ISSN2320-2882
Impact Factor9.17
UGC ParametersAligned
Low Cost₹1501 / $59
CrossRefDOI
ESTD2013
Peer ReviewTransparent
AccessOpen
Submission: Always Open
Paper Title
HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING
Authors
SWATHI R , BHUVANESWARI G , ROSHNA PARVEEN J , Dr. P. Shanmuga Priya
Keywords
Multimodal Deep Learning, YOLOv8, MobileNetV2, MFCC, Threat Detection, Public Safety
Abstract
Traditional healthcare and supply chain systems operate independently and rely heavily on manual processes, resulting in poor coordination, limited visibility, delayed decision- making, and inefficient resource utilization. In the healthcare sector, this leads to problems such as appointment congestion, lack of remote consultation facilities, delayed access to medicines, and inefficient management of medical records and supplies. Existing systems also lack intelligent support for predicting demand, managing inventory, and monitoring service usage patterns.
To address these challenges, this project proposes an integrated digital platform that combines a telemedicine system with an AI- enabled digital supply chain framework. The telemedicine module allows patients to book appointments, consult doctors remotely, receive digital prescriptions, and access medical reports through an online platform. At the same time, artificial intelligence techniques are applied to analyze healthcare usage data, appointment trends, and medicine demand to support better planning and decision-making. AI-based analysis helps in predicting service demand, identifying resource shortages, and improving the availability of medical supplies and services.
Compared to the existing system, the proposed solution reduces manual workload, improves accessibility to healthcare services, enhances decision accuracy, and enables efficient coordination between healthcare delivery and supply chain operations. The project demonstrates how integrating telemedicine with AI-driven digital supply chain concepts can improve healthcare accessibility, operational efficiency, and overall system reliability.
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IJCRT — journal style
"HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 5, page no.f720-f724, May 2026, Available :https://ijcrt.org/papers/IJCRT2605652.pdf
APA — 7th edition
R, S., G, B., J, R. P., & Priya, P. S. (2026). HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING. International Journal of Creative Research Thoughts, 14(5), f720-f724. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605652
MLA — 9th edition
R, SWATHI, et al. "HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING." International Journal of Creative Research Thoughts, vol. 14, no. 5, 2026, pp. f720-f724, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605652.
Chicago — 17th, bibliography
R, SWATHI, et al. "HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING." International Journal of Creative Research Thoughts 14, no. 5 (2026): f720-f724. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605652.
Harvard — author–date
R, S. et al. (2026) 'HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING', International Journal of Creative Research Thoughts, 14(5), pp. f720-f724. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605652
IEEE — numbered reference
S. R, B. G, R. P. J and P. S. Priya, "HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING," IJCRT, vol. 14, no. 5, pp. f720-f724, May. 2026.
Vancouver — biomedical
R S, G B, J RP, Priya PS. HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING. IJCRT. 2026 May;14(5):f720-f724.
AMA — 11th edition
R S, G B, J RP, Priya PS. HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING. IJCRT. 2026;14(5):f720-f724. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605652
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2605652,
author = {SWATHI R and BHUVANESWARI G and ROSHNA PARVEEN J and P. Shanmuga Priya},
title = {HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {5},
pages = {f720-f724},
year = {2026},
month = {May},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605652}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - R, SWATHI
AU - G, BHUVANESWARI
AU - J, ROSHNA PARVEEN
AU - Priya, P. Shanmuga
TI - HOLISTIC THREAT PREDICTION IN PUBLIC SPACES USING MULTIMODEL DEEP LEARNING
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 5
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605652
SP - f720-f724
ER -
Issue
Volume 14 Issue 5,
May-2026
Pages : f720-f724
Article Information
- Pages
- f720-f724
- Published
- May 2026
- Registration ID
- IJCRT_304506
- Published Paper ID
- IJCRT2605652
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- TIRUCHIRAPPALLI, Tamil Nadu, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 142
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2605652
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2605652.pdf
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International Journal of Creative Research Thoughts
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2320-2882
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