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Electronic health records protection strategy by using blockchain approach

Multimedia Tools and Applications 2024年第39期83卷 86883-86894页

作者： Kathole, Atul B. Patil, Sonali D. Kumbhare, Savita Vhatkar, Kapil Kshirsagar, Aniruddha P. Kimbahune, Vinod V. Computer Engineering Dr. D. Y. Patil Institute of Technology Pune India IT Dept. Pimpri Chinchwad College of Engineering Pune India Computer Science and Engineering Department K.B.P College of Engineering Satara Satara India

Electronic health records (EHRs) provide significant challenges for medical organizations due to the digitalization of traditional medical information. When using EHRs, both doctors and patients are accustomed to spending considerable time conducting the necessary queries;nevertheless, the acquired data is not always related to the sought one, and access may be revoked if this is the case. The exchange and maintenance of medical health records are among the most crucial tasks in the healthcare system. The integrity of health records is crucial, as is maintaining patient anonymity, and either would have devastating effects in the event of a breach. Therefore, it is one of the most important reasons to keep electronic health records safe. Modern healthcare infrastructure is notoriously difficult to use, wasteful, and fraught with problems related to data integrity, security, and privacy. Better EHR management and monitoring, however, has the potential to lessen the severity of the problems with security and complexity. The blockchain's potential, with its decentralized and trustworthy nature in the healthcare industry, makes this a reality. Problems with data management and the way information is validated and disseminated are at the heart of the healthcare delivery system's inherent difficulties. Improved access to medication tracking, hospital assets, drug systems, patient information, and so on is the primary benefit of employing blockchain technology in healthcare data management. Because of how important it is to have access to a patient's medical history when prescribing medication, blockchain technology has the potential to significantly enhance the current healthcare delivery system. Therefore, it is critical to develop a blockchain-based system for verifying and protecting medical records. The primary objective of this article is to create a permissioned blockchain-based Ciphertext Policy-Attribute-based encryption system to protect privacy and regulate acces

关键词： Blockchain

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Towards Robust mmWave-based Human Activity Recognition using Large Simulated Dataset for Model Pretraining

Towards Robust mmWave-based Human Activity Recognition using...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Joshi, Vinay Xu, Shengkai Cao, Qiming Zhu, Yi Wang, Pu Xue, Hongfei Unc Charlotte Dept. of Computer Science Charlotte United States Purdue University Electrical and Computer Engineering West Lafayette United States Wayne State University Dept. of Computer Science Detroit United States

ISBN: (纸本)9798350362480

Human activity recognition (HAR) is crucial for real-world applications such as healthcare, surveillance, and smart homes. Among sensing technologies, millimeter wave (mmWave) sensors stand out due to their contactless nature, high sensitivity, and ability to operate in low-light environments while preserving privacy. However, the scarcity of mmWave sensing data limits the generalizability of mmWave-based HAR systems. To address this, we propose mmAP, a data augmentation and pretraining framework that synthesizes a large mmWave dataset using human mesh data, followed by pretraining a robust and general mmWave heatmap encoder using a multi-modal masked autoencoder framework using the synthesized data. We enhance the model's robustness with heatmap-specific data perturbations and perform task-specific fine-tuning on a small real-world dataset. The experiment results over the baseline demonstrate the effectiveness of the proposed mmAP framework. © 2024 IEEE.

关键词： Large datasets

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Automating Dermatological Diagnosis - A Comparative Analysis 5

Automating Dermatological Diagnosis - A Comparative Analysis

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Johnson, Aleen Arora, Vishnupriya Aggarwal, Lakshita Goswami, Puneet Srm University Dept. Of Computer Science & Engineering Haryana Sonepat India Srm University Dept. Of Computer Science Haryana Sonepat India

ISBN: (纸本)9798350330861

Evolving and adapting healthcare along with growing technology is an urgent need of an hour using machine learning is an addition to classify health data for the large aspect of identifying dermatological illness. Convolution Neural Network (CNN) can accurately classify a variety of skin illnesses by extracting complex information from images. CNN do away with the requirement for manual feature engineering because they are able to learn from large amounts of data. They are resistant to noise and changes in lighting, ensuring accurate analysis every time. CNN help in early detection, increasing the efficacy of treatment, and lowering the possibility of a false positive. Their combination has the potential to greatly improve patient care and change dermatological procedures. Objective: The goal of this study is to determine the best effective algorithm, whether it comes from deep learning or machine learning, based on a thorough comparative analysis that considers elements like accuracy, interpret ability, and scalability. Methodology: This study uses a comparative analysis methodology to decide which machine learning and deep learning algorithms are the most effective. It entails a review of the literature, the gathering of data, the design and execution of experiments, statistical analysis, and result interpretation. Findings: This study's findings shows that deep learning approaches are more effective than machine learning ones. In contrast to machine learning, which depends on manual, subjective techniques, deep learning allows for automatic and objective feature extraction. Deep learning achieves greater prediction accuracy and generalization ability despite requiring more training time and computational resources, demonstrating its superior learning powers. © 2023 IEEE.

关键词： Learning algorithms

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Ethereum Fraud Prevention: A Supervised Learning Approach for Fraudulent Account Recognition 1

Ethereum Fraud Prevention: A Supervised Learning Approach fo...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Dutta, Sourav Sharma, Anjali Rajgor, Jaymin Lovely Professional University Punjab Dept. of Computer Science and Engg. Kolkata India Jss Academy of Technical Education Dept. of Computer Science and Engg. Bangalore India Shan and Anchor Kutchhi Engineering College Dept. of Computer Science and Engg. Mumbai India

ISBN: (纸本)9798350307795

In recent times, blockchain technology, especially Ethereum, has gained tremendous popularity for supporting various financial transactions. Ethereum, the second most prominent cryptocurrency platform after Bitcoin, processes over one million transactions daily, known for its high security and ease of use. However, despite its robust security features, Ethereum faces challenges with illegal activities, which hinder its widespread adoption. To address this issue, this study focuses on detecting illicit activities on the Ethereum blockchain, particularly identifying illegal accounts using machine learning techniques. It proposes a novel method combining a convolutional neural network with an XGBoost classifier, aimed at differentiating between normal and illicit accounts based on transaction history. The XGBoost model, a tree-based ensemble classifier, not only improves accuracy but also prevents overfitting and enhances the model's generalizability. Its parallel tree-building feature also speeds up training, making the model more scalable. The model is trained on a balanced dataset of over 4,000 Ethereum accounts, encompassing both normal and fraudulent accounts. The results are promising, with the model achieving an accuracy of 99.7% and an average AUC of 0.9998 (std: 0.0008), outperforming standard machine learning models. This approach shows significant potential in combating illegal activities within the Ethereum blockchain. © 2024 IEEE.

关键词： Blockchain

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Cloud Cryptography: Mechanism of Different Encryption Standards 18

Cloud Cryptography: Mechanism of Different Encryption Standa...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Lanke, Ravikumar Md Zubair Rahman, A.M.J. Bhardwaj, Rajat Somashekhara Reddy, D. Jain, Parth Mahesh, T.R. Dept. of Computer Science and Engineering Bangalore India Al-Ameen Engineering College Karundevanpalayam Erode India Aset Amity University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9789380544519

The term 'cloud cryptography' describes the application of cryptographic methods and protocols to protect communications and data in cloud computing settings. The provision of computing services, including storage, processing power, and applications, via the internet is known as cloud computing. Although cloud computing has numerous advantages, such as cost-effectiveness and scalability, it also poses security risks because sensitive data is processed and stored outside of the conventional network borders of an organization. Cloud cryptography plays a pivotal role in cloud security by safeguarding against unauthorized access, data disclosure, modification, or destruction. Keeping up with the most recent cryptographic methods and protocols is crucial to addressing new threats and weaknesses in this industry, which is always changing. Organizations can safeguard their data from unauthorized access, modification, and destruction by utilizing encryption techniques. This article explores the cloud cryptography along with its different encryption standards. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Architecture Benefits Cloud Computing Cryptography Encryption

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Federated Learning for Cybersecurity: A Promising Frontier 2

Federated Learning for Cybersecurity: A Promising Frontier

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

作者： Ananya, G. Bindu Madhavi, V. Murthy, Monisha Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Buddha Institute of Technology Computer Science and Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350368109

In today's world, Artificial Intelligence and Machine Learning are transforming many industries, but they rely on huge amounts of data leading to privacy issues. A better alternative is Federated learning. Federated Learning involves training the model from multiple sources using the decentralization technique, meaning each device trains the model on its local data thereby reducing the strain on the single server. This is useful in cases where the data is too large to be sent and maintained on a central server or in handling privacy and security concerns. Cybersecurity is a prime domain where vulnerable attacks and breaches can be prevented using this technique. Other domains like finance, healthcare, IoT etc. have transformed the idea of data-driven decisions. This paper gives an insight into the concept of Federated learning and why it is a better choice. It includes the various algorithms that can be implemented in multiple applications depicted through a comparative analysis. The choice of algorithm depends upon its efficiency and the desired cybersecurity application. © 2024 IEEE.

关键词： Federated learning

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JARVIS - Smart AI Support System 3

JARVIS - Smart AI Support System

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Ramya Manaswi, V. Saketh, Meduri Ravi Tejha, Mandumula Sai Karthikeya, Atluri Sai Prakash, Dharani Chandra Alkhayyat, Ahmad Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science Engineering Hyderabad India Gitam University Dept. of Computer Science Engineering with Specialisation of Cybersecurity Hyderabad India K.G Reddy College of Engineering and Technology Dept. of Electronics & Communication Engineering Telangana Hyderabad India College of Technical Engineering The Islamic University Dept. of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350354379

The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating 'Air Canvas' facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computer vision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI. © 2024 IEEE.

关键词： Face recognition

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Predicting DDoS Attacks: A Machine Learning Approach using ALDDoS Dataset 15

Predicting DDoS Attacks: A Machine Learning Approach using A...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shookdeb, Shantanu Muduli, Debendra Bhatta, Sandesh Adhikari, Aayush Sapkota, Aaron Chaturvedi, Prabhat C V Raman Global University Dept. of computer Science and Eng. Bhubaneswar India C V Raman Global University Dept. of computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

The DDoS attack stands as Distributed Denial of Service attack refers a harmful effort to intercept on the regular traffic of a targeted web service or server. DDoS attack interferes with the normal network flow of the targeted server, potentially causing significant disruption to its *** send an incoming packet to every node except the arrived node,it used a non-adaptive routing algorithm. DDoS attacks are executed by using multiple compromised systems, which included combination of multiple computers system and other IoT device resources to make a large traffic on targeted network, rendering it inaccessible to legitimate users. In our study we experimented with various machine learning model like as RandomForest, DecisionTree, Xgbbost etc. with a high accuarcy of 99.99% to predict the DDoS attack with publicly available ALDDoS (Application-Layer DDoS) Dataset by Warda (Owner). © 2024 IEEE.

关键词： ALDDos Cyber defense DDoS RandomForest Web-proxy

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Enhanced Classification of Alzheimer's Disease Using Deep Learning and Machine Learning Models with MRI Scans

Enhanced Classification of Alzheimer's Disease Using Deep Le...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Salahudin, Amna Qadeer, Eeman Ahmed, Ayesha Fahad, Labiba Gillani Dept. of Software Engineering FAST National University of Computer and Emerging Sciences Islamabad Pakistan Dept. of Computer Science FAST National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9798331510503

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease, which necessitates early and accurate diagnosis for effective management. This research presents a comprehensive approach to the classification of Alzheimer's disease using Magnetic Resonance Imaging (MRI) scans, focusing on four distinct classes: nondemented, mild, very mild, and moderate demented. Our main goals are to combine diverse datasets to improve the robustness and adaptability of the model and to fully investigate Alzheimer's disease in all four stages. To achieve these goals, we employed a combination of machine learning and deep learning models. The models utilized include Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM), Gabor filters with SVM, Gabor-Gray Level Co-occurrence Matrix (GLCM) with SVM, ResNet50, AlexNet, and InceptionV3. Among these, the InceptionV3 model achieved the highest accuracy of 96.20% using 5-fold cross-validation followed the Resnet50 model achieving accuracy of 95.43%. Our approach addresses key challenges such as class imbalance, high intra-class variation, inter-class similarities, and dataset variability, demonstrating significant potential in improving Alzheimer's disease classification and detection. © 2024 IEEE.

关键词： Gabor filters

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A Time-varying Velocity Control Strategy for Balancing Exploration and Exploitation Behaviour of a Binary PSO

A Time-varying Velocity Control Strategy for Balancing Explo...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Siddique, Md. Abu Bakkar Islam, Md. Jakirul DUET Gazipur Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

Combinatorial optimization problems (COPs) are NP-hard, and their solution space is discrete, which poses a significant challenge for optimization algorithms. Binary Particle Swarm Optimization (BPSO) is wel-known optimization technique that has been widely applied in various type of COPs. However, one of the challenges in BPSO is to balance exploration and exploitation to locate the optimal solution. This paper presents a Time-varying Velocity Control (TVC) strategy to balance exploration and exploitation in a BPSO. The TVC strategy utilizes a time-varying control parameter to adjust the velocity of the particles, allowing for more exploration in the early phases of the optimization process and more exploitation as the search progresses. The proposed strategy is evaluated on a set of benchmark combinatorial optimization problems, and the results demonstrate that it outperforms other commonly used BPSO strategies in terms of both efficiency and solution quality. Furthermore, the TVC strategy is shown to be robust to changes in the problem landscape, making it a promising approach that can be used for solving a wide range of combinatorial optimization problems. © 2023 IEEE.

关键词： Combinatorial optimization

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