2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)最新文献_第7页

AFAR-YOLO: An Adaptive YOLO Object Detection Framework AFAR-YOLO：自适应 YOLO 物体检测框架

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459422

Ainal Irham, Kurniadi, Khoirinisa Yuliandari, Farhan Mozart Aditya Fahreza, Daffa Riyadi, A. M. Shiddiqi

引用次数: 0

A Decision Support Framework for Sustainable Waste Disposal Technology Selection 可持续废物处置技术选择决策支持框架

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459408

Venkateswara Reddy Lakkireddy, R. Mohana, B. R. Ganesh, Lakkireddy Udanth Reddy, Saikat Gochhait, Shrish Chogle

{"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}

引用次数: 0

Strategic Leadership: Driving Human Resource Performance in the Modern Workplace 战略领导力：推动现代工作场所的人力资源绩效

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459520

Marwan Milhem, A. Ateeq, M. Alaghbari, Mohammed Alzoraiki, B. Beshr

{"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}

引用次数: 0

A Scientometric Analysis of Electrically Conductive Asphalt Concrete Technology 导电沥青混凝土技术的科学计量分析

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459656

Arsalaan Khan Yousafzai, M. Sutanto, Muhammad Imran Khan, Abdullah O. Baarimah, Ahmed W. Mushtaha, Nasir Khan

{"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}

引用次数: 0

A Synthetic Dataset for Deep Learning Recognition of Pathological Iris Affected by Coloboma 用于深度学习识别受色素痣影响的病理性虹膜的合成数据集

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459367

Maria Frasca, Davide La Torre

引用次数: 0

Understanding Role of Workplace Spirituality in Predicting Psychological Well-being among Faculties of Higher Education Institutes 了解职场精神在预测高等院校教师心理健康方面的作用

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459465

Shalu Kumari, Amjad Ali, Shabana Azmi, Zafrul Allam

{"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}

引用次数: 0

Utilizing Artificial Intelligence in Higher Education: A Systematic Review 在高等教育中利用人工智能：系统回顾

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459629

Salem Alateyyat, Mohamed Soltan

{"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}

引用次数: 0

The Use of Machine Learning to Forecast Financial Performance: A Literature Review 使用机器学习预测财务业绩：文献综述

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459393

Ahmed Abdulaziz Khudhur, A. Al-Alawi

{"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}

引用次数: 0

Intelligent Speed Advisory System for Optimal Energy Efficiency Based on Ambient Temperature Leveraging Communication Technology and Fuzzy Logic 基于环境温度、利用通信技术和模糊逻辑实现最佳能效的智能速度咨询系统

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459507

Tarek Othmani, S. Boubaker, F. Rehimi, S. Alimi

{"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}

引用次数: 0

DRDM: Deep Learning Model for Diabetic Retinopathy Detection DRDM：用于糖尿病视网膜病变检测的深度学习模型

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) Pub Date : 2024-01-28 DOI: 10.1109/ICETSIS61505.2024.10459593

Aya Migdady, Omar Alzoubi, Nabil El Kadhi, Samer Shorman

{"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}

引用次数: 0