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Decentralized AI for Edge Devices with Federated Learning in the Internet of Things

Decentralized AI for Edge Devices with Federated Learning in...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Gouri, M.S. Nagarajan, M. Hussain, Mohammed Waheeduddin Jayachitra, T. Jothikumar, R. Sathya, S. Baselios Mathews II College of Engineering Lake View Muthupilakadu Department of Computer Science and Engineering Kollam Kerala Sasthamcotta India Bharath Institute of Higher Education and Research Department of Electronics and Communication Engineering Chennai India Shadan College of Engineering and Technology Department of Information Technology Peerancheru Hyderabad India Sharda University School of Engineering and Technology Department of Electrical Electronics and Communication Engineering Uttar Pradesh Greater Noida India Shadan College of Engineering and Technology Department of Computer Science and Engineering Peerancheru Hyderabad India GRT Institute of Engineering and Technology Department of Computer Science and Engineering Tiruttani India

ISBN: (纸本)9798350319200

As a result of the development of the Internet of Things (IoT), connectivity and applications that are driven by data have entered a new age. Decentralised artificial intelligence with federated learning has evolved as a game-changing concept in this environment, giving answers to urgent challenges like as data privacy, network latency, and resource optimisation. This ecosystem is characterised by constant change. In this research project, we investigate the fundamentals of decentralised artificial intelligence and its consequences for edge devices in IoT networks. The research presented in this paper demonstrates, via a series of tests, that it is possible to strike an appropriate balance between data privacy and model performance by combining decentralised AI with federated learning. The research results provide system designers with helpful information by drawing attention to the trade-offs that exist between the number of devices, the number of communication cycles, and the level of privacy that is afforded. With the help of decentralised artificial intelligence, businesses are able to take advantage of the opportunities presented by the Internet of Things (IoT) without sacrificing the confidentiality of their data or the responsiveness of their real-time applications. This paper provides a cornerstone reference, setting the way for further research that may improve privacy approaches, expand to larger IoT networks, and identify fresh uses for decentralised AI in the dynamic environment of the Internet of Things (IoT). © 2023 IEEE.

关键词： Decentralized AI Experimental Results Federated Learning Internet of Things (IoT) IoT Ecosystem Latency Reduction Network Efficiency Privacy-Preserving Mechanisms Standardized Protocols

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Continual Learning of a Time Series Model Using a Mixture of HMMs with Application to the IoT Fuel Sensor Verification 18

Continual Learning of a Time Series Model Using a Mixture of...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Glomb, Przemyslaw Cholewa, Michal Foszner, Pawel Bularz, Jakub Institute of Theoretical and Applied Informatics Polish Academy of Sciences Batycka 5 Gliwice44-100 Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Aiut Sp. Z O.o. ul. Wyczókowskiego 113 Gliwice44-109 Poland

ISBN: (纸本)9788396744784

This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification. © 2023 Polish Information Processing Society.

关键词： Hidden Markov models

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Literature Survey on Face Recognition of Occluded Faces 7

Literature Survey on Face Recognition of Occluded Faces

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Anil, J. Suresh, L. Padma Muthukumar, P. Krishna Veni, S.H. Asha, P. Prasad, Rajesh Nooral Islam Centre for Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Thuckalay India Baselios Mathews II College of Engineering Sasthamcotta Department of Electrical and Electronics Engineering Kerala Kollam690 520 India Saveetha Institute of Medical and Technical Sciences Tiruvallur Saveetha School of Engineering Department of Electrical and Electronics Engineering Tamilnadu Chennai602105 India Baselios Mathews II College of Engineering Sasthamcotta Department of Computer Science and Engineering Kerala Kollam690 520 India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Stella Mary's College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu India

ISBN: (纸本)9798350372816

In this paper, a study of the recent advancements in occluded face recognition is made. Recently, this has become very relevant due to the outburst of corona virus. Because of the seriousness of the spread of the disease, every person wore a mask, and it became challenging for automated systems to identify the person. So, it became the need of the hour to find new techniques for occluded face detection. In the years 2021 to 2022, many papers have been published in this regard. Researchers all over the world have contributed significantly towards occluded face recognition. Also, the advancement of deep learning has helped researchers a lot in implementing innovative techniques to deal with the challenges in face recognition, especially occluded faces, which is considered one of the most difficult challenges to overcome. This paper intends to push the limits further and encourage researchers to find more innovative techniques for occlusion-independent face recognition. In this paper, the recent publications in this field are studied. This paper gives an insight into different algorithms which use Convolutional Neural Networks, Deep Learning, Attention mechanism, Dictionary representation, Simultaneous segmentation, Joint segmentation and identification, etc., for occluded face detection and recognition. Incorporating these strategies in face recognition has substantially improved the recognition rate, specifically for occluded face recognition. Additionally, the databases for training occluded face recognition algorithms lack adequate data. This paper also gives an insight into the recent databases available for training occluded face recognition algorithms. Also, different methods for obtaining synthesised occluded faces are discussed. © 2024 IEEE.

关键词： Convolutional neural networks

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Navigating the Generative AI Project Ecosystem with a Focus on Addressing Data Architecture Complexities and Strategic Model Selection for Optimal Outcomes

Navigating the Generative AI Project Ecosystem with a Focus ...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Gupta, Priyanka Kumari, I. Vasantha Rajalakshmi, B. Nijhawan, Ginni Praveen Mazaal, Zahraa Sahib Institute of Aeronautical Engineering Hyderabad Dundigal India Siddhartha Institute of Technology and Sciences Medchal Malkajgiri District Hyderabad Telangana India New Horizon College of Engineering Department of Computer Science Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology Collage of Dentist Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This article discusses the Generative Artificial Intelligence (AI) project's complex ecology and how to pick the optimal models and data design. Creative AI in many commercial and academic fields presents new issues and opportunities. Knowing your way around generative AI projects is crucial. Choose models carefully since data design is hard to grasp. This research provides comprehensive guidance on how to address these areas and ensure project success. Data architecture study is the first step towards generative artificial intelligence since it identifies common issues and viable solutions. Next, to determine which generative models - in terms of complexity, scalability, and ethics - are best suited for a range of tasks, we must evaluate and compare many of them. Researchers were able to validate these findings using case studies and expert interviews. The results suggest that an ordered data infrastructure is required for creative AI. This study indicated the need to boost generational artificial intelligence programs by making practitioners and researchers more aware of the challenges. The study argues that further research and development are necessary to keep up with the fast progress of creative artificial intelligence. © 2024 IEEE.

关键词： Generative adversarial networks

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An Exhaustive Meta-Analysis on Consumer Retention Forecasting Using Advanced Machine Learning Techniques

An Exhaustive Meta-Analysis on Consumer Retention Forecastin...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Lakhanpal, Sorabh Alekhya, V. Rajalakshmi, B. Khan, Nida Khan, Irfan Mohammed, Saad Khudhur Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Science Bangalore India GyanGanga Institute of Technology and Sciences DoCSE M.P Jabalpur India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Pharmacy Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

In this meta-analysis, we present a robust approach for consumer retention forecasting by integrating advanced machine learning algorithms. The method comprises three core algorithms: Consumer Behavior Neural Network (CBNN), Retention Decision Trees (RDT), and Long ShortTerm Memory for Retention (LSTM-R). Our objective is to model intricate relationships within consumer behaviors and accurately predict their retention probabilities. Consumer Behavior Neural Network (CBNN): CBNN, a key component, adeptly captures intricate patterns within consumer behavior data. It involves multiple hidden layers with activation functions, enabling accurate prediction of retention probabilities based on input features. Retention Decision Trees (RDT): This method utilizes decision tree algorithms to predict consumer retention decisions. By recursively splitting the dataset based on information gain, RDT efficiently models consumer behavior, as depicted in the flowchart. Information gain is maximized to create a robust predictive model. Long Short-Term Memory for Retention (LSTM-R): LSTM-R, tailored for temporal data in consumer retention prediction, leverages specialized LSTM architecture. It effectively processes sequential consumer interactions, crucial for accurate prediction of consumer retention probabilities. The proposed method integrates these algorithms, leveraging historical consumer data, and conducts a comprehensive meta-analysis to evaluate their predictive performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparison of SVM and Random Forest for Detection of Malicious Node in Wireless Sensor Networks 2

Comparison of SVM and Random Forest for Detection of Malicio...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Thamma, Sankara Reddy Gurumoorthi, E. Prakash, C. Om Muthusundar, S.K. Nidhya, M.S. Maram, Balajee Deloitte Consulting LLP 28027 United States DC Technical Delivery Master II Information Technology ConcordNC28027 United States School of Engineering Malla Reddy University India CMR University Bengaluru India Chennai Institute of Technology college Dept of AI & DS Chennai India School of Computer Applications Dayananda Sagar University Bangalore India School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Warangal India

ISBN: (纸本)9798331505745

Wireless Sensor Networks (WSNs) are used in various contexts and offer a wide range of applications, despite being a decentralized, resource-scarce network type;this is why this study deals with the problem of malicious nodes. Identifying such nodes is important to preventing the compromise of the network and all the nodes in it. In this paper, a comparative study of two popular classifiers named Support Vector Machine (SVM) and Random Forest (RF) in order to detect hostile nodes in WSNs. By nature, a malicious node in a network will cause significant problems to the users. There are number of models also there to detect the malicious node. Based on the above-discussed parameters the paper compares these algorithms in terms of detection accuracy, computational complexity, scalability and performance measure of imbalanced data. The comparison draws on different targets such as accuracy, capacity/large scale, speed, and small sample imbalance, and noise. As stated in this paper, both SVM and Random Forest have its own strengths where the selection between them mainly depends on the network environment and the features of the malicious activity. © 2025 IEEE.

关键词： Wireless sensor networks

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COMFIS - Comparative Visualization of Simulated Medical Flow Data

COMFIS - Comparative Visualization of Simulated Medical Flow...

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2022 Eurographics Workshop on Visual Computing for Biology and Medicine, EG VCBM 2022

作者： Meuschke, M. Voß, S. Eulzer, P. Janiga, G. Arens, C. Wickenhöfer, R. Preim, B. Lawonn, K. Department of Simulation and Graphics University of Magdeburg Germany Institute of Fluid Mechanics and Thermodynamics University of Magdeburg Germany Institute of Computer Science University of Jena Germany Department of Otorhinolaryngology University Hospital Gießen Germany Herz-Jesu Krankenhaus Dernbach Germany

ISBN: (纸本)9783038681779

Simulations of human blood and airflow are playing an increasing role in personalized medicine. Comparing flow data of different treatment scenarios or before and after an intervention is important to assess treatment options and success. However, existing visualization tools are either designed for the evaluation of a single data set or limit the comparison to a few partial aspects such as scalar fields defined on the vessel wall or internal flow patterns. Therefore, we present COMFIS, a system for the comparative visual analysis of two simulated medical flow data sets, e.g. before and after an intervention. We combine various visualization and interaction methods for comparing different aspects of the underlying, often time-dependent data. These include comparative views of different scalar fields defined on the vessel/mucous wall, comparative depictions of the underlying volume data, and comparisons of flow patterns. We evaluated COMFIS with CFD engineers and medical experts, who were able to efficiently find interesting data insights that help to assess treatment options. © 2022 The Author(s) © 2022 The Eurographics Association.

关键词： Flow patterns

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Internet-of-Explainable-Digital-Twins: A Case Study of Versatile Corn Production Ecosystem

Internet-of-Explainable-Digital-Twins: A Case Study of Versa...

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2022 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2022

作者： Bhattacharya, Pronaya Obaidat, Mohammad S. Sanghavi, Sakshi Sakariya, Vatsal Tanwar, Sudeep Hsiao, Kuei-Fang Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India Department of Computer Science Cybersecurity Center University of Texas-Permian Basin OdessaTX70762 United States King Abdullah II School of IT University of Jordan Jordan University of Science and Technology Beijing China Computer Science Department University of Texas-Permian Basin 4901 E. University Blvd. OdessaTX79762 United States

ISBN: (数字)9781665486170

ISBN: (纸本)9781665486170

In this paper, the authors present a new architectural concept named as Internet-of-Explainable-Digital-Twins (IoxDT), that holistically integrates explainable AI (xAI) and digital twin (DT) in Industry 5.0 production processes. The authors present the architecture over a versatile production system (VPS) corn production plant, where processes are sensor-driven and monitored to minimize bias. The collaboration of DT and xAI leverages the robustness, predictability, and maintainability of VPS processes. In the proposed architecture, the VPS sensor-controlled inputs are sent to the DT controller. The controller is supplied with virtual inputs, which are replicas of real inputs, and the controller behaviour is monitored. The outputs are connected to an xAI module designed with Shapley additive explanations (SHAP) model, which generates the output predictability and dependence on inputs. The unsafe outputs are rejected. A case-study evaluation is carried out in terms of the mean and standard deviation of DT module creation, and SHAP predictability outputs, which shows an accuracy of 96% of unsafe inputs. The proposed results indicate the viability of the framework in real Industry 5.0 VPS setups. © 2022 IEEE.

关键词： Ecosystems

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BeHAutH: A KNN-Based Classification Scheme for Behavior-Based Authentication in Web 3.0

BeHAutH: A KNN-Based Classification Scheme for Behavior-Base...

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2022 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2022

作者： Bhattacharya, Pronaya Trivedi, Chandan Obaidat, Mohammad S. Patel, Kartik Tanwar, Sudeep Hsiao, Kuei-Fang Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India Department of Computer Science Cybersecurity Center University of Texas Permian Basin OdessaTX70762 United States King Abdullah II School of IT University of Jordan Jordan University of Science and Technology Beijing China Computer Science Department University of Texas-Permian Basin 4901 E. University Blvd. OdessaTX79762 United States

ISBN: (数字)9781665486170

ISBN: (纸本)9781665486170

Web 3.0 would allow users and processes to communicate and interpret data semantically. It is expected to employ web user (WU) behaviour data, which is sampled and collected at different time instants for verification. Recent approaches have suggested behaviour-based authentication (BBA), but most schemes suffer from a high rate of false positives. Motivated from the gap, the paper presents a scheme named BeHAutH, that constructs a two-factor authentication (2FA) scheme for WU, based on real-time keystroke dynamics. We employ the K-nearest neighbour classification (KNN) algorithm and compute the similarity across two vectors in K dimensions. The similarity is computed in terms of both Euclidean and Manhattan distance metrics. The scheme achieves an accuracy of 87% over 146 testing samples, and a recall value of 0.95, which address the false negative issues. © 2022 IEEE.

关键词： Machine learning

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Multidimensional Impacts of Generative AI and an In-Depth Analysis of LLMs with Their Expanding Horizons in Technology and Society

Multidimensional Impacts of Generative AI and an In-Depth An...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ramu, K. Nijhawan, Ginni Lalitha, G. Praveen Asha, V. Fakher, Ahmed J. Velammal Institute of Technology Dept. of Cse TN Chennai India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India New Horizon College of Engineering Department of Computer Applications Bangalore India National University of Science and Technology College of Science Department of Forensic Sciences Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Generative AI and large language models (LLMs) have transformed technology and society, as this paper details. Generative AI advanced AI greatly. Because they can read and write, LLMs, a form of Generative AI, have altered how humans and robots communicate. This study focuses on how these technologies influence schooling, healthcare, finance, and morals. Recent studies and case studies will help us understand how LLM is applied in many sectors. Our key concerns with these activities are efficiency, efficacy, and morality. The technique also polls and interviews professionals to learn about LLM usage and issues. LLM is quicker and more precise in translating languages, creating content, and analyzing data. Sadly, prejudice, privacy, and abuse issues persisted. LLMs are versatile enough to be utilized in odd fields like individualized learning and mental health treatment, according to the research. LLMs and creative AI can advance technology and society. Despite their benefits, they pose moral and practical issues that must be addressed. This study seeks a compromise between the two LLM methods. A more moral, research-focused use might make them more beneficial and less hazardous. © 2024 IEEE.

关键词： Differential privacy

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