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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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Personalized Federated Learning for Histopathological Prediction of Lung Cancer 20

Personalized Federated Learning for Histopathological Predic...

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20th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Ayekai, Browne Judith Wenyu, Chen Sarpong Addai, Gyarteng Emmanuel Xornam, Ativi Wisdom Mawuena, Klugah Stephanie Mawuli, Cobbinah Bernard Kulevome, Delanyo Agbley, Bless Lord Turkson, Regina Esi Kuupole, Ewald Erubaar School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Cape Coast PMB University Post Office Department of Computer Science and Information Technology Cape Coast Ghana C.K. Tedam University of Tech. and Applied Sciences Navrongo Ghana

ISBN: (纸本)9798350318982

Lung cancer is a leading contributor to cancer-related fatalities worldwide, and histopathological image analysis plays a critical role in cancer detection by identifying morphological abnormalities in tissue samples. Artificial intelligence (AI) in medicine has evolved but still faces challenges, notably in maintaining data privacy and security. Federated Learning (FL) has emerged as a promising solution, enabling the training of robust models without jeopardizing data privacy. However, the effectiveness of existing FL approaches often falters in non-independent and identically distributed (non-IID) scenarios, where data distributions vary across clients. Addressing this challenge, our research presents a personalized federated learning (PFL) method specifically designed for lung cancer prediction using histopathological scans. This novel framework leverages client-specific autoencoders coupled with hierarchical clustering for personalized federated learning for lung cancer prediction. Our findings demonstrate the efficacy of the proposed method for collaborative lung cancer prediction in medical heterogeneous data environments while preserving data privacy. © 2023 IEEE.

关键词： Medical imaging

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A Throughput-Optimized Accelerator for Submanifold Sparse Convolutional Networks

A Throughput-Optimized Accelerator for Submanifold Sparse Co...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yu-Hsuan Lai Shanq-Jang Ruan Ming Fang Edwin Naroska Jeng-Lun Shieh Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Electrical Engineering and Computer Science Hochschule Niederrhein University of Applied Science Krefeld Germany

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

The inherent sparsity of 3D point clouds presents significant computational challenges for deep learning-based vision tasks. Submanifold Sparse Convolutional Networks (SSCNs) efficiently process point cloud data while maintaining this sparsity. However, current CNN accelerators need to be better suited for SSCNs. This paper introduces a specialized hardware architecture optimized for SSCNs, achieving a 2.51× improvement in throughput density over previous works, demonstrating its superior effectiveness in point cloud processing.

关键词： Point cloud compression Three-dimensional displays computer architecture Throughput Hardware Convolutional neural networks Consumer electronics

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Realistic Video Analytics Implementation on a Heterogeneous Infrastructure 4

Realistic Video Analytics Implementation on a Heterogeneous ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Kalaiselvi, S. Ganesh Kumar, N. Shyam, M. Vadivel, G. Kongu Engineering College Erode Department of Computer Technology Tamil Nadu Chennai India Deemed to Be University Gitam School of Technology Department of Computer Science and Engineering Karnataka Bangaluru India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India University of Technology and Applied Science Department of Information Technology Dhofar Salalah Oman

ISBN: (纸本)9798350300093

In real time safety administration requires exact pedestrian crash risk evaluations at various times. This work introduces real-time vehicle-pedestrian collision risk modelling for signalized crossings. Bayesian Generalized Extreme Value modelling assesses post-encroachment traffic conflict accident probability in real time. Pedestrian conflicts during the cycles of traffic signals are identified by the use of block maxima sampling using a Generalized Extreme Value distribution. Bayesian analysis handles signal cycle crash risk and traffic severe heterogeneity using various signal-level parameters. Systematic a covariate extractor from streamed video path, transportation conflict, and signal timing databases yields signal cycle-level covariates to explain cycle-dependent accident risk. Study crash data is calculated correctly. The fitted generalized extreme value distribution forecasts signal cycle-level collapses. The real-time collision risk model shows pedestrian accident risk at signalized crossings all day. This study shows that the proposed real-time system can measure signal cycle-level vehicle-pedestrian accident risk, allowing proactive safety management and pedestrian risk reduction. © 2023 IEEE.

关键词： Real time systems

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Comparative Analysis of Machine Learning Algorithms and Datasets for Detecting Cyberbullying on Social Media Platforms

Comparative Analysis of Machine Learning Algorithms and Data...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Palagati, Anusha Balan, Santhosh Kumar Arun Joe Babulo, S. Raja, Laxmi Natarajan, K.K. Kalimuthu, Rajkumar Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications K.S.R College of Arts and Science for Women Tamilnadu Tiruchengode India Department of Cyber Security Karpagam Academy of Higher Education Tamilnadu Coimbatore India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman Department of Cyber Security Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India

ISBN: (纸本)9798331510299

Cyberbullying is defined as sharing of undesirable material in the cyberspace, primarily- the use of the social sites that instigate hostility and create feelings of hatred among the individuals. Another increasing threat due to increase use of internet-based communication is cyberbullying especially on the site such as Twitter. Different ML and NLP approaches including support vector machine (SVM), Naïve bayes, Random Forest, and logistic regression is employed to detect cyberbul Bullivating. These methods for supervised learning mine patterns in text and image, allowing computers to promptly flag risky interactions. Some of these approaches also utilizes OCR for the identification of image-based bullying. Consequently, the objective of this research is to identify the correct algorithm and dataset for improving the identification of cyberbullying and solving the problem that manifests itself in the steady increase in cases of this type of bullying. © 2024 IEEE.

关键词： Adversarial machine learning

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Identifying Fake News in the Russian-Ukrainian Conflict Using Machine Learning 37th

Identifying Fake News in the Russian-Ukrainian Conflict Usin...

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Darwish, Omar Tashtoush, Yahya Maabreh, Majdi Al-essa, Rana Aln’uman, Ruba Alqublan, Ammar Abualkibash, Munther Elkhodr, Mahmoud Information Security and Applied Computing Eastern Michigan University Ypsilanti United States Computer Science Jordan University of Science and Technology Irbid Jordan Information Technology The Hashemite University Zarqa Jordan Central Queensland University Rockhampton Australia

ISBN: (纸本)9783031286933

The widespread dissemination of false information made possible by social networks' universal accessibility and ease of use may be harmful to both individuals and societies. Regrettably, one of the most popular subjects on social media right now is the crisis between Russia and Ukraine. Spreading fake news about this conflict may cause serious consequences to both countries and their citizens. Therefore, we are motivated to build a fake news detection system similar to those systems already accessible in other fields like healthcare. In this study, we build a dataset of fake and real tweets about the Russian-Ukrainian conflict and evaluate the power of machine learning in this context. The pre-train BERT and five classical machine learning algorithms;namely support vector machine (SVM), Decision Tree (DT), K-nearest neighbor (KNN), logistic regression (LR), and Naïve Bayes (NB) are trained and evaluated through different scenarios of the dataset. The results show that it is possible to develop a system that can discern between real and fake news regarding the Russian-Ukrainian conflict. Support vector machine and logistic regression outperform other learning algorithms and produce comparable prediction models with approximately 76% of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Tangispeak: A Digital Game Based Language Learning System by Using Augmented Reality technology and Tangible Objects 12

Tangispeak: A Digital Game Based Language Learning System by...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Ongoro, Catherine Akoth Fanjiang, Yong-Yi Hung, Chi-Huang Lin, Bing-Jun Lee, Mu-En Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei City Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan

ISBN: (纸本)9798350340181

In this study, Tangispeak is introduced as a language learning system for preschoolers, incorporating Digital Game-Based learning (DGBL), Augmented Reality (AR), and tangible objects. It aims to enhance English language skills through interactive and fun experiences. Tangispeak utilizes a micro-controller, gameboard, Unity 3D, and Vuforia database platform, offering scrambling and speaking steps for word formation and pronunciation. The system provides rewards, hints, and timing to engage learners while fostering cognitive and innovative skills. © 2023 IEEE.

关键词： Augmented reality

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Hemodynamics in renal arteries with partial coverage after endovascular aneurysm repair affected by renal orientations

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Medicine in Novel technology and Devices 2023年第2期18卷 81-90页

作者： Jun Wen Xinhui Liu Jianguo Lei Jiarong Wang Xiyang Chen Tinghui Zheng Ding Yuan School of Computer Science and Technology Southwest University of Science and TechnologyMianyang621010China Institute of Civil Engineering and Architecture Southwest University of Science and TechnologyMianyang621010China Department of Vascular Surgery West China HospitalSichuan UniversityChengdu610041China Department of Applied Mechanics Sichuan UniversityChengdu610000China

Endovascular aortic repair(EVAR)has been increasingly used to treat abdominal aortic aneurysm(AAA)due to its lower mortality and shorter recovery ***,the complications caused by renal artery coverage remained a major *** this study,three types of renal orientation and five types of partial renal coverage degree were adopted to numerically investigate the flow pattern,wall shear stress distribution,and blood perfusion during a cardiac *** results show that as for the mild partial coverage(=<30%),the renal artery with a downward orientation suffered fewer unfavorable wall shear stress(WSS)distribution and more blood *** should be noted that the renal artery with horizontal orientation suffered more unfavorable WSS *** terms of severe coverage(≥50%),all of the renal arteries displayed highly disturbed wall shear distribution,resulting in a significant reduction in blood perfusion It was interesting to note that the artery with an upward orientation had a smaller rate of reduction in blood *** can be concluded that as for the mild renal coverage after EVAR,renal arteries with downward orientation may have the advantage of reducing the possibility of renal occlusion,while renal arteries with horizontal orientation may increase the unfavorable wall shear *** the other hand,as for serious coverage(≥50%),high risk of renal ischemia remains the main concern for all types of renal arteries due to highly disturbed wall shear distribution would significantly increase regardless of renal orientation.

关键词： Hemodynamics Orientation Follow-up Numerical simulation Renal artery

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Adaptive particle swarm optimization model for resource leveling

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Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

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The Current Situation and Future Trend of Big Data: Visualization Analysis of literature Based on Citespace 22

The Current Situation and Future Trend of Big Data: Visualiz...

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5th International Conference on Big Data and Education, ICBDE 2022

作者： Zeng, Xianfeng Chang, Jing Lai, Yiqiang Huang, Changjiang School of Computer Science South China Business College of Guangdong University of Foreign Studies Guangzhou Guangdong510545 China College of Computer Science Guangzhou College of Applied Science and Technology Guangzhou Guangdong510030 China

ISBN: (纸本)9781450395793

More and more attentions have been paid to the value of big data, and the amount of research literature on big data is also increasing exponentially. In this paper, CiteSpace citation analysis software is used as an analysis tool and big data related literature in the Web of science core database during 2011- 2021 is used as an analysis object. The major task is to create the knowledge map of big data with multi-dimension, build knowledge graphs of big data from several perspectives and develop visual analyses of the literature. From the sight of time and space. Differences between the numbers and the research contents of literature on big data published by different countries, regions, and institutions have been analyzed and compared;By co-cited literature, the classical papers and core literature in the field of big data have been detected, and the development context of big data domain have been clarified;Cluster analysis has been conducted on hot keywords to find the research hotspots of big data domain in recent years. Burst words detection algorithm has been used to track the research frontiers and development trends in the field of big data. The results show that China has surpassed the developed countries in Europe and the United States in the number of literature on big data. However, there is still a lack of core technology literature. The development of big data and AI will be in the same direction and depend on each other;The improvement of deep learning algorithms based of big data analysis is going to become a frontier research topic in a long-term;The integration of big data and all walks of life should to be deepened, and researchers need to find the breakthrough points for the application of big data based on the characteristics of the industry. © 2022 ACM.

关键词： Big data

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