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Hydrogen Energy Storage System:Review on Recent Progress

Energy engineering 2025年第1期122卷 1-39页

作者： MilleniumWong Hadi Nabipour Afrouzi Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang TigaKuchingSarawak93350Malaysia College of Engineering Faculty of ComputingEngineering and the Built EnvironmentBirmingham City UniversityBirminghamWest MidlandsEnglandB47XGUK

A hydrogen energy storage system(HESS)is one of the many risingmodern green innovations,using excess energy to generate hydrogen and storing it for various *** that,there have been many discussions about commercializing HESS and improving it ***,the design and sizing process can be overwhelming to comprehend with various sources to examine,and understanding optimal design methodologies is crucial to optimize a HESS *** that,this review aims to collect and analyse a wide range of HESS studies to summarise recent *** different collections of studies are studied,one was sourced by the main author for preliminary readings,and another was obtained via *** findings from the Web of science platform were also examined for amore comprehensive *** findings include the People’sRepublic of China has been active in HESS research,as most works and active organizations originate from this *** has been mainly researched to support power generation and balance load demands,with financial analysis being the common scope of *** is a common tool used for HESS design,modelling,and optimization as it can handle complex *** neural network(ANN)has the potential to be used to model the HESS,but additional review is required as a formof future *** a commercialization perspective,pressurized hydrogen tanks are ideal for hydrogen storage in a HESS,but other methods can be considered after additional research and *** this review,it can be implied that modelling works will be the way forward for HESS research,but extensive collaborations and additional review are ***,this review summarized various takeaways that future research works on HESS can use.

关键词： Hydrogen energy storage system VOSViewer design review sizing

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A Comprehensive Integrated Security Model with Advanced Access Control for Enhancing WSN computing in IoT Frameworks

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Tehnicki Vjesnik 2025年第3期32卷 1040-1046页

作者： Navaneethan, S. Arun, A. Department of Networking & Communications School of Computing SRM Institute of Science and Technology SRM Nagar Kattan Kulathur Tamilnadu 603203 India Department of Networking & Communications School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Kattan Kulathur Tamilnadu 603203 India

Wireless Sensor Network (WSN) computing has emerged as a pivotal intermediary process addressing latency challenges inherent in Cloud-oriented Internet of Things (IoT) services. However, this advancement brings forth significant security and privacy concerns exacerbated by the proliferation of network nodes and the vast volumes of real-time data generated within IoT frameworks. To mitigate these issues, this research proposes an Integrated Security Model with Advanced Access Control (ISM-AAC) Mechanism. The ISM-AAC integrates Functional Encryption (FE) and Role-Based Access Control (RBAC) to fortify communication security. The model employs a comprehensive approach encompassing Access Control Processes, Integrated Encryption Processes, and Re-Encryption Processes to enhance data confidentiality and integrity. The detailed framework includes phases such as system initialization, user management, and access permission administration, ensuring robust security throughout data transactions. Evaluation of the model's efficacy is conducted based on various security metrics pertinent to WSN computing environments. This study contributes to the advancement of secure computing in IoT by proposing a sophisticated ISM-AAC framework that not only addresses current security challenges but also anticipates future threats in the landscape of WSN computing. The findings underscore the model's effectiveness in safeguarding sensitive IoT data and lay the groundwork for future enhancements in IoT security protocols. © 2025, Strojarski Facultet. All rights reserved.

关键词： Access control models

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Forward Secure Similarity Search Over Encrypted Data for Hamming Distance

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IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 2478-2491页

作者： Wang, Beining He, Kun Huang, Yingying Chen, Jing Zhao, Chenbin Du, Ruiying Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China

Similarity search on encrypted data can identify similar data and handle misspelled keywords in a privacy-preserving manner and thus has received a lot of attention. However, existing schemes suffer from imprecise or predefined distance thresholds, which means that they do not always return the expected search results. Moreover, these schemes either do not consider document addition or lack forward security in this dynamic setting. In this article, we present a Similar Keyword Matching (SKM) framework that accurately calculates the Hamming distance between keywords through a new keyword representation called uni-pos-gram. Based on our framework, we propose a basic scheme for similarity search over encrypted data called SimSE that offers adjustable Hamming distance thresholds and an enhanced scheme called SimSE-F that provides forward security. Security analysis demonstrates that our schemes effectively safeguard the privacy of documents, indexes, and searches. Empirical experiments using real-world datasets demonstrate the efficiency and practical applicability of our schemes. © 2004-2012 IEEE.

关键词： Differential privacy

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Deep Learning Approach for Identifying Red Lesions in Retinal Fundus Imagery 6th

Deep Learning Approach for Identifying Red Lesions in Retina...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Divyashrree, R. Dhawan, Samarth Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9789819774258

One dangerous side effect of diabetes that affects the eyes is called diabetic retinopathy. It happens as a result of alterations in the retina’s blood vessels, which can cause harm and even blindness. The development of red lesions in the retina is one typical sign of diabetic retinopathy. These red lesions may be a sign of microaneurysms or hemorrhages, which are important characteristics to identify and track as diabetic retinopathy progresses. This process becomes even more demanding in the early stages of the disease where symptoms are less pronounced in the images. Leveraging machine-based learning for medical image analysis, especially deep learning algorithms, has demonstrated its capability to enhance the early diagnosis of diabetic retinopathy, providing a more efficient and effective alternative to traditional manual assessments. The proposed work aims to improve the efficiency of early detection of diabetic retinopathy by utilizing Deep Learning (16-layer CNN) to determine a person’s likelihood of having diabetic retinopathy using a 02-Class model that compiles a variety of datasets, specifically IDRiD dataset. The entire dataset contains 18,590 fundus photographs, in which 3662 are allocated for training, 1928 for validation, and 13,000 for testing. All datasets had a similar distribution of output classes, which is a fundamental property of this type of data. Using the IDRiD dataset, the suggested Red Lesion detection system’s specificity and sensitivity were found to be 98% and 89%, respectively, with an accuracy of 95.32%. Using the MESSIDOR dataset, the Diabetic Retinopathy Severity Classification System’s specificity, sensitivity, and accuracy were found to be 93.8, 92.3, and 94%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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EPCNN: Efficient and Practical Privacy-Preserving Convolutional Neural Network Inference

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IEEE Transactions on Network science and engineering 2025年第3期12卷 1567-1580页

作者： He, Gang Ren, Yanli Zhao, Jun Feng, Guorui Zhang, Xinpeng Shanghai University School of Communication and Information Engineering Shanghai200444 China Nanyang Technological University School of Computing and Data Science 639798 Singapore

Convolutional Neural Networks (CNNs), as a powerful tool for efficient inference, have rapidly developed into a Machine Learning as a Service paradigm facilitated by cloud computing. Nevertheless, this service model raises privacy concerns, particularly in scenarios where relying on two non-colluding servers is unfeasible. To address this issue, we present EPCNN, an Efficient and Practical CNN inference scheme, which concurrently ensures data privacy, model privacy, and inference results with only a single server. EPCNN leverages Paillier homomorphic encryption for secure convolution operations on encrypted data and involves minimal client-server interactions. The client only participates in evaluating non-linear activation functions by judging the signs of blinded convolution results to streamline the interactions and enhance the system's practicality. Our security analysis validates the reliability of the proposed scheme, while experimental results demonstrate high inference accuracy comparable to plaintext-based methods. Compared to the state-of-the-art work, EPCNN attains huge improvements in both runtime and communication overhead. © 2013 IEEE.

关键词： Differential privacy

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Three-in-One: Robust Enhanced Universal Transferable Anti-Facial Retrieval in Online Social Networks

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4408-4421页

作者： Lv, Yunna Tang, Long Ye, Dengpan Xie, Caiyun Deng, Jiacheng He, Yiheng Shen, Sipeng Wuhan University School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan430072 China

Deep hash-based retrieval techniques are widely used in facial retrieval systems to improve the efficiency of facial matching. However, it also carries the danger of exposing private information. Deep hash models are easily influenced by adversarial examples, which can be leveraged to protect private images from malicious retrieval. The existing adversarial example methods against deep hash models focus on universality and transferability, lacking the research on its robustness in online social networks (OSNs), which leads to their failure in anti-retrieval after post-processing. Therefore, we provide the first in-depth discussion on robustness in universal transferable anti-facial retrieval and propose Three-in-One Adversarial Perturbation (TOAP). Specifically, we construct a local and global Compression Generator (CG) to simulate complex post-processing scenarios, which can be used to mitigate perturbation. Then, we propose robust optimization objectives based on the discovery of the variation patterns of model’s distribution after post-processing, and generate adversarial examples using these objectives and meta-learning. Finally, we iteratively optimize perturbation by alternately generating adversarial examples and fine-tuning the CG, balancing the performance of perturbation while enhancing CG’s ability to mitigate them. Numerous experiments demonstrate that, in addition to its advantages in universality and transferability, TOAP significantly outperforms current state-of-the-art methods in multiple robustness metrics. It further improves universality and transferability by 5% to 28%, and achieves up to about 33% significant improvement in several simulated post-processing scenarios as well as mainstream OSNs, demonstrating that TOAP can effectively protect private images from malicious retrieval in real-world scenarios. © 2005-2012 IEEE.

关键词： Social networking (online)

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Real-Time On-Site Ground-Motion Prediction Using ResNet

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Seismological Research Letters 2025年第3期96卷 1834-1845页

作者： Yang, Tzu-Yi Chen, Da-Yi Chin, Tai-Lin Mobile Computing and Networking Lab Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Seismological Center of Central Weather Administration Taipei Taiwan

On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023. © Seismological Society of America.

关键词： Convolutional neural networks

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DSAF: A Dual-Stage Attention Based Multimodal Fusion Framework for Medical Visual Question Answering

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SN Computer science 2025年第4期6卷 1-16页

作者： Mukesh, K. Jayaprakash, S.L. Prasanna Kumar, R. Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamilnadu Chennai 601103 India

In the realm of clinical healthcare, medical visual question answering systems emerge as a pivotal innovation that plays a crucial role in clinical decision-making and patient care. They are designed to interpret medical images in response to free-form natural language questions, thereby facilitating a more nuanced and comprehensive understanding of patient diagnostics. This approach seeks to bridge the gap between complex medical imagery and the clinical questions they prompt, offering a novel avenue for enhancing diagnostic accuracy and efficiency. We introduce the Dual Stage Attention-based Fusion framework, which leverages an advanced feature fusion strategy, along with image encoding techniques and state-of-the-art biomedical language models, to process and analyze medical images and textual queries concurrently. By employing a dual-stage attention mechanism, the proposed model enhances feature extraction and integration, enabling a more accurate and refined analysis of both visual and textual data. The model specifically addresses the challenges commonly associated with medical visual question answering, including the intricate nature of medical imagery and the scarcity of high-quality, annotated medical datasets. The proposed multimodal augmentation technique, involving an autoencoder and generative language model, significantly enriches the data for model training which mitigates the data scarcity issue and enhances the model’s ability to generalize across different medical queries. Empirical validation of our model on the VQA-RAD and SLAKE datasets demonstrates its exceptional performance, achieving state-of-the-art accuracies of 80.8% and 86.2% respectively. These results underscore the model’s potential in AI-enhanced medical diagnostics and decision support systems to offer unprecedented diagnostic insights and patient care solutions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data fusion Dual-stage attention Multimodal learning Vision language model Visual reasoning

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Deep learning based Image Compression, Encryption, and Recovery using Chaotic map and Compressive sensing 5

Deep learning based Image Compression, Encryption, and Recov...

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5th International Conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Tarun, R.G. Aggarwal, Priyanshu Ashwini, K. Amrita Vishwa Vidhyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522100

Images are present in everything these days, from satellite photography to medical scans, and they have a profound impact on how people view and understand the world. The daily generation of an ever-increasing volume of image data has made it imperative to identify effective ways to convey these images. This research presents a novel picture recovery approach that makes use of Chaotic Maps for increased security and Compressive Sensing for effective compression. Compressive Sensing makes use of the sparsity seen in images to minimize the number of data points required. Chaotic Map is used in encryption, is also to guarantee the security of the compressed data. The Neural network model built to recover images is a hybrid of Conditional Generative Adversarial Networks (C-GANs), patch discriminator and U-net generator. This allows for high-quality image reconstruction. This approach is suitable for usage in healthcare and marine life. © 2025 IEEE.

关键词： Image compression

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A Hybrid Approach to Dynamic Thread Allocation Driven by Workload and Task Prioritization

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SN Computer science 2025年第3期6卷 1-19页

作者： Abirami, K. Harini, N. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India

In today’s rapidly changing world, cloud service providers face numerous challenges in managing resources and meeting customer demands. To address these challenges, cloud service providers should prioritize the tasks and allocate threads efficiently. By allocating threads efficiently, service providers can optimize resource utilization and reduce response times and job completion times which in turn improves customer satisfaction. We propose a hybrid model that integrates the threshold-driven thread Balancer (TDTB) for dynamic thread allocation with a Customer Retention Model (CRM) to prioritize tasks based on customer behaviour. Our model initiates the establishment of a thread pool configured with a dynamic threshold based on workload (on-demand requests). Concurrently, a queue to handle incoming customer requests is set up alongside a CRM priority queue designed for task prioritization. Our model calculates Job duration and completion times for n priority classes. Crown consumers experience shorter durations and completion times compared to other priority classes. Tasks are completed in descending order of priority. The results of thorough experimentation clearly show that crown consumer tasks are highly satisfied. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Customer segmentation Response time Service differentiation Thread allocation Throughput Workload

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