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International Conference on Computing and Networking Technology (ICCNT)

作者： T. Gnanasekaran S Girinath K Venkatesh N. Valarmathi Sunil Kumar Bandili S Balasubramani Department of ECE R.M.K. Engineering College Kavaraipettai Tamilnadu India Department of Computer Applications Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati AP India Department of Computer Science and Engineering (Data Science) Srinivasa Ramanujan Institute of Technology Anantapur Aandra Pradesh India Department of Information Technology M.Kumarasamy College of Engineering Thalavapalayam Karur Inda Department of Computer and Information Sciences School for Professional Studies Saint Louis University St. Louis MO USA Department of ECE R.M.D. Engineering College Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Due to the broad implementation of the Internet of Things (IoT) in industrial systems, the introduction of Industry 4.0 resulted in a significant increase in operational efficiency. However, it also resulted in the introduction of new challenges when it came to identifying abnormalities. This paper presents a new method for boosting the flexibility and precision of anomaly detection models by integrating the best aspects of K-Means clustering with meta-heuristic approaches. The utilisation of K-Means meta-heuristic techniques is the driving force behind this research, which is being driven by the growing demand for reliable anomaly prediction in industrial systems that are enabled by the IoT. Aiming to discover methods that can improve the reliability of anomaly detection algorithms is the objective of this research. K-Means clustering is systematically integrated with metaheuristic techniques in this study, which allows for the optimisation of the clustering algorithm for the purpose of detecting abnormalities in industrial data that is enabled by the IoT. We use real-world datasets from a wide range of businesses in order to measure the effectiveness of the proposed strategy in comparison to more traditional ways. The results demonstrate that the K-Means meta-heuristic strategy greatly enhances the accuracy of anomaly detection in comparison to the approaches that are considered conventional. Recall, false positive rates, and precision are all areas in which it excels beyond its capabilities. The results indicate that this unique approach has the potential to be useful when applied to the challenges of anomaly detection in industrial systems that are enabled by the IoT.

关键词： Accuracy Metaheuristics Force Clustering algorithms Prediction algorithms Fourth Industrial Revolution Reliability Internet of Things Anomaly detection Testing

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Deep Learning Driven - Pest Classification and Sustainable Agriculture

Deep Learning Driven - Pest Classification and Sustainable A...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M Sirish Kumar R Usha B Rama Kantha Reddy S Ramachandra P Jaya Prakash B Swetha Dept. of Data Science School of Computing Mohan Babu University India CSE Madanapalle Institute of Technology and Science Madanapalle CSE (AI and ML) Sri Venkateswara College of Engineering Tirupati CSE Mother Theresa Institute of Engineering and Technology Palamaner Department of IT Sri Venkateswara College of Engineering Tirupati Dept. of Master of Computer Applications Mohan Babu University India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices.

关键词： Deep learning Accuracy Insects System performance Transfer learning Crops Pesticides Agriculture Convolutional neural networks Testing

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Research and Innovation In Next Generation Security and Privacy In Industry 5.0 Iot

Research and Innovation In Next Generation Security and Priv...

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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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Multi-Objective Genetic Programming Assisted Stochastic Deep Reinforcement Learning for Dynamic Knowledge Integration in Transportation Networks

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Xue, Xingsi Mao, Guojun Kumari, Saru Liu, Zhonghua Yango University College of Artificial Intelligence Fujian Fuzhou350118 China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350011 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350011 China Chaudhary Charan Singh University at Meerut Department of Mathematics Meerut250001 India Shengjing Hospital of China Medical University Computer Center Liaoning Shenyang110004 China

Transportation Networks (TNs) play a critical role in economic and social systems, yet the dynamic nature and inherent heterogeneity of TN data pose challenges for Dynamic Knowledge Integration (DKI). Traditional approaches for matching entities from different knowledge bases often struggle with the complexity and diversity of TN data, which varies across systems and sources such as traffic sensors and GPS devices. To address this issue, this paper proposes a novel Multi-Objective Genetic Programming assisted Stochastic Deep Reinforcement Learning (MOGP-SDRL) for DKI in TNs. Unlike existing methods, the proposed framework combines SDRL and MOGP to achieve superior efficiency, accuracy and adaptability in handling heterogeneous TN data. First, a novel SDRL framework is designed to automate and optimize the selection of Similarity Features (SFs) for entity matching. This framework incorporates a probabilistic action selection mechanism, which enhances exploration during the SF selection process. Second, a novel MOGP is presented to construct high-quality, diverse SFs by exploring non-dominated feature ensembles, enhancing both accuracy and adaptability in matching results, leading to more accurate and adaptable matching results compared to conventional methods. Lastly, new approximate evaluation metrics are developed to assess alignment quality without relying on predefined entity alignments, guiding the optimization process. Experimental evaluations on OAEI's knowledge graph (KG) dataset and five pairs of real-world TN dataset demonstrate the effectiveness of the MOGP-SDRL framework, which consistently produces high-quality matching results and achieves significant improvements in both accuracy and robustness over existing approaches. © 2000-2011 IEEE.

关键词： Deep reinforcement learning

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F2A2: flexible fully-decentralized approximate actor-critic for cooperative multi-agent reinforcement learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 8447-8521页

作者： Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan Hongyuan Zha School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.

关键词： cooperative MARL decentralized actor-critic primal-dual method

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A Smart Electronic Wheelchair Design Using Artificial Intelligence and Intelligent Sensors Association

A Smart Electronic Wheelchair Design Using Artificial Intell...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： R Balamurugan D. Kanchana B. Anni Princy J Jaisudha P. Malathi E Glory Department of Artificial Intelligence and Machine Learning AJK College of Arts and Science Coimbatore Tamil Nadu India Department of Computer Applications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamil Nadu India Department of Biosciences Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair’s controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair’s DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user’s pulse rate, as well as the wheelchair’s functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner.

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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Engineering applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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A GPT-4 Reticular Chemist for Guiding MOF Discovery

arXiv

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arXiv 2023年

作者： Zheng, Zhiling Rong, Zichao Rampal, Nakul Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry Kavli Energy Nanoscience Institute Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States KACST—UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia Department of Electrical Engineering and Computer Sciences Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences Department of Statistics Department of Mathematics School of Information Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States

We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthesis (chemical)

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data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles

Data Analytics for Core Temperature Estimation in Battery Ma...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Ahilya Chhetri Sumukh Surya Mohan Krishna S Vidya Rao Engineering and Technology Cactus Communications Mumbai India Bosch Global Software Technologies Pvt. Limited Bengaluru India Department of Electrical and Electronics Engineering ACED Alliance University Bengaluru India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Compared to conventional means of transport thatuse fossil fuels, electric vehicles are known to reduce pollution levels as they take power from renewable sources of energy stored in energy storage devices like batteries and fuel cells. Core temperature estimation of batteriesis extremely important inbattery management systems for preventing thermal runaway and ensuring safe operation. In this study, core temperature is estimated using a Kalman filter for two different battery chemistries viz; lithium polymer and lithium iron phosphate using a second-order thermal model. Further, a linear regression model is applied to verify the prediction over the trained and tested dataset. The lithium iron phosphate prediction curvehad a fit of approximately 82-83%, whilelithium polymerhad a fit of 72-82% during the charging and discharging of OCV-SOC variation.

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Revolutionizing IoT Network Security with Deep Learning-Anomaly Detection Model

Revolutionizing IoT Network Security with Deep Learning-Anom...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Manish Vyas R Vijayaganth Jigyasha Chandhok Aviral Srivastava S. Arumugam Mohit Tiwari Computer Science Department College Name & Address: Vikrant Institute of Technology and Management Indore Artificial Intelligence and Data Science M.Kumarasamy College of Engineering Thalavapalayam Karur Computer Science & Engineering Tula’s Institute of Engineering and Management NAAC Grade A+ Accredited institute Dhoolkot Dehradun Uttarakhand Amity University Rajasthan Amity University Rajasthan NH 11 C kant kalwar Jaipur Computer Science and Applications Periyar Maniammai Institute of Science & Technology Periyar Nagar Vallam Thanjavur Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering A-4 Rohtak Road Paschim Vihar Delhi

The term “Internet of Things” (IoT) is used to describe the collection of data and the connectivity of items to the web that requires little to no human interaction. The IoT is a network of interconnected devices that can collect and disseminate information. Increased security and privacy worries accompany the launch of new devices due to the proliferation of Internet connections and the development of cutting-edge technology like the IoT. These days, the IoT is used everywhere, but especially in logistics, manufacturing, and healthcare. While these emerging IoT applications greatly enhance the usefulness of smart objects, they also present new security risks. Because of this, adapting existing intrusion detection systems (IDS) for use with IoT networks is a topic of intense study. Many IDS experts have found success with machine learning (ML) and deep learning (DL) techniques. By combining deep extraction through the Convolutional autoencoder with deep learning to identify the best features, this work delivers an improved IDS that can be used for anomaly detection. Improves to the deep learning approach include an evaluation of hyperparameter effectiveness, a stage of feature pruning using an autoencoder neural network, and an examination of the sturdiness of the most effective deep neural networks for circumstances exaggerated by Gaussian noise over some of the features in question. Despite the noise, the results show that the formed IoT dataset is useful for anomaly detection with deep learning methods.

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