Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459422
Ainal Irham, Kurniadi, Khoirinisa Yuliandari, Farhan Mozart Aditya Fahreza, Daffa Riyadi, A. M. Shiddiqi
This study focuses on developing an advanced early warning system utilizing YOLOv5 to detect objects indicative of potential fire hazards. This research is motivated by the fact that continuous monitoring is impractical, especially in high-risk and inaccessible areas. We introduce an innovative approach: adaptive YOLO for object detection to enhance early fire detection capabilities in these challenging environments. The main contribution of this research is the development of adaptive frames per second (FPS) resolution in YOLO object detection. We found that implementing adaptive FPS alone does not significantly impact the efficiency of CPU and RAM resources in the tested devices. However, when adaptive FPS is combined with adaptive resolution, resource usage is significantly reduced–specifically, a 33% decrease in CPU usage and a 0.5-1% (200-400 MB) reduction in RAM usage. These efficiency gains are important in enhancing safety in the industrial sector.
本研究的重点是利用 YOLOv5 开发一种先进的预警系统，以探测显示潜在火灾危险的物体。之所以开展这项研究，是因为持续监测是不切实际的，尤其是在高风险和交通不便的地区。我们引入了一种创新方法：用于物体检测的自适应 YOLO，以增强在这些具有挑战性的环境中的早期火灾检测能力。这项研究的主要贡献是在 YOLO 目标检测中开发了自适应每秒帧数（FPS）分辨率。我们发现，单独实施自适应 FPS 不会对测试设备的 CPU 和 RAM 资源效率产生重大影响。但是，当自适应 FPS 与自适应分辨率相结合时，资源使用率会明显降低，特别是 CPU 使用率降低 33%，RAM 使用率降低 0.5-1%（200-400 MB）。这些效率的提高对于增强工业领域的安全性非常重要。
{"title":"AFAR-YOLO: An Adaptive YOLO Object Detection Framework","authors":"Ainal Irham, Kurniadi, Khoirinisa Yuliandari, Farhan Mozart Aditya Fahreza, Daffa Riyadi, A. M. Shiddiqi","doi":"10.1109/ICETSIS61505.2024.10459422","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459422","url":null,"abstract":"This study focuses on developing an advanced early warning system utilizing YOLOv5 to detect objects indicative of potential fire hazards. This research is motivated by the fact that continuous monitoring is impractical, especially in high-risk and inaccessible areas. We introduce an innovative approach: adaptive YOLO for object detection to enhance early fire detection capabilities in these challenging environments. The main contribution of this research is the development of adaptive frames per second (FPS) resolution in YOLO object detection. We found that implementing adaptive FPS alone does not significantly impact the efficiency of CPU and RAM resources in the tested devices. However, when adaptive FPS is combined with adaptive resolution, resource usage is significantly reduced–specifically, a 33% decrease in CPU usage and a 0.5-1% (200-400 MB) reduction in RAM usage. These efficiency gains are important in enhancing safety in the industrial sector.","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":"140530022","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/ICETSIS61505.2024.10459408
Venkateswara Reddy Lakkireddy, R. Mohana, B. R. Ganesh, Lakkireddy Udanth Reddy, Saikat Gochhait, Shrish Chogle
Waste Disposal Technology (WDT) selection is a primary issue in Municipal Solid Waste (MSW) that affects the development of the environmental and economic perspectives/aspects, particularly in developing countries. The selection of appropriate WDT is a complex Multi-Attribute Decision-Making (MADM) problem with both qualitative and quantitative elements. The existent MADM approaches with fuzzy sets (removal of uncertainty), different subjective weight methods (significance of attributes), and rank reversal phenomenon leads to improper selection of WDT due to the involvement of different opinions of decision-makers. To avoid this, a Decision Support Framework (DSF) was proposed for optimal WDT selection for the growth of economic and environmental development. The proposed DSF integrates Preference Selection Index (PSI) and a Modified-Comprehensive distance Based Ranking (M-COBRA) approaches to determine the significance of attributes and ranking the alternatives, respectively. The DSF is illustrated using a case study collected from Iran and compared with state-of-the-art MADM approaches. Further, the DSF is validated in terms of sensitivity analysis, rank reversal phenomenon, and Pearson's rank correlation coefficient to ensure the stability of ranking.
{"title":"A Decision Support Framework for Sustainable Waste Disposal Technology Selection","authors":"Venkateswara Reddy Lakkireddy, R. Mohana, B. R. Ganesh, Lakkireddy Udanth Reddy, Saikat Gochhait, Shrish Chogle","doi":"10.1109/ICETSIS61505.2024.10459408","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459408","url":null,"abstract":"Waste Disposal Technology (WDT) selection is a primary issue in Municipal Solid Waste (MSW) that affects the development of the environmental and economic perspectives/aspects, particularly in developing countries. The selection of appropriate WDT is a complex Multi-Attribute Decision-Making (MADM) problem with both qualitative and quantitative elements. The existent MADM approaches with fuzzy sets (removal of uncertainty), different subjective weight methods (significance of attributes), and rank reversal phenomenon leads to improper selection of WDT due to the involvement of different opinions of decision-makers. To avoid this, a Decision Support Framework (DSF) was proposed for optimal WDT selection for the growth of economic and environmental development. The proposed DSF integrates Preference Selection Index (PSI) and a Modified-Comprehensive distance Based Ranking (M-COBRA) approaches to determine the significance of attributes and ranking the alternatives, respectively. The DSF is illustrated using a case study collected from Iran and compared with state-of-the-art MADM approaches. Further, the DSF is validated in terms of sensitivity analysis, rank reversal phenomenon, and Pearson's rank correlation coefficient to ensure the stability of ranking.","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":"140530027","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/ICETSIS61505.2024.10459520
Marwan Milhem, A. Ateeq, M. Alaghbari, Mohammed Alzoraiki, B. Beshr
The present research, titled “Strategic Leadership: A Driver for Enhancing Human Resource Performance in the Contemporary Workplace,” delves into the intricate interplay between strategic leadership and the performance of human resources (HR). By conducting a comprehensive evaluation of relevant scholarly works and using a comparative analysis, this study sheds light on the substantial impact of strategic leadership on employee engagement, innovation in human resources practices, and the general well-being of organizations. The key results of the study indicate that the influence of strategic leadership on HR performance is generally good. However, it is important to note that the efficacy of strategic leadership in this regard is not consistent across all organizational settings and cultures. The research further underscores the difficulties encountered by strategic leaders, namely in the task of reconciling organizational goals with the varied requirements of employees within a multinational corporate setting. The significance of adaptation and contextspecific methods in leadership is highlighted via comparative examination of leadership styles. The study adds to the current academic conversation on strategic leadership by offering novel perspectives on its growing function in improving human resources performance within contemporary work environments. The paper provides pragmatic suggestions for the enhancement of leadership skills and emphasizes the need for ongoing adjustment and scholarly inquiry in this ever-evolving domain. This research serves as a significant asset for scholars and professionals in the fields of organizational leadership and human resource management.
{"title":"Strategic Leadership: Driving Human Resource Performance in the Modern Workplace","authors":"Marwan Milhem, A. Ateeq, M. Alaghbari, Mohammed Alzoraiki, B. Beshr","doi":"10.1109/ICETSIS61505.2024.10459520","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459520","url":null,"abstract":"The present research, titled “Strategic Leadership: A Driver for Enhancing Human Resource Performance in the Contemporary Workplace,” delves into the intricate interplay between strategic leadership and the performance of human resources (HR). By conducting a comprehensive evaluation of relevant scholarly works and using a comparative analysis, this study sheds light on the substantial impact of strategic leadership on employee engagement, innovation in human resources practices, and the general well-being of organizations. The key results of the study indicate that the influence of strategic leadership on HR performance is generally good. However, it is important to note that the efficacy of strategic leadership in this regard is not consistent across all organizational settings and cultures. The research further underscores the difficulties encountered by strategic leaders, namely in the task of reconciling organizational goals with the varied requirements of employees within a multinational corporate setting. The significance of adaptation and contextspecific methods in leadership is highlighted via comparative examination of leadership styles. The study adds to the current academic conversation on strategic leadership by offering novel perspectives on its growing function in improving human resources performance within contemporary work environments. The paper provides pragmatic suggestions for the enhancement of leadership skills and emphasizes the need for ongoing adjustment and scholarly inquiry in this ever-evolving domain. This research serves as a significant asset for scholars and professionals in the fields of organizational leadership and human resource management.","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":"140530034","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/ICETSIS61505.2024.10459656
Arsalaan Khan Yousafzai, M. Sutanto, Muhammad Imran Khan, Abdullah O. Baarimah, Ahmed W. Mushtaha, Nasir Khan
Plain asphalt typically is an insulator to the flow of electric current. It can be modified to conductive asphalt by adding various recyclable and environment friendly conductive additives in it. Such asphalt can provide smart and multifunctional environmentally sustainable applications in the pavement industry. Its production and performance behavior parameters are however yet to be entirely understood. This study presents an exhaustive review of literature on conductive asphalt using systematic literature review and scientometric analysis techniques to holistically understand conductive asphalt and current research developments in this field. The objective was to perform a critical review and scientometrically characterize the published research studies. Literature was acquired from credible research databases for study duration from 2009 to 2022, and subsequently filtered them using the PRISMA protocol to identify the most relevant documents. 62 bibliographic articles were consequently selected for the study. Systematic review identified the research themes and techniques adopted in the field of conductive asphalt technology, and the scientometric analysis quantified the characteristics of the articles. VOSviewer was utilized for visualizing the key findings of the quantitative analysis. Development of conductive asphalt has great research potential and improving its piezoresistivity and conductive network is the future research focus of smart asphalt technology. This review provided an in-depth understanding of conductive asphalt concrete's behavior, the emerging trends to support future studies, and helped to identify the current major research themes and the corresponding challenges.
{"title":"A Scientometric Analysis of Electrically Conductive Asphalt Concrete Technology","authors":"Arsalaan Khan Yousafzai, M. Sutanto, Muhammad Imran Khan, Abdullah O. Baarimah, Ahmed W. Mushtaha, Nasir Khan","doi":"10.1109/ICETSIS61505.2024.10459656","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459656","url":null,"abstract":"Plain asphalt typically is an insulator to the flow of electric current. It can be modified to conductive asphalt by adding various recyclable and environment friendly conductive additives in it. Such asphalt can provide smart and multifunctional environmentally sustainable applications in the pavement industry. Its production and performance behavior parameters are however yet to be entirely understood. This study presents an exhaustive review of literature on conductive asphalt using systematic literature review and scientometric analysis techniques to holistically understand conductive asphalt and current research developments in this field. The objective was to perform a critical review and scientometrically characterize the published research studies. Literature was acquired from credible research databases for study duration from 2009 to 2022, and subsequently filtered them using the PRISMA protocol to identify the most relevant documents. 62 bibliographic articles were consequently selected for the study. Systematic review identified the research themes and techniques adopted in the field of conductive asphalt technology, and the scientometric analysis quantified the characteristics of the articles. VOSviewer was utilized for visualizing the key findings of the quantitative analysis. Development of conductive asphalt has great research potential and improving its piezoresistivity and conductive network is the future research focus of smart asphalt technology. This review provided an in-depth understanding of conductive asphalt concrete's behavior, the emerging trends to support future studies, and helped to identify the current major research themes and the corresponding challenges.","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":"140530051","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/ICETSIS61505.2024.10459367
Maria Frasca, Davide La Torre
Biometric recognition systems might not work for people suffering from alteration of physical characteristics. This can also happen for well-known iris recognition systems. In this paper, we describe the creation of a synthetic dataset of eyes suffering from Coloboma, a congenital abnormality of eye membranes characterized by a “keyhole” appearance of the pupil. Due to the rarity of the disease, we apply image processing techniques on a dataset of healthy eyes to artificially simulate the effects of Coloboma. The pupil is distorted to simulate Coloboma on each of these images and the iris is compressed in the direction of the defect. A preliminary evaluation based on k-means has been performed. The dataset will be adopted for designing “non-excluding” iris recognition systems.
{"title":"A Synthetic Dataset for Deep Learning Recognition of Pathological Iris Affected by Coloboma","authors":"Maria Frasca, Davide La Torre","doi":"10.1109/ICETSIS61505.2024.10459367","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459367","url":null,"abstract":"Biometric recognition systems might not work for people suffering from alteration of physical characteristics. This can also happen for well-known iris recognition systems. In this paper, we describe the creation of a synthetic dataset of eyes suffering from Coloboma, a congenital abnormality of eye membranes characterized by a “keyhole” appearance of the pupil. Due to the rarity of the disease, we apply image processing techniques on a dataset of healthy eyes to artificially simulate the effects of Coloboma. The pupil is distorted to simulate Coloboma on each of these images and the iris is compressed in the direction of the defect. A preliminary evaluation based on k-means has been performed. The dataset will be adopted for designing “non-excluding” iris recognition 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":"140530053","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}
The purpose of the proposed investigation is to examine the relationship between spirituality in the workplace and its numerous components, including spiritual orientation, compassion, meaningful work, and value alignment, and their impact on individuals' psychological well-being. To address this purpose, 402 full-time academicians from Bihar, India's state institutions were surveyed using standardized questionnaires. Study revealed strong evidence of a favourable link between spirituality in the workplace and psychological well-being. Workplace spirituality factors such as meaningful work, spiritual orientation, compassion, and value alignment were found to be substantially predicting various measures of psychological well-being in a stepwise linear regression analysis, except of environmental mastery. This indicates that companies should create a spiritual workplace for their employees and provide them with meaningful work in order to boost their health and happiness. The study's limitations and potential applications are discussed.
{"title":"Understanding Role of Workplace Spirituality in Predicting Psychological Well-being among Faculties of Higher Education Institutes","authors":"Shalu Kumari, Amjad Ali, Shabana Azmi, Zafrul Allam","doi":"10.1109/ICETSIS61505.2024.10459465","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459465","url":null,"abstract":"The purpose of the proposed investigation is to examine the relationship between spirituality in the workplace and its numerous components, including spiritual orientation, compassion, meaningful work, and value alignment, and their impact on individuals' psychological well-being. To address this purpose, 402 full-time academicians from Bihar, India's state institutions were surveyed using standardized questionnaires. Study revealed strong evidence of a favourable link between spirituality in the workplace and psychological well-being. Workplace spirituality factors such as meaningful work, spiritual orientation, compassion, and value alignment were found to be substantially predicting various measures of psychological well-being in a stepwise linear regression analysis, except of environmental mastery. This indicates that companies should create a spiritual workplace for their employees and provide them with meaningful work in order to boost their health and happiness. The study's limitations and potential applications are discussed.","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":"140530438","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/ICETSIS61505.2024.10459629
Salem Alateyyat, Mohamed Soltan
Research on utilization of artificial intelligence in higher education has significantly expanded in recent years. However, the existing literature in this domain highlights a shortage of research in specific subareas, such as ChatGPT and the innovative utilization of advanced artificial intelligence tools. With the growing number of studies focusing on artificial intelligence in higher education, there is a need to assess to what extent the current body of research is filling the previously reported research gap. This study aims to review research published within the last 11 months in the year 2023, to assess the status and direction of recent publications in these specific areas and to provide a comprehensive summary that will assist scholars and higher education institutions in shaping their future work on artificial intelligence in higher education. Using a systematic literature review methodology, 295 articles published on the Scopus database were analyzed. The review findings indicate that the majority of papers serve a general overview purpose, with a moderate focus on generative AI, advanced integration of AI into teaching and learning, and prediction modes. On the contrary, a limited number of papers were directed toward AI for assessment, AI Chatbot, and support for administrative processes. These findings highlight the need for a shift of research efforts from more general exploration topics to a more advanced investigation into the usage of AI tools in a novel and sophisticated manner.
{"title":"Utilizing Artificial Intelligence in Higher Education: A Systematic Review","authors":"Salem Alateyyat, Mohamed Soltan","doi":"10.1109/ICETSIS61505.2024.10459629","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459629","url":null,"abstract":"Research on utilization of artificial intelligence in higher education has significantly expanded in recent years. However, the existing literature in this domain highlights a shortage of research in specific subareas, such as ChatGPT and the innovative utilization of advanced artificial intelligence tools. With the growing number of studies focusing on artificial intelligence in higher education, there is a need to assess to what extent the current body of research is filling the previously reported research gap. This study aims to review research published within the last 11 months in the year 2023, to assess the status and direction of recent publications in these specific areas and to provide a comprehensive summary that will assist scholars and higher education institutions in shaping their future work on artificial intelligence in higher education. Using a systematic literature review methodology, 295 articles published on the Scopus database were analyzed. The review findings indicate that the majority of papers serve a general overview purpose, with a moderate focus on generative AI, advanced integration of AI into teaching and learning, and prediction modes. On the contrary, a limited number of papers were directed toward AI for assessment, AI Chatbot, and support for administrative processes. These findings highlight the need for a shift of research efforts from more general exploration topics to a more advanced investigation into the usage of AI tools in a novel and sophisticated manner.","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":"140530450","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/ICETSIS61505.2024.10459393
Ahmed Abdulaziz Khudhur, A. Al-Alawi
The paper offers a comprehensive analysis of ten studies covering different facets of the application of artificial intelligence (AI) techniques for identifying financial performance. The financial stability of organizations is a major concern for decision-makers, particularly in the finance field. Diagnosing financial problems in the early stages can prevent further complications. Many of the previous papers have proved the reliability of machine learning in the prediction of financial performance. Therefore, the motivation of this systematic review is to find out how reliable is machine-learning in forecasting financial performance by exploring the pitfalls of machine-learning methods. Examining the models’ accuracies is not sufficient in determining the robustness of the methods applied, however, the harmony and quality of data used are examined as well. Financial performance is categorized as Bankruptcy and Insolvency. The financial datasets related to the study pertain to bankruptcy, data imbalance, feature dimensionality, forecasting insolvency, preprocessing issues, nonfinancial indicators, commonly used machine learning techniques, and performance metrics. Dealing with high dimensionality was suggested by feature extraction and feature selection. Whereas, data imbalance may be prevented by several techniques such as random sampling. The study's conclusions demonstrated the value of dimensionality reduction methods and data balance in data preprocessing. The study illustrates how critical and impactful when taking into consideration the mentioned strategies in enhancing the existent models. The scientific outcome of this work revolves around conceptualizing the cornerstone for building efficient models in predicting financial performance leading researchers to locate unexplored new research avenues.
{"title":"The Use of Machine Learning to Forecast Financial Performance: A Literature Review","authors":"Ahmed Abdulaziz Khudhur, A. Al-Alawi","doi":"10.1109/ICETSIS61505.2024.10459393","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459393","url":null,"abstract":"The paper offers a comprehensive analysis of ten studies covering different facets of the application of artificial intelligence (AI) techniques for identifying financial performance. The financial stability of organizations is a major concern for decision-makers, particularly in the finance field. Diagnosing financial problems in the early stages can prevent further complications. Many of the previous papers have proved the reliability of machine learning in the prediction of financial performance. Therefore, the motivation of this systematic review is to find out how reliable is machine-learning in forecasting financial performance by exploring the pitfalls of machine-learning methods. Examining the models’ accuracies is not sufficient in determining the robustness of the methods applied, however, the harmony and quality of data used are examined as well. Financial performance is categorized as Bankruptcy and Insolvency. The financial datasets related to the study pertain to bankruptcy, data imbalance, feature dimensionality, forecasting insolvency, preprocessing issues, nonfinancial indicators, commonly used machine learning techniques, and performance metrics. Dealing with high dimensionality was suggested by feature extraction and feature selection. Whereas, data imbalance may be prevented by several techniques such as random sampling. The study's conclusions demonstrated the value of dimensionality reduction methods and data balance in data preprocessing. The study illustrates how critical and impactful when taking into consideration the mentioned strategies in enhancing the existent models. The scientific outcome of this work revolves around conceptualizing the cornerstone for building efficient models in predicting financial performance leading researchers to locate unexplored new research avenues.","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":"140530077","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/ICETSIS61505.2024.10459507
Tarek Othmani, S. Boubaker, F. Rehimi, S. Alimi
Achieving sustainable transportation poses a variety of challenges and difficulties, such as economic, social, and environmental dimensions. These challenges slow down the transition towards a transportation system that minimizes environmental impact and fuel consumption. The focus has shifted towards increasing efficiency and promoting greener alternatives by integrating innovative solutions like intelligent transportation systems, communication technologies, and other approaches to address these challenges and pursue sustainable and energy-efficient transportation. This study proposes a novel approach that integrates Python, SUMO (Simulation of Urban MObility), Vehicle-to-Infrastructure (V2I) communication, and Fuzzy Logic (FL) to estimate the optimal speed for vehicles based on vehicle velocity, road speed limits, and ambient temperature. In the simulation scenarios, we considered different temperature changes to evaluate the effectiveness of the proposed approach. By using SUMO to retrieve the road speed limit through V2I communication and incorporating it into fuzzy logic, we could estimate the optimal speed for vehicles in real time. The simulation results showed a significant reduction in energy consumption and pollutant emissions compared to the unequipped vehicles with V2I and Fuzzy Logic Systems. Specifically, the study found that adopting this approach led to an average reduction in fuel consumption and CO2 emissions of around 9%. These findings highlight the potential of integrating V2I communication and Fuzzy Logic to achieve more sustainable and efficient transportation energy use.
{"title":"Intelligent Speed Advisory System for Optimal Energy Efficiency Based on Ambient Temperature Leveraging Communication Technology and Fuzzy Logic","authors":"Tarek Othmani, S. Boubaker, F. Rehimi, S. Alimi","doi":"10.1109/ICETSIS61505.2024.10459507","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459507","url":null,"abstract":"Achieving sustainable transportation poses a variety of challenges and difficulties, such as economic, social, and environmental dimensions. These challenges slow down the transition towards a transportation system that minimizes environmental impact and fuel consumption. The focus has shifted towards increasing efficiency and promoting greener alternatives by integrating innovative solutions like intelligent transportation systems, communication technologies, and other approaches to address these challenges and pursue sustainable and energy-efficient transportation. This study proposes a novel approach that integrates Python, SUMO (Simulation of Urban MObility), Vehicle-to-Infrastructure (V2I) communication, and Fuzzy Logic (FL) to estimate the optimal speed for vehicles based on vehicle velocity, road speed limits, and ambient temperature. In the simulation scenarios, we considered different temperature changes to evaluate the effectiveness of the proposed approach. By using SUMO to retrieve the road speed limit through V2I communication and incorporating it into fuzzy logic, we could estimate the optimal speed for vehicles in real time. The simulation results showed a significant reduction in energy consumption and pollutant emissions compared to the unequipped vehicles with V2I and Fuzzy Logic Systems. Specifically, the study found that adopting this approach led to an average reduction in fuel consumption and CO2 emissions of around 9%. These findings highlight the potential of integrating V2I communication and Fuzzy Logic to achieve more sustainable and efficient transportation energy use.","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":"140530384","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/ICETSIS61505.2024.10459593
Aya Migdady, Omar Alzoubi, Nabil El Kadhi, Samer Shorman
The application of Artificial Intelligence is being applied in the medical industry at a quick pace, and it is currently serving as the main source of support for clinical practice solutions. Clinical practice accuracy could be improved and costs could be decreased with the use of deep learning techniques. To diagnose Diabetic Retinopathy, an effective and dependable method for automatic screening must be identified. However, deep-learning models may face difficulties due to a lack of data in several medical fields. The Diabetic Retinopathy Detection Model (DRDM), a deep learning model, is proposed in this research to identify retinal images as either infected or uninfected. The data transformation approach is used to address the lack of Diabetic Retinopathy data, which helps prevent overfitting by doubling the data. The paper shows that building a highly complex model like EfficientNetB3 or VGG16 is not necessary to achieve high performance, where, the experiment's test results approved that the DRDM model outperforms such pre-trained models. Furthermore, it took much less time for the DRDM model to produce these results.
{"title":"DRDM: Deep Learning Model for Diabetic Retinopathy Detection","authors":"Aya Migdady, Omar Alzoubi, Nabil El Kadhi, Samer Shorman","doi":"10.1109/ICETSIS61505.2024.10459593","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459593","url":null,"abstract":"The application of Artificial Intelligence is being applied in the medical industry at a quick pace, and it is currently serving as the main source of support for clinical practice solutions. Clinical practice accuracy could be improved and costs could be decreased with the use of deep learning techniques. To diagnose Diabetic Retinopathy, an effective and dependable method for automatic screening must be identified. However, deep-learning models may face difficulties due to a lack of data in several medical fields. The Diabetic Retinopathy Detection Model (DRDM), a deep learning model, is proposed in this research to identify retinal images as either infected or uninfected. The data transformation approach is used to address the lack of Diabetic Retinopathy data, which helps prevent overfitting by doubling the data. The paper shows that building a highly complex model like EfficientNetB3 or VGG16 is not necessary to achieve high performance, where, the experiment's test results approved that the DRDM model outperforms such pre-trained models. Furthermore, it took much less time for the DRDM model to produce these 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":"140530233","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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