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Emerging Smart computing and Informatics (ESCI), Conference on

作者： K. Ananthajothi Pravin R. Kshirsagar Tan Kuan Tak Kailash Kumar Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Plant diseases directly affect farm output by lowering grain, fruit, and vegetable quality, threatening global food security. Thus, farmers inspect plant leaves with their eyes. Plant leaf monitoring is unreliable and error-prone. A multitude of deep learning algorithms have been developed to detect plant leaf diseases; however, most rely on low-resolution photos utilizing convolutional neural nets (CNNs). This research seeks to create an improved nine-layer the CNN network model for the accurate classification of leaf-related illnesses. A contrast enhancement approach is used to pre-process plant leaf pictures. Following binary thresholding, the pre-processed pictures are separated into leaf images and abnormality segmented using the "Enhanced U-Net (EU-Net)" approach. A Multilevel Feature Fusion Network Convolutional Neural Network (MFFN-CNN) is employed to categorize illnesses of leaves based on segmented images. The "Hybrid Leader Cat Swarm Optimization(HLCSO)" method improves U-Net parameter optimization. The effectiveness of the leaf diagnostic model is demonstrated experimentally employing a variety of factors through the use of numerous baseline approaches.

关键词： Deep learning Plant diseases Image segmentation Computational modeling Neural networks Convolutional neural networks Particle swarm optimization Informatics Optimization Monitoring

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Wearable Sensor-based Multimodal Physiological Responses of Socially Anxious Individuals in Social Contexts on Zoom

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IEEE Transactions on Affective computing 2025年

作者： Toner, Emma R. Rucker, Mark Wang, Zhiyuan Larrazabal, Maria A. Cai, Lihua Datta, Debajyoti Lone, Haroon Boukhechba, Mehdi Teachman, Bethany A. Barnes, Laura E. University of Virginia Department of Psychology CharlottesvilleVA United States University of Virginia Department of Systems and Information Engineering CharlottesvilleVA United States South China Normal University Aberdeen Institute of Data Science and Artificial Intelligence Guangzhou China Indian Institute of Science Education and Research Bhopal Department of Electrical Engineering and Computer Science Bhopal India Janssen Pharmaceutical Companies of Johnson & Johnson TitusvilleNJ United States

Correctly identifying an individual's social context from passively worn sensors holds promise for delivering justin-time adaptive interventions (JITAIs) to treat social anxiety. In this study, we present results using passively collected data from a within-subjects experiment that assessed physiological responses across different social contexts (i.e, alone vs. with others), social phases (i.e., pre- and post-interaction vs. during an interaction), social interaction sizes (i.e., dyadic vs. group interactions), and levels of social threat (i.e., implicit vs. explicit social evaluation). Participants in the study (N = 46) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale (≥34 out of 80). Univariate paired difference tests, multivariate random forest models, and cluster analyses were used to explore physiological response patterns across different social and non-social contexts. Our results suggest that social context is more reliably distinguishable than social phase, group size, or level of social threat, and that there is considerable variability in physiological response patterns even among distinguishable contexts. Implications for real-world context detection and future deployment of JITAIs are discussed. © 2025 IEEE.

关键词： Wearable sensors

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Beyond Radio: Het-Medium Networks and Its Cross-Medium Communication Paradigms for 6G

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IEEE Network 2025年

作者： Wu, Mingming Han, Yufeng Xiao, Yue Gao, Yulan Li, Shiying Niyato, Dusit Karagiannidis, George K. University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China QianYuan National Laboratory Hangzhou310024 China KTH Royal Institute of Technology Division of Information Science and Engineering Stockholm100 44 Sweden Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Group Electrical and Computer Engineering Dept Thessaloniki54 124 Greece

In the evolving landscape of communication technologies, the integration of Heterogeneous Medium Networks (HetMNets) has emerged as a pivotal progression, signaling a departure from traditional, radio-centric paradigms. As we edge closer to the realization of 6G networks, understanding the intricate dynamics of cross-medium communication (CMC) becomes imperative. This article indicates the multifaceted nature of HetMNets and explores the transformative CMC paradigms shaping the roadmap toward 6G. First, we outline the key performance indicators (KPIs), potential scenarios, and the emerging technologies and applications of 6G wireless systems, which serve as the driving forces behind the development of HetMNets. Subsequently, various communication media are introduced, contrasting their propagation characteristics to underscore the indispensability of HetMNets for the 6G landscape. We then enumerate the general issues involving challenges/problems and potential technical solutions in HetMNets. Moreover, we present an air-sea coordinated CMC system model, serving as an example of HetMNets built upon CMC paradigms. Specifically, we tackle a power allocation challenge, optimizing the transmit power of both underwater acoustic and radio frequency (RF) communication media at the cross-medium bidirectional relay to curtail the outage probability. The corresponding solution highlights the efficacy of the proposed CMC paradigm in supporting the reliability and resource efficiency of HetMNets. © 1986-2012 IEEE.

关键词： Radio communication

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AoI-Sensitive data Forwarding with Distributed Beamforming in UAV-Assisted IoT

arXiv

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arXiv 2025年

作者： Lang, Zifan Liu, Guixia Sun, Geng Li, Jiahui Sun, Zemin Wang, Jiacheng Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Burst-Sensitive Traffic Forecast Via Multi-Property Personalized Fusion in Federated Learning

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IEEE Transactions on Mobile computing 2025年

作者： Xue, Jingjing Sun, Sheng Liu, Min Wang, Yuwei Meng, Xuying Wang, Jingyuan Zhang, JunBo Xu, Ke Chinese Academy of Sciences University of Chinese Academy of Sciences Institute of Computing Technology Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beihang University School of Computer Science and Engineering China Beihang University MIIT Key Laboratory of Data Intelligence and Management Beijing China JD Technology JD iCity China JD Intelligent Cities Research China Tsinghua University Department of Computer Science and Technology Beijing China Zhongguancun Laboratory Beijing China

For distributed network traffic prediction with data localization and privacy protection, Federated Learning (FL) enables collaborative training without raw data exchange across Base Stations (BSs). Nevertheless, traffic across BSs exhibit inherently heterogeneous trend burst and smooth fluctuation properties, but existing FL methods model single-scale series from only one view, which cannot simultaneously capture diverse trend and fluctuation properties, especially distinct burst distributions. In this paper, we propose Personalized Federated Forecasting with Multi-property Self-fusion (P2FMS), which can represent multi-scale traffic properties from different views. With precise multi-property representations, a fusion-level prediction decision is learned for each client in a personalized manner to promptly sense traffic bursts and improve forecasting performance in non-IID settings. Specifically, P2FMS decomposes the traffic series into distinct time scales, based on which, we effectively extract closeness, period, and trend properties from different views. The closeness and period are embedded through global-view representations with spatial correlations, while non-stationary trends are individually fitted from the client-side view. Furthermore, a personalized combiner is designed to accurately quantify the proportion of general fluctuation raws (i.e., closeness and period) and specific trend property in predictions, which enables multi-property self-fusion for each client to accommodate heterogeneous traffic patterns and enhance prediction accuracy. Besides, an alternant training mechanism is introduced to optimize property representation and fusion modules with the convergence guarantee. Extensive experiments on real-world datasets show that P2FMS outperforms status quo methods in both prediction performance and convergence time. © 2002-2012 IEEE.

关键词： Weather forecasting

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MedFILIP: Medical Fine-grained Language-Image Pre-training

arXiv

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arXiv 2025年

作者： Liang, Xinjie Li, Xiangyu Li, Fanding Jiang, Jie Dong, Qing Wang, Wei Wang, Kuanquan Dong, Suyu Luo, Gongning Li, Shuo School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Thoracic Surgery at No. 4 Affiliated Hospital Harbin Medical University Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of computer and control engineering Northeast Forestry University Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH United States

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestX-ray14, VinBigdata, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69%. The code is available in https://***/ PerceptioncomputingLab/MedFILIP. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator

arXiv

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arXiv 2025年

作者： Kou, Wei-Bin Zhu, Guangxu Ye, Rongguang Wang, Shuai Tang, Ming Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen International Center For Industrial And Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the quantity and quality of the annotated training data. However, traditional manual labeling involves high cost to annotate the vast amount of required data for training robust model. To mitigate this cost of manual labeling, we propose a Label Anything Model (denoted as LAM), serving as an interpretable, high-fidelity, and prompt-free data annotator. Specifically, we firstly incorporate a pretrained Vision Transformer (ViT) to extract the latent features. On top of ViT, we propose a semantic class adapter (SCA) and an optimization-oriented unrolling algorithm (OptOU), both with a quite small number of trainable parameters. SCA is proposed to fuse ViT-extracted features to consolidate the basis of the subsequent automatic annotation. OptOU consists of multiple cascading layers and each layer contains an optimization formulation to align its output with the ground truth as closely as possible, though which OptOU acts as being interpretable rather than learning-based blackbox nature. In addition, training SCA and OptOU requires only a single pre-annotated RGB seed image, owing to their small volume of learnable parameters. Extensive experiments clearly demonstrate that the proposed LAM can generate high-fidelity annotations (almost 100% in mIoU) for multiple real-world datasets (i.e., Camvid, Cityscapes, and Apolloscapes) and CARLA simulation dataset. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

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RSRP Measurement Based Channel Autocorrelation Estimation for IRS-Aided Wideband Communication

RSRP Measurement Based Channel Autocorrelation Estimation fo...

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IEEE Conference on Wireless Communications and Networking

作者： He Sun Lipeng Zhu Weidong Mei Rui Zhang Department of Electrical and Computer Engineering National University of Singapore Singapore National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China China School of Science and Engineering Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen Guangdong China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing power-measurement-based IRS reflection designs in wideband channels.

关键词： Power measurement Upper bound OFDM Simulation Channel estimation Estimation Reflection Autocorrelation Wideband Signal to noise ratio

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Peeping into the Future: Understanding and Combating Generative AI-Based Fake News

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Cognitive Computation 2025年第3期17卷 1-33页

作者： Kumar, Sanjeev Sai, Siva Chamola, Vinay Gaur, Aanchal Agarwal, Chitwan Huang, Kaizhu Hussain, Amir University of Illinois Urbana-Champaign IL United States Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Pilani Campus Vidya Vihar Rajasthan Pilani 333031 India Department of Electrical and Electronics Engineering & APPCAIR Birla Institute of Technology and Science Pilani Pilani Campus Vidya Vihar Rajasthan Pilani 333031 India Department of Electonics and Communication Engineering Maharaja Agrasen Institute of Technology New Delhi India Data Science Research Center Duke Kunshan University Suzhou 215316 China School of Computing Edinburgh Napier University Edinburgh EH11 4BN United Kingdom

The widespread dissemination of fake news in the digital age, accelerated by generative artificial intelligence (GAI), presents a significant challenge to the integrity of information in our interconnected world. This review paper comprehensively analyzes the critical concerns surrounding GAI-generated fake news, including its origin, distribution, societal impact, and the complex challenges associated with its detection. The study explores various techniques GAI systems employ to create misleading content, ranging from textual misinformation to deepfake media, highlighting the alarming scope of fake news proliferation. Additionally, this paper examines the difficulties in detecting fake news through natural language processing, image analysis, and audio analysis, discussing both their advancements and inherent limitations. It also addresses ethical concerns tied to detection strategies, such as privacy violations and the potential erosion of public trust. Furthermore, it identifies a crucial gap in current research: the urgent demand for innovative and scalable solutions to combat the overwhelming surge of fake news in the digital ecosystem. Addressing this challenge is essential in mitigating the impact of GAI-generated fake news. One of the most pressing obstacles in the fight against misinformation today is managing the sheer volume of online fabricated content. The rapid and widespread dissemination of such content emphasizes the need for proactive strategies to curtail its influence before it inflicts significant harm. This evident knowledge gap highlights the necessity for continued research and innovation to strengthen digital security and enhance trustworthiness in online spaces. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： DeepFakes Detection Fake news Generative AI

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Modified Term Frequency and Inverse Document Frequency with Optimized Deep Learning Algorithm based Fake News Detection

Modified Term Frequency and Inverse Document Frequency with ...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Rana Veer Samara Sihman Bharattej R Prashanth V Haideer Alabdeli Sunaina Sangeet Thottan S. Ananthi Department of Computer Science Saint Leo University FL USA Department of Electrical and Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial Intelligence and Data Science Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The identification of real and fake news has become difficult task in the recent years due to more similar feature between them. As the issues of fake news are leading the miscommunication to the reads, various automated software has been introduced for the detection of fake news. The existing models tried to advance the identification of the fake new but failed to reach the expected outcomes due to overfitting issues and limited feature handling abilities. To overcome the existing issues the Latent Dirichlet Allocation based Term Frequency and Inverse Document Frequency with Brown-bear Optimization Algorithm-Bidirectional Long Short-Term Memory (LDA based TF-IDF with BOA-BiLSTM) is developed for the detection of fake news. The news data is collected and get cleaned by tokenization and normalization techniques. Then the LDA based TF-IDF model is used for the extraction of significant details of the news text data. Further the BOA-BiLSTM algorithm is used for the detection of fake news. The developed LDA based TF-IDF with BOA-BiLSTM algorithm has exhibited the better result having accuracy of 0.9489 and recall of 0.9012 for ISOT fake news dataset compared to the existing Knowledge Graphs for multi-domain fake news detection (KG-MFEND) model.

关键词： Phase frequency detectors Feature extraction Tokenization data mining Resource management Fake news Optimization Long short term memory Radio spectrum management Overfitting

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