Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459614
Raja Mouachi, Mohammed Ali Jallal, Hassnae Remmach, Mustapha Raoufi, F. Gharnati
This research presents a rigorous and intelligent techno-economic analysis of smart farm systems in the context of Morocco, employing a sophisticated hybrid metaheuristic framework. The primary objective is the meticulous evaluation of a hybrid microgrid system, intricately optimizing its dimensions and financial outlay to efficaciously energize a smart farm situated in the region. The comprehensive scope of this study encompasses the inception, optimization, and scrutiny of the smart farm system through the utilization of MATLAB, a versatile computational tool. Introducing an avant-garde metaheuristic optimization paradigm known as the Hybrid Metaheuristic, the study endeavors to discern the optimum system configuration, with a particular emphasis on ensuring unwavering electricity provision while factoring in the nuances of the levelized electricity cost (LEC). The proposed methodology establishes its mettle through empirical evidence, showcasing precision and reliability in simulation results, thereby substantiating its potential as a stalwart approach for the development of sustainable and economically sound smart farm solutions.
{"title":"Empowering Agriculture: Microgrid Optimization with Dynamic Evolutionary Swarm Algorithm for Sustainable Smart Farm in Coastal Morocco","authors":"Raja Mouachi, Mohammed Ali Jallal, Hassnae Remmach, Mustapha Raoufi, F. Gharnati","doi":"10.1109/ICETSIS61505.2024.10459614","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459614","url":null,"abstract":"This research presents a rigorous and intelligent techno-economic analysis of smart farm systems in the context of Morocco, employing a sophisticated hybrid metaheuristic framework. The primary objective is the meticulous evaluation of a hybrid microgrid system, intricately optimizing its dimensions and financial outlay to efficaciously energize a smart farm situated in the region. The comprehensive scope of this study encompasses the inception, optimization, and scrutiny of the smart farm system through the utilization of MATLAB, a versatile computational tool. Introducing an avant-garde metaheuristic optimization paradigm known as the Hybrid Metaheuristic, the study endeavors to discern the optimum system configuration, with a particular emphasis on ensuring unwavering electricity provision while factoring in the nuances of the levelized electricity cost (LEC). The proposed methodology establishes its mettle through empirical evidence, showcasing precision and reliability in simulation results, thereby substantiating its potential as a stalwart approach for the development of sustainable and economically sound smart farm solutions.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459409
Dimas Yudha Kusumawardana, Aswan Munang, Fauzan Romadlon
Cigarette filters and glass are hazardous wastes that directly impact the environment and living creatures. Cigarette and glass filter waste has increased in volume, potentially causing environmental damage by polluting soil and air quality. The research aims to recycle and determine the mechanical strength of making composites from waste cigarette filters and glass. Composites combine two or more components, where cigarette filters are used as fibers and powdered glass as a filler. Waste cigarette filters are processed into fibers and glass is ground into powder with a size of 120 mesh. The matrix uses polyester resin as a composite material binder. They are making composite specimens using a press molding machine. The DoE (Design Of Experiment) method is used in composite fractional factorial design. Tensile and impact testing was carried out twice, with the first impact testing from the pilot study being used as a parameter reference. The results of the composite composition produced eight test specimens. The optimal impact test results are the second specimen, with a value of 0.021 J/mm2, with composition resin polyester 50% and powder glass 50%. The results of the tensile testing of the second specimen, namely 32.17 Mpa. The results of testing the composite of cigarette filter waste and glass had optimal strength in the second specimen in impact and tensile testing. Glass waste influences increasing mechanical strength in making composites.
{"title":"Analysis of the Mechanical Strength of Composites Based on Cigarette Filters and Glass Powder","authors":"Dimas Yudha Kusumawardana, Aswan Munang, Fauzan Romadlon","doi":"10.1109/ICETSIS61505.2024.10459409","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459409","url":null,"abstract":"Cigarette filters and glass are hazardous wastes that directly impact the environment and living creatures. Cigarette and glass filter waste has increased in volume, potentially causing environmental damage by polluting soil and air quality. The research aims to recycle and determine the mechanical strength of making composites from waste cigarette filters and glass. Composites combine two or more components, where cigarette filters are used as fibers and powdered glass as a filler. Waste cigarette filters are processed into fibers and glass is ground into powder with a size of 120 mesh. The matrix uses polyester resin as a composite material binder. They are making composite specimens using a press molding machine. The DoE (Design Of Experiment) method is used in composite fractional factorial design. Tensile and impact testing was carried out twice, with the first impact testing from the pilot study being used as a parameter reference. The results of the composite composition produced eight test specimens. The optimal impact test results are the second specimen, with a value of 0.021 J/mm2, with composition resin polyester 50% and powder glass 50%. The results of the tensile testing of the second specimen, namely 32.17 Mpa. The results of testing the composite of cigarette filter waste and glass had optimal strength in the second specimen in impact and tensile testing. Glass waste influences increasing mechanical strength in making composites.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459600
M. Abdeldayem, H. Aldeeb, Ismail Mohamed Sharif, S. Aldulaimi
This study investigates how Batelco Telecommunication Company in Bahrain uses Artificial Intelligence (AI) to apply Total Quality Management (TQM) principles and gain a competitive edge. The study looks into how TQM practices, such as commitment from senior management, customer focus, continuous improvements, teamwork and collective participation, and error prevention-can be improved by integrating AI technologies. This study investigates the effect of AI-driven TQM implementation on competitive advantage through a thorough analysis of data gathered from a sample of Batelco employees. The results offer empirical understanding of the efficacy and possible advantages of integrating AI into TQM procedures to gain a competitive edge in the telecom sector. The study advances knowledge of the strategic role that AI plays in TQM process optimization and provides insightful advice for businesses looking to improve their competitiveness by implementing AI- driven TQM initiatives
{"title":"Examining the Impact of Total Quality Management on Competitive Advantage: A Case Study of a Private Company in the Kingdom of Bahrain","authors":"M. Abdeldayem, H. Aldeeb, Ismail Mohamed Sharif, S. Aldulaimi","doi":"10.1109/ICETSIS61505.2024.10459600","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459600","url":null,"abstract":"This study investigates how Batelco Telecommunication Company in Bahrain uses Artificial Intelligence (AI) to apply Total Quality Management (TQM) principles and gain a competitive edge. The study looks into how TQM practices, such as commitment from senior management, customer focus, continuous improvements, teamwork and collective participation, and error prevention-can be improved by integrating AI technologies. This study investigates the effect of AI-driven TQM implementation on competitive advantage through a thorough analysis of data gathered from a sample of Batelco employees. The results offer empirical understanding of the efficacy and possible advantages of integrating AI into TQM procedures to gain a competitive edge in the telecom sector. The study advances knowledge of the strategic role that AI plays in TQM process optimization and provides insightful advice for businesses looking to improve their competitiveness by implementing AI- driven TQM initiatives","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459522
Jayrhom R. Almonteros, Christine Grace A. Daray, Maria Besa Joy M. Ortuyo, Dioame Jade C. Rendon, Rejeanfe G. Sanchez
A sanitary Permit is a certification awarded to establishments that comply with the minimum sanitation requirements per Presidential Decree 522 and 856 and local ordinances. Applying or renewing authority to operate a business is a mandatory requirement. An inspection is conducted by the City Sanitary Inspectors to an establishment, ensuring that it operates according to the set of sanitation standards as stipulated in Department of Health Order No. 258-B, s. 1991. Inspections take place during application and monthly re-inspections if deemed necessary to enforce the provision of these rules and regulations. The process of scheduling inspections and releasing sanitary permits was done manually. The inspection was conducted using a pen and printed version of EHS Form No. 103; thus, the inspector manually calculated the result during the site visit and logged all the transactions in the logbooks. This work developed a platform that automates the entire process, including automatic scheduling, result calculations, and real-time report generation. Moreover, the establishment owner receives the result in real time over SMS and a detailed compliance report through email. The developed eGovernance platform obtained 92.86% agreement from the user on the app's usefulness.
{"title":"Digitalization of the Sanitary Process through Information Management: An E-Governance Platform","authors":"Jayrhom R. Almonteros, Christine Grace A. Daray, Maria Besa Joy M. Ortuyo, Dioame Jade C. Rendon, Rejeanfe G. Sanchez","doi":"10.1109/ICETSIS61505.2024.10459522","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459522","url":null,"abstract":"A sanitary Permit is a certification awarded to establishments that comply with the minimum sanitation requirements per Presidential Decree 522 and 856 and local ordinances. Applying or renewing authority to operate a business is a mandatory requirement. An inspection is conducted by the City Sanitary Inspectors to an establishment, ensuring that it operates according to the set of sanitation standards as stipulated in Department of Health Order No. 258-B, s. 1991. Inspections take place during application and monthly re-inspections if deemed necessary to enforce the provision of these rules and regulations. The process of scheduling inspections and releasing sanitary permits was done manually. The inspection was conducted using a pen and printed version of EHS Form No. 103; thus, the inspector manually calculated the result during the site visit and logged all the transactions in the logbooks. This work developed a platform that automates the entire process, including automatic scheduling, result calculations, and real-time report generation. Moreover, the establishment owner receives the result in real time over SMS and a detailed compliance report through email. The developed eGovernance platform obtained 92.86% agreement from the user on the app's usefulness.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459417
Mohammed M. Thakir
Accurate skin cancer characterization is crucial for devising effective treatment plans and ensuring optimal patient care. Although dermoscopy has proven invaluable for visualizing skin lesions, accurately determining specific phases or stages based solely on dermoscopy images remains a formidable challenge. In this research, we introduce a novel approach to skin cancer characterization, leveraging the quantification of fractal-based attributes derived from dermoscopic images. Fractal analysis provides a robust framework for capturing the intricate complexity and self-resemblance inherent in a wide array of natural and man-made structures. We harness this methodology to scrutinize the fractal attributes present in dermoscopy images, aiming to unveil distinctive patterns that correspond to different stages of skin cancer. We utilized the box-counting method to extract meaningful features that encapsulate the self-similar characteristics exhibited by skin lesions. To gauge the effectiveness of our approach, we employed an extensive dataset consisting of dermoscopy images portraying lesions in diverse stages of skin cancer. Dermatologists meticulously annotated these images, providing definitive reference information for our comparative analysis. To uncover meaningful patterns and correlations between the extracted fractal attributes and the established stages of skin cancer, we employed a wide spectrum of machine-learning techniques. These encompassed Decision Trees, Logistic Regression, Support Vector Machines, Random Forests, and Convolutional Neural Networks (CNNs). Our results show that the CNN model has the greatest accuracy of 0.77 when categorizing the fractal dimension of the input photos as a feature. We also increased the model's accuracy to 0.85 by utilizing a CNN multi-input approach. This method successfully combines image data with quantified fractal characteristics, resulting in better classification performance. While we acknowledge the difficulty of precisely defining phases merely from dermoscopy pictures, our technique offers dermatologists an additional tool to aid in their clinical decision-making. Our findings contribute to a better understanding of the possible relationships between fractal-based characteristics and skin cancer stages, opening the door for more study and the development of more comprehensive diagnostic tools. These improvements have the potential to increase dermatologists' ability to make enlightened assessments, resulting in better patient outcomes and individualized treatment methods.
{"title":"Quantifying Fractal-Based Features in Dermoscopic Images for Skin Cancer Characterization","authors":"Mohammed M. Thakir","doi":"10.1109/ICETSIS61505.2024.10459417","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459417","url":null,"abstract":"Accurate skin cancer characterization is crucial for devising effective treatment plans and ensuring optimal patient care. Although dermoscopy has proven invaluable for visualizing skin lesions, accurately determining specific phases or stages based solely on dermoscopy images remains a formidable challenge. In this research, we introduce a novel approach to skin cancer characterization, leveraging the quantification of fractal-based attributes derived from dermoscopic images. Fractal analysis provides a robust framework for capturing the intricate complexity and self-resemblance inherent in a wide array of natural and man-made structures. We harness this methodology to scrutinize the fractal attributes present in dermoscopy images, aiming to unveil distinctive patterns that correspond to different stages of skin cancer. We utilized the box-counting method to extract meaningful features that encapsulate the self-similar characteristics exhibited by skin lesions. To gauge the effectiveness of our approach, we employed an extensive dataset consisting of dermoscopy images portraying lesions in diverse stages of skin cancer. Dermatologists meticulously annotated these images, providing definitive reference information for our comparative analysis. To uncover meaningful patterns and correlations between the extracted fractal attributes and the established stages of skin cancer, we employed a wide spectrum of machine-learning techniques. These encompassed Decision Trees, Logistic Regression, Support Vector Machines, Random Forests, and Convolutional Neural Networks (CNNs). Our results show that the CNN model has the greatest accuracy of 0.77 when categorizing the fractal dimension of the input photos as a feature. We also increased the model's accuracy to 0.85 by utilizing a CNN multi-input approach. This method successfully combines image data with quantified fractal characteristics, resulting in better classification performance. While we acknowledge the difficulty of precisely defining phases merely from dermoscopy pictures, our technique offers dermatologists an additional tool to aid in their clinical decision-making. Our findings contribute to a better understanding of the possible relationships between fractal-based characteristics and skin cancer stages, opening the door for more study and the development of more comprehensive diagnostic tools. These improvements have the potential to increase dermatologists' ability to make enlightened assessments, resulting in better patient outcomes and individualized treatment methods.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459602
Tarek Othmani, S. Boubaker, F. Rehimi, Souheil El Alimi
The pressing need to address climate change by decreasing greenhouse gas emissions and improving air quality necessitates the development of eco-friendly and sustainable transportation solutions. Traditional modes of transportation largely contribute to environmental deterioration, so adopting innovative and sustainable transportation solutions is important for reducing these effects and increasing energy efficiency. This study introduces a pioneering methodology employing two separate Fuzzy Logic systems (FL) that leverage vehicle-to-infrastructure (V2I) communication technology systems. The designed FL is used to estimate a vehicle's optimal speed in order to optimize energy consumption and reduce CO2 emissions. The optimal speed is estimated based on specific factors such as vehicle velocity, road speed limit, and other parameters to estimate the optimal speed with the aim of reducing energy consumption and emissions. We have used SUMO (Simulation of Urban MObility) and Python to explore diverse scenarios, replicating road conditions with varying slopes and vehicle weights. The simulation findings highlight the transformative impacts of both FL systems combined with V2I on energy consumption and emissions, which allow cars to react and adjust their speed to changing road conditions in real-time. The vehicle's extra-weight Fuzzy Logic system and the road slope FL system exhibit a remarkable average reduction of 10% and 20%, respectively. The findings are a robust foundation for developing intelligent and eco-friendly transportation systems, contributing to the broader goal of sustainable and efficient mobility.
{"title":"Smart Driving Towards Eco-Friendly Transportation: Fuzzy Logic Approach to Optimize Vehicle Speed Based on Road Slope and Vehicle Extra Weight","authors":"Tarek Othmani, S. Boubaker, F. Rehimi, Souheil El Alimi","doi":"10.1109/ICETSIS61505.2024.10459602","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459602","url":null,"abstract":"The pressing need to address climate change by decreasing greenhouse gas emissions and improving air quality necessitates the development of eco-friendly and sustainable transportation solutions. Traditional modes of transportation largely contribute to environmental deterioration, so adopting innovative and sustainable transportation solutions is important for reducing these effects and increasing energy efficiency. This study introduces a pioneering methodology employing two separate Fuzzy Logic systems (FL) that leverage vehicle-to-infrastructure (V2I) communication technology systems. The designed FL is used to estimate a vehicle's optimal speed in order to optimize energy consumption and reduce CO2 emissions. The optimal speed is estimated based on specific factors such as vehicle velocity, road speed limit, and other parameters to estimate the optimal speed with the aim of reducing energy consumption and emissions. We have used SUMO (Simulation of Urban MObility) and Python to explore diverse scenarios, replicating road conditions with varying slopes and vehicle weights. The simulation findings highlight the transformative impacts of both FL systems combined with V2I on energy consumption and emissions, which allow cars to react and adjust their speed to changing road conditions in real-time. The vehicle's extra-weight Fuzzy Logic system and the road slope FL system exhibit a remarkable average reduction of 10% and 20%, respectively. The findings are a robust foundation for developing intelligent and eco-friendly transportation systems, contributing to the broader goal of sustainable and efficient mobility.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459437
Megha Yadav, Badruzzama Siddiqui
Biophilic design, rooted in the innate human connection to nature, has gained prominence as a strategic approach in corporate environments. It is characterized by integrating natural elements, such as greenery, natural light, and organic materials, seeking to create workspaces that mimic natural environments. This research paper investigates how the incorporation of biophilic design elements influences three critical variables: employee well-being, productivity, and job satisfaction within the corporate setting. Data for this research was collected through in-depth interviews with 16 employees from various corporate organizations. The findings shed light on how biophilic design elements contribute to enhanced wellbeing, increased productivity, and greater job satisfaction among employees in corporate environments. Moreover, this research highlights the potential of biophilic design to support the attainment of G20 goals related to sustainable development and environmental stewardship. This research not only underscores the importance of biophilic design in modern workplaces but also provides valuable insights for organizations seeking to optimize their human resource productivity and align with global sustainability initiatives.
{"title":"Do Biophilic Design Elements Impact Human Resource Productivity in a Corporate Environment?","authors":"Megha Yadav, Badruzzama Siddiqui","doi":"10.1109/ICETSIS61505.2024.10459437","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459437","url":null,"abstract":"Biophilic design, rooted in the innate human connection to nature, has gained prominence as a strategic approach in corporate environments. It is characterized by integrating natural elements, such as greenery, natural light, and organic materials, seeking to create workspaces that mimic natural environments. This research paper investigates how the incorporation of biophilic design elements influences three critical variables: employee well-being, productivity, and job satisfaction within the corporate setting. Data for this research was collected through in-depth interviews with 16 employees from various corporate organizations. The findings shed light on how biophilic design elements contribute to enhanced wellbeing, increased productivity, and greater job satisfaction among employees in corporate environments. Moreover, this research highlights the potential of biophilic design to support the attainment of G20 goals related to sustainable development and environmental stewardship. This research not only underscores the importance of biophilic design in modern workplaces but also provides valuable insights for organizations seeking to optimize their human resource productivity and align with global sustainability initiatives.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459451
A. Al-Alawi, Yahya A. Ghanem
Employee attrition, or the voluntary turnover of employees, is a major concern for businesses worldwide due to the increased competition and dynamic changes in the business environment. Predicting employee attrition can help organizations improve their retention strategies and enhance their performance. This article presents a Systematic Literature Review (SLR) of the previous studies that have applied machine learning techniques to predict employee attrition. The SLR covers the data sources, the machine learning models, and the evaluation metrics used in the existing literature. The article reveals the challenges of obtaining reliable and relevant data for attrition prediction and suggests some possible solutions. The article also compares the performance of different machine learning models, such as support vector machines (SVMs), decision trees, random forests, and neural networks, using various evaluation metrics, such as accuracy, precision, recall, and F1-score. The article shows that using multiple machine learning models and evaluation metrics can provide more reliable and robust results than relying on a single model or metric. The article concludes by highlighting the contributions and limitations of the current research and proposing some directions for future research. This article is a valuable resource for researchers and practitioners in the fields of business analytics and human resources, as it provides a comprehensive overview and analysis of the state-of-the-art in employee attrition prediction using machine learning techniques.
由于竞争的加剧和商业环境的动态变化，员工流失或员工自愿离职成为全球企业关注的主要问题。预测员工流失可以帮助企业改进留住员工的策略并提高绩效。本文对以往应用机器学习技术预测员工流失的研究进行了系统的文献综述（SLR）。系统文献综述涵盖了现有文献中使用的数据来源、机器学习模型和评估指标。文章揭示了在获取用于自然减员预测的可靠相关数据方面所面临的挑战，并提出了一些可能的解决方案。文章还比较了不同机器学习模型的性能，如支持向量机（SVM）、决策树、随机森林和神经网络，并使用了各种评价指标，如准确率、精确度、召回率和 F1 分数。文章表明，使用多种机器学习模型和评价指标能提供比依赖单一模型或指标更可靠、更稳健的结果。文章最后强调了当前研究的贡献和局限性，并提出了未来研究的一些方向。本文是商业分析和人力资源领域研究人员和从业人员的宝贵资源，因为它全面概述和分析了使用机器学习技术预测员工流失的最新进展。
{"title":"Predicting Employee Attrition Using Machine Learning: A Systematic Literature Review","authors":"A. Al-Alawi, Yahya A. Ghanem","doi":"10.1109/ICETSIS61505.2024.10459451","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459451","url":null,"abstract":"Employee attrition, or the voluntary turnover of employees, is a major concern for businesses worldwide due to the increased competition and dynamic changes in the business environment. Predicting employee attrition can help organizations improve their retention strategies and enhance their performance. This article presents a Systematic Literature Review (SLR) of the previous studies that have applied machine learning techniques to predict employee attrition. The SLR covers the data sources, the machine learning models, and the evaluation metrics used in the existing literature. The article reveals the challenges of obtaining reliable and relevant data for attrition prediction and suggests some possible solutions. The article also compares the performance of different machine learning models, such as support vector machines (SVMs), decision trees, random forests, and neural networks, using various evaluation metrics, such as accuracy, precision, recall, and F1-score. The article shows that using multiple machine learning models and evaluation metrics can provide more reliable and robust results than relying on a single model or metric. The article concludes by highlighting the contributions and limitations of the current research and proposing some directions for future research. This article is a valuable resource for researchers and practitioners in the fields of business analytics and human resources, as it provides a comprehensive overview and analysis of the state-of-the-art in employee attrition prediction using machine learning techniques.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459418
Umar Saleem, Weinjie Liu, Weilin Li, M. U. Sardar, Muhammad Mobeen Aslam, Saleem Riaz
Prognostic and Health Management (PHM) played a vital role in the industrial revolution. An efficient PHM system improves reliability and safety by detecting whether an industrial component has deviated from its normal operating condition, predicting when a fault will occur, and classifying the type of fault. Due to the rapid development of more electric aircraft in recent years, the electric power system of aircraft has become more critical in ensuring safe flying. This research mainly focuses on classifying aircraft generator faults using the Support Vector Machine (SVM). To use the SVM for fault classification, firstly, create a data set of 1112 records containing all possible types of short circuit faults and normal states using the MATLAB Simulink model. Extract features from these records by decomposing them with Wavelet Transform. The principal component analysis (PCA) optimization technique is used on detail coefficients for trained SVM that will correctly classify generator faults. Then, train the SVM at each type of fault and normal state using 70% of the data and test it on the remaining 30%. It has been observed that if the system works under normal working conditions, all SVM output will be zero. In the faulty condition, the SVM output that belongs to the type or class of fault will be one and will display the type of fault. The suggested technique has been extensively evaluated for several fault types under various operating conditions. The SVM results demonstrate impressive accuracy in fault classification and significantly improve aviation generators' PHM systems.
{"title":"Enhancing PHM System of Aircraft Generator with Machine Learning-Driven Faults Classification","authors":"Umar Saleem, Weinjie Liu, Weilin Li, M. U. Sardar, Muhammad Mobeen Aslam, Saleem Riaz","doi":"10.1109/ICETSIS61505.2024.10459418","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459418","url":null,"abstract":"Prognostic and Health Management (PHM) played a vital role in the industrial revolution. An efficient PHM system improves reliability and safety by detecting whether an industrial component has deviated from its normal operating condition, predicting when a fault will occur, and classifying the type of fault. Due to the rapid development of more electric aircraft in recent years, the electric power system of aircraft has become more critical in ensuring safe flying. This research mainly focuses on classifying aircraft generator faults using the Support Vector Machine (SVM). To use the SVM for fault classification, firstly, create a data set of 1112 records containing all possible types of short circuit faults and normal states using the MATLAB Simulink model. Extract features from these records by decomposing them with Wavelet Transform. The principal component analysis (PCA) optimization technique is used on detail coefficients for trained SVM that will correctly classify generator faults. Then, train the SVM at each type of fault and normal state using 70% of the data and test it on the remaining 30%. It has been observed that if the system works under normal working conditions, all SVM output will be zero. In the faulty condition, the SVM output that belongs to the type or class of fault will be one and will display the type of fault. The suggested technique has been extensively evaluated for several fault types under various operating conditions. The SVM results demonstrate impressive accuracy in fault classification and significantly improve aviation generators' PHM systems.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459466
Hafsa Ouchra, A. Belangour, Allae Erraissi
Precise and current land use data hold immense significance in facilitating efficient urban planning and appropriate environmental oversight. This paper proposes an approach to the unsupervised classification of Casablanca's land use using the Google Earth Engine (GEE) platform. The study relies on multispectral satellite imagery, in particular data from Landsat satellites, to extract meaningful land use categories without resorting to manual labeling. The operational process includes data collection, pre-processing, unsupervised clustering, and graphical display of results. By applying the k-means and Lvq clustering algorithms, the urban area is split into distinct groups, each representing a specific land use class. The resulting land use map provides valuable data on Casablanca's urban fabric, highlighting wooded areas, agricultural land, built infrastructure, water bodies, and barren land. This automated approach demonstrates GEE's potential as a powerful tool for analyzing land use, enabling informed, data-driven decisions on urban development and environmental monitoring. The methodology outlined can serve as a reference for similar research in other regions, helping to advance remote sensing and geospatial analysis techniques in urban and environmental studies. The effectiveness of these two algorithms is assessed in terms of overall accuracy and kappa coefficient. The k-means algorithm showed moderate accuracy, while the Lvq algorithm showed the least satisfactory results.
{"title":"Unsupervised Learning for Land Cover Mapping of Casablanca Using Multispectral Imaging","authors":"Hafsa Ouchra, A. Belangour, Allae Erraissi","doi":"10.1109/ICETSIS61505.2024.10459466","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459466","url":null,"abstract":"Precise and current land use data hold immense significance in facilitating efficient urban planning and appropriate environmental oversight. This paper proposes an approach to the unsupervised classification of Casablanca's land use using the Google Earth Engine (GEE) platform. The study relies on multispectral satellite imagery, in particular data from Landsat satellites, to extract meaningful land use categories without resorting to manual labeling. The operational process includes data collection, pre-processing, unsupervised clustering, and graphical display of results. By applying the k-means and Lvq clustering algorithms, the urban area is split into distinct groups, each representing a specific land use class. The resulting land use map provides valuable data on Casablanca's urban fabric, highlighting wooded areas, agricultural land, built infrastructure, water bodies, and barren land. This automated approach demonstrates GEE's potential as a powerful tool for analyzing land use, enabling informed, data-driven decisions on urban development and environmental monitoring. The methodology outlined can serve as a reference for similar research in other regions, helping to advance remote sensing and geospatial analysis techniques in urban and environmental studies. The effectiveness of these two algorithms is assessed in terms of overall accuracy and kappa coefficient. The k-means algorithm showed moderate accuracy, while the Lvq algorithm showed the least satisfactory results.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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