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7th International Conference on Data science and Its Applications, ICoDSA 2024

作者： Darmawahyuni, Annisa Nurmaini, Siti Tutuko, Bambang Naufal Rachmatullah, Muhammad Marcelino, Jordan Isdwanta, Rendy Firdaus, Firdaus Iriani Sapitri, Ade Islami, Anggun Faculty of Engineering Universitas Sriwijaya Indonesia Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya South Sumatera Palembang Indonesia

ISBN: (纸本)9798350365351

Feature selection (FS) analyzes the most important features to enhance classification accuracy during preprocessing. The objective of FS techniques is to reduce the number of input variables and computational load to achieve optimal classification performance. However, finding the best features is challenging due to high dimensionality, with many features but limited data samples. Metaheuristic optimization algorithms (MOAs) are crucial in creating the best subset of features through exploration and exploitation phases. This study experiments with Teaching-Learning-based Optimization (TLO) as MOA from several diverse binary medical datasets. TLO draws attention to its excellence in improving performance with minimal features. TLO is a fairly straightforward algorithm without algorithm-specific parameters, with rapid convergence and implementation yet effectiveness. In this study, we have also conducted four classifiers to obtain the best model for minimizing the features and maximizing the performance of the model, i.e., decision tree (DT), random forest (RF), k-Nearest Neighbour (KNN), and support vector machine (SVM) with the default parameter of each classifier. As the result, TLO shows its excellence in achieving minimal features, but still outstanding performance with 100% accuracy, sensitivity, specificity, and precision. TLO is successfully applied to varying diverse medical datasets with varying number of features. © 2024 IEEE.

关键词： Contrastive Learning

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Artificial intelligence for sustainable internet research

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International Journal of computers and Applications 2022年第6期44卷 501-502页

作者： Haldorai, Anandakumar Sharif, Muhammad Ravana, Sri Devi Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering COMSATS University Islamabad Wah Cantt Pakistan Department of Information System Faculty of Computer Science & Information Technology University of Malaya Malaysia

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

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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

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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A Q-Learning Hybrid BRKGA Applied to the Knapsack Problem with Forfeits 13

A Q-Learning Hybrid BRKGA Applied to the Knapsack Problem wi...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Souto, Gabriel Aritsugi, Masayoshi Mendonca, Israel Simonetti, Luidi González, Pedro Henrique Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350308365

This paper presents a hybrid BRKGA (Q-HBRKGA) that combines BRKGA with Q-learning and a Local Branching technique to solve the Knapsack Problem with Forfeits(KPF). The aim is to tackle this problem, a vari-ant of the 0-1 Knapsack Problem, where a penalty cost is imposed when both items of a forfeit pair are included in the solution. Notably, Q-HBRKGA achieves superior results compared to existing approaches, establishing a valid strategy for KPF problem-solving. Computational experiments, utilizing benchmark instances, demonstrate the efficacy of Q-HBRKGA by outperforming state-of-the-art methods documented in the literature. © 2024 IEEE.

关键词： Reinforcement learning

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Unveiling Ethical Challenges and Malpractice in Academic Research and Integrity 3

Unveiling Ethical Challenges and Malpractice in Academic Res...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Singh Bist, Ankur Rawat, Bhupesh Rahardja, Untung Hardini, Marviola Kamal, Mustofa Graphic Era Hill University Faculty of Computer Science & Engineering Uttarakhand India Graphic Era Hill University Faculty of Computing Uttarakhand India University of Raharja Faculty of Information Technology Tangerang Indonesia University of Raharja Faculty of Computer System Tangerang Indonesia

ISBN: (纸本)9798350367492

In the pursuit of knowledge and innovation, academic research stands as a cornerstone of intellectual progress. However, the integrity of this process is susceptible to malpractice, raising profound ethical concerns. This research paper delves into the nuanced landscape of malpractice and ethical considerations within the realm of academic research, aiming to shed light on the challenges that undermine the credibility and reliability of scholarly endeavours. The study explores various forms of malpractice, including plagiarism, data fabrication, and authorship disputes, which have the potential to erode the foundations of academic integrity. Through a combination of literature reviews, case studies, and expert interviews, the research seeks to identify patterns and motivations behind such malpractices. The findings reveal that systematic solutions are necessary to address these issues, and propose the integration of advanced technological tools to enhance ethical oversight. This study contributes to the ongoing discourse on ethics in academic research by providing comprehensive insights and recommendations to uphold ethical standards. © 2024 IEEE.

关键词： Confidentiality Ethics Informed Consent Plagiarism Professional Misconduct

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Myorcadial Infarction Classification with 15 Lead Electrocardiogram (ECG) Signal using Hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BILSTM) 8

Myorcadial Infarction Classification with 15 Lead Electrocar...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Mirza, Ahmad Haidar Nurmaini, Siti Partan, Radiyati Umi Herdiansyah, Muhammad Izman Universitas Sriwijaya Faculty of Computer Science Universitas Bina Darma Faculty of Engineering Palembang Indonesia University of Sriwijaya Intelligent System Research Group Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Medicine Palembang Indonesia Universitas Bina Darma Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350342604

More than three branches of death due to cardiovascular disease. Myocardial infarction (MI) is one of the most dangerous forms of coronary heart disease, with the highest mortality rate among other types of coronary heart disease. MI is caused by reduced oxygen demand in heart muscle tissue when oxygen is pumped throughout the body. To detect MI, experts can use ECG signals. ECG is a tool that can be used to record the activity of biological signals formed due to the electrical activity of the heart. Various studies have been conducted using deep learning to help detect and predict MI. This research proposes a hybrid model of CNN-LSTM and CNN-BILSTM model techniques to predict MI. The MI dataset from the Physiobank (PTB) ECG database consisting of 52 normal subjects and 148 patients was used for training models. The proposed deep learning model uses automatic feature extraction. This study suggests a simple deep learning architecture with automatic feature extraction to learn from data. Evaluation of model performance resulted in an average accuracy of 98.80% for the CNN-LSTM model and 99.90% for the CNN-BILSTM model. © 2023 IEEE.

关键词： Long short-term memory

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Revisiting the thermal ageing on the metallised polypropylene film capacitor:from device to dielectric film

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High Voltage 2023年第2期8卷 305-314页

作者： Yushuang He Feipeng Wang Guoqiang Du Lei Pan Kaizheng Wang Reimund Gerhard Ronald Plath Pawel Rozga Pavel Trnka State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina Faculty of Electrical Engineering Kunming University of Science and TechnologyKunmingChina Soft Transducer Materials and Systems Universität PotsdamPotsdamGermany Faculty IV Electrical Engineering and Computer Science Technische Universität BerlinBerlinGermany Institute of Electrical Power Engineering Lodz University of TechnologyLodzPoland Faculty of Electrical Engineering University of West BohemiaPilsenCzech Republic

Metallised polypropylene film capacitors(MPPFCs)are widely used in power electronics and are generally degraded by elevated *** work aims to determine the relationships between the structural changes of MPPFC and the microstructural variations of the PP film during the thermal ageing of MPPFC at 100℃ for 38 *** capacitance of MPPFC has a slight decrease during thermal ***,the breakdown voltage of the MPPFC decreases by 39.4%by the *** partial discharge(PD)number of MPPFC increases linearly with ageing *** tear-down analysis of the MPPFC reveals that the molecular structure of the PP film has not been altered but has led to molecular chain scission and the generation of some polar fragments/***,the relative permittivity of the PP films rises as the ageing time ***,thermal ageing causes the conversion of aluminum to alumina in the metallised electrode,which is hydrophilic for polar groups and leads to an adhesion effect between the metallised electrodes and the PP *** angle measurements prove that the surface hydrophilicity of the PP sample increased after thermal ***,the PD/breakdown voltage in the MPPFC increases/decreases due to the uneven adhesion of the metallised PP film.

关键词： film thermal ageing

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IoT and AI-Driven Conceptual Model of Museum Ecosystem

IoT and AI-Driven Conceptual Model of Museum Ecosystem

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Puspasari, Shinta Siradjuddin, Indah Agustien Rachmansyah Fahtu Rahman, M. Ade Haversyalapa, Ditho Universitas Indo Global Mandiri Faculty of Computer Science Department of Informatics Palembang Indonesia Universitas Trunojoyo Faculty of Engineering Department of Informatics Madura Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Department of Computer System Palembang Indonesia

ISBN: (纸本)9798350381290

Museum ecosystems need digital transformation to provide value added to museums. This study discusses the digital museum ecosystem conceptual model based on artificial intelligence (AI) technology and the Internet of Things (IoT). The new conceptual model integrates three elements of the museum ecosystem, people, collections & infrastructure, and museum services collaborating and communicating to achieve outputs in supporting the achievement of museum organizational goals through IoT and AI-based digital technology. The conceptual model describes various forms of implementing IoT and AI in the museum ecosystem for museum management along with the value added or results generated for museum management. The development of this ecosystem conceptual model into a physical museum has an impact on optimizing museum performance by providing value added in the form of convenience and prosperity for users and various museum stakeholders, especially in Society 5.0. © 2023 IEEE.

关键词： Internet of things

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