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Zero Trust Networks: Evolution and Application from Concept to Practice

computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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Drone-Based Intelligent System for Social Distancing Compliance Using YOLOv5 and YOLOv6 with Euclidean Distance Metric

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SN computer science 2024年第8期5卷 972页

作者： Parkavi, A. Alex, Sini Anna Pushpalatha, M.N. Shukla, Prashant Kumar Pandey, Ankur Sharma, Sachin Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering (AI and ML) M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Manav Rachna International Institute of Research and Studies Faridabad India

The COVID-19 pandemic forced rigorous social distancing measures to be put in place to halt the virus’s spread;compliance was extremely difficult. In response, we have designed a highly advanced intelligent drone system that maintains and controls distance measures following social distance principles through advanced machine learning skills. This paper describes an intelligent monitoring and compliance system in public places using drone technology with a focus on social distance requirements. Boosting the efficiency of human detection in real-time video streams acquired by drones, our solution employs the You Only Look Once version 5 (YOLOv5) and YOLO version 6 (YOLOv6) object detection algorithms which are the most potent computer vision tools today. The distances between the recognized individuals are computed based on the Euclidean distance measurement to distinguish cases of nonadherence to social distance measures. Our technique integrates the recently developed deep learning models with aerial imagery and monitoring to make it easily scalable for population health surveillance. Thus, we prove impressively that the underlying inventive method works well in different environmental conditions and can potentially contribute to people’s safety in the event of a pandemic. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Drones Euclidean distance metric Social distancing YOLOv6

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HPML-CVD: Hyperparameter Tuned Machine Learning Model to Predict Cardiovascular Disease

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Biomedical Materials and Devices 2025年 1-15页

作者： Uddin, Nazim Hossain, Md. Sahadat Mahmud, Md. Zulfiker Ferdous, Rebina Talukder, Md. Alamin Department of Information and Communication Technology Chandpur Science and Technology University Chandpur Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Basic and Applied Research on Jute Project Bangladesh Jute Research Institute Dhaka 1207 Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

Cardiovascular disease (CVD) remains a significant global health concern, necessitating early detection and accurate prediction for effective intervention. Machine learning (ML) offers a data-driven approach to analyzing patient data, identifying complex patterns, and predicting CVD risk factors such as blood pressure, cholesterol, and genetic predispositions. This study employs the Heart Disease UCI dataset, comprising 303 cases and 14 attributes, to develop predictive ML models. A comprehensive feature engineering approach was applied, including Chi-Square feature selection, where the 10 most influential features were identified to enhance model performance. After feature selection, hyperparameter tuning was performed to optimize five classifiers: Random Forest, K-Nearest Neighbors, Decision Tree, Support Vector Machine, and Logistic Regression. The results indicate significant improvements in accuracy after feature engineering and fine-tuning, with the Random Forest classifier achieving the highest accuracy of 93.41%, outperforming previous work on the same dataset by 3.41%, followed by SVM at 91.21%. These findings underscore the potential of ML in early CVD prediction, emphasizing its role in proactive risk management and healthcare. The integration of ML-driven CVD risk assessment paves the way for personalized healthcare, benefiting both patients and medical professionals by facilitating early intervention, improving patient outcomes, and optimizing healthcare resource allocation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cardiovascular disease Cleveland dataset Heart disease prediction Logistic regression Machine learning

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Vessel Segmentation Approach with Deep Neural Network Model for Detection of Diabetic Retinopathy 4

Vessel Segmentation Approach with Deep Neural Network Model ...

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4th International Conference on Recent Trends in computer science and technology, ICRTCST 2021

作者： Pahadiya, Prasham Tiwari, Prakhar Singh, Prashant Gupta, Surendra Shri G. S. Institute Of Technology And Science Electronics And Telecommunications Engineering Indore India Acropolis Institute Of Technology And Research Computer Science And Engineering Indore India Shri G. S. Institute Of Technology And Science Computer Science And Engineering Indore India

ISBN: (纸本)9781665466332

Diabetic Retinopathy is one reason of avoidable vision loss that frequently affects working people around the world. Blood vessel segmentation aids in the early identification of retinal fundus pictures, which is critical for diagnosing a variety of eye disorders. In this study, several U-net-based methods for identifying vessel segmentation pictures from the original retinal images are suggested. Pre-processing, U-net based model construction, model testing, and model assessment are the four phases in the suggested model's procedure. The original retinal images are sent via pre-processing, which uses image augmentation to enhance the amount of training data available. To train the model, augmented pictures are input into a U-net based model, which consists of encoder-convolution-decoder layers that create a U shape. On the DRIVE and STARE dataset, sensitivity, specificity, and accuracy are used to evaluate the model. State-of-the-art approaches are used to evaluate the proposed models. In terms of sensitivity, the model fared best. The model scored 94.3 percent on the DRIVE dataset and 90.6 percent on the STARE dataset, outperforming current approaches. © 2022 IEEE.

关键词： Deep neural networks

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Multiscale Feature Extraction Techniques for Emotion Recognition from Physiological Signal 1

Multiscale Feature Extraction Techniques for Emotion Recogni...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Taley, Shruti G. Pund, M.A. Prof. Ram Meghe Institute of Technology & Research Department of Computer Science & Engineering Amravati India

ISBN: (纸本)9798350356816

Emotion detection from physiological data is a growing field with important uses in affective computing, HCI, and medical imaging. To achieve significant progress in human-computer interaction, ability to recognise emotions is essential, particularly when using non-intrusive physiological sensors. This work investigates multiscale feature extraction methodologies used in emotion detection systems, with a focus on methodology and benefits. We examine physiological signals like electrocardiogram (ECG), galvanic skin response (GSR) & electroencephalogram (EEG) and we discuss state-of-the-art multiscale feature-based deep learning and machine learning methods. The aim of the paper is to present thorough assessment of state-of-the-art multi-scale machine learning techniques. © 2024 IEEE.

关键词： Electroencephalography

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Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

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computers, Materials & Continua 2023年第12期77卷 2899-2914页

作者： Vinay Kukreja Asha Abraham K.Kalaiselvi K.Deepa Thilak Shanmugasundaram Hariharan Shih-Yu Chen Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Networking and Communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabad501218India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and *** data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw *** fusion is the process of integrating multiple data *** filtering means examining a dataset to exclude,rearrange,or apportion data according to the *** sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional *** advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional ***,many open issues still exist as machine learning algorithms are used for data *** the literature,nine issues have been identified irrespective of any *** decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these *** helps to get the weights for each corresponding issue and rank issues based on these calculated *** most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted *** least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

关键词： Signal level fusion feature level fusion decision level fusion fuzzy hierarchical process machine learning

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An Overview of Telemedicine: Evolution, Application, Challenges and Future Direction 3

An Overview of Telemedicine: Evolution, Application, Challen...

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

作者： Kawde, Dhanshri Gourshettiwar, Palash Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Telemedicine is of great importance as it increases the availability of health care for people living in remote or undeveloped areas. It reduces the cost of health care, allows for early diagnosis and treatment of chronic conditions, and saves time for both patients and healthcare providers. Telemedicine also ensures continuity of care in situations like pandemics while reducing reducing the need for face-to-face consultations can reduce the chance of catching contagious diseases.. This innovation will combine wearable devices and artificial intelligence with personalized care and mental health support. This way, healthcare services would be available and efficient for everyone around the world. However, technological challenges include those stemming from poor system architecture, poor connectivity limitation in diagnosis from a physical examination privacy issues among others and diverse legal regulations. Lack of access and integration into mainstream health systems are also among the causes of discontinuities in care. Overcoming such barriers is critical, and telemedicine needs to be easy to integrate into mainstream frameworks for better access, equity, and health outcome. © 2025 IEEE.

关键词： Telemedicine

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Skin Disease Classification using Pre-Trained Convolution Neural Network with Transfer Learning 3

Skin Disease Classification using Pre-Trained Convolution Ne...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Shah, Kush Solanki, Mansi Vadi, Aditi Goel, Parth Ramoliya, Dipak Devang Patel Institute of Advance Technology and Research Computer Science & Engineering Department Anand Changa India

ISBN: (纸本)9798350381931

Skin disease diagnosis is critical for prompt intervention, effective treatment, public health management, and general well-being of people affected by dermatological disorders. Skin disease detection is a challenging task while classifying those diseases based on the images of common symptoms such as blisters- like rashes, skin lesions, pimples like papules & reddened skin is further more complex. The objective of this paper is to illustrate the effectiveness of transfer learning and application of the pre-trained models originally designed for the Imagenet Dataset in the context of detecting and categorizing various skin diseases. The aim of the model is to differentiate among monkeypox, measles, chickenpox and normal skin. The proposed framework utilizes the convolutional and pooling layers of ResNet50, DenseNet121, EfficientNetB5 and Xception which are pretrained models used for feature extraction. It incorporates a newly designed fully connected (FC) layer along with an output layer for the purpose of image classification. The proposed model is based on Monkeypox Skin Images Dataset (MSID) created at Islamic University in Bangladesh. The proposed framework exhibits that Xception achieved the best result among all the four pre-trained networks with 98.39% as training accuracy, 96.46% as validation accuracy and 95.80% as testing accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Transfer Learning based Light-weight Agricultural Pest Classification Model using Convolutional Neural Networks 7

Deep Transfer Learning based Light-weight Agricultural Pest ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Bhatt, Aarya Patel, Dharm Ajmeri, Vatsal Goel, Parth Devang Patel Institute of Advance Technology And Research Computer Science & Engineering Department Anand Changa India

ISBN: (纸本)9798350359299

A variety of pests and insects can hinder the sprouting of major field crops including wheat, rice, maize, sugarcane, and soybeans, as well as lower agricultural productivity. Since all agricultural insects have similar appearances during the initial phases of crop growth, farmers find it challenging to appropriately classify and identify these insects. Deep architecture Convolutional Neural Networks (CNNs) are employed to tackle this issue, as they can automatically extract and comprehend complex features suitable for applications in image classification. This paper seeks to highlight the competence of transfer learning and the implementation of a pre-trained model customized for the Pest Dataset in the classification of pests into 12 categories. The proposed framework was developed by incorporating the convolutional and pooling layers from pre-trained CNN frameworks, namely ResNet50, Xception, DenseNet121, and EfficientNetB5 for feature extraction. Furthermore, new fully-connected layers and an output layer were developed particularly for the categorization process. The Agricultural Pests Image Dataset is used to examine the proposed framework. The experimental findings demonstrate that Xception, when employed with the proposed architecture, obtained the highest accuracy, with a training accuracy of 92.09% and a testing accuracy of 89.35%, surpassing the performance of the other four pre-trained networks. © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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