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All Journal International Journal of Multidisciplinary Sciences and Arts BULLET : Jurnal Multidisiplin Ilmu
Ehsan Abbas
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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CUSTOMIZED AI-POWERED SECURITY AND PRIVACY CONFIGURATIONS FOR SOCIAL MEDIA WEBSITES. Ehsan Abbas; Anis Ahmed Qazi
BULLET : Jurnal Multidisiplin Ilmu Vol. 3 No. 1 (2024): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

The revolutionary field of Personalized AI-Backed Privacy and Security Settings for Social Media Web Applications is examined in-depth in this review study. The introduction lays the groundwork by outlining the escalating worries about security and privacy in the digital era, which prompts an investigation into AI-driven solutions customized to meet the demands of specific users. The background explains past struggles and the shortcomings of conventional privacy settings, laying the groundwork for the development of customized AI solutions. After that, the essay explores the necessity of customization in security and privacy settings, highlighting the variety of user preferences, the dynamic nature of threats, and the fine line that must be drawn between user experience and privacy. It presents the emergence of personalized AI solutions, propelled by sophisticated machine learning algorithms that assess user behavior in real-time and dynamically adjust security and privacy settings. This overview of privacy and security settings looks at the standard capabilities and accompanying restrictions provided by social media sites. In the part on practical implementation, case studies from well-known social media platforms are highlighted with an emphasis on best practices, lessons learned, and successful implementations. The benefits and advantages section describes how adaptive security measures, better privacy protection, and an improved user experience are all brought about by personalized AI solutions. Discussions about possible cost savings and operational efficiency arise when platforms adopt automation and AI-driven personalization.
Big Data and Java are integrated with machine learning Anis Ahmed Qazi; Ehsan Abbas
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 2 (2024): International Journal of Multidisciplinary Sciences and Arts, Article April 202
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i1.3741

Abstract

This in-depth investigation explores the revolutionary nexus of Java, Big Data, and Machine Learning (ML), clarifying the innovations and synergies that result from their integration. The voyage commences with a summary of the past, following the growth from Java's fundamental function in high-level development to the revolutionary influence of Big Data frameworks such as Apache Hadoop and Apache Spark. The story then delves into the fundamentals of machine learning in Java, highlighting its adaptability, rich library support, and crucial role in building strong ML pipelines. The investigation delves into the transformative power of Big Data frameworks, highlighting the distributed file system of Hadoop and the in-memory processing capabilities of Spark. We observe the significant effects of this convergence on a variety of industries through real-world case studies, from e-commerce personalized suggestions to fraud detection in banking. The insights gained from these implementations highlight how crucial it is to use ML models with ethical considerations, interdisciplinary cooperation, and ongoing learning. The following sections cover the nuances of data preprocessing, including the use of Java in ETL workflows, scalable feature engineering using Big Data frameworks, and data quality assurance via transformation and cleansing. ML model deployment is the major focus, along with an exploration of the Java runtime environment, micro services architecture, and crucial aspects of model robustness monitoring and maintenance. The investigation concludes with a focus on case studies and success stories that demonstrate the real-world effects of this convergence in sectors like e-commerce, finance, and healthcare. These real-world examples highlight the accomplishments of companies like Netflix, Uber, Airbnb, and others and provide insightful information about how well integration works to accomplish a range of business objectives.
Co-Authors Anis Ahmed Qazi