General AI & Machine Learning

Authors

  • Miss Shaista Shaikh, Miss Rahila Sayyed

Keywords:

Data-Driven Decision Making, Neural Networks, Supervised and Unsupervised Learning, Machine Learning, Artificial Intelligence

Abstract

General AI and Machine Learning transform how systems learn, reason, and make decisions. By analyzing vast data, they enable automation, predictive accuracy, and intelligent problem-solving across industries. Their advances drive innovation in healthcare, business, robotics, and everyday applications, shaping a more efficient and adaptive digital future.

References

(Additional resource pointers used in the review are listed inline above; readers should consult the cited items for deeper detail.)

Becker, B., & Kohavi, R. (1996). Adult [Dataset]. UCI Machine Learning RepositoryCheng, H. G., & Phillips, M. R. (2014). Secondary analysis of existing data: opportunities and implementation. [Article summarizing SDA rationale and sources]

Bhagat, P. H., & Shaikh, S. A. (2025). Managing health care in the digital world: A comparative analysis on customers using health care services in Mumbai suburbs and Pune city. IJCRT. Registration ID: IJCRT_216557.

Chakrabarty, N., & Biswas, S. (2018). A Statistical Approach to Adult Census Income Level Prediction. arXiv preprint (example of model performance reporting on Adult dataset).

Chougle, Z. S., & Shaikh, S. (2022). To understand the impact of Ayurvedic health-care business & its importance during COVID-19 with special reference to “Patanjali Products”. In Proceedings of the National Conference on Sustainability of Business during COVID-19, IJCRT, 10(1),

Machine Learning Mastery. (2020). Imbalanced Classification with the Adult Income Dataset (descriptive preprocessing summary).

Parikh, V. (2023). Whistleblowing in B-Schools, Education and Society, Vol-47, Issue – 1, Pg. 183-1

Parikh, V. C. (2022) Strategic talent management in education sector around organizational life cycle stages! JOURNAL OF THE ASIATIC SOCIETY OF MUMBAI, SSN: 0972-0766, Vol. XCV, No.11.

Paullada, A., et al. (2021). Data and its (dis)contents: A survey of dataset development and use in machine learning. [Survey article on dataset practices]

Shaikh, S. A. (2024). Empowering Gen Z and Gen Alpha: A comprehensive approach to cultivating future leaders. In Futuristic Trends in Management (IIP Series, Vol. 3, Book 9, Part 2, Chapter 2). IIP Series. https://doi.org/10.58532/V3BHMA9P2CH2

Shaikh, S. A., & Jagirdar, A. H. (2026). Beyond AI dependence: Pedagogical approaches to strengthen student reasoning and analytical skills. In S. Khan & P. Pringuet (Eds.), Empowering learners with AI: Strategies, ethics, and frameworks (Chapter 8, pp. 1–16). IGI Global. https://doi.org/10.4018/979-8-3373-7386-7.ch008

Thylstrup, N. B. (2022). Politics of data reuse in machine learning systems: Theorizing data re-use entanglements. [Article on political/ethical aspects of data reuse].

Tripathy, J. P. (2013). Secondary Data Analysis: Ethical Issues and Challenges. [Discussion of ethical issues in SDA].

Wickham, R. J. (2019). Secondary Analysis Research — review of methods and pitfalls. [Overview of SDA challenges].

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How to Cite

Miss Shaista Shaikh, Miss Rahila Sayyed. (2025). General AI & Machine Learning. International Journal of Research & Technology, 13(S4), 49–54. Retrieved from https://ijrt.org/j/article/view/651

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