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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Machine Learning for Mental Health: A Systematic Study of Seven Approaches for Detecting Mental Disorders

Machine Learning for Mental Health: A Systematic Study of Se...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Nadeem, Muhammad Rashid, Junaid Moon, Hyeonjoon Dosset, Arailym Sejong Uviversity Department of Computer Science and Engineering Seoul Korea Republic of Sejong University Department of Data Science Seoul Korea Republic of

ISBN: (纸本)9798350326413

Mental disorders are a prevalent issue among teenagers. The widespread use of smartphones and social media has revolutionized the way individuals communicate and exchange information with millions of people using these technologies every day. As a result, vast amounts of data are generated, which can be harnessed to improve mental health detection. The increasing prevalence of mental health issues and the demand for quality healthcare services have led to research exploring the potential of machine learning (ML) to address these challenges. This paper provides a systematic study of seven ML approaches used in previous studies to detect mental disorders. The study examines the datasets employed, the accuracy achieved, and the limitations of each ML approach. The seven ML approaches studied in this paper are Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO), Long Short-Term Memory (LSTM), Random Forest (RF), Logistic Regression (LR), Artificial Neural Networks (ANN), and eXtreme Gradient Boosting (XGBoost). These approaches have been utilized in various studies to detect mental disorders and this paper aims to provide a comprehensive understanding of their effectiveness. The findings indicate that machine learning approaches have demonstrated significant potential for the detection of mental disorders, with promising implications for enhancing healthcare services. Additionally, the paper discusses the open research challenges and future directions for mental health. © 2023 IEEE.

关键词： Support vector machines

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Graph Contrastive Learning for Truth Inference 40

Graph Contrastive Learning for Truth Inference

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Liu, Hao Liu, Jiacheng Tang, Feilong Li, Peng Chen, Long Yu, Jiadi Zhu, Yanmin Gao, Min Yang, Yanqin Hou, Xiaofeng Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong East China Normal University School of Data Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Aizu School of Computer Science and Engineering Japan Simon Fraser University School of Computing Science Canada

ISBN: (纸本)9798350317152

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques. © 2024 IEEE.

关键词： Crowdsourcing

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CNN-LSTM: Development of Offline Signature Authentication 1

CNN-LSTM: Development of Offline Signature Authentication

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Ganapathy, Revathy Lingeshwaran, D. Suthanthiradevi, P. Harish Kumar, M.V. Vels Insitute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350359756

Offline Signature Authentication is a critical task in the field of document authentication, and its accuracy is essential for ensuring security while transactions. This research proposes two approaches: Initially Pre-trained CNN models are used to extract features from signature images, which are then combined with handcrafted features such as HOG and some other geometric features of signature. Such combined features are passed to bidirectional LSTM model in which drop out layer undergoes classification which differentiate real and forgery signature. The proposed system has potential applications in document authentication and security, subsequently combination of CNN models and additional features provides more comprehensive representation of signature images resulting in improved accuracy. Three signature datasets are utilized namely GDPS, CEDAR, and BHSig-Bengali each with varying signature styles and image quality. Our experimental outcomes reveal that Bidirectional Convolutional LSTM along with handcraft features attained maximum accuracy in offline signature verification system. © 2023 IEEE.

关键词： Long short-term memory

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EfficientNetB1 Deep Learning Model for Microscopic Lung Cancer Lesion Detection and Classification Using Histopathological Images

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computers, Materials & Continua 2024年第10期81卷 809-825页

作者： Rabia Javed Tanzila Saba Tahani Jaser Alahmadi Sarah Al-Otaibi Bayan AlGhofaily Amjad Rehman Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan Artificial Intelligence&Data Analytics Lab. CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh145111Saudi Arabia

Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to *** cancer ranks among the most prevalent forms of cancer worldwide,affecting individuals of all *** and accurate lung cancer detection is critical for improving cancer patients’treatment outcomes and survival *** examinations for lung cancer detection,however,frequently fall short of detecting small polyps and *** address these limitations,computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients *** research implements an enhanced EfficientNetB1 deep learning model for accurate detection and classification using histopathological *** proposed technique accurately classifies the histopathological images into three distinct classes:(1)no cancer(benign),(2)adenocarcinomas,and(3)squamous cell *** evaluated the performance of the proposed technique using the histopathological(LC25000)lung *** preprocessing steps,such as image resizing and augmentation,are followed by loading a pretrained model and applying transfer *** dataset is then split into training and validation sets,with fine-tuning and retraining performed on the training *** model’s performance is evaluated on the validation dataset,and the results of lung cancer detection and classification into three classes are *** study’s findings show that an enhanced model achieves exceptional classification accuracy of 99.8%.

关键词： Colon cancer EfficientNetB1 histopathological image processing transfer learning health risks

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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A polyhedral reconstruction of a 3D object from a chain code and a low-density point cloud

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Multimedia Tools and Applications 2025年 1-28页

作者： Tapia-Dueñas, Osvaldo A. López, Hiram H. Sánchez-Cruz, Hermilo Department of Mathematics Computer Science and Data Science John Carroll University University HeightsOH United States Department of Mathematics Virginia Tech BlacksburgVA United States Departamento de Ciencias de la Computación Universidad Autónoma de Aguascalientes Aguascalientes Aguascalientes Mexico

The manipulation of 3D objects is becoming crucial for many applications, such as health, industry, or entertainment, to mention some. However, these 3D objects require substantial energy and different types of resources. With the goal of obtaining a simplified representation of a 3D object that can be easily managed, for example, for transmission, in some recent works, the authors associate low-density point clouds with a 3D object that simplifies the original 3D object. More precisely, given a 3D object in a polyhedral format, some authors associate a chain code and then use grammar-free context to obtain key points that give rise to several point clouds with different densities. In this work, we complete the cycle by developing a polyhedral reconstruction from an associated low-density point cloud and the chain code. The polyhedral reconstruction is crucial for handling 3D objects because it allows us to visualize them after they are efficiently compressed and transmitted. We apply our algorithms to well-known 3D objects in the literature. We use the Hausdorff and Chamfer distances to compare our results with the state-of-the-art proposals. We show how our proposed polyhedral reconstruction based on a helical chain code reconstructs a medical image represented or transmitted by slices into a 3D object in a polyhedral format, helping thus to mitigate and alleviate the management of 3D medical objects. The polyhedron that we propose provides better compression when compared with the original set of slices of a 3D medical object. © The Author(s) 2025.

关键词： Medical imaging

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Comparative Analysis of Traditional and Contextual Embedding for Bangla Sarcasm Detection in Natural Language Processing 12

Comparative Analysis of Traditional and Contextual Embedding...

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12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Fahim, Kaji Mehedi Hasan Moontaha, Mithila Rahman, Mashrur Rhythm, Ehsanur Rahman Rasel, Annajiat Alim Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341102

Sarcasm, a sort of sentiment characterized by a disparity between the apparent and intended meanings of the text, is a key component of sentiment analysis, opinion extraction, and social media analytics. However, sarcasm detection in Bangla has not received sufficient research attention yet. Moreover, there hasn't been a significant amount of study done comparing traditional and contextual word embeddings for the Bengali language. This study aims to address this gap by comparing traditional embedding by using the Bidirectional Gated Recurrent Unit - BiGRU model and contextual embedding by using Bidirectional Encoder Representations from Transformers - BERT for sarcasm detection in Bangla. The collection of the dataset of Bangla text was from social media platforms, containing labelled instances - whether it provides sarcasm or non-sarcasm. Pre-trained word embeddings i.e. GloVe and FastText are used as traditional embedding for this study. By using metrics like precision, recall and F1-score, the performances for both models have been obtained. When the two traditional word embedding approaches are compared, GloVe embedding with Bi-GRU has outperformed FastText embedding with a macro-averaged F1 score of 0.9395. On the other hand, contextual word embedding using BERT has outperformed both the traditional approaches having a better macro-averaged F1 score of 0.9572 and greater class-wise performance as compared with traditional embedding for both non-sarcastic (96%) and sarcastic (96%) text detection. In our findings, contextual word embedding i.e. BERT has performed better as compared with the two traditional word embeddings for this specific Bangla sarcasm detection binary classification task. © 2023 IEEE.

关键词： Embeddings

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Adaptive Three-Level BDDC Using Frugal Constraints 27th

Adaptive Three-Level BDDC Using Frugal Constraints

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27th International Conference on Domain Decomposition Methods in science and Engineering, DD 2022

作者： Klawonn, Axel Lanser, Martin Weber, Janine Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 Koln50931 Germany Center for Data and Simulation Science University of Cologne Cologne Germany

ISBN: (纸本)9783031507687

The convergence rate of both the FETI-DP (Finite Element Tearing and Interconnecting-Dual Primal) and the BDDC (Balancing Domain Decomposition by Constraints) domain decomposition methods strongly depend on the spectrum, i.e., the eigenvalues of the preconditioned system [7, 2]. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Domain decomposition methods

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Comparison of Various Computational Models for Accurate Stroke Prediction and Interpretation Using LIME and SHAP Kernels 1

Comparison of Various Computational Models for Accurate Stro...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Arun, Chalana B Anusha, M. Kodipalli, Ashwini Gargi, N. Harish, L. Devamane, Shridhar B Global Academy of Technology Dept. of Artificial Intelligence and Data Science Bangalore India School of Computing Amrita Vishwavidhyapeetam Department of Computer Science Mysuru India

ISBN: (纸本)9798331518943

Stroke is one dangerous illness that kills people. Stroke is so called because of the way it strikes people down. Across the globe, it is among the main causes of mortality and disability. It requires early identification and precise measures to be taken in order to prevent stroke and further damages to the brain and other parts of body. Our study investigates the use of machine learning techniques that includes Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Free Classifier, Random Forest Classifier, Naive Bares and multiple Boosting Classifiers. The 'Healthcare dataset Stroke data' is the dataset used, which is preprocessed and then trained upon by Ml. models. For Random Forest Classifier, the maximum accuracy observed was 94.91%, and upon Hyper parameter Tuning- there was rise in accuracy to 95.64%, Evaluation criteria like F1-score, precision, and recall provides additional evidence for the effectiveness of these models: Further in order to explain how each feature contributes differently to each prediction we have made use of two popular techniques for XAT, LIME and SHAP. LIVE and SHIP provides local, interpretable explanations and global explanations respectively. © 2024 IEEE.

关键词： Random forests

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