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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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Reinforcement Learning-based Optimization of EBike Charging infrastructure

Reinforcement Learning-based Optimization of EBike Charging ...

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IEEE/ACM International Symposium on Big Data computing (BDC)

作者： Muddsair Sharif Huseyin Seker Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham U.K. Department of Information Systems College of Computing and Informatics The University of Sharjah Sharjah UAE

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

This paper presents a novel technique for enhancing the allocation of resources in the charging infrastructure for e-bikes by employing deep reinforcement learning (DRL) in a context-specific manner. With the evolving transportation land-scape and the increasing popularity of electric micro-mobility solutions, efficient resource management plays a vital role. The methodology involves utilizing deep Q networks (DQN) in virtual environments to dynamically allocate charging resources based on contextual parameters. Through empirical analysis, we demonstrate the efficacy of our approach in achieving optimal resource allocation while considering the needs of diverse stakeholders. Findings suggest that the DQN model, when integrated with context-awareness and stakeholder objectives, enables efficient resource allocation, maximize fleet utilization, ensures charging station availability, enhances user satisfaction, and promotes environmental sustainability. The findings of this study offer valuable insights for policymakers, urban planners, and industry stakeholders seeking to enhance the sustainability and effectiveness of micro-mobility services in urban settings. Overall, this study contributes to the advancement of sustainable urban mobility solutions and underscores the potential of DRL in addressing complex urban transportation challenges.

关键词： Computational modeling Green products Transportation Charging stations Resource management Stakeholders Sustainable development Optimization Carbon emissions Context modeling

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Numerical Modelling of Capacitive Electrodes for Biomedical Signals Measurement 44

Numerical Modelling of Capacitive Electrodes for Biomedical ...

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44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021

作者： Klaic, L. Stanesic, A. Cifrek, M. Faculty of Electrical Engineering and Computing Department of Electronic Systems and Information Processing Zagreb Croatia

ISBN: (纸本)9789532331011

Surface electromyography (sEMG) uses noninvasive technique of measuring muscle action potentials on the skin surface. Numerical methods are often the first step in such a process of electrodes and biomedical electronic systems design. Thoughtful simulation, that precedes physical implementation, may speed up the product development, foreseeing its potential deficiencies in the earliest phase. Familiarisation with all the possibilities of such platforms and considerate model design in distinctively defined steps is the key to time, memory, processing power and money savings. The goal of this research is to explore the impact of the size and shape of capacitive electrode on the quality of capacitive coupling. For this purpose, the COMSOL Multiphysics software is used. In the past thirty years, a considerable number of papers have been dedicated to the implementation of various biceps muscle anatomy approximations. The primary issue of such finite element method models are realistic representations of the generation of action potentials in muscle units and their propagation through tissues and skin. In this paper, a concentric cylinder model of upper arm is proposed in order to allow stationary analysis of electrical field distribution on the electrode-fabric interface based on multiple parameters, such as electrode shape and distance or thickness and dielectric permittivity of fabric. © 2021 Croatian Society MIPRO.

关键词： Finite element method

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Application of Artificial Intelligence in Healthcare systems: A Scientometric Analysis

Application of Artificial Intelligence in Healthcare Systems...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Samuel-Soma M. Ajibade Angela Siew Hoong Lee Muhammed Basheer Jasser Tomiwa Faith Akinola Dept of Computing and Information Systems Sunway University Selangor Malaysia Dept of Comp. Engineering Istanbul Ticaret Univ Turkey Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Malaysia Department of Health Promotion Faculty of Public Health University of Ibadan Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This bibliometric study examines the worldwide progression of machine learning applications in medical and healthcare research between 2010 and 2023. A large dataset of published articles pertaining to machine learning applications in the medical and healthcare sectors is mined for useful information in this study. The data extraction procedure involves retrieving pertinent information from primary sources such as journals, books, and conference proceedings. Subsequently, the retrieved data is subjected to analysis to discern the patterns and tendencies in the use of machine learning in medical and healthcare research. A total of 1,220 publications were found in the Scopus database over the past 14 years. In addition, the study demonstrated that most AIHS research has concentrated on artificial intelligence applications to address a wide range of problems, including patient data security and chronic medical difficulties (such as cardiovascular disorders). Policymakers, healthcare practitioners, and researchers around the world may find this study's conclusions helpful. Emerging ethical concerns, integration, and real-world uses in smart healthcare systems, and the Internet of Things (IoT), could be the focus of future research.

关键词： Databases Reviews Data security Bibliometrics Medical services Machine learning Stakeholders

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IAACS: Image aesthetic assessment through color composition and space formation

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Virtual Reality & Intelligent Hardware 2023年第1期5卷 42-56页

作者： Bailin YANG Changrui ZHU Frederick WBLI Tianxiang WEI Xiaohui LIANG Qingxu WANG School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China Department of Computer Science University of DurhamDH13LEUnited Kingdom College of Intelligence and Computing Tianjin UniversityTianjin 300072China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China

Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images,correctly assessing the aesthetic quality of an image remains a challenge for deep learning. Methods To address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation(IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module(FET) and the Image Net weight-based classification model. Result By fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.

关键词： Image aesthetic assessment Color composition Space formation Deep learning

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EnsembleCAM: Unified Visualization for Explainable Cervical Cancer Identification

EnsembleCAM: Unified Visualization for Explainable Cervical ...

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International Research Conference on Smart computing and systems engineering (SCSE)

作者： Niruthikka Sritharan Nishaanthini Gnanavel Prathushan Inparaj Dulani Meedeniya Pratheepan Yogarajah Department of Computer Science and Engineering University of Moratuwa Moratuwa Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

Cervical cancer, which is ranked fourth among cancers affecting women, is highly treatable when detected early through the pap smear test. Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), analyze pap smear images, yet their "Black-Box" nature raises transparency concerns in medical diagnostics. This paper introduces a solution named EnsembleCAM to enhance interpretability by unifying visual explanations through the combination of diverse Class Activation Maps (CAMs). Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques, develop an XceptionNet based binary classification model with an accuracy of 89% and apply GradCAM, GradCAM++, Score-CAM, Eigen-CAM and LayerCAM on this classifier. Then, the novel EnsembleCAM is constructed taking the median of activation maps from the five individual CAM methods. The analysis of activation maps of each CAM method and EnsembleCAM confirmed that in activation maps of EnsembleCAM, higher activation values were more concentrated around the nucleus which is the most important region in indicating cervical malignancy. The evaluation using pixel flipping performance metric also proved that the EnsembleCAM effectively recognises regions vital to the model's decision-making through its steepest drop in the mean prediction score when the pixels in the region contributing most to the model's decision were flipped.

关键词： Visualization Decision making Closed box Predictive models Cams Reliability Medical diagnosis

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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A Novel Parallel computing Confidentiality Scheme Based on Hindmarsh-Rose Model

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Computers, Materials & Continua 2023年第8期76卷 1325-1341页

作者： Jawad Ahmad Mimonah Al Qathrady Mohammed SAlshehri Yazeed Yasin Ghadi Mujeeb Ur Rehman Syed Aziz Shah School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH105DTUK Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi112612United Arab Emirates School of Science Technology and HealthYork St John UniversityYorkYO317EXUK Research Centre for Intelligent Healthcare Coventry UniversityCoventryCV15FBUK

Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this ***,given the limitations of computers,it becomes evenmore important to employ efficient and fast image encryption *** 1D chaotic maps offer a practical approach to real-time image encryption,their limited flexibility and increased vulnerability restrict their practical *** this research,we have utilized a 3DHindmarsh-Rosemodel to construct a secure *** randomness of the chaotic map is assessed through standard *** proposed system enhances security by incorporating an increased number of system parameters and a wide range of chaotic parameters,as well as ensuring a uniformdistribution of chaotic signals across the entire value ***,a fast image encryption technique utilizing the new chaotic system is *** novelty of the approach is confirmed through time complexity *** further strengthen the resistance against cryptanalysis attacks and differential attacks,the SHA-256 algorithm is employed for secure key *** results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach,highlighting its exceptional suitability for secure ***,the security of the proposed scheme has been compared with stateof-the-art image encryption schemes,and all comparison metrics indicate the superior performance of the proposed scheme.

关键词： Hindmarsh-rose model image encryption SHA-256 parallel computing

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Signs to Speech

Signs to Speech

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Networking and Communications (ICNWC), International Conference on

作者： Harsh Vardhan Harsavardhan S M Lakshmi Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and TechnologyKattankulathur Chennai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

This research introduces an innovative Sign Language to Speech Conversion Model using Convolutional Neural Networks (CNNs) to address communication barriers for the people who are deaf and unable to hear properly. The model employs deep learning for the automatic extraction of spatial patterns from sign language images. A diverse sign language dataset is carefully curated, undergoing Pre-processing for improved quality. The CNN architecture, designed for adaptability, captures local and global features, enabling accurate *** the training phase, the model learns to map sign language gestures to spoken language representations. Performance evaluation methods which include accuracy, recall, precision and F1 score, demonstrate the effectiveness of the model. Results underscore the significance for the deaf community. The discussion explores broader implications in education, healthcare, and social interactions, acknowledging limitations and proposing future research *** conclusion, the Sign Language to Speech Conversion Model contributes to sign language recognition, offering an advanced solution for communication barriers and promoting inclusivity in diverse social settings.

关键词： Training Performance evaluation Sign language Adaptation models Refining Speech recognition Medical services

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Solving Sequential Competitive Facility Location Challenges: Using Parallel Genetic Algorithms

Solving Sequential Competitive Facility Location Challenges:...

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International Conference on Smart computing (SMARTCOMP)

作者： Sadan Kulturel-Konak Abdullah Konak Lawrence V. Snyder The Division of Engineering Business and Computing Penn State Berks Reading PA USA Department of Industrial and Systems Engineering Lehigh University Bethlehem PA USA

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

In smart computing, addressing combinatorial optimization game theory problems is essential for resolving intricate challenges involving limited resources and significant strategic interactions among decision-makers. In this paper, we solved sequential combinatorial optimization game theory problems using an evolutionary algorithm called the Game-Theoretic Genetic Algorithm (GTGA), where it is computationally infeasible to enumerate all players' decision options. The computational experiments show that the proposed fitness evaluation methods enable the GTGA to converge to true Stackelberg equilibria. Our research highlights the practical applicability of the GTGA in addressing real-world problems characterized by dynamic decision-making and strategic interactions.

关键词： Heuristic algorithms Evolutionary computation Complexity theory Game theory Optimization Genetic algorithms

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