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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Santosh Kumar, B. Viji, M. Gowtham, T. Sekar, G. Sharmila, S. Saravanan, M. New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru560103 India E.G.S. Pillay Engineering Engineering Department of Computer Science and Business Systems Nagapattinam611002 India P.A. College of Engineering and Technology Department of Electronics and Communication Engineering Pollachi642002 India Vsb College Of Engineering Department of Electronics and Communication Engineering Technical Campus Coimbatore642109 India Sir Issac Newton College of Engineering and Technology Department of Computer Science and Engineering Nagapattinam6111002 India Sri Eshwar college of engineering Department of Electronics and Communication Engineering Coimbatore641202 India

ISBN: (纸本)9798331509828

In the digital age, the spread of fake news has become ubiquitous: it weakens public speech and weakens public trust. In this paper we propose a novel method of detecting fake news using a deep learning framework. To do text analysis, we propose a hybrid model that incorporates Convolutional Neural Network and Long Short-Term Memory Network by utilizing on the strengths of each for effective text analysis. However, the LSTM layer is good in learning the context and sequence of the text, and the CNN component beats in extracting local and spatial details in textual data. The effectiveness of the proposed work is tested on a comprehensive data set of news sources (covering different sources, from different countries, and labeled for authenticity). We trained and evaluated the model on this dataset which mainly included features of linguistic and semantic nature to differentiate between genuine and fake news. We show that our results exhibit the biggest accuracy, precision, recall, and Fl-score improvement over old models. Automated fake news detection is a growing field, and this research proposes a robust and scalable solution to cut off the spread of misinformation. © 2025 IEEE.

关键词： Convolutional neural networks

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Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?

arXiv

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arXiv 2025年

作者： Wu, Guanghan Tarkoma, Sasu Morabito, Roberto Department of Computer Science University of Helsinki Finland Department of Communication Systems EURECOM France

The evolving requirements of Internet of Things (IoT) applications are driving an increasing shift toward bringing intelligence to the edge, enabling real-time insights and decision-making within resource-constrained environments. Tiny Machine Learning (TinyML) has emerged as a key enabler of this evolution, facilitating the deployment of ML models on devices such as microcontrollers and embedded systems. However, the complexity of managing the TinyML lifecycle, including stages such as data processing, model optimization and conversion, and device deployment, presents significant challenges and often requires substantial human intervention. Motivated by these challenges, we began exploring whether Large Language Models (LLMs) could help automate and streamline the TinyML lifecycle. We developed a framework that leverages the natural language processing (NLP) and code generation capabilities of LLMs to reduce development time and lower the barriers to entry for TinyML deployment. Through a case study involving a computer vision classification model, we demonstrate the framework's ability to automate key stages of the TinyML lifecycle. Our findings suggest that LLM-powered automation holds potential for improving the lifecycle development process and adapting to diverse requirements. However, while this approach shows promise, there remain obstacles and limitations, particularly in achieving fully automated solutions. This paper sheds light on both the challenges and opportunities of integrating LLMs into TinyML workflows, providing insights into the path forward for efficient, AI-assisted embedded system development. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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ECG Signal Feature Extraction and SVM Classifier Based Cardiac Arrhythmia Detection 2

ECG Signal Feature Extraction and SVM Classifier Based Cardi...

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2nd International Conference on Electrical, Electronics, Information and communication Technologies, ICEEICT 2023

作者： Venkatesan, C. Thamaraimanalan, T. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350397635

In a variety of medical applications, electrocardiogram (ECG) records are essential for the identification of cardiovascular disorders. Cardiovascular issues such cardiac arrhythmias and coronary heart disease (CHD) are among the most prevalent and can result in cardiac arrest or sudden cardiac death. This study employs ECG signal preprocessing and feature extraction to identify cardiac arrhythmias and evaluate CHD risk. This research stresses the use of a Support Vector Machine (SVM) classifier for cardiac arrhythmia identification after ECG signal preprocessing. The preprocessed ECG signal is then subjected to arrhythmic beat classification to find anomalies. Extracted R-peaks from the ECG signal are divided into normal and arrhythmic risk subjects using the SVM classification-based approach for abnormality identification. When compared to other similar classifiers, the K-Nearest Neighbor (KNN) classifier offers the highest classification accuracy of 97.5%. © 2023 IEEE.

关键词： Electrocardiograms

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Risk Management in Farming: An Overview of Sources, Decision-making and Reduction Strategies

Risk Management in Farming: An Overview of Sources, Decision...

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Energy Efficiency and Agricultural Engineering (EE&AE), International Conference on

作者： Yordanka Boneva Boriana Vatchova Krasimira Stoilova Department of Distributed Information and Control Systems Bulgarian Academy of Sciences Institute of Information and Communication Technologies Sofia Bulgaria Department of Communication and Computer Systems Bulgarian Academy of Sciences Institute of Information and Communication Technologies Sofia Bulgaria

ISBN: (数字)9798350369380

ISBN: (纸本)9798350369397

The paper considers risk management and strategies for risk reduction in farming. Different sources of risk are presented that happen on farms. The paper reveals some approaches to farmer’s decision-making to reduce the risk. An algorithm is proposed which can be applied to risk management in farms. The cycle of decision-making in farms is visually presented. Graphical representation is also available for risk management strategies. The paper presents an overview of the risk management and risk management strategies in farms.

关键词： Fuzzy logic Uncertainty Accuracy Bibliographies Decision making Insurance Production

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Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning

Synthetically Generating Human-like Data for Sequential Deci...

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Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications 2023

作者： Brandt, Bryan Dasgupta, Prithviraj Distributed Intelligent Systems Section Information Technology Division Naval Research Laboratory WashingtonDC United States Computer Science Department University of Wisconsin-Whitewater WI United States

ISBN: (纸本)9781510661721

We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can generate synthetic, human-like, decision making data while starting from a very small set of decision making data collected from humans. Our proposed algorithm integrates the concept of reward shaping with an imitation learning algorithm to generate the synthetic data. We have validated our synthetic data generation technique by using the synthetically generated data as a surrogate for human interaction data to solve three sequential decision making tasks of increasing complexity within a small computer game-like setup. Different empirical and statistical analyses of our results show that the synthetically generated data can substitute the human data and perform the game-playing tasks almost indistinguishably, with very low divergence, from a human performing the same tasks. © 2023 SPIE.

关键词： Decision making

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Energy Efficient Routing Scheme for Performance Enhancement of MANET Through Dominating Set

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第4期17卷 89-100页

作者： Sangeetha, M. Erna, G. Kannan, S. Rajes Salameh, Anas A. Kishore, Kakarla Hari Department of Data Science and Business Systems SRMIST KattankulathurChennai India Ongole India Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Management Information Systems College of Business Administration Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Mobile Ad-Hoc network is a distributed wireless network that is self-organized and self-maintained and it doesn’t require a fixed framework or central administration. Wireless nodes in a mobile ad hoc network are transient and have a short lifespan. They combine to form a self-configured, infrastructure-free network where routing is a significant difficulty. The wireless nodes in the network must synchronize in order to decide connectivity and routing. Ad hoc network synchronization includes activities including node organization, neighborhood detection, and routing. The predetermined framework resulted in an increase in network control overhead. If a node can link to every other node in the network, it is referred to as a dominating node, and a set of dominating nodes make up the dominating set. The main purpose of this article is to lessen routing overhead in the wireless network with the dominating set based routing (DSBR). In order to discover routes, shorten reroute establishment times, and increase packet delivery rates, this research presents an energy-efficient routing algorithm (EERA) that considerably increases the network performance. The effectiveness of this technique is demonstrated by the simulation analysis of the outcome. © 2023,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Mobile ad hoc networks

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Challenges and Initial Measurements on communication and Localization for Mountain Bike Safety Applications

Challenges and Initial Measurements on Communication and Loc...

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Annual Conference on Wireless On Demand Network systems and Services (WONS)

作者： Michele Zucchelli Marcus Marx Max Hörmann Frank Kargl Michele Segata Department of Information Engineering and Computer Science University of Trento Italy Institute of Distributed Systems Ulm University Germany

ISBN: (数字)9783903176713

ISBN: (纸本)9798331522025

As of today, research in vulnerable road users (VRUs) applications is mainly focused on safety in urban road scenarios. There is little to be found in the literature with respect to VRUs in mountain areas, where mountain biking and hiking also present risks of collision. Here, it is not yet clear whether existing localization and communication technologies would provide sufficient performance in such harsh environments. In this work, we start answering this question by presenting the results of a measurement campaign which took place in a mountain area in Northern Italy during Summer 2024. With respect to localization, we show that global navigation satellite system (GNSS)-based localization alone often provides unreliable results due to vegetation and terrain. Trilateration with Bluetooth Low Energy (BLE) and beacons mounted at fixed positions performs well in some circumstances and can be used to enhance GNSS, however, we also observed many unclear effects that require further investigations. Concerning communication, the results indicate that both direct short range communications (DSRC) and cellular V2X (C-V2X) works fairly well in most cases, but terrain characteristics might induce packet losses or low signal quality, whereas instabilities in GNSS fixes might also cause C-V2X outages.

关键词： Location awareness Wireless communication Global navigation satellite system Roads Vegetation mapping Packet loss communications technology Safety Bluetooth Low Energy Network systems

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Compressing Time Series Towards Lightweight Integrity Commitments 8

Compressing Time Series Towards Lightweight Integrity Commit...

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8th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Katsika, Angeliki Papageorgiou, Konstantinos Fakis, Alexandros Kakarountas, Athanasios Andritsopoulos, Fotis Plagianakos, Vassilis Spathoulas, Georgios University of the Aegean Department of Information and Communication Systems Engineering Samos Greece ITrack Athens Greece University of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece Department of Information Security and Communication Technology Gjovik Norway

ISBN: (纸本)9798350383676

In the current era of big data, the generation and preservation of time series data has seen widespread adoption in various sectors such as trade, healthcare, and industry. At the same time, data have become a prized asset, as we are witnessing an unprecedented explosion in the volume of data generated and collected. This includes data from various sources, such as sensors, social media, and transactions, offering a competitive edge to organizations that harness the power of data, leading them to informed decisions and rapid responses to changing conditions. However, it is essential to responsibly manage data, addressing privacy and security concerns to ensure their integrity and availability, while efficiently managing the cost of storage and operations. To address these challenges, there is a compelling de-mand for methods that compress these time series while ensuring that the compressed versions faithfully represent the original data and remain secure against tampering or unauthorized alterations. This paper introduces a novel approach to condensing time-series data into lightweight integrity commitments, leveraging polynomial vector commitments as a key component. These integrity commitments are integrated with blockchain technology, serving as a cornerstone in guaranteeing data integrity and authenticity, particularly in supply chain scenarios where trust and transparency are of the utmost importance. © 2023 IEEE.

关键词： Time series

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Big Data Architectures in a patient-driven healthcare system: Implications, Scrutiny and Analysis for future prospects 1

Big Data Architectures in a patient-driven healthcare system...

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1st International Conference on Innovative Engineering sciences and Technological Research, ICIESTR 2024

作者： Memon, Kamran Ali Memon, Imran Kumbhar, Farooque Bala, Abu Bakar Iqbal, Naveed Qureshi, Khurram Karim King Fahd University of Petroleum and Minerals Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Shah Abdul Latif University Department of Computer Science Shahdadkot Campus Pakistan King Fahd University of Petroleum and Minerals Electrical Engineering Department Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350348637

The term "big data"describes a significant amount of valuable information. Additionally, a sizable amount of big data related to public health comes from biological research. It is necessary to manage and analyse this data with care to get accurate information. There are several problems with every step of large data management that can only be fixed with sophisticated big data computing solutions. This is the reason health professionals need to be fully equipped with the tools necessary to efficiently collect and analyse big data to deliver appropriate solutions to promote public health. By creating new opportunities for contemporary health, proper big data management, analysis, and interpretation may completely transform the game. For this reason, several industries, including the health industry, are moving quickly to seize this chance to provide improved services and financial gains. Modern healthcare organizations have the potential to transform medical and personal medicine via the effective integration of big data and healthcare services. This article outlines many analytical paths that the patient-driven healthcare system might take, as seen from the viewpoints of different players. We have also examined several big data architectures in terms of their analytical influence, application fields, and underlying data sources and challenges in collecting the big data for healthcare systems. © 2024 IEEE.

关键词： Electronic health record

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