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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Geeta S. Navale Ravikiran Madala Manjunath Managuli N. Jayalakshmi G. Kadiravan Ruchira Rawat Department of Computer Engineering Sinhgad Institute of Technology and Science Pune Maharashtra India Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina United States Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belegavi Karnataka India Master of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Computer Science and Information Technology KL Deemed to be University Andhra Pradsh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

The objective of the newest manufacturing paradigm, known as “Industry 5.0,” is to combine human intelligence with cutting-edge technology like the Internet of Things (IoT), artificial intelligence (AI), and robots to develop production systems that are incredibly effective and adaptable. But as we rely more on these technologies, we face additional privacy and security issues that might affect how dependable and trustworthy these systems are. In this report, we examine the most recent advancements in security and privacy for Industry 5.0 IoT and pinpoint the newest trends, obstacles, and possibilities. We also cover how these changes may affect manufacturing in the future and provide advice to scholars, practitioners, and policymakers.

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Speech Abusive Language Detection System Using MFCC Speech Feature Extraction and Convolutional Neural Network

Speech Abusive Language Detection System Using MFCC Speech F...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Yi-Chang Zhu Ying-Hsiu Hung Yen-Ching Chang Jhen-Kai Tang Wen-Kai Tsai Shin-Chi Lai The Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Department of Electrical Engineering Master's Program National Formosa University Huwei Taiwan Dept. Electrical Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

As bullying rapidly spreads in schools, many parents are becoming increasingly concerned about their children being subjected to bullying by peers, which could negatively impact their physical and mental health. This paper proposes an abusive language detection system that combines Mel-frequency Cepstrum Coefficients (MFCC) speech features with a Convolutional Neural Network (CNN). Audio files which contain daily abusive phrases in the Minnan dialect are initially preprocesses to eliminate non-speech interference. Subsequently, the MFCC algorithm is applied to extract speech features that closely capture auditory characteristics. The CNN classification model is trained to learn the MFCC features and classify the phrases into their respective categories. Experimental results indicate that the CNN model achieves an average recognition accuracy of 91.9%. Finally, the CNN model was deployed on an embedded platform, where it achieves an average recognition time of 0.54 seconds per phrase and an average recognition accuracy of 86.25%.

关键词： Pediatrics Accuracy Semantics Speech recognition Mental health Speech enhancement Feature extraction Convolutional neural networks Mel frequency cepstral coefficient Optimization

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Optimal Extreme Learning Machine based Traffic Congestion Control System in Vehicular Network

Optimal Extreme Learning Machine based Traffic Congestion Co...

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International conference of electronics, Communication and Aerospace Technology (ICECA)

作者： Rajendra Kumar Bharti D. Suganthi S.K Abirami Relangi Anil Kumar B Gayathri S. Kayathri Department of CSE BTKIT Dwarahat Almora Uttarakhand India Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamilnadu India Department of Electronics and Communication Engineering Aditya College of Engineering and Technology Surampalem India Department of Computer Science Bishop Heber College Affiliated To Bharathidasan University Trichy Tamilnadu India Master of Computer Applications M. Kumarasamy College of Engineering Karur Tamilnadu India

ISBN: (纸本)9781665482721

Over the past decade, Smart cities have been advanced and minimizing traffic congestion become the main concern in the progression of smart cities. The rapid growth in the number of road vehicles has raised the number of road accidents and traffic congestion. To Solve this problem, Vehicular Network (VN) formulated several novel concepts which include traffic control, vehicular communications, and navigation. Machine Learning (ML) will be an effective technique for identifying hidden insights into ITS without being programmed clearly by learning from data. This article develops an Optimal Extreme Learning Machine based Traffic Congestion Control System (OELM-TCCS) in vehicular networks. The presented OELM-TCCS technique mainly focuses on the identification and recognition of traffic congestion in VANET. To perform this, the presented OELM-TCCS technique primarily designs a new ELM model to carry out the classification process. In addition, the presented OELM-TCCS technique executes the satin bowerbird optimization (SBO) algorithm for parameter tuning of the ELM method. To demonstrate the boosted performance of the OELM-TCCS model, a series of experiments were executed. The experimental outcomes indicate the betterment of the OELM-TCCS method over recent methods with maximum accuracy of 99.17%.

关键词： Road accidents Smart cities Extreme learning machines Road vehicles Vehicular ad hoc networks Process control Traffic control

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Ray-Tracing Calibration Based on Improved Particle Swarm Optimization in 5.9 GHz Outdoor Scenario

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Li, Zhuoyin He, Ruisi Yang, Mi Qi, Ziyi Zhang, Zhong Ai, Bo Zhang, Haoxiang Han, Jiahui Fan, Jianhua Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation the School of Electronics and Information Engineering the Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Master Planning Institute of China Academy of Industrial Internet Ministry of Industry and Information Technology of the People's Republic of China Beijing100804 China National University of Defense Technology Sixty-third Research Institute Nanjing210007 China

Ray-tracing (RT) channel simulation has been widely used for simulating and analyzing propagation of electromagnetic waves in complex environments. The electrical properties of materials play a critical role in RT simulation accuracy and calibration of material is usually required. However, the traditional calibration methods for electrical parameters, such as the genetic algorithm and simulated annealing algorithm, suffer from slow convergence and limited parallelism, making them less suitable for complex scenarios with diverse material categories. This article presents a novel approach for automatic parameter optimization based on particle swarm optimization (PSO) algorithm. The proposed method leverages fast convergence advantage of the PSO algorithm. It incorporates a linearly-varying strategy to gradually reduce inertia weight, and enhances algorithm local search capability. Additionally, random perturbation factors are employed to reassign particles exceeding boundaries, preventing them from being trapped in local optima. Measurements and calibration of 5.9 GHz outdoor channels demonstrate the method, which achieves fairly high accuracy. © 2002-2011 IEEE.

关键词： Channel simulation electromagnetic parameter calibration particle swarm optimization ray-tracing simulation

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

SSRN

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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RNN-Based Models for Predicting Seizure Onset in Epileptic Patients

RNN-Based Models for Predicting Seizure Onset in Epileptic P...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Mathan Kumar Mounagurusamy V S Thiyagarajan Abdur Rahman Shravan Chandak D. Balaji Venkateswara Rao Jallepalli Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of CSE Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Accounting and Technology School of Business & Technology Master of Science in Information Technology Emporia State University United States of America Symbiosis Centre for Management Studies Pune Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Pharmacy Practice MB School of Pharmaceutical Sciences Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing systems are often compromised by their dependence on static thresholds and basic Electroencephalogram (EEG) properties. A novel Recurrent Neural Network (RNN)-based method for seizure start prediction is proposed in the article to overcome these limitations. As opposed to conventional techniques, the proposed system makes use of Long Short-Term Memory (LSTM) networks to extract temporal correlations from unprocessed EEG data. It enables the system to adapt dynamically to the unique EEG patterns of each patient, improving prediction accuracy. The methodology of the system comprises thorough data collecting, preprocessing, and LSTM-based feature extraction. Annotated EEG datasets are then used for model training and validation. Results show a considerable reduction in false alarm rates (average of $\mathbf{6. 8 \%}$) and an improvement in prediction accuracy $(90.2 \%$ sensitivity, 88.9% specificity, and AUC-ROC of 93). Additionally, computational efficiency is significantly higher than that of existing systems (12 ms processing time, 45 MB memory consumption). About improving seizure prediction reliability, these results demonstrate the effectiveness of the proposed RNN-based strategy, opening up possibilities for its practical application to improve epilepsy treatment.

关键词： Training Accuracy Epilepsy Brain modeling Feature extraction Electroencephalography Real-time systems Hardware Data mining Long short term memory

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RNN-Based Models for Predicting Seizure Onset in Epileptic Patients

arXiv

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arXiv 2024年

作者： Mounagurusamy, Mathan Kumar Thiyagarajan, V.S. Rahman, Abdur Chandak, Shravan Balaji, D. Jallepalli, Venkateswara Rao Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of CSE Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Master of Science in Information Technology Department of Accounting and Technology School of Business & Technology Emporia State University United States Symbiosis Centre for Management Studies Pune Symbiosis International [Deemed University Pune India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Pharmacy Practice School of Pharmaceutical Sciences Mohan Babu University Andhra Pradesh Tirupati India

Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing systems are often compromised by their dependence on static thresholds and basic Electroencephalogram (EEG) properties. A novel Recurrent Neural Network (RNN)-based method for seizure start prediction is proposed in the article to overcome these limitations. As opposed to conventional techniques, the proposed system makes use of Long Short-Term Memory (LSTM) networks to extract temporal correlations from unprocessed EEG data. It enables the system to adapt dynamically to the unique EEG patterns of each patient, improving prediction accuracy. The methodology of the system comprises thorough data collecting, preprocessing, and LSTM-based feature extraction. Annotated EEG datasets are then used for model training and validation. Results show a considerable reduction in false alarm rates (average of 6.8%) and an improvement in prediction accuracy (90.2% sensitivity, 88.9% specificity, and AUC-ROC of 93). Additionally, computational efficiency is significantly higher than that of existing systems (12 ms processing time, 45 MB memory consumption). About improving seizure prediction reliability, these results demonstrate the effectiveness of the proposed RNN-based strategy, opening up possibilities for its practical application to improve epilepsy treatment. © 2024, CC BY.

关键词： Electroencephalography

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Intelligent road crack detection and analysis based on improved YOLOv8

arXiv

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arXiv 2025年

作者： Zuo, Haomin Li, Zhengyang Gong, Jiangchuan Tian, Zhen School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou China Master of Science in Computer Science DigiPen Institute of Technology RedmondWA United States Hebei Normal University Hebei Shijiazhuang China James Watt School of Engineering University of Glasgow Glasgow United Kingdom

As urbanization speeds up and traffic flow increases, the issue of pavement distress is becoming increasingly pronounced, posing a severe threat to road safety and service life. Traditional methods of pothole detection rely on manual inspection, which is not only inefficient but also costly. This paper proposes an intelligent road crack detection and analysis system, based on the enhanced YOLOv8 deep learning framework. A target segmentation model has been developed through the training of 4029 images, capable of efficiently and accurately recognizing and segmenting crack regions in roads. The model also analyzes the segmented regions to precisely calculate the maximum and minimum widths of cracks and their exact locations. Experimental results indicate that the incorporation of ECA and CBAM attention mechanisms substantially enhances the model's detection accuracy and efficiency, offering a novel solution for road maintenance and safety monitoring. © 2025, CC BY.

关键词： Image segmentation

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Analyzing Robotics and Computer Integrated Manufacturing of Key Areas Using Cloud Computing

Analyzing Robotics and Computer Integrated Manufacturing of ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Awaneesh Gupta Anil Kumar Dixit K. Suresh Kumar C Lavanya M. Kalyan Chakravarthi Durgaprasad Gangodkar Department: Master of Computer Applications United Institute of Management Prayagraj Dr. AKTU Lucknow Uttar Pradesh India Department of LCD Uttaranchal University Dehradun India Department of MBA Panimalar Engineering College Varadarajapuram Poonamallee Chennai Department of CSE Bannari Amman Institute of Technology Sathyamangalam School of Electronics Engineering VIT - AP University Amaravati Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The rising customer demands are fulfilled by the industrial sector through increased output and expedited shipping. The objectives or satisfied through the manufacturers by using cloud technologies. Tiers of manufacturing sectors adopt with the proposed technology in the future for maintaining the connection shows with the supply chain as well as customers. Cloud computing is referred as abstraction of storage, computation and infrastructure of network altogether as the platform which allows application deployment in speedy manner and dynamic scaling as well. The crucial area in cloud computing is self-service that can allow users to fill the web-form easily and quickly. A novel technical approach to job execution and resource sharing compared to conventional industrial robots is provided by Smart Manufacturing Environments, which uses cloud robotics. SME can be built up on the combination of cloud computing and industrial robots. The main focus of the proposed study is to successfully enabling the manufacturing services on demand which is provided over the internet. Manufacturing services with Cloud-based equipment can also be used by companies attempting to convert Collaborative Robots, Pa-as-you go business models, and production of increasing the productivity of production facilities, raising the caliber of products, enhancing producers’ capacity to adapt to shifting consumer needs, and entering new markets.

关键词： Cloud computing Service robots Scalability Computational modeling Supply chains Software systems Manufacturing

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