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Poor data also raises privacy, security, and compliance concerns, especially when storage and access practices fail. This study reviews modeling techniques that strengthen data regulation across multiple domains, including human-computer integration, natural language processing, medical observations, and corporate systems, with critical analysis of privacy-aware ML models and their neural and generative simulations.","Journal of Applied and Fundamental Sciences  \n| MACHINE LEARNING FUTURE DRIVE TOWARDS ENHANCED LEARNING-A REVIEW\u003Cbr>Mohiuddin Ali Khan 1*, Mujtaba Ali Khan 2, Juveria Fatima 3 Junaid Ahmed Khan4, Hafsa Konain5, and Huda\u003Cbr>Fatima6\u003Cbr>1Department of Electrical & Electronics Engineering, Jazan University, KSA\u003Cbr>2, 5 College of Computing and Informatics, University of North Carolina, USA\u003Cbr>3 College of Science, Department of Mathematics, Jazan University, Jazan, KSA\u003Cbr>4Department of Computer Science, Mahatma Gandhi Institute of Technology, India 6Department of Computer Science, College of Engineering & Computer Science, Jazan University, KSA\u003Cbr>*For correspondence. ([makhan@jazanu.edu.sa](makhan@jazanu.edu.sa)) |\n| --- |\n| Abstract: Real-world applications are strongly associated with data, their storage, their consistency etc. that require Machine Learning (ML) Techniques. While this data is maintained, it may sometimes lead to poor quality of data, insufficient information which has an adverse effect on the applications of Machine Learning techniques. Various data storage techniques may encounter issues in accessing which is adversely correlated to the data privacy, its security and the regulations that encompasses it. Considering these challenges, our study discusses extensive approach to review the existing research that discusses the various modeling techniques of machine learning that can enhance the data regulations. Our study involves various dynamics of data revolutions, Human Computer Integration, Natural Language processing, medical observations and various corporate aspects. We have also discussed the critical analysis of the data privacy integrated with machine learning models and their simulation with neural network and AI based generative models. The short-comings and opportunities that prevail in this field and their potentialities for the future developments are also discussed.\u003Cbr>Keywords: Languages; algorithm; supervised learning; unsupervised learning; data processing. |\n| 1. Introduction:\u003Cbr>Machine learning is an advanced and emerging technological development that involves the integration of the advanced techniques to make decisive conclusions focussing on the development of industrial revolutions and the challenges involved in it [1][2][3] . By the integration of the advanced techniques in computing, innovative modelling and simulations, machine learning has been a productive contribution in dealing with masses of data storage.\u003Cbr>Since computing technologies has shown emergence in transitional advancement, it must also be noted that machine-learning techniques do not give a conclusive solution. Internet of Things (IoT) [4], Deep Learning[5], etc have also shown incredible solutions and they also persistently go through challenges to overcome. As research progresses in various domains, it is significantly influential in addressing obstacles and give optimal solutions in the IT industry [11]. Since each study in the computing world deals with loads of data, the process of its collection, storage and its analysis requires a great deal of time and money. Researchers have gone through bitter experiencesand difficulties in dealing with data.\u003Cbr>Collecting and labeling data takes a lot of time and money, which creates several challenges, especially since machine learning relies so heavily on data. And thus machine learning persists to face challenges [6][7][8][9] .\u003Cbr>2. Machine Learning Challenges: |\n\nJAFS|ISSN 2395-5554 (Print)|ISSN 2395-5562 (Online)|Vol 10 |December 2025 120  \nJournal of Applied and Fundamental Sciences  \na. Data Quality and Quantity: As the ML requires a great deal of data processing and its analysis involves substantial conclusions, scarce or inadequate data can hamper the researcher’s quality analysis that can inadvertently provide unpredictable and impracticable conclusions [10] .  \nb. Data Preprocessing: The data preprocessing necessary before they train for the algorithms. For this,","cbCaipFpEQUKmaJi","https://ap.wps.com/l/cbCaipFpEQUKmaJi","pdf",820721,1,"English","en",105,"# Abstract\n# 1. Introduction\n# 2. Machine Learning Challenges\n## Data Quality and Quantity\n## Data Preprocessing\n## Overfitting and Underfitting\n## Algorithm Selection\n## Interpretability\n## Scalability\n## Ethical and Legal Concerns\n## Financed-Computing\n## Vulnerable Risks","[{\"question\":\"Why does machine learning depend heavily on data in practical applications?\",\"answer\":\"Machine learning techniques rely on data storage, consistency, and adequacy. When data quality is poor or information is insufficient, it adversely affects ML applications and can lead to weak outcomes.\"},{\"question\":\"What are the key challenges highlighted for machine learning systems?\",\"answer\":\"The document discusses data quality and quantity, preprocessing effort, overfitting/underfitting balance, algorithm selection difficulties, interpretability, scalability, and computational resource demands.\"},{\"question\":\"How do ethical and legal concerns affect machine learning deployment?\",\"answer\":\"Training data can produce biased conclusions that undermine fairness, while the use of ML in recruitment and legal or financial-like scenarios requires attention to ethical and legal compliance.\"}]","Machine Learning Future Drive Towards Enhanced Learning - A Review | PDF",1785684929,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-future-drive-towards-enhanced-learning-a-review","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-future-drive-towards-enhanced-learning-a-review/118697/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why does machine learning depend heavily on data in practical applications?","Question",{"text":74,"@type":75},"Machine learning techniques rely on data storage, consistency, and adequacy. When data quality is poor or information is insufficient, it adversely affects ML applications and can lead to weak outcomes.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What are the key challenges highlighted for machine learning systems?",{"text":79,"@type":75},"The document discusses data quality and quantity, preprocessing effort, overfitting/underfitting balance, algorithm selection difficulties, interpretability, scalability, and computational resource demands.",{"name":81,"@type":72,"acceptedAnswer":82},"How do ethical and legal concerns affect machine learning deployment?",{"text":83,"@type":75},"Training data can produce biased conclusions that undermine fairness, while the use of ML in recruitment and legal or financial-like scenarios requires attention to ethical and legal compliance.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]