Computers and Artificial Intelligence
type: channel
description: 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.
publisher: zeus press
language: en
publication name: Computers and Artificial Intelligence
issn: 3006-3728
items: SSGTN: A Graph Similarity Calculation Method Combining Subgraph and Global Features
items: MV-MoCL: A Multi-View Molecular Contrastive Learning Framework by Integrating SMILES, 2D Graph, and 3D Geometry
items: Research on the Innovation of Intelligent Teaching Models in Higher Education from the Perspective of Human–AI Teaching
items: Multi Table QA: Evaluating Modern LLM Strategies on Table Understanding
items: Survey of Autonomous Driving via Multisensor Cooperation
items: Optimization of Machine-Learning-Based Enterprise Loan Default Prediction Models: A Comparative Study with Traditional Scoring Methods
items: Intelligent Connected Technology for Campus Vehicle Applications: A Review
items: Artificial Intelligence-Driven Autonomous Vehicles: Current Developments and the Future Prospects
items: Mamba Convolutional Hybrid Spatio-Temporal Medical Image Generation Based on Diffusion Probabilistic Models
items: Optimization and Quality Control of Additives Manufacturing Based on Machine Learning: 3-year Research Review and Future Challenges
items: A Comparative Analysis of FCNN and CNN Architectures for Speech Denoising Across Diverse Noise Frequencies
items: CoA-DTI: A Drug–Target Interaction Prediction Model Based on Co-Attention
items: Design of Anti-Interference Digital Circuits for UART Communication
items: The Application of AI-Generated Content (AIGC) in E-Commerce Advertising
items: Meta-learning Driven Automatic Hyperparameter Optimization for Neural Networks in Computer Vision
items: Frontiers in Artificial Intelligence Algorithm Optimization: A Comprehensive Review of Training-Time and Inference-Time Advances
items: Efficient Classification Method for Topological Quantum Materials Based on Graph Neural Networks and Persistent Homology Theory
items: Prediction of Interatomic Potentials Combining Empirical Potential and Graph Neural Networks
items: A Lightweight Object Detection Algorithm Based on Improved YOLOv8
items: Autonomous Driving Driven by Artificial Intelligence: Development Status and Future Prospects
items: Adaptive Resource Scheduling for IoT Big Data Stream Processing Based on Deep Reinforcement Learning
items: Privacy Protection in Vehicle Platooning: Challenges, Technologies, and Future Directions
items: A Survey on Truth Discovery in Crowdsensing
items: Multi-Window Detail Enhancement Based Infrared and Visible Image Fusion
items: Research on Data Quality Analysis Based on Data Mining
items: Human-Robot Interaction in Museum Exhibition Assembly: A Framework for Adaptive Collaboration
items: The Current Development and Future Prospects of Autonomous Driving Driven by Artificial Intelligence
items: Machine Learning Analysis in Mechanical Engineering
items: An Investigation of Molecular Property Prediction and Classification Based on Machine Learning Algorithms
items: Application Research of Image Classification Based on Pytorch and Convolutional Neural Network

SSGTN: A Graph Similarity Calculation Method Combining Subgraph and Global Features
type: item
description: Graph similarity computation is one of the fundamental challenges in graph data mining, with critical applications in chemical molecule analysis and social network analysis. Traditional methods, such as graph edit distance and maximum common subgraph, are NP-hard and computationally expensive. Although neural network-based approaches have improved efficiency and accuracy, existing methods still exhibit limitations: they often focus excessively on global features while neglecting local substructures, or vice versa, and fail to evaluate the relative importance of node-level, subgraph level, and global features. To address these issues, this paper proposes Similarity Subgraph Global Tensor Network(SSGTN), a novel graph similarity computation framework that integrates subgraph and global features. Specifically, SSGTN employs graph convolutional networks to extract subgraph features and graph attention mechanisms to capture global representations. A Subgraph Global Tensor Network is then designed to dynamically fuse pooled subgraph features with global features, thereby jointly optimizing local and global characterizations. Finally, all features are aggregated to generate the graph similarity score. Experiments on real-world datasets demonstrate that SSGTN outperforms baseline models across three evaluation metrics, confirming its efficiency and accuracy.
subject: Research Articles
  topic: section
subject: graph neural network, graph similarity calculation, graph embedding, graph editing distance, maximum common subgraph
  topic: keywords
creator: Huang Xu
rights: Copyright (c) 2025 Huang Xu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-27
publication date: 2025-11-27
starting page: 158
ending page: 166
doi: 10.70267/cai.25v2n3.158166

MV-MoCL: A Multi-View Molecular Contrastive Learning Framework by Integrating SMILES, 2D Graph, and 3D Geometry
type: item
description: Machine learning-based molecular property prediction (MPP) has garnered significant attention in computer-aided drug discovery, with its primary goal being the accurate estimation of molecular properties using structural data to accelerate drug development. In recent years, multi-view-based molecular property prediction learning has aimed to integrate information from different molecular views to learn high-quality molecular representations. Furthermore, the three-dimensional geometric information of molecules encompasses a richer set of spatial features, which plays a critical role in enhancing the accuracy of molecular property prediction. However, existing models often overlook 3D information in molecules. To address this, we propose a contrastive learning model named MV-MoCL, which incorporates encoders for multiple molecular dimensions: a SMILES Transformer for the SMILES sequence view, a Graph Isomorphism Network (GIN) for 2D molecular graphs, and SchNet for 3D geometric conformations. By aligning representations from these views using a contrastive loss function, our approach captures rich, multi-faceted molecular features during pre-training, thereby improving performance on downstream molecular property prediction tasks and effectively mitigating the issue of scarce labeled data. The proposed model was evaluated on several benchmark datasets from MoleculeNet, and experiments demonstrate that MV-MoCL matches or surpasses existing models across multiple benchmarks.
subject: Research Articles
  topic: section
subject: molecular property prediction, multi-view learning, graph neural networks, deep learning, contrastive learning, bioinformatics
  topic: keywords
creator: Hang Zhao
creator: Huang Xu
rights: Copyright (c) 2025 Hang Zhao, Huang Xu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-27
publication date: 2025-11-27
starting page: 150
ending page: 157
doi: 10.70267/cai.25v2n3.150157

Research on the Innovation of Intelligent Teaching Models in Higher Education from the Perspective of Human–AI Teaching
type: item
description: 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.
subject: Research Articles
  topic: section
subject: human-AI teaching, intelligent teaching model, artificial intelligence, higher education reform, educational digitalization
  topic: keywords
creator: Min Gao
rights: Copyright (c) 2025 Min Gao (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-18
publication date: 2025-11-18
starting page: 143
ending page: 149
doi: 10.70267/cai.25v2n3.143149

Multi Table QA: Evaluating Modern LLM Strategies on Table Understanding
type: item
description: 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.
subject: Research Articles
  topic: section
subject: large language models, multi-table question answering, tool-augmented reasoning, structured data understanding, agent-based workflows, tabular data reasoning
  topic: keywords
creator: Letian Li
rights: Copyright (c) 2025 Letian Li (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-18
publication date: 2025-11-18
starting page: 117
ending page: 142
doi: 10.70267/cai.25v2n3.117142

Survey of Autonomous Driving via Multisensor Cooperation
type: item
description: 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.
subject: Research Articles
  topic: section
subject: multisensor collaboration, data fusion technology, automatic driving, sensor fusion
  topic: keywords
creator: Zhixuan Pei
rights: Copyright (c) 2025 Zhixuan Pei (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-24
publication date: 2025-10-24
starting page: 108
ending page: 116
doi: 10.70267/cai.25v2n3.108116

Optimization of Machine-Learning-Based Enterprise Loan Default Prediction Models: A Comparative Study with Traditional Scoring Methods
type: item
description: 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.
subject: Research Articles
  topic: section
subject: enterprise loan default, machine learning, credit scoring, SHAP analysis, dynamic risk
  topic: keywords
creator: Xiangyu Huang
rights: Copyright (c) 2025 Xiangyu Huang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-23
publication date: 2025-10-23
starting page: 92
ending page: 107
doi: 10.70267/cai.25v2n3.92107

Intelligent Connected Technology for Campus Vehicle Applications: A Review
type: item
description: 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 appli…
subject: Research Articles
  topic: section
subject: intelligent connected vehicles, socialization of campus vehicle use, campus fleet collaboration, scenario integration, social connectivity
  topic: keywords
creator: Ruoxi Wang
rights: Copyright (c) 2025 Ruoxi Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-21
publication date: 2025-10-21
starting page: 79
ending page: 91
doi: 10.70267/cai.25v2n3.7991

Artificial Intelligence-Driven Autonomous Vehicles: Current Developments and the Future Prospects
type: item
description: 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.
subject: Research Articles
  topic: section
subject: autonomous driving, artificial intelligence, technological challenges, deep learning: vehicle-to-everything (V2X) communication
  topic: keywords
creator: Xianni Xie
rights: Copyright (c) 2025 Xianni Xie (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-17
publication date: 2025-10-17
starting page: 75
ending page: 78
doi: 10.70267/ajp73f40

Mamba Convolutional Hybrid Spatio-Temporal Medical Image Generation Based on Diffusion Probabilistic Models
type: item
description: 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.
subject: Research Articles
  topic: section
subject: medical image generation, Mamba, 4-dimensional data, long-range dependencies, diffusion model
  topic: keywords
creator: Mei Zhang
creator: Zhengjie Liang
rights: Copyright (c) 2025 Mei Zhang, Zhengjie Liang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-11
publication date: 2025-10-11
starting page: 55
ending page: 62
doi: 10.70267/cai.25v2n3.5562

Optimization and Quality Control of Additives Manufacturing Based on Machine Learning: 3-year Research Review and Future Challenges
type: item
description: 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.
subject: Research Articles
  topic: section
subject: additive manufacturing, machine learning, optimization, quality control, data-driven
  topic: keywords
creator: Haochen Linghu
rights: Copyright (c) 2025 Haochen Linghu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-11
publication date: 2025-10-11
starting page: 63
ending page: 74
doi: 10.70267/cai.25v2n3.6374

A Comparative Analysis of FCNN and CNN Architectures for Speech Denoising Across Diverse Noise Frequencies
type: item
description: 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.
subject: Research Articles
  topic: section
subject: speech denoising, FCNN, CNN, noise frequency analysis, SNR, RMSE, common voice dataset
  topic: keywords
creator: Xueqing Ma
rights: Copyright (c) 2025 Xueqing Ma (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 45
ending page: 54
doi: 10.70267/cai.25v2n3.4554

CoA-DTI: A Drug–Target Interaction Prediction Model Based on Co-Attention
type: item
description: 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.
subject: Research Articles
  topic: section
subject: drug–target interaction, graph neural networks, deep learning, co-attention mechanism, cross-modal feature fusion
  topic: keywords
creator: Bolun Qi
rights: Copyright (c) 2025 Bolun Qi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-12
publication date: 2025-09-12
starting page: 20
ending page: 28
doi: 10.70267/cai.25v2n3.2028

Design of Anti-Interference Digital Circuits for UART Communication
type: item
description: 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.
subject: Research Articles
  topic: section
subject: UART, serial communication, Verilog HDL, anti-interference, digital circuit, simulation verification
  topic: keywords
creator: Linxi Jin
rights: Copyright (c) 2025 Linxi Jin (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-11
publication date: 2025-09-11
starting page: 1
ending page: 9
doi: 10.70267/cai.25v3n1.0109

The Application of AI-Generated Content (AIGC) in E-Commerce Advertising
type: item
description: 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.
subject: Research Articles
  topic: section
subject: AIGC, artificial intelligence, E-commerce, digital advertising
  topic: keywords
creator: Kaiyue Xu
rights: Copyright (c) 2025 Kaiyue Xu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-12
publication date: 2025-09-12
starting page: 41
ending page: 44
doi: 10.70267/cai.25v2n3.4144

Meta-learning Driven Automatic Hyperparameter Optimization for Neural Networks in Computer Vision
type: item
description: 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.
subject: Research Articles
  topic: section
subject: meta-learning, computer vision, hyperparameter optimization, MAML, reptile
  topic: keywords
creator: Xin Zhang
rights: Copyright (c) 2025 Xin Zhang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-12
publication date: 2025-09-12
starting page: 10
ending page: 19
doi: 10.70267/cai.25v2n3.1019

Frontiers in Artificial Intelligence Algorithm Optimization: A Comprehensive Review of Training-Time and Inference-Time Advances
type: item
description: 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.
subject: Research Articles
  topic: section
subject: artificial intelligence optimization
  topic: keywords
subject: deep learning efficiency
  topic: keywords
subject: large language models (LLMs)
  topic: keywords
subject: training-time acceleration
  topic: keywords
subject: inference-time acceleration
  topic: keywords
subject: reinforcement learning with human feedback (RLHF)
  topic: keywords
subject: sustainable AI
  topic: keywords
creator: Juntong Lu
rights: Copyright (c) 2025 Juntong Lu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-10
publication date: 2025-09-10
starting page: 29
ending page: 40
doi: 10.70267/cai.25v2n3.2940

Efficient Classification Method for Topological Quantum Materials Based on Graph Neural Networks and Persistent Homology Theory
type: item
description: 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.
subject: Research Articles
  topic: section
subject: topological quantum materials, graph isomorphism networks (GIN), atomic-specific persistent homology (ASPH), XGBoost classification
  topic: keywords
creator: Yuanyuan Xu
rights: Copyright (c) 2025 Yuanyuan Xu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-15
publication date: 2025-08-15
starting page: 53
ending page: 65
doi: 10.70267/cai.25v2n2.5365

Prediction of Interatomic Potentials Combining Empirical Potential and Graph Neural Networks
type: item
description: 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.
subject: Research Articles
  topic: section
subject: molecular dynamics simulation, graph neural network, ZBL potential, machine learning interatomic potential
  topic: keywords
creator: Tao Ming
creator: Guochao Wan
rights: Copyright (c) 2025 Tao Ming, Guochao Wan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-07-25
publication date: 2025-07-25
starting page: 44
ending page: 51
doi: 10.70267/cai.25v2n2.4451

A Lightweight Object Detection Algorithm Based on Improved YOLOv8
type: item
description: 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.
subject: Research Articles
  topic: section
subject: lightweight object detection, LAD-YOLO, large separable kernel attention, lightweight convolution
  topic: keywords
creator: Guochao Wan
creator: Tao Ming
rights: Copyright (c) 2025 Guochao Wan, Tao Ming (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-07-25
publication date: 2025-07-25
starting page: 37
ending page: 43
doi: 10.70267/cai.25v2n2.3743

Autonomous Driving Driven by Artificial Intelligence: Development Status and Future Prospects
type: item
description: 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.
subject: Research Articles
  topic: section
subject: artificial intelligence, computer vision, deep learning, reinforcement learning, autonomous driving, vehicle‒road coordination
  topic: keywords
creator: Lichao Geng
rights: Copyright (c) 2025 Lichao Geng (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-07-02
publication date: 2025-07-02
starting page: 29
ending page: 36
doi: 10.70267/cai.25v2n2.2936

Adaptive Resource Scheduling for IoT Big Data Stream Processing Based on Deep Reinforcement Learning
type: item
description: 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.
subject: Research Articles
  topic: section
subject: deep reinforcement learning, Internet of Things, big data stream processing, adaptive resource scheduling, intelligent decision-making
  topic: keywords
creator: Yuqi Zhou
rights: Copyright (c) 2025 Yuqi Zhou (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-07-01
publication date: 2025-07-01
starting page: 16
ending page: 28
doi: 10.70267/cai.25v2n2.1628

Privacy Protection in Vehicle Platooning: Challenges, Technologies, and Future Directions
type: item
description: 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.
subject: Research Articles
  topic: section
subject: key word, vehicle platooning, privacy protection, internet of vehicles
  topic: keywords
creator: Tianxi Wang
creator: Yadong Peng
creator: Lilong Sima
rights: Copyright (c) 2025 Tianxi Wang, Yadong Peng, Lilong Sima (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-07-01
publication date: 2025-07-01
starting page: 9
ending page: 15
doi: 10.70267/cai.25v2n2.0915

A Survey on Truth Discovery in Crowdsensing
type: item
description: 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.
subject: Research Articles
  topic: section
subject: crowdsensing, data quality, truth discovery, privacy protection
  topic: keywords
creator: Yadong Peng
creator: Tianxi Wang
creator: Qing Lang
rights: Copyright (c) 2025 Yadong Peng, Tianxi Wang, Qing Lang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-07-01
publication date: 2025-07-01
starting page: 1
ending page: 8
doi: 10.70267/cai.25v2n2.0108

Multi-Window Detail Enhancement Based Infrared and Visible Image Fusion
type: item
description: 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.
subject: Research Articles
  topic: section
subject: infrared and visible image fusion, multi-window attention, deep learning
  topic: keywords
creator: Long Wang
creator: Xinbo Wang
rights: Copyright (c) 2025 Long Wang, Xinbo Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-27
publication date: 2025-06-27
starting page: 25
ending page: 31
doi: 10.70267/cai.25v2n1.2531

Research on Data Quality Analysis Based on Data Mining
type: item
description: 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.
subject: Research Articles
  topic: section
subject: data mining, data quality, analysis
  topic: keywords
creator: Jieting Lian
rights: Copyright (c) 2025 Jieting Lian Lian (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-27
publication date: 2025-04-27
starting page: 1
ending page: 7
doi: 10.70267/cai.25v2n101

Human-Robot Interaction in Museum Exhibition Assembly: A Framework for Adaptive Collaboration
type: item
description: 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.
subject: Research Articles
  topic: section
subject: museum
  topic: keywords
subject: human-robot interaction
  topic: keywords
subject: exhibition assembly
  topic: keywords
subject: adaptive design
  topic: keywords
creator: Yixuan Zhou
creator: Hanlei Sun
creator: Bingxue Zhang
rights: Copyright (c) 2025 Yixuan Zhou, Hanlei Sun, Bingxue Zhang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-28
publication date: 2025-04-28
starting page: 16
ending page: 24
doi: 10.70267/cai.25v2n1.1624

The Current Development and Future Prospects of Autonomous Driving Driven by Artificial Intelligence
type: item
description: 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.
subject: Research Articles
  topic: section
subject: autonomous driving, artificial intelligence, multimodal perception, reinforcement learning, intelligent transportation system
  topic: keywords
creator: Zichen Fu
rights: Copyright (c) 2025 Zichen Fu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-27
publication date: 2025-04-27
starting page: 8
ending page: 15
doi: 10.70267/cai.25v2n1.0815

Machine Learning Analysis in Mechanical Engineering
type: item
description: 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.
subject: Research Articles
  topic: section
subject: data-driven, neural network, machine learning, artificial intelligence
  topic: keywords
creator: Huangyu Zhao
rights: Copyright (c) 2024 Huangyu Zhao (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-11-21
publication date: 2024-11-21
starting page: 1
ending page: 5
doi: 10.70267/2xfync82

An Investigation of Molecular Property Prediction and Classification Based on Machine Learning Algorithms
type: item
description: 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.
subject: Research Articles
  topic: section
subject: model prediction, classification, random forest
  topic: keywords
creator: Yanzi Chen
rights: Copyright (c) 2024 Yanzi Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-09-25
publication date: 2024-09-25
starting page: 40
ending page: 44
doi: 10.70267/a5ghvb15

Application Research of Image Classification Based on Pytorch and Convolutional Neural Network
type: item
description: 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.
subject: Research Articles
  topic: section
subject: Image classification, neural network, PyTorch, Model, Training
  topic: keywords
creator: Zhaoyu Li
rights: Copyright (c) 2024 Zhaoyu Li (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-10-09
publication date: 2024-10-09
starting page: 28
ending page: 39
doi: 10.70267/eta2tt72