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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Virendra Singh Kushwah Sivaneasan Bala Krishnan Kamal Upreti Manoj Kumar Pravin R. Kshirsagar Vinai K. Singh Singapore Institute of Technology SINGAPORE Department of Computer Science School of Sciences Christ University Ghaziabad INDIA Department of Mathematics and Statistics Gurukula Kangri University Haridwar INDIA Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur INDIA Department of Computer Science & Engineering GL Bajaj Group of Institutions Mathura India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Type II diabetes mellitus, on the other hand has been regarded as one of the growing concerns globally and thus clearly raises the need for making accurate forecasts of diabetes. The risk for Type II diabetes can be predicted using Ma-chine Learning as well as any other form to make the predictions much more enhanced than the traditional methods. This paper aims to give a broad overview of literature that has so far been available on the ML algorithms used in the management of Type II diabetes including such supervised algorithms as logistic regression, alphabet regression, random forest, support vector regression along with other methods such as, ensemble learning, deep learning, and hybrid. Analysis of the main aspects for the performance model such as parameter selection, the way to face and cope with imbalance parameters, interpretability and generalizability across different populations, another aspect that was regarded is the possibility of using real-time data collected with wearable devices and applying tissue and other biomarkers for better prediction. Finally, the key obstacles and future directions towards developing ML algorithms and models explainable and clinically relevant have been introduced to help researchers and practitioners toward effective, personalized, and scalable interventions.

关键词： Deep learning Support vector machines Adaptation models Accuracy Biological system modeling Predictive models Prediction algorithms Data models Diabetes Wearable devices

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Privacy Preserving Charge Location Prediction for Electric Vehicles

arXiv

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arXiv 2025年

作者： Marlin, Robert Jurdak, Raja Abuadbba, Alsharif Miller, Dimity School of Computer Science Australia School of Electrical Engineering and Robotics Queensland University of Technology Australia CSIRO’s Data61 Cyber Security Cooperative Research Centre Australia CSIRO’s Data61 Australia

By 2050, electric vehicles (EVs) are projected to account for 70% of global vehicle sales. While EVs provide environmental benefits, they also pose challenges for energy generation, grid infrastructure, and data privacy. Current research on EV routing and charge management often overlooks privacy when predicting energy demands, leaving sensitive mobility data vulnerable. To address this, we developed a Federated Learning Transformer Network (FLTN) to predict EVs’ next charge location with enhanced privacy measures. Each EV operates as a client, training an onboard FLTN model that shares only model weights, not raw data with a community based Distributed Energy Resource Management System (DERMS), which aggregates them into a community global model. To further enhance privacy, non-transitory EVs use peer-to-peer weight sharing and augmentation within their community, obfuscating individual contributions and improving model accuracy. Community DERMS global model weights are then redistributed to EVs for continuous training. Our FLTN approach achieved up to 92% accuracy while preserving data privacy, compared to our baseline centralised model, which achieved 98% accuracy with no data privacy. Simulations conducted across diverse charge levels confirm the FLTN’s ability to forecast energy demands over extended periods. We present a privacy focused solution for forecasting EV charge location prediction, effectively mitigating data leakage risks. © 2025, CC BY-NC-ND.

关键词： Differential privacy

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ADAPTIVE CAMERA SENSOR FOR VISION MODELS

arXiv

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arXiv 2025年

作者： Baek, Eunsu Han, Sung-Hwan Gong, Taesik Kim, Hyung-Sin Graduate School of Data Science Seoul National University Seoul Korea Republic of Department of Computer Science & ngineering Seogang University Seoul Korea Republic of Department of Computer Science & Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of

Domain shift remains a persistent challenge in deep-learning-based computer vision, often requiring extensive model modifications or large labeled datasets to address. Inspired by human visual perception, which adjusts input quality through corrective lenses rather than over-training the brain, we propose Lens, a novel camera sensor control method that enhances model performance by capturing high-quality images from the model’s perspective, rather than relying on traditional human-centric sensor control. Lens is lightweight and adapts sensor parameters to specific models and scenes in real-time. At its core, Lens utilizes VisiT, a training-free, model-specific quality indicator that evaluates individual unlabeled samples at test time using confidence scores, without additional adaptation costs. To validate Lens, we introduce ImageNet-ES Diverse, a new benchmark dataset capturing natural perturbations from varying sensor and lighting conditions. Extensive experiments on both ImageNet-ES and our new ImageNet-ES Diverse show that Lens significantly improves model accuracy across various baseline schemes for sensor control and model modification, while maintaining low latency in image captures. Lens effectively compensates for large model size differences and integrates synergistically with model improvement techniques. Our code and dataset are available at ***/Edw2n/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Federated Learning for Diffusion Models

arXiv

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arXiv 2025年

作者： Peng, Zihao Wang, Xijun Chen, Shengbo Rao, Hong Shen, Cong School of Mathematics and Computer Science Nanchang University Nanchang330000 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China School of Software Nanchang University Nanchang330000 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States

Diffusion models are powerful generative models that can produce highly realistic samples for various tasks. Typically, these models are constructed using centralized, independently and identically distributed (IID) training data. However, in practical scenarios, data is often distributed across multiple clients and frequently manifests non-IID characteristics. Federated Learning (FL) can leverage this distributed data to train diffusion models, but the performance of existing FL methods is unsatisfactory in non-IID scenarios. To address this, we propose FEDDDPM—Federated Learning with Denoising Diffusion Probabilistic Models, which leverages the data generative capability of diffusion models to facilitate model training. In particular, the server uses well-trained local diffusion models uploaded by each client before FL training to generate auxiliary data that can approximately represent the global data distribution. Following each round of model aggregation, the server further optimizes the global model using the auxiliary dataset to alleviate the impact of heterogeneous data on model performance. We provide a rigorous convergence analysis of FEDDDPM and propose an enhanced algorithm, FEDDDPM+, to reduce training overheads. FEDDDPM+ detects instances of slow model learning and performs a one-shot correction using the auxiliary dataset. Experimental results validate that our proposed algorithms outperform the state-of-the-art FL algorithms on the MNIST, CIFAR10 and CIFAR100 datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Leveraging IoT and AI in Precision Agriculture for Efficient Water Management

Leveraging IoT and AI in Precision Agriculture for Efficient...

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Computational, Communication and Information technology (ICCCIT), International Conference on

作者： RVS Praveen Mudit Mittal Prasanta Parida Yogendra Kumar Atul Singla Nagarjuna Thandra Digital Engineering and Assurance LTIMindtree Limited Hyderabad Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India School of Rural Management KIIT University Bhubaneswar Odisha India Department of Electrical Engineering GLA University Mathura India Lovely Professional University Phagwara India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Where water is limited the challenge of getting the best management of the resource become a major issue in farming. In this study, therefore, we focus on the following question: What role may AI and the IoT play in precision agriculture towards enhancing sustainability through the conservation of water resources? IoT devices that can provide information on the environmental conditions include; sensors for moisture level in the soil, and weather systems. To such data artificial intelligence algorithms are applied to automate the process of irrigation, determine possible water consumption and avoid excess. Real-time and historical data can also be evaluated using machine learning approaches of the system. From this it is then able to predict when crops will need water and then align their irrigation times from this information. Moreover, the analytic model using AI may find potential issues with irrigation and present mitigating steps. Juxtaposing IoT and AI, it is revealed in this research how the two technologies can improve water management decisionmaking with the aim of minimizing water depletion as well as over-irrigation. Integrated soil nutrients management and conservation agriculture are recommended and the overall atmospheric water scarce zone sustainable agricultural productivity is enhanced by the proposed framework while increasing water productivity. The study reveals that it is possible to steer the future of water management through the interconnection of IoT and AI in precision agriculture. It may therefore result in higher production, efficiency in resource utilization and ultimate sustainability.

关键词： Precision agriculture Productivity Irrigation Crops Water conservation Soil Internet of Things Artificial intelligence Sustainable development Water resources

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FLyer: Federated Learning-based Crop Yield Prediction for Agriculture 5.0

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Dey, Tanushree Bera, Somnath Mukherjee, Anwesha De, Debashis Buyya, Rajkumar Maulana Abul Kalam Azad University of Technology Department of Computer Science and Engineering West Bengal India Mahishadal Raj College Department of Computer Science West Bengal Mahishadal721628 India The University of Melbourne Research Visitor of the CLOUDS Lab School of Computing and Information Systems Australia Lab School of Computing and Information Systems ParkvilleVIC3052 Australia

Crop yield prediction is a significant area of precision agriculture. In this paper, we propose a crop yield prediction framework named FLyer, based on federated learning and edge computing. In FLyer, the soil and environmental data are locally processed inside the edge servers, and the model parameters are transmitted between the edge servers and the cloud with encrypted gradients. LSTM is used as the local and global models for data analysis. As the LSTM model can capture the temporal dependencies and hold the sequential nature of the data, we use LSTM in FLyer. By encrypting the gradients, the gradient information leakage ratio is reduced, and data privacy is protected. For gradient encryption, we use AES-256, and for data encryption during local storage we use RSA and AES-256. The results demonstrate that FLyer diminishes the latency by 39% and energy consumption by 40% than the conventional edge-cloud framework respectively. The experimental results show that the global model in FLyer achieves above 99% accuracy, precision, recall, and F1-score in crop yield prediction. The results also present that the local models also achieve >94% accuracy in crop yield prediction. © 2020 IEEE.

关键词： Federated learning

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A Theoretical Analysis of Analogy-Based Evolutionary Transfer Optimization

arXiv

引用

arXiv 2025年

作者： Xue, Xiaoming Feng, Liang Feng, Yinglan Liu, Rui Zhang, Kai Tan, Kay Chen Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong College of Computer Science Chongqing University China School of Civil Engineering Qingdao University of Technology China

Evolutionary transfer optimization (ETO) has been gaining popularity in research over the years due to its outstanding knowledge transfer ability to address various challenges in optimization. However, a pressing issue in this field is that the invention of new ETO algorithms has far outpaced the development of fundamental theories needed to clearly understand the key factors contributing to the success of these algorithms for effective generalization. In response to this challenge, this study aims to establish theoretical foundations for analogy-based ETO, specifically to support various algorithms that frequently reference a key concept known as similarity. First, we introduce analogical reasoning and link its subprocesses to three key issues in ETO. Then, we develop theories for analogy-based knowledge transfer, rooted in the principles that underlie the subprocesses. Afterwards, we present two theorems related to the performance gain of analogy-based knowledge transfer, namely unconditionally nonnegative performance gain and conditionally positive performance gain, to theoretically demonstrate the effectiveness of various analogy-based ETO methods. Last but not least, we offer a novel insight into analogy-based ETO that interprets its conditional superiority over traditional evolutionary optimization through the lens of the no free lunch theorem for optimization. 1156v1 [***] 27 Mar Copyright © 2025, The Authors. All rights reserved.

关键词： Optimization algorithms

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation

Multi-Layer Perceptron Regression Model for Material Charact...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Hanima Kannan C H Deepa Gupta Parul Mathur Dhanesh G Kurup Tilesh Sanjay Chaudhari Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Chemical Engineering Creative and Advanced Research Based on Nanomaterials Laboratory Indian Institute of Technology Hyderabad School of Engineering and Applied Science Bennett University Greater Noida U.P. India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an $R^{2}$ of 99.991% and MSE of $6.65 \times 10^{-5}$ , with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions.

关键词： Power engineering Dielectric constant Predictive models Mathematical models Software Scattering parameters Probes Permittivity Standards Load modeling

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A Method for Extracting Small Water Areas Based on an Improved FPN Network

A Method for Extracting Small Water Areas Based on an Improv...

引用

International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yufeng Yu Meixiang Wang Qun Zhao Yong Wang Qingqing Chen Department of Computer and Software Hohai University Nanjing Jiangsu China School of Computer Engineering Nanjing Institute of Technology Nanjing Jiangsu China Bank of Nanjing Co. Jiangsu Nanjing China Aerosun Corporation Information Center Nanjing Jiangsu China

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Small water areas such as river branches, lakes, and ponds are vital components of aquatic ecosystems closely linked to human activities. Extracting small water areas from remote sensing images holds significant importance in water resource planning, investigation, sustainable water resource management, and environmental protection. However, the current small water area extraction methods based on remote sensing often encounter the problems of small target, few features, difficult localization and easy loss of edge features. Therefore, this paper proposes a small water area extraction model based on IFPN_BEM network. The model leverages a Transformer Encoder module with deformable attention mechanisms to globally model deep features of the FPN network and optimizes small water area edges using the BEM module, which further improves the extraction accuracy of small water areas. Experimental results on the GID dataset demonstrate that this model provides more complete and accurate extraction of small water areas, achieving precision, F1 score, and IoU accuracy metrics of 81.3%, 84.4%, and 73.4% respectively. These metrics show improvements of 9.5%, 8.2%, and 8.5% over the original FPN network, outperforming UNet, DeepLabV3+, and the improved FPN + PointRend networks across all metrics.

关键词： Measurement Deformable models Accuracy Attention mechanisms Image edge detection Feature extraction Transformers Data mining Water resources Remote sensing

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A base station microgrid traffic prediction method based on IOOA-CNN-BiLSTM

A base station microgrid traffic prediction method based on ...

引用

3rd International Conference on Smart Energy and Electrical engineering, SEEE 2024

作者： Yan, Ming Guo, Wenhao Xian, Shixuan Qin, Tuanfa School of Electrical Engineering Guangxi University Nanning530004 China School of Computer Electronics and Information Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning530004 China

The rapid advancement of 5G technology has raised significant concerns regarding the energy consumption of base stations for mobile network operators. Integrating traditional base station power supply systems with microgrids to maximize the utilization of renewable energy has demonstrated considerable potential in addressing the energy consumption challenges faced by base stations. However, the inherent randomness of communication traffic loads adversely affects the reliable operation of base station microgrids. To tackle this issue, we propose a traffic prediction model for base station microgrids based on deep learning methods. Initially, a reference scenario for the base station microgrid is established, followed by the employment of an Improved Osprey Optimization Algorithm (IOOA) to enhance convergence speed and mitigate the risk of local optima. Ultimately, the key parameters of the CNN-BiLSTM model are optimized using the IOOA. Experimental results from real datasets corroborate the superiority of the proposed model concerning MAPE and R2 indicators, as well as perform effectively in energy savings. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Energy utilization

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