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# International Journal of Innovative Technology and Exploring Engineering: The International Journal of Innovative Technology and Exploring Engineering (IJITEE) aims to publish articles in Engineering and Technology.

## Sitemaps
[XML Sitemap](https://www.ijitee.org/sitemap_index.xml): Includes all crawlable and indexable pages.

## Posts
- [I32310789S319](https://www.ijitee.org/i32310789s319/): 2B.K. Roy, National Institute of Technology, Silchar, India.
- [J101908810S219](https://www.ijitee.org/j101908810s219/): 3Deepak Verma, Department of Mechanical Engineering, Graphic Era Hill University, Uttarakhand, India.

## Pages
- [Published in Year 2026](https://www.ijitee.org/published-in-year-2026/): VOLUME-15 ISSUE-10, SEPTEMBER 2026: Last Date of Article Submission - 30 August 2026 (Open) | Date of Publication - 30 September 2026
VOLUME-15 ISSUE-9, AUGUST 2026: Last date of Article Submission has been closed - The articles are under process and can be viewed and downloaded after 30 August 2026.
VOLUME-15 ISSUE-8, JULY 2026
VOLUME-15 ISSUE-7, JUNE 2026
VOLUME-15 ISSUE-6, MAY 2026
VOLUME-15 ISSUE-5, APRIL 2026
VOLUME-15 ISSUE-4, MARCH 2026
VOLUME-15 ISSUE-3, FEBRUARY 2026
VOLUME-15 ISSUE-2, JANUARY 2026
- [Generative AI Tools or Chatbots](https://www.ijitee.org/generative-ai-tools/): Generative AI Tools or Chatbots:
- [Diversity, Equity, Inclusivity, and Accessibility (DEIA)](https://www.ijitee.org/diversity-equity-inclusivity-and-accessibility-deia/): Diversity, Equity, Inclusivity, and Accessibility (DEIA):
- [Complaints and Appeals](https://www.ijitee.org/complaints-and-appeals/): Complaints and Appeals:
- [Published in Year 2025](https://www.ijitee.org/published-in-year-2025/): VOLUME-15 ISSUE-1, DECEMBER 2025
VOLUME-14 ISSUE-12, NOVEMBER 2025
VOLUME-14 ISSUE-11, OCTOBER 2025
VOLUME-14 ISSUE-10, SEPTEMBER 2025
VOLUME-14 ISSUE-9, AUGUST 2025
VOLUME-14 ISSUE-8, JULY 2025
VOLUME-14 ISSUE-7, JUNE 2025
VOLUME-14 ISSUE-6, MAY 2025
VOLUME-14 ISSUE-5, APRIL 2025
VOLUME-14 ISSUE-4, MARCH 2025
VOLUME-14 ISSUE-3, FEBRUARY 2025
VOLUME-14 ISSUE-2, JANUARY 2025
- [Published in Year 2024](https://www.ijitee.org/published-in-year-2024/): VOLUME-14 ISSUE-1, DECEMBER 2024
VOLUME-13 ISSUE-12, NOVEMBER 2024
VOLUME-13 ISSUE-11, OCTOBER 2024
VOLUME-13 ISSUE-10, SEPTEMBER 2024
VOLUME-13 ISSUE-9, AUGUST 2024
VOLUME-13 ISSUE-8, JULY 2024
VOLUME-13 ISSUE-7, JUNE 2024
VOLUME-13 ISSUE-6, MAY 2024
VOLUME-13 ISSUE-5, APRIL 2024
VOLUME-13 ISSUE-4, MARCH 2024
VOLUME-13 ISSUE-3, FEBRUARY 2024
VOLUME-13 ISSUE-2, JANUARY 2024
- [Advertising](https://www.ijitee.org/advertising/): Advertising:
- [Responsibilities and Selection Process of the Editorial Board](https://www.ijitee.org/responsibilities-and-selection-process-of-the-editorial-board/): The Editorial Board comprises various distinguished positions, including Associate Editor, General Editor, and Editor-in-Chief. These positions are responsible for ensuring the publication's quality and integrity.
- [Journal Metrics](https://www.ijitee.org/journal-metrics/): Journal Metrics:
- [Archiving Policy](https://www.ijitee.org/archiving-policy/): Archiving:
- [Imprint](https://www.ijitee.org/imprint/): Full Journal Title: International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075 (Online) Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) Publisher Location: India. Postal Address: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), # G: 18, Block-A, Tirupati Abhinav Commercial Campus, Tirupati Abhinav Homes, Ayodhya Bypass Road, Damkheda, Bhopal (Madhya Pradesh)-462037, India. Editors: Editorial Board Publication Frequency: Monthly Publication Medium: Online (Electronic Only) Publication Website: www.ijitee.org First Year Published: 2012 Indexing Databases: Indexing & Abstracting
Journal DOI: https://doi.org/10.35940/ijitee
Publication Language: English
Primary Field: Engineering and Technology
Archive: https://www.ijitee.org/archive/
CrossRef: Yes
Guidelines for Authors: https://www.ijitee.org/instructions-for-authors/
Editorial and Publishing Policies: https://www.ijitee.org/ethics-policies/
Publisher License under: CC-BY-NC-ND 4.0
- [Citations](https://www.ijitee.org/citation/): Citations play a crucial role in academic and professional writing, serving as a means to credit original authors and sources. Properly formatted citations enhance the credibility of a work and allow readers to trace the origins of the ideas presented. Adhering to established citation styles is essential for maintaining consistency and professionalism in scholarly communication. As such, meticulous attention to citation details is imperative for the integrity of any research or publication.
- [Declaration Statement](https://www.ijitee.org/declaration-statement/): Declaration Statement:
- [Acknowledgements](https://www.ijitee.org/acknowledgements/): Acknowledgements:
- [Corrections, Retractions, and Post Publication](https://www.ijitee.org/corrections-retractions-removal-and-republications/): Corrections, Retractions, and Post Publication:
- [Authorship](https://www.ijitee.org/authorship/): Authorship:
- [Competing Interests/ Conflicts of Interest](https://www.ijitee.org/competing-interests/): Competing Interests/ Conflicts of Interest:
- [Animal and Human Research Participants: Clinical Trials, Nomenclatures, and Abbreviations](https://www.ijitee.org/code-of-conduct-for-medical-ethics/): The document titled Animal and Human Research Participants: Clinical Trials, Nomenclatures, and Abbreviations serves as a comprehensive guide to understanding the terminology and abbreviations commonly used in clinical research involving both animal and human subjects. It delineates the ethical considerations and regulatory frameworks governing such studies, ensuring clarity and precision in the communication of research findings. This resource is essential for researchers, clinicians, and regulatory personnel engaged in the design, implementation, and evaluation of clinical trials.
- [Data Access Statement and Material Availability](https://www.ijitee.org/availability-of-data-and-material/): Data Access Statement and Material Availability:
- [Repositories](https://www.ijitee.org/repositories/): Repository:
- [Image Integrity and Processing](https://www.ijitee.org/image-integrity-and-standards/): Image Integrity and Processing:
- [Published in Year 2023](https://www.ijitee.org/published-in-year-2023/): VOLUME-13 ISSUE-1, DECEMBER 2023
VOLUME-12 ISSUE-12, NOVEMBER 2023
VOLUME-12 ISSUE-11, OCTOBER 2023
VOLUME-12 ISSUE-10, SEPTEMBER 2023
VOLUME-12 ISSUE-9, AUGUST 2023
VOLUME-12 ISSUE-8, JULY 2023
VOLUME-12 ISSUE-7, JUNE 2023
VOLUME-12 ISSUE-6, MAY 2023
VOLUME-12 ISSUE-5, APRIL 2023
VOLUME-12 ISSUE-4, MARCH 2023
VOLUME-12 ISSUE-3, FEBRUARY 2023
VOLUME-12 ISSUE-2, JANUARY 2023
- [Frequently Asked Questions (FAQ)](https://www.ijitee.org/faq/): Authors should first read the FAQ, then submit a query if necessary.
- [Important Dates](https://www.ijitee.org/dates/): Authors can electronically submit articles throughout the year using the Article Submission System. Authors can format their article either in (i) a single-column format or (ii) as per the journal template. The submitted articles should not have been previously published or are currently under consideration for publication elsewhere. The journal does not accept brief or short notes for publication. The editors retain the right to reject any articles that lack quality or originality without sending them for review. All articles must fall within the journal’s scope and will undergo a double-anonymized peer-review process. Authors must confirm that they have read and understood the content of their submitted article and ensure that it meets acceptable English grammar and usage standards. To help with the proofreading process, authors can use tools like Grammarly or similar applications. As an open-access journal, authors must pay an Article Processing Charge (APC) to publish their articles and retain copyright. Additionally, authors should familiarise themselves with the editorial and publishing policies of the journal.
- [Published in Year 2022](https://www.ijitee.org/published-in-year-2022/): VOLUME-12 ISSUE-1, DECEMBER 2022
VOLUME-11 ISSUE-12, NOVEMBER 2022
VOLUME-11 ISSUE-11, OCTOBER 2022
VOLUME-11 ISSUE-10, SEPTEMBER 2022
VOLUME-11 ISSUE-9, AUGUST 2022
VOLUME-11 ISSUE-8, JULY 2022
VOLUME-11 ISSUE-7, JUNE 2022
VOLUME-11 ISSUE-6, MAY 2022
VOLUME-11 ISSUE-5, APRIL 2022
VOLUME-11 ISSUE-4, MARCH 2022
VOLUME-11 ISSUE-3, FEBRUARY 2022
- [Confidentiality and Privacy](https://www.ijitee.org/confidentiality-policy/): Confidentiality and Privacy:
- [Selection Process of Editors](https://www.ijitee.org/selection-of-editors/): The International Journal of Innovative Technology and Exploring Engineering (IJITEE) has ISSN: 2278-3075 (online), which is an online, open access, peer reviewed, periodical monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in the months of January, February, March, April, May, June, July, August, September, October, November and December.
- [Published in Year S 2018](https://www.ijitee.org/published-in-year-s-2018/): VOLUME-8 ISSUE-2S2, DECEMBER 2018
VOLUME-8 ISSUE-2S, DECEMBER 2018
- [Published in Year S 2019](https://www.ijitee.org/published-in-year-s-2019/): VOLUME-9 ISSUE-2S5, DECEMBER 2019
VOLUME-9 ISSUE-2S4, DECEMBER 2019
VOLUME-9 ISSUE-2S3, DECEMBER 2019
VOLUME-9 ISSUE-2S2, DECEMBER 2019
VOLUME-9 ISSUE-2S, DECEMBER 2019
VOLUME-9 ISSUE-1S, NOVEMBER 2019
VOLUME-8 ISSUE-12S3, OCTOBER 2019
VOLUME-8 ISSUE-12S2, OCTOBER 2019
VOLUME-8 ISSUE-12S, OCTOBER 2019
VOLUME-8 ISSUE-11S2, SEPTEMBER 2019
VOLUME-8 ISSUE-11S, SEPTEMBER 2019
VOLUME-8 ISSUE-10S2, AUGUST 2019
VOLUME-8 ISSUE-10S, AUGUST 2019
VOLUME-8 ISSUE-9S4, JULY 2019
VOLUME-8 ISSUE-9S3, JULY 2019
VOLUME-8 ISSUE-9S2, JULY 2019
VOLUME-8 ISSUE-9S, JULY 2019
VOLUME-8 ISSUE-8S3, JUNE 2019
VOLUME-8 ISSUE-8S2, JUNE 2019
VOLUME-8 ISSUE-8S, JUNE 2019
VOLUME-8 ISSUE-7C2, MAY 2019
VOLUME-8 ISSUE-7C, MAY 2019
VOLUME-8 ISSUE-7S2, MAY 2019
VOLUME-8 ISSUE-7S, MAY 2019
VOLUME-8 ISSUE-6S4, APRIL 2019
VOLUME-8 ISSUE-6S3, APRIL 2019
VOLUME-8 ISSUE-6S2, APRIL 2019
VOLUME-8 ISSUE-6S, APRIL 2019
VOLUME-8 ISSUE-6C2, APRIL 2019
VOLUME-8 ISSUE-6C, APRIL 2019
VOLUME-8 ISSUE-5S, MARCH 2019
VOLUME-8 ISSUE-4S3, MARCH 2019
VOLUME-8 ISSUE-4S2, MARCH 2019
VOLUME-8 ISSUE-4S, FEBRUARY 2019
VOLUME-8 ISSUE-3C, JANUARY 2019
- [Article Submission System](https://www.ijitee.org/article-submission-system/): If there is any problem in uploading the article through the form given below, the author can also email the article to submit2@ijitee.org with the following details: Your name, Mobile No, WhatsApp No, Country Name, Email, Other email (optional), Scope of the article, Author(s) Name (Min-01, Max 05), Title of the Article, Name of the journal.
- [Published in Year S 2020](https://www.ijitee.org/published-in-year-s-2020/): VOLUME-9 ISSUE-9S, JULY 2020
VOLUME-9 ISSUE-7S, MAY 2020
VOLUME-9 ISSUE-4S3, FEBRUARY 2020
VOLUME-9 ISSUE-4S2, MARCH 2020
VOLUME-9 ISSUE-4S, MARCH 2020
VOLUME-9 ISSUE-3S2, JANUARY 2020
VOLUME-9 ISSUE-3S, JANUARY 2020
- [Published in Year 2021](https://www.ijitee.org/published-in-year-2021/): VOLUME-11 ISSUE-2, DECEMBER 2021
VOLUME-11 ISSUE-1, NOVEMBER 2021
VOLUME-10 ISSUE-12, OCTOBER 2021
VOLUME-10 ISSUE-11, SEPTEMBER 2021
VOLUME-10 ISSUE-10, AUGUST 2021
VOLUME-10 ISSUE-9, JULY 2021
VOLUME-10 ISSUE-8, JUNE 2021
VOLUME-10 ISSUE-7, MAY 2021
VOLUME-10 ISSUE-6, APRIL 2021
VOLUME-10 ISSUE-5, MARCH 2021
VOLUME-10 ISSUE-4, FEBRUARY 2021
VOLUME-10 ISSUE-3, JANUARY 2021
- [Article Processing Charge (APC)](https://www.ijitee.org/article-processing-charge-policy/): Authors must pay a fixed APC to publish their articles in the journal to retain copyright. The APC is payable only upon acceptance, not before or upon rejection.
- [Published in Year 2012](https://www.ijitee.org/published-in-year-2012/): VOLUME-2, ISSUE-1, DECEMBER 2012
VOLUME-1, ISSUE-6, NOVEMBER 2012
VOLUME-1, ISSUE-5, OCTOBER 2012
VOLUME-1, ISSUE-4, SEPTEMBER 2012
VOLUME-1, ISSUE-3, AUGUST 2012
VOLUME-1, ISSUE-2, JULY 2012
VOLUME-1, ISSUE-1, JUNE 2012
- [Published in Year 2013](https://www.ijitee.org/published-in-year-2013/): VOLUME-3, ISSUE-7, DECEMBER 2013
VOLUME-3, ISSUE-6, NOVEMBER 2013
VOLUME-3, ISSUE-5, OCTOBER 2013
VOLUME-3, ISSUE-4, SEPTEMBER 2013
VOLUME-3, ISSUE-3, AUGUST 2013
VOLUME-3, ISSUE-2, JULY 2013
VOLUME-3, ISSUE-1, JUNE 2013
VOLUME-2, ISSUE-6, MAY 2013
VOLUME-2, ISSUE-5, APRIL 2013
VOLUME-2, ISSUE-4, MARCH 2013
VOLUME-2, ISSUE-3, FEBRUARY 2013
VOLUME-2, ISSUE-2, JANUARY 2013
- [Published in Year 2014](https://www.ijitee.org/published-in-year-2014/): VOLUME-4, ISSUE-7, DECEMBER 2014
VOLUME-4, ISSUE-6, NOVEMBER 2014
VOLUME-4, ISSUE-5, OCTOBER 2014
VOLUME-4, ISSUE-4, SEPTEMBER 2014
VOLUME-4, ISSUE-3, AUGUST 2014
VOLUME-4, ISSUE-2, JULY 2014
VOLUME-4, ISSUE-1, JUNE 2014
VOLUME-3, ISSUE-12, MAY 2014
VOLUME-3, ISSUE-11, APRIL 2014
VOLUME-3, ISSUE-10, MARCH 2014
VOLUME-3, ISSUE-9, FEBRUARY 2014
VOLUME-3, ISSUE-8, JANUARY 2014
- [Published in Year 2015](https://www.ijitee.org/published-in-year-2015/): VOLUME-5, ISSUE-7, DECEMBER 2015
VOLUME-5, ISSUE-6, NOVEMBER 2015
VOLUME-5, ISSUE-5, OCTOBER 2015
VOLUME-5, ISSUE-4, SEPTEMBER 2015
VOLUME-5, ISSUE-3, AUGUST 2015
VOLUME-5, ISSUE-2, JULY 2015
VOLUME-5, ISSUE-1, JUNE 2015
VOLUME-4, ISSUE-12, MAY 2015
VOLUME-4, ISSUE-11, APRIL 2015
VOLUME-4, ISSUE-10, MARCH 2015
VOLUME-4, ISSUE-9, FEBRUARY 2015
VOLUME-4, ISSUE-8, JANUARY 2015
- [Published in Year 2016](https://www.ijitee.org/published-in-year-2016/): VOLUME-6, ISSUE-7, DECEMBER 2016
VOLUME-6, ISSUE-6, NOVEMBER 2016
VOLUME-6, ISSUE-5, OCTOBER 2016
VOLUME-6, ISSUE-4, SEPTEMBER 2016
VOLUME-6, ISSUE-3, AUGUST 2016
VOLUME-6, ISSUE-2, JULY 2016
VOLUME-6, ISSUE-1, JUNE 2016
VOLUME-5, ISSUE-12, MAY 2016
VOLUME-5, ISSUE-11, APRIL 2016
VOLUME-5, ISSUE-10, MARCH 2016
VOLUME-5, ISSUE-9, FEBRUARY 2016
VOLUME-5, ISSUE-8, JANUARY 2016
- [Published in Year 2017](https://www.ijitee.org/published-in-year-2017/): VOLUME-7, ISSUE-3, DECEMBER 2017
VOLUME-7, ISSUE-2, NOVEMBER 2017
VOLUME-7, ISSUE-1, SEPTEMBER 2017
VOLUME-6, ISSUE-12, AUGUST 2017
VOLUME-6, ISSUE-11, JULY 2017
VOLUME-6, ISSUE-10, JUNE 2017
VOLUME-6, ISSUE-9, APRIL 2017
VOLUME-6, ISSUE-8, MARCH 2017
- [Published in Year 2018](https://www.ijitee.org/published-in-year-2018/): VOLUME-8 ISSUE-2, DECEMBER 2018
VOLUME-8 ISSUE-1, NOVEMBER 2018
VOLUME-7 ISSUE-12, SEPTEMBER 2018
VOLUME-7 ISSUE-11, AUGUST 2018
VOLUME-7 ISSUE-10, JULY 2018
VOLUME-7 ISSUE-9, JUNE 2018
VOLUME-7 ISSUE-8, MAY 2018
VOLUME-7 ISSUE-7, APRIL 2018
VOLUME-7 ISSUE-6, MARCH 2018
VOLUME-7, ISSUE-5, FEBRUARY 2018
VOLUME-7, ISSUE-4, JANUARY 2018
- [Published in Year 2019](https://www.ijitee.org/published-in-year-2019/): VOLUME-9 ISSUE-2, DECEMBER 2019
VOLUME-9 ISSUE-1, NOVEMBER 2019
VOLUME-8 ISSUE-12, OCTOBER 2019
VOLUME-8 ISSUE-11, SEPTEMBER 2019
VOLUME-8 ISSUE-10, AUGUST 2019
VOLUME-8 ISSUE-9, JULY 2019
VOLUME-8 ISSUE-8, JUNE 2019
VOLUME-8 ISSUE-7, MAY 2019
VOLUME-8 ISSUE-6, APRIL 2019
VOLUME-8 ISSUE-5, MARCH 2019
VOLUME-8 ISSUE-4, FEBRUARY 2019
VOLUME-8 ISSUE-3, JANUARY 2019
- [Published in Year 2020](https://www.ijitee.org/published-in-year-2020/): VOLUME-10 ISSUE-2, DECEMBER 2020
VOLUME-10 ISSUE-1, NOVEMBER 2020
VOLUME-9 ISSUE-12, OCTOBER 2020
VOLUME-9 ISSUE-11, SEPTEMBER 2020
VOLUME-9 ISSUE-10, AUGUST 2020
VOLUME-9 ISSUE-9, JULY 2020
VOLUME-9 ISSUE-8, JUNE 2020
VOLUME-9 ISSUE-7, MAY 2020
VOLUME-9 ISSUE-6, APRIL 2020
VOLUME-9 ISSUE-5, MARCH 2020
VOLUME-9 ISSUE-4, FEBRUARY 2020
VOLUME-9 ISSUE-3, JANUARY 2020
- [Purchases](https://www.ijitee.org/purchases/)
- [Cart](https://www.ijitee.org/cart/)
- [Special Issue](https://www.ijitee.org/special-issue/): The International Journal of Innovative Technology and Exploring Engineering (IJITEE) publish two types of issues: (1) Regular Issues and (2) Theme Based Special Issues (announced from time to time). The Authors may submit articles electronically throughout the year using the Article Submission System. After the final acceptance of the article, based upon the detailed review process, the article will immediately be published online. For Theme-Based Special Issues, time-bound special calls for articles will be announced. Authors are allowed to download published articles. There is no need to pay the fee for downloading articles.
- [National Conference on Smart Computation and Technology (NCSCT-2017) | April 07-08, 2017 | Jaipur, INDIA](https://www.ijitee.org/ncsct-2017/): S. No
- [Board of Referees (BoR)](https://www.ijitee.org/board-referees-bor/): Dr. Shiv Kumar
Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE, Member of the Elsevier Advisory Panel
Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India
Dr. Gamal Abd El-Nasser Ahmed Mohamed Said
Ph.D(CSE), MS(CSE), BSc(EE)
Department of Computer and Information Technology , Port Training Institute, Arab Academy for Science, Technology and Maritime Transport, Egypt
Dr. Mayank Singh
PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT
Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-Natal, Durban, South Africa.
Prof. (Dr.) Hamid Saremi
Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran
Dr. Moinuddin Sarker
Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor) Stamford, USA.
Prof. (Dr.) Nishakant Ojha
Principal Advisor (Information &Technology) His Excellency Ambassador Republic of Sudan& Head of Mission in New Delhi, India
Dr. Shanmugha Priya. Pon Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania, East Africa, Tanzania
Dr. Veronica Mc Gowan
Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman, China.
Dr. Fadiya Samson Oluwaseun Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern Cyprus, Turkey.
Dr. Robert Brian Smith
International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie Centre, North Ryde, New South Wales, Australia
Prof. MPS Chawla
Member of IEEE, Professor-Incharge (head)-Library, Associate Professor in Electrical Engineering, G.S. Institute of Technology & Science Indore, Madhya Pradesh, India, Chairman, IEEE MP Sub-Section, India
- [Indexing and Abstracting](https://www.ijitee.org/indexing/): Indexing details available in the journal website.
- [Open Access Publishing](https://www.ijitee.org/open-access-license/): Open Access Publishing:
- [Intellectual Property](https://www.ijitee.org/copyright-grants-ownership-declaration/): These permissions are granted under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. The International Journal of Innovative Technology and Exploring Engineering (IJITEE) encourage users to share and disseminate the work, providing appropriate credit to the original authors and refraining from altering or using the content commercially. This inclusive approach allows a broader audience to benefit from shared knowledge.

## Downloads
- [Volume-15 Issue-8, July 2026](https://www.ijitee.org/download/volume-15-issue-8/): Editor-In-Chief
- [Volume-15 Issue-7, June 2026](https://www.ijitee.org/download/volume-15-issue-7/): Editor-In-Chief
- [Volume-15 Issue-6, May 2026](https://www.ijitee.org/download/volume-15-issue-6/): Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
- [Volume-15 Issue-5, April 2026](https://www.ijitee.org/download/volume-15-issue-5/): Editor-In-Chief
- [Volume-15 Issue-4, March 2026](https://www.ijitee.org/download/volume-15-issue-4/): Editor-In-Chief
- [Volume-15 Issue-3, February 2026](https://www.ijitee.org/download/volume-15-issue-3/): Editor-In-Chief
- [Volume-15 Issue-2, January 2026](https://www.ijitee.org/download/volume-15-issue-2/): Editor-In-Chief
- [Volume-15 Issue-1, December 2025](https://www.ijitee.org/download/volume-15-issue-1/): Editor-In-Chief
- [Volume-14 Issue-12, November 2025](https://www.ijitee.org/download/volume-14-issue-12/): Editor-In-Chief
- [Volume-14 Issue-11, October 2025](https://www.ijitee.org/download/volume-14-issue-11/): Editor-In-Chief
- [Volume-14 Issue-10, September 2025](https://www.ijitee.org/download/volume-14-issue-10/): Editor-In-Chief
- [Volume-14 Issue-9, August 2025](https://www.ijitee.org/download/volume-14-issue-9/): Editor-In-Chief
- [Volume-14 Issue-8, July 2025](https://www.ijitee.org/download/volume-14-issue-8/): Editor-In-Chief
- [Volume-14 Issue-7, June 2025](https://www.ijitee.org/download/volume-14-issue-7/): Editor-In-Chief
- [Volume-14 Issue-6, May 2025](https://www.ijitee.org/download/volume-14-issue-6/): Editor-In-Chief
- [Volume-14 Issue-5, April 2025](https://www.ijitee.org/download/volume-14-issue-5/): Editor-In-Chief
- [Volume-14 Issue-4, March 2025](https://www.ijitee.org/download/volume-14-issue-4/): Editor-In-Chief
- [Volume-14 Issue-3, February 2025](https://www.ijitee.org/download/volume-14-issue-3/): Editor-In-Chief
- [Volume-14 Issue-2, January 2025](https://www.ijitee.org/download/volume-14-issue-2/): Editor-In-Chief
- [Volume-14 Issue-1, December 2024](https://www.ijitee.org/download/volume-14-issue-1/): Editor-In-Chief
- [Volume-13 Issue-12, November 2024](https://www.ijitee.org/download/volume-13-issue-12/): Editor-In-Chief
- [Volume-13 Issue-11, October 2024](https://www.ijitee.org/download/volume-13-issue-11/): Editor-In-Chief
- [Volume-13 Issue-10, September 2024](https://www.ijitee.org/download/volume-13-issue-10/): Editor-In-Chief
- [Volume-13 Issue-9, August 2024](https://www.ijitee.org/download/volume-13-issue-9/): Editor-In-Chief
- [Volume-13 Issue-8, July 2024](https://www.ijitee.org/download/volume-13-issue-8/): Editor-In-Chief
- [Volume-13 Issue-7, June 2024](https://www.ijitee.org/download/volume-13-issue-7/): Editor-In-Chief
- [Volume-13 Issue-6, May 2024](https://www.ijitee.org/download/volume-13-issue-6/): Editor-In-Chief
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## Portfolio Items
- [I128615090826](https://www.ijitee.org/portfolio-item/i128615090826/): The increasing usage of distributed renewable energy resources has rapidly increased the transition to decentralised smart grids. This is where secure and efficient peer-to-peer (P2P) energy trading is crucial. However, conventional blockchain-based energy trading schemes suffer from high computational overhead, communication latency, and limited scalability, making them unsuitable for resource-constrained Internet of Things (IoT) networks. This research proposes a lightweight blockchain framework that incorporates Hyperledger Fabric with Practical Byzantine Fault Tolerance (PBFT) consensus, ZigbeePro communication, and Long Short-Term Memory (LSTM)-based energy demand forecasting to facilitate secure and intelligent decentralised energy trading. The framework was evaluated using MATLAB/Simulink simulation, NS-3, Hyperledger Fabric, and a Raspberry Pi/ESP32 prototype. The results of the experiment show that the proposed framework achieved an average latency of 48.9 ms, throughput of 185 transactions per second, packet delivery ratio of 97.8%, and support for up to 250 IoT nodes while maintaining low energy overhead. The LSTM forecasting model attained an R² of 0.964 with a MAPE of 4.7%, delivering accurate demand prediction for intelligent energy allocation. Compared with centralised and Proof-of-Work blockchain models, the proposed framework enhanced communication efficiency, scalability, and security integrating while reducing computational cost. These results demonstrated that lightweight blockchain, low-power communication, and Artificial Intelligence-based forecasting provides a practical and scalable solution for decentralised energy trading for the next-generation smart grids.
- [I128715090826](https://www.ijitee.org/portfolio-item/i128715090826/): This paper proposes a method for building a specialised sign language dataset for deaf-mute sign language recognition in digital libraries, comprising 120 signs and 65 phrases, collected from 25 people with hearing impairments in Hanoi and Ho Chi Minh City. The data were collected under various conditions and yielded approximately 125,000 video samples after processing. Based on experimental results with Random Forest, pure LSTM, and CNN-LSTM models, the CNN-LSTM model yields the best results and is selected for the development of Vietnamese sign language recognition services, demonstrating high performance, stability, and suitability for practical implementation requirements.
- [H128415080726](https://www.ijitee.org/portfolio-item/h128415080726/): Digital communication strategies have emerged as critical enablers of successful global Information Technology (IT) transformation, particularly as organisations navigate the complexities of digitalisation, distributed workforces, and cross cultural collaboration. While technological advancements provide the infrastructure for transformation, the effectiveness of digital initiatives largely depends on communication practices that facilitate stakeholder engagement, knowledge sharing, organisational alignment, and adoption of change. This study explores the strategic role of digital communication in supporting global IT transformation by integrating perspectives from digital transformation theory, organizational communication, change management, and information systems research. The research investigates how digital communication platforms, collaborative technologies, leadership communication, and data-driven communication strategies influence organizational readiness, employee engagement, innovation capability, and project success across geographically dispersed and culturally diverse environments. It further examines the impact of emerging technologies—including artificial intelligence (AI), cloud-based collaboration platforms, enterprise social networks, and real-time communication systems—on enhancing transparency, decision making, and knowledge exchange during large-scale IT transformation initiatives. Particular attention is given to challenges such as digital communication overload, cybersecurity concerns, resistance to change, cultural diversity, language barriers, and governance issues that affect the effectiveness of communication in multinational organisations. Employing a mixed-methods research design, the study proposes a comprehensive conceptual framework examining the relationships among digital communication capabilities, organisational culture, leadership effectiveness, technological readiness, and transformation performance. Quantitative analysis is complemented by qualitative insights from global IT organisations, providing a multidimensional understanding of communication practices and their influence on digital transformation outcomes. Advanced analytical techniques, including structural equation modelling (SEM) and thematic analysis, are utilised to validate the proposed framework and identify key determinants of communication effectiveness. The study contributes to the theoretical advancement of the digital transformation and organisational communication literature by establishing digital communication strategy as a strategic capability that mediates the relationship between technology adoption and organisational performance. In practice, the findings offer evidence-based recommendations for policymakers, business leaders, and IT managers seeking to design resilient communication frameworks that enhance collaboration, accelerate technology adoption, strengthen stakeholder trust, and improve the success of transformation in global enterprises. Ultimately, the research underscores that effective digital communication is not merely a supporting function but a strategic driver of sustainable, agile, and inclusive global IT transformation in the era of Industry 4.0 and beyond.
- [E124715050426](https://www.ijitee.org/portfolio-item/e124715050426/): Named Data Networking (NDN) is considered a promising paradigm that enables content-centric communication and in-network caching. However, challenges of mobility, scalability, and security limit its effectiveness and impact in dynamic IoT environments. Existing mobility management approaches and strategies, including anchor-based and anchor-free schemes, are unable to jointly optimise latency, security, and multihoming efficiency, even under highly dynamic conditions. This research work proposes a Mobile Producer Handoff in the Named-data Emulated Mobility framework known as MP-HNEM. This is a blockchain-based adaptive routing strategy that integrates predictive handoff, cross-layer optimisation, and a lightweight Proof-of-Authority consensus mechanism to provide and enhance mobility support in multihomed NDN-based wireless sensor networks. The framework also addresses a crucial gap in secure and latency-aware producer mobility. A hybrid simulation and emulation method is employed using ndnSIM v2.9 and a Mini-NDN to evaluate MP-HNEM performance under varying mobility patterns, trust thresholds, and network densities. We analyzed latency, throughput, packet delivery ratio, energy consumption, and trust validation delay as key metrics. MP-HNEM results show a 42.7% reduction in latency, a 73% increase in throughput, and a 39% reduction in energy consumption compared to baseline schemes. The packet delivery ratio increases by 31.5%, indicating improved reliability across all handoff events. Security analysis shows detection accuracy over 90% and block validation success rates over 98% under mobility conditions. Using ANOVA, we conducted Statistical validation and achieved p < 0.05, confirming the impact and significance of these improvements. The major contributions of this research work are: (i) a blockchain-integrated ARS developed to secure multihoming mobility, (ii) a reinforcement learning-based predictive handoff mechanism for smart support, and (iii) a hybrid validation framework that combines both simulation and emulation procedures. The results indicate that MP-HNEM is a scalable, energy-efficient, and secure mobility solution for NDN-based IoT systems, suitable for applications such as smart healthcare and industrial IoT. Future work intends to focus on real-world deployment and heterogeneous IoT integration.
- [H127515080726](https://www.ijitee.org/portfolio-item/h127515080726/): Thalassemia is a hereditary condition that affects haemoglobin formation, leading to ineffective erythropoiesis, anaemia, and iron overload, resulting in serious complications and reduced quality of life. While modern medical care, such as blood transfusions and iron chelation, has considerably extended patients’ lifespan, prolonged therapy has been accompanied by various side effects, financial burden, and low compliance. In addition, due to the complex pathogenesis of thalassemia, which includes oxidative stress, inflammation, disrupted iron homeostasis, and cellular damage, there is a need to develop new methods that can address all these processes simultaneously. This fact has led researchers to show great interest in plant-based remedies containing naturally active biological substances. In this study, we explored the therapeutic efficacy of the plant Phyllanthus niruri by investigating its association with target proteins and biological pathways related to iron homeostasis in thalassemic patients, employing a comprehensive bioinformatics approach. Active compounds found in Phyllanthus niruri were recognized and filtered. Then, target identification, along with disease-gene associations, was performed for common diseases and compounds. For biological insights, targets associated with thalassemia and phytoconstituents were analysed employing network pharmacology, protein-protein interaction networks, Gene Ontology enrichment, and KEGG pathway analyses. In vitro molecular docking experiments to predict interactions between phytoconstituents and their targets were performed, and ADMET analysis was conducted to assess pharmacokinetics and drug likeness. Among the identified hub genes were those associated with iron metabolism, oxidative stress, inflammation, and erythrocyte formation. The pathways enriched in the functional enrichment analysis included those associated with iron metabolism, cytokine signalling, apoptosis, and cellular responses to stress. The molecular docking study revealed favourable interactions between the phytoconstituents, particularly corilagin, geraniin, and quercetin, and the target proteins. ADMET predictions indicated favourable pharmacokinetics for some compounds. All in all, the results demonstrate the multitarget potential of Phyllanthus niruri as an important source of bioactive substances that can address complications of thalassemia, especially in cases of iron overload. The study lays an important foundation for the subsequent experimental verification of the results.
- [F127415070626](https://www.ijitee.org/portfolio-item/f127415070626/): Investigation and prediction of defects in software is one of the important solutions to ensure software quality and reliability. Machine learning algorithms are used across a wide array of fields to solve real-world problems by building large, complex models. Many researchers have made significant contributions by developing predictive models for software defects using statistical and machine-learning approaches. But only a few frameworks have discussed the issue of building a universal software defect prediction model. Most existing models have been trained on limited datasets, which results in good performance on the training data but poor performance on unseen data. These limitations have motivated researchers to explore and develop more generalised and universal models for software defect prediction. Moreover, the growing complexity of contemporary software systems. Such limitations have encouraged researchers to investigate and build more generalised and universal models for software defect prediction. In addition, the increasing complexity of modern software systems and the rapid growth of software repositories have driven a demand for intelligent prediction techniques capable of handling heterogeneous data. Research is being conducted to investigate advanced machine learning and deep learning methods, including ensemble learning and transfer learning, to enhance prediction accuracy and adaptability across different software projects. These approaches aim to reduce development and maintenance costs and increase the overall reliability and performance of software products.
- [E127115060526](https://www.ijitee.org/portfolio-item/e127115060526/): Industrial process control has relied on proprietary DCS for over five decades. The model worked. But working and working well are different things, and in 2026, the gap between what proprietary DCS delivers and what operators in manufacturing, oil and gas, chemical processing, and water treatment need has grown wide enough to drive real change. Vendor lock-in, hardware obsolescence that stretches across decades, and maintenance contracts that give a single supplier complete leverage over upgrade decisions are no longer tolerable when the alternative, open and software-defined control, has been proven at an industrial scale. The Open Process Automation Standard (O-PAS), the IEC 61499 function block model, and OPC UA connectivity together provide the technical foundation for software-defined control systems (SDCS) that break these dependencies. Documented lifecycle cost savings reach approximately 52% over twenty-five years when compared to equivalent proprietary DCS platforms . This paper reviews the standards, examines real deployments, and confronts the barriers that still slow adoption, particularly the near-complete absence of IT-domain competencies in OT workforces. An original Software Defined Automation Risk Mapping Model (SD-ARMM) is proposed, providing practitioners with a five-dimensional, risk driven tool to determine which migration strategy fits their specific organisational and operational reality.
- [E126515060526](https://www.ijitee.org/portfolio-item/e126515060526/): The study evaluates the return on investment (ROI) benefits of transitioning from a traditional reactive construction workflow to a proactive hybrid geotechnical resilience workflow for high-density urban infrastructure projects. This paper addressed the complexities often seen in dense-urban environments characterised by high-moisture basins, hydraulic instability, and substructure compromised by environmental unpredictability, often leading to systemic delays, soil collapse, and material wastage. These complexities are addressed by implementing lean construction principles that focus on mitigating Muda (waste), Mura (unevenness), and Muri (overburden), and on enhancing the project’s predictability and structural safety. The lean strategies to overcome the structural difficulties involved establishing a responsive feedback loop. Firstly, by achieving precise excavation using hydraulic machinery with 3D-GPS guidance kits synchronised with Digital Terrain Modelling (DTM). This helped eliminate the 10% standard manual over-dig typically encountered in traditional depth control. Thereby optimising excavation volumes and reducing redundant soil hauling. Secondly, a real-time monitoring network comprising vibrating-wire piezometers and inclinometers was used to monitor pore-water pressure and soil displacement. This sensor-driven approach enabled a Jidoka (built-in quality) protocol, in which automated alerts for pressure spikes triggered immediate stabilisation measures that helped prevent catastrophic failures that historically stall urban developments. In the study, a comparative performance analysis of a traditional workflow and a lean-integrated workflow demonstrates that the proactive lean-integrated workflow results in a quantifiable reduction in the construction timeline and labour volatility. Specifically, the excavation and the shoring durations were reduced by up to 40% through data-driven execution and Target Value Design (TVD). The findings validate that incorporating digital intelligence during the substructure phases helps achieve a net fiscal recovery of over ₹2.17 crores by preventing rework and resource wastage. By providing a scalable model for geotechnical resilience, this study helps optimise operations and improve ROI for projects in complex urban settings.
- [E125515060526](https://www.ijitee.org/portfolio-item/e125515060526/): Eye diseases are becoming common in day-to-day life and affecting all age groups of people. The ratio of ophthalmologists to patients suggests the need for an automated technique to detect eye diseases. Conjunctivitis is of many types, including adenoviral conjunctivitis, ocular drug toxic conjunctivitis, pollen-allergic conjunctivitis, bacterial conjunctivitis, and many others. Conjunctivitis can be automatically detected using conventional image processing techniques, but with lower accuracy and precision, and more computational time is required compared to deep learning and AI techniques. This paper presents a novel deep-learning-assisted segmentation technique for the automatic detection of conjunctivitis that overcomes the limitations of conventional methods. The proposed method uses Attention-U-Net++ with Transformer Encoder (Global Context), Swin / ViT CNN +, Transformer Monte-Carlo Dropout Layer Enabled at inference time, Uncertainty-aware and Segmentation Mask + Uncertainty Map, which provides better results with Accuracy= 90.3%, sensitivity= 0.87, specificity= 0.93, precision=0.88, recall=0.87, F1-Score= 0.89, ROC-Auc=0.96.
- [E125015050426](https://www.ijitee.org/portfolio-item/e125015050426/): Open bore wells pose significant safety risks, especially to children, with many incidents leading to life threatening situations from accidental falls. Traditional rescue methods are often slow, complex, and lack real-time monitoring, which delays decisions and increases danger to victims. To address these issues, this research introduces a smart, cloud-based rescue management system to improve the efficiency and effectiveness of borehole rescues. The system uses sensor-based monitoring and IoT technology for rapid detection and response. A Passive Infrared (PIR) sensor detects movement and confirms the presence of a trapped person, while a gas sensor monitors for hazardous gases. When a victim is detected, a NodeMCU microcontroller processes the data and automatically activates an air pump to maintain safe oxygen levels. A robotic arm with a mechanical gripper assists in the physical rescue. All components connect to a cloud-based IoT platform, enabling real-time data transmission, remote monitoring, and coordinated control by emergency teams. This connectivity improves situational awareness and supports faster, more informed decisions during rescues. The study shows that integrating sensor technology, automation, and cloud-based communication can reduce response times and increase rescue success rates. This research aims to provide a safer, more reliable, and more advanced solution for borewell rescues, ultimately reducing fatalities and improving emergency response outcomes.
- [E124615050426](https://www.ijitee.org/portfolio-item/e124615050426/): Purpose: The rapid escalation of ransomware and zero-day malware attacks poses a significant challenge to conventional signature-based detection systems, which cannot generalise to previously unseen threats. This study aims to develop a robust, scalable, and behaviour-aware malware-detection framework capable of accurately identifying ransomware and zero-day attacks across heterogeneous computing environments. Design/methodology/approach: A novel multi-stage hybrid detection pipeline is proposed that integrates advanced feature selection, deep sequential learning, attention mechanisms, and ensemble classification. Initially, irrelevant and redundant features are eliminated using correlation thresholding, Chi-square analysis, mutual information, and variance-based ranking. To capture latent behavioral patterns, a hybrid Gated Recurrent Unit Temporal Convolutional Network (GRU-TCN) architecture is employed to model long- and short-term temporal dependencies. These representations are further refined using squeeze-and excitation attention-enhanced TCN blocks. Finally, an XG-Fusion framework that combines GRU encoding, dilated residual TCNs, attention-based feature fusion, and focal loss optimisation is introduced to address class imbalance, with XGBoost serving as a meta-classifier for final decision-making. Findings: Experimental evaluations conducted on multiple benchmark datasets demonstrate that the proposed framework consistently outperforms traditional machine learning and baseline deep learning models. Superior performance is achieved in terms of accuracy, precision, recall, F1 Score, and ROC AUC. The hierarchical and attention-driven architecture effectively abstracts malicious behavioral patterns and enhances generalization to previously unseen malware variants. Originality: This work introduces a novel multi-stage hybrid deep learning architecture that synergistically combines sequential behavioural modelling, attention-enhanced feature learning, and ensemble-based classification. The proposed approach offers a forward-looking and reliable solution for proactive detection of ransomware and zero-day malware threats.
- [D475715040426](https://www.ijitee.org/portfolio-item/d475715040426/): The application of Infrastructure as Code (IaC) has enhanced cloud environment scalability and automation, but configuration drift and security misconfigurations remain critical operational and security issues. Current drift detection and remediation solutions rely largely on reactive, rules-based, and human intervention; therefore, they are ineffective in dynamic, multi-cloud environments. This research aims to develop and deploy a self-healing infrastructure architecture that autonomously identifies and recovers from configuration drift and security misconfigurations in real time. The paper suggests the following to accomplish this: a new multi-agent architecture based on Large Language Models (LLMs), in which Drift detectors, security reasoners, root-cause analysers, remediation generators, and post-remediation validators operate within a closed-loop pipeline. To evaluate the framework, a publicly available IaC dataset (written in Terraform) of simulated drift situations is used. According to experimental results, the proposed LLM-agent system outperforms rule-based and semi automated systems, with a drift detection rate of 96.8, a security misconfiguration detection rate of 95.2, and a mean time to remediation of 6.9 minutes. The framework is also very effective in reducing false positives and manual intervention, as well as getting high policy compliance. Such findings affirm the usefulness of autonomous LLM agents in empowering proactive, intelligent and scalable self-healing infrastructure management in contemporary cloud systems.
- [E124215050426](https://www.ijitee.org/portfolio-item/e124215050426/): A fundamental component of digitalisation solutions that have garnered significant attention in the digital sphere is machine learning, which is primarily an area of artificial intelligence. The author’s goal in this study is to provide a concise overview of the most widely utilised machine learning algorithms for this purpose. To assist in selecting the best learning algorithm to meet the application’s specific needs, the author aims to highlight the advantages and limitations of machine learning algorithms from the standpoint of their application. This paper provides a brief overview and outlook on the various uses of machine learning techniques.
- [A834415010526](https://www.ijitee.org/portfolio-item/a834415010526/): Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder in women, and it is difficult to diagnose at an early stage due to varying symptoms and limitations of traditional diagnostic methods. Early detection of PCOS is important to prevent long-term health complications. In this study, a hybrid machine learning model is proposed for PCOS detection using clinical and hormonal data. A dataset containing 541 patient records was used for analysis. Missing values were imputed using K-Nearest Neighbour (KNN), and the most relevant features were selected using Mutual Information. To address class imbalance, SMOTE was applied to the training data. Individual machine learning models were first evaluated, and based on their performance, a hybrid model was developed using a weighted soft-voting approach that combines Gaussian Naïve Bayes, Logistic Regression, and Random Forest. The experimental results suggest that the hybrid model strikes a better balance between accuracy, precision, and recall than any single model on its own. This makes it a more trustworthy approach for predicting PCOS. This project marks just the first phase of research, laying the groundwork for future studies that will combine these findings with ultrasound image analysis.
- [C122215030226](https://www.ijitee.org/portfolio-item/c122215030226/): The study of pedestrian operational performance at crosswalks is essential for enhancing pedestrian safety, streamlining traffic, and boosting overall urban mobility. Five points were selected in Hyderabad where the road intersection has a signal and a pedestrian crosswalk. The pedestrian crosswalk footage was recorded between 8 a.m. and 10 a.m. and between 4 p.m. and 6 p.m. The data was extracted from the same footage and used to determine the number of different vehicles and pedestrians using the crosswalk. The key parameters chosen for study are the number of pedestrians, their gender, walking speed, and the time they wait at the crossing. It is noted that the number of persons walking and their walking speeds varied in that selected time period. The purpose of the study is to determine how improvements, such as modifying traffic light timings or improving pedestrian-friendly crossings, affect the safety and comfort of walking for people. This study will help improve roadways and crossings, making them safer and more pleasant for pedestrians. This implies that roads and crossings must be planned to facilitate safe walking. Therefore, policymakers and urban planners can create safer, more accessible urban settings by designing more effective, pedestrian-friendly infrastructure informed by an understanding of pedestrian behaviour, crossing patterns, and interactions with automobiles.
- [D474915040426](https://www.ijitee.org/portfolio-item/d474915040426/): The increased use of Electronic Health Records (EHRs) has increased access to patient information in medical facilities, but has also raised long-term concerns regarding the security, confidentiality, and partial control of records. In most work environments, medical information is scattered across hospitals, laboratories, and clinics, hindering transparency and making its sharing and analysis extremely difficult. Most existing systems operate on centralised architectures that are not as hard to manage but can be undermined by data breaches, unauthorised access, and single points of failure. To address these shortcomings, this paper presents a management and prediction model for medical reports that integrates blockchain technology, decentralised storage, and machine learning-based analytics. A permissioned consortium blockchain is responsible for metadata, ownership, and access control of large medical files stored off-chain in the InterPlanetary File System (IPFS) to maximise scalability and efficiency. Anonymised and aggregated data have been analysed using machine learning models to enable predictive analysis without exposing sensitive patient data. The proposed system was tested in controlled experimental scenarios using a simulated healthcare dataset. The results demonstrate improved data integrity, clearer control over access, and greater storage efficiency compared to conventional centralised approaches. Although certain scalability, data availability, and real-world application issues remain, the findings demonstrate that the recommended architecture provides a viable and secure foundation for patient-centred healthcare data management and predictive support.
- [D123015040326](https://www.ijitee.org/portfolio-item/d123015040326/): Artificial intelligence (AI) has proven to be an asset in reducing human intervention in predicting and decision-making for many applications. Electrification through the addition of both electric vehicles (EVs) and charging stations is resulting in several core challenges, including improving utilisation, increasing availability, and reducing charging times. This paper aims to develop concepts that AI can be trained on to enable applications such as predicting battery life, making accurate charging-time predictions, and identifying the cheapest available charging options for EV owners. Additionally, a model was proposed to predict with available variables to provide major stakeholders, such as customers with EV purchasing timeframes, businesses with suitable chargers, governments for policy updates, and other stakeholders for carbon footprint reduction measures. Although this concept limits data collection from both manufacturers and customers, relaxing government policies to allow data access may lead to improved AI models. Upgrading charging equipment to enable data collection on customer charger utilisation is a challenge from both manufacturers’ and users’ perspectives. Users become conscious of their data privacy while sharing information about their vehicles and charging frequencies. Manufacturers become more conscious of their data privacy when sharing typical battery and other equipment characteristics curves, which are more confidential, to ensure they are not readily available to competitors. The holistic conceptual model developed in this paper served as the basis for AI training. The model offers significant opportunities to learn from other published predictive techniques and data analysis methods for critical infrastructure, thereby increasing the safety, quality, and reliability of electrical power.
- [F833614060326](https://www.ijitee.org/portfolio-item/f833614060326/): The paper proposes a practical, scalable, and non-intrusive system for the automatic detection of tool wear under real industrial conditions that does not require process metadata (e.g., spindle speed, feed rate) or specialised equipment. The proposed method is based solely on the analysis of triaxial vibration signals and combines multiple signal-processing methods (time, frequency, and time-frequency) to enable deeper analysis. Various time-domain features, such as RMS, standard deviation, kurtosis, and crest factor, are combined with spectral analysis via Welch power spectral density (PSD) estimation and the continuous wavelet transform (CWT) for time-frequency analysis. To make features comparable across machines and operating conditions, the features are combined using median statistics and normalised relative to the median (Δ%). Experimental validation was performed with multiple machines and measurement axes on real industrial datasets that were heavily imbalanced. It was shown that there is a direct and consistent correlation between the condition of worn tools and the overall increase in tool vibration energy, as evidenced by significantly higher RMS and standard deviation values. On the other hand, higher-order statistical measures, such as kurtosis and crest factor, were less consistent when used alone due to their sensitivity to operational variability. Frequency domain analysis showed that the wear of the tool could not only be described by a general rise in energy, but also by the significant increase of certain frequency components in the spectrum. Most notably, the peaks centred at 200 Hz were found to be significantly raised, along with their harmonics at around 600 Hz and 800 Hz, when the tool was worn, and this was true for all the axes of the measurements. These frequencies can thus be considered reliable and repeatable indicators of tool wear. The continuous wavelet transforms (CWT), a complementary method to time-localised time-frequency representations, confirmed the results and, notably, revealed that the signal’s high-energy bursts are time-recurrent and running; thus, they can be seen as a series of bright spots closely packed in time in the scalogram. The strength of the proposal is its rational and transparent integration of several well-known signal-processing methods into a single, coherent concept that encompasses the full spectrum of changes, from the global increase in vibration energy to the localised, frequency-specific excitations that are the hallmark of wear progression. By combining global energy indicators with spectral and time frequency features, the approach enhances diagnostic reliability while improving physical understanding of tool-wear mechanisms. It is robust, practical, and readily transferable, offering strong potential for scalable predictive maintenance applications in machining environments.
- [C122715030226](https://www.ijitee.org/portfolio-item/c122715030226/): Rolling element bearings are fundamental parts of rotating machinery, and their sudden breakdown may cause abnormal vibrations, an unplanned production halt, and higher maintenance costs. To address this problem, this paper presents a hybrid data-driven approach for the automatic diagnosis and classification of bearing faults using vibration signals from the IMS dataset. The proposed method first divides the raw vibration signals into fixed-length windows and computes seven statistical features to characterise the signals. An unsupervised Isolation Forest technique is then used to detect anomalous signal segments, providing an early warning of potential faults. Thus, it does not require prior knowledge of the normal condition for fault detection. Subsequently, the detected anomalies are classified using two ensemble-based supervised learning models: Extra Trees and XGBoost. The experimental results indicate that the tree-based ensemble classifiers outperform the other models tested. Specifically, XGBoost achieves an F1 score of 97. 41%, whereas Extra Trees achieves an F1 score of nearly 97%, indicating its high potential for accurately detecting different types of bearing faults. The results confirm that the proposed hybrid two-stage model is an efficient and reliable tool for bearing fault diagnosis, supporting early fault detection, and enhancing system reliability in industrial settings.
- [A371016010326](https://www.ijitee.org/portfolio-item/a371016010326/): We have witnessed a significant amount of fraud and security issues in modern life. Numerous biometric characteristics, such as the eyes, face, fingers, and palms, are used to address these problems. Among these, facial recognition is considered one of the least intrusive methods and is frequently used to identify or verify an individual. Face recognition is one of the most effective applications of computer vision, and has achieved considerable attention in recent years. Deep learning networks have achieved state-of-the-art performance in still-image-based face recognition. Video-based face recognition is a more complex task than still-image-based face recognition due to video quality, pose variation, occlusion, and illumination, and it also entails processing a large volume of data. We address these challenges by developing an efficient deep learning model trained, tested, and evaluated on the YouTube Face Dataset, designed for unconstrained face recognition in videos. In this paper, a deep learning face detection algorithm, Multi-task Cascaded Convolutional Neural Network (MTCNN), is employed to detect and localise faces in videos. Feature extraction and face recognition have been performed by using a convolutional neural network (CNN). This model has been proposed for accurate face detection and recognition from unconstrained video and performs better on the YouTube face dataset. The test accuracy of the proposed model is 93.11%. This work has been conducted to improve face recognition accuracy in the presence of intra-video variations.
- [E3247038519](https://www.ijitee.org/portfolio-item/e3247038519/): Dynamic Stress-strain Compressive Response of Soft Tissue using Polymeric split- Hopkinson Pressure Bar
Somnath H. Kadhane1, Hemant N. Warhatkar2
1Somnath H. Kadhane, Mechanical Engineering Department, DBATU, Lonere - 402103, India.
2Hemant N. Warhatkar, Mechanical Engineering Department, DBATU, Lonere - 402103, India. Manuscript received on 26 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 341-347 | Volume-8 Issue-9, July 2019 | Retrieval Number: E3247038519/19©BEIESP | DOI: 10.35940/ijitee.E3247.078919 Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting © The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The characterization of soft tissues subjected to higher compressive strain rates is of increasing importance as the material properties of soft tissues were commonly used in impact applications such as automotive safety and crashworthiness for biofidible numerical modeling of the human body. Due to less availability of stain rate dependent data in the open literature and various challenges in the characterisation of soft tissues under impacts, need were felt for characterisation of soft tissues at higher strain rates. The purpose of this study is to investigate the dynamic mechanical behavior of soft tissues at varying compressive strain-rates. The viscoelastic split Hopkinson pressure bar (SHPB) is designed and developed to predict the high rate compressive behavior of soft tissues. The primary benefit in using viscoelastic bars is the reduced bar impedance allowing for high-quality measurements of the transmitted and reflected stress wave signals. The dynamic behavior of goat muscles has been measured at higher strain rates ~500-1200 s-1 using a polymeric SHPB apparatus. The dynamic stress-strain response of muscle tissue exhibits non-linear behavior under compressive loadings and is strain-rate dependent.
Index Terms: Strain Rate, Mechanical Behavior, SHPB, Soft Tissue
Scope of the Article: Mechanical Engineering Download PDF
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