Journal of Computer Science and Technology Studies最新文献

{"title":"A Comparative Assessment of Machine Learning Algorithms for Detecting and Diagnosing Breast Cancer","authors":"Md Zahidul Islam, Md Nasiruddin, Shuvo Dutta, Rajesh Sikder, Chowdhury Badrul Huda, Md Rasibul Islam","doi":"10.32996/jcsts.2024.6.2.14","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.14","url":null,"abstract":"The principal goal of this study was to explore machine-learning techniques deployed for the early detection of breast cancer in the United States. Specifically, three algorithms were trained on a breast cancer dataset: Decision Tree, Random Forest, and Linear Regression. Each model was further evaluated for its performance, to ascertain the best model. Upon review, the Random Forest provided higher classification performance. It was postulated that the Random Forest offered higher accuracy models on the test data because Decision Trees and Linear Regression require more extensive data for them to be more precise in making high-precision predictions. Out of all the models, the Random Forest provided suitable accuracy on test data. Therefore, in this research scope, Random Forest was the most successful and proved effective in accurately identifying breast cancer malignancies. In that light, the proposed random forest can benefit healthcare organizations by facilitating in detection of breast cancer disease by identifying patients in high-risk groups at an early and more treatable stage of disease for improved outcomes and lower healthcare costs. Besides, Random Forest models can assist in identifying high-risk patients in advance for prompt treatment. In that regard, such detection saves lives and decreases long-term healthcare costs for the US government.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":"46 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348947","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}

{"title":"Using Intuitionistic Fuzzy Set to Classify Uncertain and Linearly Non-Separable Data","authors":"Shubair Abdulla","doi":"10.32996/jcsts.2024.6.2.12","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.12","url":null,"abstract":"The problem of non-linearly separable data points requires more efforts to classify the data sample with high accuracy. This paper proposes a new classification approach that employs intuitionistic fuzzy sets to accurately classify non-separable datasets and to efficiently deal with uncertain labelled datasets. The dataset used contains 124 students with 9 features and 1 class for each student. First, the dataset is normalized to train and test the proposed approach. Second, the intuitionistic fuzzy sets were constructed using three features and the fuzzy model was created by calculating the equation of the straight line passing through the intuitionistic fuzzy sets of dataset classes. Finally, the classification is performed by calculating the distance between each class and the unseen sample that is subject to classification. Experimental results show that the classification performance of the proposed approach is competitive and superior to that of other state-of-the-art algorithms on the aforementioned dataset.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370782","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}

{"title":"Synergizing AI and Blockchain: Innovations in Decentralized Carbon Markets for Emission Reduction through Intelligent Carbon Credit Trading","authors":"Luka Baklaga","doi":"10.32996/jcsts.2024.6.2.13","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.13","url":null,"abstract":"This study aims to enhance the paradigm of decentralized carbon markets by proposing an innovative integration of artificial intelligence (AI) and blockchain technology for intelligent carbon credit trading with the goal of attaining sustainable emission reduction. Blockchain systems powered by artificial intelligence (AI) have the potential to boost the effectiveness of current systems and expedite the global implementation of emissions trading. Although still in its infancy, blockchain artificial intelligence (AI) presents a promising solution to some of the world's most pressing environmental issues. Environmental sustainability is greatly affected by artificial intelligence because of its decentralized computation architecture. The Artificial Intelligence and blockchain are outstanding direction for today’s environmental issues starting from carbon footprint emission to earth market unstable management, whereby the AI facilitates the best possible operational control of power systems and the blockchain offers decentralized trading platforms for the energy markets. The paper's theoretical framework, based on advanced mathematical models, serves as the foundation for this study, in which AI algorithms are methodically constructed to anticipate carbon emissions with unprecedented accuracy. Using sophisticated coding simulations and complicated mathematical formulas, the study boldly transitions into a realistic digital implementation that builds on this theoretical foundation. This complex experiment not only validates the theoretical ideas but also illustrates the complex relationship between blockchain and AI in the decentralized carbon market ecosystem. This experiment's mathematical basis is the creation of an integrated pricing model that seamlessly blends blockchain-based trading dynamics with AI-driven forecasts. The model incorporates a dynamic, self-adjusting system that responds to current market conditions, in addition to optimizing the pricing calculation of carbon credits. Complex market dynamics, player tactics, and the overall equilibrium of the carbon credit market are all modeled by mathematical simulations. The project goes deeper into building blockchain-based smart contracts, which enable safe and transparent transactions. The comprehensive mathematical results of the experiment shed light on the best way to price carbon credits while underscoring the disruptive potential of blockchain and artificial intelligence in terms of sustainable emission reduction strategies used in carbon markets. Major conclusions about the potential advantages of Blockchain AI for guaranteeing emissions reduction are drawn from the current study. Additionally, it presents a roadmap for future research in this area.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":" 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368256","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}

{"title":"AI-Driven Cybersecurity: Balancing Advancements and Safeguards","authors":"Atia Shahana, Rakibul Hasan, Sayeda Farjana Farabi, Jahanara Akter, Md Abdullah al Mahmud, F. Johora, Gurkan Suzer","doi":"10.32996/jcsts.2024.6.2.9","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.2.9","url":null,"abstract":"As Artificial Intelligence (AI) continues its rapid evolution, its profound influence on cybersecurity becomes increasingly evident. This study delves into the pivotal role of AI in fortifying cybersecurity measures, emphasizing its capacity for enhanced threat detection, automated response mechanisms, and the development of resilient security frameworks. However, alongside its promise, recognition of AI's susceptibility to exploitation in sophisticated cyber-attacks exists, underscoring the imperative for continual advancements in AI-driven security solutions. This research offers a nuanced perspective on AI's impact on cybersecurity, advocating for the proactive integration of AI strategies, sustained research efforts, and formulating ethical guidelines. Adopting supervised machine learning (ML) algorithms like decision trees, support vector machines, and neural networks aims to harness AI's potential to bolster cybersecurity while concurrently addressing associated risks, paving the way for a secure digital landscape. Regarding accuracy, the neural network outperforms other models by 98%.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140992217","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}

{"title":"Advancements in Early Detection of Lung Cancer in Public Health: A Comprehensive Study Utilizing Machine Learning Algorithms and Predictive Models","authors":"Mohammad Shafiquzzaman Bhuiyan, Imranul Kabir Chowdhury, Mahfuz Haider, Afjal Hossain Jisan, Rasel Mahmud Jewel, Rumana Shahid, Mst Zannatun Ferdus","doi":"10.32996/jcsts.2024.6.1.12","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.1.12","url":null,"abstract":"Lung cancer stands as the leading cause of death in the United States, attributed to factors such as the spontaneous growth of malignant tumors in the lungs that can metastasize to other parts of the body, posing severe threats. Notably, smoking emerges as a predominant external factor contributing to lung problems and ultimately leading to lung cancer. Nevertheless, early detection presents a pivotal strategy for preventing this lethal disease. Leveraging machine learning, we aspire to develop robust algorithms capable of predicting lung cancer at its nascent stage. Such a model could prove instrumental in aiding physicians in making informed decisions during the diagnostic process, determining whether a patient necessitates an intensive or standard level of diagnosis. This approach holds the potential to significantly reduce treatment costs, as physicians can tailor the treatment plan based on accurate predictions, thereby avoiding unnecessary and costly interventions. Our goal is to establish a sustainable model that accurately predicts the disease, and our findings reveal that XGBoost outperformed other models, achieving an impressive accuracy level of 96.92%. In comparison, LightGBM, AdaBoost, Logistic Regression, and Support Vector Machine achieved accuracies of 93.50%, 92.32%, 67.41%, and 88.02%, respectively.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":"1 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139524114","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}

{"title":"Digitalization of Student Administration Services at Politeknik Negeri Ujung Pandang","authors":"Andi Gunawan, Masita, Asima, Nahiruddin, Hirman, Andi Yusrill Ihza Mahendra","doi":"10.32996/jcsts.2024.6.1.11","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.1.11","url":null,"abstract":"The process of handling student correspondence submitted to the Study Program / Department, then processed by academics and signed by Deputy Director 1 has experienced obstacles in terms of the time to complete the various kinds of letters needed by students. Sometimes it takes up to one week to complete the process. If a student sends a Professional Work Practice (PKP) application letter to the Industry and the letter is rejected, the student must repeat the process of proposing the application letter with a long time. This is because the correspondence process still uses conventional methods. This research aims to develop a web application that is integrated with various related units in handling student correspondence at Ujung Pandang State Polytechnic. The waterfall method is used in its development, including needs analysis, design, development, testing, and implementation. The result is an application that facilitates the management of student letter administration with a barcode system for verification and signing, ensuring efficiency and effectiveness in the process. The results of this research are as follows: 1) Assist the storage of data management of academic administration services; 2) The process of inputting statement letter data becomes easier and faster; 3) Make it easier for department / study program / related unit admins to manage statement letters; 4) searching for administrative service data and statement letters is easier and faster; 5) integrated student data and statement letters can improve the quality of administrative services at Politeknik Negeri Ujung Pandang.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":"117 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614197","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}

{"title":"Dominance of AI and Machine Learning Techniques in Hybrid Movie Recommendation System Applying Text-to-number Conversion and Cosine Similarity Approaches","authors":"MD Rokibul Hasan, Janatul Ferdous MSc","doi":"10.32996/jcsts.2024.6.1.10","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.1.10","url":null,"abstract":"This research explored movie recommendation systems based on predicting top-rated and suitable movies for users. This research proposed a hybrid movie recommendation system that integrates both text-to-number conversion and cosine similarity approaches to predict the most top-rated and desired movies for the targeted users. The proposed movie recommendation employed the Alternating Least Squares (ALS) algorithm to reinforce the accuracy of movie recommendations. The performance analysis and evaluation were undertaken by employing the widely used \"TMDB 5000 Movie Dataset\" from the Kaggle dataset. Two experiments were conducted, categorizing the dataset into distinct modules, and the outcomes were contrasted with state-of-the-art models. The first experiment attained a Root Mean Squared Error (RMSE) of 0.97613, while the second experiment expanded predictions to 4800 movies, culminating in a substantially minimized RMSE of 0.8951, portraying a 97% accuracy enhancement. The findings underscore the essence of parameter selection in text-to-number conversion and cosine and the gap for other systems to maintain user preferences for comprehensive and precise data gathering. Overall, the proposed hybrid movie recommendation system demonstrated promising results in predicting top-rated movies and offering personalized and accurate recommendations to users.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139620015","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}

{"title":"Securing Against Advanced Cyber Threats: A Comprehensive Guide to Phishing, XSS, and SQL Injection Defense","authors":"Sunil Sukumaran Nair","doi":"10.32996/jcsts.2024.6.1.9","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.1.9","url":null,"abstract":"In an era dominated by digital connectivity, the proliferation of advanced cyber threats poses a formidable challenge to organizations worldwide. This comprehensive guide delves into the intricacies of safeguarding against three prevalent and insidious threats: Phishing, Cross-Site Scripting (XSS), and SQL Injection. The guide begins by dissecting the anatomy of phishing attacks, exploring the psychological tactics employed by threat actors to manipulate individuals into divulging sensitive information. It provides an in-depth analysis of various phishing techniques and offers practical strategies for both individuals and organizations to fortify their defenses against these deceptive practices. Moving on to XSS vulnerabilities, the guide elucidates the mechanics behind this web application threat. It offers a detailed exploration of how attackers exploit code injection to compromise user data and system integrity. The guide provides a robust framework for developing secure coding practices, implementing web application firewalls, and conducting regular security audits to detect and mitigate XSS vulnerabilities. The third facet of defense focuses on SQL injection, a persistent threat to database-driven applications. The guide elucidates the intricacies of SQL injection attacks, emphasizing the potential impact on data confidentiality and integrity. Practical measures for securing databases, input validation, and the use of parameterized queries are extensively discussed to empower organizations in safeguarding against SQL injection threats. Throughout the guide, a holistic approach to cybersecurity is advocated, emphasizing the integration of technological solutions, employee training, and proactive risk management. Real-world case studies and practical examples enrich the content, providing a valuable resource for security professionals, developers, and decision-makers striving to fortify their digital assets against the ever-evolving landscape of advanced cyber threats.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":"89 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530427","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}

{"title":"Deep Learning in Stock Market Forecasting: Comparative Analysis of Neural Network Architectures Across NSE and NYSE","authors":"Bishnu Padh Ghosh, Mohammad Shafiquzzaman Bhuiyan, Debashish Das, Tuan Ngoc Nguyen, Mahmud Jewel, Md Tuhin Mia, Duc M Cao","doi":"10.32996/jcsts.2024.6.1.8","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.1.8","url":null,"abstract":"This research explores the application of four deep learning architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN)—in predicting stock prices using historical data. Focusing on day-wise closing prices from the National Stock Exchange (NSE) of India and the New York Stock Exchange (NYSE), the study trains the neural network on NSE data and tests it on both NSE and NYSE stocks. Surprisingly, the CNN model outperforms the others, successfully predicting NYSE stock prices despite being trained on NSE data. Comparative analysis against the ARIMA model underscores the superior performance of neural networks, emphasizing their potential in forecasting stock market trends. This research sheds light on the shared underlying dynamics between distinct markets and demonstrates the efficacy of deep learning architectures in stock price prediction.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":"17 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139531014","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}

{"title":"Harmonizing Macro-Financial Factors and Twitter Sentiment Analysis in Forecasting Stock Market Trends","authors":"Md Shahedul Amin, Hossain Ayon, Bishnu Padh Ghosh, Md Salim Chowdhury, Mohammad Shafiquzzaman Bhuiyan, Rasel Mahmud Jewel, Ahmed Ali Linkon","doi":"10.32996/jcsts.2024.6.1.7","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.1.7","url":null,"abstract":"The surge in generative artificial intelligence technologies, exemplified by systems such as ChatGPT, has sparked widespread interest and discourse prominently observed on social media platforms like Twitter. This paper delves into the inquiry of whether sentiment expressed in tweets discussing advancements in AI can forecast day-to-day fluctuations in stock prices of associated companies. Our investigation involves the analysis of tweets containing hashtags related to ChatGPT within the timeframe of December 2022 to March 2023. Leveraging natural language processing techniques, we extract features, including positive/negative sentiment scores, from the collected tweets. A range of classifier machine learning models, encompassing gradient boosting, decision trees and random forests, are employed to train on tweet sentiments and associated features for the prediction of stock price movements among key companies, such as Microsoft and OpenAI. These models undergo training and testing phases utilizing an empirical dataset gathered during the stipulated timeframe. Our preliminary findings reveal intriguing indications suggesting a plausible correlation between public sentiment reflected in Twitter discussions surrounding ChatGPT and generative AI and the subsequent impact on market valuation and trading activities concerning pertinent companies, gauged through stock prices. This study aims to forecast bullish or bearish trends in the stock market by leveraging sentiment analysis derived from an extensive dataset comprising 500,000 tweets. In conjunction with this sentiment analysis derived from Twitter, we incorporate control variables encompassing macroeconomic indicators, Twitter uncertainty index and stock market data for several prominent companies.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":"43 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139535982","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}