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Secure and Privacy-Preserving Human Interaction Recognition of Pervasive Healthcare Monitoring

IEEE Transactions on Network Science and engineering 2023年第5期10卷 2439-2454页

作者： Yang, Chao-Lung Tampubolon, Hendrik Setyoko, Aji Hua, Kai-Lung Tanveer, Mohammad Wei, Wei National Taiwan University of Science and Technology Department of Industrial Management Taipei106 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan Indian Institute of Technology Indore Department of Mathematics Indore453552 India Xi'An University of Technology School of Computer Science and Engineering Shaanxi 710048 China

A cloud-based Artificial Intelligence (AI) service has recently empowered the Internet of Medical Things (IoMT) in many applications on the remote Human Interaction Recognition of Pervasive Healthcare Monitoring (HIR-PHM). In this work, a framework of the skeleton-based HIR-PHM under secure Edge-Fog-Cloud computing (EFCC) was proposed to manage the computation and storage resources, latency, cyber-attack, and privacy-preserving simultaneously. At the Edge, IoMT with a camera, record the human interaction as videos and sends them to the Fog, which installed a human pose estimation model, PoseNet, to convert the videos into human skeleton data. At the Cloud, a skeleton-based Spatial-Temporal Graph Convolution Network with Pairwise Adjacency Matrix (STGCN-PAM) was employed to recognize the human interaction. A Hybrid of One Time Password (OTP), Hashed Messages Authentication Code (HMAC), and symmetric cipher were employed to secure the skeleton data. The UT-Interaction dataset was used to evaluate the proposed framework. Besides, the computation performance and latency under EFCC were compared with Edge-Fog and Edge-Cloud deployments. Experimental results confirmed two major contributions: 1) the proposed framework has promising performance compared with other methods on the dataset. 2) The skeleton-based HIR-PHM with secure EFCC shows the advantages of better computation performance in terms of frame per second. © 2013 IEEE.

关键词： Authentication

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Real-time Obesity and Heart Disease Monitoring with IoT and Deep Learning

Real-time Obesity and Heart Disease Monitoring with IoT and ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Singh, Jagendra Govindaraju, P. Kumar, Vinish Yamsani, Nagendar Umamaheswari, J. Munagala, Madhu Bennett University School of Computer Science Engineering & Technology Greater Noida India K S R Institute for Engineering and Technology Department of Electronics and Communication Engineering Tiruchengode India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India SR University School of Computer Science and Artificial Intelligence Warangal India Brainware University Department of Computer Science Kolkata India Koneru Lakshmaiah Education Foundation Department of of English Vaddeswaram India

ISBN: (纸本)9798350394412

The accelerating spread of junk food consumption is progressively putting the younger generations at risk of obesity. The concept of health prioritization is deeply rooted in the development of nurturing and general welfare in society. The job has further become more complex by the glaring statistics of people across the globe who are fighting obesity. The World Health Organization (WHO) puts a staggering estimate that approximately 1 billion people across the globe suffer from obesity-related issues, a statistic heavy in terms of impact primarily because obesity greatly affects one's health but also holds fatal risks. Diagnosing the early stages of obesity can just be simple and prevent one from being obese;however, several obstacles stand in the way of its early diagnosis. Symptoms or signs that one is in the trajectory of suffering from obesity generally go unnoticed by the individual, and the availability of specialists to diagnose such risks persistently on time is not always possible. In this regard, the current paper shall showcase an application hitherto unknown using cloud-based diagnosis, in collaboration with machine learning through Secure Access Internet of Things to predict obesity-related risks without having to depend on glaring physical parameter indicators - in this regard, redefining how such information could be sourced even without the need of regularly visiting health professional people. To ensure the best possible health predictability from this machine learning, we gathered and plotted the datasets consisting of obese and overweight people. Other than machine learning, we use a Secure Access IoT sensor network. This allows for real-time monitoring and diagnosis, making early detection achievable. Consequently, this system paves the way for mitigating and substantially reducing the complications associated with obesity. © 2024 IEEE.

关键词： Nutrition

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Interpretable Deep Learning For Automated Retinal Damage Detection in Optical Coherence Tomography

Interpretable Deep Learning For Automated Retinal Damage Det...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Suganya, R. Sharma, Abhiram Srivats, R. Kalyanasundaram, V. Shanmugam, Selvanayaki Kolandapalayam Vellore Institute of Technology School of Computer Science and Engineering Chennai India Ashland University Department of the Mathematics and Computer Science AshlandOH United States

ISBN: (纸本)9798350362480

This work portrays developing and evaluating deep learning models for automatically identifying retinal injuries using Optical Coherence Tomography (OCT) images. In particular, this study optimizes three pre-trained Convolutional Neural Networks (CNNs), VGG16, EfficientNetB3, and ResNet50, using transfer learning to classify retinal images into four categories, Drusen, Normal, Diabetic Macular Edema (DME), and Choroidal Neovascularization (CNV). In addition to high-performance classification, the study integrates Explainable Artificial Intelligence (XAI) methods, utilizing Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the retinal regions influencing the model's predictions visually. This offers critical interpretability for clinical settings. The Key performance metrics indicators include accuracy, precision, and F1-score and help to evaluate the models. With the available selected models, EfficientNetB3 achieved the highest performance with an accuracy of 93%, outperforming VGG16 and ResNet50. The application of Grad-CAM highlights the increase in transparency and prediction trust of this model's decision-making process. This study demonstrated the profound learning model efficacy, particularly EfficientNetB3, for accurate retinal injury detection and provides valuable insights into model interpretability for clinical use. © 2024 IEEE.

关键词： Convolutional neural networks

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Decoding SARS-CoV-2 Variants: An in-silico approach to RNA-Seq feature extraction using K-mers and N-grams

Decoding SARS-CoV-2 Variants: An in-silico approach to RNA-S...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Iqbal, Naiyar Bhardwaj, Aditya Maulana Azad National Urdu University Department of Computer Science and Information Technology Telangana Hyderabad India International Institute of Information Technology Department of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350348460

The world's enormous challenge is to combat the COVID-19 pandemic, which has spread by the SARS-CoV-2 virus over the past three years. Many waves are formed with diverse variations categorized as variants of concern and variants of interest by the World Health Organization (WHO). Moreover, the emergence of a new coronavirus and its susceptibility to various pandemic conditions throughout the world strongly imply that the virus is taking new shapes, resulting in divergent variations, and predicting the virus becomes more ***: This article proposes an integrated intelligent prediction model to predict coronavirus variants of concern, such as alpha, beta, gamma, delta, and omicron, using RNA-Seq and machine learning ***: We proposed machine learning-based SARS-CoV-2 variants prediction model with 2028 RNA-Seq of alpha, beta, gamma, delta, and omicron were gathered to train prominent classifiers for the prediction of any unseen sequences of these variants. In this order, the feature vector was created from the motif of the sequence obtained through K-mer and N-gram methodologies to build prediction ***: The findings show that the developed prediction model based on Logistic Regression (LR) performs better by reaching an aggregated accuracy of 99% during training and testing compared to other models. We also validated the models using unseen variant sequences and found a significant prediction ***: Our experiment showed that the suggested SARS-CoV-2 variants prediction model is capable of predicting the SARS-CoV-2 variants of concern using a logistic regression trained model. © 2024 IEEE.

关键词： Classification Omicron RNA-Seq SARS-CoV-2 Text Mining Variants of Concern

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Development of Automotive-mobility Training in the TRIREME project 30

Development of Automotive-mobility Training in the TRIREME p...

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30th IEEE International Symposium for Design and technology in Electronic Packaging, SIITME 2024

作者： Medgyes, Balint Farkas, Csaba Spanyik, Marek Danieli, Stefano Cantiani, Elena Stolfa, Jakub Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Electronics Technology Budapest Hungary VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Computer Science Ostrava Czech Republic Mylia-Adecco Formazione Milan Italy

ISBN: (纸本)9798331539511

To achieve climate neutrality by 2050, the consistent transformation of the mobility sector is one of the most important pillars of the European Green deal. As a result of green and sustainable approaches, novel automotive concepts and massive digitalisation in the mobility ecosystem is undergoing a continuous development towards eco-friendly solutions and novel business models, and the changing landscape of automotive industry comes with a significant social impact as well. The overall horizontal collaboration on skills agenda in the Automotive-Mobility Ecosystem is assured throughout the Automotive Skills Alliance (ASA) [1], which is the Large-scale Pact for Skills Partnership. The latest strategic project of the partnership to support its activities is the Digital & Green Skills Towards Future of the Mobility Ecosystem (TRIREME) which promotes a collaboration between industry stakeholders and training providers including Vocational education Trainings (VETs), Higher Educations (HEs) and universities), moreover, also emphasizing the widespread public representation of different project segments and evaluating the social impacts and regional impact of the changes in the Automotive-Mobility Ecosystem. In parallel to the development of sectoral skills strategy, our department (department of Electronics technology-ETT) is contributing to deliver specific training materials and courses within Work Package 4 (WP4), which is based on the gathered and processed intelligence from previous desk research, survey activity and participation in professional events. The methodology of course development relies on the concept of modularity, our professional competences in the field of Automotive-mobility, and the fulfillment of requirements to cover the needed skills, the competence and the occupational profile subsets. © 2024 IEEE.

关键词： Automotive Mobility Modular Courses Skills Training Development

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Image steganography using F5 algorithm 2

Image steganography using F5 algorithm

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Sushil, Vaibhavi Srivastav, Shashank Buddha Institute of Technology GIDA Department of Computer Science and Engineering U.P. Gorakhpur India

ISBN: (纸本)9798350368109

One approach used to protect confidential data from unauthorised usage is steganography. The process of extracting the concealed text from a stego picture is called steganalysis. This research proposes a steganalysis based on the image's DCT value. The F5 algorithm for steganography is more secure than other algorithms. In this research, we offer a steganalytic approach that uses the steganographic algorithm F5 to consistently detect messages (and estimate their size) buried in JPEG images. Estimating the cover-image histogram from the stego- image is a crucial component of the methodology. To achieve this, the stegoimage is first decompressed, then it has four pixels cropping in each direction to eliminate final step is frequency domain quantisation, followed by recompression using the same quality factor as the stegoimage. Using the least square fit method, the quantity of relative modifications brought about by F5 is ascertained by contrasting the calculated histograms of specific coefficients of DCT using the stego picture. According to experimental findings, it is possible to successfully identify relative alterations as tiny as 10% of the usable DCT coefficients. A wide range of test photos, including both raw and modified JPEG and BMP files, are used to evaluate the technique. To increase embedding performance, F5 uses matrix encoding. As a result, fewer adjustments are required. Permutative straddling is used by F5 to evenly distribute the modifications throughout the whole steganogram. In this study, we also present a method for compressing a message or doc file into a picture, audio file, or video. © 2024 IEEE.

关键词： Frequency domain analysis

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A Review on Unconstrained Real-Time Rotation-Invariant Face Detection 3

A Review on Unconstrained Real-Time Rotation-Invariant Face ...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Agrwal, Shubh Lakshmi Sharma, Sudheer Kumar Kant, Vibhor Manipal University Jaipur Lnm Institute of Information Technology Department of Computer Science & Engineering Jaipur India Lnm Institute of Information Technology Department of Computer Science & Engineering Jaipur India Banaras Hindu University Department of Computer Science & Engineering Rgsc Varanasi India

ISBN: (纸本)9781665453578

With the amazing growth of image and video databases, there is a vast need for intelligent systems to automatically understand and look at information since doing it by hand is getting very hard. Faces are significant in social interactions because they show the feelings and identity of a person. People are not much better than machines at recognizing different faces. The automatic face detection system is a key in head pose tracking, face verification, face recognition, face tracking, face animation, face modeling, facial expression recognition, age and gender recognition, and behavior analysis in a crowd. Face detection is a way for a computer to find out the size and location of a face in an image. Face detection has been an outstanding issue in computer vision literature. This paper provides an overview of pose and rotation invariant face detection approaches with architecture designs and performance on popular benchmark datasets. The benchmark datasets used for face detection are listed as their key features. This paper also talks about different applications and challenges with face detection. Also, we set up special discussions on the practical aspects of making a face-detection system that works well. We end this paper by suggesting a few promising directions for future research. © 2023 IEEE.

关键词： Face recognition

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Optimized Homomorphic Encryption (OHE) algorithms for protecting sensitive image data in the cloud computing environment

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International Journal of Information technology (Singapore) 2024年第7期16卷 4143-4153页

作者： Dewangan, Revati Raman Soni, Sunita Mishal, Ashish Department of Computer Science and Engineering Bhilai Institute of Technology Durg (C.G.) Durg India Department of Information Technology NIT Raipur Raipur India

This research aims to enhance photo encryption security by developing a sophisticated technique. This method uses homomorphic encryption to address challenges in encrypting visible spectrum pictures. Each red–green–blue (RGB) channel of the image is divided into smaller sub-values, encrypted separately using an optimized homomorphic encryption algorithm, and then combined for further encryption. Additionally, a novel approach involves combining surrounding pixels to embed extra data during encryption. The process allows for compression and decompression of encrypted components for easier storage or transmission. After decryption, the initial pixel values are recovered, removing any unnecessary data and condensing each channel's pixel intensity into just two sub-values. Multiple security evaluations confirm the method's robustness and resistance, emphasizing its strong security features for encrypted images. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Cryptographic scheme Homomorphic encryption etc Improved image encryption/decryption RGB still image encryption/decryption Still image encryption/decryption

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Digital Secure Storage for Healthcare Data Information Using Blockchain 3

Digital Secure Storage for Healthcare Data Information Using...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naveenkumar, H. Ranjana, P. Hindustan Institute of Technology & Science Department of Computer Application Chennai603103 India Hindustan Institute of Technology & Science Department of Computer Science Engineering Chennai603103 India

ISBN: (纸本)9798350399264

Healthcare Blockchains grant access to a modern technique to accumulate healthcare information, achieve healthcare, and make a belief for medical records distribution and records combination in a decentralized unlock healthcare network surroundings. Even though the healthcare blockchain technologies have paid attention to extroverted benefits in addition to a concentration in assiduousness, management as well as university circles, the safety and confidentiality concerns stay behind the center of attention of deliberate while organizing Blockchains for information sharing across company operations and research collaboration in the healthcare sector. Medical data sharing via Blockchains are the subject of research that focuses on security and privacy needs. Together with technological solution techniques and strategies, the research provides a complete evaluation of the safety and confidentiality risks, requirements, and technology solutions. In the beginning, we discuss the security and privacy requirements for using healthcare Blockchains to share electronic medical records. Second, we group current initiatives into three reference blockchain procedure scenarios for the sharing of electronic medical records. We consider the technologies that can be used to implement this secure store for healthcare data properties in the two categories of healthcare Blockchains procedure scenarios, including anonymous signatures, and attribute-based encryption. Source to implement the access control for hospital, patient, doctor, admin. © 2023 IEEE.

关键词： Blockchain

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Convolutional Neural Network Based IOT Intrusion Detection System using Edge-IIoTset

Convolutional Neural Network Based IOT Intrusion Detection S...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Singh, Mahima Chauhan, Naveen National Institute of Technology Department of Computer Science Engineering H.P. Hamirpur177005 India

ISBN: (纸本)9798350364088

The world of IoT security is rapidly evolving, especially with the rise of advanced technologies. The paper is a study of how Convolutional Neural Networks into Intrusion Detection Systems (IDS) for Edge Industrial Internet of Things (EdgeIIoT) datasets can greatly improve the security of IoT devices. By utilizing deep learning and CNNs in IoT security, we can develop advanced anomaly detection algorithms capable of identifying unusual patterns and behaviors that may indicate potential security threats. The primary benefit of utilizing CNNs for Intrusion Detection Systems (IDS) in EdgeIIoT security lies in their capacity to autonomously learn and extract features from unprocessed data. This capability proves especially valuable when managing the extensive streams of varied data produced by EdgeIIoT devices. This paper explores how CNN and its variations, Xception and VGG16 (Visual Geometry Group 16), help improve the security of an Edge-IIoTset. Generic CNN tops both Xception and VGG16 models with the highest accuracy of 98.98%. The vast field of IoT security is growing rapidly, especially because of advanced technologies. This case study delves into the application of CNNs in IDS for EdgeIIoT datasets, displaying a boost in security measures for IoT devices. © 2024 IEEE.

关键词： Convolutional neural networks

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