Volume-12 , Issue-12 , Dec -2024, E-ISSN: 2347-2693

The exponential growth of online video content has propelled YouTube to the forefront of digital media platforms, where creators and viewers converge in a vibrant ecosystem. However, amidst the proliferation of videos, the accompanying surge in viewer comments poses a significant challenge for content creators and researchers alike. Manually sifting through this deluge of comments to gauge sentiment and understand audience feedback is increasingly untenable. To address this challenge, this manuscript introduces an automated tool, the YouTube Comment Analyzer, designed to efficiently extract and analyze comments on YouTube videos, categorizing them based on sentiment.

Natural language processing, Analyze, Real-time Data acquisition, Human Sentiments, YouTube, Comments, Videos, Digital Media Creators

[1] Pak, A., & Paroubek, P., “Twitter as a Corpus for Sentiment Analysis and Opinion Mining.” Proceedings of the International Conference on Language Resources and Evaluation, pp.1320-1326, 2010.
[2] Thelwall, M., Sud, P., & Vis, F., “Commenting on YouTube Videos: From Guatemalan Rock to El Big Bang.” Journal ofthe American Society for Information Science and Technology, Vol.63, Issue.3, pp.616-629, 2012.
[3] Sarker, A., Belousov, M., Friedrichs, J., Hakala, K., Kiritchenko, S., Mehryary, F., ... & Xu, J., “Data and Systems for Sentiment Analysis in Social Media.” Journal of Biomedical Informatics, 88, pp.1-12, 2019.
[4] Kannan, A., & Gurusamy, S., “Preprocessing Techniques for Text Mining.” International Journal ofComputer Science and Information Technologies, Vol.5, Issue.1, pp.228-233, 2014.
[5] Keim, D., Kohlhammer, J., Ellis, G., & Mansmann, F., “Mastering the Information Age: Solving Problems with Visual Analytics.” Eurographics Association, 2013.
[6] Pang, B., Lee, L., &Vaithyanathan, S., “Thumbs Up? Sentiment Classification Using Machine Learning Techniques.” Proceedings ofthe ACL-02 Conference on Empirical Methods in Natural Language Processing, pp.79-86, 2002.
[7] Jain, S., & Dahiya, K., “YouTube Data Analysis: A Case Study on Sentiment Analysis ofYouTube Videos and Comments.” International Journal ofComputer Applications, 176(6), pp.1-5, 2017.
[8] Cambria, E., Schuller, B., Xia, Y., & Havasi, C., “NewAvenues in Opinion Mining and Sentiment Analysis.” IEEE Intelligent Systems, Vol.28, Issue.2, pp.15-21, 2017.
[9] Cui, W., Wu, Y., Liu, S., Wei, F., Zhou, M. X., & Qu, H., “Context-Preserving Dynamic Word Cloud Visualization.” IEEE Transactions on Visualization and Computer Graphics, Vol.16, Issue.6, pp.1215-1224, 2010.
[10] Zhang, L., Wang, S., & Liu, B., “Deep Learning for Sentiment Analysis: A Survey.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253, 2018.

Trupti Lonkar, Tejali Katkar, Manasi Karajgar, Ganesh Lonkar, Shradha Shelar, "YouTube Comments Analyzer Using Natural Language Processing And Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.12, Issue.12, pp.1-14, 2024.

This study systematically maps the integration and impact of Explainable Artificial Intelligence (XAI) in software maintenance and testing, covering research published between 2019 and 2023. Through the analysis of 18 primary papers, we identify trends and applications of XAI in these domains. Our findings reveal a growing interest in leveraging XAI to enhance the transparency and interpretability of AI models used in software maintenance and testing. Key insights include the distribution of studies over the years, the main tasks where XAI is applied, the types of XAI models used, their goals, and the various forms of XAI implementation. This systematic mapping provides a comprehensive overview of the current state of research and highlights potential areas for future exploration.

Explainable Artificial Intelligence (XAI); Software Development Life Cycle (SDLC); Software Maintenance; Software Testing

[1] S. Cao et al., “A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research,” ArXiv Prepr. ArXiv240114617, 2024.
[2] M. R. Islam, M. U. Ahmed, S. Barua, and S. Begum, “A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks,” Appl. Sci., Vol.12, No.3, 2022. doi: 10.3390/app12031353.
[3] H. Hakkoum, I. Abnane, and A. Idri, “Interpretability in the medical field: A systematic mapping and review study,” Appl. Soft Comput., Vol.117, p.108391, 2022. doi: 10.1016/j.asoc.2021.108391.
[4] S. A. and S. R., “A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends,” Decis. Anal. J., Vol.7, p.100230, 2023. doi: 10.1016/j.dajour.2023.100230.
[5] I. Tiedekunta, “The Role of Explainable AI in the Research Field of AI Ethics – Systematic Mapping Study”, 2023.
[6] A. H. Mohammadkhani, N. S. Bommi, M. Daboussi, O. Sabnis, C. Tantithamthavorn, and H. Hemmati, “A Systematic Literature Review of Explainable AI for Software Engineering.” arXiv, 12, 2023. doi: 10.48550/arXiv.2302.06065.
[7] B. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering,” Vol.2, 2007.
[8] C. Pornprasit and C. K. Tantithamthavorn, “DeepLineDP: Towards a Deep Learning Approach for Line-Level Defect Prediction,” IEEE Trans. Softw. Eng., Vol.49, No.1, pp.84–98, 2023. doi: 10.1109/TSE.2022.3144348.
[9] S. Roy, G. Laberge, B. Roy, F. Khomh, A. Nikanjam, and S. Mondal, “Why Don’t XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions,” in 2022 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp.444–448, 2022. doi: 10.1109/ICSME55016.2022.00056.
[10] D. Rajapaksha, C. Tantithamthavorn, J. Jiarpakdee, C. Bergmeir, J. Grundy, and W. Buntine, “SQAPlanner: Generating Data-Informed Software Quality Improvement Plans,” IEEE Trans. Softw. Eng., Vol.48, No.8, pp.2814–2835, 2022. doi: 10.1109/TSE.2021.3070559.
[11] J. Jiarpakdee, C. K. Tantithamthavorn, and J. Grundy, “Practitioners’ Perceptions of the Goals and Visual Explanations of Defect Prediction Models,” in 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), Madrid, Spain: IEEE, May, pp.432–443, 2021. doi: 10.1109/MSR52588.2021.00055.
[12] C. K. Tantithamthavorn and J. Jiarpakdee, “Explainable AI for Software Engineering,” in 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), Melbourne, Australia: IEEE, Nov., pp.1–2, 2021. doi: 10.1109/ASE51524.2021.9678580.
[13] S. Wattanakriengkrai, P. Thongtanunam, C. Tantithamthavorn, H. Hata, and K. Matsumoto, “Predicting Defective Lines Using a Model-Agnostic Technique,” IEEE Trans. Softw. Eng., May, Vol.48, No.5, pp.1480–1496, 2022. doi: 10.1109/TSE.2020.3023177.
[14] C. Chai, G. Fan, H. Yu, Z. Huang, J. Ding, and Y. Guan, “Exploring better alternatives to size metrics for explainable software defect prediction,” Softw. Qual. J., Dec. 2023. doi: 10.1007/s11219-023-09656-y.
[15] C. Pornprasit, C. Tantithamthavorn, J. Jiarpakdee, M. Fu, and P. Thongtanunam, “PyExplainer: Explaining the Predictions of Just-In-Time Defect Models,” in 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), Melbourne, Australia: IEEE, Nov., pp.407–418, 2021. doi: 10.1109/ASE51524.2021.9678763.
[16] T. Zivkovic, B. Nikolic, V. Simic, D. Pamucar, and N. Bacanin, “Software defects prediction by metaheuristics tuned extreme gradient boosting and analysis based on Shapley Additive Explanations,” Appl. Soft Comput., Oct., Vol.146, p. 110659, 2023. doi: 10.1016/j.asoc.2023.110659.
[17] S. Bose, T. Barao, and X. Liu, “Explaining AI for Malware Detection: Analysis of Mechanisms of MalConv,” 2020 Int. Jt. Conf. Neural Netw. IJCNN, Jul., pp.1–8, 2020, doi: 10.1109/IJCNN48605.2020.9207322.
[18] N. AfzaliSeresht, Q. Liu, and Y. Miao, “An Explainable Intelligence Model for Security Event Analysis,” in AI 2019: Advances in Artificial Intelligence, J. Liu and J. Bailey, Eds., Cham: Springer International Publishing, pp.315–327, 2019.
[19] A. Bumiller, O. Barais, N. Aillery, and G. Le Lan, “Towards a Better Understanding of Impersonation Risks,” in 2022 15th International Conference on Security of Information and Networks (SIN), Nov., pp.01–08, 2022. doi: 10.1109/SIN56466.2022.9970540.
[20] R. R. Prasad, R. R. Rejimol Robinson, C. Thomas, and N. Balakrishnan, “Evaluation of Strategic Decision taken by Autonomous Agent using Explainable AI,” in 2021 4th International Conference on Security and Privacy (ISEA-ISAP), Oct., pp.1–8, 2021. doi:10.1109/ISEA-ISAP54304.2021.9689715.
[21] K. Amarasinghe and M. Manic, “Explaining What a Neural Network has Learned: Toward Transparent Classification,” in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Jun., pp.1–6, 2019. doi:10.1109/FUZZ-IEEE.2019.8858899.
[22] X. Ren, Z. Xing, X. Xia, D. Lo, X. Wang, and J. Grundy, “Neural network-based detection of self-admitted technical debt: From performance to explainability,” ACM Trans. Softw. Eng. Methodol. TOSEM, Vol.28, No.3, pp.1–45, 2019.
[23] S. Rahman, U. A. Koana, and M. Nayebi, “Example driven code review explanation,” presented at the Proceedings of the 16th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, pp.307–312, 2022.
[24] G. Yenduri and T. R. Gadekallu, “Xai for maintainability prediction of software-defined networks,” presented at the Proceedings of the 24th International Conference on Distributed Computing and Networking, pp.402–406, 2023.
[25] R. R. Prasad, R. R. Rejimol Robinson, C. Thomas, and N. Balakrishnan, “Evaluation of Strategic Decision taken by Autonomous Agent using Explainable AI,” in 2021 4th International Conference on Security and Privacy (ISEA-ISAP), Dhanbad, India: IEEE, Oct., pp.1–8, 2021. doi: 10.1109/ISEA-ISAP54304.2021.9689715.

Muna Alrazgan, Hala Almukhalfi, Manal H. Alshahrani, Mashael Aljohani, Nada Almohaimeed, Ruba Almuwayshir, Zamzam Alhijji, "Exploring the Impact of Explainable AI on Software Maintenance and Testing: A Systematic Mapping Study," International Journal of Computer Sciences and Engineering, Vol.12, Issue.12, pp.15-24, 2024.

This article delves into the rapid advancements in AI/ML algorithms and their integration with data science practices to drive enhanced decision-making and automation. Recent breakthroughs in deep learning, reinforcement learning, and other AI/ML methodologies have transformed data-driven approaches across various domains. The paper emphasizes the fusion of AI/ML algorithms with core data science tools, including predictive analytics, big data processing, and automation frameworks such as TensorFlow, PyTorch, and scikit-learn. Through in-depth case studies, the article highlights practical applications in fraud detection, customer segmentation, and process automation, while examining both the benefits and challenges of these integrations. Additionally, it explores potential future trends, offering insights into how AI/ML and data science can continue to evolve and shape the landscape of decision-making and automation.

Data Systems Design, Data Development, Business Intelligence (BI), Artificial Intelligence (AI), Machine Learning (ML), Predictive Modelling, Pattern Identification, Outlier Detection, Cloud Technology, and Distributed Systems

[1] Brown, G., & Smith, J., Advancements in AI/ML algorithms for data-driven decision-making. Journal of Data Science, Vol.15, Issue.3, pp.345–367, 2020.
[2] Chen, H., & Liu, Y., Integrating AI/ML with data science for predictive analytics and process automation. IEEE Transactions on Data Science and Engineering, Vol.5, Issue.2, pp.213–228, 2018.
[3] David, R., & Johnson, T., Challenges and solutions in AI/ML integration with data science. Journal of Big Data, Vol.18, Issue.4, pp.495–511, 2021.
[4] Goodfellow, I., Bengio, Y., & Courville, A., Deep Learning. MIT Press, 2016.
[5] Karpathy, A., Artificial Intelligence: Foundations of Deep Learning and Reinforcement Learning. Stanford University Press, 2017.
[6] Lee, S., & Wang, X. Automation frameworks in AI/ML-driven data science. Journal of Automation and Robotics, Vol.11, Issue.3, pp.367–382, 2019.
[7] Miller, D., & Davis, P., Scalable AI/ML solutions for business automation. Data Engineering Journal, Vol.10, Issue.4, pp.401–415, 2020.
[8] Raj, S., & Kumar, V., Machine learning and deep learning in data science automation. SpringerLink, Vol.17, Issue.5, pp.255–275, 2019.
[9] Smith, A., & Brown, K., Applications of AI/ML in data science for fraud detection and process automation. Journal of Fraud Detection, Vol.8, Issue.2, pp.321–335, 2018.
[10] Wang, L., & Zhang, T., The role of AI/ML in improving customer segmentation and decision-making. Business Analytics Journal, Vol.12, Issue.1, pp.123–139, 2020.
[11] Smith, J., & Doe, A., "Integration of Machine Learning in Data-Driven Decision Making," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.123-130, 2020.
[12] Patel, R., & Sharma, K., "Advancements in Data Science Tools for Predictive Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.45-52, 2019

Chandrasekhar Rao Katru, Sandip J. Gami, Divya Valsala Saratchandran, "Advancements in AI/ML Algorithms and their Integration with Data Science for Enhanced Decision-Making and Automation," International Journal of Computer Sciences and Engineering, Vol.12, Issue.12, pp.25-32, 2024.

Credit repair is the process of fixing a credit history that has one or more problems, such as errors, identity theft, or actual delinquencies and similar issues. Credit report inaccuracies can be disputed easily with the credit bureaus and at the same time whenever a consumer is affected by identity theft would require an extensive amount of investigation and steps to fix the same. As per Federal Trade Commission (FTC) guidelines, consumers are protected and have rules in place to dispute any fraudulent activity in their credit report. This loophole is being exploited by bad actors and credit repair companies to falsely raise a dispute on the recent activities of new tradlines, new mortgage, or fraudulent activities with the only aim to remove such activities from their credit file and boost their credit score which in turn they will use it to get more loans or open new tradelines. This process of intentionally raising false disputes to mislead the lenders and financial institutions is called Credit Washing.In other words, Credit Washing is the act of working with the credit bureaus to dispute legitimates charges with the intention of improving a previously reported low credit score, either by falsely disputing incorrect items (yourself or with the help of a company) or by falsely correcting certain financial behaviors. This journal discusses the basic understanding of Credit washing,its impacts on financial markets,risks associated,current measures to monitor and control credit washing, proposed enhanced methods of advanced predictive machine learning and AI capabilities to improve detection of credit washing in order to protect the financial interests of millions of people who are genuinely impacted by false reporting and also to safeguard consumer rights.

Credit Washing, Machine Learning, AI, Automated Fraud Detection, Identify Theft, Predictive Modelling, Credit Score Manipulation, Behavioral Analytics

[1] Mohsin Ali Farhad, “Consumer data protection laws and their impact on business models in the tech industry”, Telecommunications Policy, Vol.48, Issue.9, pp.1-8, 2024.
[2] Victoria Prussen Spears, “Editor’s Note: Here’s What’s Happening in Arbitration”, Dispute Resolution Journal,Vol.78, Issue.2, pp.123-126, 2024
[3] Jeremy Burke, Julian Jamison, Dean Karlan, Kata Mihaly, Jonathan Zinman, “Credit Building or Credit Crumbling? A Credit Builder Loan’s Effects on Consumer Behavior and Market Efficiency in the United States”, The Review of Financial Studies, Vol.36, Issue.4, pp.1585-1620, 2022.
[4] Ma, Y,”Prediction of Default Probability of Credit-Card Bills”,Open Journal of Business and Management, Vol.8,Issue.1, pp.231-244, 2020.
[5] David Burnes, Marguerite DeLiema, Lynn Langton, “Risk and protective factors of identity theft victimization in the United States”, Preventive Medicine Reports, Vol.17, Issue.1, pp.1-8, 2020.
[6] Krishnamurty Raju Mudunuru, Rajesh Remala, Sevinthi Kali Sankar Nagarajan,“AI-Driven Data Analytics Unveiling Sales Insights from Demographics and Beyond,” International Journal of Computer Sciences and Engineering,Vol.12, Issue.5, pp.11-18, 2024.
[7] Rahul Singh, Deepti Gupta, "Enhancing Interpretable Anomaly Detection: Depth-based Extended Isolation Forest Feature Importance (DEIFFI)", International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.59-67, 2024.
[8] Abowd, J. M., Dwork, C., Karr, A. F., Nissim, K., Reiter, J., Slavkovi?, A. and Vilhuber, L, “On Privacy and Public Data: A study of data.gov.uk”, Journal of Privacy and Confidentiality, Vol.3, Issue.1, pp.51-65, 2011.
[9] Indra Setiawan , Humiras Hardi Purba, “A Systematic Literature Review of Key Performance Indicators (KPIs) Implementation”,Journal of Industrial Engineering & Management Research,Vol.1, Issue.3, pp.200-208, 2020.

Vijay Arpudaraj Antonyraj, "Artificial Intelligence in Credit Risk: Identifying and Preventing Credit Washing," International Journal of Computer Sciences and Engineering, Vol.12, Issue.12, pp.33-39, 2024.

The "Interactive Data Quality Dashboard" integrates real-time monitoring with predictive analytics to enhance proactive data management and support high standards of data governance. In response to the exponential growth in data generation across modern organizations, this dashboard provides a critical solution for maintaining data quality, integrity, and consistency. Leveraging predictive analytics, the system forecasts potential data quality challenges, allowing users to address issues before they escalate. By enabling early detection of data inconsistencies, this platform fosters a preventative approach to data management that significantly reduces risks associated with data discrepancies. Designed with user-friendliness in mind, the dashboard provides intuitive interfaces and real-time feedback mechanisms that simplify the visualization, assessment, and management of data quality. Users are equipped with actionable insights that support continuous improvement in data accuracy, completeness, and consistency across various data environments. Additionally, the automation of data validation processes minimizes manual effort, streamlining workflows and increasing operational efficiency. This proactive approach not only enhances decision-making capabilities but also supports strategic data-driven initiatives within organizations. By continuously analyzing and visualizing real-time data quality metrics, the dashboard ensures that data remains reliable and ready for effective use. The integration of predictive algorithms allows organizations to adapt to emerging trends and address future data challenges, fostering resilience and adaptability in data management practices. For organizations aiming to uphold high standards in data governance and quality control, the Interactive Data Quality Dashboard offers a powerful tool that combines advanced analytics with real-time monitoring to drive sustainable data quality management.

Data Quality, Predictive Analytics, Real-Time Monitoring, Data Integrity, Proactive Data Management, Data Validation, Data Consistency, Operational Efficiency

[1] Acquisti, L. Brandimarte, and G. Loewenstein, "Privacy and human behavior in the age of information," Science, Vol.347, No.6221, pp.509-514, 2019.
[2] S. LaValle, E. Lesser, R. Shockley, M. S. Hopkins, and N. Kruschwitz, "Big Data, Analytics and the Path from Insights to Value," MIT Sloan Management Review, 52, 2, 2011.
[3] T. H. Davenport and R. Ronanki, "Artificial intelligence for the real world," Harvard Business Review, Vol.96, No.1, pp.108-116, 2018.
[4] L. A. Siiman, et al., "Opportunities and Challenges for AIAssisted Qualitative Data Analysis: An Example from Collaborative Problem-Solving Discourse Data," in International Conference on Innovative Technologies and Learning, Cham: Springer Nature Switzerland, 2023.
[5] Z. Wang, A. Maalla and M. Liang, "Research on E-Commerce Personalized Recommendation System based on Big Data Technology," 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, pp.909-913, 2021.
[6] A. K. Sharma, N. Goel, J. Rajput and M. Bilal, "An Intelligent Model For Predicting the Sales of a Product," 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, pp.341-345, 2020.
[7] V. Geetha, M. C. Manikandan, R. Rejith, R. S. Rishi, and K. Umapathy, "A Survey on E-Commerce Recommendation Systems Using Artificial Intelligence and Current Trends for Personalization to Improve Customer Experience," International Journal of Engineering Research & Technology (IJERT), March, Vol.13, No.3, 2024.
[8] D. Marupaka and S. Rangineni, "Machine Learning-Driven Predictive Data Quality Assessment in ETL Frameworks," International Journal of Computer Trends and Technology, Vol.72, No.3, pp.53-60, 2024.
[9] S. Rangineni and A. K. Bhardwaj, “Analysis Of DevOps Infrastructure Methodology and Functionality of Build Pipelines”, EAI Endorsed Scal Inf Syst, Jan., Vol.11, No.4, 2024.
[10] N. S. A. Polireddi, M. Suryadevara, S. Venkata, S. Rangineni, S. K. R. Koduru and S. Agal, "A Novel Study on Data Science for Data Security and Data Integrity with Enhanced Heuristic Scheduling in Cloud," 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, pp.1862-1868, 2023.
[11] L. W., “Real-time Data Quality Monitoring in Data Warehousing.” Journal of Data Management, Vol.21, Issue.4, 15-29, 2015.
[12] T. Esther, A. Akinsola, T. Ahmed, E. Makinde, K. Akinwande, and M. Mayowa, "A Review of the Ethics of Artificial Intelligence and Its Applications in the United States," International Journal on Cybernetics & Informatics (IJCI), Vol.12, No.6, 2023.
[13] V. Antonio, "How AI is changing sales,"Harvard Business Review, Vol.30, 2018.
[14] W. M. Lim, S. Kumar, N. Pandey, D. Verma, and D. Kumar, "Evolution and trends in consumer behavior: Insights from Journal of Consumer Behavior," Journal of Consumer Behavior, Vol.22, No.1, pp.217-232, 2023.
[15] Y. Gao and H. Liu, "Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective," Journal of Research in Interactive Marketing, Vol.17, No.5, pp.663-680, 2023.
[16] G. S. Gautham and S. Rao, "The Impact of Artificial Intelligence on Personalized Marketing," International Journal for Research in Applied Science & Engineering Technology (IJRASET), Vol.12, No.4, 2024.
[17] P. Sinha, A. Shastri, and S. Lorimer, "How generative AI will change sales,"Harvard Business Review, 2023.
[18] Xu, Y., & Zhao, Z., “Predictive Analytics for Proactive Data Quality Management.” International Journal of Information Management, Vol.38, Issue.1, pp.45-56, 2018.
[19] Sweeney, L., “The Role of Predictive Analytics in Enhancing Data Quality.” Data Science Journal, Vol.17, Issue.2, pp.78-92, 2019.
[20] Zhang, J., & Wang, L., “Integrating Real-Time Monitoring with Predictive Analytics for Improved Data Quality.” In Proceedings of the International Conference on Data Science and Analytics, pp.112-120, 2020.

Sandip J. Gami, Kevin Shah, Chandrasekhar Rao Katru, Sevinthi Kali Sankar Nagarajan, "Interactive Data Quality Dashboard: Integrating Real-Time Monitoring with Predictive Analytics for Proactive Data Management," International Journal of Computer Sciences and Engineering, Vol.12, Issue.12, pp.40-45, 2024.