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AMCB: enhancing the authentication process with blockchain integrated with PUF and machine learning

Multimedia Tools and Applications 2025年第8期84卷 4809-4835页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundoora Australia

The rapid advancement of technology has given rise to medical cyber-physical systems (MCPS), a subset of cyber-physical systems (CPS) specifically tailored for patient care and healthcare providers. MCPS generate substantial volumes of big data, posing challenges in terms of processing, storage, and safeguarding against unauthorized access. This paper introduces the Authenticated Medical Cyber-Physical Blockchain (AMCB) model within MCPS, leveraging Blockchain technology, machine learning, and physically unclonable functions (PUF) to fortify the authentication process, particularly for accessing electronic health records (EHR) stored on cloud servers. The model also involves analyzing data from authenticated devices to ensure its integrity. Experiments on the AMCB model evaluate its efficiency across four layers. In the physical layer, PUF keys are extracted using a built Static Random-Access Memory (SRAM) circuit and stored in a simulated Blockchain, yielding a Hamming Distance standard deviation of 6.60%. The inspection layer analyzes data from authenticated devices using machine learning, with the Random Forest achieving the highest accuracy at 99.21%. In the application layer, centered on user authentication, the average transaction time is 36.15 MS. Overall, the AMCB model requires an average of 49.82 ms to execute operations across all layers. These findings underscore the effectiveness of the proposed model in securing EHR in the dynamic landscape of MCPS. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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A novel end-to-end privacy preserving deep Aquila feed forward networks on healthcare 4.0 environment

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Discover Internet of Things 2025年第1期5卷 1-20页

作者： Kalpana, Ponugoti Tappari, Sunitha Smitha, L. Madhavi, Dasari Naresh, K. Vijayalakshmi, Maddala Department of Computer Science and Engineering AVN Institute of Engineering and Technology Telangana Hyderabad501510 India Department of Electronics and Telematics Engineering G Narayanamma Institute of Technology and Science Telangana Hyderabad India Department of Information Technology G Narayanamma Institute of Technology and Science Telangana Hyderabad India Sridevi Women’s Engineering College Telangana Hyderabad India Department of Computer Science and Engineering TKR College of Engineering & Technology Telangana Hyderabad500097 India

Healthcare 4.0 is considered to be the most resilient technology with the integration of Internet of Things (IoT), Artificial Intelligence (AI), and 5G wireless communication. These Internet-enabled devices compile human data and deliver it to remote clinical centers for efficient diagnosis and treatment processes. Though sensor-driven devices have largely eased everyday lives, these healthcare systems have been suffering from various security breaches and data privacy problems. This evokes a need for designing intelligent systems to eradicate data breaches and privacy problems. This research presents a groundbreaking framework that uniquely combines privacy-preserving optimized deep learning to effectively diagnose cardiac troubles utilizing edge and fog computing devices. The recommended framework is tested with real-time datasets and employs federated learning for training the network. To ensure high prediction performance, Gated Aquila Optimized Deep Feedforward Networks (GAODP) are used to predict heart diseases. In the primary instance, information is acquired by the IoT sensors and stored in fog gateways. Subsequently, the federated mode of GAODP is adopted for the prediction of heart diseases. The ablation tests are conducted utilizing IoT nodes connected to medical sensors, with fog gateways implemented using Embedded Jetson Nano gadgets. Extensive evaluations are carried out using the gathered datasets, and performance metrics are examined and analyzed. The experimentation involves various traditional deep learning frameworks to prove the performance of privacy-preserving models. Moreover, the duration required to construct the model is calculated to confirm the recommended model's intricacy. The outcome indicate that the recommended model has attained notable predictive accuracy, including precision (0.98), recall (0.975), specificity (0.98), F1-score (0.99) and accuracy (0.99) with less computational overhead. © The Author(s) 2025.

关键词： Diagnosis

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QR Code-Based Visual Secret Sharing Scheme: A Review

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2642-2661页

作者： Chu, Shu-Chuan Liu, Tao Pan, Jeng-Shyang Information Engineering College Guangzhou City Construction College Guangzhou510925 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Visual secret sharing (VSS) scheme is the technology that divides a secret into shares. The decoding operation on the share is performed to restore the secret image. The quick response (QR) code is an image carrying the information. The standard of it is public. When it was obtained, anyone can decode the QR code. The VSS scheme and QR code are combined to improve the security of daily life. They can enhance the security of mobile payments, authentication, privacy data, cheater prevention, and so on. This study mainly aims to summarize previous research achievements. Comparative analysis of relevant results is conducted to illustrate the future research direction. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： information hiding

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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Enhancing Skin Disease Diagnosis: Light GBM-DMS Algorithm for Accurate Image Classification

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Cognitive Computation 2025年第3期17卷 1-18页

作者： Kalpana, B. Senthilselvi, A. Muruganandam, S. Kumar, S. Vinodh Department of Information Technology RMD Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science and Business Systems Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Thandalam Chennai India

Globally, skin diseases are emerging as the most common health problem. It initiates depressive disorder, and it also causes physical health distress. It rarely led to skin cancer in extreme cases. Diagnosing skin disease is subjective and time-consuming when it is performed by health experts manually. Hence, automatic skin disease prediction is required by both dermatologists and patients to enhance and accelerate treatment plans. Even though many machine-learning techniques are available to detect skin diseases, the timely prediction and accurate classification of skin lesions are still in demand. Hence, the Discrete Mycorrhizal Search Algorithm is used to optimize the novel LightGBM to meet this objective. Firstly, intrusions like noise elimination and hair removal are performed and then the image resizing takes place by utilizing the preprocessing pipelines. Subsequent to data pre-processing, the prime features are extracted using the convolutional self-attention based L2 regularization, thus minimizing the data complexity. At last, the extracted features are shifted to the final categorization phase which is done by the novel approach for effective classification of skin diseases. Two skin disease classification images, including datasets, the Skin Lesions Dermatoscopic Image dataset and Skin Cancer MNIST HAM10000, are selected to validate the classification performance. Comprehensive analyses are performed with significant measures that showed the proposed model attained better performances of 98.6%, 0.95, and 10.43 s from accuracy, intersection of union, and execution time, respectively. The stability and robustness under different environmental conditions for clinical applications in skin disease detection and classification tasks are analyzed for making decisions in real-time scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Dermatitis

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Dynamic Neurological Insights: Real-Time Stress Analysis Utilizing Portable EEG Kits 21

Dynamic Neurological Insights: Real-Time Stress Analysis Uti...

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21st International engineering Research Conference 2024, EURECA 2024

作者： Saranya, K. Gokila, N.M. Computer Science Engineering Kumaraguru College of Technology India Information Science and Engineering Kumaraguru College of Technology India

Clinical Electroencephalogram(EEG) devices are effective in hospitals, but for everyday monitoring, portable options are more accessible and convenient, albeit less accurate. In this research, the working of three portable devices -Neurosky MindWave Mobile 2, Muse 2 headband and Emotiv Insight that have different channels 1,4,5 respectively are explored. They are used to understand students' state of stress in two different scenarios-listening to online lecture videos (Scenario 1) and live lecture classes in their classrooms (Scenario 2). Twelve engineering college students participated in the experiment and were asked to watch 10 video lectures from Coursera each video is 4 to 8-minute duration and their EEG data was collected with all the three devices. The same participants were engaged in EEG data collection during their live lecture classes for one hour. A questionnaire consisting of a Likert scale like Self-Assessment Manikin (SAM) was prepared to assess the levels of arousal and valence experienced by the participants. The collected EEG signals from both scenarios are tested with the pre-trained model (Artificial neural network (ANN )model trained using DEAP (A dataset for emotion analysis using EEG, physiological and video signals dataset) and the results of real-time data are analysed. In Scenario 1 which is short duration, EEG recordings showed an accuracy of 55% in Neurosky and 63% in Muse and 76% in Emotiv Insight and in Scenario 2 which is long duration EEG recordings showed an accuracy of 47% in Neurosky and 60% in the muse and 67% in Emotiv Insight. Emotiv shows high accuracy, but Muse proves more comfortable for extended use due to its consistent accuracy in both scenarios. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Wood surface defect detection based on improved YOLOv8

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Signal, Image and Video Processing 2025年第8期19卷 1-12页

作者： Ge, Yilin Ji, Haowen Liu, Xingli College of Computer and Information Engineering Heilongjiang Science and Technology University Heilongjiang Puyuan Harbin150027 China College of Computer and Control Engineering Northeast Forestry University Heilongjiang Hexing Harbin150040 China

The detection of wood surface defects is crucial for ensuring the quality of wood products in industries such as furniture manufacturing and construction. This study proposes DMD-YOLO, an advanced object detection model based on YOLOv8, specifically designed for wood surface defect detection. The model incorporates three key enhancements: Depth Attention Convolution (DAConv), Multi-Scale Attention Feature Fusion (MAFF), and a Decoupled Weighted Head (DWHead). DAConv integrates depthwise separable convolution with channel and spatial attention mechanisms to improve feature extraction efficiency and defect localization accuracy. MAFF enhances the fusion of multi-scale features by leveraging channel and spatial attention, enabling the model to capture global and local defect details. DWHead decouples classification and localization tasks, optimizing the detection of diverse and complex defects. Experimental results demonstrate that DMD-YOLO achieves state-of-the-art performance, with a mean Average Precision (mAP50) of 93.8%, a Recall of 90.4%, and an F1 score of 94.7%, significantly outperforming existing models such as YOLOv5s, Faster R-CNN and RT-DETR. The model shows excellent robustness in detecting small and complex defects while reducing false positives and missed detections, making it suitable for industrial applications and meeting the need for accuracy and robustness in quality inspection tasks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Furniture manufacture

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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Differential Evolution Strategy with Chebyshev Chaos Based Mutation

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 613-628页

作者： Sitenda, Amos Song, Pei-Cheng Chu, Shu-Chuan Chen, Shi-Huang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science and Information Engineering Shu-Te UniversityKaohsiung Kaohsiung824 Taiwan

Differential Evolution (DE) is a potent stochastic evolutionary optimization algorithm garnering increasing research attention. Over the years, it has been found applicable in solving diverse real-world problems. DE employs mutation, crossover, and selection operators to guide populations toward optimal or nearly optimal solutions. However, the standard DE mutation strategies have found limitations in balancing exploration and exploitation effectively, thus prompting research into possible improvements. This study introduces a novel mutation strategy named Chebyshev Infused Chaos Mutation Strategy (CICMS). where a chaotic sequence partially guides the process of donor vector generation. Rigorous evaluations were conducted, comparing our modified DE against the standard DE and seven other metaheuristic algorithms, including Genetic Algorithm, Particle Swarm Optimization and Gravitational Search Algorithm. Experiments were performed using the challenging CEC 2014 benchmark functions, consisting of 30 objective functions. Results indicate substantial improvements in convergence speed and solution quality, highlighting the potential of our novel mutation strategy to enhance DE’s practicality in addressing complex optimization problems. This research contributes valuable insights to the dynamic field of optimization algorithms with implications for a wide range of applications. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Chaos theory

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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