Pub Date : 2024-04-25DOI: 10.32996/jcsts.2024.6.2.8
Shan Guo, Shiyu Dong
With the aging of China's population, as a new model combining information technology and high-quality elderly care services, the topic of smart elderly care continues to warm up and has immediately attracted widespread attention. With the innovation of Internet technology, elderly people and families are in demand of the smart pension industry, and national policies have issued a series of policies and plans to encourage the development of smart pensions, allowing the innovation and design of China's smart elderly service system to fully expand. However, the research shows that from the perspective of the macro development of China's smart pension industry, the overall operation system is not mature, the talent gap is more accurate, there are fewer services, and it is still in the market development stage. This paper focuses on the Shenzhen Longhua District, which is a local part of the community wisdom endowment service industry chain investigation. The analysis of the current pension service system development is not mature enough, and it does not completely combine Internet technology and wisdom endowment. Additionally, because the economic strength and cultural level limit of wisdom endowment service acceptance are not high, the policy support for community wisdom endowment is not large enough. On this basis, this paper draws on the excellent successful experience at home and abroad. From the perspective of three aspects and put forward opinions for innovation, first, the innovation of community smart elderly care service technology, which combines Internet information technology and elderly care services organically, improves the quality of life and the happiness index of elderly people. Second, the innovation of community smart elderly care services, including the full use of medical institutions to provide 24-hour rehabilitation monitoring, remote monitoring services, and personalized and differentiated services, are tailored for elderly people. Third, the national policy innovation of community elderly care services, through policy guidance and support, promotes the healthy development of community elderly care services to provide better quality and convenient pension services for elderly people. The author believes that in the future, community elderly care services will be more professional and standardized, and a set of digital systems and service standards with scientific standards and rules will be established to ensure the quality of service and personalized demand.
{"title":"Research and Innovation of a Community Intelligent Pension Service System: Taking Longhua District, Shenzhen, China, as an Example","authors":"Shan Guo, Shiyu Dong","doi":"10.32996/jcsts.2024.6.2.8","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.8","url":null,"abstract":"With the aging of China's population, as a new model combining information technology and high-quality elderly care services, the topic of smart elderly care continues to warm up and has immediately attracted widespread attention. With the innovation of Internet technology, elderly people and families are in demand of the smart pension industry, and national policies have issued a series of policies and plans to encourage the development of smart pensions, allowing the innovation and design of China's smart elderly service system to fully expand. However, the research shows that from the perspective of the macro development of China's smart pension industry, the overall operation system is not mature, the talent gap is more accurate, there are fewer services, and it is still in the market development stage. This paper focuses on the Shenzhen Longhua District, which is a local part of the community wisdom endowment service industry chain investigation. The analysis of the current pension service system development is not mature enough, and it does not completely combine Internet technology and wisdom endowment. Additionally, because the economic strength and cultural level limit of wisdom endowment service acceptance are not high, the policy support for community wisdom endowment is not large enough. On this basis, this paper draws on the excellent successful experience at home and abroad. From the perspective of three aspects and put forward opinions for innovation, first, the innovation of community smart elderly care service technology, which combines Internet information technology and elderly care services organically, improves the quality of life and the happiness index of elderly people. Second, the innovation of community smart elderly care services, including the full use of medical institutions to provide 24-hour rehabilitation monitoring, remote monitoring services, and personalized and differentiated services, are tailored for elderly people. Third, the national policy innovation of community elderly care services, through policy guidance and support, promotes the healthy development of community elderly care services to provide better quality and convenient pension services for elderly people. The author believes that in the future, community elderly care services will be more professional and standardized, and a set of digital systems and service standards with scientific standards and rules will be established to ensure the quality of service and personalized demand.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140657738","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}
Cardiovascular diseases, including myocardial infarction, present significant challenges in modern healthcare, necessitating accurate prediction models for early intervention. This study explores the efficacy of machine learning algorithms in predicting myocardial infarction, leveraging a dataset comprising various clinical attributes sourced from patients with heart failure. Six machine learning models, including Logistic Regression, Support Vector Machine, XGBoost, LightGBM, Decision Tree, and Bagging, are evaluated based on key performance metrics such as accuracy, precision, recall, F1 Score, and AUC. The results reveal XGBoost as the top performer, achieving an accuracy of 94.80% and an AUC of 90.0%. LightGBM closely follows with an accuracy of 92.50% and an AUC of 92.00%. Logistic Regression emerges as a reliable option with an accuracy of 85.0%. The study underscores the potential of machine learning in enhancing myocardial infarction prediction, offering valuable insights for clinical decision-making and healthcare intervention strategies.
{"title":"Improving Cardiovascular Disease Prediction through Comparative Analysis of Machine Learning Models","authors":"Nishat Anjum, Cynthia Ummay Siddiqua, Mahfuz Haider, Zannatun Ferdus, Md Azad Hossain Raju, Touhid Imam, Md Rezwanur Rahman","doi":"10.32996/jcsts.2024.6.2.7","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.7","url":null,"abstract":"Cardiovascular diseases, including myocardial infarction, present significant challenges in modern healthcare, necessitating accurate prediction models for early intervention. This study explores the efficacy of machine learning algorithms in predicting myocardial infarction, leveraging a dataset comprising various clinical attributes sourced from patients with heart failure. Six machine learning models, including Logistic Regression, Support Vector Machine, XGBoost, LightGBM, Decision Tree, and Bagging, are evaluated based on key performance metrics such as accuracy, precision, recall, F1 Score, and AUC. The results reveal XGBoost as the top performer, achieving an accuracy of 94.80% and an AUC of 90.0%. LightGBM closely follows with an accuracy of 92.50% and an AUC of 92.00%. Logistic Regression emerges as a reliable option with an accuracy of 85.0%. The study underscores the potential of machine learning in enhancing myocardial infarction prediction, offering valuable insights for clinical decision-making and healthcare intervention strategies.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680769","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 : 2024-04-20DOI: 10.32996/jcsts.2024.6.2.6
Md Rokibul Hasan
The agricultural sector plays a paramount role in the economy of the United States, contributing significantly to the GDP and affirming sustainability for American residents. This study explored the application of Artificial Intelligence and Machine Learning techniques in maximizing crop yields in America. This research employed various software tools, comprising Python programming language, Pandas library for data manipulation and analysis, Scikit-learn library for machine learning models and evaluation metrics, and LIME library for explainable AI. The crop yield datasets for the current research were sourced from Kaggle. This dataset provided substantial insights regarding crop cultivation practices within the USA context. This study proposes the "XAI-CROP" algorithm, which is a novel explainable artificial intelligence (XAI) model developed particularly to reinforce the interpretability, transparency and trustworthiness of crop recommendation systems (CRS). From the experimentation, the XAI-CROP model excelled at forecasting crop yield, as demonstrated by its lowest MSE value of 0.9412, suggesting minimal errors. Besides, Its MAE of 0.9874 suggests an average error of less than 1 unit in forecasting crop yield. Furthermore, the R2 value of 0.94152 suggests that the algorithm accounts for 94.15% of the data's variability.
{"title":"AI and Machine Learning for Optimal Crop Yield Optimization in the USA","authors":"Md Rokibul Hasan","doi":"10.32996/jcsts.2024.6.2.6","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.6","url":null,"abstract":"The agricultural sector plays a paramount role in the economy of the United States, contributing significantly to the GDP and affirming sustainability for American residents. This study explored the application of Artificial Intelligence and Machine Learning techniques in maximizing crop yields in America. This research employed various software tools, comprising Python programming language, Pandas library for data manipulation and analysis, Scikit-learn library for machine learning models and evaluation metrics, and LIME library for explainable AI. The crop yield datasets for the current research were sourced from Kaggle. This dataset provided substantial insights regarding crop cultivation practices within the USA context. This study proposes the \"XAI-CROP\" algorithm, which is a novel explainable artificial intelligence (XAI) model developed particularly to reinforce the interpretability, transparency and trustworthiness of crop recommendation systems (CRS). From the experimentation, the XAI-CROP model excelled at forecasting crop yield, as demonstrated by its lowest MSE value of 0.9412, suggesting minimal errors. Besides, Its MAE of 0.9874 suggests an average error of less than 1 unit in forecasting crop yield. Furthermore, the R2 value of 0.94152 suggests that the algorithm accounts for 94.15% of the data's variability.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680422","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 innovative study "Fuzzy Logic Empowered NetWatch: Revolutionizing Aquaculture with IoT-based Intelligent Monitoring and Management in Bangladesh" is a step towards the dawn of a new era in fish farming techniques that emphasize accuracy and efficiency. Using fuzzy logic controllers in the NetWatch system, stakeholders involved in aquaculture can access a degree of intelligence and adaptability that is not possible with standard management techniques. Fuzzy logic techniques are included in NetWatch, allowing it to make intelligent judgments based on the intricate and frequently unpredictable nature of aquaculture systems, in addition to monitoring and controlling environmental parameters and water quality. Because of its dynamic adaptability, the system can mitigate risks and optimize results in real time while successfully responding to changing situations. Furthermore, NetWatch offers a comprehensive picture of the aquaculture ecosystem by combining pond-specific data with more general environmental insights, facilitating better-informed macro and micro decision-making. With this thorough knowledge, fish farmers can allocate resources more efficiently, reduce waste, and sustainably increase productivity. Moreover, Fuzzy Logic Empowered NetWatch's revolutionary potential offers opportunities for the aquaculture industry, transcending the boundaries of individual fish ponds. Bangladesh can establish itself as a global leader in sustainable aquaculture methods and set new benchmarks for production, efficiency, and environmental stewardship using IoT-based intelligent monitoring and management. Fuzzy Logic Empowered NetWatch catalyzes a systemic shift in how we approach aquaculture management rather than merely technology. Bangladesh may achieve previously unattainable levels of sustainability and productivity by utilizing fuzzy logic and the Internet of Things. This would guarantee a better future for the country's aquaculture sector and the communities it serves.
{"title":"Fuzzy Logic Empowered NetWatch: Revolutionizing Aquaculture with IoT-based Intelligent Monitoring and Management in Bangladesh","authors":"Purna Chakraborty, Arnab Chakraborty, Arnab Chakraborty, Abhijit Pathak","doi":"10.32996/jcsts.2024.6.2.5","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.5","url":null,"abstract":"The innovative study \"Fuzzy Logic Empowered NetWatch: Revolutionizing Aquaculture with IoT-based Intelligent Monitoring and Management in Bangladesh\" is a step towards the dawn of a new era in fish farming techniques that emphasize accuracy and efficiency. Using fuzzy logic controllers in the NetWatch system, stakeholders involved in aquaculture can access a degree of intelligence and adaptability that is not possible with standard management techniques. Fuzzy logic techniques are included in NetWatch, allowing it to make intelligent judgments based on the intricate and frequently unpredictable nature of aquaculture systems, in addition to monitoring and controlling environmental parameters and water quality. Because of its dynamic adaptability, the system can mitigate risks and optimize results in real time while successfully responding to changing situations. Furthermore, NetWatch offers a comprehensive picture of the aquaculture ecosystem by combining pond-specific data with more general environmental insights, facilitating better-informed macro and micro decision-making. With this thorough knowledge, fish farmers can allocate resources more efficiently, reduce waste, and sustainably increase productivity. Moreover, Fuzzy Logic Empowered NetWatch's revolutionary potential offers opportunities for the aquaculture industry, transcending the boundaries of individual fish ponds. Bangladesh can establish itself as a global leader in sustainable aquaculture methods and set new benchmarks for production, efficiency, and environmental stewardship using IoT-based intelligent monitoring and management. Fuzzy Logic Empowered NetWatch catalyzes a systemic shift in how we approach aquaculture management rather than merely technology. Bangladesh may achieve previously unattainable levels of sustainability and productivity by utilizing fuzzy logic and the Internet of Things. This would guarantee a better future for the country's aquaculture sector and the communities it serves.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140697572","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}
This examination researches the use of profound learning methods, explicitly utilizing Convolutional Brain Organizations (CNNs), for ongoing recognition of vehicles and path limits in roadway driving situations. The study investigates the performance of a modified Over Feat CNN architecture by making use of a comprehensive dataset that includes annotated frames captured by a variety of sensors, including cameras, LIDAR, radar, and GPS. The framework shows heartiness in identifying vehicles and anticipating path shapes in 3D while accomplishing functional rates of north of 10 Hz on different GPU setups. Vehicle bounding box predictions with high accuracy, resistance to occlusions, and efficient lane boundary identification are key findings. Quiet, the exploration underlines the likely materialness of this framework in the space of independent driving, introducing a promising road for future improvements in this field.
{"title":"Real-Time Vehicle and Lane Detection using Modified OverFeat CNN: A Comprehensive Study on Robustness and Performance in Autonomous Driving","authors":"Monowar Hossain Saikat, Sonjoy Paul, Kazi Toriqul Islam, Tanjida Tahmina, Md Shahriar Abdullah, Touhid Imam","doi":"10.32996/jcsts.2024.6.2.4","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.4","url":null,"abstract":"This examination researches the use of profound learning methods, explicitly utilizing Convolutional Brain Organizations (CNNs), for ongoing recognition of vehicles and path limits in roadway driving situations. The study investigates the performance of a modified Over Feat CNN architecture by making use of a comprehensive dataset that includes annotated frames captured by a variety of sensors, including cameras, LIDAR, radar, and GPS. The framework shows heartiness in identifying vehicles and anticipating path shapes in 3D while accomplishing functional rates of north of 10 Hz on different GPU setups. Vehicle bounding box predictions with high accuracy, resistance to occlusions, and efficient lane boundary identification are key findings. Quiet, the exploration underlines the likely materialness of this framework in the space of independent driving, introducing a promising road for future improvements in this field.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140715224","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 : 2024-04-11DOI: 10.32996/jcsts.2024.6.2.3x
N. Gurung, Md Rokibul Hasan, Sumon Gazi, Faiaz Rahat Chowdhury
Understanding consumer churn is pivotal for companies in the USA to develop efficient strategies for consumer retention and reduce its negative effects on revenue and profitability. To start with, understanding client churn entails pinpointing the factors that contribute to it. This research paper delved into the application of machine learning algorithms such as Random Forests and Decision Trees for designing churn prediction models and exploring key factors that churn probabilities. The dataset used in this study was sourced from the prominent UCI repository of machine learning databases, preserved at the University of California, Irvine. This dataset provided extensive information on a total of 3333 clients, facilitating in-depth analysis and insights. Models performance evaluation comprised examining the model's efficiency using a confusion matrix. Random Forest seemed to be a relatively better performing model than Decision Tree for this specific classification task. In particular, Random Forest attained higher accuracy (96.25%), precision (91.49), Recall (83.49%), F-measure (0.87), and Phi coefficient (0.85). By deploying Random Forest and Decision Tree models, government companies can get an in-depth comprehension of the factors that lead to consumer churn. As a result, this information may enable them to tailor targeted retention strategies and interventions. By effectively retaining consumers, government organizations can maintain a stable customer base, leading to sustained revenue and economic growth.
{"title":"AI-Based Customer Churn Prediction Model for Business Markets in the USA: Exploring the Use of AI and Machine Learning Technologies in Preventing Customer Churn","authors":"N. Gurung, Md Rokibul Hasan, Sumon Gazi, Faiaz Rahat Chowdhury","doi":"10.32996/jcsts.2024.6.2.3x","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.3x","url":null,"abstract":"Understanding consumer churn is pivotal for companies in the USA to develop efficient strategies for consumer retention and reduce its negative effects on revenue and profitability. To start with, understanding client churn entails pinpointing the factors that contribute to it. This research paper delved into the application of machine learning algorithms such as Random Forests and Decision Trees for designing churn prediction models and exploring key factors that churn probabilities. The dataset used in this study was sourced from the prominent UCI repository of machine learning databases, preserved at the University of California, Irvine. This dataset provided extensive information on a total of 3333 clients, facilitating in-depth analysis and insights. Models performance evaluation comprised examining the model's efficiency using a confusion matrix. Random Forest seemed to be a relatively better performing model than Decision Tree for this specific classification task. In particular, Random Forest attained higher accuracy (96.25%), precision (91.49), Recall (83.49%), F-measure (0.87), and Phi coefficient (0.85). By deploying Random Forest and Decision Tree models, government companies can get an in-depth comprehension of the factors that lead to consumer churn. As a result, this information may enable them to tailor targeted retention strategies and interventions. By effectively retaining consumers, government organizations can maintain a stable customer base, leading to sustained revenue and economic growth.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140713921","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 : 2024-04-07DOI: 10.32996/jcsts.2024.6.2.3
Mingzheng Wang
The rapid development of Generative Artificial Intelligence (GAI) technology has shown tremendous potential in various fields, such as image generation, text generation, and video generation, and it has been widely applied in various industries. However, GAI also brings new risks and challenges to cybersecurity. This paper analyzes the application status of GAI technology in the field of cybersecurity and discusses the risks and challenges it brings, including data security risks, scientific and technological ethics and moral challenges, Artificial Intelligence (AI) fraud, and threats from cyberattacks. On this basis, this paper proposes some countermeasures to maintain cybersecurity and address the threats posed by GAI, including: establishing and improving standards and specifications for AI technology to ensure its security and reliability; developing AI-based cybersecurity defense technologies to enhance cybersecurity defense capabilities; improving the AI literacy of the whole society to help the public understand and use AI technology correctly. From the perspective of GAI technology background, this paper systematically analyzes its impact on cybersecurity and proposes some targeted countermeasures and suggestions, possessing certain theoretical and practical significance.
{"title":"Generative AI: A New Challenge for Cybersecurity","authors":"Mingzheng Wang","doi":"10.32996/jcsts.2024.6.2.3","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.3","url":null,"abstract":"The rapid development of Generative Artificial Intelligence (GAI) technology has shown tremendous potential in various fields, such as image generation, text generation, and video generation, and it has been widely applied in various industries. However, GAI also brings new risks and challenges to cybersecurity. This paper analyzes the application status of GAI technology in the field of cybersecurity and discusses the risks and challenges it brings, including data security risks, scientific and technological ethics and moral challenges, Artificial Intelligence (AI) fraud, and threats from cyberattacks. On this basis, this paper proposes some countermeasures to maintain cybersecurity and address the threats posed by GAI, including: establishing and improving standards and specifications for AI technology to ensure its security and reliability; developing AI-based cybersecurity defense technologies to enhance cybersecurity defense capabilities; improving the AI literacy of the whole society to help the public understand and use AI technology correctly. From the perspective of GAI technology background, this paper systematically analyzes its impact on cybersecurity and proposes some targeted countermeasures and suggestions, possessing certain theoretical and practical significance.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140733723","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 : 2024-04-06DOI: 10.32996/jcsts.2024.6.2.1
Md Rokibul Hasan, Sumon Gazi, N. Gurung
Credit Card Fraud presents significant challenges across various domains, comprising, healthcare, insurance, finance, and e-commerce. The principal objective of this research was to examine the efficacy of Machine Learning techniques in detecting credit card fraud. Four key Machine Learning techniques were employed, notably, Support Vector Machine, Logistic Regression, Random Forest, and Artificial Neural Network. Subsequently, model performance was evaluated using Precision, Recall, Accuracy, and F-measure metrics. While all models demonstrated high accuracy rates (99%), this was largely due to the dataset's size, with 284,807 attributes and only 492 fraudulent transactions. Nevertheless, accuracy solely did not provide a comprehensive comparison metric. Support Vector Machine showed the highest recall (89.5), correctly identifying the most positive instances, highlighting its efficacy in detecting true positives. On the other hand, the Artificial Neural Network model exhibited the highest precision (79.4, indicating its capability to make accurate identifications, making it proficient in optimistic predictions.
{"title":"Explainable AI in Credit Card Fraud Detection: Interpretable Models and Transparent Decision-making for Enhanced Trust and Compliance in the USA","authors":"Md Rokibul Hasan, Sumon Gazi, N. Gurung","doi":"10.32996/jcsts.2024.6.2.1","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.1","url":null,"abstract":"Credit Card Fraud presents significant challenges across various domains, comprising, healthcare, insurance, finance, and e-commerce. The principal objective of this research was to examine the efficacy of Machine Learning techniques in detecting credit card fraud. Four key Machine Learning techniques were employed, notably, Support Vector Machine, Logistic Regression, Random Forest, and Artificial Neural Network. Subsequently, model performance was evaluated using Precision, Recall, Accuracy, and F-measure metrics. While all models demonstrated high accuracy rates (99%), this was largely due to the dataset's size, with 284,807 attributes and only 492 fraudulent transactions. Nevertheless, accuracy solely did not provide a comprehensive comparison metric. Support Vector Machine showed the highest recall (89.5), correctly identifying the most positive instances, highlighting its efficacy in detecting true positives. On the other hand, the Artificial Neural Network model exhibited the highest precision (79.4, indicating its capability to make accurate identifications, making it proficient in optimistic predictions.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140734704","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 : 2024-03-18DOI: 10.32996/jcsts.2024.6.1.28
Kazi Nafisa, Anjum, Md Azad, Hossain Raju, Monowar Hossain Saikat, ✉. Sonjoy, Paul Avi, Kazi Toriqul Islam, Rhine Hoque, Touhid Imam, Monowar Hossain, Saikat
The evolution of cities into sustainable and intelligent entities is undergoing a significant transformation with the integration of Artificial Intelligence (AI) and the Internet of Things (IoT). This study systematically examines 133 papers published between 2014 and 2021, predominantly sourced from Scopus (90%) and WoS (70%). Focusing on key smart city domains such as healthcare, education, environment, waste management, mobility, agriculture, risk management, and security, the analysis explores the applications of AI. As cities increasingly embrace AI for operational automation, data-driven decision-making, and environmental improvements, regulatory challenges surface, spanning concerns related to privacy, service delivery discrimination, and ethical considerations. The impact of AI adoption, especially in healthcare following the 2019 global health crisis, is underscored, emphasizing the pivotal role of AI algorithms, including ANN, RNN/LSTM, CNN/R-CNN, DNN, and SVM/LS-SVM, in shaping urban development trajectories. This research provides insights into the multifaceted implications of AI in smart cities, offering a comprehensive overview of the benefits, challenges, and transformative potential of these technologies across diverse urban sectors.
{"title":"Exploring the Multifaceted Impact of Artificial Intelligence and the Internet of Things on Smart City Management","authors":"Kazi Nafisa, Anjum, Md Azad, Hossain Raju, Monowar Hossain Saikat, ✉. Sonjoy, Paul Avi, Kazi Toriqul Islam, Rhine Hoque, Touhid Imam, Monowar Hossain, Saikat","doi":"10.32996/jcsts.2024.6.1.28","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.1.28","url":null,"abstract":"The evolution of cities into sustainable and intelligent entities is undergoing a significant transformation with the integration of Artificial Intelligence (AI) and the Internet of Things (IoT). This study systematically examines 133 papers published between 2014 and 2021, predominantly sourced from Scopus (90%) and WoS (70%). Focusing on key smart city domains such as healthcare, education, environment, waste management, mobility, agriculture, risk management, and security, the analysis explores the applications of AI. As cities increasingly embrace AI for operational automation, data-driven decision-making, and environmental improvements, regulatory challenges surface, spanning concerns related to privacy, service delivery discrimination, and ethical considerations. The impact of AI adoption, especially in healthcare following the 2019 global health crisis, is underscored, emphasizing the pivotal role of AI algorithms, including ANN, RNN/LSTM, CNN/R-CNN, DNN, and SVM/LS-SVM, in shaping urban development trajectories. This research provides insights into the multifaceted implications of AI in smart cities, offering a comprehensive overview of the benefits, challenges, and transformative potential of these technologies across diverse urban sectors.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140234783","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 : 2024-03-14DOI: 10.32996/jcsts.2024.6.1.27
Taufik Rendi Anggara
Nowadays, many people spend their time on the Internet, and the number of people subscribed to mobile phones is 69.4% of the 5.61 billion population in the world. To handle this situation, we need to implement a high-performance Distributed Cluster System (DCS) in the correct architecture as well. We separated the cluster for each purpose and gave it a unique VLAN. This study uses a mix of methodologies between case study and system development with evaluation after implementation. We observe all aspects of built-in technologies. In this research, monolith spikes us for performance issues, and also, the infrastructure is messy implemented. Event Based System (EBS) helps DCS to absorb high processing tasks in peak situations. EBS can easily lose a couple as needed. Labeling the incoming data assists us in managing inconsistent distributed data in the environment. Our research was evaluated for two weeks. The result is very pleasant, and the requirements in this research were satisfied.
{"title":"A Case Study of Implementation Strategy for Performance Optimization in Distributed Cluster System","authors":"Taufik Rendi Anggara","doi":"10.32996/jcsts.2024.6.1.27","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.1.27","url":null,"abstract":"Nowadays, many people spend their time on the Internet, and the number of people subscribed to mobile phones is 69.4% of the 5.61 billion population in the world. To handle this situation, we need to implement a high-performance Distributed Cluster System (DCS) in the correct architecture as well. We separated the cluster for each purpose and gave it a unique VLAN. This study uses a mix of methodologies between case study and system development with evaluation after implementation. We observe all aspects of built-in technologies. In this research, monolith spikes us for performance issues, and also, the infrastructure is messy implemented. Event Based System (EBS) helps DCS to absorb high processing tasks in peak situations. EBS can easily lose a couple as needed. Labeling the incoming data assists us in managing inconsistent distributed data in the environment. Our research was evaluated for two weeks. The result is very pleasant, and the requirements in this research were satisfied.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242892","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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