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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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Multi-strategy Hybrid Coati Optimizer:A Case Study of Prediction of Average Daily Electricity Consumption in China

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Journal of Bionic Engineering 2024年第5期21卷 2540-2568页

作者： Gang Hu Sa Wang Essam H.Houssein Department of Applied Mathematics Xi’an University of TechnologyXi’an710054China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China Faculty of Computers and Information Minia UniversityMinia61519Egypt

The power sector is an important factor in ensuring the development of the national *** simulation and prediction of power consumption help achieve the balance between power generation and power *** this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity ***,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning *** improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the ***,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test ***,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting *** experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.

关键词： Forecast of average daily electricity consumption in China Coati optimization algorithm Three-parameter combination optimization model Circle chaotic mapping

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Classification of Functions Using Machine Learning

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International Journal of Unconventional Computing 2023年第2-3期18卷 217-247页

作者： Lukac, Martin Yessenbayeva, Aigerim Lewis, Michael Podlaski, Krzysztof Nazarbayev University Department of Computer Science Astana01000 Kazakhstan University of Lodz Faculty of Physics and Applied Informatics Lodz Poland

We study the approximation of function classification by machine learn-ing. We first determine all the NPN and NPNP classes of three-bit reversible and one to five-bits irreversible functions. Then we apply machine learning to learn the labels for each set of functions. We then search the parameter space for several optimization criteria, such as the size of the neural network, the architecture, and learning parameters that would allow the network under training to learn the NPN classification problem with up to 100% accuracy. We also determine if, for n-bit functions, a classification can be performed using two smaller net-works, each classifying n-1-bit functions. Additionally, we discuss the logic solvability of all four-bit functions using the game of Nono-grams. We determine a relation between the learnability of the classification and logic solvability. We determined that while there is a set of functions that can be classified by representing a function as a prod-uct of two subfunctions, a more complex model is needed to classify a n-bit NPN functions as a function of n-1-bit NPN functions. In addi-tion, we determine that approximate classification is always possible, with certain NPN classes being perfectly classified while others are not. Moreover, we can observe a direct relationship between the learnability of certain classes and the speed of finding a solution for these classes when used in a logic game. Finally, we show that the approximation of the classification using machine learning can replace standard database-based classification. The advantages of machine learning methods are end-to-end learning, the lack of circuit design requirements for classifi-cation, and the ability to easily learn the class distinction problem. © 2023 Old City Publishing, Inc.

关键词： Classification (of information)

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Fog Computing - Desktop Review 4

Fog Computing - Desktop Review

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4th International Multidisciplinary Information technology and Engineering Conference, IMITEC 2024

作者： Sithole, Sicelo Prince Sonhera, Naume Zuva, Tranos Vaal University of Technology Faculty of Applied and Computer Sciences Department of Computer Science Vanderbijlpark South Africa

ISBN: (纸本)9798350387988

Fog computing is an innovative approach that brings cloud computing capabilities to the edge of the network, and it has great potential for enterprises looking to improve real-time data processing and lower latency. However, the adoption of fog computing in organizations remains relatively unexplored. This review paper aims to investigate the factors influencing fog computing adoption in organizations. Through a systematic review of existing literature, case studies, and expert opinions, this paper identifies and analyses the unique adoption factors. The review examines challenges, characteristics, benefits, and Open Fog Computing Issues with the Internet of Things (IoT), shaping fog computing adoption decisions in organizations across various industries. This analysis highlights fog computing advantages over traditional cloud computing approaches, such as low latency, improved data privacy, and enhanced scalability. Furthermore, this review investigates the challenges hindering the adoption of fog computing, including security concerns, implementation complexity, connectivity, and lack of awareness, as well as open fog computing issues with IoT. Key findings indicate that while fog computing can offer low-latency solutions, organizations face considerable challenges, including security risks, implementation complexity, and a lack of skilled personnel. By synthesizing and analysing the findings, this paper provides valuable insights for decision-makers, researchers, and policymakers seeking to promote and facilitate fog computing adoption. © 2024 IEEE.

关键词： Fog computing

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Enhance Efficient of Support Vector Machine with CNN and PSO for solving Handwritten Digits Recognition 28

Enhance Efficient of Support Vector Machine with CNN and PSO...

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28th International computer science and Engineering Conference, ICSEC 2024

作者： Janprasit, Siwach Punkong, Narong Ratanavilisagul, Chiabwoot Kosolsombat, Somkiat Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand Digital Technology for Business Faculty of Management Science Kanchanaburi Thailand Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand Thammasat University Data Science and Innovation College of Interdisciplinary Studies Thailand

ISBN: (纸本)9798350366860

handwritten digit recognition is a crucial task in various fields such as postal mail sorting, bank check processing, and digitizing handwritten documents. This research aims to compare the effectiveness of using Convolutional Neural Network (CNN) for feature extraction combined with Support Vector Machine (SVM) for classification, against the traditional method of using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for feature extraction. Additionally, Particle Swarm Optimization (PSO) is applied to optimize SVM parameters to further enhance model performance. The experiments are conducted on two datasets: The MNIST dataset and an individual handwriting dataset from 50 participants. The results indicate that the proposed method of using CNN for feature extraction and PSO-optimized SVM achieves superior accuracy compared to traditional DWT and DCT methods. Specifically, the proposed method achieved an accuracy of 99.05% on the MNIST dataset and 67.79% on the individual handwriting dataset, demonstrating its robustness and effectiveness in handwritten digit recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Development of Robot to Improve Learning of Programming Skills among Students

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第3期4卷 1-11页

作者： Hashim, Ahmad Sobri Aminu Muazu, Aminu Yusof, Mohammad Ateff Md. Arshad, Noreen Izza Computer & Information Sciences Department Faculty of Science and Information Technology Universiti Teknologi PETRONAS Malaysia Computer Sciences Department Faculty of Natural and Applied Science Umaru Musa Yar’adua University Katsina Nigeria

The Fourth Industrial Revolution (IR4.0) has shifted the mindsets of engineering students on the importance of IT skills for current and future engineering related jobs. Nowadays, programming is the most fundamental skill that needs to be learnt by the students using mBot technology. The mBot technology is consider as programmable educational robot designed for beginners to learn basic programming concepts which can be assessed and evaluated via bloom's taxonomy framework. It can be a daunting task to learn programming, especially to new students who do not have any prior experience in coding. Average and low performing students are lacking algorithmic skills, where they could not visualize how the programming concepts work. Therefore, this paper presents the effectiveness of using robot to improve students’ learning of the programming concepts. In designing the learning modules, bloom’s taxonomy model and problem-based learning are adopted using mBot. Moreover, a low-cost and pre-programmed line follower robot has been used to demonstrate the outputs from the programs written in a more interactive manner. For the evaluation, pre-test and post-test of Quasi experimental design have been applied involving 40 students who were identified and categorized as average and low performing groups in the course. The findings show that a significant improvement has been observed from students’ performance and motivation. As such, the students’ performance is measured based on two phases of experiments. Whereas the students’ motivation is measured based on four motivation attributes: self-efficacy, active learning strategy, programming learning value and performance goal. © 2023 College of Education, Al-Iraqia University. All Rights Reserved.

关键词： Students

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Dzyaloshinskii-Moriya-type spin-spin interaction from mixed-parity superconductivity

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Physical Review B 2025年第6期111卷 L060504-L060504页

作者： Jabir Ali Ouassou Takehito Yokoyama Jacob Linder Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences NO-5528 Haugesund Norway Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Center for Quantum Spintronics Department of Physics Norwegian University of Science and Technology NO-7491 Trondheim Norway

Interacting impurity spins adsorbed on surfaces have been suggested as basic components for applications in quantum computation and spintronics. Such spins usually prefer a parallel or antiparallel configuration, but weakly noncollinear alignments are possible due to the Dzyaloshinskii-Moriya interaction (DMI) that arises in the presence of relativistic spin-orbit coupling. Here, we show that an effective Dzyaloshinskii-Moriya-type interaction (DMTI) can emerge purely from superconducting correlations without any spin-orbit interaction. We give an analytical proof and provide a numerical study which shows that DMTI arises in mixed-parity superconductors solely from the superconducting pairing. Moreover, we show that the same effect can be realized in Josephson junctions between s-wave and p-wave superconductors, where a phase bias toggles the DMTI entirely on and off. These results enable a way to engineer spin textures using superconducting order.

关键词： Shear waves

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Numerical solution hybridized by machine- leaning-based algorithm to provide an efficient method for analyzing thermomechanical shock behavior of the circumferentially-graded graphene-plates reinforced composite sandwich panel (sept, 10.1080/15376494.2023.2195417, 2023)

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第29期31卷 12345-12348页

作者： Han, Shaoyong Ye, Qianqian Hakamy, A. Abd-Elnaiem, Alaa M. Changzhou Vocational Institute of Industry Technology Changzhou Jiangsu China Postdoctoral Scientific Research Workstation Bank of Zhengzhou Zhengzhou Henan China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou Zhejiang China Department of Physics Faculty of Applied Science Umm Al-Qura University Makkah Saudi Arabia Physics Department Faculty of Science Assiut University Assiut Egypt

The present research bridges the gap between a numerical solution with efficient machine-learning-based algorithms to provide a strong platform for predicting thermomechanical shock behavior of the sandwich cylindrical panels with polymer-made core and circumferentially-graded graphene-plates reinforced (CG-GPLR) nanocomposite face-sheets. The comprehensive declaration of the elasticity theory is considered to establish the governing equations of the system in three orthogonal directions. The well-known Lord-Shulman heat transfer theorem is implemented to present the time-variant nature of the system’s heat. The differential quadrature method (DQM) is used as a numerical approach to solve the spatial dependency of the governing differential equations. While the Laplace transform solves the time dependency of the governing differential equations. In order to decline the computational cost of predicting the time-variant analysis of the thermomechanical shock response of the system, an efficient machine-learning-based algorithm is employed in a way that it only requires the thermomechanical shock information of the selective set of points known as the training set. After completion of the training procedure, this machine-learning predictor can easily predict the thermoelastic response of all points included either as the train set or a new point. Validation of the applied solution is performed through a comparative process between the present results with those determined in the published literature. The results of this study are in excellent compatibility with those obtained in a solo numerical solution.

关键词： Coupled thermoelasticity analysis sandwich cylindrical shell optimized machine-learning regressor spatial and temporal stress distribution thermomechanical shock response

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Electrochemical performance of a symmetric supercapacitor device designed using laser-induced multilayered graphene

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新型炭材料(中英文) 2024年

作者： Gargi Dhiman Kavita Kumari Bon-Heun Koo Faheem Ahmed Nagih M.Shaalan Saurabh Dalela Parvez A.Alvi Ranjeet Kumar Brajpuriya Shalendra Kumar Department of Physics School of Advanced EngineeringUPES Industrial Technology Research Institute Changwon National University Changwon Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia Department of Physics College of Science King Faisal University Physics Department Faculty of Science Assiut University Department of Pure & Applied Physics University of Kota Department of Physical Science

This study demonstrates a facile and economical approach for fabricating laser-induced graphene（LIG）, which results in improved electrochemical performance. Polyimide polymer was used as a starting material for LIG synthesis and irradiated under ambient conditions using a CO2laser. The prepared LIG samples were characterized by Raman spectroscopy and FTIR, which validated the formation of multilayered graphene containing sp2 hybridized C=C bonds. The FE-SEM revealed three-dimensional sheetlike structures, while HR-TEM images displayed lattice planes with an interplanar spacing of approximately 0.33 nm, corresponding to the（002） plane of graphene. The electrochemical performance of the as-prepared LIG samples demonstrated a remarkable areal specific capacitance（CA） of 51 mF cm-2(170 F g-1) at 1 mA cm-2(3.3 A g-1) in a three-electrode configuration with 1M KOH as the aqueous electrolyte. The LIG electrodes produced an energy density of ～3.5 μWh cm-2and a power density of ～350 μW cm-2,demonstrating significant energy storage capabilities. Additionally, the electrodes exhibited excellent cyclic stability, retaining 87%of their specific capacitance after 3 000 cycles at 1 mA/cm2. The symmetric supercapacitor fabricated with LIG electrodes and the tested 1M KOH electrolyte showed a specific capacitance of 23 mF cm-2and maintained excellent retention after 10,000 cycles,showing LIG's potential for supercapacitor applications.

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and Engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical sciences. All rights reserved.

关键词： Electroencephalography

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