International Journal on Science and Technology

E-ISSN: 2229-7677 • Follows UGC-CARE Guidelines • Impact Factor: 9.88

An online refereed journal (peer reviewed journal). An Openly Accessible, Widely Indexed, Multidisciplinary, Scholarly / Academic, International Journal

Our Other Journals:

Indexed by Google Scholar and many other research databases

Academia
ResearchGate
Index Copernicus

Peer Reviewed

Open Access

Multidisciplinary

Widely Indexed

About IJSAT

International Journal on Science and Technology (IJSAT) is a widely indexed, open access, refereed / peer reviewed, multidisciplinary, international, scholarly online journal, and helping researches to share their research work since 2010.

As being a multidisciplinary journal, we are accepting research work from all branches of Engineering, all branches of Ph.D., all fields of Medical & Pharmacy, all branches of Business Administration (MBA), all disciplines of Physical Sciences, all streams of Computer Applications (MCA) and all areas of Mathematics, Economics, Social Sciences and Arts. Click here to view all the disciplines and research areas.

UGC-CARE (India) recommends publishing papers in either peer reviewed journals (refereed journals), or in the journals approved by UGC-CARE, or journals indexed in Scopus or Web of Science. IJSAT is a peer reviewed journal (refereed journals) and therefore fitting within UGC's recommendations.

The research papers published in the journal are indexed by and submitted to various research indexing websites and databases, which include Google Scholar, Academia.edu, Index Copernicus, Zenodo, and more.

As being an international, open access and online journal, papers published in the journal can reach to very wide demography.

  • Publish research paper in almost all academic/research disciplines (multidisciplinary).
  • Publish research paper in almost all spoken languages.
  • Unique Crossref for each published papers.
  • Indexing of papers in all major online journal / research databases / repositories.
  • Easy and fast review and publication system.
  • Authors can check review and publication process status online anytime.
  • Email notification sent to the authors at each process stage.
  • Quick support through email, phone and social media (WhatsApp, Facebook).
  • HTTPS-SSL website for secured encrypted access.

Current Issue

Volume 17, Issue 4
(October-December 2026)


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Indexing

Academia
Advanced Sciences Index
Bielefeld Academic Search Engine
Cabell's Directories
CiteSeer
Crossref
DRJI
Google Scholar
Index Copernicus
Independent Search Engine & Directory Network
Issuu
Mendeley Research Networks
RefSeek
ResearcherId - Thomson Reuters
ResearchGate
Scirus
Scribd
Semantic Scholar
Wiki for Call for Papers
WorldCat
Zenodo

Latest Published Research Papers

IntelliFinance: A Rule-Based and AI-Driven Smart Financial Decision Support System for Personalized Financial Planning—Effective personal financial management remains difficult for individuals who lack access to professional, personalized financial advice suited to their income, expenses, and goals. Most existing solutions address only a fragment of this problem: budgeting and expense-tracking applications record transactions but leave analysis and decision-making to the user; robo-advisory platforms focus narrowly on investment allocation; and conversational finance chatbots often generate numerical guidance directly from a language model, which can be inconsistent or unverifiable. This paper presents IntelliFinance, a web-based Smart Financial Decision Support System (SFDSS) that unifies these fragmented capabilities into a single, explainable platform. The system separates deterministic financial computation from natural-language explanation: a rule-based calculation engine computes budget allocations, emergency-fund targets, SIP/investment contributions, EMI and debt-repayment strategies, and an overall Financial Health Score, while a large-language-model-based AI Advisor interprets these results for the user in plain language and answers user-specific financial queries. The platform further introduces a Family Financial Mode for household-level planning and visibility. Drawing on evidence from a randomized controlled trial of low-income personal finance apps, a review of AI-based robo-advisory systems, and a machine-learning-based expense analytics platform, this paper positions IntelliFinance within the existing literature, identifies the specific gaps it addresses, and describes its proposed architecture, data flow, research hypotheses, and technology stack. The system is intended to function as an accessible, transparent, and trustworthy decision-support and financial-education tool, particularly for students, young professionals, and first-time earners.