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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Rhaif Alsahlanee, Abbas Thajeel Singh, Jagendra Garg, Neha Rajpoot, Abha Kiran Tiwari, Mohit Elangovan, Muniyandy Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9789819788644

This research presents a composite approach to bank security issues by integrating the Internet of Things (IoT) threat reduction and artificial intelligence (AI) into systems. In this research, machine learning (ML) techniques are deployed across key banking operations such as ATM false alarms, health monitoring of hardware components, and adding security to locker rooms. Through the utilization of ML models such as CNN, SVM, KNN, and RNN, significant advancements are achieved in detecting fraudulent activities within ATM transactions. ML-driven approaches have high precision, recall and accuracy rates in bolstering ATM network security. In addition, predictive maintenance models trained on sensor data from ATM hardware components enable proactive maintenance interventions. This in turn leads to improved ATM availability, reduced downtime, and cost savings. Biometric authentication systems based on ML models appear to have promising effects in locker room security. It is aimed at enabling security measures to be improved intelligently. Overall, this research shows how banks can use AI to tackle threats such as those maxing home machines or video-recordings by infiltrating introductory courses. By making the most of IoT-linked data and ML algorithms, the ability of banks to detect and prevent fraudulent activities can be improved in addition to having a preventive maintenance program where fees are paid in ATM withdrawals. These advancements contribute not only to safeguarding the financial assets and customer data but also to fostering trust and confidence in the banking systems. Continual research and innovation in AI and IoT technologies are crucial in order to strengthen banking security measures against the digital challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive maintenance

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

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computer Modeling in engineering & sciences 2024年第6期139卷 3085-3099页

作者： Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie School of Computer Science and Technology Harbin University of Science and TechnologyHarbin150080China Shanghai Futures Information Technology Co. Ltd.Shanghai201201China Heilongjiang Province Cyberspace Research Center Harbin150001China Information Network Engineering and Research Center South China University of TechnologyGuangzhou510641China

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic *** applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be ***,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured ***,privacy of patients’data needs to be protected,as these data contain the most sensitive and private ***,considering the practicality of the model,the computational requirements should not be too *** address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage *** method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an *** also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in ***,this paper designs and conducts experiments to evaluate the proposed *** experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关键词： Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data

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S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning 41

S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning

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41st International Conference on Machine Learning, ICML 2024

作者： Wan, Guancheng Tian, Yijun Huang, Wenke Chawla, Nitesh V. Ye, Mang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Department of Computer Science University of Notre Dame United States Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

Graph Contrastive Learning (GCL) has emerged as a highly effective self-supervised approach in graph representation learning. However, prevailing GCL methods confront two primary challenges: 1) They predominantly operate under homophily assumptions, focusing on low-frequency signals in node features while neglecting heterophilic edges that connect nodes with dissimilar features. 2) Their reliance on neighborhood aggregation for inference leads to scalability challenges and hinders deployment in real-time applications. In this paper, we introduce S3GCL, an innovative framework designed to tackle these challenges. Inspired by spectral GNNs, we initially demonstrate the correlation between frequency and homophily levels. Then, we propose a novel cosine-parameterized Chebyshev polynomial as low/high-pass filters to generate biased graph views. To resolve the inference dilemma, we incorporate an MLP encoder and enhance its awareness of graph context by introducing structurally and semantically neighboring nodes as positive pairs in the spatial domain. Finally, we formulate a cross-pass GCL objective between full-pass MLP and biased-pass GNN filtered features, eliminating the need for augmentation. Extensive experiments on real-world tasks validate S3GCL proficiency in generalization to diverse homophily levels and its superior inference efficiency. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Generating Pixel Enhancement for Road Extraction in High-Resolution Aerial Images

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第10期9卷 1-13页

作者： Liu, Ruiqi Li, Fangting Jiang, Weiping Song, Chengfang Chen, Qusen Li, Zhao Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China Hubei Institute of Surveying and Mapping Engineering Wuhan China GNSS Research Center Hubei Luojia Laboratory Wuhan University Wuhan China

Road extraction is a powerful technique support to autonomous driving as it provides routable road information for motion planning algorithms. High-resolution aerial images offer comprehensive road information, facilitating the establishment of efficient and accurate road networks to monitor road changes in a timely manner. However, widespread occlusions and abundant local details pose challenges to highly accurate and continuous extraction, especially in areas with road bifurcations. To simultaneously improve both accuracy and connectivity of road extraction, in this paper, we propose a novel approach TPEGAN to integrate pixel-level segmentation and graph inference based on road pixel enhancement. By generating road pixels enhanced images, the generative adversarial network exploits the consistency among road pixels to embed pixel-level accuracy into the segmentation module. The multi-scale dual-branch segmentation module employs graph convolution reasoning to capture dependencies across different spatial regions, maintaining the connectivity of road networks. Extensive experiments on three public datasets demonstrate that TPEGAN outperforms SOTA methods with a considerable performance gap. As the complexity of road networks increases, the performance of TPEGAN degrades more slowly than SOTA method. Even in challenging urban scenes where the proportion of road pixels is more than 15%, TPEGAN retains its high performance and achieves a rIoU of 0.664 with an APLS of 72.81%, amounting to improvements of 4.1% and 2.62% over SOTA method, respectively. IEEE

关键词： Image segmentation

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Lumbar spine localisation method based on feature fusion

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 931-945页

作者： Yonghong Zhang Ning Hu Zhuofu Li Xuquan Ji Shanshan Liu Youyang Sha Xiongkang Song Jian Zhang Lei Hu Weishi Li Robotics Institute School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina Beijing Zhuzheng Robot Co. LTDBeijingChina Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKnoxvilleTennesseeUSA Department of Orthopaedics Peking University Third HospitalBeijingChina Engineering Research Center of Bone and Joint Precision Medicine Ministry of EducationBeijingChina Beijing Key Laboratory of Spinal Disease Research BeijingChina School of Biological Science and Medical Engineering Beihang UniversityBeijingChina Department of Computer Science University of WarwickCoventryUK

To eliminate unnecessary background information,such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning,a two‐stage approach for localising lumbar segments is ***,based on the multi‐scale feature fusion technology,a non‐linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine,effectively eliminating useless background information,such as soft *** the second stage,we directly realised the precise positioning of each segment in the lumbar spine space region based on the non‐linear regression method,thus effectively eliminating the interference caused by the adjacent *** 3D Intersection over Union(3D_IOU)is used as the main evaluation indicator for the positioning *** an open dataset,3D_IOU values of 0.8339�0.0990 and 0.8559�0.0332 in the first and second stages,respectively is *** addition,the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s ***,the proposed method performs very well in terms of both pre-cision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.

关键词： CT image lumbar spatial orientation multi‐scale information fusion

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A bitcoin service community classification method based on Random Forest and improved KNN algorithm

IET Blockchain

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IET Blockchain 2024年第3期4卷 276-286页

作者： Gao, Muyun Lin, Shenwen Tian, Xin He, Xi He, Ketai Chen, Shifeng School of Mechanical Engineering University of Science and Technology Beijing Beijing China Internet Financial Security Technology Key Laboratory National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

There are service communities with different functions in the Bitcoin transactions system. Identifying community categories helps to further understand the Bitcoin transactions system and facilitates targeted regulation of anonymized Bitcoin transactions. To this end, a Bitcoin service community classification method based on Random Forest and improved K-Nearest Neighbor (KNN) algorithm is proposed. First, the transaction characteristics of different types of communities are analyzed and summarized, and the corresponding transaction features are extracted from the address and entity levels;then multiple classification algorithms are compared, the optimal model to filter the effective features is selected, and the feature vector of entity addresses is constructed. Finally, a classification model is constructed based on Random Forest and improved KNN algorithm to classify the entities. By constructing different classification models for experimental comparison, the accuracy and stability advantages of the proposed method for classification in service community classification research are verified. © 2024 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Classification (of information)

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The Current Status, Hotspots, and Trends of Artificial Intelligence Research in Africa-A Bibliometric Analysis Based on CiteSpace from 2003 to 2023 24

The Current Status, Hotspots, and Trends of Artificial Intel...

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5th International Conference on computer science and Management Technology, ICCSMT 2024

作者： Dong, Miao Wu, Yuliang Zhang, Peipei Mubeen, Afzal School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Center for West African Studies University of Electronic Science and Technology of China Sichuan Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798400709999

Based on the CiteSpace bibliometric method, 273 research papers from the Web of science Core Database from 2003 to 2023 were analyzed. This study delves into external characteristics such as publication volume, participating countries, research institutions, and authorship to uncover the key issues and evolving trends in the field of artificial intelligence (AI) research in Africa. The research shows that the number of AI studies in Africa has seen an"explosive" growth in recent years, with AI solutions being applied on a large scale. The development of AI in Africa primarily revolves around addressing practical needs in areas such as agriculture, food security, disease, and education, leading to significant achievements. However, challenges remain, including a lack of widespread international collaboration, a small core research community, and issues related to the localization of data and technology application. Looking ahead, strengthening international cooperation, enhancing local research and innovation capabilities, and addressing challenges related to data and technology application will be key to the continued growth of AI research in Africa. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Bibliographies

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Exploring the Power of Entangled Data in Quantum Machine Learning

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Wuhan University Journal of Natural sciences 2024年第3期29卷 193-194页

作者： WANG Xinbiao DU Yuxuan TU Zhuozhuo LUO Yong YUAN Xiao TAO Dacheng Institute of Artificial Intelligence School of Computer ScienceWuhan UniversityWuhan 430072HubeiChina National Engineering Research Center for Multimedia Software Wuhan UniversityWuhan 430072HubeiChina JD Explore Academy Beijing 101111China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore School of Computer Science Faculty of EngineeringUniversity of SydneyNSW 2008Australia Center on Frontiers of Computing Studies Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China

Quantum entanglement is a key resource for achieving superiority of quantum ***,scientists are extensively focusing on how to integrate quantum entanglement into various components of quantum machine learning(QML)models,aiming to surpass the performance of traditional machine learning *** successes include the use of entangled measurements^([1-3])and entangled channels^([4]),which have been shown to reduce query complexity or improve the prediction precision for specified QML *** entangled data,capable of encoding more information compared to classical data of the same size,is recognized for its potential to achieve quantum ***,the impact of the entanglement degree in quantum data on model performance remains a challenging and unresolved research question.

关键词： quantum specified integrate

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Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 14536-14545页

作者： Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Ghaffar, Zahid Khan, Muhammad Khurram Das, Ashok Kumar National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan King Saud University Center of Excellence in Information Assurance Riyadh11653 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol's security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols. © 2000-2011 IEEE.

关键词： Hash functions

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