Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64419
Volume:
10 |
5 |
May 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of General Engineering
Annai Mathammal Sheela Engineering College

Department of Computer Science
Nehru Memorial College
Artificial Intelligence (AI) has become an important component of modern education, research, and professional writing. This review presents a detailed discussion of AI-based tools that support machine learning development, scientific research, and academic content creation. Major machine learning frameworks such as Scikit-learn, TensorFlow, Theano, Keras, Caffe, MXNet, and Google ML Kit are examined for their capabilities in data analysis, neural network implementation, and model deployment. The paper also reviews AI-assisted research platforms including PDFgear, Consensus, Scite, Elicit, Semantic Scholar, SciSpace, OpenRead, ResearchRabbit, Humata AI, Perplexity AI, ChatDOC, and PDF.ai. These tools help researchers locate relevant literature, summarize complex documents, evaluate citations, and improve information retrieval. In addition, AI writing assistants such as Grammarly, QuillBot, Writesonic, ChatGPT, TextCortex, Zeno Assistant, and ZenoChat are discussed for their ability to enhance grammar, paraphrasing, summarization, and content generation. The review highlights the growing impact of AI technologies on academic productivity, knowledge acquisition, and research quality.
KEYWORDS
Artificial intelligence tools have become indispensable resources for modern learners, educators, researchers, and writers. Machine learning frameworks simplify the development of intelligent systems, while research assistance platforms accelerate literature review and knowledge discovery. Writing assistants enhance the quality, clarity, and originality of academic documents. The continued advancement of AI technologies is expected to further improve productivity, accuracy, and accessibility across educational and research environments. Organizations and institutions that effectively integrate these tools can achieve greater efficiency and innovation in knowledge creation and dissemination.
[1] Mulam, V. R. (2024). Machine Learning and AI Innovations with Python: Trends and Future Directions. International Journal for Multidisciplinary Research (IJFMR), 6(3), 1–12. [2] Medavarapu, S. S. (2024). Advancements in Deep Learning: A Review of Keras and TensorFlow Frameworks. Journal of Scientific and Engineering Research, 11(5), 282–286. [3] Rao, M. N. (2023). A Comparative Analysis of Deep Learning Frameworks and Libraries. International Journal of Intelligent Systems and Applications in Engineering, 11(2s). [4] Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. [5] Abadi, M., et al. (2016). TensorFlow: A System for Large-Scale Machine Learning. OSDI Conference Proceedings, 265–283. [6] Al Rfou, R., et al. (2016). Theano: A Python Framework for Fast Computation of Mathematical Expressions. arXiv preprint arXiv: 1605.02688. [7] Chollet, F. (2015). Keras: Deep Learning Library for Theano and TensorFlow. GitHub Repository. [8] Jia, Y., et al. (2014). Caffe: Convolutional Architecture for Fast Feature Embedding. ACM Multimedia, 675–678. [9] Chen, T., et al. (2015). MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. arXiv preprint arXiv: 1512.01274. [10] Scite Inc. (2023). Scite: Smart Citation Indexing Platform.
Publication Date
6/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
5
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...