Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085428
Sohaib Ali Khan, Hafiz Zia Ur Rehman, A. Waqar, Z. Khan, Engr. Dr. Muntazir Hussain, U. Masud
This paper presents a framework for the implementation of a digital twin (DT) in electrical grid management. Automation in the electrical energy network has resulted in the transformation into Smart grid, which is utilized for the generation, transmission, and distribution of electrical power as well as interconnecting microgrids with dynamic scheduling and trading options. The evolution of the digital twin offers added advantages including real-time condition monitoring based maintenance of assets based on data analytics, energy forecasting, and prediction for appropriate decision making by investors. Thus, fault diagnosis and detection can be easily handled in the advanced automated future grid. These features have enhanced reliability and offer optimized energy management by incorporating a virtual DT domain. In this paper, some major benefits of establishing a digital twin for the smart-grid is highlighted followed by the case study on monitoring a single component of the Smart grid that is evaluated for the remaining useful life (RUL) of the equipment by using artificial intelligence (AI) algorithm. This approach of preventive maintenance based on DT can be effectively utilized for all the key components in the smart grid-connected via a sensor network for data sampling to reduce downtime and improve the reliability of the overall system.
{"title":"Digital Twin for Advanced Automation of Future Smart Grid","authors":"Sohaib Ali Khan, Hafiz Zia Ur Rehman, A. Waqar, Z. Khan, Engr. Dr. Muntazir Hussain, U. Masud","doi":"10.1109/ICAISC56366.2023.10085428","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085428","url":null,"abstract":"This paper presents a framework for the implementation of a digital twin (DT) in electrical grid management. Automation in the electrical energy network has resulted in the transformation into Smart grid, which is utilized for the generation, transmission, and distribution of electrical power as well as interconnecting microgrids with dynamic scheduling and trading options. The evolution of the digital twin offers added advantages including real-time condition monitoring based maintenance of assets based on data analytics, energy forecasting, and prediction for appropriate decision making by investors. Thus, fault diagnosis and detection can be easily handled in the advanced automated future grid. These features have enhanced reliability and offer optimized energy management by incorporating a virtual DT domain. In this paper, some major benefits of establishing a digital twin for the smart-grid is highlighted followed by the case study on monitoring a single component of the Smart grid that is evaluated for the remaining useful life (RUL) of the equipment by using artificial intelligence (AI) algorithm. This approach of preventive maintenance based on DT can be effectively utilized for all the key components in the smart grid-connected via a sensor network for data sampling to reduce downtime and improve the reliability of the overall system.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121022986","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 : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085581
Fatimah A. Alghamdi, Waad S. AlAnazi, S. Snoussi
Digital privacy awareness has become a priority in the area of computer science, especially among young Saudi Arabian adults. The purpose of this study is to assess Saudi Arabian computer science students’ awareness of mobile operating system privacy. This study involved sixty-six computer science students at universities across Saudi Arabia who filled out an online questionnaire that contained twenty-six questions about mobile privacy and it was divided into five sections: browsers, accounts and passwords, applications and permissions, public networks, and information protection. The survey shows that while computer science students are informed about the possible risks of personal information being disclosed through their mobile devices, more than half of them are still willing to share personal details through applications that require private or sensitive information. Based on this study, although there is a decent amount of mobile privacy awareness among Saudi Arabian computer science students, there is still a serious need to improve it by raising awareness of the dangers of mobile devices and the risks involved in disclosing private information, and by presenting information in a more interactive format.
{"title":"Awareness of Mobile Operating System Privacy Among Computer Science Students","authors":"Fatimah A. Alghamdi, Waad S. AlAnazi, S. Snoussi","doi":"10.1109/ICAISC56366.2023.10085581","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085581","url":null,"abstract":"Digital privacy awareness has become a priority in the area of computer science, especially among young Saudi Arabian adults. The purpose of this study is to assess Saudi Arabian computer science students’ awareness of mobile operating system privacy. This study involved sixty-six computer science students at universities across Saudi Arabia who filled out an online questionnaire that contained twenty-six questions about mobile privacy and it was divided into five sections: browsers, accounts and passwords, applications and permissions, public networks, and information protection. The survey shows that while computer science students are informed about the possible risks of personal information being disclosed through their mobile devices, more than half of them are still willing to share personal details through applications that require private or sensitive information. Based on this study, although there is a decent amount of mobile privacy awareness among Saudi Arabian computer science students, there is still a serious need to improve it by raising awareness of the dangers of mobile devices and the risks involved in disclosing private information, and by presenting information in a more interactive format.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126737072","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 : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085668
Tarik F. Himdi
Traditional higher education in universities has been unchanged for long times. When the internet was introduced in the early 2000, it gave great opportunities by shifting from traditional learning to E-Learning. Some universities moved toward online degrees for several majors. The majority of E-learning models shared the following systems: Learning Management System (LMS), Virtual Class-Room System (V.C.S), Digital Library system, Content Management System (C.M.S), and Admission & Registration system. However, most of those E-Learning models have several limitations compared to traditional learning. Various limitations for example: the lack of high speed of the internet, unavailability of good learning contents, and the shortage of student’s comprehension of learning resources due to lost connections between online learning and real-world cases. This paper will study how to improve higher education in Smart City Environment by tighten online learning resources with real world scenario. Our case study will be in a computer school where an under development Smart Education System (SES) will track learning resources based on Course Learning Outcomes (CLOs) of computer courses either locally or remotely.
{"title":"Higher Education Model in Smart Cities: A case study in computer school","authors":"Tarik F. Himdi","doi":"10.1109/ICAISC56366.2023.10085668","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085668","url":null,"abstract":"Traditional higher education in universities has been unchanged for long times. When the internet was introduced in the early 2000, it gave great opportunities by shifting from traditional learning to E-Learning. Some universities moved toward online degrees for several majors. The majority of E-learning models shared the following systems: Learning Management System (LMS), Virtual Class-Room System (V.C.S), Digital Library system, Content Management System (C.M.S), and Admission & Registration system. However, most of those E-Learning models have several limitations compared to traditional learning. Various limitations for example: the lack of high speed of the internet, unavailability of good learning contents, and the shortage of student’s comprehension of learning resources due to lost connections between online learning and real-world cases. This paper will study how to improve higher education in Smart City Environment by tighten online learning resources with real world scenario. Our case study will be in a computer school where an under development Smart Education System (SES) will track learning resources based on Course Learning Outcomes (CLOs) of computer courses either locally or remotely.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128415034","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 growing use of dialect around the country has recently drawn interest from speech technology and research communities in dialect detection. This article aimed to identify Arabic speech dialects and classify them according to the country of speaking. This study presents an analysis and preprocessing system for audio inputs that express the Arabic dialects within 8 Arab dialects. The dataset contains 672 data and eight main subgroups, 84 samples for each of the eight Arabic dialects. Arabic dialect features are extracted and modeled using Convolutional Neural Network (CNN) techniques. The study shows the suitability and efficiency of the system, deep learning models are used instead of machine learning models. The overall results reveal that CNN’s implementation of our proposed system for identifying Arabic dialects reaches a degree of accuracy of 83%. This paper has proposed a system that showed its superiority in performance. The system converts the speech into images using the spectrogram feature, and CNN is used because it can extract features from images automatically. The study contributes to enhancing the classification process of Arabic speech dialects which is an essential issue as many of the studies working on Modern Standard Arabic (MSA), while the majority of Arabs speak local dialects, it is necessary to identify the dialect used by speakers in order to communicate with one another or before machine translation takes place.
{"title":"Arabic Speech Dialect Classification using Deep Learning","authors":"Meaad Alrehaili, Tahani Alasmari, Areej Aoalshutayri","doi":"10.1109/ICAISC56366.2023.10085647","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085647","url":null,"abstract":"The growing use of dialect around the country has recently drawn interest from speech technology and research communities in dialect detection. This article aimed to identify Arabic speech dialects and classify them according to the country of speaking. This study presents an analysis and preprocessing system for audio inputs that express the Arabic dialects within 8 Arab dialects. The dataset contains 672 data and eight main subgroups, 84 samples for each of the eight Arabic dialects. Arabic dialect features are extracted and modeled using Convolutional Neural Network (CNN) techniques. The study shows the suitability and efficiency of the system, deep learning models are used instead of machine learning models. The overall results reveal that CNN’s implementation of our proposed system for identifying Arabic dialects reaches a degree of accuracy of 83%. This paper has proposed a system that showed its superiority in performance. The system converts the speech into images using the spectrogram feature, and CNN is used because it can extract features from images automatically. The study contributes to enhancing the classification process of Arabic speech dialects which is an essential issue as many of the studies working on Modern Standard Arabic (MSA), while the majority of Arabs speak local dialects, it is necessary to identify the dialect used by speakers in order to communicate with one another or before machine translation takes place.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128727051","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 : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085307
Muhammad Akhlaq, Z. Rasheed
Permeability is an important property of a petroleum reservoir that indicates the amount of oil in the reservoir and its ability to flow. The ability to predict reservoir permeability can significantly improve oil field operations and management. One method to obtain reliable permeability data is to analyze cores in the laboratories, which is very expensive, time consuming and not applicable in all cases. Another method better suited to smart cities is to use log data from oil wells to predict permeability, which is fast, reliable, and very cheap. In this study, we apply multiple artificial intelligence (AI) techniques to well logs to predict oilfield permeability in search of a more powerful hybrid model. In this paper, we propose Genetic Algorithm Neural Network (GANN), a hybrid model for permeability prediction, using the neural network as the primary model to calculate weights for the prediction and the Genetic Algorithm as the secondary model to optimize the results generated by the Neural Network be used. The experimental results show that the GANN model can estimate the permeability of oil reservoirs with higher correlation coefficients and lower mean square errors compared to the individual AI techniques.
{"title":"GANN: A Hybrid Model for Permeability Prediction of Oil Reservoirs","authors":"Muhammad Akhlaq, Z. Rasheed","doi":"10.1109/ICAISC56366.2023.10085307","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085307","url":null,"abstract":"Permeability is an important property of a petroleum reservoir that indicates the amount of oil in the reservoir and its ability to flow. The ability to predict reservoir permeability can significantly improve oil field operations and management. One method to obtain reliable permeability data is to analyze cores in the laboratories, which is very expensive, time consuming and not applicable in all cases. Another method better suited to smart cities is to use log data from oil wells to predict permeability, which is fast, reliable, and very cheap. In this study, we apply multiple artificial intelligence (AI) techniques to well logs to predict oilfield permeability in search of a more powerful hybrid model. In this paper, we propose Genetic Algorithm Neural Network (GANN), a hybrid model for permeability prediction, using the neural network as the primary model to calculate weights for the prediction and the Genetic Algorithm as the secondary model to optimize the results generated by the Neural Network be used. The experimental results show that the GANN model can estimate the permeability of oil reservoirs with higher correlation coefficients and lower mean square errors compared to the individual AI techniques.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130589191","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}
Federated learning (FL) is an emerging trend related to the concept of distributed Machine Learning (ML). It focuses on a collaborative training process locally conducted on the dataset of the client devices in order to preserve the users’ privacy. Nonetheless, this solution still suffers from many challenges dealing with privacy, security, and performance. In this work, we introduce a novel policy-based FL approach for improving privacy, security, and performance in federated learning. Our proposed solution ensures reliability, communications security, and heterogeneous privacy (i.e., the users have different privacy attitudes and expectations). In addition, it guarantees performance in terms of the dataset’s quality and scalability. To prove the effectiveness of our model, we perform a security and performance evaluation by assuming a threat model with attackers having different behaviors.
{"title":"Towards Privacy Preserving and Efficiency in Fog Selection for Federated Learning","authors":"Noura Alhwidi, Noura Alqahtani, Latifah Almaiman, Molka Rekik","doi":"10.1109/ICAISC56366.2023.10085094","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085094","url":null,"abstract":"Federated learning (FL) is an emerging trend related to the concept of distributed Machine Learning (ML). It focuses on a collaborative training process locally conducted on the dataset of the client devices in order to preserve the users’ privacy. Nonetheless, this solution still suffers from many challenges dealing with privacy, security, and performance. In this work, we introduce a novel policy-based FL approach for improving privacy, security, and performance in federated learning. Our proposed solution ensures reliability, communications security, and heterogeneous privacy (i.e., the users have different privacy attitudes and expectations). In addition, it guarantees performance in terms of the dataset’s quality and scalability. To prove the effectiveness of our model, we perform a security and performance evaluation by assuming a threat model with attackers having different behaviors.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125433345","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 : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085473
Chada Lakshma Reddy, K. B. Reddy, G. R. Anil, S. Mohanty, Abdul Basit
Online laptop sales are at an all-time high as a result of the pandemic. A laptop is a must-have for working from home, as well as e-learning and other activities. The buyer is aided in making a purchasing decision by a feature-based pricing prediction algorithm. Based on real-time data scraped from an e-commerce website, this study proposes a model for predicting laptop costs. The suggested method collects data from a real-time environment and predicts the model’s pricing with high accuracy. This study employs Support Vector Regression, Decision Tree Regression and Multi-Linear Regression to forecast laptop price.
{"title":"Laptop Price Prediction Using Real Time Data","authors":"Chada Lakshma Reddy, K. B. Reddy, G. R. Anil, S. Mohanty, Abdul Basit","doi":"10.1109/ICAISC56366.2023.10085473","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085473","url":null,"abstract":"Online laptop sales are at an all-time high as a result of the pandemic. A laptop is a must-have for working from home, as well as e-learning and other activities. The buyer is aided in making a purchasing decision by a feature-based pricing prediction algorithm. Based on real-time data scraped from an e-commerce website, this study proposes a model for predicting laptop costs. The suggested method collects data from a real-time environment and predicts the model’s pricing with high accuracy. This study employs Support Vector Regression, Decision Tree Regression and Multi-Linear Regression to forecast laptop price.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131532189","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}
With technological progress, the risk factor resulting from malware is increasing dramatically. In this paper, we present the most prominent techniques and tools used in malware forensics to combat this threat. The malware designed by attackers is multiform and has the potential to spread and harm the global economy and corporate assets every day. Thus, there is an urgent need to analyze and detect malware before important assets worldwide are damaged. In this study, we discuss various techniques for malware analysis, such as static, dynamic, hybrid, and memory forensic, as well as malware-detection techniques, such as signature, anomaly, and specification. Moreover, we present the most prominent tools used to analyze and detect malware. These tools are divided into two categories: static and dynamic. The paper focus in studying the main features and limitations of the current malware forensic techniques and tools.
{"title":"A Survey off Malware Forensics Analysis Techniques And Tools","authors":"Shahad Al-Sofyani, Amerah Alelayani, Fatimah Al-zahrani, Roaa Monshi","doi":"10.1109/ICAISC56366.2023.10085474","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085474","url":null,"abstract":"With technological progress, the risk factor resulting from malware is increasing dramatically. In this paper, we present the most prominent techniques and tools used in malware forensics to combat this threat. The malware designed by attackers is multiform and has the potential to spread and harm the global economy and corporate assets every day. Thus, there is an urgent need to analyze and detect malware before important assets worldwide are damaged. In this study, we discuss various techniques for malware analysis, such as static, dynamic, hybrid, and memory forensic, as well as malware-detection techniques, such as signature, anomaly, and specification. Moreover, we present the most prominent tools used to analyze and detect malware. These tools are divided into two categories: static and dynamic. The paper focus in studying the main features and limitations of the current malware forensic techniques and tools.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125904223","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 : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085320
Ripto Mukti Wibowo, Bahjat Fakieh, M. S. Ramzan, A. Alzahrani, M. Siddiqui, B. Alzahrani
Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 percent.In this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities.
{"title":"Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category)","authors":"Ripto Mukti Wibowo, Bahjat Fakieh, M. S. Ramzan, A. Alzahrani, M. Siddiqui, B. Alzahrani","doi":"10.1109/ICAISC56366.2023.10085320","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085320","url":null,"abstract":"Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 percent.In this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128448082","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}
In this paper, we present a two-step automatic blood cell counting approach for accurately and efficiently determining the complete blood count (CBC). The approach involves using two convolutional neural networks (CNNs) for the segmentation of red blood cells, white blood cells, and platelets, and then applying three different algorithms (Watershed, Connected Component Labeling, and Circle Hough Transform) to count the cells present in the masks produced by the CNNs. We also introduce a loss function for the Circle Hough Transform algorithm to further improve its accuracy. Our approach shows good results compared to other methods in the literature and has the potential to significantly reduce the time and effort required for manual blood cell counting.
{"title":"Blood cells image segmentation and counting using deep transfer learning","authors":"Gharbi Aghiles, Neggazi Mohamed Lamine, Touazi Faycal, Gaceb Djamel, Yagoubi Mohamed Riad","doi":"10.1109/ICAISC56366.2023.10085605","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085605","url":null,"abstract":"In this paper, we present a two-step automatic blood cell counting approach for accurately and efficiently determining the complete blood count (CBC). The approach involves using two convolutional neural networks (CNNs) for the segmentation of red blood cells, white blood cells, and platelets, and then applying three different algorithms (Watershed, Connected Component Labeling, and Circle Hough Transform) to count the cells present in the masks produced by the CNNs. We also introduce a loss function for the Circle Hough Transform algorithm to further improve its accuracy. Our approach shows good results compared to other methods in the literature and has the potential to significantly reduce the time and effort required for manual blood cell counting.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128581219","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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