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

作者： Ansari, Khustar Srivastava, Priyanka Sarala Birla University Jharkhand Ranchi835103 India Bit Sindri Dhanbad Department of Computer Science and Engineering Jharkhand Sindri828123 India Sarala Birla University Computer Science and Engineering Jharkhand Ranchi835103 India

ISBN: (纸本)9798331509675

Video caption generation is an extremely challenging computer vision task that automatically generates the caption for video clips with a clear understanding of the embedded semantics. Due to the extensive use of images for conveying large volumes of information, there exists a huge demand for image analytics in information processing systems. Various techniques are designed for automatic image caption generation to solve the computer vision challenges, but highlighting the contextual information more accurately still results in a challenging task. To overcome such issues, a Hybrid Attention Vision Transformer-based Convolutional Neural Network-Gated Recurrent Unit-Bidirectional Long Short-Term Memory (HAVisT-CNN-GRU-BiLSTM) is developed for video caption generation. Specifically, the Vision transformer (VisT) effectively captures the global dependencies and learns the intricate relationships present in the image. Moreover, the proposed approach combines the salient information extracted from the CNN, GRU, and BiLSTM models resulting in less memory usage, high dimensional data processing, and improved convergence speed. Moreover, the hybrid attention-based Vision transformer facilitates capturing the global context information and enhances computational efficiency. The experimental results demonstrate that the HAVisT-CNN-GRU-BiLSTM model achieves the efficient metric values of BLEU, METEOR, and ROUGE as 0.48, 0.28, and 0.71 respectively outperforming the other existing techniques. © 2025 IEEE.

关键词： Convolutional neural networks

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Advancements in Efficient Underwater Image Restoration Using ETransMapNet for Enhanced Dehazing

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Tehnicki Vjesnik 2025年第3期32卷 1125-1132页

作者： Indumathi, C.P. Alshahrani, Haya Mesfer N.A., Natraj C.H., Sarada Devi Department of Computer Science and Engineering University College of Engineering BIT Campus Anna University Tamilnadu Tiruchirapalli24 India Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Maharashtra Pune India Department of CSE Meenakshi College of Engineering India

Underwater (UW) information is essential for advancing human exploration and utilization of the underwater world, including fields such as UW Paleology, UW Target Detection, UW Object Tracking, UW Surveillance, and related activities. Visual media like movies and images enhance our natural understanding of underwater objectives. In the past decade, underwater photo restoration and enhancement have gained increasing attention. This study proposes a novel approach employing the recently developed Convolutional Neural Network (CNN) for dehazing, named ETransMapNet (Efficient Transmission Map Network). ETransMapNet is designed with convolution layers and nonlinear activations to execute four sequential processes: nonlinear regression, local maxima detection, multi-scale decomposition, and convolutional feature extraction. Unlike traditional CNNs, ETransMapNet replaces the initial layer's Rectified Linear Unit (ReLU) activation with a convolution layer utilizing a Maxout activation function. ETransMapNet extracts features using three convolution kernels of different sizes (3 × 3, 5 × 5, and 7 × 7). The method suppresses noise in the estimated transmittance map, while local extremum values maintain local consistency within the transmittance map. This study adopts Bilateral ReLU (BReLU) for normalizing network outputs within a 0 to 1 range. Additionally, Adaptive Bilateral Filtering (ABF) is applied to remove redundant artifacts from the predicted transmission map. White balancing addresses color divergence, and Laplacian pyramid fusion combines the color-corrected and dehazed images. In the final stage, the resultant image is transformed into the Wavelet and Directional Filter Banks (WDFB) domain for denoising and edge enhancement. Performance metrics reveal that the proposed ETransMapNet approach improves performance by 38%-50% compared to previous methods. © 2025, Strojarski Facultet. All rights reserved.

关键词： Underwater photography

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Cyber-Resilient Detection of Power Quality Events With NSCT and PCA-SVM

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IEEE Access 2025年 13卷 72292-72313页

作者： Chatterjee, Sayanti Sinha, Pampa Gatla, Ranjith Kumar Kumar, Devineni Gireesh Rao, D. S. Naga Malleswara Paul, Kaushik Ustun, Taha Selim Onen, Ahmet Department Hyderabad500090 India KIIT Deemed to be University School of Electrical Engineering Bhubaneswar751024 India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad500043 India B. V. Raju Institute of Technology Department of EEE Narsapur502313 India Gokaraju Rangaraju Institute of Engineering and Technology EEE Department Hyderabad500090 India BIT Sindri Electrical Engineering Department Dhanbad828123 India Fukushima Renewable Energy Institute AIST Koriyama963-0298 Japan University of Doha for Science and Technology College of Engineering and Technology Doha Qatar

The increasing reliance on smart grids, coupled with the integration of renewable energy and growing cyber-physical interactions, has heightened the vulnerability of power systems to both power quality (PQ) disturbances and cyber-attacks. This paper presents an innovative detection framework that combines Nonsubsampled Contourlet Transform (NSCT) with Principal Component Analysis (PCA) and Support Vector Machine (SVM) classification to accurately detect and classify PQ disturbances under the influence of cyber threats, such as False Data Injection (FDI) and Denial of Service (DoS) attacks. The proposed methodology leverages NSCT’s multiscale decomposition capabilities to extract fine-grained signal features, while PCA optimizes feature selection for enhanced computational efficiency. Comprehensive experiments conducted on synthetic and real-world datasets validate the framework’s effectiveness, demonstrating superior detection accuracy, robustness to noise, and resilience against cyber-attacks. The proposed NSCT-PCA- SVM approach represents a significant step forward in ensuring secure and reliable smart grid operations. © 2013 IEEE.

关键词： Cyber attacks

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When Generative AI Meets Semantic Communication: Optimizing Radio Map Construction and Distribution in Future Mobile Networks

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IEEE Network 2025年第3期39卷 47-55页

作者： Zhou, Li Deng, Xinfeng Ning, Zhaolong Zhao, Haitao Wei, Jibo Leung, Victor C. M. National University of Defense Technology College of Electronic Science and Technology Changsha410073 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shenzhen MSU-BIT University AI Research Institute Shenzhen518000 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518000 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

With the rapid development of the internet of things and smart cities, the demand for effective spectrum collaboration has grown *** maps play a crucial role in understanding the spatial radio environment, which is essential for wireless applications such as cell planning and radio resource management. However, generating radio maps is a resource-intensive task, as the construction process is complex and the distribution process consumes substantial bandwidth. This article proposes a method that combines semantic communication (SemCom) and generative artificial intelligence (GAI) to optimize the construction and distribution processes of radio maps. By incorporating SemCom, our method transmits only key semantic information during the distribution process, significantly reducing bandwidth usage while ensuring accurate and efficient information transfer. While the diverse generative capabilities of GAI introduce some instability, which could be a limitation in radio map construction, this article achieves precise content decoding through prompts based on multi-modal semantic information, enabling accurate construction and restoration of radio maps. By utilizing SemCom and GAI technologies, the cost of applications of radio maps can be significantly reduced, which is beneficial for stimulating the pace of intelligence evolution in future mobile networks. © 1986-2012 IEEE.

关键词： Bandwidth

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iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations

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BMC Biology 2025年第1期23卷 1-14页

作者： Wei, Hang Hou, Jialu Liu, Yumeng Shaytan, Alexey K. Liu, Bin Wu, Hao School of Computer Science and Technology Xidian University Shaanxi Xi’an 710126 China SMBU-MSU-BIT Joint Laboratory On Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Guangdong Shenzhen 518172 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen 518118 China Department of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer Science HSE University Moscow 109028 Russian Federation Zhongguancun Academy Beijing 100094 China

Background: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns. Results: In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. Additionally, it integrates their refined and macroscopic inferences to derive the final prediction result. Conclusions: The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance. © The Author(s) 2025.

关键词： Graph ensemble learning Local context graph piRNA-disease association identification

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Hybrid Attention Vision Transformer-based Deep Learning Model for Video Caption Generation

Hybrid Attention Vision Transformer-based Deep Learning Mode...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Khustar Ansari Priyanka Srivastava Sarala Birla University Ranchi Jharkhand India Department of Computer Science and Engineering BIT Sindri Dhanbad Sindri Jharkhand India Computer Science and Engineering Sarala Birla University Ranchi Jharkhand India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

关键词： Deep learning computer vision Computational modeling Semantics Bidirectional long short term memory Transformers Hybrid power systems Vectors Convolutional neural networks Meteors

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ICAA-Mamba: Vision Mamba for Image Color Aesthetics Assessment

ICAA-Mamba: Vision Mamba for Image Color Aesthetics Assessme...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qinghao Xie Wenchao Jiang Zhihua Wang Qiuping Jiang School of Computer Science Guangdong University of Technology Guangzhou China Department of Engineering Shenzhen MSU-BIT University Shenzhen China School of Information Science and Engineering Ningbo University Ningbo China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Image Color Aesthetics Assessment (ICAA) focuses on evaluating the aesthetic quality of color composition within images. This task involves analyzing and quantifying the visual appeal of color arrangements, taking into account factors such as harmony, contrast, and balance, to provide an objective assessment of color aesthetics. In this paper, we investigate the application of the State Space Model (Mamba) to the ICAA task, with a focus on exploring the perceptual capabilities of vision Mamba. To this end, we propose a novel framework that employs Mamba as the backbone to extract informative patterns from images, leveraging its global receptive field and linear complexity with respect to input length. Instead of relying solely on a final-layer feature, followed by fully connected layers for quality prediction, we exploit a comprehensive set of multi-scale features, thereby constructing a richer global representation of color information. To fuse these multi-scale features effectively, we introduce a Weighted Feature Fusion Module (WFFM), which adaptively assigns weights to emphasize salient color-related information. We conduct extensive experiments on two benchmark datasets, ICAA17K and SPAQ, successfully demonstrating its effectiveness for the ICAA task. The code is available at https://***/qinghaoya/icaa-mamba.

关键词： Deep learning Visualization Codes Image color analysis Fuses Signal processing Feature extraction Complexity theory Data mining Speech processing

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Deep Reinforcement Learning-Based Computation Rate Maximization for RIS-Aided Edge Computing in Wireless Consumer Application Networks

Deep Reinforcement Learning-Based Computation Rate Maximizat...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Tong Liu Dongyang Xu Tiantian Zhang Shilong Zhang Jinhua Chen Keping Yu Victor C. M. Leung Graduate School of Science and Engineering Hosei University Japan School of Information and Communications Engineering Xi'an Jiaotong University China Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Computer Science and Software Engineering Shenzhen University China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The accelerated development of the Industrial Internet of Things (IoT) has significantly enhanced the computational efficiency and processing capabilities of Wireless Consumer Application Networks (WCAN). Despite these advancements, the performance of WCAN remains constrained by the inherent limitations in computational capacity and energy resources of wireless devices. To address these issues, this paper proposes a wireless powered-mobile edge computing system with Reconfigurable Intelligent Surface (RIS) assistance for WCAN, where wireless consumer devices can not only harvest energy from access point, but also offload computing tasks to edge server with the help of RIS. Aiming to maximize the system computation rate, a non-convex problem is developed and a block optimization with deep reinforcement learning (BO-DRL) scheme is proposed. First, the RIS phase shifts in the Wireless Power Transfer (WPT) stage and offloading stage are optimized using closed-form solutions and the gradient ascent algorithm, respectively. Subsequently, the offloading strategies are optimized using deep reinforcement learning, and a convex optimization algorithm is employed to obtain the time allocation for the WPT and offloading stages. Numerical results illustrate that the BO-DRL algorithm greatly enhances computation rate by 20% compared to without RIS.

关键词： Wireless communication Training Performance evaluation Wireless power transfer Reconfigurable intelligent surfaces Deep reinforcement learning Convex functions Resource management Servers Uplink

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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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Secure Energy Efficiency for ARIS Networks with Deep Learning: Active Beamforming and Position Optimization

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5282-5296页

作者： Wang, Dawei Wang, Zijun Zhao, Hongbo Zhou, Fuhui Alfarraj, Osama Yang, Weichao Mumtaz, Shahid Leung, Victor C. M. Northwestern Polytechnical University School of Electronics and Information Shaanxi Xi'an710072 China Beihang University School of Electronic and Information Engineering Beijing100191 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210000 China King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia Nottingham Trent University Department of Computer Science United Kingdom Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Incorporating an active reconfigurable intelligent surface on an unmanned aerial vehicle (UAV), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in UAVs, energy management emerges as a key challenge within UAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the UAV's position and designing the active beamforming. For the UAV's position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the UAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in UAV networks. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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