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Blockchain-based color medical image cryptosystem for industrial Internet of Healthcare Things (IoHT)

Multimedia Tools and Applications 2024年 1-55页

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Ahram Canadian University 6th October City Giza Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a substantial growth. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptography techniques inadequate. To address these challenges, blockchain (BC) technology has emerged as a promising solution. This study introduces an efficient framework for the Internet of Healthcare Things (IoHT), presenting a novel cryptosystem for color medical images using BC technology in conjunction with the IoT, Secure Hash Algorithm 256-bit (SHA256), shuffling, and bitwise XOR operations. The encryption scheme is specifically designed for an IIoT grid network computing system, relying on diffusion and confusion principles. In this paper, the proposed cryptosystem strength is evaluated against differential attacks with several comprehensive metrics. Simulation results and theoretical analysis demonstrate the cryptosystem effectiveness, showcasing its ability to provide high levels of security and immunity to data leakage. The proposed cryptosystem offers a versatile range of technical solutions and strategies that are adaptable to various scenarios. The evaluation metrics, with approximate values of 99.61% for Number of Pixels Change Rate (NPCR), 33.46% for Unified Average Changed Intensity (UACI), and 8 for information entropy, closely align with the desired ideal outcomes. Consequently, this paper contributes to the advancement of secure and private systems for medical image encryption based on BC technology, potentially mitigating the risks associated with cyberattacks on smart medical devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Guarding Voices, Protecting Homes: A Comprehensive Case Study on Voice Assistant Security in Smart Living 12

Guarding Voices, Protecting Homes: A Comprehensive Case Stud...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Rehman, Fazalur Hashmi, Joudat Abdullah, Moiz Zaman, Hafsah School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan College of Engineering Georgia Tech University Department of Analytics United States Air University Faculty of Computing and Artificial Intelligence Department of Cyber Security Islamabad Pakistan

ISBN: (纸本)9798350330366

The evolution of technologies in the last decades has transformed the whole human interaction experience with the digital world. Integrated into users' home voice assistant devices has rationalized personal interaction with the technology however convenience of intelligent voice assistant presents security and privacy risks. This paper investigates the security vulnerabilities of smart voice assistant devices specifically focusing on Google Home Mini. Through a series of simulated attacks and comprehensive analysis, this study uncovers potential risks ranging from unauthorized access to user data privacy compromise within home networks. The objective is to raise awareness about these vulnerabilities and their implication urging the need for robust security measures and providing valuable observation for future research. As voice devices are becoming an integral part of daily life addressing these challenges is paramount to ensuring user privacy data integrity and overall security of the smart home ecosystem. © 2024 IEEE.

关键词： Risk assessment

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Detection of Aphasia Using Speech Characteristics: A Comparative Analysis of Learning Models 5th

Detection of Aphasia Using Speech Characteristics: A Compara...

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5th International Conference on computing and Network Communications, CoCoNet 2023

作者： Surisetty, Hima Varshini Gottimukkala, Sarayu Varma Vekkot, Susmitha Gupta, Deepa Alotaibi, Yousef A. M. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

ISBN: (纸本)9789819747108

Aphasia is one of the brain disorders that affects the part of the brain that is responsible for communication. This work deals with the detection of aphasia from speech features. The features extracted from the speech samples are MFCC, mel spectrogram, zero crossing rate, chroma, energy, spectral centroid, spectral roll-off, fundamental frequency and utterance duration. These different speech features were then ranked based on the SHapley Additive exPlanations (SHAPs), and the most important features were retained in an optimized feature set that was given to various machine learning models like support vector machine and decision trees and deep learning models like LSTM, LSTM with GRU, Bi-LSTM and Bi-LSTM with GRU. These models were then evaluated based on their train and test set accuracy, F1 score, recall, precision, plots of training loss and validation loss and confusion matrix. Based on the analysis of the performance metrics, Bi-LSTM with GRU was found to be the best model among all the models used. The model’s performance was evaluated using metrics like accuracy, F1 score, recall and precision when trained and tested with the optimal feature set which were 87.5%, 79.7%, 90% and 75%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

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computers, Materials & Continua 2023年第5期75卷 3189-3201页

作者： Shakti Raj Chopra Parulpreet Singh Ahmed Alhussen Nitin Mittal MohdAnul Haq School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia University Centre for Research and Development Chandigarh UniversityMohaliPunjab140413India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Many extensive UAV communication networks have used UAV cooperative *** networking services can be offered using unmanned aerial vehicles(UAVs)as aerial base *** only is coverage maximization,but also better connectivity,a fundamental design challenge that must be *** number of applications for unmanned aerial vehicles(UAVs)operating in unlicensed bands is fast expanding as the Internet of Things(IoT)*** bands,however,have become overcrowded as the number of systems that use them *** Radio(CR)and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks,and hence as technological solutions for future generations,from this *** a result,combining CR with UAVs has the potential to give significant benefits for large-scale UAV *** paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient *** maximization,power control,and enhanced connection quality are the three steps of the proposed *** satisfy the desired signal-to-noise ratio,the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural,suburban,and urban macro deployment scenarios of the ITU-R M.2135 standard(SNR).

关键词： UAV networks multi-UAV coverage energy-efficient FER SNR

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IoT Based Battery Monitoring System for Electric Vehicle using ESP32 2

IoT Based Battery Monitoring System for Electric Vehicle usi...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Usha Rani, R. Divya, G. Lakshmi Swarupa, M. Navaneetha, Kandle Department of Computer Science Engineering CVR College of Engineering Ibrahimpatnam Hyderabad India Department of Electrical and Electronics Engineering CVR College of Engineering Ibrahimpatnam Hyderabad India

ISBN: (纸本)9798350305418

Since the system is powered by the battery, it is the most vital part of any gadget. Incorrect or excessive charging or draining of the battery might damage the battery and lead to system failure, hence it is important to monitor the battery's voltage level. By far most of current electrical and electronic gear utilize a committed instrument called a battery the board framework (BMS). The battery the board framework (BMS) screens and controls the battery's fundamental capabilities such voltage, current, temperature, and auto-off. The appropriate and get treatment of lithium-particle or lithium-polymer batteries is guaranteed along these lines. More established BMS just observed the battery and notified the user when there was a problem. Thanks to IoT, however, we can now provide instantaneous alerts to far-flung targets. They may check the remaining juice from any location using their phones or computers. The goal is to demonstrate the viability of the idea's basic premise. The hardware for the battery monitoring device is developed as part of the system development. In this study, the battery status data is sent to the Arduino IoT cloud using an IoT-based battery monitoring system, node, and MCU using an ESP32 chip. Whether the battery is being charged or discharged, the Arduino IoT cloud will display the voltage and percentage of charge remaining. © 2023 IEEE.

关键词： Battery management systems (BMS) Battery Voltage Electric Vehicles Internet of Things Lithium-ion Batteries SoC measurement

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A New Model for Predicting Pandemic Impact on Worldwide Academic Rankings

A New Model for Predicting Pandemic Impact on Worldwide Acad...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Sharada, K.A. Swathi, R. Basi Reddy, A. Selvan, R.Senthamil Sivaranjani, Lingala Hkbk College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Tirupati India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India Mohan Babu University School of Computing Tirupati India

ISBN: (纸本)9798350317060

People must stay home during pandemics because physical life stops. Pandemic dread closes schools nationwide, hurting students academically. If so, educating kids properly to avoid academic issues is difficult. During the COVID-19 epidemic, schools provided pupils with online instruction. Career and academic tenure were unaffected. This runs counter to what happened during various pandemics throughout history when school years were lost due to widespread illness. Human-computer interface (HCI) technology advancements have made this all feasible. Interference. Many schools made substantial efforts to adapt to the modern teaching paradigm, yet they still ranked poorly internationally in at least one crucial area. As a result, they're worldwide standing took a dramatic nosedive, which will take a lot of time and work to recover from. This study highlights why certain top universities fell in the worldwide rankings during global disruption, such as the recent COVID-19 epidemic, and what may be done to reverse this trend. Potential future pandemics. The unique methodology provided in this paper aids academics in anticipating the influence of epidemics on their overall worldwide rankings, allowing them to make timely recovery choices and strategies. The task is to build a model using sophisticated Machine Learning techniques, with a massive ranking dataset as input. © 2023 IEEE.

关键词： COVID-19

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Stationary Processes on Directed Graphs 12

Stationary Processes on Directed Graphs

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12th Iran Workshop on Communication and Information Theory, IWCIT 2024

作者： Iraji, Mohammad Bagher Eini, Mohammad Amini, Arash Rini, Stefano Sharif University of Technology Department of Electrical Engineering Tehran Iran National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Hsinchu City Taiwan

ISBN: (纸本)9798350387094

In this paper, we extend the concepts of kernels, weak stationarity and white noise to the case of directed graphs (digraphs). We shall determine the attributes of two types of kernels (I and II) based on the Jordan decomposition of the shift operator. Using kernels of type I and II, we define the stationarity concept and filtering properties on digraphs. We further generalize the concept of Wiener filter and the related optimization framework to digraphs. To validate the concepts, we apply our theories numerically to denoise and recover temperature values in some cities in France based on real recordings that are manually noise contaminated. © 2024 IEEE.

关键词： Directed graphs

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Uncovering the Potential for a Weakly Supervised End-to-End Model in Recognising Speech from Patient with Post-Stroke Aphasia 5

Uncovering the Potential for a Weakly Supervised End-to-End ...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Sanguedolce, Giulia Naylor, Patrick A. Geranmayeh, Fatemeh Department of Computing Department of Electrical & Electronic Engineering Department of Brain Sciences Imperial College London United Kingdom Department of Electrical & Electronic Engineering Imperial College London United Kingdom Department of Brain Sciences Imperial College London United Kingdom

ISBN: (纸本)9781959429883

Post-stroke speech and language deficits (aphasia) significantly impact patients' quality of life. Many with mild symptoms remain undiagnosed, and the majority do not receive the intensive doses of therapy recommended, due to healthcare costs and/or inadequate services. Automatic Speech Recognition (ASR) may help overcome these difficulties by improving diagnostic rates and providing feedback during tailored therapy. However, its performance is often unsatisfactory due to the high variability in speech errors and scarcity of training datasets. This study assessed the performance of Whisper, a recently released end-to-end model, in patients with post-stroke aphasia (PWA). We tuned its hyperparameters to achieve the lowest word error rate (WER) on aphasic speech. WER was significantly higher in PWA compared to age-matched controls (10.3% vs 38.5%, p © 2023 Association for Computational Linguistics.

关键词： Diagnosis

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Design of 32 Bit RISC V Processor 15

Design of 32 Bit RISC V Processor

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

作者： Meeradevi, T. Mohanraj, K. Mourissh, B.M. Santhosh Sivaa, V. Samikannu, Ravi Sasikala, S. Kongu Engineering College Department of ECE Perundurai Erode India International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana

ISBN: (纸本)9798350370249

This paper presents the design and implementation of a RISC-V processor core with a single-stage architecture, focusing on the execution of the base 32I instruction set. The processor core features a 32-bit address and data architecture, with 32 general-purpose registers for data manipulation. The design adheres strictly to the RISC-V ISA specifications, ensuring compatibility with existing software and toolchains. Key components of the processor core include a fetch unit, decode unit, ALU unit, register file, memory control unit, and instruction memory unit. Through rigorous design and simulation, the processor core achieves single-clock-cycle execution, enabling efficient processing of instructions at an operating frequency of 2.2 KHz. The instruction memory unit facilitates the retrieval of instructions from a 4.5 KB memory array, while simultaneous read and write capabilities are supported for both registers and data memory. Additionally, 13 pseudo-instructions have been incorporated to enhance programming ease and readability. Performance evaluation of the processor core demonstrates its effectiveness in executing a variety of computational tasks, showcasing its reliability and efficiency within the specified operational parameters. The design serves as a foundation for future enhancements, including the integration of pipeline architecture, branch prediction mechanisms, and frequency optimization. © 2024 IEEE.

关键词： Memory management units

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Enhancing Electric Vehicle Charging Predictions: A Physics-Informed Neural Network Approach 50

Enhancing Electric Vehicle Charging Predictions: A Physics-I...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Kamal, Naheel Faisal Sharida, Ali Bayhan, Sertac Abu-Rub, Haitham Alnuweiri, Hussein Department of Electrical and Computer Engineering Texas A&M University at Qatar Qatar Foundation Doha Qatar Electrical and Computer Engineering Department Texas A&M University College Station United States HBKU Qatar Foundation Doha Qatar Department of Electrical and Computer Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

ISBN: (纸本)9781665464543

Electric vehicle (EV) charging stations have been evolving to offer better and more efficient power delivery methods. A key area of research in this field involves predicting the power consumption of EV charging stations. Many researchers have addressed this issue using machine learning and deep learning methods, however, forecasting models struggle to predict individual charging sessions with high resolutions. In this paper, a deep neural network (DNN) is constructed to forecast the EV charging current profile and state of charge (SoC). Charging current and SoC are mathematically formalized and embedded into the DNN’s loss calculation to build a physics-informed neural network (PINN). The performance of the models is assessed through analysis of their prediction error, utilizing actual data from real EV charging sessions. © 2024 IEEE.

关键词： Charging stations

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