Computers and Artificial Intelligence is a double-blind peer-reviewed, open-access journal dedicated to publishing high-quality research in the fields of computer science and artificial intelligence. The journal provides an essential platform for researchers, practitioners, and educators to share innovative ideas, findings, and advancements that are shaping the future of these rapidly evolving fields.
Research on the Innovation of Intelligent Teaching Models in Higher Education from the Perspective of Human–AI Teaching
The breakthrough development of artificial intelligence is reshaping the instructional patterns of higher education. As an emerging teaching paradigm in which teachers and AI collaborate to jointly support learning, Human–AI Teaching has become an important direction for the intelligent transformation of universities. From the perspective of Human–AI Teaching, this study systematically analyzes the innovation and optimization pathways of intelligent teaching models in higher education. Drawing on constructivist learning theory and other theoretical foundations, the study proposes the Four-Dimension Driving Pathway Model: “Technology support, scenario integration, intelligent feedback, and competence cultivation” to reveal the internal mechanisms of Human–AI Teaching. Representative cases from Zhejiang University and other institutions are selected to examine the practical characteristics of Human–AI Teaching in instructional design, classroom interaction, learning feedback, and competence development. The results show that this model significantly enhances teaching efficiency and student engagement, strengthens teachers’ AI literacy and students’ self-directed learning abilities, and promotes continuous improvement in teaching quality. Based on these findings, the study constructs an innovative framework for Human–AI Teaching in universities. Guided by the four-dimensional model of the technology system, scenario system, feedback system, and competence system, it develops a Five Key Elements and ten strategies Innovation System Intelligent Teaching Innovation System consisting of top-level planning, platform construction, classroom innovation, feedback loops, and Human–AI collaboration. The study argues that Human–AI Teaching is not merely a result of technological empowerment; rather, it represents a systematic reconstruction of educational philosophy and pedagogical ecology.
human-AI teaching, intelligent teaching model, artificial intelligence, higher education reform, educational digitalization
Copyright (c) 2025 Min Gao (Author) https://creativecommons.org/licenses/by/4.0
Multi Table QA:Evaluating Modern LLM Strategies on Table0 Understanding
Tables constitute the majority of structured data in enterprise environments. While single-table question answering has received significant attention, research on multi-table reasoning remains limited. Compared to single-table QA, multi-table QA requires schema alignment, relational inference, and scalable context man- agement. We introduce tool-augmented reasoning as a paradigm for multi-table QA, and systematically study two complementary strategies: (1) free-form tool interaction, where models iteratively call exploration and computation tools, and(2) structured agent workflows, which stage tool-use into exploration, preparation, and analysis phases. Using the TQA-Bench dataset, we evaluate GPT-4o-mini and GPT-5-mini across both small (8k) and large (128k) database scales under varying tool context constraints. We show that tool augmentation substantially improves robustness and accuracy over direct prompting, with gains up to +28 percentage points. Structured workflows yield further benefits for weaker models on large databases, but regress for stronger models, revealing a scale–capacity trade-off in the value of structure. These results establish tool-augmented reasoning as a powerful paradigm for multi-table QA. Structure aids weaker models under scale but constrains stronger models, underscoring that tool-use strategies must be adapted jointly to model ability and database complexity.
large language models, multi-table question answering, tool-augmented reasoning, structured data understanding, agent-based workflows, tabular data reasoning
Copyright (c) 2025 Letian Li (Author) https://creativecommons.org/licenses/by/4.0
Survey of Autonomous Driving via Multisensor Cooperation
Against the background of rapid progress in science and technology and continuous optimization of intelligent transportation systems, a survey of autonomous driving based on multisensor cooperation has focused on multisensor cooperation technology in the field of autonomous driving. First, the research background, purpose and significance of this technology are introduced, and the value of this technology in theory and practical application is clarified. Second, the concept of multisensor collaboration is expounded, including the characteristics of different sensors, application scenarios, key technologies, architecture, advantages and challenges. Then, the data fusion technology is discussed in detail, including an overview, common methods, applications and limitations in autonomous driving. It also illustrates the application of multisensor collaboration in the perception layer, decision layer and execution layer of an autonomous driving system and shows the effect with a case study. Finally, its importance and potential development direction are summarized, which provides a comprehensive technical reference for related researchers and engineers.
multisensor collaboration, data fusion technology, automatic driving, sensor fusion
Copyright (c) 2025 Zhixuan Pei (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Machine-Learning-Based Enterprise Loan Default Prediction Models: A Comparative Study with Traditional Scoring Methods
This study leverages public datasets such as CSMAR and Wind to conduct a comparative analysis between machine learning models (XGBoost, LightGBM) and traditional credit scoring models (Logistic Regression, Altman Z-score) for optimizing enterprise loan default prediction models. The research addresses three critical challenges: data imbalance, integration of non-financial information, and model interpretability. By employing the SMOTE oversampling technique and SHAP value analysis, the model performance was significantly enhanced. Experimental results demonstrate that the XGBoost model achieved an AUC of 0.85, markedly outperforming the traditional Logistic Regression model (AUC=0.72). Furthermore, incorporating sentiment data improved the recall rate by 15%. The contributions of this study are threefold: first, it systematically compares the performance differences between machine learning and traditional models in enterprise loan default prediction; second, it proposes a dynamic risk assessment framework integrating financial and non-financial features, enhancing the model’s timeliness and adaptability; third, it improves model transparency through interpretable AI techniques (SHAP analysis), aligning with the regulatory requirements of Basel III and providing theoretical and practical support for risk management in commercial banks.
enterprise loan default, machine learning, credit scoring, SHAP analysis, dynamic risk
Copyright (c) 2025 Xiangyu Huang (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Connected Technology for Campus Vehicle Applications: A Review
Intelligent connected technology (ICT), a product of the deep integration of artificial intelligence (AI), the Internet of Things (IoT), communication technologies, big data, and autonomous driving, aims to increase transportation safety, efficiency, and comfort through synergistic “intelligence” and “connectivity.” This synergy enables real-time information interaction and collaborative decision-making among vehicles, road infrastructure, users, and cloud platforms, ultimately fostering a smarter, greener, and more efficient transportation system. With the increasing application of ICT in confined environments, university campuses, as typical microcosms of urban settings, are witnessing a transformation in vehicle usage demands—shifting from mere commuting to social, collaborative, and scenario-integrated needs. This review focuses on the closed-campus context, investigating the mechanisms through which ICT drives the socialization, collaboration, and scenario fusion of campus vehicle use, thereby providing theoretical support for tailoring technology to campus-specific scenarios. Specifically, we synthesize research progress across three critical dimensions: social connectivity, fleet collaboration, and scenario integration. Employing a critical analysis approach, we examine the methodological designs and conclusion applicability of existing studies, highlight their limitations, and substantiate our arguments with empirical cases from pilot initiatives at Chinese universities. Moving beyond the prevailing singular focus on “technical functionality,” this paper pioneers a “demand-technology-scenario” triadic interactive perspective to reveal the adaptation gap between technology and social needs in campus mobility. On the basis of an analysis of 35 literature sources, primarily from the last five years, we identify significant shortcomings in technology-scenario adaptation. Future efforts should focus on interdisciplinary integration, targeted innovation, and application scenario expansion to promote the deep integration of ICT with campus vehicle ecosystems.
intelligent connected vehicles, socialization of campus vehicle use, campus fleet collaboration, scenario integration, social connectivity
Copyright (c) 2025 Ruoxi Wang (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence-Driven Autonomous Vehicles: Current Developments and the Future Prospects
Artificial intelligence (AI) technology is profoundly transforming the field of autonomous driving, propelling it from theory to practical application. This paper systematically reviews the key technological advancements in AI-driven autonomous driving. Recognition and control algorithms based on deep learning and reinforcement learning have enhanced the safety of real-time decision-making. Multisensor fusion and vehicle-to-everything (V2X) communication technologies have strengthened environmental perception and vehicle–road cooperation capabilities. The combination of computer vision and lidar has enabled high-precision 3D modeling. Currently, the global market is experiencing rapid growth. China, which relies on the “5+6” strategy and policy pilots, is accelerating the implementation of this technology. Levels 2 and 3 (L2/L3) systems have been commercialized, and Level 4 (L4) systems have entered the demonstration operation stage. However, an insufficient perception of complex environments, the “black box” problem of decision-making algorithms, and hardware computing power bottlenecks remain the main challenges for higher-level autonomous driving. In the future, promoting the development of technology toward Level 5 (L5) through the research and development of explainable AI algorithms, breakthroughs in domestic chips, and cross-industry collaboration. At the same time, an ethical framework centered around people and an intelligent transportation ecosystem should be constructed.
autonomous driving, artificial intelligence, technological challenges, deep learning: vehicle-to-everything (V2X) communication
Copyright (c) 2025 Xianni Xie (Author) https://creativecommons.org/licenses/by/4.0
Mamba Convolutional Hybrid Spatio-Temporal Medical Image Generation Based on Diffusion Probabilistic Models
We introduce a novel hybrid deep learning module, termed the Mamba-Spatial-Temporal Generator (MSTG), which integrates the strengths of Convolutional Neural Networks (CNNs) with the advanced Mamba architecture. While conventional CNNs are effective in extracting local features within diffusion models, their limited receptive field restricts their capacity to capture long-range dependencies. To overcome this limitation, MSTG first employs CNN-based convolutional and pooling layers to extract multi-level local features, and subsequently incorporates Mamba blocks founded on State Space Models (SSMs). Owing to its linear computational complexity and powerful long-sequence modeling capability, Mamba adaptively selects and fuses global contextual information. Through this synergistic design, MSTG retains the local perceptual advantages of CNNs while simultaneously leveraging the global dynamic modeling capacity of Mamba. As a result, it significantly improves the understanding of complex spatial and sequential dependencies without compromising computational efficiency. This module has a clear structure and good scalability, providing a new and effective way to improve the performance of cardiac medical image generation tasks for 4D data.
medical image generation, Mamba, 4-dimensional data, long-range dependencies, diffusion model
Copyright (c) 2025 Mei Zhang, Zhengjie Liang (Author) https://creativecommons.org/licenses/by/4.0
Optimization and Quality Control of Additives Manufacturing Based on Machine Learning: 3-year Research Review and Future Challenges
This paper aims to review the research progress in the optimization and quality control of additive manufacturing methods based on machine learning over the past three years. By systematically reviewing and analysing the relevant contributions, this paper discusses the application of machine learning in additive manufacturing, such as parameter optimization, material performance prediction, real-time process monitoring and data-driven quality evaluation. Machine learning technology significantly improves the stability and product quality of additive manufacturing, but it also faces challenges such as data standardization, multiscale modelling and interdisciplinary cooperation. Finally, this paper proposes the following: research directions and policy recommendations to promote the development of additive manufacturing technology.
additive manufacturing, machine learning, optimization, quality control, data-driven
Copyright (c) 2025 Haochen Linghu (Author) https://creativecommons.org/licenses/by/4.0
A Comparative Analysis of FCNN and CNN Architectures for Speech Denoising Across Diverse Noise Frequencies
Speech denoising remains a critical challenge in audio signal processing, especially under non-stationary noise conditions. While convolutional neural networks (CNNs) have been widely adopted for speech enhancement, the potential of fully connected neural networks (FCNNs) remains underexplored, particularly under frequency-varying noise scenarios. This study presents a systematic comparative analysis of FCNN and CNN architectures for speech denoising across multiple noise frequencies. Using the Common Voice dataset, we introduced diverse noise types at 8 kHz, 16 kHz, and 44 kHz to evaluate the denoising performance of both models. Experimental results demonstrate a frequency-dependent performance disparity: at 8 kHz, both models perform similarly, with CNN showing marginally higher Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE). At 16 kHz, CNN achieves significantly higher SNR albeit with increased RMSE, indicating a trade-off between noise suppression and spectral fidelity. At 44 kHz, CNN comprehensively outperforms FCNN, attaining superior SNR (4.80, +0.04) and lower RMSE (2.6826, –0.1556). These findings underscore the architectural advantages of CNNs in broad-frequency and complex noise environments, while revealing FCNN’s applicability in narrowband scenarios. This research highlights the necessity of frequency-aware model selection and provides novel insights into the comparative efficacy of FCNN and CNN in speech denoising.
speech denoising, FCNN, CNN, noise frequency analysis, SNR, RMSE, common voice dataset
Copyright (c) 2025 Xueqing Ma (Author) https://creativecommons.org/licenses/by/4.0
CoA-DTI: A Drug–Target Interaction Prediction Model Based on Co-Attention
Accurate prediction of drug–target interactions (DTIs) is a fundamental task in AI-driven drug discovery and repositioning. While traditional experimental methods are reliable, they are often time-consuming and expensive, limiting their scalability. Recent computational approaches have shown promise but still face challenges in extracting informative representations and modeling cross-modal interactions between drugs and proteins. In this work, we propose CoA-DTI, a novel deep learning framework that integrates Graph Convolutional Networks (GCNs), Convolutional Neural Networks (CNNs), and a Co-Attention mechanism for DTI prediction. Drug molecules are encoded as molecular graphs and processed through multi-layer GCNs to capture topological and atom-level features. Protein sequences are encoded via CNNs to learn local biochemical patterns. A co-attention module is then employed to model bidirectional interactions between the drug and protein representations, enabling fine-grained and context-aware feature fusion. Extensive experiments on the benchmark Davis dataset show that CoA-DTI achieves superior performance over competitive baselines in terms of accuracy and robustness. Ablation studies further validate the effectiveness of each module. This work demonstrates the potential of integrating graph-based learning and cross-modal attention in bioinformatics applications, offering a generalizable and interpretable approach for interaction prediction.
drug–target interaction, graph neural networks, deep learning, co-attention mechanism, cross-modal feature fusion
Copyright (c) 2025 Bolun Qi (Author) https://creativecommons.org/licenses/by/4.0
Design of Anti-Interference Digital Circuits for UART Communication
This paper designs and implements a UART receiving module with anti-interference capability based on Verilog HDL. During the design process, the working principle of the UART protocol was first analyzed in depth, clarifying the data frame structure and baud rate sampling mechanism. On this basis, a receiving module was constructed, incorporating functions such as start-bit detection, baud-rate division, data-bit acquisition, and stop-bit verification. In addition, multi-point sampling and majority-voting logic were introduced to effectively suppress input interference signals. Simulation results demonstrate that under simulated interference conditions, the receiving module can correctly parse serial data, providing stable and reliable output with significant anti-interference performance. This design ensures communication accuracy while maintaining extensibility, thereby offering a feasible reference for the development of subsequent embedded system communication modules.
UART, serial communication, Verilog HDL, anti-interference, digital circuit, simulation verification
Copyright (c) 2025 Linxi Jin (Author) https://creativecommons.org/licenses/by/4.0
The Application of AI-Generated Content (AIGC) in E-Commerce Advertising
In recent years, e-commerce platforms have increasingly relied on content marketing, where advertising is expected to deliver more diverse and information-rich messages. The rapid development of artificial intelligence-generated content (AIGC) offers new tools that allow companies to adapt to this environment more efficiently. With the maturation of technologies such as AI-driven copywriting, image design, and video generation, AIGC has begun to play a central role in advertising content production and distribution. This paper explores how AIGC reduces production costs, enables scalable and personalized advertising, and reshapes consumer engagement. Moreover, it analyses emerging challenges, including the homogenization of content, data security risks, and copyright disputes. The paper concludes with an outlook on the future of AIGC in advertising and proposes directions for responsible and innovative development.
AIGC, artificial intelligence, E-commerce, digital advertising
Copyright (c) 2025 Kaiyue Xu (Author) https://creativecommons.org/licenses/by/4.0
Meta-learning Driven Automatic Hyperparameter Optimization for Neural Networks in Computer Vision
With the growing complexity of computer vision tasks and the explosive expansion of data scales, the optimization of neural network hyperparameters has become increasingly critical to model performance. Efficient hyperparameter optimization enables breakthroughs in tackling complex tasks and adapting to intricate scenarios. Traditional optimization methods rely heavily on manual experience and suffer from low efficiency. In contrast, meta-learning empowers hyperparameter optimization through its core logic of “learning to learn”: it extracts generalizable experience to build meta-cognition, thereby enhancing computational efficiency while achieving strong generalization capabilities. This paper first systematically introduces basic concepts such as neural networks, then elaborates on classic traditional optimization methods and meta-learning-based approaches, and then presents the experimental results of selected algorithms. Finally, it identifies unresolved issues and provides an outlook on future development trends.
meta-learning, computer vision, hyperparameter optimization, MAML, reptile
Copyright (c) 2025 Xin Zhang (Author) https://creativecommons.org/licenses/by/4.0
Frontiers in Artificial Intelligence Algorithm Optimization: A Comprehensive Review of Training-Time and Inference-Time Advances
The rapid progress of artificial intelligence (AI) has been largely driven by the scaling of deep neural networks, advances in hardware accelerators, and the availability of large-scale datasets. However, the computational, memory, and energy demands of training and deploying foundation models such as GPT-5 and LLaMA-3 have created scalability and sustainability bottlenecks. Algorithmic optimization has emerged as a central strategy to alleviate these challenges across training-time efficiency, inference-time acceleration, long-context extension, and alignment learning. This article provides a comprehensive review of the state of the art in AI algorithm optimization, systematically categorizing approaches, benchmarking them under unified metrics (memory, throughput, latency, perplexity, stability, complexity, portability), and identifying failure modes and boundary conditions. We further present reproducibility artifacts, including minimal training and inference stacks (GaLore + Sophia optimizer; vLLM + FlashAttention-3 + QServe) and standardized datasets (MMLU, GSM8K, LongBench, DCLM). Our synthesis underscores that algorithm–system co-design—spanning optimizer innovations, quantization-aware serving, context length generalization, and efficient preference alignment—is critical to achieving both efficiency and ethical sustainability in next-generation AI systems.
reinforcement learning with human feedback (RLHF)
Copyright (c) 2025 Juntong Lu (Author) https://creativecommons.org/licenses/by/4.0
Efficient Classification Method for Topological Quantum Materials Based on Graph Neural Networks and Persistent Homology Theory
Topological quantum materials hold considerable promise for applications in quantum computing and spintronic devices due to their unique electronic properties. However, traditional Density Functional Theory (DFT) methods encounter difficulties in predicting these topological properties, including high computational costs and classification errors. This study proposes a machine learning framework that combines Graph Isomorphism Networks (GIN) with Atomic-Specific Persistent Homology (ASPH) to achieve efficient classification of topological materials by integrating global crystal structure features with local atomic topological descriptors. GIN is used to capture the global graph representation of periodic crystal structures, while ASPH extracts local features of atomic environments through multiscale topological analysis. These two feature sets are integrated following dimensionality reduction and subsequently classified using XGBoost. Principal Component Analysis (PCA) was employed to reduce the dimensionality of the high-dimensional ASPH feature vectors, thereby enhancing both model efficiency and accuracy. Experimental results indicate that this method performs exceptionally well in binary classification (topologically trivial/non-trivial), achieving an accuracy of 87.32%, which is significantly better than models based on a single feature. However, performance in ternary classification (trivial, semi-metal, topological insulator) declines to 75.04% due to class imbalance and feature overlap. The study validates the feasibility of combining graph neural networks with topological data analysis, providing an efficient computational framework for high-throughput screening of topological materials and offering new ideas for the application of multimodal feature fusion in materials science.
topological quantum materials, graph isomorphism networks (GIN), atomic-specific persistent homology (ASPH), XGBoost classification
Copyright (c) 2025 Yuanyuan Xu (Author) https://creativecommons.org/licenses/by/4.0
Prediction of Interatomic Potentials Combining Empirical Potential and Graph Neural Networks
Molecular dynamics (MD) simulation is constrained by the time and spatial scales as well as computational efficiency of traditional methods. While machine learning interatomic potentials (MLIPs) based on graph neural networks (GNNs) improve modeling accuracy, they suffer from insufficient short-range interaction modeling and lack of physical constraints. This paper proposes the DimezblNet model, which explicitly embeds the ZBL empirical potential function into the DimeNet framework. Through physically guided hybrid potential modeling, the description of short-range repulsive interactions is strengthened. On the MD17 dataset, the model reduces the mean absolute error (MAE) of energy and atomic force predictions for molecules like aspirin by 3.7%-5.3% compared to DimeNet. In the QM9 molecular property prediction task, the MAE of polarizability (α) is 0.0452 Å³, outperforming DimeNet (0.0469 Å³, a 3.6% improvement), and the MAE of dipole moment (μ) is 0.0273 D. Experiments show that combining physical priors with data-driven strategies significantly enhances the model's generalization ability and interpretability in complex molecular systems.
molecular dynamics simulation, graph neural network, ZBL potential, machine learning interatomic potential
Copyright (c) 2025 Tao Ming, Guochao Wan (Author) https://creativecommons.org/licenses/by/4.0
A Lightweight Object Detection Algorithm Based on Improved YOLOv8
Lightweight object detection algorithms are crucial in the field of computer vision, directly affecting whether computer vision algorithms can be deployed on resource-constrained devices and meet the real-time requirements of daily life. To address the above problems, this paper proposes a lightweight object detection algorithm LAD-YOLO based on improved YOLOv8. First, we optimize the point-wise convolution in depthwise separable convolution to enhance the model's learning ability, introduce depthwise separable convolution into the backbone network and neck network to reduce the model size, and construct a lightweight detection head. Meanwhile, the LSKA (Large Separable Kernel Attention) mechanism is introduced to help the model capture multi-scale information and achieve better detection performance. Extensive experiments conducted on the VOC dataset show that the proposed LAD-YOLO algorithm improves the precision (P) and mAP0.5:0.95 by 2.5% and 1.8% respectively compared with YOLOv8n, while maintaining lower parameters and computational complexity.
lightweight object detection, LAD-YOLO, large separable kernel attention, lightweight convolution
Copyright (c) 2025 Guochao Wan, Tao Ming (Author) https://creativecommons.org/licenses/by/4.0
Autonomous Driving Driven by Artificial Intelligence: Development Status and Future Prospects
This paper aims to explore the current status and future development trends of artificial intelligence technology in the field of autonomous driving. By analyzing the application of artificial intelligence technologies such as computer vision, deep learning and reinforcement learning in autonomous driving, this paper shows that autonomous driving is currently a hot topic in society. At present, L2 and L3 autonomous driving systems have been launched. In the future, autonomous driving may develop in the direction of vehicle‒road collaboration and L4 unmanned delivery. In addition, we still face many challenges, such as the accuracy attenuation of computer vision algorithms in extreme weather and the proportion of responsibility between car companies and users in autonomous driving accidents.
artificial intelligence, computer vision, deep learning, reinforcement learning, autonomous driving, vehicle‒road coordination
Copyright (c) 2025 Lichao Geng (Author) https://creativecommons.org/licenses/by/4.0
Adaptive Resource Scheduling for IoT Big Data Stream Processing Based on Deep Reinforcement Learning
With the rapid development of Internet of Things (IoT) technology and the explosive growth of data scale, traditional static resource scheduling methods can no longer meet the dynamic and heterogeneous requirements of IoT big data stream processing. This paper proposes an adaptive resource scheduling method based on deep reinforcement learning, which achieves intelligent resource scheduling decisions in IoT environments by constructing multi-level state space models, designing intelligent action spaces, and multi-objective reward functions. Experiments were conducted based on three typical application scenarios: smart cities, industrial IoT, and smart grids, establishing large-scale testing environments containing over 28,000 devices. Results demonstrate that compared to traditional scheduling methods, the proposed method achieves improvements of 42.3%-84.0% in response time, 56.7%-70.2% enhancement in system throughput, 34.1%-50.1% improvement in resource utilization, and 50.2%-60.3% enhancement in energy efficiency. During 18 months of actual deployment, cumulative operational cost savings reached 101.8 million yuan, with a payback period of only 1.8 years. Long-term stability testing shows that the algorithm processed 1.52 billion scheduling decisions during 30 days of continuous operation, with performance fluctuations controlled within 6.4%, demonstrating excellent convergence and robustness. The research findings provide theoretical foundations and technical support for intelligent resource management in IoT big data stream processing, holding significant importance for promoting the industrial application of IoT technology.
deep reinforcement learning, Internet of Things, big data stream processing, adaptive resource scheduling, intelligent decision-making
Copyright (c) 2025 Yuqi Zhou (Author) https://creativecommons.org/licenses/by/4.0
Privacy Protection in Vehicle Platooning: Challenges, Technologies, and Future Directions
Vehicle platooning technology significantly enhances traffic efficiency and safety but introduces substantial privacy challenges. These include traditional threats like information leakage, data correlation, and sophisticated forgery attacks, which can mislead vehicle decisions and behaviors. To mitigate these, current research explores core protection strategies such as anonymization, differential privacy, various encryption techniques, trust management, and fine-grained access control. This paper also highlights the burgeoning role of blockchain in providing decentralized management and facilitating privacy-preserving machine learning within platooning systems. Despite advancements, significant hurdles persist concerning performance optimization, balancing security with privacy, ensuring real-time processing, guaranteeing robustness against evolving threats, and achieving standardization. This study systematically analyzes existing privacy protection technologies tailored for vehicle platooning, providing a comprehensive overview and identifying critical future research directions to foster secure and widespread adoption of this transformative technology.
key word, vehicle platooning, privacy protection, internet of vehicles
Copyright (c) 2025 Tianxi Wang, Yadong Peng, Lilong Sima (Author) https://creativecommons.org/licenses/by/4.0
A Survey on Truth Discovery in Crowdsensing
In recent years, the rapid proliferation of smartphones and wearable devices has significantly propelled the development of crowdsensing. As a prerequisite for ensuring the secure operation of crowdsensing services, data quality has emerged as a critical issue that demands urgent resolution. This paper first introduces the main components and system workflows of crowdsensing, followed by an account of the basic concepts, principles, and key research focuses of truth discovery methods in crowdsensing. By categorizing truth discovery approaches based on modeling paradigms, this study systematically reviews the current research landscape, analyzes and compares existing methods, and constructs a comprehensive framework for their evaluation. Finally, it synthesizes the findings and identifies future challenges for truth discovery in crowdsensing, aligning with the evolving application demands of the field.
crowdsensing, data quality, truth discovery, privacy protection
Copyright (c) 2025 Yadong Peng, Tianxi Wang, Qing Lang (Author) https://creativecommons.org/licenses/by/4.0
Multi-Window Detail Enhancement Based Infrared and Visible Image Fusion
The goal of infrared and visible image fusion is to combine complementary information from infrared and visible images of the same scene to generate a high-quality composite image that integrates the advantages of both modalities. Although many current fusion methods achieve satisfactory results, they still suffer from limitations such as insufficient feature resolution extraction from infrared images and inadequate texture information extraction from visible images. This paper proposes a novel fusion method designed to enhance the resolution, detail preservation, and visual consistency of the fused image. The method integrates multi-window detail enhancement with multi-layer residual connections, employing a detail selector and a global feature extractor to separately capture high-frequency and low-frequency features from the infrared and visible images. Experimental results demonstrate that, compared to existing approaches, the proposed method achieves superior fusion quality and better preservation of image details, providing higher-quality data for subsequent image processing tasks.
infrared and visible image fusion, multi-window attention, deep learning
Copyright (c) 2025 Long Wang, Xinbo Wang (Author) https://creativecommons.org/licenses/by/4.0
Research on Data Quality Analysis Based on Data Mining
As the process of digitalization continues to advance, big data plays a pivotal role across various industries. However, the quality of data directly impacts the results of data analysis and the accuracy of decision-making. High-quality data can provide a reliable basis for decisions, whereas poor-quality data may lead to erroneous judgments and cause incalculable losses. Therefore, the management and enhancement of data quality have become central issues in contemporary data management. Data mining technology, as a powerful tool, excels not only in extracting valuable information from large-scale datasets but also in demonstrating significant potential in improving data quality. By mining and analyzing the patterns and characteristics hidden within data, it is possible to identify and rectify defects, thereby enhancing the overall quality of data and increasing the reliability of data-driven decision-making. This paper delves into the definition and dimensions of data quality, explores the fundamental principles of data mining technology, and examines its specific applications in data quality analysis, aiming to offer new insights and methods for data quality management.
Copyright (c) 2025 Jieting Lian Lian (Author) https://creativecommons.org/licenses/by/4.0
Human-Robot Interaction in Museum Exhibition Assembly: A Framework for Adaptive Collaboration
Exhibition assembly in museums represents a critical phase in translating abstract curatorial concepts into tangible physical forms. Unlike structured industrial settings, museum environments are often unstructured and unpredictable, posing considerable challenges for human-robot interaction (HRI). This study proposes an adaptive collaboration framework for HRI, specifically tailored to meet the unique demands of museum exhibition assembly. The framework is structured around three core interaction mechanisms: behavioral coordination, intention sharing, and role collaboration. It addresses key challenges such as the sensitivity of cultural artifacts, environmental complexity, and the necessity of establishing trust between humans and robots. To improve collaborative accuracy, the framework incorporates hierarchical task modeling, adaptive behavior adjustment, and multimodal information exchange. For intention sharing, it introduces multidimensional situational modeling, context-aware intention inference algorithms, and perceivable feedback systems. To optimize role allocation, it employs a flexible role assignment strategy, adaptive interaction patterns, and clear role status indicators. By extending HRI research beyond industrial applications, the framework offers novel insights into collaborative systems within complex cultural contexts. Nevertheless, further empirical validation and context-specific analysis are required to fully bridge the gap between theoretical development and practical implementation.
Copyright (c) 2025 Yixuan Zhou, Hanlei Sun, Bingxue Zhang (Author) https://creativecommons.org/licenses/by/4.0
The Current Development and Future Prospects of Autonomous Driving Driven by Artificial Intelligence
This paper explores the application and development of artificial intelligence in autonomous driving and analyses its current status, challenges, and future trends. Autonomous driving systems integrate multiple core technologies in vehicle perception and driving decision-making, achieving a leap from assisted driving to commercial deployment. Leveraging emerging methods such as machine learning, deep learning, and reinforcement learning, autonomous driving systems have significantly improved perception accuracy, decision-making capabilities, and environmental adaptability. However, current autonomous driving systems still face technical bottlenecks, including insufficient model generalizability and low training efficiency, while also encountering legal and societal challenges such as data privacy protection, accident liability determination, and algorithmic ethical biases. In the future, high-precision multimodal perception architectures, edge computing deployment solutions, and the construction of a vehicle‒road collaborative ecosystem will be key breakthrough directions for enabling fully autonomous driving across all scenarios.
autonomous driving, artificial intelligence, multimodal perception, reinforcement learning, intelligent transportation system
Copyright (c) 2025 Zichen Fu (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning Analysis in Mechanical Engineering
Despite the rapid development of the AI industry and the programming industry in the machinery manufacturing or production industry, there is no systematic explanation as to why the use of machine learning can enhance the development of related industries. This paper summarizes the reasons for the industry’s development direction from the three aspects of machine learning’s automation design, expected maintenance, and production efficiency improvement. The learning types are briefly introduced to indicate the application of machine learning in mechanical engineering. By providing relevant examples, the superiority of machine learning in mechanical engineering is reflected, and specific practical cases are given to show the development trend of machine learning in the manufacturing industry in the future. Finally, we make predictions on the main development directions of machine learning in the future industry.
data-driven, neural network, machine learning, artificial intelligence
Copyright (c) 2024 Huangyu Zhao (Author) https://creativecommons.org/licenses/by/4.0
An Investigation of Molecular Property Prediction and Classification Based on Machine Learning Algorithms
With the rapid advancement of science and technology, chemical and physical research has entered an era of complexity and high dimensionality, where traditional research paradigms struggle to optimize vast chemical parameter searches. The Machine Chemist platform was developed in this context, leveraging big data and intelligent models to automate chemical synthesis, characterization, and testing processes. This study aims to predict the y1, y2 and y3 properties and classes of 2,580 molecules based on their physicochemical properties and improve model accuracy through data analysis and modeling. Data preprocessing involved removing missing values, duplicates, and outliers using the quartile method, resulting in an analyzable dataset. A scatter plot of y2 and id suggested a univariate polynomial function relationship, leading to the construction of a univariate nonlinear regression model. The model achieved a high prediction accuracy, with a low root mean square error and a high coefficient of determination. Additionally, the relationship between class and y1~y3 , x1~x100 indicators was examined, revealing mostly nonlinear relationships. A random forest model was established to classify 2,580 molecules into 1 to 4 classes based on the properties of 200,000 chemical molecules. The model's performance was evaluated using decision trees, confusion matrices, precision, and recall metrics. Finally, the SHAP method assessed the impact of feature indicators on classification outcomes, contributing to the development of a reliable molecular category prediction model.
model prediction, classification, random forest
Copyright (c) 2024 Yanzi Chen (Author) https://creativecommons.org/licenses/by/4.0
Application Research of Image Classification Based on Pytorch and Convolutional Neural Network
The image classification problem of convolutional neural network (CNN) on CIFAR-10 dataset is studied, and the model is implemented and experimented with PyTorch. Through the training and testing of the model, we analyze the performance of the model and explore the possibility of improvement. The experimental results show that the model achieves a certain accuracy in the image classification task, but there is space for improvement in some categories.
Image classification, neural network, PyTorch, Model, Training
Copyright (c) 2024 Zhaoyu Li (Author) https://creativecommons.org/licenses/by/4.0
Digital Art Creation under AIGC Technology Innovation: Multidimensional Challenges and Reflections on Design Practice, Creation Environment and Artistic Ecology
With the rapid development of science and technology, artificial intelligence generated content (AIGC) technology has gradually become a great help in the field of digital art creation. It not only changed the mode of traditional artistic creation, but also brought unprecedented multi-dimensional challenges and reflections to design practice, creative environment and artistic ecology. This paper will discuss these two aspects and discuss the opportunities and difficulties faced by digital art creation under the technical innovation of AIGC.
AIGC, art design, artistic creation, multidimensional challenge
Copyright (c) 2024 Qiyue Ren, Yiling Tang, Yuxi Lin (Author) https://creativecommons.org/licenses/by/4.0
Leveraging Machine Learning for Telecom Banking Card Fraud Detection: A Comparative Analysis of Logistic Regression, Random Forest, and XGBoost Models
In recent years, telecommunication bank card fraud has become a major threat to financial security, so it is necessary to develop robust detection mechanisms for telecommunication bank card fraud. This study examines the application of machine learning techniques (specifically logistic regression, random forests, and XGBoost) in identifying fraudulent telecom bank transactions. Using a dataset consisting of one million transaction records from the 2024 National Student Data Statistics and Analytics Competition, we implemented and evaluated these models based on key performance metrics such as accuracy, precision, recall, F1 score and ROC-AUC. The results show that XGBoost outperforms the other models, achieving superior accuracy and robustness in fraud detection, while Random Forest also performs well, achieving almost perfect classification accuracy. Logistic regression, while effective, lagged behind in terms of handling the complexity of the data. The analyses in this paper further highlight the critical role of features such as transaction amount ratios and online transaction status in predicting fraud. These findings suggest that advanced machine learning models, especially ensemble methods such as XGBoost, are highly effective in combating telecom banking fraud and should be integrated into existing detection systems to enhance their predictive capabilities.
Telecom Banking, Fraud Detection, Machine Learning, Logistic Regression, Random Forest, XGBoost, Predictive Modeling.
Copyright (c) 2024 Guanyu Liu (Author) https://creativecommons.org/licenses/by/4.0