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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

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

ISBN: (纸本)9798331509675

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. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Deep Transfer Learning for Multiclass Dermatology Classification: A Comprehensive Study 3rd

Deep Transfer Learning for Multiclass Dermatology Classifica...

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3rd International Conference on Emerging Electronics and Automation, E2A 2023

作者： Rao, S. V. Achuta Murty, P. T. Satyanarayana Maram, Balajee Garg, Ankit Daniya, T. Kumar, B. Santhosh Data Science Research Laboratories Sree Dattha Institute of Engineering and Science Sheriguda Telangana Hyderabad501 510 India Shri Vishnu Engineering College for Women Bhimavaram India AIT- Department of Computer Science and Engineering Chandigarh University Punjab Gharuan Mohali140055 India GMR Institute of Technology Rajam532127 India Telangana Ibrahimpatnam501506 India

ISBN: (纸本)9789819768011

Accurate and prompt diagnosis is crucial to the treatment of patients with dermatological diseases, which are a significant cause for worry in the medical community. By automating the diagnosing process, deep learning algorithms have recently demonstrated significant promise in the field of dermatology. In-depth research on the use of deep transfer learning for multiclass dermatological classification is presented in this publication. To train deep neural networks, the study makes use of a broad and comprehensive dermatology dataset that includes a variety of skin disorders. Modern pre-trained models are used with transfer learning techniques to make it easier to extract discriminative features. In order to improve classification performance, we delve into the complex process of model fine-tuning and investigate data augmentation techniques. Our research shows that multiclass dermatology classification accuracy has significantly improved, with models delivering reliable findings for a variety of skin disorders. We offer a thorough evaluation of the model's performance, emphasizing its ability to distinguish between disorders that appear to be visually identical and assisting dermatologists in their diagnostic procedures. The field of dermatology will be significantly impacted by the study's findings. Our work helps to the creation of precise, effective, and available dermatological diagnostic tools by bridging the gap between conventional diagnosis techniques and cutting-edge deep learning technologies. We also go over potential difficulties, ethical issues, and prospective future research routes, highlighting how crucial it is to keep this field innovative. This thorough study highlights the importance of artificial intelligence in improving patient care in the field of dermatology and the possibility of deep transfer learning to revolutionize dermatology diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis

arXiv

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arXiv 2025年

作者： Kumar, Akash Parhi, Rahul Belkin, Mikhail Department of Computer Science and Engineering University of California San Diego United States Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Science and Engineering Halıcıoğlu Data Science Institute University of California San Diego United States

Recent works have characterized the function-space inductive bias of infinite-width bounded-norm single-hidden-layer neural networks as a kind of bounded-variation-type space. This novel neural network Banach space encompasses many classical multivariate function spaces including certain Sobolev spaces and the spectral Barron spaces. Notably, this Banach space also includes functions that exhibit less classical regularity such as those that only vary in a few directions. On bounded domains, it is well-established that the Gaussian reproducing kernel Hilbert space (RKHS) strictly embeds into this Banach space, demonstrating a clear gap between the Gaussian RKHS and the neural network Banach space. It turns out that when investigating these spaces on unbounded domains, e.g., all of d, the story is fundamentally different. We establish the following fundamental result: Certain functions that lie in the Gaussian RKHS have infinite norm in the neural network Banach space. This provides a nontrivial gap between kernel methods and neural networks by the exhibition of functions in which kernel methods can do strictly better than neural networks. © 2025, CC BY.

关键词： Sobolev spaces

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FEDERATED INVERSE PROBABILITY TREATMENT WEIGHTING FOR INDIVIDUAL TREATMENT EFFECT ESTIMATION

arXiv

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arXiv 2025年

作者： Yin, Changchang Chen, Hong-You Chao, Wei-Lun Zhang, Ping Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Department of Computer Science and Engineering Department of Biomedical Informatics Translational Data Analytics Institute The Ohio State University ColumbusOH United States

Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most existing ITE estimation methods are designed for centralized settings. However, in real-world clinical scenarios, the raw data are usually not shareable among hospitals due to the potential privacy and security risks, which makes the methods not applicable. In this work, we study the ITE estimation task in a federated setting, which allows us to harness the decentralized data from multiple hospitals. Due to the unavoidable confounding bias in the collected data, a model directly learned from it would be inaccurate. One well-known solution is Inverse Probability Treatment Weighting (IPTW), which uses the conditional probability of treatment given the covariates to re-weight each training example. Applying IPTW in a federated setting, however, is non-trivial. We found that even with a well-estimated conditional probability, the local model training step using each hospital’s data alone would still suffer from confounding bias. To address this, we propose FED-IPTW, a novel algorithm to extend IPTW into a federated setting that enforces both global (over all the data) and local (within each hospital) decorrelation between covariates and treatments. We validated our approach on the task of comparing the treatment effects of mechanical ventilation on improving survival probability for patients with breadth difficulties in the intensive care unit (ICU). We conducted experiments on both synthetic and real-world eICU datasets and the results show that FED-IPTW outperform state-of-the-art methods on all the metrics on factual prediction and ITE estimation tasks, paving the way for personalized treatment strategy design in mechanical ventilation usage. © 2025, CC BY-NC-SA.

关键词： Inverse problems

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Incentivized Federated Learning and Unlearning

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IEEE Transactions on Mobile Computing 2025年

作者： Ding, Ningning Sun, Zhenyu Wei, Ermin Berry, Randall Data Science and Analytics Thrust Internet of Things Thrust Information Hub Guangzhou511453 China Northwestern University Department of Electrical and Computer Engineering EvanstonIL60208 United States Northwestern University Electrical and Computer Engineering Department Industrial Engineering and Management Sciences Department EvanstonIL60208 United States

To protect users' right to be forgotten in federated learning, federated unlearning aims at eliminating the impact of leaving users' data on the global learned model. The current research in federated unlearning mainly concentrates on developing effective and efficient unlearning techniques. However, the issue of incentivizing valuable users to remain engaged and preventing their data from being unlearned is still under-explored, yet important to the unlearned model performance. This paper focuses on the incentive issue and develops an incentive mechanism for federated learning and unlearning. We first characterize the leaving users' impact on the global model accuracy and the required communication rounds for unlearning. Building on these results, we propose a four-stage game to capture the interaction and information updates during the learning and unlearning process. A key contribution is to summarize users' multi-dimensional private information into one-dimensional metrics to guide the incentive design. Interestingly, we prove that allowing federated unlearning can result in reduced payoffs for both the server and users, compared to a scenario without unlearning. Numerical results demonstrate the necessity of unlearning incentives for retaining valuable leaving users, and also show that our proposed mechanisms decrease the server's cost by up to 53.91% compared to state-of-the-art benchmarks. © 2002-2012 IEEE.

关键词： federated learning federated unlearning incentive mechanism

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CAMP-GNN: A Constraint-Aware Message-Passing Model for Optimal Resource Allocation in Software Projects

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IEEE Access 2025年 13卷 87965-87977页

作者： Muhammad Asif Fahd M. Aldosari Donia Y. Badawood Abdu Salam Department of Computer Science Bacha Khan University Charsadda Chārsadda Pakistan Department of Computer and Networks Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Data Science Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Abdul Wali Khan University Mardan Mardan Pakistan

Resource allocation in software projects is a critical challenge, with inefficiencies in handling dynamic constraints such as skill mismatches, evolving task dependencies, and budget overruns leading to widespread cost and schedule overruns. To address these issues, this paper introduces CAMP-GNN, a novel constraint-aware graph neural network (GNN) framework designed to optimize resource allocation in software projects, addressing challenges like skill mismatches, evolving task dependencies, and budget overruns. These issues often lead to significant cost and schedule overruns, and CAMP-GNN aims to resolve them by dynamically adapting to project constraints. The framework integrates urgency, skill alignment, and budget slack directly into its message-passing layers, allowing for more precise and context-aware task-resource assignments. A key contribution is SoftStat, the first structured dataset that combines resource pools, allocations, and project details, enabling the modeling of complex task-resource dependencies and financial/temporal constraints. CAMP-GNN also introduces domain-specific message aggregation rules to unify urgency, skill alignment, and budget slack, advancing GNNs beyond basic relational modeling. A novel preprocessing pipeline is proposed to convert raw project timelines, skill dependencies, and budget thresholds into machine-learnable features, ensuring effective integration with computational models. Validated on both synthetic and real-world datasets, CAMP-GNN demonstrates superior performance with a 0.12 MAE, RMSE of 0.18 in allocation, F1 of 0.89 in budget violations, and 085 in deadline violations, 18% cost savings, and a 27% reduction in deadline breaches, outperforming existing methods. Future work will focus on scaling the framework for large-scale projects and improving interpretability for broader adoption.

关键词： Resource management Software Optimization Costs Mathematical models Computational modeling Dynamic scheduling Project management Deep learning data models

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A Systematic Review on Cyber Security Forensics: An AI-Driven Approach 12th

A Systematic Review on Cyber Security Forensics: An AI-Drive...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Bogiri, Nagaraju Limkar, Suresh Dhage, Prashant Sharma, Rahul Choudhari, Amit More, Amar Hemant Department of Computer Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Artificial Intelligence and Data Science AISSMS Institute of Information Technology Maharashtra Pune India Nagpur Campus Pune India Department of Electronics and Communication Arya College of Engineering Rajasthan Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Maharashtra Pune India Maharashtra Pune India

ISBN: (纸本)9789819601462

The growing threat posed by cybercriminals to an organization’s digital security is driving it to look for more intelligent ways to protect the system. Artificial intelligence (AI) is being used by many businesses to safeguard their resources. data indicates that over the past few years, the use of AI in cyber security has increased rapidly across the globe, indicating that an increasing number of businesses are looking for more efficient solutions than the conventional ones. However, some people are hesitant to put it into practice. Previous studies indicate that the cyber security branch discusses this issue. In order to better grasp AI’s potential applications and the difficulties that organizations confront, researchers are working to learn more about it. A wide range of businesses, including those that use AI-based cyber security solutions and those that do not, were questioned about their perspectives on the subject using semi-structured interviews as the main research approach. The study aims to understand the perceptions of artificial intelligence (AI) in cyber security, as well as the perceived risks and opportunities connected with AI adoption. Additionally, the research will investigate prospective avenues for future research and development in this area. The study comes to the conclusion that businesses should think about the ethical and legal ramifications as well as the significance of selecting the appropriate AI solutions when expressing interest in strengthening their security using AI. In light of the growing and dynamic nature of cyber threats, experts advise businesses looking to strengthen their cyber security defenses to consider implementing artificial intelligence (AI). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels

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IEEE Internet of Things Journal 2025年第11期12卷 17123-17136页

作者： Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal Peng Cheng Laboratory Shenzhen518066 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi'an710071 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multi-user interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multi-user interference. More importantly, SFDMA can protect the privacy of users' semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta-Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme. © 2014 IEEE.

关键词： Image reconstruction

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Label-Free Biomolecule Detection With InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate

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IEEE Access 2025年 13卷 89667-89684页

作者： Hashim Elshafie Abdulrahman Saad Alqahtani Azath Mubarakali Adarsh Rag S M. Venkatesh P. Parthasarathy Department of Computer Engineering College of Computer Science King Khalid University Abha Saudi Arabia College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Electronics and Communication Engineering CMR Institute of Technology Bengaluru Karnataka India

This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of $2.5 \times 10$ cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of $3.5 \times 10 ^{3}$ , outperforming configurations without overlap ( $2 \times 10 ^{3}$ ), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection.

关键词： Biosensors Logic gates Sensitivity Tunneling Dielectric constant Performance evaluation Metals Biological system modeling TFETs Electrostatics

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Interference-Based Reliability and Capacity Analysis for IEEE 802.11 Broadcast Ad-Hoc Networks on the Highway 11

Interference-Based Reliability and Capacity Analysis for IEE...

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11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Li, Zhijuan Wu, Xintong Li, Xiaokun Ma, Xiaomin Department of Computer and Big Data Heilongjiang University Harbin150080 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd. Harbin150090 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China College of Science and Engineering Oral Roberts University TulsaOK74171 United States

ISBN: (纸本)9789897587450

Interference is a critical factor that degrades wireless network performance. In IEEE 802.11 wireless broadcast networks, hidden terminals and concurrent transmissions are the primary sources of interference due to the carrier sense multiple access (CSMA) mechanism. Therefore, analyzing the signal-to-interference-plus-noise ratio (SINR) distribution is essential for evaluating network performance, whose derivation relates to the transmission probabilities of hidden terminals and concurrent transmissions. In this study, we utilize the existing semi-markov process (SMP) model to calculate these transmission probabilities. Subsequently, we employ the Laplace transform to analyze the SINR distribution in IEEE 802.11 broadcast ad-hoc networks on highways. Based on the derived SINR distribution, we further evaluate the reliability and capacity. This approach can be readily extended to two-dimensional (2D) or three-dimensional (3D) scenarios by employing d-dimensional (1 ≤ d ≤ 3, dD) point process. Experimental results demonstrate that the proposed model achieves high accuracy under small to medium interference ranges. Additionally, the analysis remains highly accurate for receivers within 70 meters, even in scenarios with large interference ranges. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Markov processes

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