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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Machine Learning Based Pattern Recognition with HOG Features for Detection of Islanding 3

Machine Learning Based Pattern Recognition with HOG Features...

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3rd IEEE International Conference on electrical Power and Energy Systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Bera, Bidisha Basu, Sarmistha National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Global Institute of Science and Technology Department of Computer Science and Technology Haldia India Calcutta Institute of Engineering and Management Department of Physics Kolkata India

ISBN: (纸本)9798350390728

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a Machine Learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. Images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds. © 2024 IEEE.

关键词： Spectrographs

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A Novel Vision Transformer Approach for Driver Fatigue Detection from EEG Signal with Correlation-Based Channel Selection

A Novel Vision Transformer Approach for Driver Fatigue Detec...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Khan, Robiul Roy, Dipon Ali, Md. Wajed Refat, Kawsar Ahmed Naziullah, Shekh Pabna University of Science and Technology Department of Electrical and Electronic Engineering Pabna Bangladesh Daffodil International University Department of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Driver fatigue poses a critical threat to road safety, necessitating the development of robust detection methods to minimize traffic accidents and societal burdens. Deep neural networks have recently been effectively applied to Electroencephalography (EEG)-based driving fatigue detection. Nevertheless, most of the existing models, particularly those relying on extensive pooling, often struggle to capture long-range dependencies within images. To address this issue, we propose a Correlation-based Channel Selection (CCS) with a Vision Transformer (ViT) approach for driver fatigue detection using an EEG. Our methodology integrates a pioneering Channel Selection (CS) block to extract discriminative channels via CCS. This mechanism systematically identifies the most informative EEG channels crucial for fatigue detection. Subsequently, we leverage Continuous Wavelet Transform (CWT) to convert the selected EEG channels into a time-frequency spectral image. Finally, the resulting time-frequency spectral images, encompassing both temporal and spectral information, are concatenated and fed into the Vision Transformer (ViT) model to classify them as either normal or fatigued. The proposed model is evaluated on a publicly available EEG dataset containing recordings from twelve subjects. The model achieved superior accuracy: 95.83% for the effective selected combined subject and 99.925% for the average accuracy of each subject, demonstrating its potential for robust driver fatigue detection. © 2024 IEEE.

关键词： Deep neural networks

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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Highly Differentiated Target Detection under Extremely Low-Light Conditions Based on Improved YOLOX Model

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computer Modeling in engineering & sciences 2024年第8期140卷 1507-1537页

作者： Haijian Shao Suqin Lei Chenxu Yan Xing Deng Yunsong Qi School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas VegasNV89154USA Zhejiang Geely Automobile Research Institute Ningbo315336China

This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy,specifically tailored for environments characterized by markedly low luminance *** methodologies struggle with the challenges posed by luminosity fluctuations,especially in settings characterized by diminished radiance,further exacerbated by the utilization of suboptimal imaging *** envisioned approach mandates a departure from the conventional YOLOX model,which exhibits inadequacies in mitigating these *** enhance the efficacy of this approach in low-light conditions,the dehazing algorithm undergoes refinement,effecting a discerning regulation of the transmission rate at the pixel level,reducing it to values below 0.5,thereby resulting in an augmentation of image ***,the coiflet wavelet transform is employed to discern and isolate high-discriminatory attributes by dismantling low-frequency image attributes and extracting high-frequency attributes across divergent *** utilization of CycleGAN serves to elevate the features of low-light imagery across an array of stylistic *** computational methodologies are then employed to amalgamate and conflate intricate attributes originating from images characterized by distinct stylistic orientations,thereby augmenting the model’s erudition *** validation conducted on the PASCAL VOC and MS COCO 2017 datasets substantiates pronounced *** refined low-light enhancement algorithm yields a discernible 5.9%augmentation in the target detection evaluation index when compared to the original *** Average Precision(mAP)undergoes enhancements of 9.45%and 0.052%in low-light visual renditions relative to conventional YOLOX *** envisaged approach presents a myriad of advantages over prevailing benchmark methodologies in the realm of target detection within environments marked by an acute scarcity of lumi

关键词： Target detection extremely low-light wavelet transformation highly differentiated features YOLOX

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Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete EVent system specification

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International Journal of Vehicle Systems Modelling and Testing 2024年第1期18卷 33-61页

作者： Maatoug, Abdelfettah Kharoubi, Sahraoui Maaskri, Moustafa Belalem, Ghalem Mahmoudi, Said Faculty of Applied Sciences Science and Technology Department University of Tiaret Tiaret14000 Algeria Faculty of Mathematics and Computer Science Computer Science Department University of Tiaret Tiaret14000 Algeria Faculty of Applied Sciences Electrical Engineering Department University of Tiaret Tiaret14000 Algeria Faculty of Exact and Applied Sciences Computer Science Department University of Oran 1 Ahmed Ben Bella Oran31000 Algeria Faculty of Engineering Computer Science Department University of Mons Mons7000 Belgium

This work provides a basis for studying energy management optimisation in power-split hybrid electric vehicles (PSHEVs) to reduce fuel consumption and increase powertrain efficiency by enforcing a strategy related to battery level and vehicle speed. Three modes were used to operate the vehicle to meet different situations related to available energy and desired speed. These are particularly abundance mode, ascetic mode and optimal mode. For this, a system was designed based on several sub-models written in the discrete EVent system (DEVS) specification. The vehicle's power train and control strategy models were created in the DEVSimPy environment, and a joint simulation was carried out from the composite sub models until the overall model is reached. Simulation results under several driving cycles showed that the proposed system improves the energy consumption of the vehicle in different operating modes of the engines and gives more opportunities to adopt electric motors in vehicle transmission. © 2024 Inderscience Enterprises Ltd.

关键词： Energy management

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Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-11页

作者： Du, Quancheng Wang, Xiao Yin, Shouguo Li, Lingxi Ning, Huansheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence Anhui University Hefei China School of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indianapolis IN USA

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). The primary goal is to extract social interactions among agents more accurately. SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatio-temporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Experimental results on publicly available datasets demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. IEEE

关键词： Recurrent neural networks

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Isolation Enhancement in a Compact Four-Element MIMO Antenna for Ultra-Wideband Applications

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computers, Materials & Continua 2023年第4期75卷 911-925页

作者： Awais Khan Shahid Bashir Salman Ghafoor Hatem Rmili Jawad Mirza Ammar Ahmad Department of Electrical Engineering University of Engineering and TechnologyPeshawar25000Pakistan Department of Electrical Engineering University of Science and TechnologyBannu28100Pakistan School of Electrical Engineering and Computer Science(SEECS) National University of Sciences and Technology(NUST)Islamabad44000Pakistan Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah21589Saudi Arabia SEECS Photonic Research Group National University of Sciences and TechnologyIslamabad44000Pakistan

Mutual coupling reduction or isolation enhancement in antenna arrays is an important area of research as it severely affects the performance of an *** this paper,a new type of compact and highly isolated Multiple-Input-Multiple-Output(MIMO)antenna for ultra-wideband(UWB)applications is *** design consists of four radiators that are orthogonally positioned and confined to a compact 40×40×0.8 mm3 *** final antenna design uses an inverted L shape partial ground to produce an acceptable reflection coefficient(S11<−10 dB)in an entire UWB band(3.1–10.6)giga hertz(GHz).Moreover,the inter-element isolation has also been enhanced to>20 db for majority of the UWB *** antenna was fabricated and tested with the vector network analyzer(VNA)and in an anechoic chamber for scattering parameters and radiation ***,different MIMO diversity performance metrics are also measured to validate the proposed *** simulation results and the experimental results from the constructed model agree quite *** proposed antenna is compared with similar designs in recently published literature for various performance *** of its low envelope correlation coefficient(ECC<0.1),high diversity gain(DG>9.99 dB),peak gain of 4.6 dB,reduced channel capacity loss(CCL<0.4 b/s/Hz),and average radiation efficiency of over 85%,the proposed MIMO antenna is ideally suited for practical UWB applications.

关键词： Multiple input multiple output antennas envelope correlation coefficient defected ground structure ultra wide band diversity gain

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A Deep Neural Network Approach for Fault Detection in Industrial Motors: Enhancing Reliability and Efficiency in Industry 4.0 21

A Deep Neural Network Approach for Fault Detection in Indust...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ali, Hassan Ahyen, Mostafa Yaqub, Raziq Badawi, Ahmed Shifa, Noora Khandakar, Amith University of Doha for Science and Technology Department of Electrical Engineering Doha Qatar Alabama A and M University Department of Electrical Engineering and Computer Science Huntsville United States Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350378078

In the era of Industry 4.0, the integration of sophisticated monitoring and control technologies is essential for the reliability and efficiency of industrial equipment. This paper presents a deep neural network (DNN)-based approach for fault detection and classification in industrial motors. The approach identifies common motor faults, including bent shaft, loose vibration, loosely mounted components, electrical faults, and bearing vibration. The proposed DNN model demonstrated robust performance, achieving an overall training accuracy of 83.49% ± 0.43% and a test accuracy of 81.91 % ± 0.82%. The model also achieved a mean precision of 82.05% ± 0.77%, mean recall of 81.91 % ± 0.82%, and mean F1-Score of81.86%± 0.78%. The 95% confidence intervals for accuracy, precision, recall, and F1-Score were (80.77%, 83.04%), (80.98%, 83.11 %), (80.77%, 83.04%), and (80.78%,82.95%), respectively. These results demonstrate the DNN model's ability to precisely classify various motor faults with minimal variability, as evidenced by its narrow confidence intervals and low standard deviations. The model's reliability and efficacy for real-world applications in proactive maintenance and fault detection are confirmed by the consistent performance that is observed across cross-validation folds. © 2024 IEEE.

关键词： Predictive maintenance

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Ensemble Learning for Predicting Task Connectivity over Time in Cloud Data Centers

Ensemble Learning for Predicting Task Connectivity over Time...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Daraghmeh, Mustafa Agarwal, Anjali Jararweh, Yaser Electrical and Computer Engineering Concordia University Montreal Canada Jordan University of Science and Technology Computer Science Irbid Jordan

ISBN: (纸本)9781665494274

The rapid growth and interdependencies of cloud-hosted services and applications have made it imperative to optimize data center resource management while maintaining low operating costs. Inefficient resource use, rising energy consumption, and operating costs impact cloud provider's ability to provide high-quality services on an elastic basis. However, changing the selection and decision-making processes based on how the tasks are set up can improve scheduling and resource management in cloud data centers. In this paper, we develop a multivariate time series prediction model for task connectivity based on windowing characterization and ensemble learning methods. The high cardinality features are handled using a counter encoder, and the task trace data is transformed using a sliding window, from which features are extracted and used in conjunction with the task profile data to train and tune the candidate estimators. The best model outcomes are then used to construct an ensembled estimator. As part of the evaluation, a baseline comparison is performed in order to determine how well ensemble learning predicts task connectivity over time. The model outcomes are assisted using standard classification metrics such as accuracy, precision, and recall, including the F1 score, Kappa, and Matthews correlation coefficient. The results show that the proposed model outperformed the traditional models in most performance metrics, indicating the successful implementation of an ensemble learning approach for task connectivity predictions in large-scale cloud data centers. © 2023 IEEE.

关键词： Learning systems

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