Huatao Li
Abstract: As the number of individuals sharing their thoughts on Twitter continues to grow, comprehending the underlying sentiment behind these tweets becomes increasingly crucial for researchers. To identify the optimal model capable of accurately distinguishing tweet sentiment, the author uses a dataset published in 2022, containing tweet texts annotated with corresponding sentiments. Six basic machine learning classification methods are used for model training: Logistic Regression, Naïve Bayes …
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Yuheng Sun
Abstract: With the widespread application of smart homes, the study and design of control systems for smart electric stoves, an indispensable part of kitchen life, have garnered significant attention. This research is dedicated to exploring the theoretical foundations and practical applications of smart electric stove control systems, aiming to enhance the user experience of smart stoves while focusing on energy efficiency and environmental impact, thus contributing to the sustainable development of smart …
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Wenbo Ma, Zejun Ni
Abstract: The SM9 algorithm represents a sophisticated advancement in cryptographic protocols, characterized by its unique implementation of bilinear pairing techniques and identity-based encryption mechanisms. Unlike conventional approaches, SM9 leverages the user's identity directly in the generation of cryptographic keys, streamlining the process and enhancing security. This method stands in contrast to traditional schemes like SM2, which typically rely on external certificate management for public key …
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Mashuo Ding
Abstract: The article commences by laying the foundational knowledge of public key cryptographic systems, with a focus on RSA encryption and digital signature technology. It then utilizes RSA encryption as a paradigm to elucidate the application of this encryption in digital signature schemes, offering an algorithmic perspective. A critical link is established between the security of digital signatures and the robustness of RSA public key cryptographic systems. Subsequently, the article presents a concise …
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Honghao Liu
Abstract: This scholarly discourse delineates the foundational principles and prevalent algorithms underpinning information encryption technology, illuminating its critical role in fortifying information security through the lenses of data protection and privacy safeguarding. Encryption technology serves as a stalwart guardian of data confidentiality, integrity, and availability, undergirding the secure and robust functioning of digital networks and systems. As the landscape of cyber threats undergoes …
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Jiacheng Cai, Zhide Zheng
Abstract: Electrical stimulation to treat diseases can be tranced back to the pharaonic era. Since then, people have found using the electrical pulse generated by electric cat-fish can mange the patient’s pain effectively. Foreign studies started early, and the technology was relatively mature. Spinal cord stimulation was produced in the 1960s by inserting stimulating electrodes into the spinal canal's epidural area. At the same time, the pulse generator was implanted subcutaneously to achieve the purpose …
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Zhide Zheng, Jiacheng Cai
Abstract: Spinal cord stimulation is currently widely used in the treatment of chronic pain, which has a prominent effect on reducing pain in the trunk and limbs. With the development of related technology, spinal cord stimulation has shown great advantages in the field of pain management and a lot of stimulation methods have been born. This paper will first introduce the working principle of spinal cord stimulation and introduce several typical open-loop stimulation methods, including conventional tonic …
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Wenshuo Wang
Abstract: Sentiment analysis, as one of the key technologies of natural language processing, has been widely used in medical, film and television fields. In order to increase sentiment analysis's precision, it is particularly important to integrate multi-modal data. This paper presents a pioneering fusion strategy that amalgamates the cutting-edge Efficient Multimodal Transformer (EMT) model with the innovative Bi-Bimodal Fusion Network (BBFN) to revolutionize emotion analysis. By synergistically …
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Qiyang Du
Abstract: The article delves into the transformative realm of Smart Retail, exploring its evolution, technological foundations, and real-world applications. With a focus on cutting-edge technologies such as Smart Shelf Systems, Radio Frequency Identification (RFID) Tags, Near Field Communication (NFC) Technology, Smart Carts, and advanced algorithms like eXtreme Gradient Boosting (XGBoost) and Asynchronous Advantage Actor-Critic (A3C), this paper offers a comprehensive analysis of the role of these …
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He Ren
Abstract: Spinal cord stimulation (SCS) is a neurostimulation technique that has emerged as a promising therapeutic option for the management of chronic pain and other neurological disorders. An SCS consists of a implantable pulse generator (IPG) connected to a lead with a cathode (negative electrode) and an anode (positive electrode). The cathode and anode create an electrical field within the biological tissue that can depolarize the target nerves. In order to achieve the best results, the stimulation …
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Zhihui Lin
Abstract: Chronic pain, notably in conditions like failed back surgery syndrome (FBSS), challenges healthcare systems, prompting a search for effective therapies. However, pharma treatment triggers human body’s resistance which disfunction in long-term treatment. Therefore, spinal cord stimulation (SCS) offers a promising avenue. According to the gate control theory of pain control, it is possible to control the pain signal by sending new electrical waves through spinal cord to the brain. In this paper, …
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Jingqi Chen
Abstract: The power of Deep Neural Networks relies heavily on the quantity and quality of training data. Usually, it is expensive and time-consuming for people to collect and annotate data on a large scale. Traditional methods, including data augmentation, do not always have the effect, especially in some biomedical fields where some large-size anonymous datasets are generally not publicly available. The study investigates the effectiveness of Deep Convolutional Generative Adversarial Networks …
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Jiawen Dong
Abstract: Spinal cord stimulators (SCS) are a method used to treat chronic pain and are widely used in the field of modern neuroregulation. Based on the existing literature, this paper compares and discusses the principle and structure of a new wireless spinal cord stimulator with a small range of precise stimulation and the choice of working plan. In this spinal cord stimulator, a bipolar structure combined with multi-channel electrodes is considered, and a passive resonator is used to improve the …
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Keyi Guo, Haoyu Ren, Peiyu Wang
Abstract: In recent years, blockchain technology has evolved significantly, enabling a decentralized network application model that offers both user anonymity and transparency. This unique characteristic of blockchain has led to its adoption in various sectors, including healthcare, finance, and transportation. The advancement of modern zero-knowledge proof technology has further enhanced blockchain's applications across these fields, bolstering privacy protection. Zero-knowledge proofs have become a key …
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Zhili Lin
Abstract: With urbanization progressing rapidly, the availability of transportation resources in large cities has become increasingly strained. Among these resources, parking spaces are crucial for maintaining the smooth functioning of cities and ensuring the efficient operation of vehicles. However, due to spatial limitations, it is not always feasible to continuously increase the number of parking spaces. Hence, accurate prediction of future parking space demand has become imperative. The purpose of …
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Rong Song
Abstract: Books are one of the main ways for people to acquire knowledge, so usually reading books is very important. Reading books can not only enrich people's knowledge base but also make them more cheerful and help people resist loneliness. With the development of the times, reading has become an indispensable part of people's lives, people have gradually shifted from buying books from bookstores to buying books from online shopping platforms. This study utilized data set information from Amazon Kindle …
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Changju Yu
Abstract: This paper describes the application of SLAM in complex indoor environment. Through the joint work of sensor, LiDAR and visual camera, the real-time self-positioning and map construction are realized at the same time. However, in the practical application, there are also many challenges, the layout of the environment, the brightness of the environment, the presence of noise, the presence of dynamic objects and other environmental factors will lead to different degrees of data errors in SLAM …
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Hoiho Wen
Abstract: In this paper, there is a brief introduction about Kalman filteration. Then, analyze the working principle of Simultaneous Localization and Mapping, also called Concurrent Localization and Mapping. Give an introduction about different types of SLAM and the sensors required. After then, give some applications to the SLAM. A practical solution of laparoscopic sequence for surgery, which applied some feature that occur in the realistic use and overcome these obstacles. After then introduce a …
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Shiyu Zhang, Yueye Zhu, Yuxin Lin, Shiying Deng
Abstract: Population aging is one of the basic national conditions in China. The implementation of the strategy of actively coping with the aging of the population is of great significance to the construction of the working pattern of the aging which is compatible with the modernization of the Chinese style and the promotion of the modernization of the Chinese style population. Conducting a national survey on the quality of life of the elderly and constructing a comprehensive evaluation system to measure …
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Mingjie Lin
Abstract: Path planning is the core of the field of mobile robots. In order to solve the problem of finding the optimal solution, experts have conducted extensive research on it. The document offers a concise overview of rigorously conducted research on prevalent path planning approaches for mobile robots up to this point. There are essentially two main categories of path planning methods for mobile robots: Classical methods and Heuristic methods. I have provided a more detailed classification of these …
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Kairan Zhou
Abstract: Disasters often manifest as multifaceted crises that pose significant challenges for emergency responders, particularly when accessing hazardous environments such as fire scenes or chemically contaminated areas. The complexity of these situations necessitates the exploration of innovative solutions to augment the efficacy of rescue operations. In this context, the present study introduces a novel approach through the development of a disaster drone control system, predicated on the principles of …
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Mingyu Yao
Abstract: In the realm of medical technology advancements, there has been a significant enhancement in methodologies for investigating patient ailments and devising therapeutic interventions, surmounting myriad complex challenges in the process. Concurrently, the healthcare sector has adopted a diverse array of tools to accommodate various treatment modalities. Among these, the infusion device, a staple in clinical settings, is ubiquitously employed yet is considered to be archaic in its operational …
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Kaixiang Chu
Abstract: Natural disasters do great to human life and post-disaster relief is significantly essential. Moreover, the post-disaster emergency rescue robots play an important role in searching, rescuing, transportation and detection. This paper wants to enhance the stiffness and force limit through the characteristics of the mortise and tenon structure by applying the mortise and tenon structure to the mechanical arm of the post-disaster emergency rescue robot. By analyzing the mechanism of robotic arm …
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Mingyue Yang
Abstract: The subway industry has brought about a significant change in transportation by effectively reducing ground traffic congestion, resulting in an increasing demand for predicting subway passenger flow based on historical data. Researchers are constantly striving to improve the diversity and accuracy of data prediction models. This paper examines the daily passenger flow data of Nanjing Metro in 2023 and makes medium-term predictions using the ARIMA model to explore the feasibility and …
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Hao Cheng, Muze Li, Haoting Zhang
Abstract: This study forecasts the demand for shared bicycles using the ARIMA model, incorporating insights from extensive literature on urban bike-sharing usage. Through in-depth analysis, first-order differencing was identified as crucial for achieving stationarity, leading to the recommendation of the ARIMA (312) model. This model effectively encapsulates the dynamics of shared bicycle usage, evident from the significant ADF test results and the careful selection of model parameters based on ACF and …
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Yaxin Gao
Abstract: The improvement and perfection of higher education work relies on correct feedback from student evaluation of teaching, however, problems such as strong subjectivity and generalization of the indicator system are common in college student evaluation. Therefore, this article designs a student evaluation model based on emotion recognition through classroom monitoring. First, use classroom surveillance to collect class videos of teachers and students. After that the emotional information of …
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Zhiqiao Kang
Abstract: Traditional machine learning models are often only able to classify or predict data, and cannot generate new simulated data, while Generative Adversarial Network (GAN) provides the possibility for machines to generate high-quality and diverse data. GAN can be used for image data generation, image-to-image transformation, translation of image information and text information into each other, and so on. There are many different types of GAN can realize different types of functions. GAN has a …
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Yingying Peng
Abstract: In the field of artificial intelligence, image-generation techniques have been a hotspot for research. Two generative models that have garnered a lot of attention are diffusion models and generative adversarial networks (GANs). This review paper aims to compare and analyze GAN and Diffusion Models in the field of picture generation, as well as to give a thorough discussion of their features, applications, benefits, and drawbacks. Firstly, the work related to the working principle of GAN and …
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Chucheng Zhou
Abstract: In recent years, the development of Large Language Models (LLMs) has reached a state of maturity, resulting in texts that are becoming increasingly indistinguishable from those written by humans. This progress has sparked a growing need for precise and efficient methods to detect Artificial Intelligence (AI)-generated texts, as the blend of human and machine authorship becomes more seamless. This study utilizes data from student-written papers and articles generated by various LLMs to develop a …
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Zhouyu Ding
Abstract: Climate prediction refers to the use of scientific methods and techniques to predict and analyze changes in the natural environment at a certain point in the future. This paper aims to improve climate prediction by analyzing its parameters and impacts using Random Forest (RF) techniques. Specifically, firstly, data frames (DF) are manipulated to explore data features, perform visualization, and perform preprocessing. Second, the paper introduces RF as a base model as a backbone network to …
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Yimin Yang
Abstract: Machine learning is now being used to detect heart disease. Considering that failure to diagnose a heart disease patient can lead to serious consequences, including delayed treatment, worsening of the condition, and even life-threatening situations, it is crucial to ensure that as many true patients as possible are confirmed. Thus, it is important to consider recall rates while maintaining a focus on accuracy. This article compares the application of four machine learning models, including …
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Yilin Wang
Abstract: Stock price prediction can help investors to create initial pre-scenarios. The topic of this research is to predict the stock market scenario through machine learning methods. By successfully predicting the stock market situation, the movement of different stocks, etc., the possibility of buying the wrong stocks can be greatly reduced, making it possible to make huge profits by buying and selling stocks. The purpose of the research is threefold. This paper uses a market capitalization weighted …
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Shuaijie Shao
Abstract: Long Short-Term Memory (LSTM) networks and their modified versions, Stacked LSTM, are often used for time series prediction due to their powerful ability to model long-range dependencies. However, few papers have delved into the relationship between the number of layers of LSTM and model performance. For this purpose, this study used LSTM and stacked LSTM at different levels to predict the univariate time series dataset of Apple Inc. stock prices. The goal is to study the relationship between …
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Yuxuan Wang
Abstract: The National Basketball Association (NBA) game has a high international profile, and the level of NBA players and salary projections can help clubs make the right decisions. This study introduces the research topic of predicting NBA salaries for the 2022-23 season. The proposed method utilizes a gradient boosting algorithm to analyze player performance metrics and historical salary data. The specific process includes data preprocessing, feature selection, model selection and evaluation. The …
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Yiquan Ding
Abstract: Virtual Reality (VR), Augmented Reality (AR), and other similar technologies are becoming increasingly popular nowadays. However, despite the increasing number of related devices being launched, the feedback tends to be disappointing. A significant contributor to this dissatisfaction is the blurred viewpoints and unclear levels, which often lead to a suboptimal user experience. To address this issue, this research focuses on training a modified version of the Cycle-Consistent Generative …
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Yixuan Xin
Abstract: Among generative models, Generative Adversarial Network (GAN) has been sought after by researchers since its proposal. The researching fields of image generation, style transformation, data augmentation, super-resolution, image restoration, and image transformation have shined because of GAN. Deep Convolution Generative Adversarial Network (DCGAN), as an early neural network to improve GAN, solved the problem of unstableness during training. It can be easily scaled to deal with larger datasets …
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Yujie Yang
Abstract: Image generation and manipulation are important research directions in the field of computer vision and graphics. Traditional methods of image generation and manipulation often necessitate substantial manual intervention or are constrained by the quality and diversity of the dataset, leading to a lack of authenticity and control in the images produced. This paper provides an extensive examination of image generation and manipulation using Deep Convolutional Generative Adversarial Networks …
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Zhaoxiang Tong
Abstract: Cycle-Consistent Generative Adversarial Networks (CycleGAN) has demonstrated remarkable proficiency in its ability to undertake image transference between differing domains without the need for paired examples, overcoming a major limitation of traditional image-to-image translation methods. These advantages make it a valuable addition to the toolbox of computer vision researchers and practitioners. However, there persists ample potential for further advancements. This article explores the …
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Ruijing Zhang
Abstract: Automatic Speech Recognition (ASR) is a technology that leverages artificial intelligence to convert spoken language into written text. It utilizes machine learning algorithms, specifically deep learning models, to analyze audio signals and extract linguistic features. This technology has revolutionized the way that people interact with voice-enabled devices, enabling efficient and accurate transcription of human speech in various applications, including voice assistants, captioning, and …
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Baiyuan Chen
Abstract: This paper presents an innovative enhancement to the Sphere as Prior Generative Adversarial Network (SP-GAN) model, a state-of-the-art GAN designed for point cloud generation. A novel method is introduced for point cloud generation that elevates the structural integrity and overall quality of the generated point clouds by incorporating topological priors into the training process of the generator. Specifically, this work utilizes the K-means algorithm to segment a point cloud from the repository …
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Jiameng Li
Abstract: Sentiment analysis technology is widely used in film and television media, social media, entertainment industry, medical and other fields, and has great commercial use value. In the early stages of research, sentiment analysis methods based on traditional machine learning are not effective, which greatly limits its application in the commercial field. In recent years, with the development of artificial intelligence, deep learning-based sentiment analysis have made great advances. However, the …
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Ding Wang
Abstract: Stock price prediction is currently a research focus in the financial field, especially in blockchain research. The central focus of this research is to forecast Bitcoin's closing price through the integration of deep learning techniques, specifically employing Long Short-Term Memory (LSTM). This study takes into account that Bitcoin is a mainstream virtual currency, and predicting its future price can help investors make better judgments in trading. The goal of this exploration is to identify …
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Wenqi Zhao
Abstract: This paper explores the importance of low-power digital integrated circuits (IC) in technological development and everyday applications, especially their key role in modern electronic devices such as smartphones and advanced medical devices. With the advent of the digital age, the design efficiency and energy efficiency of low-power IC have a direct impact on device performance and sustainability. Based on the design basis of low power IC, CMOS technology and its power consumption model are …
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Qianyu Tang
Abstract: In order to better meet user needs, user-oriented interactive design introduces computer graphics technology to generate, process, and transmit graphical information more effectively, thereby enhancing the practicality, simplicity, convenience, and enjoyment of interactive media. This paper aims to discuss the application of computer graphics in interactive media design. In order to analyze and compare the use of different computer graphics (CG) technologies in different scenarios, this paper …
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Aohua Zhang
Abstract: Object detection is commonly utilized in fundamental computer vision research fields: medical, autonomous driving, sensing monitoring, and other fields. With the advancement of high-performance computing technology, the computing speed of hardware has made a significant advancement. Meanwhile, machine learning-related algorithms, especially deep learning-based algorithms, greatly boosted the detection speed and accuracy. In practical applications, engineers have little understanding of the …
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Yiwen Fang
Abstract: With the development of social economy, the number of road vehicles is increasing, and road traffic safety has become one of the major factors threatening people's life and property safety. Abnormal driving behavior of drivers represented by mobile phone operation is one of the important causes of road traffic safety accidents. Real-time monitoring of drivers' mobile phone use and implementing appropriate measures when abnormal behavior occurs are of great significance to protect the life and …
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Tong Yin
Abstract: Vitiligo is a common depigmented skin disease. Changes in appearance and unstable treatment outcomes have a negative impact on the physical and mental health of patients. Not only can it cause psychological problems such as social anxiety and depression, but it can also affect the effectiveness of disease treatment. Aim of the study: In order to help vitiligo patients reduce social anxiety and improve the applicability and practicality of psychotherapy, this article proposes a self-help …
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Yunqi Zhang
Abstract: Understanding housing price predictions assists the government in better adjusting and formulating relevant policies to promote economic stability and sustainable social development. In this study, researchers collected a large amount of data on the Seattle real estate market, including housing characteristics (such as area, number of bedrooms, number of bathrooms), geographical location (such as neighborhood, nearby facilities), and historical price data. This paper used this data to train and …
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Perry Wu
Abstract: The Organic Field-Effect Transistor (OFET) boasts several salient features including flexibility, reduced power consumption, and enhanced biocompatibility, positioning it as a pivotal component in the advancement of flexible electronics, wearable technologies, and medical electronic devices. Despite its considerable attributes, the OFET's widespread adoption is hindered by inherent limitations, notably its low carrier mobility. Consequently, there is a concerted effort within the research …
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Yuyao Tang
Abstract: The binary absolute value detector is crucial in today's computer domain, especially in computer storage and analysis systems. It ensures the integrity and accuracy of data. Therefore, this paper proposes an optimized design of a 4-bit absolute value detector aimed at finding the circuit with the lowest energy consumption. Firstly, this paper introduces a design different from the traditional absolute value calculator, resulting in a reduced total number of stages in the circuit. Secondly, this …
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Ruotong Li
Abstract: As integrated circuits and technology have advanced, people's requirements for integrated circuits are getting higher and higher, and integrated circuits with high performance, low latency and low power consumption characteristics are needed to satisfy all kinds of human needs. However, meeting these needs requires a balanced optimization of circuit delay and energy consumption. The gate size and voltage need to be varied simultaneously, and the most suitable voltage and gate are found by …
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Fangyi Yu
Abstract: In the contemporary digital era, artificial intelligence (AI) emerges as a pivotal industry, playing a critical role across various sectors. Concurrently, the integrated circuit (IC) industry represents a highly specialized field characterized by swift technological advancements and innovation. Anchored on this premise, this paper embarks on a comprehensive exploration of the IC domain, delineating its evolutionary trajectory and current dynamics. Subsequently, this paper extends to a detailed …
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Dingnan Shi
Abstract: Point cloud-based 3D modeling is a cutting-edge technology that leverages point cloud data from sensors like lidar and cameras to recreate detailed 3D models of objects and environments. This paper serves to outline the fundamental route, key principles, and prevalent methods within this field, with a specific emphasis on the preprocessing registration techniques for point cloud data and the advancements in 3D model reconstruction technologies.Furthermore, the article will delve into …
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Fengze Du
Abstract: Bio-inspired motion control in robotics draws inspiration from biological systems to enhance the movement capabilities of robots. This article explores the integration of bionics techniques in robots’ path planning, motion control, and design of moving parts, offering advantages over traditional robots control systems. In path planning, bio-inspired approaches, such as swarm intelligence algorithms and artificial neural networks, optimize trajectories and enable obstacle avoid ability in complex …
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Mingwei Liu
Abstract: Semantic segmentation is based on the image information to detect and identify various categories of objects and output these objects mask. Compared with object detection and image classification, semantic segmentation has a more accurate recognition effect, and has a wide range of applications in automatic driving and other fields. With the development of deep learning, semantic segmentation methods based on deep learning have achieved good results, such as mask rcnn. Nowadays, Mask RCNN …
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Qisong Liu
Abstract: This article introduces the concept and practical applications of deep reinforcement learning (DRL), describes the core principles and main algorithms of DRL, and summarizes the advantages of DRL and its differences from traditional reinforcement learning (RL). At the same time, it introduces the broad application prospects of DRL in various fields and lists some examples about the mechanical field. This includes applications for classifying colored objects, agricultural applications for robotic …
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Shunyu Yang
Abstract: Recently, the publication of the Neural Radiance Field (NeRF) has sparked a surge in further research, unveiling a wealth of innovative solutions in related domains. With its potent predictive capabilities and operational efficiency, neural networks have addressed the traditional trade-off dilemma, enabling real-time rendering at state-of-the-art (SOTA) quality. This breakthrough has introduced a novel rendering approach in computer graphics (CG) and unlocked fresh opportunities for real-time …
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Jiaqi Zhu
Abstract: Nowadays, there are a lot of 3D models in the world, but how to build a Chinese Dragon is relatively few methods.Aiming at this problem, this paper put forward a method which made up for the deficiency in this aspect. In order to build this model, OpenSCAN software was used in this paper. This paper also divided Chinese Dragon into four parts: head, body, claws and tail, and used different methods to model these four parts. At the same time, in order to make the Chinese Dragon look more …
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Yanfeng Li
Abstract: The path planning problem of autonomous mobile robots is a very important problem in today's robotics field, and after decades of development, many path planning algorithms have been derived, among which the A* algorithm and the Rapidly Exploring Random Trees (RRT) algorithm are more widely used. These two methods use completely different basic principles, the A* algorithm relies on global search, while the RRT algorithm uses random search, in order to explore the advantages and disadvantages of …
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Yuhai Yang
Abstract: Amidst the swift advancement of intelligent vehicles, computer vision technology has become an increasingly important core technology in the field of autonomous driving, especially in providing safety guarantees for drivers. Given the common Incidence of traffic accidents resulting from driver fatigue. The use of deep learning technology to prevent driver fatigue behavior has become an indispensable protective application in intelligent cockpit human-computer interaction. This article introduces …
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Ruiyun Hu, Haoming Shi, Jiandong Wen
Abstract: With the improvement of contemporary biotechnology and the rapid development of exoskeleton robot technology, the demand for exoskeleton rehabilitation robots is constantly increasing, and a large number of movements in daily life require the participation of the limbs. Therefore, it is of profound significance to study limb exoskeleton rehabilitation robots, but there are few products on the market to help customers recover more smoothly. This paper designs an interactive client framework that …
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Hancheng Li, Yurui Li, Yaowen Qian
Abstract: The paper aims to explore the feasibility of using artificially generated facial expressions with different emotions to enhance the features and increase the data volume for emotion recognition in the context of intelligent cockpit. The paper first introduces the background and significance of the research, which is motivated by the increasing number of private cars in China, the development of intelligent cockpit technology, and the importance of emotion recognition for driving safety. The …
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Tony Wayne Wang
Abstract: This study conducts a comparison of machine learning algorithms, including Logistic Regression (LR), Decision Tree Classifier (DT), and Random Forest Classifier (RF), to predict the survival outcomes of passengers on the Titanic. The dataset used in the study includes variables such as socio-economic status, age, gender, and family relationships; this paper meticulously prepares and analyzes the data to train and evaluate these models. The study's objective is to determine the impact of various …
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Zhixin Meng
Abstract: With the rapid development of the vehicle and information industries, the traditional vehicle has been transformed from a simple travel tool to an intelligent vehicle with comprehensive functions, intelligence, and rich information. As a standard function of intelligent vehicles, automated driving will greatly reduce or even replace humans for vehicle control, and the human-machine interaction system pays more attention to displaying the driving status and prompting the driver in an intuitive …
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Yue Wang
Abstract: With the rapid development of artificial intelligence (AI) technology, its application in agriculture is continuously expanding, bringing new intelligent and precise solutions to agricultural production. The continuous development of artificial intelligence technology has brought great changes and opportunities to the agricultural field.This paper discusses the current status of artificial intelligence technology in agriculture and its significant implications for sustainable agricultural …
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Siqi Wang, Jihong Zhuang
Abstract: In the confluence of natural language processing and machine vision, the field of image captioning has experienced exponential growth since the introduction of the BLEU evaluation algorithm by IBM in 2002. This discipline serves to bridge the "semantic gap" between human and machine perception, translating visual information into semantic narratives. Such technology is extensively applied in areas like human-computer interaction, video subtitling, quiz generation, and image-based search …
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Yi Sun
Abstract: Single Image Super-Resolution (SISR) is a process designed to transform Low-Resolution (LR) images into High-Resolution (HR) counterparts. This technology finds critical applications in various sectors, including gaming, photography, and medical imaging. With the advent and widespread success of deep learning, this approach has been increasingly applied in the realm of SISR. Deep learning-based SISR models are primarily categorized into three types based on their nonlinear module structures: …
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Jin Xing
Abstract: The power of deep neural networks relies heavily on the quantity and quality of training data. However, it is expensive and time consuming for people to collect and annotate data on a large scale. Traditional methods, including modifying the copies of existing data, do not always have the effect, especially in some biomedical fields where some large-size anonymous datasets are generally not publicly available. So, this paper tried to tackle this problem by generating specific data using Deep …
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Jiemin Wang
Abstract: Power quality classification is essential for identifying and categorizing various power quality issues within electrical systems. With the increasing integration of electronic devices into power grids, including converters, rectifiers, and inverters, the significance of real-time monitoring and fault prediction has grown. This paper explores the application of image classification techniques, specifically Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP), in addressing power …
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Yichen Hu
Abstract: The study delves into the intricate relationship between hyperparameters and image translation quality in Cycle-Consistent Generative Adversarial Network (CycleGAN), which is one of the most widespread tools for unsupervised image-to-image translation. The hyperparameters investigated include learning rate, identity loss, and cycle consistency loss, which are aimed at enhancing the model's quality of producing realistic and good images. Furthermore, by employing horse2zebra dataset author was …
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Yongkang Hu, Chuyue Jing, Ziyuan Li, Xiao Liu
Abstract: This manuscript delves into the pivotal role of feature optimization within the realm of deep learning-driven super-resolution techniques, a topic of considerable importance across diverse domains such as medical imaging and electron microscopy. The paper confronts the inherent challenges posed by deep learning in super-resolution tasks, including issues related to complexity, computational burden, and the paucity of model generalizability. It underscores the significance of feature optimization …
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Hao Chen, Sicheng Lei, Zhengliang Lyu, Naitian Zhang
Abstract: Weakly supervised object detection represents a burgeoning field within the realm of computer vision, reflecting the growing interest in developing models that can effectively identify and classify objects with minimal labeled data. This paper offers a comprehensive classification of contemporary, state-of-the-art deep learning models tailored for weakly supervised target detection. The classification encompasses four principal categories: Multi-Instance Learning (MIL), Class Activation Mapping …
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Jiaxi Huang, Yaokun Dong, Yichen Zhang
Abstract: This paper delves into the current research landscape of traffic sign recognition and detection, aiming for a comprehensive understanding of the field's developmental trajectory, significant achievements, and persistent enigmas. This is achieved through an extensive review of pertinent literature from both domestic and international sources. Within the research backdrop, it explores the diverse methodologies and challenges encountered in traffic sign detection and recognition, with a specific …
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Ge Chen
Abstract: This paper presents an in-depth exploration of text-to-image synthesis, categorizing the field into three main areas based on deep learning methodologies. Each category undergoes meticulous analysis to chart its development and dissect its fundamental mechanisms. The emphasis is on the significant advancements in the field, with a special focus on major breakthroughs and the continual evolution of these technologies. Concurrently, the paper critically evaluates the challenges and limitations …
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Yuqi Jiang
Abstract: This detailed review explores key developments and ongoing challenges in image style transfer, emphasizing the transformative role of deep learning approaches, notably Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The paper examines the fundamental principles of this field, particularly the intricate process of blending 'style' and 'content' via advanced neural network designs. It chronicles critical breakthroughs, and progresses to contemporary solutions …
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Junhong Yin
Abstract: This manuscript delves into the intricate realm of small target detection technology in Unmanned Aerial Vehicles (UAVs), a pivotal element in contemporary aviation dynamics, with widespread implications across military, civilian, and emergency response domains. Central to this is the detection of diminutive targets, a process entailing sophisticated sensors and image processing algorithms, contending with the intricacies of identifying small, non-distinct targets against multifaceted …
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Honglin Wang
Abstract: With the popularity of artificial intelligence models and the increasing expectation of artificial intelligence applications in many fields, reference image segmentation (RIS) has attracted much attention from researchers. RIS, as one of the most basic and challenging visual language cross-modal tasks in the intersection of computer vision and natural language processing, aims to segment an instance from an image corresponding to a given natural language representation. This paper aims to …
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Hang Yin
Abstract: Object detection technology is a crucial research direction in the field of computer vision. Its primary task is to identify and locate objects within images. This technology finds extensive applications across various domains such as autonomous driving, medical diagnostics, and security monitoring. With the development of machine learning, especially the progress of deep learning in related fields of image processing, the accuracy rate of object detection has achieved better results. However, …
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Yifei Zhao
Abstract: Object detection, as one of the basic issues in the computer vision (CV) field, has huge application value in realm such as autonomous driving and face recognition. Since the object differences in images in different scenes, angles, and lighting environments are too great, the number and size of objects are also different, those greatly increase the difficulty of detection. Therefore, how to detect objects quickly and accurately has attracted widespread attention from researchers. This article …
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Junliang Lyu, Ouze Wang, Yinghao Zhang
Abstract: This paper introduces machine learning models for constructing a comprehensive quality prediction model to forecast National Basketball Association (NBA) players' specific scores. The objective is to facilitate the analysis, guidance, and evaluation of players' relevant value. Initially, data processing involves renaming the dataset and dividing it into an 80% training set and a 20% dataset through preprocessing, simultaneously addressing missing row. Subsequent steps include visualizing the …
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Jiale Li
Abstract: The main purpose of this paper is to use machine learning methods to identify factors related to the survival of Titanic passengers and analyze model parameters. The study first speculated on the survival factors of Titanic passengers and conducted data visualization tests on these speculations. Subsequently, this article cleaned up the relevant data, abandoned the factor of excessive missing data, made up for a small amount of missing data, and then constructed a relevant machine model. …
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Xingyan Wu
Abstract: In today's fast-paced and rapid development era, people's pressure is also increasing rapidly, too much pressure also makes depression become the focus of public attention, and effective prevention has become an urgent problem to be solved. College students are the backbone of social progress, but a large number of college students suffer from depression, which makes it very important to find out the main causes of depression. Whether to effectively prevent depression among college students is …
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ShiRu Lyu
Abstract: With the increasing coverage of subways in cities, people travel more than just by bus or walking. Nowadays, subway stations have become sites with high population density in the city. Due to the increasing travel demand of residents. Excessive congestion in subway stations will lead to inconvenience and even accidents. The subway passenger flow prediction can avoid many potential problems. Passenger flow prediction can not only optimize the subway scheduling, but also help the operating company …
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Xintong Huang
Abstract: With the progressive development of the “sharing economy”, sharing bicycles are becoming increasingly popular in cities, and citizens’ last-mile travel problems are greatly alleviated. In addition, bicycles are also a good solution for short-distance travel. However, sharing bicycle operators do not have a good approach to dealing with supply and demand relationships, and bicycles are distributed in a position where supply is greater than demand, making it convenient for considerable discounts. …
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Xiaofan Sun
Abstract: Dut to the limitations inherent in traditional music education approaches, the remarkable predictive and analytical prowess of artificial intelligence has been integrated to amplify the quality of music education. This paper investigates the integration of ChatGPT, an emerging machine learning technology based on deep learning and Natural Language Processing, with music education, and analyzes various approaches. These include the utilization of contemporary spectral analysis techniques to …
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Ziqi Ren
Abstract: The realm of pedestrian re-identification (Re-ID) technology, a vital component in areas such as video surveillance and intelligent security, has witnessed significant advancements, particularly in the context of deep learning. This domain, despite its nascent start in the broader image processing landscape, has seen rapid evolution with the advent of robust deep learning techniques. The present article delves into a range of methodologies pertinent to pedestrian Re-ID, emphasizing both the …
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Yucheng Xiang
Abstract: Today's automotive intelligent cabin gradually tends to multi-screen, multi-screen interconnection research, design and development, intelligent cabin is being gradually applied to more and more production models, the car has become including LCD dashboard, head-up display, streaming media rearview mirror, intelligent driving assistance, voice recognition control, gesture control and other complex electronic equipment collection. “One core and multiple screens” is the way most car companies …
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Boyu Shen
Abstract: Nowadays, intelligentized human-machine interactions technologies are developing in a incredible speed, more areas these technologies are applied in. In recent years, intelligent cabins become a main-stream topic which is a tech aims for serving drivers and improving the driving experience. As the trend towards increasingly intelligent vehicles, more scientific researchers put emphasis on researching practical applications which can improve driving experience and satisfaction of drivers and …
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Guanzheng An
Abstract: Drivers' fatigue driving detection is very important to traffic safety, and is closely related to the safety of human life and property. It is a key research topic for researchers. Effective fatigue identification technology can effectively reduce the traffic accidents caused by fatigue. This paper provides a systematic review of the detection methods for driver fatigue driving. The concept of driver fatigue and its necessity for detection are introduced, and the characteristics of fatigue …
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Chengyu Wang, Zihe Zhong
Abstract: In the arena of Alzheimer's Disease (AD) research, the utilization of Artificial Intelligence (AI) for the early diagnosis and prediction of the disease is identified as a crucial avenue of exploration. The impetus for this line of inquiry is rooted in the urgent need for treatments and strategies that can slow the progression of AD, particularly through early detection and prognostication. The application of AI, in conjunction with big data, facilitates the integration of diverse datasets …
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Yuhao Zhang
Abstract: With the continuous progress of modern society, more and more families have their own private cars for the convenience of travel and other needs, making it easier and faster to go anywhere. However, with the increase in the number of private cars, parking spaces in parking lots cannot keep up with the growing number of cars. It is increasingly difficult to find parking spaces everywhere when driving, and even if they are found, they are often full. The problems of chaotic management in parking …
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Zihang Cheng, Zihan Shao, Xinyi Wang
Abstract: The research introduces traffic accident predictive models known as the Convolutional Long Short-Term Memory Model and a K-means cluster algorithm with Random Forest Network, which aims to provide precise forecasts of incident rates Through the amalgamation of the strengths inherent in Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) neural network, K-means cluster algorithm and Random Forest algorithms. These fusion models surpass the performance of the standalone LSTM model, …
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Zefei Guo
Abstract: OpenSCAD, an open-source 3D modeling software, has been widely favored by creators for its powerful functions and flexible scalability. Although OpenSCAD has a wide range of applications in the field of 3D modeling, there are still few studies on how to use it to build animated models with specific cultural characteristics. As one of the important symbols of traditional Chinese culture, the Chinese dragon has unique morphology and cultural connotation. Therefore, we hope to provide new ideas and …
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Haoran Jia
Abstract: This paper provides an in-depth look at the application and impact of Artificial Intelligence (AI) in the animation industry, while also pointing out the existing limitations. In recent decades, AI has been seamlessly integrated into all aspects of life, and the field of animation creation is no exception.AI has not only automated tedious tasks, increased efficiency, and lowered costs, but also revolutionized the traditional animation ecosystem by providing animators with entirely new ways of …
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Yilin Li
Abstract: Saturn V, utilized by NASA during the 1960s Apollo program, stands as a pivotal heavy-lift rocket in space exploration history. Its design and fabrication mark a significant chapter in spaceflight history. Through an amalgamation of Saturn V's authentic design parameters and Juno New Origins' sophisticated modeling tools, we proficiently resurrected this emblematic rocket system. This investigation delves into design methodology, model validation, and the advantages of Juno New Origins for …
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Lu Ge
Abstract: Deep learning applications have been applied extensively and have made tremendous strides in the 3D reconstruction field in recent years. This paper offers a methodical review of deep learning-based techniques for single-view images, multi-view images, and video-based sequence approaches. For single-view methods, we focus on depth estimation using Convolutional Neural Networks (CNNs) and image-to-depth mapping using Generative Adversarial Networks (GANs). For multi-view methods, we explore 3D …
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Wenkang Wu
Abstract: At present, path-planning algorithms in the field of robotics is a problem of research value. Especially in the field of robotics, it has great significance. A good algorithm can improve the efficiency of the robot and ensure the safety of the robot in complex environments. In addition, the path planning algorithm can improve the autonomy and intelligence of the robot, help the robot to realize autonomous navigation and decision-making, and through different algorithms, the robot can also play …
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Weixiang Xiong
Abstract: Gallium nitride (GaN) is a desirable substance for electronics that require high frequencies and high power. The research and development of vertical GaN devices on GaN substrates has made significant progress in recent years, thanks to the availability of high-quality free-standing bulk GaN substrates. Despite some inherent difficulties associated with substrate fabrication and the intricate process of metal doping for sensor applications, the prospects for the future of GaN appear quite …
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Zhuwen Wei, Yifan Zhang
Abstract: Movie, also known as moving or moving pictures, or "reflections," is a form of visual art that communicates ideas, stories, cognitions, emotions, values, or various atmospheric simulated experiences through the use of moving images. As the mainstay of cultural industry, film has brought huge economic and social benefits, and the box office is the most important index to measure the economic benefits of film. In this study, the prediction results of the random forest analysis model are …
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Shuai Hu, Zhongluo Yu
Abstract: This investigation targets to research on construction of prediction model of traffic density and discuss suitable methods to decrease heavy traffic. The data is obtained from the public website of Government of Shenzhen which provides updates of traffic monitoring, time sequence analysis is put in practical manipulation, and it is verified that traffic undergoes regular alternations responsible for specific time periods every day. Data sets of traffic density of routes are analysed using ACF …
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Xiongzhewen Zhang
Abstract: The EEG, or Electroencephalogram, is a non-invasive tool that records electrical activity along the scalp, offering a window into the complex workings of the brain. By analyzing these signals, researchers can gain insights into cognitive processes such as attention, memory, and decision-making. This investigation utilizes Convolutional Neural Networks (CNN) for the classification of EEG signals into numerical digits, tackling the challenge of deciphering cognitive states via non-invasive …
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Wenkai Xiao, Xinhao Ye
Abstract: Automatic car obstacle avoidance technology mainly utilizes advanced sensor technology to enhance the car's perception ability of the driving environment, feedback real-time information such as vehicle speed and position obtained by the perception system to the system, and judge and analyze potential safety hazards based on comprehensive information of road conditions and traffic flow. Mobile robots are increasingly used in applications, including in industrial production, agriculture, …
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Qikang Huang
Abstract: Current film and television platforms still struggle to effectively gather information for users and companies through public film reviews. There is a lack of applications or research on keyword extraction from film reviews. This study aims to evaluate the effectiveness and feasibility of the KeyBERT model for extracting keywords from film reviews. The precision and recall rate are utilized to assess the impact of model extraction. The test results indicate that the average precision and recall …
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Chenhe Yang
Abstract: Recent advancements in deep learning have enabled the recognition of images, objects, observations, texts, and other complex structures with increasing accuracy. In recent years, scene text recognition has stimulated a lot of researchers in the computer vision community. However, it still needs improvement due to the poor performance of existing scene recognition algorithms. This paper aims to combine deep learning research to recognize texts and formulas in images and convert them into LaTeX …
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Siqi Zhu
Abstract: As Multi-Armed Bandit (MAB) applications grow increasingly complex, particularly when multiple agents collaborate or compete, traditional bandit algorithms face fresh challenges, underscoring the rising importance of research in multi-agent multi-armed bandits (MAMAB). Developments in MAMAB algorithms have spurred significant advances across a variety of fields, addressing challenges in dynamic and uncertain environments. This paper offers an exhaustive review of recent progress in MAMAB …
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Qianqian Zhang
Abstract: In the rapidly evolving landscape of decision-making systems, the significance of Multi-Armed Bandit (MAB) algorithms has surged, showcasing a remarkable ability to address the exploration-exploitation dilemma across diverse domains. Originating from the probabilistic and statistical decision-making framework, MAB algorithms have established a critical role by offering a systematic approach to making choices in uncertain environments with limited information. These algorithms ingeniously balance …
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Qianwei Ke
Abstract: As one of the important applications in the field of human-computer interaction, input word prediction has made great progress in recent years.This paper reviews and summarizes the development process of input word prediction technology, from the early statistical model-based method to the current deep learning-based technology application, describes its development trajectory and the representative algorithms of each technology.In addition, this paper also analyzes the current problems and …
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Zelong Huang
Abstract: With the rapid development of smart devices, a huge amount of images are appearing in people's lives, so the processing of images, especially recognition and classification processing, becomes more and more important. This paper aims to provide researchers with an overview of current traditional machine learning (ML) and deep learning (DL) in solving image recognition and classification problems, specifically analyzing architectures such as KNN, SVM, Random forest algorithms, and CNN …
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Lingyun Yan
Abstract: The gradient optimization algorithms and network architectures play pivotal roles in the field of artificial intelligence. However, there is limited research comparing multiple optimization algorithms across different network structures. In this paper, the effectiveness of several optimization techniques in image processing tasks is investigated, along with an investigation of their effects on different neural network architectures. LeNet, AlexNet, and the backpropagation neural network are the …
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Chengxiang Sun
Abstract: With the development of natural language processing techniques, sentiment analysis techniques are constantly updated. However, most of the sentiment analysis techniques are limited to handling binary sentiment analysis and perform poorly on multivariate sentiment analysis tasks. This paper explores the optimization in the field of sentiment analysis by using the BERT model combined with fuzzy logic to improve the performance of the model on multivariate sentiment classification tasks. The study …
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Yu Zeng
Abstract: Heart failure is a clinically complex syndrome that affects millions of people globally and posts a heavy strain on healthcare systems because of its high rates of morbidity and mortality. To manage and treat it effectively, early detection and accurate prediction are essential. Machine learning algorithms present a viable technique. This research attempts to forecast heart failure under specific circumstances, using a heart disease dataset from kaggle.com that is accessible to the public. Five …
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Wenxin Guo
Abstract: In recent years, federated learning has become a hot research topic in the machine learning community. It aims to reduce the potential data security and privacy risks caused by the centralized training paradigm of traditional machine learning through local training and global aggregation. Although federated learning methods have been widely applied in numerous fields such as finance, healthcare, autonomous driving, and smart retail, there are still urgent issues to be addressed in the field of …
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Ziwei He
Abstract: Ischemic stroke is a dangerous disease that endangers human health and is one of the important causes of disability and death. Early detection and prediction of ischemic stroke are essential for timely intervention and prevention of serious consequences. Fortunately, deep learning-based medical image research has made great progress, which can assist doctors in diagnosing conditions. This paper analyzes the overall effectiveness of existing machine learning (ML) methods in predicting final …
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Zizheng Li
Abstract: The property market is closely related to the regional economy. This study focuses on exploring the problem of house price prediction in the property market. This paper aims to reveal the performance of extreme gradient boosted tree regression, ridge regression, decision tree, and random forest regression models in feature selection and data processing, and assess their effectiveness in house price prediction. Aspects compared include prediction accuracy, data required for fitting, and running …
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Zihang Li
Abstract: The rapid development of deep learning models has been a hallmark of recent years. As the world moves towards greater intelligence, there is an urgent need for advancements in technologies like question-answering systems, chatbots, and search and recommendation engines, especially within the service and e-commerce sectors. At the heart of these advancements lies the task of detecting semantic similarity in natural language processing. Against this backdrop, this paper examines the accuracy of …
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Jieqi Li
Abstract: Current departmental guidance platforms in intelligent systems predominantly rely on selective options, necessitating patients to select pertinent medical information before scheduling an appointment. However, most patients' lack of specialized medical knowledge frequently results in misregistration incidents. Moreover, the focus of existing research on medical guidance systems does not emphasize or distinguish among specialized departments, leading to reduced consultation efficiency, …
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Sa Xiao
Abstract: In the current online environment, music reviews can be a good reflection of the popularity of the music, which can attract others to listen and pay attention to the music. A sentimental analysis of these reviews and the identification of emotional attitudes are essential for the dissemination of music. Based on the classic Chinese R&B album "David Tao" and its related reviews, this paper aims to analyze sentiment tendencies, analyze word frequency, and extract comment topics based on the Baidu …
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Yang Cao, Tianle Chen, Qian Wu
Abstract: Humor is one of the measures of human intelligence, and similarly, humor is a major indicator of chatbot intelligence. To make chatbot answers more lively and interesting, thus increasing the interaction and entertainment between users and bots and providing a more interesting user experience. This paper collects a large text dataset and use models such as humor detection and humor scoring to detect the humor ability of chatbots, and improve the future work based on the results obtained from the …
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Hanyue Liao
Abstract: In the era of big data, businesses are inundated with vast amounts of information across a wide spectrum of types, creating both opportunities and challenges in data utilization. Among the various algorithms adapted for this environment, the Thompson Sampling algorithm stands out due to its exceptional capability to handle dynamic data and complex problems. This algorithm leverages probabilistic models to continuously update and optimize decision-making processes based on incoming data streams. …
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Xinyan Hu
Abstract: This paper introduces the Adaptive Thompson Sampling (AdaTS) algorithm, specifically designed for the challenges posed by opportunistic bandit problems. Unlike conventional multi-armed bandit scenarios where the optimality of actions remains relatively static, opportunistic bandit environments are significantly influenced by fluctuating external conditions. These variations necessitate a highly adaptive strategy for decision-making. The AdaTS algorithm meets this requirement through a novel …
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Shuohan Gu
Abstract: An in-depth discussion will explore the advanced methodologies designed to minimize regret in recommendation systems, shedding light on the principal strategies and assessing their effectiveness. This analysis will also consider additional factors that could profoundly impact the decision-making mechanisms within these systems. Special focus will be placed on user-specific variables such as contextual nuances, temporal dynamics, and individual preferences that, although frequently overlooked, …
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Xiaorui Li
Abstract: The market position of the RMB has gradually increased, and the price change of the US dollar against the RMB has become increasingly important. Therefore, it is vital to use scientific means to forecast the exchange rate forecasting model. In this thesis, the BP neural network model is established by MATLAB, and the price change of the USD/RMB exchange rate is analyzed and processed according to the characteristics of the exchange rate prediction model. Part of the data is elected for the …
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Junrui Song, Fanzhen Xiao
Abstract: This project predicts the trend of gold price change against the US dollar and provides a more scientific and effective basis for investment decisions. The whole system is divided into two aspects: data visualization and BP neural network model application. For visualization and analysis, the powerful drawing function of Matlab was used for perspective drawing, and line graphs were created to show the degree of fit of the predicted values with the accurate values and the trend of change. For the …
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Zulin Wu, Yihan Xie
Abstract: As global base currencies, the fluctuations in the prices of the pound and dollar have an impact on the global economic situation. The exchange rate of currency not only affects the decline of the economy but also affects the stability and development of political and trade relations in various countries. Due to the numerous and non-linear factors that affect the currency exchange rates between the two countries, the accuracy of existing exchange rate prediction research is still relatively low. …
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Ze Xu
Abstract: With the global economy getting closer, the fluctuation of the foreign exchange market has an increasingly significant impact on the world economy. As one of the important foreign currencies, the research on the price change of the dollar against the yen is of great significance. In recent years, the emergence of neural network models has made remarkable achievements in the field of forecasting the price of USD against JPY. This paper discusses the feasibility of using the neural network model …
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Zhi Inn Ong
Abstract: Pedicle fixation during spinal surgery done with robot assistance has sparked a wave of contemporary discourse within the surgical field. This contrasts with conventional methods such as freehand with fluoroscopy assistance. The comparison between the novel and conventional techniques, the gradual application of robotics into spine surgical field, prospects, and limitations of applying robotics in spinal surgery will be discussed in this review. Spine surgery with robot assistance was shown to …
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Jiahao Lin
Abstract: This paper explores the utilization of physiological signals, including heart rate variability (HRV) and electroencephalography (EEG), in emotion recognition through wearable devices. Heart Rate Variability (HRV) is closely linked to emotional arousal. HRV can detect subtle changes in heart rate patterns, which are indicative of different emotional states. By analyzing these patterns, researchers can identify and differentiate between various emotions someone may be experiencing. The integration …
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Xiangru Chen
Abstract: There is recently a large amount of deep learning algorithms developed for sleep staging using the signals, especially EEG, recorded by sleep monitoring devices for sleep related patients. Among them, those based on CNN architecture have some advantages over the others. This paper mainly discusses the CNN-based sleep staging algorithms for EEG, and meanwhile considers their upstream monitoring devices and the downstream clinical tasks. The relationship between these three parts of the sleep …
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Junjie Yang
Abstract: This article summarizes research on classifying electroencephalogram (EEG) signals of Parkinson's disease (PD) patients using deep learning technologies. Parkinson's disease is a common neurodegenerative disorder that severely affects quality of life. EEG signals provide valuable information about brain activity. Therefore, analyzing and classifying the EEG signals of PD patients can aid in early diagnosis and treatment. The paper outlines the assessment metrics and cross-validation methods in …
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Binghua Xi
Abstract: Cancer remains one of the leading causes of mortality worldwide. Predicting the survival of cancer patients holds immense significance in guiding treatment decisions and enhancing patient quality of life. To provide a comprehensive understanding of the application of machine learning methods and to explore future research directions in survival prediction, this paper reviews the current landscape of machine learning algorithms in this domain. The paper discusses the significance and challenges …
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Xuanpeng Zhang
Abstract: Electroencephalography (EEG) signals denoising is crucial for neural signal interpretation, particularly in complex noise conditions. Traditional methods often fail to address these conditions effectively. In contrast, deep learning-based techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Generative Adversarial Networks (GANs), and Transformers offer enhanced adaptability and robustness by tuning to diverse noise characteristics. This paper presents …
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Xin Wang
Abstract: Road accident threaten people’s life safety as well as wealth seriously, therefore predicting and analysing car accidents have great significance. This research first compared the performance of four machine learning models in analysing the severity of traffic accidents, including Random Forest, Naïve Bayes, Logistic Regression and Multi-Layer Perceptron. Naïve Bayes performs badly with a low accuracy while other three models have equal level of performance. Multi-Layer Perceptron performs …
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Shangming Zhang
Abstract: Due to the complexity and diversity of features, accurately recognizing their classification accuracy is significant for remote sensing data processing. Machine learning algorithms have high accuracy and can be applied to a variety of classification problems, providing excellent solutions to the problem of feature classification. In order to achieve fast and accurate classification of features and screen out the best model, this paper takes forests and airplanes as an example and compares and …
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Mingrui Zhong
Abstract: In the current era, there exists an uneven distribution of global medical resources, making it a crucial focal point for medical digital transformation. This paper aims to explore the application status, potential challenges, and future development prospects of ChatGPT, a large language model based on OpenAI, in the field of medicine. Given the current scarcity of global medical resources, this study analyzes existing medical use cases and examines the ethical and regulatory environment …
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Bowen Ma
Abstract: Under the current age in which data privacy and security become more and more important, as the short-coming of traditional distributed machine learning algorithms, the Federated Learning algorithm (FL), became more apparent. We need a better algorithm framework to handle more complex training scenarios. The focus of this paper is on multi-master Federated Learning, specifically on the last part of how to handle the weights from different master nodes. In this algorithm, instead of using only …
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Xinyi Sun
Abstract: Artificial Intelligence has had a huge influence on various fields since its appearance. The main focus of this paper is on the revolutionary impact of artificial intelligence on education. The major applications in the field of education are personalized learning, adaptive learning, virtual reality technology and teaching assessment. Virtual reality mainly focuses on virtual classroom, virtual laboratory, and smart campus, while teaching assessment analyzes and exemplifies self-service grading …
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Kangjie Lu
Abstract: Differential privacy technology is more and more widely used in the field of machine learning, especially in the gradient descent algorithm (SGD). Protecting data privacy by adding noise has become a hot topic of research. This paper reviews the noise addition strategy of differential privacy SGD from multiple dimensions, including adjustment based on noise distribution, adjustment based on gradient norm, adjustment based on privacy budget, and method based on model architecture. Each strategy …
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Ziyue Tian
Abstract: In recent years, federated learning technology has developed rapidly and been widely used in the field of data processing. This paper makes a comprehensive discussion on the privacy protection methods in federated learning,and makes a detailed analysis of three basic methods:data encryption,data perturbation and trusted hardware-based,introduces the principle of each method,and objectively analyzes their performance in practical applications. This paper gives a visual example to compare the …
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Weidan Chen
Abstract: Fruits are very popular in people's lives because they contain vitamins and dietary fiber, making them an important source of human diet. According to fruit production statistics, global fruit production reaches millions of metric tons, so it is necessary for people to establish an advanced fruit recognition system. However, traditional methods, including physical inspect, are inefficient, labor-intensive, and have a high probability of errors. Based on these facts, developing more efficient …
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Pengyu Chen
Abstract: For a long time, people have been trying to predict the direction of stocks, employing various methods. This paper explores the application of machine learning algorithms in predicting stock market trends, aiming to address the challenges of traditional methods and leverage the advantages of data-driven approaches. Highlights the limitations of conventional techniques and elucidates the potential of machine learning in capturing complex market dynamics. This paper discusses various machine …
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Tongyu Wang
Abstract: This paper aims to explore the visual information encoding mechanism based on neuronal cell assembly theory, focusing on improving the interpretability and credibility of visual information processing models. By comprehensively applying multidisciplinary theories and methods such as neuroscience, computational neuroscience, and information theory, the effect of neuronal activity in the process of visual information processing is analyzed step by step, and the way visual information is encoded in …
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Xitan Zhang
Abstract: The lower part of the eye is filled with abundant blood vessels, in the normal course of infusion of blood vessels, and the reason for this phenomenon is various eyeground vessels, for example, diabetes retinopathy, retina venous obstruction, hypertension retinopathy, hyperoptic retinopathy, etc. If the bleeding volume is small, people don't need to look for a doctor, but eyeground can be self-relieved. If, however, the hemorrhage in the blood vessel is extensive and hard to heal, it may be …
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Jiaxuan Zhang, Jiuji Rong
Abstract: The property insurance industry faces significant challenges against the backdrop of the high incidence of extreme weather events around the world. This study focuses on insurers writing policies in high-risk areas and explores how communities and real estate developers are adapting their insurance models to enhance property resilience. We provide an in-depth analysis of the impact of climate change on the sustainability of the insurance industry and use ARIMA and CGDAM-WRIR models to predict …
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Weihang Yuan
Abstract: This article examines the transformative impact of machine learning (ML) and artificial intelligence (AI) on surgical robotics, highlighting the advancements that have significantly enhanced precision, efficiency, and safety in surgeries. The integration of these technologies has enabled surgical robots to perform complex tasks autonomously, with accuracy rates approaching those of human surgeons. Key developments include improved surgical tool tracking, real-time data analysis, and enhanced …
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Ruining Yu
Abstract: This study evaluates traditional and deep learning-based image recognition technologies in autonomous navigation robots, detailing their strengths and limitations. Traditional image recognition techniques, which rely on predefined algorithms and pattern recognition, have proven efficient and stable for navigation in controlled environments. Conversely, deep learning approaches, notably through convolutional neural networks (CNNs), excel in dynamic and unpredictable settings by adapting more …
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Zhe Yang
Abstract: Fatigue, significantly impacting daily work and life, is a common state triggered by overexertion. Given these statistics, the development of reliable fatigue driving detection technology becomes critical to enhancing road safety. Therefore, fatigue driving detection technology is essential to reduce accidents and improve traffic safety. Effectively detecting fatigue has become a significant topic of interest among researchers. Methods for detecting fatigue that rely on physiological data are …
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Siyuan Wang
Abstract: In recent years, the application of deep learning techniques in financial forecasting has garnered increasing attention due to their potential to capture complex patterns in market data. This study employed a Bidirectional Long Short-Term Memory (BiLSTM) neural network to predict the stock price trends of Apple Inc. By analyzing data sourced from Yahoo Finance, a predictive model capable of capturing stock price trends and patterns was developed. The study demonstrated satisfactory performance …
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Yimin Yuan
Abstract: Agriculture is the most basic, fundamental and important industry. Now, amid global climate change and resource shortages, agriculture must deal with the challenges of growing demand as the world's population increases This article organizes three aspects of agriculture that need improvement: anticipatory preparation before production, improvement of production methods, and detection and classification of agricultural products, and analyzes how machine learning can help agricultural progress in …
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Hanwen Jian
Abstract: Path planning algorithm is a critical technology enabling mobile robots to realize autonomous navigation. In this paper, the path-planning technology of mobile robots is deeply discussed, and the operation mechanism and principle of different algorithms are analyzed in detail. Based on the understanding of the characteristics of mobile robot path planning algorithms, these algorithms are divided into three categories: traditional planning algorithm, intelligent planning algorithm, and …
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Baixu Chen
Abstract: This paper studies the application of simultaneous localization and mapping (SLAM) technology in the design of mobile robots. Traditional SLAM research lacks a systematic approach and adaptability to actual work tasks, making it difficult for researchers to choose reasonable architectures for optimized design, thereby limiting the further development of SLAM technology. The paper begins by introducing the definition and development history of SLAM technology, then introduces six kinds of SLAM …
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Tianwei Sun
Abstract: Sleep quality is an important factor affecting human health, and more and more factors in daily life can affect sleep quality. Although there have been numerous studies demonstrating the relationship between sleep quality and health, there are still research gaps in different populations and key influencing factors. This study discusses the relationship between factors that affect sleep quality and health and conducts in-depth research on their impact on human health development. It conducted a …
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Bin Tang, Mengyao Sun, Haowen Zheng, Haiyun Han, Yu Jin
Abstract: In recent years, the continuous innovation of computer vision processing technology has greatly improved the quality of people's production and life, while the accompanying tampering risk has also gradually increased. There are frequent incidents of people with ulterior motives using computer image processing technology to tamper with digital media and falsifying and cheating others. This paper mainly analyzes the technology and method of digital media image tampering and forgery, the social …
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Wei Liang
Abstract: Facial expression recognition is of great significance in the fields of computer vision and human-computer interaction. Given the problems of over-fitting, high computational complexity, and vanishing gradient in VGG19, this paper proposes an improved VGG19 network model. By introducing depth wise separable convolution, the number of parameters of the model is significantly reduced and the computational efficiency is improved. This paper applies Batch Normalization and Dropout technology to …
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Xuhao Shen, Guangrui Luo, Chuanxia Hu
Abstract: Modern society is characterized by high levels of human activity and pollution, triggering numerous extreme weather events. This has resulted in numerous disaster losses, and ancient buildings of conservation value are often damaged by extreme weather. To mitigate the crisis caused by the increasingly severe climate risk to the protection of ancient buildings, this paper will measure the level of exposure of ancient buildings to climate risk this paper constructs a comprehensive evaluation index …
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Yiyi Lin, Yuhang Xiang, Yuhan Huang
Abstract: In tennis, "momentum" is one of the most important factors affecting the results of the game. First, this paper establishes the Multiscale Momentum-Success Test Model and calculates the proportion of the winning side’s momentum at four different scales (score, game, set and match). The proportions of the four scales are 70.3%,78.1%,84.6%,93.5%. Therefore, it can be proved that momentum plays a role in the game. Also, the fluctuation and success in the game are not random. Afterward, this paper …
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Runze Peng
Abstract: In the Great Lakes of the United States, lampreys, as an invasive species, have seriously affected the survival of native fish and destroyed the ecological environment. To find natural ways to control its population, it is necessary to study the relationship between lamprey and other species. The sex ratio of lampreys has an enormous impact on their population size. To study the impact of lampreys on ecosystem, it’s important to investigate the relationship between the sex ratio of lampreys and …
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Haojing Zhai
Abstract: The choice of a cultural heritage conservation scheme is crucial for the protection of cultural relics. A scientific cultural relics protection scheme can effectively reduce the risks and protection costs, and realize the sustainable and efficient development of cultural relics protection. In order to be able to choose the protection scheme more scientifically, this paper establishes an extreme weather risk assessment model based on the entropy weight method, a local underwriting value …
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Ruoxi Yun, Yunlong Wei, Kaiji Wei
Abstract: Marine surveying plays an important role in nautical safety and marine resources development. Among them, survey line planning and seafloor surface fitting are two key problems in marine surveying. In this paper, we propose a comprehensive solution to these problems. Firstly, for line planning, we establish a mathematical model for determining the shortest line in rectangular sea area, and derive the optimal line path through iterative solving. Second, for the seafloor surface fitting problem, …
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Kaiwen Luo, Hengyi Jiang
Abstract: This study explores the growth and population dynamics of lampreys, focusing on the relationship between growth rate, sex ratio, and interspecific interactions. Utilizing the Von Bertalanffy growth equation, we establish a non-monotonic growth rate function and an asymmetrical S-shaped weight gain curve for lampreys. Population distribution analysis reveals a normal distribution for mass and age, with a joint probability density function for coupled distributions. The study delves into the …
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Jingze Dai
Abstract: Illegal wildlife trade (IWT) stands as a silent yet devastating force. In this paper a 5-year project is proposed to have a measurable impact on reducing the illicit trade based on data-driven analyses. First, we develop the Client Performance Evaluation Model to identify the ideal client. Secondly, the AI-Powered Wildlife Guardian Project (AWGP) is designed to monitor and manage the IWT chain, from supply to demand. Additional powers and resources for the project’s success are identified. The …
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Yujun Yue, Xingqi Dong
Abstract: The aim of this paper is to apply models and algorithms to address the problem of searching and rescuing lost underwater vehicles. This paper main work is divided into three steps: establishing a geographic model, establishing a simulation model, and optimizing the search model. A model is built to predict the drift path of underwater vehicles in the ocean, and an optimization search model based on simulated annealing algorithm is used to find the optimal search and rescue strategy to locate the …
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Wenting Ma, Jiawei Yang, Haitao Zhao
Abstract: This article studies the automatic pricing and replenishment decision-making of vegetable products. Through factor analysis and time series analysis, analyze the inherent laws, and combine mathematical programming with genetic algorithm to make sales decisions for vegetable products. Firstly, the data is preprocessed using time series analysis to predict and fill in missing values from September 12, 2022 to October 14, 2022, when the data is empty. Secondly, the relationship between vegetable …
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Zimu Jia, Zhuoye Li
Abstract: From the score data of the Wimbledon tennis matches, we observed unexpected score fluctuations and even set-level variations for the leading side. This article aims to evaluate which player performs better at any given moment during the match and quantify the extent of their performance advantage, which this paper refers to as momentum. Firstly, this paper defines momentum as the player's winning probability, convert match data into a Markov chain, and establish a time-predictive model based on …
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Jinsheng Liu, Tong Jing, Xuejiayi Xu, Guohao Song, Xiaotong Li
Abstract: Understanding the intricate dynamics of the Great Lakes water levels, shaped by environmental factors like rainfall and evaporation, is crucial for devising sustainable water management strategies. This study develops a model to manage the Great Lakes' water levels by analyzing the interconnectedness of the lake network. Utilizing principal component analysis, key variables like rainfall and evaporation were evaluated for their impact on water levels. System dynamics models integrated with flow …
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Shaoxukang Liu
Abstract: To improve the ability to prevent potential natural disasters, countries around the world are actively formulating emergency plans to protect people's lives and property. In this study, Sichuan Province was used as a case study to explore the allocation of medical supplies in a single distribution center using data from 21 cities and a storage base of medical supplies in Sichuan province. By constructing integer mixed nonlinear programming models-Planar helicopter Problem (HRP) and …
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Chuanhui Cui, Meiting Dong, Haiqing Li
Abstract: The occurrence of extreme weather events affects the natural economic and social security, and has a serious impact on some industries, so the prediction and evaluation of disaster risk level is particularly important. This paper collected data on nine indicators from 25 regions and divided them into four areas: extreme weather, natural, social, and economic. the ARIMA prediction model is used to predict the probability and intensity of extreme weather in the future. The combined weights of each …
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Bowei Dong, Kaizhi Dong
Abstract: In the men's singles final of the 2023 Wimbledon Open, rising star Carlos Alcaraz defeated veteran Novak Djokovic, an astonishing turnaround and result that sparked an in-depth exploration of the sports phenomenon of "momentum". When one side of a match gains momentum, its technical level and adaptability will be greatly utilized, which will directly affect the victory or defeat of the match. Most of the past researches focus on verifying the authenticity of momentum in sports games and the …
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Yiwei Ge
Abstract: Tennis, with its playful and elegant nature, has perennially captivated individuals of all ages. Beyond being a mere game, it represents a clash of momentum. In modern sports, the term "momentum" is used to describe a player's drive and momentum during a game. In the tennis match, it can affect the player's rhythm of the match and the result, is the key factor to determine the result of the match. But the quantification of momentum has no definitive reference in sport. This study attempts to …
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Weiyu Tang, Xuanzeng Yi, Huimin Zhao
Abstract: This paper introduces a research framework for sports science, particularly in competitive tennis, focusing on momentum as a crucial factor impacting match outcomes. It addresses controversies surrounding momentum's definition, quantification methods, and its influence on results, highlighting the lack of research on momentum strategies. The study proposes a paradigm for calculating potential energy and employs the XGBOOST model optimized with DA to predict players' potential energy values with …
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Yunzhi Zhou, Xiubi Huang
Abstract: In recent years, the world has suffered from a variety of extreme weather events, which are gradually becoming a crisis for insurance companies and homeowners. To address the challenges of insurance companies in their underwriting decisions for a specific region, this paper proposes a binary classification model for determining whether they should be underwritten, with an output of 1 (underwritten) or 0 (not underwritten). Firstly, extreme weather events are identified by screening the number of …
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Yongkuan Li
Abstract: Establishing an accurate vegetable demand forecasting model is crucial for fresh supermarkets to optimize replenishment and maximize profits amidst fluctuating market demand driven by the perishable nature of vegetables and changing consumer purchasing decisions. This study takes a certain fresh supermarket as an example, targeting the strong periodicity and seasonality of the vegetable market demand characteristics, using SPSS for data analysis and ARIMA time series modeling. Through stationary …
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Lei Luo
Abstract: Nowadays, heliostat field has been gradually put into practical production, how to design heliostat field to make the highest efficiency of light energy utilization is an important problem. By establishing the adjustment model of heliostat, the design problem of heliostat field is studied by using the mesh search optimization algorithm. First, the position of the absorption tower is determined, so that the heliostat is closely arranged, and then the annual average output thermal power per unit …
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Guanyu Liu
Abstract: Addressing the critical challenge of extreme weather events, this study introduces a comprehensive model for enhancing insurance underwriting strategies through predictive analytics and risk assessment methodologies. Utilizing an integrated approach that combines Autoregressive Integrated Moving Average (ARIMA) for forecasting extreme weather occurrences, Fuzzy Comprehensive Evaluation for assessing regional payment capabilities, and Monte Carlo simulations for detailed risk quantification, the …
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Junjie Wang, Yu Yu, Xuechun Wang
Abstract: The Maasai Mara National Reserve is rated as one of the seven natural wonders of Africa and one of the ten wonders of the world. To reduce the impact of human activities on wildlife and further consolidate the balance between humans and nature, this paper proposes a research analysis method for the Massai Mara National Reserve. First, the influencing factors were divided into four indicators: wildlife, economy, resident life and non-animal resources. For these four indicators, eight main factors …
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Xinyi You, Jiaying Wu
Abstract: Lampreys are regarded as parasites with significant impacts on the ecosystem in some lake habitats, and analyzing the relationship between the population characteristics of lampreys and the ecosystem is of great significance for improving the ecological environment and maintaining the ecological balance. The sex ratio of lampreys varies depending on the external environment. To gain insight into the issue of sex ratios of lampreys in ecosystems and their dependence on local conditions, this …
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Zhiyi Chen, Zixin Guo, Ruirui Wu
Abstract: This study explores the concept of green GDP and its relationship with eco-environmental cost, aiming to study the development of green GDP and eco-environmental cost based on regression model. First, green GDP is a new type of GDP after deducting the cost of resource and environment degradation and increasing environmental profit based on traditional GDP. Then, it introduces its accounting method to realize sustainable development by calculating resource and environmental cost and environmental …
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Sitong Liu, Ziwen Tong, Yuhe Liu
Abstract: In this paper, a number of mathematical models were established to study the effects of sex ratio changes in lampreys on this ecosystem. Logistic and Verhulst models were established to describe the population dynamics of Lampreys. Then a food web model and a niche model were construct-ed to describe the food relationships and interactions between different species. The effects of sex ratio of lampreys on ecosystem stability and species interaction were analyzed. Finally, TOPSIS evaluation model …
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Fan Wang, Tong Wang, Jiayu Fan
Abstract: Gaussian process regression, as a nonparametric statistical method capable of fitting nonlinear functions, holds an important place in the realm of quantitative finance. However, when applied to the prediction of noisy stock price time series, a single Gaussian process regression is prone to overfitting, and its computational efficiency diminishes with an increase in data volume. Considering this, we propose an ensemble learning model for Gaussian processes based on Bootstrap and stacking …
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Zenan Huang, Kai Wang
Abstract: In supermarkets or shopping malls that mainly deal with fresh food, vegetable commodities are generally difficult to store for a long time with a short freshness period. And the quality of vegetables and storage time present a certain relationship, the longer the storage time the worse the quality of vegetables, and even the existence of varieties if the day did not sell the next day will not be able to sell. Therefore, supermarkets need to replenish goods on the same day according to the past …
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Jiayi Mao, Jiashi Qi, Pengcheng He
Abstract: In this paper, a dispersal prediction model was established to predict the dispersal of dandelion by taking dandelion as the research object and analyzing the climatic conditions of the key parameters of dandelion seeds. Firstly, the key parameters of dandelion seeds were introduced, including the number of crown hairs, the average length of crown hairs, and the diameter of crown hairs. Then a dispersal prediction model was established to predict the dispersal of dandelion in different regions …
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Shuo Wu, Zeshuo Zhang, Yixuan Ru
Abstract: In the field of e-commerce, accurately predicting the future demand for specific products is crucial for efficient inventory management and supply chain optimization. Although traditional time series forecasting methods have shown reliability in handling rich data, they are significantly affected by seasonal fluctuations and sudden market events. This is especially true for newly launched products or long-tail products with insufficient sales data, which often results in poor performance, …
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Tianyuan Zhang
Abstract: Under the current study, there are fewer ways to solve the higher-order full differential of multivariate functions. This paper introduces Yanghui Triangle and the higher-dimensional form of Yanghui Triangle to study the field of higher-order full differential solution of multivariate functions, to summarize the general formulas and to improve the efficiency of operation. Based on the assumptions of infinite partial derivatives and continuity of partial derivatives, this paper first proves the …
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Tao Shen, Zheng Liu, Kejin Li
Abstract: Extreme-weather events are becoming a crisis for property owners and insurers, which has drawn widespread attention. For insurance companies, predicting regional losses from Extreme-weather events and studying the participation decisions of insurers can inform insurance strategies. This paper primarily focuses on the impact of extreme-weather events on insurance behavior, based on ten years of extreme-weather data of Michigan, we use Fourier fitting to predict the frequency of extreme-weather …
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Sayang Mu
Abstract: In this paper, the competitive relationship between trout and perch populations was analyzed by model differential equations, and the stability and final stabilization points under different parameters were explored. It is found that under the premise that global stability is better than local stability, trout and perch can coexist and reach the final stable state when the competition factors satisfy specific conditions; while under other competition conditions, it may lead to the number of one …
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Kaiyue Wang, Changcong Xu
Abstract: Extreme weather can cause economic losses to residents which can be reduced by purchasing insurance. From the perspective of insurance company, to earn maximum profit, they need to carefully consider whether to provide insurance services in a region, especially for where natural disasters happen frequently. The paper puts forward Natural disaster prediction model and Insurance company undertaking model. In the first part, the paper uses ARIMA algorithm to forecast when will the natural disasters …
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Yanze Sun, Zhaoqi Chen, Qiutong Wang
Abstract: This paper aims to study the state volatility of players in tennis matches and proposes a conceptual model of "momentum" based on sliding window technology. Firstly, Pearson correlation analysis is used to screen out the indicators that are highly relevant to the victory of the match, and the index system including point advantage, service advantage, break point, unforced error, ace, game won and distance run is constructed. Then, a formula for calculating "momentum" is proposed, and the sliding …
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Feifan Yu, Lei You, Xinyue Han, Haoze Ma, hijun Sun, Liyin Zhang, Yuesheng Zhao
Abstract: In fresh food superstores, vegetable commodities are characterized by a short shelf life and deterioration in quality over time. To meet market demand and profit, merchants usually sell vegetables with shipping losses and deteriorating quality at a discount. Therefore, the pricing and replenishment strategies of vegetables have an important impact on the profit maximization of fresh produce superstores. The aim of this paper is to reveal the patterns by processing and analyzing the past sales …
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Xingyu Bao, Shiya Li, Zihan Chen, Zihan Chen
Abstract: The lampreys, a parasitic fish native to the Atlantic Ocean, is an invasive non-native species in the Laurentian Great Lakes of North America and an endangered species in most of its native regions in North America and Europe. This paper addresses the study of demographic changes in the population of lampreys in the Great Lakes basin of North America, which is of reference significance to the control of the population of lampreys in the Great Lakes basin of the United States and the protection …
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Yan Ding, Chi Zhang
Abstract: A tennis player's performance during a match affects the outcome of the match. The study of the issue can provide advice to tennis players on how to prepare for the tournament, and to coaches and their teams on how to develop training programs that will help athletes get better results in the tournament. Initially, according to the entropy weight method to assess the player's performance on the field. Two factor layers were used to finally get a score measuring the performance at each point. …
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Weihan Wang, Jianshu Zheng, Jinqiu Zhang
Abstract: This study provides an in-depth examination of nature reserve programs and their feasibility, focusing on the challenges posed by illegal wildlife trade. Through an extensive literature review of scientific databases such as SCI and EI, the thesis illuminates the frontiers of current conservation efforts and assesses the compatibility of nature reserve programs with stakeholders. The research includes a comprehensive case study of wetlands in Patagonia, Brazil, analyzing the correlation between …
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Yuanyuan Qin, Yuanyuan Qin, Yunzhuo Cha, Hanhong Liu
Abstract: Real estate location selection is integral to real estate decision-making. Accurate real estate site selection is of great significance to ensure profits for real estate developers, increase real estate flexibility, and improve the serviceability of the real estate industry. To accurately locate real estate, this paper takes Dallas County, which is a high-risk area, as the research object, and establishes a real estate location optimization model combined with property insurance pricing model. …
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Zhisang Zhou, Haiwang Gong, Mingyan Tang, Wenrui Xiao, Mengqi Lou
Abstract: Momentum in tennis matches can greatly affect the performance of tennis players, and dynamic monitoring of momentum can help players to achieve better results in the competition. This paper constructs a tennis ball momentum prediction model based on the random forest algorithm. The model selects the nine indicators of the process of winning the competition in the current set, whether to score continuously, whether to serve, gain or lose the point by untouchable shot, relative fatigue, error …
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Yi Lu, Dezhi Luo, Xi Yang
Abstract: The greatness of nature is often astounding, but few people realize that behind its greatness lies a host of impacts from extreme weather, and with global climate change continuing to intensify, it poses unprecedented challenges to the property insurance industry. In this paper, we develop a broadly applicable risk scoring model that provides the property insurance industry with critical insights for more accurate risk assessment and adjustment of underwriting strategies. In this paper, we use …
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Xuyi Shi, Jiachen Guang
Abstract: In the analysis of functional data, functional data regression is a crucial statistical model. The preceding step is to smooth the discrete function, usually using the basis function expansion method. However, due to the diversity of basic functions, it is a difficult problem to choose the basis function suitable for specific data. In view of this, an adaptive basis function expansion function regression model based on Group LASSO is proposed in this paper, which is suitable for the scenario …
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Binglin Cheng, Wenchao Wang, Xiaohao Lei, Suien Ma
Abstract: This study employs mathematical modeling to investigate the impact of sex ratio changes on the sea lamprey population dynamics. Utilizing differential equations and specific models for population growth, reproduction success, and resource utilization efficiency, the study examines the effects of sex ratio on population dynamics, reproduction success rates, and resource usage efficiency. Diagrams illustrating variations in sex ratio and population quantities offer insights into their effects on …
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Rui Xu, Mengwen Sun, Qing Lv
Abstract: Based on the working principle of multibeam bathymetric system, this paper establishes a multibeam line design optimization model and coverage width geometric model by establishing a spatial right-angled coordinate system, dichotomy method, moving surface method and other methods. A two-dimensional geometric model of the coverage width and the overlap rate of adjacent strips is established in the two-dimensional plane. Then, based on the geometric model, the relationship equations of seawater …
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Jingzhe Peng, Feiyang Pan, Jiaxing Li, Yixin Duan
Abstract: This study aims to build an accurate performance prediction model to assist the training and development of skilled workers. This article uses the advanced LightGBM algorithm and combines it with rich technical worker training data in a certain industry to predict trainees' training results. In the data processing stage, this article conducted detailed data cleaning to eliminate outliers and ensure the accuracy and reliability of the data. At the same time, by introducing new variables, this …
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Shaohang Chen, Jiacheng Chen, Ruizhe Jiang, Yian Xuan, Siming Liang
Abstract: This paper examines the relationship between "Momentum" and victory in the imbledon 2023 men's tournament, particularly between the semi-finals and the final. The definition and ormula of the Pearson's correlation coefficient are presented, and the Pearson's correlation oefficient is analysed based on the match data between Carlos Alcaraz and Novak Djokovic. The results show that there is a positive correlation between "Momentum" and winning. Then the logistic regression model was used to …
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Huanyu Gong, Xuanxuan Guo, Zhuohui Gao
Abstract: In tennis, player performance is often attributed to "momentum," pivotal for game control. We analyzed tennis rules, extracting performance indicators such as point differentials, dynamic scoring, and serving advantages. Utilizing a Random Forest model with player performance as the dependent variable, we mapped features to momentum and determined feature importance, achieving an AUC of 0.8796. We then introduced the Comprehensive Ability Volatility Index (CVI) based on factors like serve win …
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Yurun Xia, Yujie He
Abstract: With environmental changes and increased human activities, changes in the sex ratio of Lampreys, a key secondary consumer, have a significant impact on the health and stability of aquatic ecosystems. To address this, this study created a seven-gill eel population prediction model based on the Lotka-Volterra Model incorporating a Bayesian Hierarchical Model suitable for changes in the seven-gill eel population. The model predicted the population density of Lampreys based on the sex ratio of …
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Xiangyu Wu, Haoxuan Xu, Xuan Zeng
Abstract: The study employed the Adaboost algorithm to identify optimal indicators of momentum, demonstrating that momentum could significantly predict the likelihood of a player's subsequent wins or losses. This study offers a novel exploration into the dynamics of tennis matches, focusing on the concept of "momentum" and its impact on match outcomes. Through an analysis of the 2023 Wimbledon Men’s Singles final, the research demonstrates how momentum significantly influences the likelihood of a player's …
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Yuxuan Zhang, Zixin Kang, Wenjie Lyu
Abstract: Sports events have been a significant component of global culture and economics, and analyzing the fluctuations in athletes' performance is one crucial factor in predicting the result of matches. Quantitative research on players’ momentum has long been a popular topic. Building upon prior research, we have developed a mathematical model based on two combined machine learning methods. Specifically, this paper utilized a random forest model to analyze the importance of various metrics of athletes …
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Minxiao Wu, Yuchen Liu, Yuren He
Abstract: This paper uses particle swarm and neural network algorithms to construct an efficient submarine search and rescue (SAR) solution based on an unmanned underwater detector cluster, aiming to provide stronger safety guarantees for tourists who experience the submarine project. Specifically, this article constructs a position prediction model and a search optimization model respectively. The former will accurately simulate the submarine's motion trajectory and various parameters through a …
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Quankun Zang, Zetong Li, Jiren Hu, Tingxu Zhang, Weiqi Zhang
Abstract: Tennis is a beloved sport worldwide, and spectators often eagerly predict the winners of both sides based on various playing conditions. Research has shown that there is a significant correlation between players' performance in the game and their momentum. To explore the impact of players' momentum on match results, this paper establishes a TOPSIS evaluation model based on entropy weight method to measure players' momentum value at every moment. Additionally, this paper establishes a decision …
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Wenqian Sun, Heng Hua
Abstract: Momentum shifts in competitive sports have a significant impact on game outcomes and strategy development, and despite their widely recognized importance, there is a relative lack of relevant systematic research. In this regard, this study quantified the relationship between momentum shifts and player success, using the metrics analysis method to analyze their correlation and stochasticity. The relevant metrics mainly comprised Spearman rank correlation analysis, and P-value test calculations, …
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Huaizheng Lu, Xinyi Wu, Dedong Zhang
Abstract: Potholes on roads not only jeopardize traffic safety and driving comfort but also necessitate efficient detection and maintenance measures. Traditional manual detection methods are labor-intensive and time-consuming. Thus, developing automated solutions is imperative. This study addresses this challenge by constructing a dataset and evaluating various deep learning models, including GoogleNet, VGG16, ResNet50, AlexNet, YOLOV5, and YOLOV8, for pothole detection. YOLOV8 emerges as the optimal …
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Yadi Peng, Xinzhou Du
Abstract: The quantitative analysis of momentum is of great guiding significance to the adjustment of competition strategy and state of coaches and athletes in the field of sports. However, the existing momentum research is mainly explained from the perspective of economics, psychology and other theories, without quantitative analysis. Or after quantitative analysis, the model can only predict the results of the whole game, and cannot accurately predict the changes during the game. Therefore, using data …
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Yonghe Peng, Junhe Yang, Rui Liu
Abstract: With the deepening of cultural relics conservation and archaeological research, changes in the chemical and physical properties of ancient glass artifacts, especially the phenomenon of surface weathering, have become an important basis for the study of ancient civilizations and for guiding the authentication and preservation of cultural relics. To help reveal whether the glass surface is weathered or not and accurately classify the glass, this paper firstly preprocesses the sample point data of …
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Yuxin Zhao, Zhaoyang Wang, Yucheng Chen, Xin Li
Abstract: In terms of sorting center volume forecasting, the traditional ARIMA model is difficult to cope with the rapid changes in the market and environment, so an innovative new method based on machine learning is proposed. Then features such as trend, period and lag are constructed for each sorting center to accurately capture the temporal features and improve the prediction accuracy. By comparing various models such as random forest, neural network, support vector machine and linear regression, and …
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Yuhao Fu
Abstract: In recent years, the landscape of the electronic gaming industry has been profoundly reshaped by advancements in computer hardware and software capabilities. Notably, the proliferation of network and mobile gaming platforms has catalyzed unprecedented growth within the sector. This surge in popularity underscores the increasing demand for immersive and engaging gaming experiences across diverse demographics. This paper endeavors to delve into the intricacies of AI’s role in gaming, providing an …
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Jinjin Xu
Abstract: In this paper, the application of high-performance computing technology in real-time polarization parameter estimation algorithm of conformal array is discussed, aiming at solving the problems of high computational complexity and poor real-time performance in traditional polarization parameter estimation methods. By introducing parallel computing and distributed computing technology, a new real-time polarization parameter estimation algorithm framework is proposed, which can significantly …
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Meng Lin
Abstract: In recent years, deep learning has significantly advanced image classification. However, as models grow more complex, their computational demands increase, prompting a shift toward lightweight models. The MobileNet series, known for its efficiency in resource-constrained environments, is a prime example. Despite its popularity, performance comparisons among different MobileNet versions for specific tasks like cat and dog image classification remain underexplored. This study addresses this gap by …
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Haotang Wang
Abstract: As autonomous driving has become more popular, research on the technology has proliferated. There are many schemes to realize autonomous driving, including visual perception, lidar, and radar. Among them, visual perception implements advanced neural networks and transfer learning to achieve excellent performance. This article mainly discusses the advantages of visual perception, the challenges it faces, and possible solutions, while comparing it with other schemes from various aspects at the …
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Xianshun Jiang
Abstract: This report details a machine learning project aimed at predicting car prices, which is crucial and meaningful for business owners to forecast the market, avoid financial loss, and for buyers to have a rough overview of each car’s price. To accomplish the goal, this project using the Car Price Prediction Challenge dataset from Kaggle. The dataset includes a variety of automobile attributes such as brand, model, year of manufacture, engine specifications, mileage, and other relevant factors. The …
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Wenxuan Zhang
Abstract: Remote sensing image recognition is an important direction in remote sensing data processing. Among them, deep neural networks have achieved results far beyond traditional methods on many challenging image datasets in remote sensing images. There are various network models for convolutional neural networks, for better remote sensing image recognition, this paper has selected the AlexNet model and ResNet model for comparison to select the network model with more accurate results. After running, …
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Xinbu Zhao
Abstract: As deep learning technology has rapidly developed in recent years, image recognition and classification have become a hotspot research direction in artificial intelligence. Accurate identification of target objects in images has great significance for practical applications like autonomous driving, medical image analysis, and biodiversity conservation. Pandas and brown bears are both endangered species of worldwide conservation concern, but manually classifying large volumes of images can be …
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Bohan Xuan
Abstract: Deep learning has emerging and numerous applications for most areas, including finance, physics and medical science, etc. The deep-based models achieve satisfying performance on those tasks. In this paper, we aim to provide a summary of deep learning-based temporal stock prediction. Specifically, we first categorize the models into three aspects, including CNN-based models, RNN-based models, and hybrid models, by combining CNN and RNN. Then, we detail the preliminary knowledge for deep-based …
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Xiangjun Zhao, Zirui Qiao, Xiaoyang Li, Lingfei Zhu
Abstract: In the future, cloud computing technology will continue to evolve in the direction of more openness, intelligence and integration; big data analytics will develop in the direction of generation prediction, personalized recommendation and autonomous driving. The integration of cloud computing technology and big data analytics will breed more opportunities and industrial changes. The two are closely related and the development trend of mutual promotion is unstoppable, worthy of in-depth attention …
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Xiyi Xiong
Abstract: This research focuses on developing a robust face alignment system crucial for applications in facial recognition, expression analysis, and augmented reality. Utilizing a dataset of 2811 training images and 554 test images each annotated with 44 facial landmarks, the study explores several models including Convolutional Neural Networks (CNN), Cascade Regression, and Residual Networks (ResNet). Through rigorous experimentation, it was observed that while CNNs and Cascade Regression models …
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Zicheng Zhang
Abstract: Brain tumors represent a severe threat to human health due to their potential to disrupt vital functions controlled by the brain, such as movement, sensation, and cognition. This study aims to classify a comprehensive dataset of 7,023 magnetic resonance imaging (MRI) images from Figshare, SARTAJ, and BR35H into four distinct classes (Glioma, Meningioma, Pituitary, and No-Tumor). After evaluating the state-of-the-art models including VGG16, DenseNet201, ResNet50, and V3 Inception, the study …
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Jun Zhou
Abstract: The evolution of memristors and their successful applications have positioned them as formidable candidates for the next generation of computer systems. With the rapid advancement of foundational ar- tificial intelligence applications, there is an increasing demand for computational power, energy efficiency, and stability. Memristors and the Neuromorphic Computing (NMC) systems they underpin hold signifi- can’t potential to break through the von Neumann bottleneck. However, technical challenges …
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Lingwen Jiang
Abstract: Puzzle game wordle has been wildly popular since its launch, its simple and unique game rules have generated a lot of data while gaining a large number of users in a short period of time, making wordle prediction a hot topic. In this paper, we construct four models based on the properties of words: interpretive prediction report counting model, correlation judgment model, percentage prediction model and word difficulty category classification model to provide a solution for predicting words. In …
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Ruitao Zhu, Linjie Wang, Yunbo Feng
Abstract: This paper discusses the characteristics and development trend of intelligent mechanical engineering. Intelligent mechanical engineering is characterized by high quality, high efficiency, four-dimensional intersection, energy saving and environmental protection. The development trends include networking, informatization, integration, automation control, product intelligence and artificial intelligence, etc. These trends will push mechanical engineering towards a new stage of more intelligent, …
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Wumiao Xie, Kaicheng Frank Yang
Abstract: Colorectal Cancer is one of the most common cancers worldwide, and as the probability of cure decreases as the stages progress, the early-stage detection of possible indications of colorectal cancer (i.e., polyps) during colonoscopy exams can contribute greatly to its prevention and curation. Since some of the indications of colorectal cancer are hard to identify by the human eye, it becomes crucial to develop an algorithm that can assist in the early identification of abnormal tissue walls that …
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Dingtian Pu, Mingran Sun, Jingyi Yuan
Abstract: Deepening globalization has made the illegal wildlife trade a growing problem, and this paper uses modern information technologies, such as big data analysis and machine learning, for monitoring and evaluation, which are essential for understanding trade dynamics, predicting trends, and assigning responsibilities. This study uses Klein's comprehensive national power equation to establish a scoring system to select the countries or organizations with the most rights, resources, and interest in …
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Pingxin Liu, Maoliang Tian, Chenzhe Dang
Abstract: Momentum in sports, particularly in tennis, refers to the perceived shift in performance and psychological advantage that can influence the outcome of a match. It encompasses both physical and psychological aspects and is often characterized by sequences of winning points, games, or sets. In tennis match, the flow of the match tends to change based on athlete’s individual ability, real-time status and other objective factors.In these factors, a growing number of matches have shown that the …
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Xin Gao, Shaojia Yuan, Jiazheng Zhu, Yifei Zhao
Abstract: In response to the pressing need for intelligent navigation technology in modern agriculture, this paper introduces a visual navigation system for unmanned agricultural machinery based on deep convolutional neural networks (CNNs). The system integrates the strong representational power of deep learning with rich visual information to facilitate high-precision, adaptive autonomous navigation in complex agricultural environments. Key innovations encompass: (1) the development of a customized deep …
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Yiyang Zhou, Xingyu Chen, Zijin Wang
Abstract: The paper mainly builds a machine learning model for tennis match score prediction and conducts significance analysis on the effects of concomitant conditions using PSM. Firstly, the paper constructed a set of new metrics system, including whether the athlete is on the serve side, his/her personal skill, the level of fatigue, and his/her mentality during the match, tested the significant effect of these metrics on the model prediction by binary logistic regression, and used various machine …
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Zibin Geng, Junchen Liu, Jiuzhou Wang
Abstract: In this study, through the use of machine learning models and BP neural network, a large amount of financial data is deeply mined and analysed. The research results show that the constructed model improves the financial fraud recognition accuracy to 0.94, which can achieve the discrimination of financial fraudulent account behaviours at a high accuracy rate, provide strong support for financial institutions, and effectively prevent financial fraud risks. This study aims to solve the problem of …
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Tianyu Du, Qingwen Yin
Abstract: With the rise of e-commerce and the ever-changing consumer demand, logistic network sorting centers face increasingly complex and significant challenges in cargo volume forecasting. To predict the cargo volume for various routes, this paper selects the Random Forest model for forecasting. Feature data were extracted, and the dataset was divided into a training set and a test set. The model was evaluated using the test set. Ultimately, a high coefficient of determination and low mean squared …
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Yongchao Wang, Xingcheng Wu, Jianni Hou, Mengda Hu
Abstract: To enhance the accuracy and efficiency of color matching for opaque products, a relevant model is employed to predict their color schemes. By extracting the spectral reflectance of dyed panels as input and dyeing ratios as output, it is possible to reduce color matching errors and improve production efficiency, thereby advancing the dyeing technology of opaque products and achieving color matching freedom. This study initially applies a logarithmic transformation to the K/S (absorption …
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Tao Jiang, Ni Fang, Mengyao Deng
Abstract: This paper explores the optimization of mine equipment configuration and operations using quantum computing and the Quadratic Unconstrained Binary Optimization (QUBO) model. It highlights quantum computing's ability to enhance problem-solving efficiency and solution quality, especially for complex and multivariable optimization problems, compared to traditional methods.The research focuses on two main areas: optimizing equipment configuration and scheduling operations. For equipment …
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Haiyi Wang
Abstract: The safety and efficiency of autonomous driving technology rely heavily on precise simulation training, which is greatly limited by the quality and diversity of the training environments. Generative Adversarial Networks (GANs), as a powerful generative tool, can create highly realistic virtual images and environments, providing complex and variable training data for autonomous vehicles, thus enhancing the algorithms' generalization ability and adaptability. This study first reviews the major …
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Dishen Yang, Qingyang Zhang, Baihe Luan
Abstract: Momentum is a critical factor influencing the dynamics and outcomes of tennis matches. To enhance the predictive accuracy of these fluctuations, this study utilizes machine learning techniques and big data analytics to improve the prediction of these fluctuations. The study conducts a comprehensive analysis to identify the correlation between a player's momentum and 14 key features in a tennis match. An optimized BP neural network model, based on Levenberg-Marquardt theory, was developed to …
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Tingyue Wang
Abstract: Quantitative investment is a hot topic in recent years, through machine learning algorithms to deal with a large amount of data, saving investors' time and energy, to provide investors with reliable investment prediction results, the use of machine learning algorithms in quantitative investment has a broad prospect. This paper is based on the machine learning model to predict the rise and fall of the SSE index, using evaluation indicators to measure the model performance, select the best model …
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