Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459486
Mouaissa Mohamed Salah, Marouf Hafida, Ali-Dahmane Tewfik, Benosman Ahmed Sofiane, Aissa Mamoune Sidi Mohammed
Geopolymers can be considered as geomaterials due to their composite nature and applications in various areas of construction and civil engineering. Geopolymers are inorganic materials that can synthesized from natural raw materials, such as fly ash, rice husk ash, metakaolin, or clays, through reaction with alkaline solutions. They exhibit interesting properties such as mechanical strength, and they stand out as eco-friendly binders due to their ability to reduce carbon footprint and promote the sustainability of construction structures. This article aimed to review the current state of the art in the production of geopolymer pastes and mortars based on aluminosilicate sources and their properties, with a particular focus on geopolymers incorporating dredged sediment. The review includes a brief assessment of the use of aluminosilicate sources in designing geopolymer mixes, in addition to identifying key factor influencing the performance of geopolymers containing sediments. The latest data related to the mechanical and durability properties of geopolymers are presented, while also addressing the environmental impacts.
{"title":"Developement of a Next-Generation Geomaterial Based on Aluminosilicate Sources: A Review","authors":"Mouaissa Mohamed Salah, Marouf Hafida, Ali-Dahmane Tewfik, Benosman Ahmed Sofiane, Aissa Mamoune Sidi Mohammed","doi":"10.1109/ICETSIS61505.2024.10459486","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459486","url":null,"abstract":"Geopolymers can be considered as geomaterials due to their composite nature and applications in various areas of construction and civil engineering. Geopolymers are inorganic materials that can synthesized from natural raw materials, such as fly ash, rice husk ash, metakaolin, or clays, through reaction with alkaline solutions. They exhibit interesting properties such as mechanical strength, and they stand out as eco-friendly binders due to their ability to reduce carbon footprint and promote the sustainability of construction structures. This article aimed to review the current state of the art in the production of geopolymer pastes and mortars based on aluminosilicate sources and their properties, with a particular focus on geopolymers incorporating dredged sediment. The review includes a brief assessment of the use of aluminosilicate sources in designing geopolymer mixes, in addition to identifying key factor influencing the performance of geopolymers containing sediments. The latest data related to the mechanical and durability properties of geopolymers are presented, while also addressing the environmental impacts.","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":"140530485","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/ICETSIS61505.2024.10459527
Ahmed W. Mushtaha, W. Alaloul, M. A. Musarat, Abdullah O. Baarimah, F. Rabah, A. M. Alawag
The post-disaster phase, which ensues immediately after a catastrophic event, encompasses vital activities related to reconstruction, often varying in duration based on the disaster's magnitude and local conditions. This period presents unique challenges, including data scarcity and the intricate nature of infrastructure utilities. The lack of comprehensive information regarding infrastructure utilities can lead to damages, accidents, fatalities, disruptions, and project delays. Additionally, the post-disaster phase places immense pressure on local governments to swiftly make informed decisions and execute effective responses. To address these challenges, the integration of Building Information Modeling (BIM) and Geographical Information System (GIS) technologies has emerged as a promising solution. This integration has been explored in various studies, ranging from incorporating GIS into project management software applications to devising software architectures for the seamless integration of BIM into GIS, resulting in more efficient infrastructure management. The amalgamation of BIM and GIS is instrumental in post-disaster infrastructure management, offering a plethora of benefits such as improved decision-making, cost reduction, and enhanced collaboration among stakeholders. This paper aims to fulfill several objectives, including identifying contemporary trends in BIM and GIS research, documenting the existing body of knowledge related to their fusion, and providing recommendations for future research endeavors. In this study, a comprehensive literature review was conducted. The analysis delves into the utilization of BIM-GIS integration, its present applications, and potential future prospects in the context of sustainable built environments.
{"title":"BIM-GIS Integration for Infrastructure Management in Post-Disaster Stage","authors":"Ahmed W. Mushtaha, W. Alaloul, M. A. Musarat, Abdullah O. Baarimah, F. Rabah, A. M. Alawag","doi":"10.1109/ICETSIS61505.2024.10459527","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459527","url":null,"abstract":"The post-disaster phase, which ensues immediately after a catastrophic event, encompasses vital activities related to reconstruction, often varying in duration based on the disaster's magnitude and local conditions. This period presents unique challenges, including data scarcity and the intricate nature of infrastructure utilities. The lack of comprehensive information regarding infrastructure utilities can lead to damages, accidents, fatalities, disruptions, and project delays. Additionally, the post-disaster phase places immense pressure on local governments to swiftly make informed decisions and execute effective responses. To address these challenges, the integration of Building Information Modeling (BIM) and Geographical Information System (GIS) technologies has emerged as a promising solution. This integration has been explored in various studies, ranging from incorporating GIS into project management software applications to devising software architectures for the seamless integration of BIM into GIS, resulting in more efficient infrastructure management. The amalgamation of BIM and GIS is instrumental in post-disaster infrastructure management, offering a plethora of benefits such as improved decision-making, cost reduction, and enhanced collaboration among stakeholders. This paper aims to fulfill several objectives, including identifying contemporary trends in BIM and GIS research, documenting the existing body of knowledge related to their fusion, and providing recommendations for future research endeavors. In this study, a comprehensive literature review was conducted. The analysis delves into the utilization of BIM-GIS integration, its present applications, and potential future prospects in the context of sustainable built environments.","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":"140530210","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/ICETSIS61505.2024.10459360
R. Tiwari, Ankit Kumar Rai
This research aims to identify the maturity of mangoes by proposing a hybrid approach that combines a convolutional neural network (CNN) and support vector machine (SVM). Sorting mangoes according to ripeness is a vital agricultural exercise that increases yield productivity and reduces overages during storage. The suggested hybrid model aims to improve the efficiency and accuracy of existing methods for classifying mango ripeness. The hybrid CNN-SVM model was trained and tested using the dataset containing approx. thousand images of mangoes in three stages (unripe, ripe and overripe). The proposed hybrid method combines CNN's capability to extract characteristics from visual input with the accuracy of SVM classification. With a farfetched 98.53% accuracy rate, experiments with the hybrid model show that it performs better than both traditional machine learning and deep learning approaches. These results demonstrate how hybrid models may be used to assess the maturity of mangos quickly and accurately, which might improve agricultural decision-making.
{"title":"Hybridizing Convolutional Neural Networks and Support Vector Machines for Mango Ripeness Classification","authors":"R. Tiwari, Ankit Kumar Rai","doi":"10.1109/ICETSIS61505.2024.10459360","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459360","url":null,"abstract":"This research aims to identify the maturity of mangoes by proposing a hybrid approach that combines a convolutional neural network (CNN) and support vector machine (SVM). Sorting mangoes according to ripeness is a vital agricultural exercise that increases yield productivity and reduces overages during storage. The suggested hybrid model aims to improve the efficiency and accuracy of existing methods for classifying mango ripeness. The hybrid CNN-SVM model was trained and tested using the dataset containing approx. thousand images of mangoes in three stages (unripe, ripe and overripe). The proposed hybrid method combines CNN's capability to extract characteristics from visual input with the accuracy of SVM classification. With a farfetched 98.53% accuracy rate, experiments with the hybrid model show that it performs better than both traditional machine learning and deep learning approaches. These results demonstrate how hybrid models may be used to assess the maturity of mangos quickly and accurately, which might improve agricultural decision-making.","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":"140530226","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 provision of sustainability information on a local government's website plays a crucial role in facilitating communication, engagement, and collaboration in the pursuit of Sustainable Development Goals (SDGs). This study aims to evaluate the level of website sustainability disclosure (WSD) in Indonesian district/ city local governments and examine the influence of government size and human development (HD) on WSD. The number of samples was determined using Slovin's formula and selected randomly using an online randomizing tool. The final number of samples is 228 primary official websites of the Indonesian local governments. The level of WSD was determined using the content analysis method and scoring method (0 = undisclosed item, 1 = limited disclosure, 2 = fully disclosed). Using the multiple regression method, this study revealed that WSD in Indonesian local governments is still far from expected and cannot be a decision aid to evaluate and compare SDG achievement. Government size, proxied by total budgeted income, does not influence WSD, while HD, proxied by the human development index, has a positive and significant impact on WSD. Thus, this study calls for extending and improving sustainability disclosure in Indonesian local governments, particularly in social and environmental areas.1
{"title":"Website Sustainability Disclosure, Government Size, and Human Development: Evidence from Indonesian Local Governments","authors":"Safridha Ulyati, Adelia, Mutia Sabila, Khafia Mutia, Rahmawaty, Darwanis, Heru Fahlevi","doi":"10.1109/ICETSIS61505.2024.10459537","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459537","url":null,"abstract":"The provision of sustainability information on a local government's website plays a crucial role in facilitating communication, engagement, and collaboration in the pursuit of Sustainable Development Goals (SDGs). This study aims to evaluate the level of website sustainability disclosure (WSD) in Indonesian district/ city local governments and examine the influence of government size and human development (HD) on WSD. The number of samples was determined using Slovin's formula and selected randomly using an online randomizing tool. The final number of samples is 228 primary official websites of the Indonesian local governments. The level of WSD was determined using the content analysis method and scoring method (0 = undisclosed item, 1 = limited disclosure, 2 = fully disclosed). Using the multiple regression method, this study revealed that WSD in Indonesian local governments is still far from expected and cannot be a decision aid to evaluate and compare SDG achievement. Government size, proxied by total budgeted income, does not influence WSD, while HD, proxied by the human development index, has a positive and significant impact on WSD. Thus, this study calls for extending and improving sustainability disclosure in Indonesian local governments, particularly in social and environmental areas.1","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":"140530230","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/ICETSIS61505.2024.10459618
Hana Amani Fatihah, Hasmawati, I. Kurniawan
The main cause of Tuberculosis (TB), a specific infectious disease that affects people worldwide, is Mycobacterium Tuberculosis (MTB). An estimated 30% of the population worldwide has a TB infection, which causes over 20 million deaths annually. Also, 37.7 million people are afflicted with HIV and TB together. Detecting TB in HIV patients is crucial due to the high risk associated with TB. To identify HIV-positive patients, RNA-based methods are used to find host gene expression signatures associated with different aspects of the disease. Nevertheless, no group in this method describes gene signatures that can be used to identify patients who are co-infected with TB and HIV. Therefore, a method is needed to identify TB in HIV patients. This study aims to classify high-dimensional micro array data using Grey Wolf Optimization (GWO) with Support Vector Machines (SVM). To improve the performance of the model, hyperparameter tuning was carried out. Based on the results, we obtained the optimal SVM model using a linear kernel that outperforms other kernels in terms of accuracy, with Fl-score values of 0.78 and 0.80, respectively.
{"title":"Prediction of Tuberculosis on HIV Patients Based on Gene Expression Data Using Grey Wolf Optimization-Support Vector Machine","authors":"Hana Amani Fatihah, Hasmawati, I. Kurniawan","doi":"10.1109/ICETSIS61505.2024.10459618","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459618","url":null,"abstract":"The main cause of Tuberculosis (TB), a specific infectious disease that affects people worldwide, is Mycobacterium Tuberculosis (MTB). An estimated 30% of the population worldwide has a TB infection, which causes over 20 million deaths annually. Also, 37.7 million people are afflicted with HIV and TB together. Detecting TB in HIV patients is crucial due to the high risk associated with TB. To identify HIV-positive patients, RNA-based methods are used to find host gene expression signatures associated with different aspects of the disease. Nevertheless, no group in this method describes gene signatures that can be used to identify patients who are co-infected with TB and HIV. Therefore, a method is needed to identify TB in HIV patients. This study aims to classify high-dimensional micro array data using Grey Wolf Optimization (GWO) with Support Vector Machines (SVM). To improve the performance of the model, hyperparameter tuning was carried out. Based on the results, we obtained the optimal SVM model using a linear kernel that outperforms other kernels in terms of accuracy, with Fl-score values of 0.78 and 0.80, respectively.","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":"140530232","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/ICETSIS61505.2024.10459447
Mokammel Hossain Tito, Md Arifuzzaman, Alifa Nasrin, Shahzad Khan, M. Asaduzzaman, Muhammad Shahzad Chohan, Ali Nabil Al-Duais
This study compares three deep learning algorithms for cardiovascular disease risk prediction. While RBFN boasts the highest accuracy (84.07%), wekaDeeplearning4j excels in identifying high-risk individuals via better AUC and PRC area, valuable for prioritizing early intervention despite slightly lower overall accuracy (81.85%). Conversely, MLP's low mean absolute error indicates high precision in individual case prediction, ideal for personalized treatments. However, tradeoffs exist: wekaDeeplearning4j requires longer training times, and MLP's precision may sacrifice sensitivity. Choosing the optimal algorithm depends on context and priorities. High accuracy and speed favor RBFN, while superior high-risk identification or precise individual predictions favor wekaDeeplearning4j or MLP, respectively. Understanding these trade-offs is crucial for maximizing deep learning's effectiveness in cardiovascular disease risk prediction.
{"title":"Deep Learning for Prediction of Cardiovascular Disease","authors":"Mokammel Hossain Tito, Md Arifuzzaman, Alifa Nasrin, Shahzad Khan, M. Asaduzzaman, Muhammad Shahzad Chohan, Ali Nabil Al-Duais","doi":"10.1109/ICETSIS61505.2024.10459447","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459447","url":null,"abstract":"This study compares three deep learning algorithms for cardiovascular disease risk prediction. While RBFN boasts the highest accuracy (84.07%), wekaDeeplearning4j excels in identifying high-risk individuals via better AUC and PRC area, valuable for prioritizing early intervention despite slightly lower overall accuracy (81.85%). Conversely, MLP's low mean absolute error indicates high precision in individual case prediction, ideal for personalized treatments. However, tradeoffs exist: wekaDeeplearning4j requires longer training times, and MLP's precision may sacrifice sensitivity. Choosing the optimal algorithm depends on context and priorities. High accuracy and speed favor RBFN, while superior high-risk identification or precise individual predictions favor wekaDeeplearning4j or MLP, respectively. Understanding these trade-offs is crucial for maximizing deep learning's effectiveness in cardiovascular disease risk prediction.","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":"140530086","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/ICETSIS61505.2024.10459691
Fatima Wardi, Mohamed Louzazni, Mohamed Hanine
The research presents an original approach to estimate and extract the electrical intrinsic characteristics of flexible hydrogenated amorphous silicon (a-Si:H) solar cells using Earthworm Optimization Algorithm (EOA) The EOA metaheuristic algorithm has gained popularity for optimizing non-linear and complicated systems in various fields. Additionally, the current-voltage curve is used to calculate the offered restricted objective function. In addition, the obtained results using EOA are compared with two algorithms named; quasi-Newton technique (Q-N) and self-organizing migration algorithm (SOMA). Finally, to validate the performance of the used algorithm statistical evaluations are calculated to determine the correctness of the calculated parameters. The compared results show that the theoretical results exhibit great agreement with experimental data, demonstrating higher accuracy when compared to Q-N and SOMA.
{"title":"Optimum Parameters Extraction of Flexible Photovoltaic Cell Using Earthworm Optimization Algorithm","authors":"Fatima Wardi, Mohamed Louzazni, Mohamed Hanine","doi":"10.1109/ICETSIS61505.2024.10459691","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459691","url":null,"abstract":"The research presents an original approach to estimate and extract the electrical intrinsic characteristics of flexible hydrogenated amorphous silicon (a-Si:H) solar cells using Earthworm Optimization Algorithm (EOA) The EOA metaheuristic algorithm has gained popularity for optimizing non-linear and complicated systems in various fields. Additionally, the current-voltage curve is used to calculate the offered restricted objective function. In addition, the obtained results using EOA are compared with two algorithms named; quasi-Newton technique (Q-N) and self-organizing migration algorithm (SOMA). Finally, to validate the performance of the used algorithm statistical evaluations are calculated to determine the correctness of the calculated parameters. The compared results show that the theoretical results exhibit great agreement with experimental data, demonstrating higher accuracy when compared to Q-N and SOMA.","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":"140530090","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/ICETSIS61505.2024.10459429
Anugrah Pangeran, Muhammad Shalahuddin, Raditya Prabaswara, Ardhy Lazuardy, R. Nurcahyo, M. Habiburrahman
The growth of the automotive industry in Indonesia is increasingly rapid, especially in Jakarta, the capital city of Indonesia. This has raised concerns about increasing CO2 emissions. This study discusses the effectiveness of “towards environmentally friendly transportation in Jakarta” by examining the factors influencing individuals' willingness to buy electric motorcycles (EM). Despite government efforts to promote the adoption of EM, the number of registered EM still lags behind targets. Findings show 64 percent desire to purchase an EM, with essential influences being government regulatory reform, awareness of EM, perception of supporting infrastructure, and knowledge of regulations supporting EMs. Aligning these factors with the goals of Jakarta's green transport initiatives is critical to the city's success in combating CO2 emissions and developing a more sustainable urban transport system.
{"title":"Factors Influencing Community Willingness to Buy Electric Motorcycles for Green Transportation in Indonesia: Towards a Sustainable and Eco-Friendly City","authors":"Anugrah Pangeran, Muhammad Shalahuddin, Raditya Prabaswara, Ardhy Lazuardy, R. Nurcahyo, M. Habiburrahman","doi":"10.1109/ICETSIS61505.2024.10459429","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459429","url":null,"abstract":"The growth of the automotive industry in Indonesia is increasingly rapid, especially in Jakarta, the capital city of Indonesia. This has raised concerns about increasing CO2 emissions. This study discusses the effectiveness of “towards environmentally friendly transportation in Jakarta” by examining the factors influencing individuals' willingness to buy electric motorcycles (EM). Despite government efforts to promote the adoption of EM, the number of registered EM still lags behind targets. Findings show 64 percent desire to purchase an EM, with essential influences being government regulatory reform, awareness of EM, perception of supporting infrastructure, and knowledge of regulations supporting EMs. Aligning these factors with the goals of Jakarta's green transport initiatives is critical to the city's success in combating CO2 emissions and developing a more sustainable urban transport system.","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":"140530100","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/ICETSIS61505.2024.10459494
Nowshin Tasnim, Kazi Rifah Noor, Mursalina Islam, Mohammad Nurul Huda, Iqbal H. Sarker
Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology.
{"title":"A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction","authors":"Nowshin Tasnim, Kazi Rifah Noor, Mursalina Islam, Mohammad Nurul Huda, Iqbal H. Sarker","doi":"10.1109/ICETSIS61505.2024.10459494","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459494","url":null,"abstract":"Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology.","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":"140530262","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/ICETSIS61505.2024.10459549
Mohamed Albaz, Mahmoud Khalifa
The main principle of AI is to simulate and go beyond the way humans understand and interact with the world around us, which has quickly become the cornerstone of innovation. Artificial intelligence improves the performance and productivity of institutions by automating processes or tasks that previously required human strength, and the emergence of solutions and tools that rely on artificial intelligence means that more companies can benefit from artificial intelligence at a lower cost and less time. Hence, the adoption of competitive advantage (CA) is one of the most significant challenges for business organization management because of the urgent need to acquire a competitive advantage, depending on the extent to which the industrial sector can create a good working environment and formulate a strategy that supports innovation and its ability to respond to scientific progress and possess good knowledge and skills to achieve excellence in the internal and external environment. Therefore, the current research aimed to systematically analyze the scientific literature related to the application of artificial intelligence and machine learning (ML) in industry to achieve competitive advantage in business organizations. It has been shown that artificial intelligence has a positive impact on achieving competitive advantage in business organizations, and the research relied on The descriptive analytical approach to determine the framework to develop the proposed framework to clarify the relationship between artificial intelligence and competitive advantage in business organizations. An analysis of the literature on artificial intelligence and the competitive advantage of business organizations has been used to answer the main question of research: what is the role of artificial intelligence applications in achieving the competitive advantage of industrial business organizations?
{"title":"The Role of Artificial Intelligence Applications in Achieving Competitive Advantage for Business Organizations - Challenges and Proposed Solutions","authors":"Mohamed Albaz, Mahmoud Khalifa","doi":"10.1109/ICETSIS61505.2024.10459549","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459549","url":null,"abstract":"The main principle of AI is to simulate and go beyond the way humans understand and interact with the world around us, which has quickly become the cornerstone of innovation. Artificial intelligence improves the performance and productivity of institutions by automating processes or tasks that previously required human strength, and the emergence of solutions and tools that rely on artificial intelligence means that more companies can benefit from artificial intelligence at a lower cost and less time. Hence, the adoption of competitive advantage (CA) is one of the most significant challenges for business organization management because of the urgent need to acquire a competitive advantage, depending on the extent to which the industrial sector can create a good working environment and formulate a strategy that supports innovation and its ability to respond to scientific progress and possess good knowledge and skills to achieve excellence in the internal and external environment. Therefore, the current research aimed to systematically analyze the scientific literature related to the application of artificial intelligence and machine learning (ML) in industry to achieve competitive advantage in business organizations. It has been shown that artificial intelligence has a positive impact on achieving competitive advantage in business organizations, and the research relied on The descriptive analytical approach to determine the framework to develop the proposed framework to clarify the relationship between artificial intelligence and competitive advantage in business organizations. An analysis of the literature on artificial intelligence and the competitive advantage of business organizations has been used to answer the main question of research: what is the role of artificial intelligence applications in achieving the competitive advantage of industrial business organizations?","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":"140530448","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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