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Finger-vein Secure Access Based on Lightweight Dual-attention Convolutional Neural Network for Quality Distance Education

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Sensors and Materials 2024年第3期36卷 945-955页

作者： Ke, Liang-Ying Hsia, Chih-Hsien Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan Department of Business Administration Chaoyang University of Technology Taichung Taiwan

As global average fertility rates decline annually, a crisis brought by declining birthrates gradually emerges, further impacting the existing education system. In response to the impact of declining birthrates on education, countries have recently accelerated the development of distance learning. Distance learning provides guaranteed access to comprehensive and quality education while encouraging continuous learning opportunities for everyone. However, the identity recognition technology used in the current distance learning platform seldom addresses the problem of inconsistent image quality in actual scenarios, making it difficult to ensure the practical application of the model. To solve these issues, we propose a lightweight dual-attention convolutional neural network (LDA-FV) constructed through a dual attention-based inverted residual block (DA-IRB) and implemented using an adaptive margin loss (AML) function for finger-vein recognition. This method not only extracts effective finger-vein features through DA-IRB but also adjusts the training difficulty in accordance with the image quality. Furthermore, owing to its lightweight design, the model can be more flexibly deployed on existing hardware devices (e.g., mouse) in distance education scenarios. The experimental results of this study indicate that the proposed method effectively enhanced the model's recognition capability using the finger-vein database of the University Sains Malaysia (FVUSM) and the PLUSVein dorsal-palmar finger-vein (PLUSVein-FV3) public database, achieving correct identification rates (CIRs) of 99.90% and 97.50%, respectively. © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Convolution

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An Investigation on Open-RAN Specifications:Use Cases,Security Threats,Requirements,Discussions

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computer Modeling in engineering & sciences 2024年第10期141卷 13-41页

作者： Heejae Park Tri-HaiNguyen Laihyuk Park Department of Computer Science and Engineering Seoul National University of Science and TechnologySeoul01811Republic of Korea Faculty of Information Technology School of TechnologyVan Lang UniversityHo Chi Minh City70000Vietnam

The emergence of various technologies such as terahertz communications,Reconfigurable Intelligent Surfaces(RIS),and AI-powered communication services will burden network operators with rising infrastructure ***,the Open Radio Access Network(O-RAN)has been introduced as a solution for growing financial and operational burdens in Beyond 5G(B5G)and 6G networks.O-RAN promotes openness and intelligence to overcome the limitations of traditional *** disaggregating conventional Base Band Units(BBUs)into O-RAN Distributed Units(O-DU)and O-RAN Centralized Units(O-CU),O-RAN offers greater flexibility for upgrades and network ***,this openness introduces new security challenges compared to traditional *** existing studies overlook these security requirements of the O-RAN *** gain deeper insights into the O-RAN system and security,this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G *** then delve into specifications of O-RAN security threats and requirements,aiming to mitigate security vulnerabilities *** providing a comprehensive understanding of O-RAN architecture,use cases,and security considerations,thisworkserves as a valuable resource for future research in O-RAN and its security.

关键词： O-RAN architecture use cases security issues security requirements security discussions

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Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

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Tsinghua science and technology 2024年第5期29卷 1283-1299页

作者： Wei Li Xiangfang Yan Ying Huang School of Information Engineering Jiangxi University of Science and TechnologyGanzhou 341000China School of Mathematics and Computer Science Gannan Normal UniversityGanzhou 341000China

With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing *** Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research ***,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this *** algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for *** problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing ***,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its *** KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in *** effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.

关键词： Ant Colony Optimization(ACO) Job shop Scheduling Problem(JSP) knowledge learning cooperative guidance

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Machine and deep learning techniques for the prediction of diabetics: a review

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Multimedia Tools and Applications 2025年第18期84卷 19425-19549页

作者： Modak, Sandip Kumar Singh Jha, Vijay Kumar Department of Computer Science & Engineering Sarala Birla University Jharkhand Ranchi India Department of Computer Science & Engineering Birla Institute of Technology Mesra Ranchi India

Diabetes has become one of the significant reasons for public sickness and death in worldwide. By 2019, diabetes had affected more than 463 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 700 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multi factorial metabolic disease, its diagnostic criteria are difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of the medical diagnosis process. With the development of Data mining, researchers find that machine learning and deep learning, playing an important role in diabetes prediction research. This paper is an in-depth study on the application of machine learning and deep learning techniques in the prediction of diabetics. In addition, this paper also discusses the different methodology used in machine and deep learning for prediction of diabetics since last two decades and examines the methods used, to explore their successes and failure. This review would help researchers and practitioners understand the current state-of-the-art methods and identify gaps in the literature. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data mining

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Con-Fog: Consensus-Driven Fog Node Selection in FU-Serve Platform for IoT Applications

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IEEE Internet of Things Journal 2025年第13期12卷 24686-24697页

作者： Imandi, Raju Roy, Arijit Sethi, Kamalakanta Pavan, Kumar Guizani, Mohsen Indian Institute of Information Technology Computer Science and Engineering Group Sri City India Indian Institute of Technology Department of Computer Science and Engineering Patna India Mohamed bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

The rapid expansion of Internet of Things (IoT) devices and applications necessitates the need for more efficient computational and data management strategies. The Fog-enabled UAV-as-a-Service (FU-Serve) platform addresses these demands by integrating fog computing to enhance the operational efficiency of UAVs in IoT environments. Despite its advantages, the FU-Serve platform faces significant challenges, including data transmission latency, resource allocation, and energy management, contributing to the underutilization of UAVs and fog nodes. To address these challenges, this paper introduces a consensus-driven approach, Con-Fog, that optimizes the selection of fog nodes for UAVs within the FU-Serve platform. Con-Fog evaluates potential fog nodes within the communication range by computing utility values based on geographical distance, link quality, available computational resources, and residual energy. UAVs rank these nodes according to their utility values and select the most suitable ones through a consensus-based approach, ensuring alignment with the operational demands of IoT devices. Additionally, we apply an optimal best-fit algorithm to refine fog node allocation, maximizing resource utilization while keeping it below each node’s capacity threshold (T%). Our simulation results show that Con-Fog significantly enhances key IoT performance metrics. Transmission time and the number of unassigned UAVs decrease by 10%-30% and 20%-40%, respectively, while residual energy increases by 30%-50% compared to existing systems. These improvements enhance the management of UAV and fog node resources, thereby advancing the effectiveness of IoT applications within the FU-Serve platform. © 2014 IEEE.

关键词： Fog computing

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Biomarkers to Predictive Modeling: Leveraging Feature Selection and Ensemble Learning for Ovarian Cancer Prognosis 2024

Biomarkers to Predictive Modeling: Leveraging Feature Select...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Srivastava, Jaya Rathi, Megha Department of Computer Science & Engineering Jaypee Institute of Information Technology India

ISBN: (纸本)9798400709722

Treatment outcomes and patient survival rates are greatly improved by early identification of ovarian cancer. However, to increase diagnostic accuracy, effective predictive modeling is required due to the biomarkers' variability and complexity. This research proposes a complex methodology that combines feature selection and ensemble learning approaches to create a forecast model for the diagnosis of ovarian cancer. Recursive feature elimination (RFE), a thorough feature selection technique, was originally used to identify important biomarkers in order to lower dimensionality and improve model interpretability. We then used ensemble learning techniques, which fuse together several machine learning models, to boost the prognosis's stability and predictive performance. Using a dataset containing clinical and genetic data from ovarian cancer patients, ensemble techniques called Adaboost, Catboost, Random Forest, and Gradient boosting were put into practice and validated. Performance metrics like precision, accuracy, F1-score, recall and AUC-ROC curve were used to measure the techniques' efficacy. The findings show that our approach not only performs better than conventional single-predictor models, with results that are roughly 99% correct, but it also offers important insights into the biomarkers which are most symptomatic of the course of ovarian cancer. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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A user-adaptive privacy-preserving authentication of IoMT using zero knowledge proofs with ECC

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Multimedia Tools and Applications 2025年 1-32页

作者： Misra, Garima Hazela, Bramah Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science & Engineering Pranveer Singh Institute of Technology Utter Pradesh Kanpur India

The Internet of Things (IoT) has developed into a crucial component for meeting the connection needs of the current smart healthcare systems. The Internet of Medical Things (IoMT) consists of medical devices that are connected to patients in order to sense their medical parameters and exchange that information with healthcare professionals so they can deliver remote healthcare services. The need to protect the patient’s private information from being misused makes IoMT security an urgent issue that merits further study. Elliptic Curve cryptography (ECC), a popular substitute for traditional public key encryption like RSA, has several advantages. ECC is a prime contender for implementation on constrained devices with confined memory and speed, as well as wireless communication protocols that use low power like IoMT environment. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the IoMT that is based on elliptic curve encryption based zero-knowledge proof (ZKP) protocols. The proposed mechanism completes in order of milliseconds up to 300 ms for 160 bits. The research will include the comparison between computational delays in traditional protocols and ZKP-ECC protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP-ECC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Authentication Protocol

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WGSDMM+GA: A genetic algorithm-based service clustering methodology assimilating dirichlet multinomial mixture model with word embedding

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Future Generation computer Systems 2023年 145卷 254-266页

作者： Agarwal, Neha Sikka, Geeta Awasthi, Lalit Kumar Computer Science and Engineering Department Indian Institute of Information Technology Karnataka Raichur584135 India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi110036 India 246174 India

Advancement of web 2.0 results in the expeditious growth of services in repositories and service portals, which raises the demand for service management. With the clusters of services, different processes like discovery, selection, ranking, etc., can be expedited efficiently. K-Means clustering is highly recommended for service clustering but it can be stuck in the local optimum values. Developers generally use short text to describe the functionality of Web services. For effective service clustering, it is required that relevant features of services are mapped in vector space based on semantic relations. Gibbs Sampling algorithm for Dirichlet Multinomial Mixture model (GSDMM) model is able to provide good performance on short text because it works on the assumption that there will be one topic related to one document. In this paper, a novel technique for web service clustering, i.e., WGSDMM+GA, is proposed in which relevant features, semantic relations, and their mappings in vector space are analyzed based on an assimilated approach of Word2Vec and GSDMM models. As K-means clustering cannot provide effective results due to local optima problem, GA-based clustering is applied in which fitness function is based on Manhattan distance. The proposed model is applied to five real-time datasets, and the performance is evaluated based on standard evaluation measures. Experimental results illustrate that WGSDMM+GA yields better results, and the accuracy score is increased by 23%, 17%,17%, 21%, and 25% on five real-time datasets. © 2023 Elsevier B.V.

关键词： Genetic algorithms

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CCGS-based discriminatory recognition of skeleton-based actions

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Multimedia Tools and Applications 2025年第6期84卷 3161-3174页

作者： Sameem, M Shujah Islam Department of Computer Science College of Computer Sciences & Information Technology King Faisal University Al Ahsa Saudi Arabia

As life forms feature joints, dynamic skeletons have been studied extensively as a potential tool for action detection. As a result of their focus on modelling skeletons, older methods were unable to adequately convey the whole range of motion at more erratic skeletal joints. Despite introducing spatial methods for unstructured data, detecting many simultaneous motions remains challenging. As part of this study, now article proposes a CCGS for skeleton-based action recognition by depicting skeletons organically on joints and therefore attempting to capture the spatiotemporal variation in the data. Let here propose a motion regression to learn the shape statistically from numerous data points since the representation of the joints is crucial to motion convolution. To achieve this sparsity in the underlying CCGS for efficient representation, now give spatial modelling of skeletons and formulate an optimisation problem with a slip-critical joint. Each joint has a strong or weak connection to the other joints in the same frame and to key joints in the action frames that have been processed. The coordinates of the skeleton sequence and the positions of the joints are then sent to the motion-caused graph, which has been tuned, so that features based on Chebyshev, spatial, and angular approximations of the motion may be learned. Also, the article examines the motion features approximation's representation of variation. The results of the experiments show that the recommended joints regression works and the proposed CCGS achieves state-of-the-art performance on the most popular datasets. The present research suggests a CCGS-based method for skeleton-based action recognition that captures spatiotemporal variations by intuitively depicting skeletons on joints. The experimental results suggest that the proposed method obtains state-of-the-art performance, with accuracy enhancements of up to 2% over existing methods on prominent datasets. © The Author(s), under exclusive licenc

关键词： Chebyshev approximation

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Optimized digital watermarking: Harnessing the synergies of Schur matrix factorization, DCT, and DWT for superior image ownership proofing

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Multimedia Tools and Applications 2025年第18期84卷 19817-19852页

作者： Al-Aiash, Issa Alquran, Rabee AlJamal, Mahmoud Alsarhan, Ayoub Aljaidi, Mohammad Al-Fraihat, Dimah Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan

The contemporary context of abundant digital dissemination inherently gives rise to the need for media protection and the clear identification of ownership rights. This paper responded to this nagging issue of unauthorized media capture by developing a new digital watermarking approach uniting the Discrete Wavelet Transform DWT, Discrete Cosine Transform DCT, and Schur matrix factorization. The proposed watermark, which was a positive vector uniformly distributed, presented a high degree of robustness to several adversarial operating conditions, including histogram equalization, salt and pepper noise, ripple impact, smoothing attack, and cropping. The obtained results reveal the effectiveness of our approach. The correlation coefficient between watermarked and original images remains exceptionally high at 0.999998, ensuring minimal visual distortion. Mean Squared Error (MSE) graphs for both high-frequency (HH) and low-frequency (LL) domains demonstrate negligible changes after attacks, confirming the watermark's robustness. Notably, the watermark extraction process remains successful even after histogram equalization, as indicated by significantly lower MSE compared to random vectors. Moreover, the average processing times for different attacks are promising;Histogram Equalization and Salt and Pepper attacks each took 0.3 s, Ripple Attack required 0.28 s, Smoothing Attack completed in 0.29 s, and the Cropping Attack took 0.6 s. The robustness of our approach appears against the salt and pepper attack, which historically presented a vulnerability when employing Schur matrix factorization in image watermarking, indicating the novelty of and this contribution to improving media ownership verification. By combining disparate transformation methodologies, this technique offers a promising avenue for reinforcing the integrity and authenticity of digital media, thereby strengthening the foundations of intellectual property rights in the digital domain. © The Author(s), und

关键词： Discrete wavelet transforms

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