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Fuzzy-Deep Learning-Based Artificial Intelligence for Edge computing and Real-Time Decision-Making in Uncertain IoT Environments 1

Fuzzy-Deep Learning-Based Artificial Intelligence for Edge C...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Joshi, B.P. Singh, Akhilesh Kumar, Nagendra Rautela, Siddharth Graphic Era Hill University Department of Mathematics Department of Computer Science & Engineering India VMSB UTU Department of Electrical Engineering Campus Pithoragarh India GL Bajaj Institute of Technology & Management Department of Electrical & Electronics Engineering Greater Noida201306 India Central Board of Secondary Education Department of Mathematics Uttarakhand Bhimtal India

ISBN: (纸本)9798331518578

Owing to the Internet of Vehicles (IoV) quick growth, both academics and the sector have paid close emphasis to vehicular edge computation (VEC). Nevertheless, because of the unbalanced congestion and the strict delay requirements, task offloading in various junction situations continues to struggle from inefficient resource allocated and poor operation implementation standards. This study proposes a task-offloading technique using a fuzzy decision-making method to deal with ambiguity and uncertainties to solve these problems. Roadside Utilities (RSUs) placed alongside remote roadways typically have limited energy resources, thus they must offer energy-effective planning assistance with the distribution of duties to VEC. However, planning decisions for regional task execution incur computational costs, and assigning duties to edge automobiles incurs transmission costs, making energy usage management difficult. Task data transmission to edge automobiles results in increased RSU power usage even while task offloading lowers response delay. To meet task schedule and supply restrictions, this study proposes an energy-effective automobile planning issue for offloading functions to mobile edge units. This study develops a planning technique depending on on-policy deep reinforcement learning (DRL) and a fuzzy-based DRL to address the extremely complex problem brought on by a rise in the number of automobiles under RSU service. When contrasted to the Q-learning method, this FRL not only speeds up the learning procedure but also enhances long-term payoff. © 2025 IEEE.

关键词： Energy utilization

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The Research on V2V Cooperative Caching Method Based on Vehicle Attributes 24

The Research on V2V Cooperative Caching Method Based on Vehi...

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2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Wang, Zirui Wang, Hui Wan, Jianxiong Liu, Chuyi Sun, Pengfei Li, Leixiao School of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Autonomous Region Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Autonomous Region Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798400709869

To address the increasingly severe issue of insufficient vehicular caching in the Internet of Vehicles (IoV), Vehicular edge computing uses base stations (BS) and vehicles with computing and storage capabilities as edge servers to cache content. However, most existing studies focus on reducing task costs, assuming vehicles can simultaneously act as requesters and providers, without considering whether a vehicle is suitable to serve as a provider, willing to share resources or ensure secure data transmission, thus lowering matching efficiency. Deep reinforcement learning and blockchain technology were integrated into the Internet of Vehicles to address distributed vehicle-to-vehicle collaborative caching. An optimal collaborative caching strategy was designed, considering dynamic vehicle attributes, with the system's objective being to maximize vehicle utility to encourage resource sharing. Simulation results confirm that the proposed blockchain-based vehicular edge computing collaborative caching scheme with deep reinforcement learning significantly outperforms other strategies. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blockchain

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A Lightweight Deep Leakage Prevention Model with Mixing Multi-gradients 2nd

A Lightweight Deep Leakage Prevention Model with Mixing Mul...

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2nd international conference on Security and Information technologies with AI, Internet computing and Big-data Applications, SITAIBA 2023

作者： Hsiao, Wei-Chun Wang, Chih-Hung Department of Computer Science and Information Engineering National Chiayi University Chiayi Taiwan

ISBN: (纸本)9789819777853

In deep learning model, the training procedure can obtain appropriate weights by leveraging the information of gradient descent. However, the gradients between the weights may leak the original neural network training data if they are not well-designed. Zhu et al. (advances in neural information processing systems, Curran Associates, Inc., [5]) as proposed a kind of attacking scenario named deep leakage from gradients. There were many mitigation strategies proposed in the literature, such as mixing Gaussian noises and model quantization. However, they usually somewhat lost classification accuracy during prevention process. This paper proposes a lightweight attachment model which can be used as an extension on the original classification model to prevent deep leakage from gradients with low information loss. This approach can complete the goal of mitigation on trying to minimize the changes of the original structure of the model and can be easily implemented on some existent deep learning classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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IoT-Enabled Asthma Trackers for Children with KNN for Improved Breathing Control 5

IoT-Enabled Asthma Trackers for Children with KNN for Improv...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Sathyanathan, P. Balachander, K. Pandey, Pramod Srinivasan, V. Prasanna Ganeshbabu, T.R. Muthulekshmi, M. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

Managing asthma in children is a complex task that needs constant vigilance and personalized treatments to get the best potential control. For better respiratory management, an Internet of Things (IoT)-enabled pediatric asthma tracking system that uses machine learning (ML) methods, particularly for K-nearest neighbors (KNN), is presented. The system's integration of wearable sensors, which continuously monitor vital signs and environmental conditions, provides a comprehensive view of the child's respiratory health. Asthma exacerbation causes, and trends may be discovered by analyzing the obtained data using KNN algorithms. Analysis allows for the generation of real-time warnings and feedback, allowing caregivers to take proactive steps in reducing risks and optimizing treatment procedures. Our IoT-enabled method for improving respiratory control and decreasing asthma-related consequences in children is feasible and effective in pilot research, including children with asthma. Our research highlights the promise of IoT devices and ML algorithms to transform how pediatric asthma is treated by providing tailored, evidence-based treatments that lead to better health outcomes. © 2025 IEEE.

关键词： Pediatrics

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Consumer Behavior in the Post-Pandemic Era: Insights from Machine Learning and AI-Driven Analytics 5

Consumer Behavior in the Post-Pandemic Era: Insights from Ma...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Nimma, Divya Anudeep, Ch. Sahu, Tripti Shah, Nusrat Hamid Shah, Lubna Hamid Bala, B. Kiran University of Southern Mississippi Ummc United States Department of Management studies Bhimavaram India Mima Institute of Management Maharashtra Pune India College of Engineering and Computer Science Jazan University Saudi Arabia College of Business Jazan University Department of Management and Marketing Saudi Arabia K.Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

COVID-19 has been the disruptor in the world, and the society has been changed from one perspective of the consumer behavior in terms of digital platforms and their preferences for safety, convenience and sustainability. It is therefore very important for any business to acquaint itself with these changes so that may be in a position to effectively compete for the market within the current ever-changing business environment. Conventional consumer behavior models, on the other hand, rely on fragmented data sources and analytical tools that may not reflect the current post-pandemic consumption patterns. Such models are also incapable of incorporating multiple data types and can also fail to address changing social factors. In response, a Multi-Modal Consumer Behavior Prediction Framework (MCBP) that considers a vast array of data sources is introduced. BERT and GNNs are used to give a detailed, real-time interpretation of consumer behaviour accomplished by the MCBP framework. It uses BERT to extract and analyze sentiment and topics of the textual data and GNNs to capture influence and peer effects on purchase decisions. The findings of the study show the effectiveness of the proposed framework with an accuracy of 99%. 5% and the implementation is done using Python. The high level of accuracy shown assures the success of the MCBP model in measuring and forecasting consumers' behaviors in the post-pandemic world. © 2025 IEEE.

关键词： Consumer behavior

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Secure Resource Allocation via Constrained Deep Reinforcement Learning 6th

Secure Resource Allocation via Constrained Deep Reinforceme...

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6th international conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Sun, Jianfei Gao, Qiang Wu, Cong Li, Yuxian Wang, Jiacheng Niyato, Dusit School of Computing and Information Systems Singapore Management University Singapore188065 Singapore School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9789819645657

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO ’s potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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WeedsGalore: A Multispectral and Multitemporal UAV-Based dataset for Crop and Weed Segmentation in Agricultural Maize Fields

WeedsGalore: A Multispectral and Multitemporal UAV-Based Dat...

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2025 IEEE/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Celikkan, Ekin Kunzmann, Timo Yeskaliyev, Yertay Itzerott, Sibylle Klein, Nadja Herold, Martin Gfz German Research Centre for Geosciences Germany Humboldt-Universität zu Berlin Germany Scientific Computing Center Karlsruhe Institute of Technology Germany

ISBN: (纸本)9798331510831

Weeds are one of the major reasons for crop yield loss but current weeding practices fail to manage weeds in an efficient and targeted manner. Effective weed management is especially important for crops with high worldwide production such as maize, to maximize crop yield for meeting increasing global demands. advances in near-sensing and computer vision enable the development of new tools for weed management. Specifically, state-of-the-art segmentation models, coupled with novel sensing technologies, can facilitate timely and accurate weeding and monitoring systems. However, learning-based approaches require annotated data and show a lack of generalization to aerial imaging for different crops. We present a novel dataset for semantic and instance segmentation of crops and weeds in agricultural maize fields. The multispectral UAV-based dataset contains images with RGB, red-edge, and nearinfrared bands, a large number of plant instances, dense annotations for maize and four weed classes, and is multitemporal. We provide extensive baseline results for both tasks, including probabilistic methods to quantify prediction uncertainty, improve model calibration, and demonstrate the approach's applicability to out-of-distribution data. The results show the effectiveness of the two additional bands compared to RGB only, and better performance in our target domain than models trained on existing datasets. We hope our dataset advances research on methods and operational systems for fine-grained weed identification, enhancing the robustness and applicability of UAVbased weed management. The dataset and code are available at https://***/GFZ/weedsgalore. © 2025 IEEE.

关键词： Weed control

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Robotic Precision Fitness: Accurate Pose Training for Elderly Rehabilitation 25th

Robotic Precision Fitness: Accurate Pose Training for Elderl...

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25th international conference on Intelligent data Engineering and Automated Learning

作者： Rincon, J. A. Marco-Detchart, C. Univ Burgos Dept Digitalizac Escuela Politen Super Ctra Oron 28 Burgos 09200 Spain Univ Politec Valencia UPV Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031777370;9783031777387

This study presents a novel robotic assistant tailored for elderly rehabilitation. The system combines state-of-the-art artificial intelligence, computer vision, and robotics to provide customized exercise routines and instant feedback. The core of the assistant is a Grove AI V2 vision system, driven by a dual-core Arm Cortex-M55 processor and an Arm Ethos-U55 neural network component. A custom-built robotic torso fitted with 12 servo motors supports the demonstration of a wide variety of upper body exercises. A crucial element in the experiments is the use of Restricted Radial Equivalence Functions (RREFs) and Radial Similarity Measures (RSMs) to precisely match human poses with the robot's demonstrations. This method ensures an accurate evaluation of exercise performance, enhancing the efficacy of physical therapy for the elderly. Using edge computing technologies, the system provides real-time monitoring and personalised care while addressing important privacy issues. The assistant is additionally characterized by a small-sized design that includes a 124x64 pixel OLED display, capable of displaying bio-signal monitoring and an animated face to engage users. This research advances AI-assisted elderly care, aiming to increase independence, physical health, and overall quality of life for aging populations while offering support to healthcare systems and caregivers.

关键词： Edge computing Elderly Rehabilitation Pose Estimation Robotic Assistance

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advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution 2nd

Advances in smart farming for precision agriculture: Green-I...

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2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

作者： Digra, Monia Rajput, Preeti Sharma, Pooja Model Institute of Engineering and Technology J&K Jammu India School of Computer Science and Engineering Shri Mata Vaishno Devi University J&K Katra India

ISBN: (纸本)9781032738659

Agricultural development is crucial to ending poverty and boosting economic growth in emerging nations. The global population is expected to reach 9.7 billion by 2050. Smart and precise agriculture must enhance agricultural productivity without compromising quality, nutrition, or natural resources. IoT and AI/ML have significant uses in agriculture due to internet technologies. Although IoT has many benefits, the enormous number of networked devices may raise carbon emissions and e-waste due to energy consumption. Inspired by the goal of sustainable smart agriculture, this article addresses major results and research gaps in IoT-based smart agriculture and provides solutions for energy-saving Green-IoT (G-IoT) applications in agriculture. We start with G-IoT’s evolution and smart agriculture applications. We also examine how green architecture may efficiently apply AI/ML in smart agriculture. We propose an AI/ML G-IoT framework for smart agriculture with green sensor cloud and green AI. This unique G-IoT paradigm provides sustainable and energy-efficient agricultural architecture. We investigate statistical and experimental data and emphasise the limitations of adopting G-IoT and AI/ML architecture in agriculture. In conclusion, G-IoT and AI/ML architecture can enable sustainable, energy-efficient, smart precision agriculture. © 2025 The Author(s).

关键词： Energy utilization

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Integrating Blockchain and Artificial Intelligence for Securing Payment processes Against Fraudulent Transactions 5

Integrating Blockchain and Artificial Intelligence for Secur...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Ramesh, J. V. Naga Shah, Nusrat Hamid Alazzam, Malik Bader Nimma, Divya Selvi, V. Balakumar, A. Graphic Era Hill University Department of Cse Dehradun India Graphic Era Deemed To Be University Department of Cse Uttarakhand Dehradun India College of Engineering and Computer Science Jazan University Saudi Arabia Jadara University Faculty of Information Technology Irbid Jordan University of Southern Mississippi Ummc United States Mother Teresa Women's University Department of Computer Science Kodaikanal India K. Ramakrishnan College of Engineering Department of Ece Trichy India

ISBN: (纸本)9798350356236

In recent years, the fast growth of digital payment processes has raised large safety concerns, which include fraud and data breaches, prompting the need for robust safety mechanisms. Existing techniques, which include conventional rule-based systems, regularly fall quick in adaptability and accuracy, leading to multiplied vulnerabilities within the economic zone. This study proposes a unique integration of Blockchain technology with Artificial Intelligence (AI), specially making use of Generative Adversarial Networks (GANs), to enhance the security of monetary transactions. The technique includes growing a decentralized ledger device to make sure transparency and immutability, coupled with a GAN framework for real-time anomaly detection. Then behavior experiments the use of a complete dataset of monetary transactions to validate the effectiveness of the included technique. The consequences imply a considerable improvement in detecting fraudulent activities, accomplishing higher accuracy and decrease false positive rates in comparison to conventional methods. This research no longer most effective addresses the crucial safety gaps found in contemporary monetary transaction systems however additionally demonstrates the potential of integrating Blockchain and AI to create an extra stable monetary environment. The findings recommend that this integrated technique can serve as a scalable solution for enhancing transaction safety, providing valuable insights for both academia and industry practitioners. © 2025 IEEE.

关键词： Intelligent systems

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