Enhancing Fantasy Novel Illustrations through AI-Driven Unpaired Image Translation and Diffusion Models

Junyi Luo

pp.1-6 DOI: 10.62051/x3m55b72

Abstract: Fantasy fiction captivates readers by transporting them to magical worlds filled with mythical creatures and vast landscapes. Illustrations are essential in enhancing this immersive experience by visually bringing these imaginative realms to life. Traditionally, creating such illustrations relies on skilled artists, a process that is time-consuming and resource-intensive. However, recent advances in artificial intelligence, particularly generative models, offer new possibilities for automating … Read More

Joint Restoration Method Based on Super-resolution Network and Diffusion Model

Mengran Xin, Chenghan Li, Mengyao Xin

pp.7-15 DOI: 10.62051/dwx7ap48

Abstract: To address the challenges of low image resolution obtained by underwater robots and poor mask inpainting effect, a joint restoration method based on super-resolution network and diffusion model is proposed. Through the degradation model and deep learning, the mapping between high and low resolution images is achieved, and combined with the reverse optimization ability of the diffusion model, the image clarity and physical authenticity are significantly improved. Experiments show that the … Read More

Application of Pre-training Model in Natural Language Processing

Zhaosicheng Chu

pp.16-23 DOI: 10.62051/8wsmzk54

Abstract: Natural language processing is a very important research area in the field of artificial intelligence, with the goal of enabling computers to understand human language, this research area brings together information from multiple disciplines such as linguistics, computer science, machine learning, mathematics, and cognitive psychology, it has two sides: cognition and understanding of natural language and natural language processing. Of these, natural language recognition and understanding allow … Read More

Deep Learning-Based Exploration of Text Meaning

Junlin Huang, Xuan Liu

pp.24-29 DOI: 10.62051/5ndjf655

Abstract: In the field of Natural Language Processing (NLP), Recognizing of Textual Implications (RTE) belongs to one of the core tasks, aiming at recognizing semantic relations among texts. With the continuous improvement of related models, the application of Transformer has pushed the development of this field, but the existing models are still insufficient in numerical reasoning and multilingual scenarios. Therefore, this paper proposes and analyzes an improved hybrid transformer-QRNN model, exploring … Read More

A Survey of Text Aspect-level Sentiment Classification Focusing on Relevance

Xinyu Du

pp.30-39 DOI: 10.62051/zz3ch192

Abstract: Sentiment classification is an essential part of sentiment analysis. In recent years, it has emerged as a major research area in natural language processing. This field of research has high application value in social media, product reviews, and other areas. With the advent of deep learning, research for sentiment classification is evolving from coarse-grained towards fine-grained approaches. The merge is relatively oriented towards aspect-level sentiment classification. The underlying … Read More

Expanding Movie Sentiment Analysis with Fastai

Shunqian Mao

pp.40-43 DOI: 10.62051/gzc1np77

Abstract: Sentiment analysis, a crucial component of natural language processing (NLP), isolates personal information from intellectual information, such as digital responses. Moving learning-based deep learning systems like Fastai have performed incredibly well in general text classification. However, they haven't been tested on their adaptability to domain-specific tasks like video sentiment analysis. Applying the Kaggle videos, this research examines Fastai's success. Using worksheet data to make … Read More

A Review of Applications of Speech Synthesis Technology

Wenhao Xiong

pp.44-52 DOI: 10.62051/f4gcpp59

Abstract: Currently, the development of visual technologies and applications is much more advanced than that of speech, but as both speech and vision are equally important and attractive, their potentials should be comparable. In order to develop speech technology and prove its application potential, this paper presents current status of speech synthesis technology in four applications: spoken language education, digital music, virtual character, and language protection and dissemination, and then points … Read More

Progress of Pathological Image Analysis Based on deep Learning

Zibing Hua

pp.53-59 DOI: 10.62051/y27v8m74

Abstract: Pathological image analysis plays a crucial role in the diagnosis and treatment of diseases. However, due to its complexity and diversity, traditional methods are difficult to meet the growing demand. A novel method for analyzing pathological images has emerged in recent years because of advancements in deep learning technology, particularly the usage of convolutional neural networks (CNNs). This study compared the performance of various models through a systematic literature review and … Read More

Analysis and Application Research of Typical Technologies of Generative AI in the Context of Games

Shuzhao Shen

pp.60-70 DOI: 10.62051/mps9h313

Abstract: With the rapid development of artificial intelligence technology, generative AI technology is gradually reshaping the paradigm of game development. It not only greatly improves the efficiency of game content generation, but also enhances the interactivity and personalized experience of games, which will be a unique new experience for developers and players. This paper focuses on the typical technical architecture and innovative applications of generative AI in the game field, and systematically … Read More

A Study of Exploration-Exploitation Strategies in Unconventional Situations

Zhangqi Zheng

pp.71-79 DOI: 10.62051/88exap78

Abstract: The exploration-exploitation problem is a central challenge in Reinforcement Learning (RL), and the Multi-Armed Bandits (MAB) serve as its foundation, providing a classical paradigm for exploration and exploitation strategies. With the development of big data and deep learning, the application of RL models in online learning, recommender systems, and other fields has become increasingly complex, giving rise to variants of models such as multi-objective optimization and stochastic adversarial. … Read More

Research Progress and Challenges of Computer Vision Object Detection Technology

Chun Ho Ma

pp.80-85 DOI: 10.62051/wzzqzf67

Abstract: With the continuous increase in the number of motor vehicles in the world, traffic accidents caused by drowsy driving have become a major social safety problem. This paper systematically analyzes the evolution path of the current fatigue driving detection technology, focusing on two major methodologies based on the fusion of single features and multiple features. Although the method based on a single feature has the advantages of low computing cost and easy deployment, it is limited by … Read More

Towards a Unified and Adaptive Smart Home System: Deep Learning and iot Integration

Denghan Xu

pp.86-93 DOI: 10.62051/wd65nf07

Abstract: With the wide application of IoT technology in daily life, the smart home industry has achieved rapid development in recent years. However, due to the different communication methods and fragmented functions between different smart home devices, this makes users face higher complexity in management and use. The goal of the smart home management and control platform is to integrate the data monitoring and control functions of multiple devices in a heterogeneous network environment to provide … Read More

Typical Method Combination of Global Path Planning Technology Based on SLAM Technology

Xinyue Li

pp.94-100 DOI: 10.62051/68cdnf60

Abstract: As automation and intelligent technologies progress rapidly, robotics has seen broad adoption in multiple sectors such as autonomous transportation, UAV operations, home automation, and industrial manufacturing. In order to achieve autonomous navigation of robots in unknown, dynamic, and complex environments, global path planning technology has become a key research direction. This article explores the typical methods and optimization research of global path planning based on SLAM (Simultaneous … Read More

Design and Application Research of Dynamic NPC System Based on Local Large Language Models

Xinrui Li

pp.101-107 DOI: 10.62051/5qe7kq62

Abstract: With the Development of the video game industry, the problem of traditional Non-Player Characters (NPCs) became rigid because they rely on pre-written scripts. Although the Large Language Models (LLMs) offer a new way to generate dynamic conversations, most useful ways nowadays from using cloud-base solutions suffer from high response delay (>500ms) and privacy risks.  Recent research has highlighted the need for on-device AI solutions to address these performance and security concerns. This … Read More

Research and Analysis on Text Interaction Methods Based on Large Language Models

Shangze Yu

pp.108-114 DOI: 10.62051/rs49my32

Abstract: Large Language Models (LLMs), as a cutting-edge technology in the field of artificial intelligence, have revolutionized human-computer interaction paradigms by integrating natural language processing, Transformer architecture, and reinforcement learning techniques, thereby achieving in-depth understanding and generation of linguistic logic and emotions. Their technological core lies in the Transformer architecture trained on massive datasets, which can capture complex semantics and contextual … Read More

Research on the Application of Image-based Gesture Recognition Technology in Classical Industries

Yang Wang

pp.115-119 DOI: 10.62051/2mt36577

Abstract: In today's era of rapid technological development, the innovation of human-computer interaction technology is profoundly changing the way people live and work. Among them, gesture recognition technology, as a promising means of interaction, has gradually become an unstoppable trend in various classic industries. At present, gesture recognition technology is mainly divided into two categories: sensor-based data capture and vision-based non-contact perception. The former relies on high-precision … Read More

A Systematic Survey of Multi-Agent Reinforcement Learning

Junyi Leng

pp.120-125 DOI: 10.62051/dvnhyg89

Abstract: Multi-Agent Reinforcement Learning (MARL) solves collaboration and competition problems in complex dynamic environments through distributed decision-making mechanisms, and has made significant progress in recent years in areas such as autonomous driving and robot cluster control. In this paper, we systematically sort out the theoretical framework, mainstream methods (e.g., MADDPG, QMIX), commonly used datasets (SMAC, Pommerman), and evaluation criteria (win rate, convergence speed) of MARL, and … Read More

Advances in Image Inpainting: Global Context Modeling via Transformers and Diffusion Models

Jiaoyang Li

pp.126-133 DOI: 10.62051/anzvbz05

Abstract: Image inpainting, a critical task in computer vision, has significantly benefited from the rapid development of deep learning techniques, particularly Transformers and Diffusion Models. Traditional methods relying on texture matching and PDE-based diffusion strategies demonstrate limited effectiveness in complex or extensive damaged regions. Recent advancements employing Transformer architectures effectively exploit global context via self-attention mechanisms, ensuring structural coherence in … Read More

Modeling and Multi Source Signal Processing of High Precision Perception Sensing System in Intelligent Robots

Sitong Man

pp.134-138 DOI: 10.62051/dfw66m04

Abstract: With the development of intelligent robot technology, the demand for high-precision perception of complex environment is increasing. This article focuses on the modeling of high-precision sensing system, aiming at improving the accuracy and adaptability of robot environmental perception. Compared with the traditional modeling method based on physical characteristics, the data-driven modeling method is emphasized. In this article, a fusion algorithm of convolutional neural network (CNN) and … Read More

Understanding Convolutional Neural Networks for Image Classification

Jinghan Xu

pp.139-145 DOI: 10.62051/rwf1e012

Abstract: In an era where artificial intelligence is becoming increasingly globalized, the development of AI-based image processing has undergone a significant transformation compared to previous generations of mechanical algorithms. Whether compared to classical machine learning classification models or traditional image classification methods that do not involve machine learning, technologies based on Convolutional Neural Networks (CNNs) demonstrate notable advantages—including higher accuracy, stronger … Read More

Advancements in Semantic Segmentation Using Deep Learning Approaches

Chenhuan Ni

pp.146-153 DOI: 10.62051/wkgenq24

Abstract: Semantic segmentation plays an important part in computer vision by assigning semantic labels to each pixel in an image. Many models have been created to enhance segmentation performance as deep learning develops, from traditional models CNNs, FCNs, and U-Net, to the advanced models DeepLab series, GANs, and Transformer-based models. This paper offers a comprehensive overview of semantic segmentation methods based on deep learning. It begins with an introduction to the task and related … Read More

Predicting Heart Disease Risk Using Machine Learning: A Comparative Analysis of Linear and Nonlinear Models

Xuchong Su

pp.154-159 DOI: 10.62051/9qd8ax37

Abstract: Heart disease is one of the leading causes of mortality worldwide, and early risk prediction plays a vital role in reducing its impact. Traditional assessment methods such as the Framingham Risk Score are widely used but rely on linear assumptions, which can overlook complex interactions between clinical factors. Machine Learning (ML) offers promising alternatives by modeling these nonlinear relationships. In this study, the predictive capabilities of two interpretable machine learning … Read More

Optimal Machine Learning Algorithms for Predicting the Popularity of Songs

Zhuoyang Tao

pp.160-164 DOI: 10.62051/34tchf58

Abstract: Predicting song popularity has become a hot topic of research in recent years due to its necessity. This study investigates the effectiveness of different machine learning models in predicting the popularity of songs on Spotify using audio features. By comparing Linear Regression, Random Forest, and K-Nearest Neighbors (KNN), the research aims to identify the most suitable algorithm for this task. A dataset containing key musical attributes such as danceability, loudness, energy, tempo, and … Read More

Comparing Linear Regression and Random Forest for Housing Price Prediction: Insights from the Boston Housing Dataset

Guoguo Chen

pp.165-171 DOI: 10.62051/d5x1ss82

Abstract: Housing price prediction is a critical task in real estate and economic analysis, providing valuable insights for stakeholders such as homebuyers, sellers, and policymakers. This study focuses on the Boston Housing dataset, a benchmark with 505 samples and 14 features, to predict the median value of owner-occupied homes (MEDV) using Linear Regression and Random Forest Regression. Exploratory data analysis reveals non-linear patterns, such as the right-skewed distribution of MEDV (skewness = … Read More

Advances in Deep Learning-Based Bone Age Assessment Research

Zhenyu Du

pp.172-179 DOI: 10.62051/jf5p0b04

Abstract: In clinical and forensic medicine, bone age assessment (BAA) is crucial for tracking children's growth and development as well as diagnosing and identifying diseases. While statistical machine learning and mapping and expert discrimination are two examples of traditional BAA techniques, deep learning (DL)-based BAA techniques are progressively becoming more popular as deep learning technology advances. The use of convolutional neural networks in BAA is explained in length in this study, along … Read More

The Investigation on the Influence of Dimension Manipulation on Regret Performance Using Upper Confidence Bound Algorithm

Wentao Qian

pp.180-184 DOI: 10.62051/byxt6m28

Abstract: This paper explores the impact of dimensionality manipulation on recommendation algorithm performance amid the $5 trillion global e-commerce landscape, where traditional methods suffer from the curse of dimensionality-feature redundancy eroding efficiency and distorting recommendations. Focusing on the "exploration-exploitation" balance, we use Principal Component Analysis (PCA) for dimensionality reduction, combined with the Upper Confidence Bound (UCB) algorithm to quantify regret performance … Read More

Data Annotation Methodologies for Fake News

Ruiyi Wang

pp.185-190 DOI: 10.62051/tx2dxj37

Abstract: With the development of technology, information dissemination has become faster and more convenient. Fake news has drawn much attention due to its characteristics, such as rapid spread, strong disguise ability, and great harm. The performance of existing fake news detection models is highly dependent on the quality of training datasets. It is crucial to construct high-quality and lower-cost training datasets. The research progress of fake news dataset construction is systematically reviewed in … Read More

Application Review of Network Attack Detection Based on Deep Learning

Yixuan Liu, Jiaohang Yu

pp.191-196 DOI: 10.62051/9dg86559

Abstract: With the rapid development of the Internet, network attacks (NA) have become increasingly diverse and sophisticated. Traditional detection methods relying on rules systems and statistical analysis now face significant challenges, including strong dependency on feature engineering and limited generalization ability. The effectiveness of detection systems is not only limited, but the difficulty of addressing new threats is also increased. Deep learning has been recognized as a novel solution for … Read More

Comparative Study on Fusion Method Based on Multimodal Speech Emotion Recognition of Speech and Text

Xinjie Xie

pp.197-207 DOI: 10.62051/qadyms14

Abstract: With the rapid development of artificial intelligence and deep learning technologies, emotion recognition has gradually become an important research area in human-computer interaction. However, in the current gaming industry, emotion recognition is rarely utilized to optimize NPC (non-player character) intelligence to enhance immersion. Therefore, this study primarily explores the feasibility of applying multimodal emotion recognition in gaming scenarios, aiming to improve the accuracy of … Read More

Image-Based Chip Defect Detection

Zehou Liu

pp.208-215 DOI: 10.62051/pnp6ps15

Abstract: This paper reviews the research status, key technologies, and application prospects of image-based chip defect detection techniques. With the advancement of chip manufacturing processes, traditional inspection methods face challenges in terms of accuracy, efficiency, and automation. Deep learning algorithms (such as YOLO, Faster R-CNN, SSD, etc.) have provided new solutions for defect detection. This paper analyzes the advantages and disadvantages of these algorithms and summarizes improved … Read More

Fine - Tuning and Optimization of Live2D Facial Expression Recognition Based on Vision Transformer (ViT)

Ziyang Chen

pp.216-222 DOI: 10.62051/v27mjf51

Abstract: With the development of fields such as virtual reality, the expressiveness of virtual character facial expressions has become increasingly crucial. Traditional CNN - based facial expression recognition methods have problems such as limited local feature extraction, sensitivity to pose changes, and high computational complexity. This research is based on the Vision Transformer (ViT) model, exploring its optimization and application in the facial expression recognition task, and combining with … Read More

A Review on Semantic Understanding of Road Incidents Based on Vehicle-mounted Vision Systems

Zesheng Ma

pp.223-230 DOI: 10.62051/kje1rw34

Abstract: Road incidents pose serious threats to driving safety. In recent years, thanks to rapid advances in vehicle-mounted hardware and artificial intelligence technologies, it has become possible to apply these technologies for real-time road monitoring. This paper reviews methods for detecting and semantically understanding road incidents based on vehicle-mounted vision systems, focusing specifically on the applications and challenges of object detection and semantic segmentation in traffic … Read More

Analysis of Depth-based and Diffusion Model-based Toon Shading in 3D and 2D Rendering

Shiming Huang

pp.231-235 DOI: 10.62051/9bvgfs21

Abstract: This paper explores toon shading, a rendering technique widely applied to 3D models to create cartoon or anime-style visuals characterized by flat surfaces and shadows. While toon shading can also be used at the 2D level, it is less common and not as broadly applicable compared to its 3D counterpart. This paper aims to review existing toon shading methods and assess their efficiency in stylization and overall product quality. Specifically, this paper examines depth-based toon shading, which … Read More

Artificial Intelligence-Driven 3D Modeling: Transformative Applications Analysis

Kunliang Rao

pp.236-245 DOI: 10.62051/f5wn2q81

Abstract: The convergence of Artificial Intelligence (AI) and 3D modeling technologies profoundly changes several key industries. This study analyzes the innovative applications and practical value of AI-powered 3D modeling technology in four major fields: architecture, education, healthcare, and gaming. First, AI technology optimizes design solutions in the construction industry through intelligent algorithms to significantly improve buildings' functionality, energy efficiency, and structural stability. … Read More

Evolution of Shader Technology: From Fixed Pipeline to Real-Time Ray Tracing, A Case Study on Racing Games

Yujie Feng

pp.246-252 DOI: 10.62051/tfm0jg64

Abstract: This study examines the evolution of shader technologies in racing games, focusing on their impact on visual fidelity, performance, and user experience. The research highlights how these innovations have transformed the graphics landscape in racing simulations by exploring the development of various shader frameworks, such as fixed-function pipelines, programmable pipelines, and real-time ray tracing. The findings reveal that advanced visual effects significantly enhance player immersion and … Read More

Research on Intelligent 3D Modeling and Rendering and Application Analysis

Fengyuan Liu

pp.253-257 DOI: 10.62051/necnrf10

Abstract: This paper investigates the evolution of 3D modeling and rendering technologies, from traditional manual techniques to Artificial Intelligence (AI)-assisted processes. It explores the transition from time-intensive manual modeling to procedural and AI-based approaches, highlighting the impact of artificial intelligence in automating and enhancing the modeling process. The study also examines modern rendering engines, discussing integrating real-time performance with visual fidelity, focusing on … Read More

Fluid Simulation Analysis of Eulerian, Lagrangian, and Hybrid Approaches for Graphics and CFD Applications

Yuming Zhang

pp.258-263 DOI: 10.62051/6bg3ab97

Abstract: Fluid simulation has emerged as a key area bridging computer graphics and computational fluid dynamics (CFD), enabling realistic simulation of liquids, gases, and reacting phenomena in entertainment, engineering, and scientific applications. This review systematically evaluates three major simulation paradigms—Eulerian mesh-based techniques, Lagrangian particle-centric methods, and hybrid approaches—by analyzing their theoretical foundations, implementation challenges, and performance results. … Read More

A Technical and Practical Comparison of Traditional and Neural Rendering Methods in Games

Daiyao Lin

pp.264-270 DOI: 10.62051/rkr5g596

Abstract: Rendering technology, a significant component of modern computer graphics, plays a vital role in the game field. With the growing demand for visual realism and real-time performance in game development, rendering technology has been continuously developed and optimized. This paper presents a comparative study of the three techniques - rasterization, ray tracing, and neural rendering- within an interactive gaming environment. By analysing their fundamental principles and practical applications in … Read More

Exchange Rate Prediction: Micro and Macro Factors, Machine Learning, and Future Directions

Bohan Wang

pp.271-277 DOI: 10.62051/rv2qre97

Abstract: This review synthesizes current research on the dynamics of exchange rates, examining the influence of microeconomic factors, such as market sentiment, and macroeconomic factors, including political stability and economic governance. It also explores the application of modern predictive techniques, notably machine learning and deep learning technologies, in anticipating exchange rate movements. The review emphasizes how these cutting-edge methods can be used to better foresee and capture … Read More

Forecasting USD/CNY Exchange Rates Using Machine Learning

Ruihua Gong

pp.278-286 DOI: 10.62051/ve4mrv31

Abstract: Exchange rate prediction is important in global trade, investment, and financial decision-making. The USD/CNY exchange rate in particular has received much attention due to China’s growing role in the global economy. However, predicting exchange rates is difficult because the data is complex, nonlinear, and often influenced by many factors. This research focuses on predicting the USD/CNY exchange rate using machine learning-based approaches. Three models are evaluated and compared in this work: … Read More

Knowledge Graphs: Technical Construction, Cross-Domain Applications and Future Challenges

Xuanyu Chen

pp.287-293 DOI: 10.62051/gv3gxt42

Abstract: The Knowledge Graph has become a crucial technology in the field of Artificial Intelligence, structurally representing real-world entities and their semantic relationships through the Semantic Web. The utilization of knowledge graphs has been demonstrated to facilitate the enhancement of semantic understanding and knowledge extraction for computers. Despite the wide applicability of knowledge bases, there are still challenges in dealing with complex knowledge types and the accuracy of … Read More

Stock price prediction model based on machine learning

Jiajun Sun

pp.294-298 DOI: 10.62051/0e4z6k33

Abstract: This paper reviews the problem of stock price prediction and discusses the current application status of traditional statistical models, machine learning and deep learning methods in financial data processing. By analyzing the limitations of traditional methods such as ARIMA and regression models in capturing market nonlinear characteristics and noise processing, it is pointed out that these methods have deviations in actual predictions; while models such as support vector regression, random … Read More

Prediction of Chronic Kidney Disease Based on Comparison of Machine Learning Models

Pai Li

pp.299-304 DOI: 10.62051/aqad4c86

Abstract: Chronic kidney disease (CKD) has become an important issue affecting global public health security due to its complex and variable pathogenic factors. Machine learning models with good predictive performance for CKD can break the limitations of traditional diagnostic methods and effectively control the burden of CKD on patients and the harm to human health security. In this paper, eXtreme Gradient Boost (XGBoost), logistic regression, and Support Vector Machine (SVM) were adopted for the … Read More

Predicting Student Depression Using Machine Learning

Bo Peng

pp.305-312 DOI: 10.62051/5x88k313

Abstract: The rising prevalence of depression among students has drawn significant attention. As data mining and artificial intelligence technologies continue to evolve, leveraging behavioral and textual data for early depression prediction offers new opportunities for timely mental health interventions. This study uses 17 depression-related features, including gender, financial stress, academic pressure, and working hours. To evaluate model performance, this study focused on three representative … Read More

Customer Churn Prediction System Based on Flask

Jiasong Ye

pp.313-318 DOI: 10.62051/bg4yj973

Abstract: The article designs and implements a customer churn prediction system based on Flask, aimed at helping businesses identify potential customer churn risks through machine learning techniques. The system first loads and processes the input customer data, then utilizes various classifiers (such as Random Forest, Support Vector Machine, and XGBoost) to predict churn probabilities and provide corresponding customer retention suggestions. Methodologically, this study employs grid search for … Read More

Towards Efficient LLMs: Analyzing Computational Bottlenecks and Optimization Strategies

Taowen Qian

pp.319-323 DOI: 10.62051/m633as12

Abstract: The study major focuses on the efficiency of the current Large Language Model (LLMs). By researching several papers that focus on it, the limitations of the current efficiency in LLM are significant problems that need to be considered by academia. Then, the study will provide some research on the progress of solving issues and explain each solution clearly. Finally, the study will focus on the further needs for developing each solution. This study is conducted on the USER-LLM, OPTIMA, and … Read More

Analysis of Rendering Technology on the development of the film and television Industry and Global Cultural Exchange

Weiyu Ouyang

pp.324-330 DOI: 10.62051/wxkq6c21

Abstract: This study examines the impact of rendering technology on the development of the film and television industry as well as global cultural exchange. With the rapid growth of computer graphics, advanced rendering technologies have become an essential part of visual narratives and cross-cultural communication. The study examines how ray tracing, High Dynamic Range (HDR) imaging, and neural rendering affect content dissemination, cultural presentation, and production efficiency. A mixed-methods … Read More

Analysis of Key Mechanics for The Impact of Game Design on Player Experience

Dongwei Wang

pp.331-336 DOI: 10.62051/qs63cj67

Abstract: The development of the Internet and technology has transformed the gaming industry, leading to more immersive, evolving games, from 2D to 3D, independent to online, and from simple to highly interactive experiences. This paper explores the relationship between game design and player experience, specifically examining how different game elements influence player immersion, challenge, and emotional feedback. The study employs a combination of literature review and case studies, referencing key … Read More

Stock Prediction Based on Machine and Deep Learning

Qilong Zhong

pp.337-344 DOI: 10.62051/we7zxt74

Abstract: Amidst the ongoing evolution of capital markets and accelerated progress in digital technologies, forecasting equity market trends has emerged as a pivotal investigation domain within computational finance and intelligent systems research. This study systematically examines two predominant predictive methodologies: algorithmic learning techniques and neural network architectures. Initially, this paper establish the contextual relevance and academic value of financial market forecasting while … Read More

Lung Cancer Prediction Using Machine Learning: A Comparative study of Classification Algorithms

Yikun Zhao

pp.345-350 DOI: 10.62051/8wkw1c74

Abstract: Lung cancer is a leading cause of death globally, often diagnosed at advanced stages due to the lack of early symptoms. Early detection is crucial for improving treatment outcomes and survival rates. With rising global cases, predicting lung cancer before it becomes critical is a key public health challenge. Symptoms like persistent cough, shortness of breath, and weight loss can signal the disease but are also common in other conditions, making early prediction vital for timely intervention. … Read More

Performance Comparison of GPipe-Optimized VGG16 and LeNet Networks for Malaria Microscopy Image Classification

Zhentao Lu

pp.351-360 DOI: 10.62051/q9pv4x06

Abstract: This study addresses the memory–accuracy trade-off that limits deep-learning malaria diagnostics on commodity hardware. This paper curated 27 558 high-quality thin-smear images from the public NLM corpus, split 80/20 for training-test, and benchmarked two convolutional architectures. A compact LeNet was trained on one RTX 2080 Ti, whereas a VGG16 was trained in single-GPU mode and with GPipe pipeline parallelism across two, three and four identical GPUs. The evaluation used accuracy, F1-score, … Read More

Application of Multiple Machine Learning Models based on Python in Predicting the Risk of Esophageal Cancer

Junhan Zhao

pp.361-367 DOI: 10.62051/r1yhmf35

Abstract: As health awareness rises and technology advances, machine learning has garnered significant attention in cancer prediction. This study focuses on esophageal cancer, a common and high-mortality digestive tract tumor, aiming to evaluate the predictive accuracy of different machine learning models, identify optimal models, and examine the role of hyperparameter tuning through random search in enhancing prediction accuracy for models with lower performance. The findings of the study indicate that … Read More

Evaluating Machine Learning Models in Housing Price Forecasting

Kailin Wang

pp.368-373 DOI: 10.62051/ev013e77

Abstract: This study aims to compare the performance of different machine learning algorithms in predicting housing prices and to construct an experimental process based on data preprocessing, feature selection, and comparative analysis of multiple regression models. Initially, a Random Forest will filter out the ten most significant variables. This variable will be utilized to build Multivariable Linear Regression (MLR). To find the best subset, lasso, best subset, and 5-fold Cross-Validation methods … Read More

Comparative Analysis of Traditional Statistical and Machine Learning Approaches in Credit Scoring Applications

Leung Hon Sum

pp.374-383 DOI: 10.62051/9fcack29

Abstract: This paper compares the performance of traditional statistical approaches, such as logistic regression (LR), and machine learning approaches, like support vector machines (SVMs), in credit scoring. In this paper, a dataset is simulated containing borrower characteristics like income, wealth, repayment history, length of requested loans, and total debt, which can be altered to represent different macroeconomic scenarios. Mathematica is used to train or fit and ultimately test both LR and SVM … Read More

Artificial Intelligence in Games: Enriching Game Content and Enhancing Player Experience

Chengzi Jiang

pp.384-391 DOI: 10.62051/22cf0v54

Abstract: This study explores the application of generative artificial intelligence (AI) in video games, focusing on improving content richness and personalized player experience. Traditional non-player character (NPC) systems have limitations in adaptability and interactivity. This study explores how generative AI can improve NPC behavior and dialogue. Then, this paper proposes a framework that combines generative agents, Transformer-based dialogue models, and reinforcement learning. Specifically, the … Read More

Comparative Analysis of Video Frame Interpolation from Optical Flow to Diffusion Models

Tianlang Yin

pp.392-398 DOI: 10.62051/qxv9y811

Abstract: Video Frame Interpolation (VFI) is essential in handling video processing to fill in the gaps between the initial and final frames and increase temporal resolution. This method is critical in applications like frame rate up-sampling, slow-motion rendering, and video improvement. This work compares and evaluates the merits and limitations of several different VFI methods based on their structures and interpolation performance. This paper summarizes conventional optical flow-based methods, … Read More

Applications and Analysis of Artificial Intelligence in Modern Education

Jinrong Guo

pp.399-405 DOI: 10.62051/hkn3xj93

Abstract: The application of artificial intelligence in education has changed the traditional teaching methods and provides innovative solutions to enhance learning experiences and effects. This study analyzes the significance of artificial intelligence in modern education and its impact on educational methods. The study also examines artificial intelligence applications in contemporary education, including machine learning, deep learning, and natural language processing. Specifically, this paper examines … Read More

A Review of Image Restoration Based on Deep Learning

Hongze Zuo

pp.406-415 DOI: 10.62051/meghns59

Abstract: Image restoration is a key task in the field of computer vision, which aims to recover high-quality clear images from degraded images. Degraded images may be produced for various reasons, such as noise, blurring, compression, etc. In recent years, the rapid development of deep learning technology has brought new breakthroughs in the field of image restoration, and various image restoration methods based on deep learning continue to emerge, and their performance is better than the previous … Read More

Key Applications of AI Question-Answering Systems: Research and Technical Analysis Based on Game Text and Dialogue

Qineng Wu

pp.416-422 DOI: 10.62051/fhj73a81

Abstract: With the rapid development of the global gaming industry, artificial intelligence (AI) applications in games have garnered increasing attention. In role-playing and open-world games, particularly, the intelligence of non-Player Characters’ (NPCs) dialogue systems is critical to enhancing player experience and interactivity. This paper investigates the applications of AI question-answering (QA) systems in gaming contexts by analyzing mainstream algorithms and models, their implementation … Read More

Design and Optimization of a Chinese Chess engine Based on MCTS

Jiashu Chang

pp.423-428 DOI: 10.62051/9w1tb254

Abstract: Chinese chess, as part of Chinese traditional culture, presents unique challenges and value in the field of artificial intelligence research. This study focuses on innovatively applying the Monte Carlo Tree Search (MCTS) algorithm to the design of a Chinese chess engine, breaking through the limitations of traditional engines that rely on Alpha-Beta pruning and manual evaluation functions. By implementing a mixed strategy generation framework, the engine achieves decision-making and optimization … Read More

Applications of Artificial Intelligence in Rare Disease Research: A Technical Framework and Future Prospects

Yunfan Chen, Hairui Hu, Ziqin Xue

pp.429-437 DOI: 10.62051/yncd5x09

Abstract: In rare disease studies, researchers have consistently encountered issues like data shortage and diagnostic uncertainty—challenges largely stemming from these diseases’ low prevalence, varied symptoms, and complex diagnostic procedures. Traditional research methods frequently produce unreliable results, largely because of persistent issues such as limited data and clinical complexity. In recent years, the advancement of artificial intelligence, particularly foundation models, has opened up new … Read More

The Influence of Moral Choices in Video Games on Real-World Decision-Making and Empathy

Bangqiao Wang, Youyou Wu, Zirui Xu

pp.438-443 DOI: 10.62051/pbv5gm54

Abstract: This paper explores the impact of moral choices in video games on players' real-world decision-making and ethical behavior. This study highlights the psychological benefits of engaging with morally complex decisions, while video games are often criticized for promoting violence and addiction. By analyzing some typical video game instances such as Detroit: Become Human, Red Dead Redemption 2, and The Last of Us Part II, the research shows how game decisions can enhance substitution, empathy, and … Read More

Interactive Volumetric Fog System in Unreal Engine 5 Using Distance Fields and GPU Particle Simulation

Xi Wei

pp.444-449 DOI: 10.62051/te0qnc44

Abstract: Real-time volumetric fog is a crucial visual effect in modern 3D environments, enhancing depth perception, atmosphere, and immersion. However, conventional fog rendering methods often lack physical accuracy and interactivity, disconnecting players and their environment. To address this issue, this study proposes an interactive volumetric fog system based on Unreal Engine 5, designed to provide realistic static fog rendering and dynamic responses to user interaction. The main objective is to … Read More

Analysis of Procedural Content Generation and Stylisation Techniques in Developing Video Games

Junkai Xie

pp.450-455 DOI: 10.62051/0swzbh75

Abstract: This paper explores the evolving role of Procedural Content Generation (PCG) technology in developing video games, highlighting its importance in enabling automatic generation of scalable, dynamic, and visually distinct gameplay experiences. This research aims to provide an overview of the current methods of procedural terrain generation and assess how emerging generative artificial intelligence (AI) technologies can address their limitations. Specifically, the study reviews methods, including … Read More

Research on Optimization of Fine 3D Reconstruction Process Based on SfM-MVS

Zhuoyuan Wu

pp.456-467 DOI: 10.62051/6fcb3285

Abstract: Aiming at the optimization problem of motion recovery structure and multi-view stereo (MVS) vision process, this study systematically explores the influence of shooting parameters on 3D reconstruction quality by establishing data acquisition standards, filling the gap of standardization research in this field. This study aims to quantify the effect of variables such as lighting conditions, shooting equipment, and background complexity on reconstruction accuracy and propose a scientific … Read More

Multi-agent Game Theory Applied in Artificial Intelligence

Chang Yuan

pp.468-472 DOI: 10.62051/e7hser67

Abstract: With the further development of machine learning methods, algorithms are in the face of a more complicated environment than ever. The massive amount of data has brought severe pressure to present computing and decision-making strategies. In order to augment the performance of artificial intelligence in multi-agent interacting scenarios, researchers have adopted game theories to machine learning strategies. The article introduces the application of game theory from three aspects: the theoretical … Read More

Research on Network Security Attack Defense Mechanism and Its Development Trend

Xiulin Yang

pp.473-477 DOI: 10.62051/q1gsgq83

Abstract: In the digital era, network security has become a core issue concerning the global informatization process. This article first explores the current research progress in the field of network security from two dimensions: network attack and network defense. It systematically analyzes the classic methods of each. Then, this article delves into the application prospects of blockchain technology and reinforcement learning methods in network security. Blockchain technology, with its decentralized and … Read More

Application of Five Machine Learning Techniques for Stellar Classification on SDSS DR18

Haiqi Su

pp.478-487 DOI: 10.62051/v7mpk191

Abstract: Systematic classification of celestial objects is a cornerstone of modern astronomy, as it underpins the understanding of the universe’s large-scale structure and long-term evolution. Moreover, it facilitates the discovery of rare or extreme phenomena, offering clues to the underlying physical laws that govern them. This study presents a comprehensive evaluation of five machine learning models—Random Forest, Adaptive Boosting, Extreme Gradient Boosting, Deep Neural Network, and TabNet—on the … Read More

Application of Data Mining in Education

Qihao Chen

pp.488-494 DOI: 10.62051/f68v1y26

Abstract: With the rapid growth of educational data, how to extract valuable information from multi-source heterogeneous data has become an important challenge in educational research. The existing technology applications are scattered and lack systematic integration, which makes it difficult for educational practitioners to comprehensively evaluate the applicability of different methods. This paper systematically reviews the research progress in student behavior analysis, academic prediction and … Read More

Interface Optimization: Progress and Applications Based on Multidimensional Technological Integration

Zijun Ma

pp.495-500 DOI: 10.62051/vphx4e23

Abstract: In today’s digital world, where users expect fast, seamless, and personalized interactions, interface optimization has become a crucial area of focus. The evolution of technology over the past decades has dramatically reshaped how people engage with digital systems. Early user interfaces, often characterized by static pages and limited interactivity, reflected the constraints of hardware performance and network capabilities at the time. As processing power increased and internet access expanded, … Read More

Predicting the Strength of Concrete in an Intelligent Manner: A Research Grounded on Random Forest

Xuanhuai Song

pp.501-506 DOI: 10.62051/t17fhq72

Abstract: Concrete is one of the most extensively used materials in civil engineering due to its versatility, durability, and cost-effectiveness. As a fundamental construction component, its mechanical properties, especially compressive strength, play a crucial role in ensuring the structural integrity and safety of buildings and infrastructure. Accurate prediction of compressive strength is therefore essential in both the design and quality control phases of construction projects. Traditional prediction … Read More

Research on Stock Index Prediction Based on SARIMA-BP-KPCA+RF Model

Fengying Yan

pp.507-514 DOI: 10.62051/1t4vhs76

Abstract: The prediction of stock prices plays a vital role in guiding investors' decision - making processes and managing risks within the financial market. The stock price of ADANIPORTS is influenced by multidimensional data and exhibits nonlinear dynamic characteristics. This study is based on Yahoo Finance data, calculating 8 technical indicators including KDJ and RSI. After feature screening, seasonal autoregressive integral moving average model (SARIMA), backpropagation neural network model (BP … Read More

Applications and Challenges of Artificial Intelligence in the Field of Cybersecurity

Qiyue Yang

pp.515-521 DOI: 10.62051/syb39j62

Abstract: With the rapid development and widespread application of computer technologies, cyber-attacks have become increasingly diverse and intelligent. Traditional cyber defense methods are no longer sufficient to meet current security demands. Therefore, introducing more efficient and intelligent protection technologies has become critically important. This paper focuses on the applications and challenges of artificial intelligence (AI) in the field of cybersecurity. Through a combination of literature … Read More

Algorithm Evolution and Technical Challenges in Autonomous Driving Object Detection

Xu Gao

pp.522-526 DOI: 10.62051/kxdmeg28

Abstract: The rapid evolution of autonomous vehicle technologies has positioned object detection systems as pivotal components for reliable environmental perception. This review systematically examines three critical dimensions: architectural advancements from traditional CNN-based models to Transformer architectures, strategies for mitigating environmental interference, and practical implementation challenges in edge computing. Through comprehensive analysis of 35 peer-reviewed studies (2018–2023), … Read More

Comparative Analysis Research on Machine Learning Models in Credit Risk Assessment

Bohan Zhang

pp.527-538 DOI: 10.62051/19wa7a05

Abstract: Credit risk assessment is crucial for the risk management and control of financial institutions, but it faces challenges such as sample imbalance, complex characteristics and the lack of model interpretability. In this study, two public datasets, "Give Me Some Credit" and "Loan Default", were used. The Synthetic Minority Over-Sampling Technique (SMOTE) was employed to balance the sample distribution and conduct feature engineering. Construct new features such as the income-debt ratio … Read More

Comparing Machine Learning Models for Predicting YouTube Video Like-to-View Ratios

Xinming Zhao

pp.539-546 DOI: 10.62051/ybmr8j39

Abstract: With YouTube's dominance as a video-sharing platform, the like-to-view ratio (LVR) has emerged as a critical metric for quantifying audience engagement beyond raw view counts. Current approaches lack systematic model comparisons and actionable optimization strategies. This study bridges these gaps by rigorously evaluating tree-based ensembles (Random Forest, XGBoost, LightGBM) on raw metadata, demonstrating XGBoost's superior performance (R²=0.8703) over both individual models and a Voting … Read More

A Comprehensive Review of Stock Index Prediction Methods: From Traditional Econometrics to Deep Learning with Attention Mechanisms

Zihang Sun

pp.547-552 DOI: 10.62051/g34zas22

Abstract: In the era of rapid digitalization and data-driven decision-making, financial forecasting has emerged as a critical area of research and application. Stock index prediction, in particular, plays a vital role in guiding investment strategies, managing risks, and shaping economic policies. With the increasing availability of high-frequency and high-dimensional financial data, selecting appropriate modeling techniques has become both more challenging and more essential. This paper provides a … Read More

Diabetes Risk Prediction Model Using Machine Learning

Boyi Yang

pp.553-558 DOI: 10.62051/nzr6tw29

Abstract: Diabetes is a major global health challenge, contributing to increased mortality and long-term complications worldwide. Early diagnosis and effective risk stratification are critical to reducing the disease burden. This research aims to evaluate and compare the predictive performance of five machine learning (ML) models—Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM)—using the Pima Indians Diabetes Dataset. A standardized … Read More

Research and Model Development for Gastric Cancer Risk Prediction

Tianyu Li

pp.559-563 DOI: 10.62051/gjgj3p83

Abstract: Gastric cancer stands out as one of the most widespread deadly cancers which produces substantial rates of sickness and death throughout the world. The ability to predict gastric cancer risk early is vital to enhance patient recovery and survival statistics. The study integrated clinical and lifestyle data from public databases and simulated data which underwent preprocessing through missing value imputation and feature engineering steps including BMI creation, dietary score calculations and age … Read More

Dynamic Feature Engineering for Breast Cancer Risk Stratification: A Machine Learning System Integrating Clinical Guidelines

Borui Liao

pp.564-568 DOI: 10.62051/p44xjf14

Abstract: Breast cancer is one of the most common malignancies among women worldwide, posing a major public health challenge due to its high incidence and complex biological characteristics. Breast cancer screening is the cornerstone of tumor prevention and requires the systematic integration of morphological biomarkers and clinical guidelines. This study proposes a dynamic feature engineering framework, which encodes tumor biology through nonlinear transformations, including the square root … Read More

Could AI Anchors Replace Real Anchors in News Reporting?

Bowen Yang

pp.569-578 DOI: 10.62051/hsjafy94

Abstract: In today's highly developed artificial intelligence technology, AI anchors are widely used in the field of news reporting. With the emergence of zero-error broadcasting by AI anchors, whether the position of real anchors will be shaken by AI anchors, and whether AI anchors can completely replace real anchors in news reporting are the main issues discussed in this paper. Based on CASA paradigm, this paper adopts the experimental method, divides the subjects into AI anchor group and real anchor … Read More

SmarTax: An Intelligent Tax Policy Maker via Multi-agent Reinforcement Learning and Irrationality of Humans

Tianlang Xiong

pp.579-591 DOI: 10.62051/m1e5qs94

Abstract: Effective tax policies are difficult to design since taxpayers’ behaviors often deviate from the traditional economic models, which assume rational decision-making. Tax policymakers need tools that predict how individuals will respond to different tax policies, accounting for the irrational behaviors caused by biases, emotions, and imperfect information. This study introduces SmarTax, the first income tax policy-making simulator which integrates economics models, multi-agent reinforcement … Read More

Implementation of Random Forest Algorithm Based Medal Count Prediction

Shuai Chen, Junjie Wang, Yuhang Wu

pp.592-599 DOI: 10.62051/mddr0e54

Abstract: This paper proposes a data trend prediction model based on random forest regression, focusing on the comprehensive use of machine learning and association rule mining in complex data prediction. Firstly, considering various factors, we constructed a medal prediction model based on random forest regression, assessed the accuracy of data, predicted the medal list of the 2028 Olympic Games and the first medal prediction results of the non-winning countries, and at the same time, we used the Apriori … Read More

Application of Ant Colony Algorithm and Genetic Algorithm in Multi-Objective Dynamic Equalization Scheduling of AGVs

Lu Liang, Mingyuan Liu, Ziyi Xiong

pp.600-607 DOI: 10.62051/w5tkr951

Abstract: In this paper, an optimization method based on heuristic genetic algorithm is proposed for AGV multi-objective dynamic balanced scheduling. Firstly, the warehouse environment is constructed by raster modeling method, and the AGV path planning is transformed into a node traversal situation in a two-dimensional coordinate system, and the spatial relationship of multiple elements is defined. Then, a single-objective constraint model is established to minimize the longest path AGV picking time as … Read More

The Impact of Artificial Intelligence on Supply Chain Resilience: An Empirical Study

Zhiwei Xu

pp.608-628 DOI: 10.62051/qxg0k209

Abstract: The rapid development and widespread application of artificial intelligence (AI) across various industries have garnered significant attention. This paper investigates the impact of AI on supply chain resilience using data from A - share listed companies in China from 2012 to 2022. The findings indicate that AI has a significant positive impact on supply chain resilience, which remains robust after multiple robustness tests and endogeneity treatments. Heterogeneous analysis shows that the … Read More

Predicting House Prices Using Machine Learning Models

Tianying Zhao

pp.629-633 DOI: 10.62051/e9x5cc73

Abstract: Predicting house prices is crucial for stakeholders such as buyers, developers, financial institutions, and policymakers. This paper explores how machine learning models can help with house price prediction. Using the Ames Housing Dataset, we compare three models: linear regression, decision tree, and random forest. The goal is to understand which model gives better results in terms of accuracy and how well it works with new data. Data preprocessing, including imputation for missing values, … Read More

Using Machine Learning Algorithms for Assisting Heart Disease Diagnosis

Jiaxi Miao

pp.634-638 DOI: 10.62051/ct25ge41

Abstract: Heart disease continues to be one of the top causes of death around the world, which highlights the importance of catching it early to ensure timely treatment. This study explores the use of various body health information, such as Age, Gender etc. and physiological responses such as chest pain, shortness of breath, Fatigue, Dizziness etc. The meaning of this study is to find out how does different body responses correspond to if the patient catches heart disease and which kind of method of … Read More

Application of Machine Learning Methods in Predicting Chemical Solubility

Shengye Shi

pp.639-643 DOI: 10.62051/b1df7k17

Abstract: Solubility is an important property in chemistry, especially in drug development, as it affects how well a drug can work in the human body. Traditionally, scientists predicted solubility by studying the structure of molecules, but these methods are not always accurate. In recent years, machine learning has become popular in many fields, and this study explores how it can help improve solubility prediction. A dataset from Kaggle with four features including MolLogP, MolWt, NumRotatableBonds, and … Read More

Diamond Price Prediction Based on Regression Model: Comparison and Analysis of Different Algorithms

Qifeng Xiao

pp.644-650 DOI: 10.62051/a61en476

Abstract: Diamonds, as a valuable commodity, have investment value. Through price prediction, investors can determine the trend of diamond prices and decide when to buy or sell diamonds to achieve maximum investment returns. For some consumers who plan to purchase diamonds as collectibles or investments, price forecasting can help them determine whether the purchased diamonds have appreciation potential and make more informed consumption decisions. The government or relevant industry associations can … Read More

Efficient Rendering Analysis for Converting 3D to 2D Cartoon Style

Tingkai Hu

pp.651-656 DOI: 10.62051/hgnpcm53

Abstract: This paper explores using non-photorealistic rendering (NPR) techniques in Blender to simulate a 2D cartoon aesthetic using 3D models. NPR has become increasingly helpful in various fields like animation, education, and games due to its special aesthetic style, which resembles 2D animations and is low-cost. This study investigates how NPR, including flat shading and tone flattening, can be applied in Blender to build an efficient and wonderful NPR workflow. A toon shader pipeline is constructed … Read More

From Mode Collapse to Quantum Generation: Architectural Innovations, Evaluation Dilemmas, and Multimodal Fusion Paths for Generative Adversarial Networks (GAN)

Boyu Zhang

pp.657-663 DOI: 10.62051/ch3cn431

Abstract: In this paper, based on papers from the last five years, this paper systematically sorts out the technical evolution, application expansion and existing challenges of Generative Adversarial Networks (GAN). Generative Adversarial Networks (GANs), as the core framework of deep generative models, have shown revolutionary potential in the fields of image synthesis, time-series prediction, and cross-modal generation in the past decade. For example, the variants represented by Wasserstein GAN and … Read More

Development Trends and Future Prospects of Artificial Intelligence in Medical Image Processing

Cheng Luo

pp.664-673 DOI: 10.62051/qc7fac76

Abstract: In recent years, the momentum of the development of artificial intelligence has become increasingly strong, and the integration of artificial intelligence and various disciplines has also been applied to all aspects of human life. At the same time, in the medical industry related to human health, research on artificial intelligence + medicine has been carried out for many years. In the medical field, the processing of medical images already accounts for 80%-90% of the medical data sources, … Read More

A Review of the Safety Survey of Intelligent Driving

Hao Wang

pp.674-679 DOI: 10.62051/f94s5m17

Abstract: With the rapid advancement of artificial intelligence and sensing technologies, intelligent driving systems have transitioned from high‑end model exclusivity to widespread adoption across diverse vehicle segments. This survey reviews three major object detection methods—traditional object detection, object detection based on Yolo, and object detection based on Transformer—and evaluates their respective strengths and limitations in autonomous driving contexts. We summarize representative … Read More

How Machine Learning Drives Intelligent Transformation in Manufacturing

Yiming Zhang

pp.680-683 DOI: 10.62051/ka1mxv92

Abstract: The manufacturing industry is undergoing a transformative shift driven by machine learning (ML), addressing critical inefficiencies in traditional methods such as manual quality control, reactive maintenance, and inflexible supply chains. ML applications—including convolutional neural networks for defect detection (e.g., Tesla’s 99.5% accuracy), LSTM-based predictive maintenance (reducing downtime by 30% at Siemens), and reinforcement learning for dynamic scheduling—demonstrate enhanced … Read More

A Review of Machine Learning and Deep Learning Approaches for Predicting Greenhouse Gas Emissions

Xiaowen Chen

pp.684-690 DOI: 10.62051/mam9km07

Abstract: Accurate prediction of greenhouse gas (GHG) emissions is critical in guiding informed climatic policymaking but has traditionally suffered from spatial biases, reporting lags, and inconsistent data. Machine learning (ML) and deep learning (DL) have in recent times become viable options for dealing with the complex, non-linear relationships in emissions data. These data-oriented methodologies have been used widely across sectors, showing high accuracy in prediction and flexibility. Here, this … Read More

Review on Dexterous Hand Technology Empowered by Computer Science: Multidimensional Integration and Innovative Progress

Di Li

pp.691-702 DOI: 10.62051/abgdx687

Abstract: Dexterous hand technologies have emerged as a critical frontier in robotics, driven by growing demands for human-like manipulation in complex and dynamic environments. This review explores how advancements in computer science—particularly in intelligent control, sensor fusion, and machine learning—have transformed the design and deployment of dexterous robotic hands. We analyze three major integration pathways: data-driven control algorithms that improve real-time adaptability, … Read More

From Rule-Driven to Data-Driven: Technological Evolution, Challenges, and Future Trends in Smart Contract Vulnerability Detection

Qiheng Yu

pp.703-709 DOI: 10.62051/c9jgtf18

Abstract: Smart contract vulnerability detection is undergoing a paradigm shift from rule-driven to data-driven approaches. While traditional methods relying on predefined patterns struggle with evolving attack vectors, machine learning enables novel solutions through multi-level feature learning. This study systematically traces technological evolution: Early research established foundational frameworks using syntax tree features with classical classifiers. Deep learning advancements introduced temporal … Read More

Data Prediction and Intervention Effect Analysis Based on Random Forest and DID Algorithm

Yue Zhong, Bin Wu

pp.710-718 DOI: 10.62051/1mh1r427

Abstract: In this paper, negative binomial regression model, random forest model, PageRank algorithm and PSM-DID algorithm are proposed, focusing on the application of multi-models in the prediction of count data, the assessment of importance of feature variables, the analysis of association networks and the quantification of intervention effects. First, to address the over-dispersion of count data, a negative binomial regression model is used, which solves the limitations of traditional Poisson … Read More