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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Gulfam, Muhammad Seals, Cheryl D. Vargas-Nordcbeck, Adriana Dozier, Gerry V. Computer Science and Software Engineering Auburn University AuburnAL United States National Center for Asphalt Technology Auburn University AuburnAL36849 United States

ISBN: (纸本)9783031823763

Pavement crack detection is an integral part of maintaining pavements. Identifying the pavement areas that require maintenance through manual processes is a laborious and time-consuming task. Latest researches have employed machine learning and deep learning to devise automatic and semi-automatic solutions for pavement distress detection. In this research, we propose a lightweight deep convolutional neural network (DCNN) model for crack recognition in pavement images, with 20 times fewer parameters than ResNet34 model. The model was tested on three public datasets that include Concrete Crack Images for Classification (CCIC) dataset, Sharif University of Technology Crack (SUT-Crack) dataset, and bridge crack detection dataset. The model obtained an average F1-score of 99.98%, 98.75% and 100% on the above-mentioned datasets respectively. We also tested the model on our own dataset that has pavement images with finer cracks which are harder to detect. Our dataset consists of 5,506 images in total. The model obtained 89.13% average F1-score. We explored the transfer learning technique on the proposed model as well. We fine-tuned the model (pre-trained on CCIC dataset) on our dataset which improved the results by 6.34% and increased the average F1-score to 95.47%. The model shows good generalizability with just a little over a million trainable parameters. Model explainability analysis using Captum AI library confirmed that the model is focusing on the cracked sections of the pavement images to classify the images as cracked. Having relevant areas of the image contribute more toward the prediction increases the confidence in the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review

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Artificial Intelligence Review 2025年第8期58卷 1-78页

作者： Al-antari, Mugahed A. Salem, Saied Raza, Mukhlis Elbadawy, Ahmed S. Bütün, Ertan Aydin, Ahmet Arif Aydoğan, Murat Ertuğrul, Bilal Talo, Muhammed Gu, Yeong Hyeon Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of Department of Artificial Intelligence College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of Department of Computer Engineering Faculty of Engineering Fırat University Elazığ Turkey Department of Computer Engineering Faculty of Engineering Inonu University Malatya Turkey Department of Software Engineering Faculty of Technology Fırat University Elazığ Turkey Department of Neurosurgery Faculty of Medicine Fırat University Elazığ Turkey Department of Computer Science & Engineering University of North Texas DentonTX76205 United States

Lumbar spinal stenosis (LSS) involves the narrowing of the spinal canal, leading to compression of the spinal cord and nerves in the lower back. Common causes include injuries, degenerative age-related changes, congenital conditions, and tumors, all of which contribute to back pain. Early diagnosis is critical for symptom management, preventing progression, and preserving quality of life. This study systematically reviews AI-based approaches for predicting LSS using MRI axial and sagittal imaging. The review focuses on various AI tasks: detection, segmentation, classification, hybrid approaches, spinal index measurements (SIM), and explainable AI frameworks. The aim is to highlight current knowledge, identify limitations in existing models, and propose future research directions. Following PRISMA guidelines and the PICO method (Population, Intervention, Comparison, Outcome), the review collects data from databases like PubMed, Web of science, scienceDirect, and IEEE Xplore (2005–2024). The Rayyan AI tool is used for duplicate removal and screening. The screening process includes an initial review of titles and abstracts, followed by full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) assesses the quality of selected articles. Of 1323 records, 97 duplicates were removed. After screening, 895 records were excluded, leaving 331 for full-text review. Among these, 184 articles were excluded for lacking AI relevance. Ultimately, 95 key articles (91 technical papers and 4 reviews) were identified for their contributions to AI-based LSS prediction. This review provides a comprehensive analysis of AI techniques in LSS prediction, guiding future research and advancing understanding in areas like explainable AI and large language models (LLMs). © The Author(s) 2025.

关键词： Explainable artificial intelligence (XAI) Harmonization Large language models (LLMs) LSS prediction Lumbar spinal stenosis (LSS) Spinal LSS indices measurements

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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Adaptive Local Neighborhood Window Laplacian Eigenmaps 24

Adaptive Local Neighborhood Window Laplacian Eigenmaps

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Xu, Jiayi Huang, Qirui Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400717482

Manifold learning method is a dimensionality reduction method that treats the data in non-Euclidean space as a Euclidean space in a local scope. However, most existing manifold learning methods cannot obtain the true neighborbood relationship of each sample point well. Therefore, we propose a manifold learning method based on adaptive local neighborhood. This method can adaptively select a neighborhood window. It can be approximated as a local Euclidean space based on the sample characteristics. To implement this method, a specific manifold learning method, named Laplacian Eigenmap of Adaptive Local Neighborhood Window (ALNWLE) is proposed. ALNWLE mainly contains two modules, namely the manifold structure learning module (MSLM) and the manifold structure pattern recognition module (MSPR). MSLM constructs an adaptive local neighborhood adjacency matrix to constrain the learning of the manifold structure. MSPR uses the interpolation method to recognize new sample patterns. The adaptive local neighborhood method proposed in this paper is a general method which is applicable to any manifold learning method. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

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Ensemble Classifier for EEG-Based Stress Classification: An Empirical Study on Stacking Classifiers 3rd

Ensemble Classifier for EEG-Based Stress Classification: An ...

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Shikha, Shikha Sethia, Divyashikha Indu, S. Department of Computer Science and Engineering Delhi Technological University Delhi India Department of Software Engineering Delhi Technological University Delhi India Department of Electronics and Communication Delhi Technological University Delhi India

ISBN: (纸本)9789819719426

In the last few years, combining multiple algorithms to improve the performance of machine learning models has been a common practice. However, its application to stress detection still needs to be explored. This paper uses a stacking ensemble technique to introduce a novel stress classification approach using electroencephalogram (EEG) signals. Specifically, the study evaluates the effectiveness of various classifiers, including Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Adaboost (AB), individually and in combination with RF as the base model, for stress classification tasks. The paper uses the SAM40 Dataset consisting of 32-channel EEG data to extract features in time and frequency domains for stress classification. The results show that the stacked classifiers outperform single classifiers, with RF + KNN providing the highest accuracy of 98.55%. The findings suggest that a stacked classifier is a promising approach for stress classification, as it can leverage the strengths of different algorithms and improve generalization performance. It holds promising future applicability in personalized stress management, healthcare interventions, and addressing societal concerns related to mental well-being. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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A Question-and-Answer Scheme of Clinical Practice Guidelines Based on SDA 24

A Question-and-Answer Scheme of Clinical Practice Guidelines...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Zhang, Zhizheng Yang, Qian Du, Yan School of Computer Science and Engineering Southeast University Jiangsu Nanjing China Joint Graduate School Southeast University Jiangsu Nanjing China School of Software Engineering Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400717826

This paper proposes a medical diagnosis and treatment knowledge question and answer scheme based on clinical practice guidelines, aiming at the need of standardization of medical process and popularization of medical knowledge. The scheme realizes computer intelligent diagnosis and treatment assistance through diagnosis and treatment knowledge base construction, problem analysis, information retrieval and answer generation. The SDA*(State-DecisionAction) modeling method and question formal definition for hypertension diagnose and treatment are proposed, and the answers are generated based on the answer set program (ASP). For the transformation of natural language and ASP rules, large language model (LLM) technology is adopted to automatically identify question terms, generate ASP rules and process the generated answers. To verify the scheme’s ability to generate reliable answers, accuracy for problem classification and ASP rule generations are confirmed. The experimental results show that the scheme could provide the ability to solve medical problems based on the knowledge of clinical practice guidelines. © 2024 Copyright held by the owner/author(s).

关键词： Hyperthermia therapy

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NumScout: Unveiling Numerical Defects in Smart Contracts Using LLM-Pruning Symbolic Execution

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IEEE Transactions on software engineering 2025年第5期51卷 1538-1553页

作者： Chen, Jiachi Shao, Zhenzhe Yang, Shuo Shen, Yiming Wang, Yanlin Chen, Ting Shan, Zhenyu Zheng, Zibin Hangzhou Normal University Intelligent Transportation and Information Security Laboratory Hangzhou311121 China Sun Yat-sen University School of Software Engineering Zhuhai519082 China University of Electronic Science and Technology of China School of Computer Science and Engineering School of Cyber Security Chengdu611731 China Kashi Institute of Electronics and Information Industry Kashi844000 China

In recent years, the Ethereum platform has witnessed a proliferation of smart contracts, accompanied by exponential growth in total value locked (TVL). High-TVL smart contracts often require complex numerical computations, particularly in mathematical financial models used by many decentralized applications (DApps). Improper calculations can introduce numerical defects, posing potential security risks. Existing research primarily focuses on traditional numerical defects like integer overflow, and there is currently a lack of systematic research and effective detection methods targeting new types of numerical defects. In this paper, we identify five new types of numerical defects through the analysis of 1,199 audit reports by utilizing the open card method. Each defect is defined and illustrated with a code example to highlight its features and potential consequences. We also propose NumScout, a symbolic execution-based tool designed to detect these five defects. Specifically, the tool combines information from source code and bytecode, analyzing key operations such as comparisons and transfers, to effectively locate defects and report them based on predefined detection patterns. Furthermore, NumScout uses a large language model (LLM) to prune functions which are unrelated to numerical operations. This step allows symbolic execution to quickly enter the target function and improve runtime speed by 28.4%. We run NumScout on 6,617 real-world contracts and evaluated its performance based on manually labeled results. We find that 1,774 contracts contained at least one of the five defects, and the tool achieved an overall precision of 89.7%. © 1976-2012 IEEE.

关键词： Smart contract

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Generative Diffusion Model-Based Deep Learning Framework for Remaining Useful Life Prediction

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IEEE Internet of Things Journal 2025年第11期12卷 18431-18434页

作者： Ha, Sangjun Sung, Mingyu Saeed, Faisal Yun, Sangseok Kim, Il-Min Kang, Jae-Mo Kyungpook National University Department of Artificial Intelligence Daegu41566 Korea Republic of Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Pukyong National University Department of Information and Communications Engineering Busan48513 Korea Republic of Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada

In this letter, we propose a novel and high-performing deep learning framework for remaining useful life (RUL) prediction, called RUL-Diff, by leveraging a generative diffusion model. It is composed of two modules that are connected in tandem: (i) a feature extractor corresponding to the encoder part of our customized U-Net and (ii) a RUL predictor constructed by a multi-layer perceptron. We further devise an effective two-stage training methodology for the proposed RUL-Diff, in which the feature extractor is initially pre-trained for high-quality feature learning, and then, is re-trained jointly with the RUL predictor for accurate RUL prediction. Extensive experimental results on NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) datasets demonstrate the superiority and effectiveness of the proposed scheme. © 2014 IEEE.

关键词： Prediction models

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A Paradigm Shift to Causal Model-Driven Decision-Making With Generative AI 13th

A Paradigm Shift to Causal Model-Driven Decision-Making Wit...

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13th International Conference on AI and Multimodal Services, AIMS 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： He, Sheng Ning, Yishuang Zhang, Liang-Jie Lei, Kai National Engineering Research Center for Supporting Software of Enterprise Internet Services Shenzhen518057 China Kingdee Research Kingdee International Software Group Co. Ltd. Shenzhen518057 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Peking University Shenzhen518055 China

ISBN: (纸本)9783031776809

In recent years, the rise of big data has popularized data-driven decision-making. However, the interpretability shortcomings of artificial intelligence (AI) models limit their reliability for critical decisions. This paper proposes a paradigm shift from conventional data-driven to causal model-driven decision-making, leveraging advancements in causal inference and large language models (LLMs). By applying the capabilities of generative AI, this paradigm shift enables the fusion of extensive domain knowledge, facilitating the development of causal models that capture the complexity of real-world systems and problems. The model-driven approach provides a better understanding of the causal mechanisms, relationships, and dynamics compared to correlational data-driven methods. Moreover, we introduce the concept of a composable business model based on modular causal components. We present a methodology for constructing robust causal model-driven decision frameworks, emphasizing the comprehensive utilization of generative AI to incorporate domain knowledge with causal inferences. Through an in-depth analysis of case studies across multiple domains, this model-driven approach empirically showcases its potential to improve decision quality, optimize resource allocation, and enhance process efficiency. Additionally, we critically discuss future research directions and challenges in the evolutionary trajectory of causal model-driven decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Online Credibility Assessment of Equipment Digital Twin for Discrete Manufacturing

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IEEE Transactions on Automation science and engineering 2025年 22卷 14763-14774页

作者： Lu, Han Zhang, Lin Deen, M. Jamal Wang, Kunyu Cheng, Hongbo Yang, Laurence T. Beihang University Hangzhou International Innovation Institute Zhejiang 311115 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China AI Atlas Inc Canada Zhengzhou University School of Computer and Artificial Intelligence School of Software Engineering Zhengzhou China St. Francis Xavier University Department of Computer Science Antigonish Canada

The equipment digital twins (EDTs) for discrete manufacturing should be calibrated quickly to avoid irreversible physical damage to the equipment caused by biased control commands. Therefore, an online credibility assessment method for EDTs is urgently needed. However, existing assessment approaches consume too much time, and thus could not reveal dynamic faults in time. In this paper, the dynamic relationship between online evolution and actual applications of EDTs is investigated. Then, two steps are proposed to accelerate the assessment process significantly. One involves pre-constructing a performance-deviation-agent (PDA), and the other involves dynamically fitting the application-time-window (ATW) probability distribution. The methodology is applicable to discrete manufacturing processes. The dynamic credibility of EDT evolution process can be updated after every iteration of the model evolution. Sorting manufacturing equipment was used as a case study to demonstrate the effectiveness of this method. The time consumption was reduced by 90% compared with traditional assessment methods in the case. © 2025 IEEE.

关键词： Fabrication

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