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Anonymous and robust biometric authentication scheme for secure social IoT healthcare applications

Journal of engineering and Applied Science 2024年第1期71卷 8页

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

In the era of rapid technological advancement, the Internet of Things (IoT) has revolutionised healthcare through systems like the Telecare Medicine Information System (TMIS), designed to streamline patient-doctor interactions and enhance medical treatment. However, the transmission of sensitive patient data over inherently insecure Internet channels exposes it to a spectrum of security risks. Protecting patient medical privacy and ensuring system reliability necessitate mutual authentication between both patients and medical servers. TMIS relies on robust authentication mechanisms, and combining passwords and smart cards has been a recognised approach for mutual authentication. This research introduces an innovative three-factor authentication technique with perfect forward secrecy by leveraging the power of Elliptic Curve Cryptography (ECC) in tandem with smart cards. Additionally, we have incorporated biometric authentication with a Fuzzy Extractor technology to enhance the security and reliability of the system, setting a new standard for user authentication within the realm of Social IoT healthcare. The use of ECC in the method is justified due to its compact key size and robust security measures, making the solution both efficient and secure. The proposed method safeguards user privacy by permitting registered users to change their passwords without divulging their identity to the server. The Burrows–Abadi–Needham logic (also known as the BAN logic) serves as a proof-of-concept for the proposed scheme’s security. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computation cost and communication cost of up to 71.03% compared to the other four relevant techniques, making it more useful in real-world scenarios. © 2023, The Author(s).

关键词： Smart cards

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Dermo classify: A dermatologist skin disease detection and classification using DCNN

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Sudar, K. Muthamil Nagaraj, P. Muneeswaran, V. Panda, Baidyanath Bhoi, Akash Kumar Department of Computer Science and Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India LTIMindtree 1 American Row 3 Floor HartfordCT06103 United States Directorate of Research Sikkim Manipal University Gangtok Sikkim737102 India

Purpose: In current circumstances, investigators who are doing exploration in various regions remote from their old neighborhood are experiencing different skin issues like dermatitis, dry skin, and psoriasis because of environment or weather condition changes, food sensitivities, and water changes. This results in itching and dry patching on their skin. An affected individual should investigate and analyze the skin sickness, take appropriate precautions to keep the illness from spreading and seek therapy from a skin expert as quickly as time permits. Since the investigators come from different parts of the country, they are unfamiliar with hospitals and places, so they avoid such skin diseases without realizing the severity of the sickness. To address this, we propose a computer-aided system that detects and classifies skin diseases early, helping individuals to identify their condition and seek appropriate treatment quickly. Methods: To detect skin disease at an early stage, this work epitomizes a strategy using a computer-aided model according to sophisticated deep learning neural network techniques such as convolutional neural network (CNN), Residual neural networks, and Xception neural network to detect skin disease. We use the HAM-10000 data set, which contains data on seven different types of skin diseases. Results: The CNN model produced the most significant performance accuracy of 98.3718% in the experiments. Conclusion: The paper presents a promising approach for skin disease detection and classification using DCNNs. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2024.

关键词： Convolutional neural networks

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A Comprehensive Study and Research Perception towards Secured Data Sharing for Lung Cancer Detection with Blockchain Technology

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Annals of Data Science 2025年第2期12卷 757-797页

作者： Kalidindi, Hari Krishna Srinivasu, N. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of CSE S.R.K.R Engineering College Andhra Pradesh Bhimavaram534204 India

Modernization in the healthcare industry is happening with the support of artificial intelligence and blockchain technologies. Collecting healthcare data is done through any Google survey from different governing bodies and data available on the Web of Sciences. However, the researchers continually suffered on developing effective classification approaches. In the recently developed models, deep learning is used for better generalization and training performance using a massive amount of data. A better learning model is built by sharing the data from organizations like research centers, testing labs, hospitals, etc. Each healthcare institution requires proper data privacy, and thus, these industries desire to use efficient and accurate learning systems for different applications. Among various diseases in the world, lung cancer is one of a hazardous diseases. Thus, early identification of lung cancer and followed by the appropriate treatment can save a life. Hence, the computer Aided Diagnosis (CAD) model is essential for supporting healthcare applications. Therefore, an automated lung cancer detection models are developed to identify cancer from the different modalities of medical images. As a result, the privacy concern in clinical data restricts data sharing between various organizations based on legal and ethical problems. Hence, for these security reasons, the blockchain comes into focus. Here, there is a need to get access to the blockchain by healthcare professionals for displaying the clinical records of the patient, which ensures the security of the patient’s data. For this purpose, artificial intelligence utilizes numerous techniques, large quantities of data, and decision-making capability. Thus, the medical system must have democratized healthcare, reduced costs, and enhanced service efficiency by combining technological advancement. Therefore, this paper aims to review several lung cancer detection approaches in data sharing to help future research. Her

关键词： Blockchain

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Learning label-specific features for decomposition-based multi-class classification

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Frontiers of computer Science 2023年第6期17卷 101-110页

作者： Bin-Bin JIA Jun-Ying LIU Jun-Yi HANG Min-Ling ZHANG College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast UniversityNanjing 210096China

Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative *** this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative ***,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster *** clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.

关键词： machine learning multi-class classification error-correcting output codes label-specific features

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Traffic Detection and Enhancing Traffic Safety: YOLO V8 Framework and OCR for Violation Detection Using Deep Learning Techniques

Traffic Detection and Enhancing Traffic Safety: YOLO V8 Fram...

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2024 International Conference on Science, Technology, engineering and Management, ICSTEM 2024

作者： Nagaraj, P. Muthamil Sudar, K. Gurusigaamani, A.M. Sai Vasanth, B. Saranya, V.S.S. Reddy, P. Girish Kumar Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India Department of Computer Science and Engineering Mepco Schlenk Engineering College Virudhunagar Sivakasi India

ISBN: (纸本)9798350376913

Making sure traffic is safe and well-managed has become a top priority in the world of contemporary transportation. Using the robust YOLO (You Only Look Once) v8 model in conjunction with Optical Character Recognition (OCR) technology, this research explores the creation and deployment of a state-of-the-art traffic detection system. The main goal is to make roads safer by detecting traffic in real-time and automatically identifying violations. This research is built around the YOLO v8 framework, which is known for its fast and accurate object detection. With YOLO v8 and OCR technology integrated, the system's capabilities are greatly enhanced. Extracting textual information from licence plates allows the system to automatically detect violations like speeding. In order to make it work in a wide variety of international contexts, the OCR component is strong and can handle different fonts, sizes, and languages. To sum up, intelligent traffic management systems have come a long way using the YOLO v8 Framework and optical character recognition (OCR) for violation detection and traffic detection and enhancement. © 2024 IEEE.

关键词： Optical character recognition

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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Design and Analysis of 6 Degree of Freedom Robotic Whiteboard Cleaner 6th

Design and Analysis of 6 Degree of Freedom Robotic Whiteboar...

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6th International Conference on Mechanical engineering Research, ICMER 2021

作者： Estrera, Mark Ryan M. Genobiagon, Cresencio P. Mechanical Engineering Program College of Engineering Education University of Mindanao Matina Davao City Philippines

ISBN: (纸本)9789811915765

The design and analysis of a six DOF robotic white board cleaner is the focus of this project. A kinematic model for a six-degree-of-freedom (DOF) white board cleaner is presented in this study. Moreover, forward kinematics and inversed kinematics model is developed, and Denavit-Hartenberg (D-H) parameters are used to find solutions. The forward model is used to determine the position and orientation of the end-effector whereas inversed kinematics is used analyze its results associated with solidworks. The experiment is then repeated to confirm the simulation results. Results exhibit that the analytical data are consistent with the measured points and orientation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robotics

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Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation

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Chinese Journal of Electronics 2023年第4期32卷 896-907页

作者： ZHANG Zhe WANG Bilin YU Zhezhou ZHAO Fengzhi College of Computer Science and Technology Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry

Weakly supervised semantic segmentation using only image-level labels is critical since it alleviates the need for expensive pixel-level labels. Most cuttingedge methods adopt two-step solutions that learn to produce pseudo-ground-truth using only image-level labels and then train off-the-shelf fully supervised semantic segmentation network with these pseudo labels. Although these methods have made significant progress, they also increase the complexity of the model and training. In this paper, we propose a one-step approach for weakly supervised image semantic segmentation—attention guided enhancement network(AGEN), which produces pseudopixel-level labels under the supervision of image-level labels and trains the network to generate segmentation masks in an end-to-end manner. Particularly, we employ class activation maps(CAM) produced by different layers of the classification branch to guide the segmentation branch to learn spatial and semantic ***, the CAM produced by the lower layer can capture the complete object region but with many ***, the self-attention module is proposed to enhance object regions adaptively and suppress irrelevant object regions, further boosting the segmentation *** on the Pascal VOC 2012 dataset demonstrate that AGEN outperforms alternative state-of-the-art weakly supervised semantic segmentation methods exclusively relying on image-level labels.

关键词： Training Annotations Semantic segmentation Scalability Semantics Benchmark testing Boosting

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An optimal deep learning-based framework for the detection and classification of android malware

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Journal of Intelligent and Fuzzy Systems 2023年第6期44卷 9297-9310页

作者： Jebin Bose, S. Kalaiselvi, R. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Tamil Nadu Kumaracoil India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Gummidipoondi Tamil Nadu Puduvoyal India

The use of smartphones is increasing rapidly and the malicious intrusions associated with it have become a challenging task that needs to be resolved. A secure and effective technique is needed to prevent breaches and detect malicious applications. Through deep learning methods and neural networks, the earliest detection and classification of malware can be performed. Detection of Android malware is the process to identify malicious attackers and through the classification method of malware, the type is categorized as adware, ransomware, SMS malware, and scareware. Since there were several techniques employed so far for malware detection and classification, there were some limitations like a reduced rate of accuracy and so on. To overcome these limitations, a deep learning-based automated process is employed to identify the malware. In this paper, initially, the datasets are collected, and through the preprocessing method, the duplicate and noisy data are removed to improve accuracy. Then the separated malware and benign dataset from the preprocessing phase is dealt with in feature selection. The reliable features are extracted in this process by Meta-Heuristic Artificial Jellyfish Search Optimizer (MH-AJSO). Further by the process of classification, the type of malware is categorized. The classification method is performed by the proposed Dense Dilated ResNet101 (DDResNet101) classifier. According to the type of malware the breach is prevented and secured on the android device. Although several methods of malware detection are found in the android platform the accuracy is effectively derived in our proposed system. Various performance analysis is performed to compare the robustness of detection. The results show that better accuracy of 98% is achieved in the proposed model with effectiveness for identifying the malware and thereby breaches and intrusion can be prevented. © 2023-IOS Press. All rights reserved.

关键词： Smartphones

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Implementing a Robust Method for Detecting Human Actions in Health Monitoring by Employing Sensors on Mobile Internet of Things Devices and Utilizing a One-Dimensional Convolutional Neural Network

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Journal of Technology in Behavioral Science 2025年第1期10卷 79-91页

作者： Vignesh, T. Thyagharajan, K.K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai India

This research is focused on developing a precise human action recognition system by utilizing accelerometer and gyroscope sensor data. The aim is to analyze various human actions like jogging, sitting, standing, walking, going upstairs, going downstairs, and falling, using multiple sensors found in modern smartphones. Previous studies have explored human action recognition using different sensors, cameras, and machine learning and deep learning techniques. However, some existing approaches, particularly those based on channel state information (CSI), have shown less satisfactory outcomes. In contrast, this paper employs deep learning techniques, specifically one-dimensional convolutional neural networks (1D-CNN), for more accurate human action analysis. One significant advantage of the proposed system is its potential as a wearable device. The paper will introduce a one-dimensional convolutional neural network tailored for human action recognition and an optimized approach for determining the magnitude of accelerometer's X, Y, and Z coordinates, as well as gyroscope sensor values. By utilizing these coordinates, the One-Dimensional Convolutional Neural Networks can effectively classify various human actions like jogging, sitting, standing, walking, going upstairs, going downstairs, and falling. To evaluate the performance of the 1D-CNN approach, a comparison is made with other methods, including SOM, SVM, and FCM. The results demonstrate that the proposed method (1D-CNN) outperforms other classification techniques with an impressive accuracy of 92.42 per cent. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Acceleration Gyroscope Human action recognition One-dimensional convolutional neural network Sensors

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