检索结果-内蒙古大学图书馆

Machine Learning-based Optimal Framework for Internet of Things Networks

computers, Materials & Continua 2022年第6期71卷 5355-5380页

作者： Moath Alsafasfeh Zaid A.Arida Omar A.Saraereh Department of Computer Engineering College of EngineeringAl-Hussein Bin Talal UniversityMa’anJordan Abdul Aziz Ghurair School of Advanced Computing(ASAC) LTUCAmmanP11118Jordan Department of Electrical Engineering Engineering FacultyThe Hashemite UniversityZarqa13133Jordan

Deep neural networks(DNN)are widely employed in a wide range of intelligent applications,including image and video ***,due to the enormous amount of computations required by ***,performing DNN inference tasks locally is problematic for resourceconstrained Internet of Things(IoT)*** cloud approaches are sensitive to problems like erratic communication delays and unreliable remote server *** utilization of IoT device collaboration to create distributed and scalable DNN task inference is a very promising *** existing research,on the other hand,exclusively looks at the static split method in the scenario of homogeneous IoT *** a result,there is a pressing need to investigate how to divide DNN tasks adaptively among IoT devices with varying capabilities and resource constraints,and execute the task inference *** major obstacles confront the aforementioned research problems:1)In a heterogeneous dynamic multi-device environment,it is difficult to estimate the multi-layer inference delay of DNN tasks;2)It is difficult to intelligently adapt the collaborative inference approach in real *** a result,a multi-layer delay prediction model with fine-grained interpretability is proposed ***,for DNN inference tasks,evolutionary reinforcement learning(ERL)is employed to adaptively discover the approximate best split *** show that,in a heterogeneous dynamic environment,the proposed framework can provide considerable DNN inference *** the number of devices is 2,3,and 4,the delay acceleration of the proposed algorithm is 1.81 times,1.98 times and 5.28 times that of the EE algorithm,respectively.

关键词： IoT distributed computing neural networks reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

引用

computer Systems Science & engineering 2022年第10期43卷 87-102页

作者： Safaa El-Gazar Walid El-Shafai Ghada El-Banby Hesham F.A.Hamed Gerges M.Salama Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department of Electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last *** achieves safe access to systems using biometrics instead of the traditional *** utilization of a biometric in its original format makes it usable only ***,a cancelable biometric template should be used,so that it can be replaced when it is *** biometrics aims to enhance the security and privacy of biometric *** encryption is an efficient technique to be used in order to generate cancelable biometric *** this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification *** considered biometric in this paper is the speech *** speech signal is transformed into its ***,the spectrogram is encrypted using two cascaded optical encryption *** first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption *** OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase Masks(RPMs).After the two cascaded optical encryption algorithms,the cancelable template is *** verification is implemented through correlation estimation between enrolled and test templates in their encrypted *** the correlation value is larger than a threshold value,the user is *** threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two *** implementation of optical encryption is adopted using its software rather than the optical *** efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation *** can improve the biometric data security without deteriorating the recognition *** results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Charact

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

来源：评论

学校读者我要写书评

暂无评论

Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Hamiltonian learning using machine-learning models trained with continuous measurements

引用

Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

来源：评论

学校读者我要写书评

暂无评论

Developing Engagement in the Learning Management System Supported by Learning Analytics

引用

computer Systems Science & engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

来源：评论

学校读者我要写书评

暂无评论

Machine Learning based Comparative Analysis of Cervical Cancer Risk Classifications Algorithms 2

Machine Learning based Comparative Analysis of Cervical Canc...

引用

2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Ganguly, Tania Pati, Peeta Basa Deepa, K. Singh, Tripty Ozer, Tolga Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India Afyon Kocatepe University Technology Faculty Electric Electronic Engineering Afyonkarahisar Turkey

ISBN: (纸本)9798350315905

Cervical cancer is a major health concern for women worldwide, and early detection is essential for successful treatment. Since symptoms often do not appear until later stages, early screening is necessary. Machine learning can help classify cervical cancer risk by analyzing patient datasets and identifying the important factors that predict the likelihood of emerging cervical cancer. This paper evaluates six different machine-learning approaches for analyzing risk factors associated with cervical cancer using a dataset of 838 instances with 36 features. Results show that the SVM classifier performs the best, with an accuracy of 99.60% which emphasize the possibility of utilizing machine learning to enhance the precision of cervical cancer risk assessment. This can result in the development of better screening and prevention techniques for cervical cancer, which can be more effective in identifying and managing this disease. © 2023 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

引用

IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1026-1036页

作者： Jingyang Mao Xiangyu Meng Derui Ding Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China Division of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803 USA IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership ***,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy ***,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and *** the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation ***-time recursive algorithms are given to find the minimal ellipsoid containing the true ***,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

关键词： Affine model membership functions set-membership filtering stability Takagi-Sugeno fuzzy modeling

来源：评论

学校读者我要写书评

暂无评论

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

引用

computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimizing Thermo-Electric Generation with Quasi-Z-Source Converter in Energy Harvesting for Wearable Devices 3

Optimizing Thermo-Electric Generation with Quasi-Z-Source Co...

引用

3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Lotfy, Mostafa Wageh Hussein, Aziza I. Dept. of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (纸本)9781643685649

Recent advances in semiconductors industry and microelectronics have created new opportunities for integrating various technologies in energy harvesting projects. These advancements have simplified the process of capturing energy from different sources, such as thermal energy, and converting it into electrical power. Thermo-Electric Generators (TEGs) are commonly used in many applications such as wearable devices. However Thermo-Electric Generators produce very low output voltages, which is insufficient for most low-power electronic circuits to operate. Therefore, a boost converter is essential to step up the voltage output from the TEG to a usable level. In this paper, a DC-DC converter with a high performance in terms of voltage gain is implemented. The proposed quasi-Z-source (q-ZSC) DC-DC converter not only offers high voltage gain but also several significant advantages. The proposed q-ZSC is capable of both boosting and regulating the voltage output efficiently, even under variable input conditions, making it highly suitable for low-power energy harvesting systems such as Thermo-Electric Generators. The design is simulated using MATLAB Simulink software, focusing on integrated circuits, with a strong emphasis on optimizing the circuit's efficiency. This work underscores critical factors in improving energy harvesting circuits, enhancing their performance and effectiveness in low-power applications for wearable technology. © 2024 The Authors.

关键词： DC generator motors

来源：评论

学校读者我要写书评

暂无评论

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

引用

computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：