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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Garima Jain Prasidhi Nandwani Rupayali Swaroop Harshlata Vishakarma Sanat Jain School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computer Science and Engineering JECRC University Jaipur India School of Computer Science and Engineering VIT Bhopal University Sehore India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This study proposes a thorough investigation of countless object detection models, focusing uniquely on various YOLO versions. The paper begins with a thorough evaluation of object detection models including YOLO, ResNet, SSD, etc. by comparing their performance concerning their accuracy and effectiveness and their relative strengths and weaknesses in real-time object detection. Fabricating on the comparative analysis. This study proposes an innovative approach for object detection that integrates distance estimation and voice commands. The suggested research is aimed at enhancing real-time interaction with the surroundings, enabling users to receive immediate feedback about their surroundings. For identification and categorization, the model depends on live video input. Then estimates the distance of the object from the user, and finally gives an audio output. This study can be applied in various fields like robotics, autonomous vehicles, assistive technologies, and smart environments where object detection is crucial.

关键词： YOLO Technological innovation Estimation Assistive technologies Streaming media User experience Real-time systems Convolutional neural networks Object recognition Robots

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Comparative Analysis of Real-Time Multi-Person Pose Detection in Electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose 4

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big data, ICEIB 2024

作者： Tsai, Shang-Te Wu, Zong-Rong Lin, Pin-Hsuan Chen, Chin-Hsiung Chien, Wei Chang, Yu-Cheng School of Art and Design Sanming University Fujian365004 China National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan National Central University Department of Business Administration Taoyuan Taiwan Commercial Industrial Vocational Senior High School Department of Electrical Engineering New Taipei Municipal Tamsui New Taipei Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350360721

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy. © 2024 IEEE.

关键词： Accident prevention

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Advanced Remote Sensing and Generative Models for Comprehensive Water Quality Management in a Changing Climate

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 596-611页

作者： Thopanaiah, Chandrashekhara Kenkere Gireesh Babu C N Gurani, Vijayakumar Rajani, Thota Pavani, Avula Muthukumaran, D. Deivasigamani, Shanmugavel Department of CSE/ISE BMS Institute of Technology and Management Bengaluru India P.G. Department of Computer Science Karnatak University Dharwad India Department of English Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Chennai India

Water quality is a pivotal factor for maintaining human and ecological health. Traditional water quality assessments often depend on ground sampling and lab tests, which are costly, slow, and constrained by geographic limitations. The emergence of remote sensing technologies now allows for extensive and timely monitoring of water quality across vast regions. This study introduces an innovative approach that utilizes remote sensing data alongside a hybrid Generative Adversarial Network-Long Short-Term Memory (GAN-LSTM) model to transform the monitoring of water quality, focusing on pollution and sanitation management. We employed a comprehensive dataset from Kaggle, which includes 3276 data points and 10 essential water quality indicators, integrated with historical remote sensing data. The GAN model is designed to produce realistic synthetic datasets, which are then used by the LSTM model to predict water quality trends with high accuracy. The methodology achieved notable results, with an accuracy of 98%, precision of 97%, recall of 99%, and an F1-score of 98%. This approach leverages cutting-edge modeling techniques and extensive datasets to significantly improve the monitoring and management of water quality. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： GAN-LSTM Machine learning Pollution management Real-time monitoring Remote sensing Sanitation management Synthetic data Water potability Water quality monitoring

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Detection of Alzheimer’s Disease progression from Structural MRIs using ConvMixer based Deep Learning Model

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Procedia computer science 2025年 258卷 420-429页

作者： Mahak Lashkary Kanika Bansal Samyak Jain Praveen Kumar Shukla Hitesh Tekchandani Vijaypal Singh Dhaka School of Computing and Intelligent Systems Manipal University Jaipur Jaipur-303007 Rajasthan India School of Computer Science and Engineering Manipal University Jaipur Jaipur-303007 Rajasthan India Department of artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad-500075 Telangana India

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive functions and quality of life. Affecting over 50 million people worldwide, early and accurate detection of AD progression is crucial for timely intervention and management. This paper presents a reliable method for classifying Alzheimer’s stages using Deep Learning based ConvMixer model on structural MRI data. The proposed deep learning based convmixer model significantly utilizes the patch based learning with depthwise separable convolution to achieve improved performance. The dataset used in this work has been extracted from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and comprises of three classes: Alzheimer’s Disease (AD), Cognitive Impairment (CI), and Cognitively Normal (CN). The proposed approach demonstrated exceptional performance, achieving an accuracy of 99.88%, specificity of 99.87%, sensitivity of 99.90 and precision of 99.95%. The achieved results demonstrate the proposed model’s accuracy in distinguishing Alzheimer’s stages, making it a promising tool for early diagnosis and disease progression monitoring.

关键词： Alzheimer’s Disease Cognitive Impairment (CI) Cognitively Normal (CN) ConvMixer deep learning structural MRI neuroimaging Classification early detection machine learning

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Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond

arXiv

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arXiv 2024年

作者： Wang, Qi Li, Jindong Wang, Shiqi Xing, Qianli Niu, Runliang Kong, He Li, Rui Long, Guodong Chang, Yi Zhang, Chengqi School of Artificial Intelligence Jilin University Jilin Changchun China School of Computer Science and Technology Jilin University Jilin Changchun China MRS Meta AI Menlo ParkCA United States Australian Artificial Intelligence Institute University of Technology Sydney SydneyNSW Australia Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

Large language models (LLMs) have not only revolutionized the field of natural language processing (NLP) but also have the potential to bring a paradigm shift in many other fields due to their remarkable abilities of language understanding, as well as impressive generalization capabilities and reasoning skills. As a result, recent studies have actively attempted to harness the power of LLMs to improve recommender systems, and it is imperative to thoroughly review the recent advances and challenges of LLM-based recommender systems. Unlike existing work, this survey does not merely analyze the classifications of LLM-based recommendation systems according to the technical framework of LLMs. Instead, it investigates how LLMs can better serve recommendation tasks from the perspective of the recommender system community, thus enhancing the integration of large language models into the research of recommender system and its practical application. In addition, the long-standing gap between academic research and industrial applications related to recommender systems has not been well discussed, especially in the era of large language models. In this review, we introduce a novel taxonomy that originates from the intrinsic essence of recommendation, delving into the application of large language model-based recommendation systems and their industrial implementation. Specifically, we propose a three-tier structure that more accurately reflects the developmental progression of recommendation systems from research to practical implementation, including representing and understanding, scheming and utilizing, and industrial deployment. Furthermore, we discuss critical challenges and opportunities in this emerging field. A more up-to-date version of the papers is maintained at: https://***/jindongliAi/Next-Generation-LLM-based-Recommender-Systems-Survey. © 2024, CC BY.

关键词： Generative adversarial networks

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on intelligence technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Label Noise Correction for Federated Learning: A Secure, Efficient and Reliable Realization 40

Label Noise Correction for Federated Learning: A Secure, Eff...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Wang, Haodi Jiang, Tangyu Guo, Yu Guo, Fangda Bie, Rongfang Jia, Xiaohua School of Artificial Intelligence Beijing Normal University Beijing China Institute of Computing Technology Chinese Academy of Sciences CAS Key Laboratory of AI Safety & Security Beijing China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

ISBN: (纸本)9798350317152

Federated learning has emerged as a promising paradigm for large-scale collaborative training tasks, harnessing diverse local datasets from different clients to jointly train global models. In real-world implementations, client data could have label noise, causing the quality of the global model to be influenced. Existing label-correction solutions assume all the clients are discreet and fail to consider detecting the malicious clients, thus are not practical or privacy-preserving. In this paper, we present zkCor, an efficient and reliable label noise correction scheme with zero-knowledge confidentiality. Our method is designed upon FedCorr [1], but with more relaxed security assumptions. zkCor is established from the ingenious synergy of the label noise correction protocol and the zero-knowledge proof (ZKP), requiring each client to provide a computation integrity proof to the aggregator in each iteration. Thus, clients are forced to jointly guarantee label-correction reliability. We further devise a batch ZKP that is efficient and more suitable for federated learning settings. We rigorously illustrate the building blocks of zkCor and complete the prototype implementation. The extensive experiments demonstrate that zkCor can gain at least 2 to 30 times better performance than the baseline approach on verification workloads with nearly no extra proof time cost from clients. © 2024 IEEE.

关键词： Iterative methods

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Automatic Generation of Smart Contracts from Real-World Contracts in Natural Language 9

Automatic Generation of Smart Contracts from Real-World Cont...

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9th IEEE Smart World Congress, SWC 2023

作者： Fan, Yuqing Chen, E. Zhu, Yan He, Xiao Yau, Stephen S. Pandya, Krutik University of Science and Technology Beijing School of Computer and Communication Engineering Beijing10083 China Arizona State University School of Computing and Augmented Intelligence TempeAZ85281 United States

ISBN: (纸本)9798350319804

Real-world contract texts are in natural languages, and normally suffer from lack of accuracy and are subject to interpretations. Hence, it is difficult to automatically generate smart contracts from real-world contracts for monitoring and enforcement of the execution of real-world contracts. Smart contracts can be viewed as a digital twin of real-world contracts because smart contracts in the digital world have the same objective as their corresponding real-world contracts in natural languages. In this paper, an approach to automatic generation of smart contracts from real-world contracts in a natural language is presented. In this approach, a language, called the Contract Text Markup Language (CTML), is used to facilitate automatic abstraction of the details of the real-world contract and convert them to a marked-up contract, which can be automatically compiled to generate the corresponding smart contract executable code for monitoring and enforcing the execution of the real-world contract. A legal contract in English is used to illustrate this approach. In addition, an online smart contract library is presented to facilitate the applications of this approach. © 2023 IEEE.

关键词： and Contract Text Markup Language automatic generation blockchain digital twin real-world contracts smart contracts

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Predicting Fish Habitat in the Persian Gulf Using artificial intelligence 1

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International Conference on Advances in Communication technology and computer Engineering, ICACTCE 2023

作者： Hai, Tao Zhou, Jincheng Ahmadi, Hoorieh Ekiye, Ayibatonbo Ebiare Wei, Yangping Iwendi, Celestine Boulouard, Zakaria School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Creative Technologies University of Bolton Bolton United Kingdom LIM Hassan II University of Casablanca Casablanca Morocco School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

The Persian Gulf is one of the most important habitats in the Middle East. It can be extremely beneficial to aquatic species’ survival and environmental preservation to continuous monitoring and collect data about aquatic animals, their habitats, and behaviours. Finding a novel and suitable method to carry out accurate and automatic monitoring with low timing and low cost for monitoring aquatic species’ behaviour in this high potential area is helpful. To predict fish habitat in Persian Gulf Convolutional Neural Network method and Naïve Bayes algorithm are used. Deep learning convolutional neural network technology is mostly used for data science classification and recognition because of its exceptional accuracy and to solve search and optimization issues, the Naïve Bayes algorithm is employed. Results indicate for predicting fish habitat in the Persian Gulf, the accuracy of the Convolutional Neural Network algorithm and the Naïve Bayes algorithms is 97.32% and 95.47%, respectively. With p = 0.025 (p0.05), there is a substantial difference between the Naïve Bayes method and the Convolutional Neural Network algorithm. Therefore, The Convolutional Neural Network method seems to be more accurate than the Naïve Bayes method at predicting fish habitat in the Persian Gulf. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ecosystems

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Parallel Driving with Big Models and Foundation intelligence in Cyber-hysical-ocial Spaces

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Research 2024年第2期2024卷 1-17页

作者： Xiao Wang Jun Huang Yonglin Tian Chen Sun Lie Yang Shanhe Lou Chen Lv Changyin Sun Fei-Yue Wang School of Artificial Intelligence Anhui UniversityHefeiChina Macao University of Science and Technology MacaoChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina MVSLab Department of Mechanical and Mechatronics EngineeringUniversity of Waterloo200 University Ave WestWaterlooON N2L3G1Canada School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.

关键词： breakthrough integrate corners

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