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Cyber Resilience (ICCR), International Conference on

作者： Abduljaleel Al-Hasnawi Yitong Niu Jamal F. Tawfeq Manas Ranjan Pradhan Mohammed Salahat Taher M. Ghazal Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Communication Technology Engineering College of Information Technology Imam Ja'afar Al-Sadiq University Baghdad Iraq School of Aeronautical Engineering AnYang University Anyang China Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq School of Computing Skyline University College University City Sharjah Sharjah UAE College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Computer Science Department Centre for Cyber Physical Systems Khalifa University Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The privacy of sensitive data within the Internet of Things (IoT) is at risk due to potential privacy violations. These violations can occur through illegal access by external entities, as well as inadvertent disclosures or intentional attacks by those within the system who exceed their authorized access privileges. Furthermore, there needs to be more control experienced by data owners regarding their sensitive data as it transitions from their domain to other entities within the Internet of Things (IoT) ecosystem. Numerous methods have been presented in the academic literature over the past few years to effectively tackle the privacy concerns associated with the Internet of Things (IoT). This paper aims to classify existing literature by organizing it according to the specific technology or technique employed to safeguard privacy. The classification encompasses four primary areas of privacy solutions, specifically, solutions based on encryption, solutions based on identity management, solutions based on policy enforcement, and solutions focused on self-protection. The paper examines various state-of-the-art solutions within each category. It begins by providing a broad comparison between these solutions and our work. Subsequently, it focuses on a specific comparison of the privacy objectives of the offered PEFM solutions.

关键词： Privacy Data privacy Reviews Ecosystems Encryption Internet of Things Resilience

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Adaptive Optimal Electrical Resistance Tomography for Large-Area Tactile Sensing

Adaptive Optimal Electrical Resistance Tomography for Large-...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wendong Zheng Huaping Liu Di Guo Wuqiang Yang Department of Computer Science and Technology Tsinghua University Beijing China the State Key Laboratory of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Electronic Engineering The University of Manchester Manchester U.K.

It is critical to perceive physical contact for intelligent robots to safely interact in dynamic, unstructured environments. As physical contacts can occur at any location, a well-performing tactile sensing system should be able to deploy a large area on robotic surface. Some researchers have implemented large-area tactile sensors by using sensing arrays, but it is challenging to deploy many sensing elements. Electrical resistance tomography (ERT) has recently been introduced into tactile sensing to overcome some of the limitations with conventional tactile sensing arrays, and good results have been achieved for some robotic applications. However, a particular challenge is that spatial resolution is low. Although various attempts have been made to improve the performance of ERT-based tactile sensors, the intrinsic resolution issue remains unsolved. In this paper, we propose a novel adaptive optimal drive strategy for efficient ERT-based large-area tactile sensing for robotic applications, which can adaptively select the current injection and voltage measurement pattern for optimal tactile stimulus. In particular, regions of tactile contacts are preliminarily detected and localized by a base scanning pattern with only a few measurement data. According to this detected region, the adaptive strategy can select the optimal current injection and voltage measurement pattern to improve the sensing performance by maximizing the current density. To verify the effectiveness of the proposed strategy, the proposed method is comprehensively evaluated by simulation and experiments. The results revealed that the optimal strategy can effectively improve both spatial and temporal resolution.

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Deterministic spin-orbit torque switching including the interplay between spin polarization and kagome plane in Mn3Sn

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Physical Review B 2024年第13期109卷 134433-134433页

作者： Zhengde Xu Xue Zhang Yixiao Qiao Gengchiau Liang Shuyuan Shi Zhifeng Zhu School of Information Science and Technology ShanghaiTech University Shanghai 201210 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China University of Chinese Academy of Sciences Beijing 100049 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Industry Academia Innovation School National Yang-Ming Chiao Tung University Hsinchu City 300093 Taiwan Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing 100191 China Shanghai Engineering Research Center of Energy Efficient and Custom AI IC Shanghai 201210 China

Previous research has demonstrated the spin-orbit torque (SOT) switching of Mn3Sn in configuration I, where the spin polarization σ resides within the kagome plane. However, this configuration has yielded several unexpected outcomes, giving rise to debates concerning the fundamental physics governing the switching process. Alternatively, in configuration II, σ is perpendicular to the kagome plane, which bears greater resemblance to the ferromagnetic system. In this study, we show successful SOT switching of Mn3Sn in configuration II, demonstrating behaviors more akin to ferromagnets, e.g., the critical switching current density (Jcrit) and external field (Hext) are in the order of 1010A/m2 and tens of Oersted, respectively. The switching result is also independent of the initial state. We further show that the distinctive spin structure of Mn3Sn leads to unique switching characteristics, including Jcrit increasing linearly with Hext and the opposite switching polarity to ferromagnetism. A switching phase diagram is further provided as a guideline for experimental demonstrations, offering a clear physical picture for the observed phenomena.

关键词： Antiferromagnetism Spin dynamics Spin-orbit torque Antiferromagnets Chiral magnets Landau-Lifschitz-Gilbert equation

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Reviewing the Impact of Machine Learning on Disease Diagnosis and Prognosis: A Comprehensive Analysis

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Open Pain Journal 2024年 17卷

作者： Chandan, Radha Raman Singh, Jagendra Ravi, Vinayakumar Shivahare, Basu Dev Alahmadi, Tahani Jaser Singh, Prabhishek Diwakar, Manoj Department of CS School of Management Science Uttar Pradesh Varanasi India School of Computer Science Engineering & Technology Bennett University Greater Noida India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of CSE Galgotias University Greater Noida India Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun India Graphic Era Hill University Dehradun India

Aim: This study aimed to explore how machine learning algorithms can enhance medical diagnostics through the analysis of illness imagery and patient data, assessing their effectiveness and potential to improve diagnostic accuracy and early disease detection. Background: This study highlights the critical role of machine learning in healthcare, particularly in medical diagnostics. By leveraging advanced algorithms to analyse medical data and images, machine learning enhances disease detection and diagnosis, contributing significantly to improved patient outcomes and the advancement of precision medicine. Objective: The objective of this study was to thoroughly analyse and evaluate the efficacy of machine learning algorithms in medical diagnostics, focusing on their application in interpreting illness images and patient data. The goal was to ascertain the algorithms' accuracy in disease diagnosis and prognosis, aiming to demonstrate their potential in revolutionizing healthcare through improved diagnostic precision and early disease detection. Methods: A systematic approach has been used in this study to evaluate machine learning algorithms' effectiveness in diagnosing diseases from medical images and data. It involved selecting pertinent datasets, applying and comparing models, like SVM and K-nearest neighbors, and assessing their diagnostic accuracy and performance, aiming to identify the most effective methodologies in medical diagnostics. Results: The results have highlighted the varying accuracy of machine learning algorithms in medical diagnostics, with a focus on the performance of models, such as SVM and K-nearest neighbors. A comparative analysis has illustrated the differential effectiveness of these algorithms across various diseases and datasets, underscoring their potential to enhance healthcare diagnostics. Conclusion: The study has concluded that machine learning algorithms have significantly improved medical diagnostics, offering varied effectiveness a

关键词： Algorithms Disease Disease diagnosis Health system Human Image-based disorders Machine learning

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A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

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Microsystems & Nanoengineering 2022年第3期8卷 277-290页

作者： Hao Wang Yue Wu Quchao Zou Wenjian Yang Zhongyuan Xu Hao Dong Zhjing Zhu Depeng Wang Tanxing Wang Ning Hu Diming Zhang State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Research Center for Itelligent Sensing Systems Zhejiang LabHangzhou 311121China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemistryThe Second Affiliated Hospital Zhejiang University School of MedicineDepartment of Clinical Medical EngineeringZhejiang UniversityHangzhou 310058China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-LinkCare Meditech Co LtdHangzhou 310011China State Key Laboratory of Transducer Technology Chinese Academy of SciencesShanghai 200050China

Cardiovascular disease is the number one cause of death in ***,cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug ***,cell-based techniques developed for arhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large *** carried out the frst report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm(MNDA)analysis for druginduced arrhythmia recognition and *** biosensing system cultured cardiomyocytes as physiologically relevant models,used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes,and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating *** best performing MNDA parameter,approximate entropy,enabled the system to recognize the appearance of sertindoleand norepinephrine-induced arrhythmia in the *** MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of *** new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

关键词： arrhythmia cardio analysis

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Delving into Machine Learning's Influence on Disease Diagnosis and Prediction

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Open Public Health Journal 2024年 17卷

作者： Shivahare, Basu Dev Singh, Jagendra Ravi, Vinayakumar Chandan, Radha Raman Alahmadi, Tahani Jaser Singh, Prabhishek Diwakar, Manoj Department of CSE Galgotias University Greater Noida India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CS School of Management Science Uttar Pradesh Varanasi India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun India Graphic Era Hill University Dehradun India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

Introduction/ Background: Medical diagnoses have increasingly depended on digitized images obtained through cutting-edge technology. These algorithms offer a promising avenue to transform diagnostic processes in healthcare, with their application scope continually widening due to ongoing advancements. This paper explores machine learning's role in clinical analysis and prediction, examining various studies that apply these techniques in clinical diagnosis, focusing on their use in analyzing images and providing accurate diagnoses. Materials and Methods: This study employs a comparative analysis approach, utilizing diverse machine learning algorithms like SVM, K-nearest neighbors, Random Forests, and Decision Trees to analyze digitized medical images and patient records. We extracted data from several medical databases, ensuring a varied and comprehensive dataset. We also evaluated the impact of different data characteristics on the algorithms' effectiveness, aiming to understand the variability in their diagnostic precision across various conditions. Results: The results indicate that machine learning algorithms, particularly SVM, K-nearest neighbors, Random Forests, and Decision Trees, demonstrate significant accuracy in diagnosing diseases from digitized images and medical records. SVM and Random Forests showed particularly high effectiveness in clinical diagnosis, suggesting their robustness across different medical conditions and datasets. These findings underscore the potential of machine learning to enhance diagnostic precision and predict illnesses early, aligning with the growing trend of technology-driven medical diagnostics. Discussion: The findings reinforce the pivotal role of machine learning in transforming medical diagnostics. The variability in algorithm performance highlights the necessity for tailored approaches, considering dataset specifics and the medical condition being diagnosed. This study underscores the potential of machine learning to enha

关键词： Algorithms Disease diagnosis Health system Human and disease Image-based disorder Machine learning

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Ethical Considerations in Explainable AI: Balancing Transparency and User Privacy in English Language-based Virtual Assistants

Ethical Considerations in Explainable AI: Balancing Transpar...

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Intelligent Communication Technologies and Virtual Mobile networks (ICICV), International Conference on

作者： Franciskus Antonius Alijoyo S. Sandya Sneha Sri Purnachandra Rao Alapati Dilyorjon Yuldashev Ponni Valavan M Center for Risk Management and Sustainability Indonesia School of Business and Information Technology STMIK LIKMI Bandung Indonesia Krishna University Andhra Pradesh Prasad V Potluri Siddhartha Institute of Technology Kanuru Vijayawada Andhra Pradesh India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy Tamil Nadu India

English Language-Based Virtual Assistants (ELB-VAs) are AI-powered systems designed to comprehend and respond to user queries in the English language, exemplified by virtual assistants like Siri or Alexa. The need for balancing transparency and user privacy in ELB-VAs is paramount due to their pervasive integration into daily life. Ensuring transparency imbues user trust, while safeguarding privacy addresses ethical concerns associated with personal data. Existing methods involve clear privacy policies, user-controlled data sharing settings, and encryption. However, drawbacks include user confusion and potential biases. To address these limitations, this study proposes a novel approach. Methodologically, it integrates pre-processing techniques such as lowercasing and tokenization, coupled with a Natural Language Understanding model. This model undergoes intent and entity recognition training, enhancing accuracy, and incorporates privacy-aware response generation, ensuring informative yet privacy-conscious interactions. The implementation of the study's results is carried out using Python tools, showcasing improved metrics and response times. This approach contributes to a more transparent and privacy-respecting user experience, aligning with evolving ethical norms and setting the stage for advancements in ELB-VA technology. This comprehensive exploration bridges existing gaps, emphasizing the ethical imperative of user-centric and privacy-aware AI interactions in ELB- VAs. The proposed NLU model exhibits a substantial increase in accuracy compared to other methods, with an impressive accuracy value of 99.1 % • On average, it outperforms the Random Forest and Decision Tree models by 15.7 percentage points, highlighting its superior predictive capabilities in the evaluated task. This comprehensive exploration aligns with evolving ethical norms and establishes a foundation for future advancements in ELB-VA technology.

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Smart Auditing Rules for Rental Subsidy Using Pfhrcnns Algorithm

SSRN

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SSRN 2023年

作者： Chen, Jieh-Haur Su, Mu-Chun Hsu, Shu-Chien Wei, Hsi-Hsien Su, Chih-Ko Department of Civil Engineering Taiwan Research Center of Smart Construction National Central University Jhongli Taoyuan320317 Taiwan APEC Society of Project Forensics Jhongli Taoyuan320317 Taiwan Safety and Health Association of Taiwan Miaoli Zhunan350007 Taiwan Department of Computer Science and Information Engineering Taiwan College of Electrical Engineering and Computer Science National Central University Jhongli Taoyuan320317 Taiwan Department of Civil and Environmental Engineering Hong Kong Polytechnic University Kowloon Hong Kong Department of Building and Real Estate The Hong Kong Polytechnic University Kowloon Hong Kong School of Civil Engineering Purdue University West LafayetteIN47907 United States

The aim of this research is to create an efficient and precise tool that can rapidly screen and eliminate unqualified applications, while also establishing consistent review guidelines across various cities and townships in Taiwan. The proposed approach involves developing a tool that employs PSO-based Fuzzy Hyper-rectangular Composite Neural networks (PFHRCNNS). Utilizing a dataset of 36,086 entries from the government data bank, each application encompasses 10 distinct features that can be further scrutinized. The resultant tool achieves an impressive accuracy rate of 98.6% and generates 66 recommended rules for assessing eligibility for rental subsidies. © 2023, The Authors. All rights reserved.

关键词： Fuzzy neural networks

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IoT based cost-effective smart parking system

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AIP Conference Proceedings 2023年第1期2724卷

作者： Haneef Khan Shams Tabrez Siddiqui Naved Alam Mohammad Rafeek Khan Md Imran Alam Department of Computer and Network Engineering College of Computer Science and Information Technology Jazan Saudi Arabia Jazan University 45142 Saudi Arabia Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia 45142 Saudi Arabia Department of Electrical Engineering Jamia Millia Islamia New Delhi 110025 India

The number of vehicles on the road has been increasing rapidly, especially in metropolitan cities. It's observed finding parking space is a time consuming and hectic task for the drivers, moreover, the environment is also get affected due to the level of fuel burn in the parking lot. Therefore, a practically feasible solution is demanded to find out parking space in small spam of time to save the driver's time and to keep our cities clean. In this paper, a framework is proposed to provide parking space for vehicles by using IoT and some other technologies. Moreover, application based hardware is proposed using microcontrollers, IoT devices and web applications. The proposed design also gives multiple options for the payment to save time and deadlocks in the parking area.

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A gazelle optimization expedition for key term separated fractional nonlinear systems with application to electrically stimulated muscle modeling

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Chaos, Solitons and Fractals 2024年 185卷

作者： Khan, Taimoor Ali Chaudhary, Naveed Ishtiaq Hsu, Chung-Chian Mehmood, Khizer Khan, Zeshan Aslam Raja, Muhammad Asif Zahoor Shu, Chi-Min International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of Information Management National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of Electrical and Computer Engineering International Islamic University Islamabad Pakistan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan

Various real-time processes are effectively represented with a fractional nonlinear autoregressive exogenous (F-NARX) model where estimation of model parameters is considered as an essential task. In this study, a population-based gazelle optimization algorithm (GOA) inspired by the evolutionary characteristics of gazelle's is exploited for the parameter estimation of the key term-separated F-NARX system. The Grünwald-Letnikov derivative, a fractional order calculus operator is integrated to develop the F-NARX system from a conventional non-linear autoregressive system. The mean square error-based merit function is developed and the effectiveness of the GOA for the F-NARX system is analyzed in terms of speedy convergence, estimation accuracy, complexity and robustness for different noise scenarios. The extendibility of the GOA is assessed through the estimation of stiff parameters of the electrically stimulated muscle model (ESMM) required for rehabilitation of paralyzed muscles. The efficacy of the GOA is endorsed through Wilcoxon signed rank statistical test in comparison with recent counterparts of Runge Kutta optimization algorithm, Whale optimization algorithm, and Harris Hawks optimization algorithm. © 2024 Elsevier Ltd

关键词： Parameter estimation

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