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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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The Use of Data Mining to Identify Primary Teacher Qualification Demand in Indonesia 2

The Use of Data Mining to Identify Primary Teacher Qualifica...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Fahsyah Nurzaman, Adam Ubaidah Alexander, Dennis Rombot, Olifia School of Information Systems Binus University Information Systems Department Greater Jakarta Indonesia Binus University Primary Teacher Education Faculty of Humanities Greater Jakarta Indonesia School of Computer Science Binus University Mathematics Departement Greater Jakarta Indonesia

ISBN: (纸本)9798350327533

The use of big data for knowledge retrieval has been a significant trend in recent years, transforming how information is accessed and utilized. Data mining plays a crucial role in supporting research trends in big data by enabling the extraction of valuable patterns, insights, and knowledge from large and complex datasets. One of the potentials of big data that can be optimized is job vacancy data which can provide direction for the development of a profession. One of the professions with rapidly developing needs along with the development of society is the teacher profession. Job vacancy data with large volume, variety, and velocity can be a source of knowledge that can provide direction for knowledge development and research to support the development of the teacher profession, especially in Indonesia. There is not much research that analyzes teacher-related job vacancy data into knowledge with valuable patterns, insights, and knowledge. This research aims to provide knowledge by data mining on teacher qualifications needed in schools and educational institutions in Indonesia. This research uses a digital humanities approach by implementing data mining to optimize job vacancy information big data of elementary schools and educational institutions in Indonesia, in the job search portals. This research proposed knowledge containing information from 215 schools and educational institutions that classified teacher qualification needs in terms of pedagogical, professional, social, and personality competency aspects. The results of this research can serve as an input for education stakeholders in higher education institutions, researchers, and individual teacher candidates in preparing quality teachers for the sake of equalizing the quality of education in Indonesia. © 2024 IEEE.

关键词： Big Data Knowledge Retrieval Qualification Gap

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Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

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computers, Materials & Continua 2023年第5期75卷 3413-3429页

作者： Badriyya B.Al-onazi Mohamed K.Nour Hussain Alshahran Mohamed Ahmed Elfaki Mrim M.Alnfiai Radwa Marzouk Mahmoud Othman Mahir M.Sharif Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sign language includes the motion of the arms and hands to communicate with people with hearing *** models have been available in the literature for sign language detection and classification for enhanced *** the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural *** paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)*** presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language *** presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)*** gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language ***,the DHO algorithm is utilized for parameter optimization of the MLP *** experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct *** comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.

关键词： Machine learning sign language recognition multilayer perceptron deer hunting optimization densenet

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Machine Learning for Indoor Localization using CSI Technique 21

Machine Learning for Indoor Localization using CSI Technique

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Lee, Wilaiporn Suwanasri, Cattareeya Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand

ISBN: (纸本)9798350377088

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others. © 2024 IEEE.

关键词： Adversarial machine learning

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Examining User Acceptance of MOOCs: The Role of Openness, Task Technology Fit, and Self-Efficacy 7

Examining User Acceptance of MOOCs: The Role of Openness, Ta...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Harnadi, Bernardinus Prasetya, Fx Hendra Widiantoro, Albertus Dwiyoga Computer Science Faculty Soegijapranata Catholic University Information Systems Department Semarang Indonesia

ISBN: (纸本)9798350345711

This study has purpose to understand the main psychological processes in technology acceptance of massive open online courses (MOOCs). We proposed extending expectation confirmation model (ECM) model with openness, task technology fit, and self-efficacy. A sample of 210 high school and university students participated as respondents in this study. After the sample data passed the validity and reliability checking, the research hypotheses were examined with correlation analysis to prove the relationship among variables and causal effect analysis using structural equation analysis (SEM) to prove the causal effect among the variables. The results revealed that: openness and task technology fit have significantly direct effect on perceived usefulness and then on continue intention to use MOOCs;self-efficacy and task technology fit have significantly direct effect on satisfaction and then on continue intention;and the last, task technology fit has significantly direct effect on confirmation and then on perceived usefulness and satisfaction. This significantly factors of the study can contribute to people who interest on the development of MOOCs leaning model through keeping the factors to gain continue intention to use MOOCs. © 2022 IEEE.

关键词： Reliability analysis

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Understanding Behavioral Intention to Use Social Media Technology: Two Comparing Model, TAM and UTAUT 6

Understanding Behavioral Intention to Use Social Media Techn...

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6th International Conference on information Technology, InCIT 2022

作者： Harnadi, Bernardinus Prasetya, Fx Hendra Widiantoro, Albertus Dwiyoga Soegijapranata Catholic University Computer Science Faculty Information Systems Department Semarang Indonesia

ISBN: (纸本)9781665489126

This study has purpose to investigate behavioral intention to use social media technology using two model TAM and UTAUT. The proposed models were examined by 326 sample data from university students and reveal that all variables on TAM and UTAUT show their significantly direct effect among variables in the models. Hedonic motivation as main adding variable in the two model also proves the significantly direct effect on behavioral intention. By way of investigating the strong variables on previous related studies and employing the variables on the proposed model, the study reveals the prominent variables in user acceptance of social media technology including perceived usefulness, perceived ease of use, social influence, facilitating condition, and hedonic motivation. This study contributes to people who interests to the result of this study in revealing the prominent variables in research on user acceptance of social media. The recommendation of this study is the reducing or increasing user intention to use social media can be done by reducing or increasing the influence of factors or variables related to it. © 2022 IEEE.

关键词： Economic and social effects

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Personalized Shopping Experiences: Leveraging Big Data Analytics in E-commerce for Enhanced Customer Engagement

Personalized Shopping Experiences: Leveraging Big Data Analy...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Raja, Abdullah Siddique, Fatima Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Altaf, Junaid Kousar, Shagufta University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan University Putra Malaysia Researcher Cyber Security Center Malaysia Bahria University Lahore Campus Department of Computer Science Lahore Pakistan Gulab Devi Educational Complex Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan

ISBN: (纸本)9798350369106

E-commerce systems have integrated big data analytics (BDA) as a core element, enabling personalized shopping experiences, crucial for customer loyalty and satisfaction. This research explores how intentional use of data analytics impacts customers and signals the combined effect of technology and market data in tailoring customer needs through innovation. We developed unique data mining processes from e-commerce archives to uncover patterns in customer behavior, aiming to simplify consumer analysis and identify factors influencing purchasing and loyalty barriers. Our research also tests personalization strategies on sample data to improve key performance indicators like conversion rates, average order value, and customer retention. Ethical data use and privacy are prioritized, ensuring trust and transparency between e-commerce platforms and consumers. This study is innovative, demonstrating the power of analytics to revolutionize e-commerce by prioritizing customer needs and sustainable growth. © 2024 IEEE.

关键词： Marketplaces

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Gamifying Python: Enhancing Learning Outcomes and Motivation through a Game-Based Approach

Gamifying Python: Enhancing Learning Outcomes and Motivation...

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International Conference on information Technology (INCIT)

作者： Nicha Sirikong Porawat Visutsak Department of Computer and Information Science Faculty of Applied Science KMUTNB Bangkok Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

This work aimed to enhance Python programming education by developing a learning game that utilizes gamification techniques. The study focused on Mathayom Suksa 1 (7th grade in the U.S. education system) students who often face challenges in understanding complex programming concepts due to traditional teaching methods. The developed game incorporated gamification elements like points, rewards, leaderboards, and time constraints to create an engaging and interactive learning environment. The study compared students’ learning achievements before and after using the game and assessed their satisfaction levels. The findings indicated that the gamified learning game significantly improved students’ learning outcomes, with post-test scores (M = 12.90, SD = 4.27) being notably higher than pre-test scores (M = 7.28, SD = 1.60), t(39) = 11.82, p < .05. Furthermore, students expressed high levels of satisfaction with the gamified learning experience (M = 3.88, SD = 0.69) on a 5-point Likert scale, indicating its potential to foster positive attitudes towards learning Python. The study highlights the potential of gamification in addressing the challenges of teaching programming to novice learners and promoting effective and enjoyable learning experiences.

关键词： Education Collaboration Games Planning Time factors Problem-solving information technology Programming profession Faces Python

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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FAQNet: Frequency-Aware Quaternion Network for Endoscopic Highlight Removal

FAQNet: Frequency-Aware Quaternion Network for Endoscopic Hi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Dingzhou Huang, Guoheng Yuan, Xiaochen Chen, Xuhang Zhong, Guo Pun, Chi-Man Deng, Jie Guangdong University of Technology School of Computer Science and Technology China Macao Polytechnic University Faculty of Applied Sciences China Huizhou University School of Computer Science and Engineering China University of Macau Department of Computer and Information Science China Guangdong University of Foreign Studies School of Information Science and Technology China First Affiliated Hospital of Sun Yat-Sen University China

ISBN: (纸本)9798350386226

Due to the built-in light source within the endoscope, the illumination of bodily mucous can cause the formation of highlight regions due to reflection. This not only interferes with the diagnosis conducted by doctors but also poses a challenge to subsequent computer vision tasks. To tackle this issue, we introduce FAQNet, a network specifically designed for endoscopic image highlight removal. FAQNet seamlessly integrates multi-channel information leveraging quaternion convolution and spatial channel attention within our Quaternion Multi-Channel Fusion (QMCF) Module. This allows it to capture intricate details of color, texture, spatial information, and highlight characteristics within the imaged organ. Additionally, by employing frequency domain transformation and dilated convolution, the Contextual information Integration (CII) Module effectively enlarges the receptive field, organizing contextual information between highlight regions and their surrounding areas. Lastly, the PixelShuffle Upsampling (PSU) Module generates the restored image. We validate our model's performance on two benchmark datasets, demonstrating its superiority over existing highlight removal methodologies. © 2024 IEEE.

关键词： Endoscopy

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