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":"276 2","pages":"842-846"},"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":"268 3","pages":"639-643"},"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}
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":"207 2","pages":"1-6"},"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/ICETSIS61505.2024.10459424
Muhamad Farhan Wirasantoso, Hasmawati, I. Kurniawan
One of significant parameters of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) is Human Oral Bioavailability (HOB) which is crucial for determining the total of consumed drugs inside humans body circulation. Poor HOB results in undeterminable drug effects in the human body, with approximately 50% of drug candidates failing due to low oral availability. As many as 80% of drugs in the world use the oral route of entry into the body, so HOB prediction is very important to reduce side effects and the risk of toxicity brought by drugs. Unfortunately, oral bioavailability is currently predominantly measured in vivo consequently, developing in-silico methods is considered crucial. To reckon the human oral bioavailability of medication candidates, we used the Hybrid Bat Algorithm method for feature selection and the Ensemble method, i.e. Random Forest, AdaBoost, and XGBoost for the prediction model. The result showed that XGBoost as the best model in which the value of accuracy and Fl-score were 0.776, and 0.802, respectively.
{"title":"Implementation of Hybrid Bat Algorithm-Ensemble on Human Oral Bioavailability Prediction of Drug Candidate","authors":"Muhamad Farhan Wirasantoso, Hasmawati, I. Kurniawan","doi":"10.1109/ICETSIS61505.2024.10459424","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459424","url":null,"abstract":"One of significant parameters of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) is Human Oral Bioavailability (HOB) which is crucial for determining the total of consumed drugs inside humans body circulation. Poor HOB results in undeterminable drug effects in the human body, with approximately 50% of drug candidates failing due to low oral availability. As many as 80% of drugs in the world use the oral route of entry into the body, so HOB prediction is very important to reduce side effects and the risk of toxicity brought by drugs. Unfortunately, oral bioavailability is currently predominantly measured in vivo consequently, developing in-silico methods is considered crucial. To reckon the human oral bioavailability of medication candidates, we used the Hybrid Bat Algorithm method for feature selection and the Ensemble method, i.e. Random Forest, AdaBoost, and XGBoost for the prediction model. The result showed that XGBoost as the best model in which the value of accuracy and Fl-score were 0.776, and 0.802, respectively.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"3 1","pages":"1663-1667"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530194","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":"370 6","pages":"371-374"},"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/ICETSIS61505.2024.10459520
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":"395 8","pages":"1958-1962"},"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/ICETSIS61505.2024.10459458
Jie Sh'ng Yeow, Muhammad Ehsan Rana, Nur Amira Abdul Majid
While the potential of AI in software development is undeniable, integrating advanced models like GPT-3.5 into its core processes like requirements engineering remains largely unexplored. This research investigates the effectiveness of GPT-3.5 in automating key tasks within software requirements engineering. The primary objective is to comprehensively explore the capabilities, limitations, and potential applications of GPT-3.5 in software requirements engineering. Subsequently, the research undergoes thorough analysis and evaluation to gather insights into the strengths and limitations of GPT-3.5 in the requirement-gathering process. The research concludes by identifying the limitations and putting forth recommendations for future research endeavours aimed at integrating GPT-3.5 into software requirement engineering processes. While GPT-3.5 demonstrates proficiency in aspects like creative prototyping and question generation, limitations in areas like domain understanding and context awareness become evident. By outlining these limitations, the authors offer concrete recommendations for future research focusing on the seamless integration of GPT-3.5 and similar models into the broader framework of software requirements engineering.
{"title":"An Automated Model of Software Requirement Engineering Using GPT-3.5","authors":"Jie Sh'ng Yeow, Muhammad Ehsan Rana, Nur Amira Abdul Majid","doi":"10.1109/ICETSIS61505.2024.10459458","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459458","url":null,"abstract":"While the potential of AI in software development is undeniable, integrating advanced models like GPT-3.5 into its core processes like requirements engineering remains largely unexplored. This research investigates the effectiveness of GPT-3.5 in automating key tasks within software requirements engineering. The primary objective is to comprehensively explore the capabilities, limitations, and potential applications of GPT-3.5 in software requirements engineering. Subsequently, the research undergoes thorough analysis and evaluation to gather insights into the strengths and limitations of GPT-3.5 in the requirement-gathering process. The research concludes by identifying the limitations and putting forth recommendations for future research endeavours aimed at integrating GPT-3.5 into software requirement engineering processes. While GPT-3.5 demonstrates proficiency in aspects like creative prototyping and question generation, limitations in areas like domain understanding and context awareness become evident. By outlining these limitations, the authors offer concrete recommendations for future research focusing on the seamless integration of GPT-3.5 and similar models into the broader framework of software requirements engineering.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"50 6","pages":"1746-1755"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530197","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":"252 1","pages":"78-82"},"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}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459504
Mahmoud Khalifa, W. Nageab
In recent years, Geospatial Information Systems (GIS) have witnessed a significant shift towards smart spatial analytics, which involves the processing of large volumes of geographic data, known as big geographic data (Geodata Big). This processing and analysis enable the extraction of valuable information that can be utilized in decision-making processes. Smart maps, as geographic information platforms, play a crucial role in decision-making by providing data, statistics, and information about geographic features and phenomena. Three-dimensional maps offer the capability to visualize and analyze geographic and temporal data on Earth's surface or custom maps. They enable users to observe changes over time and create interactive visual tours that can be shared with others. The applications of smart maps are diverse and include surveillance, monitoring, navigation, and determining optimal routes to specific locations. Artificial-Geo intelligence (AI-Geo) applications have further enhanced GIS capabilities by utilizing intelligent models trained on various datasets. These models enable the identification and extraction of geographic phenomena from satellite imagery, aerial photographs, and other sources. AIGeo applications have found utility in numerous domains beyond mapping, providing valuable insights and facilitating decision-making processes. This abstract highlights the growing importance of smart spatial analytics in GIS, particularly through the use of smart maps and AI-Geo applications. By leveraging these technologies, organizations can harness the power of geospatial data to make informed decisions and gain deeper insights into geographic phenomena.
{"title":"Use of Three-Dimensional Maps in the Study of the Political Geography Course as One of the Applications of Artificial Intelligence","authors":"Mahmoud Khalifa, W. Nageab","doi":"10.1109/ICETSIS61505.2024.10459504","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459504","url":null,"abstract":"In recent years, Geospatial Information Systems (GIS) have witnessed a significant shift towards smart spatial analytics, which involves the processing of large volumes of geographic data, known as big geographic data (Geodata Big). This processing and analysis enable the extraction of valuable information that can be utilized in decision-making processes. Smart maps, as geographic information platforms, play a crucial role in decision-making by providing data, statistics, and information about geographic features and phenomena. Three-dimensional maps offer the capability to visualize and analyze geographic and temporal data on Earth's surface or custom maps. They enable users to observe changes over time and create interactive visual tours that can be shared with others. The applications of smart maps are diverse and include surveillance, monitoring, navigation, and determining optimal routes to specific locations. Artificial-Geo intelligence (AI-Geo) applications have further enhanced GIS capabilities by utilizing intelligent models trained on various datasets. These models enable the identification and extraction of geographic phenomena from satellite imagery, aerial photographs, and other sources. AIGeo applications have found utility in numerous domains beyond mapping, providing valuable insights and facilitating decision-making processes. This abstract highlights the growing importance of smart spatial analytics in GIS, particularly through the use of smart maps and AI-Geo applications. By leveraging these technologies, organizations can harness the power of geospatial data to make informed decisions and gain deeper insights into geographic phenomena.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"237 4","pages":"230-234"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530241","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/ICETSIS61505.2024.10459594
Nada Tarek
Through a case study of a 19th century residential structure in Glina, Croatia, this research paper investigates preservation engineering application of an earthquake-damaged historic buildings. Following the 2020 Petrina quakes, the cultural site of the case study received studies and minimally invasive retrofits to strike a ratio between preservation and seismic resistance. fabric-reinforced cementitious matrix FCRM and adding concrete to the floors was employed to improve structural integrity and increase its ability to resist seismic waves. The research paper concludes to effective approaches for preserving authentic heritage elements while improving life safety by combining preservation standards and structural engineering.
{"title":"Preservation Conservation Engineering: A Case Study","authors":"Nada Tarek","doi":"10.1109/ICETSIS61505.2024.10459594","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459594","url":null,"abstract":"Through a case study of a 19th century residential structure in Glina, Croatia, this research paper investigates preservation engineering application of an earthquake-damaged historic buildings. Following the 2020 Petrina quakes, the cultural site of the case study received studies and minimally invasive retrofits to strike a ratio between preservation and seismic resistance. fabric-reinforced cementitious matrix FCRM and adding concrete to the floors was employed to improve structural integrity and increase its ability to resist seismic waves. The research paper concludes to effective approaches for preserving authentic heritage elements while improving life safety by combining preservation standards and structural engineering.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"185 1","pages":"1996-2000"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530250","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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