Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Journal of Electronics, Electromedical Engineering, and Medical Informatics
Shinde, Shilpa
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Energy Conservation Clustering through Agent Nodes and Clusters (EECANC) for Wearable Health Monitoring and Smart Building Automation in Smart Hospitals using Wireless Sensor Networks Mirkar, Sulalah Qais; Shinde, Shilpa
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1082

Abstract

Wireless Sensor Networks (WSNs) play a vital role in enabling real-time patient monitoring, medical device tracking, and automated management of building operations in smart hospitals. Wearable health sensors and hospital automation systems produce a constant flow of data, resulting in elevated energy usage and network congestion. This study introduces an advanced framework named Energy Conservation via Clustering by Agent Nodes and Clusters (EECANC), designed to improve energy efficiency, extend the network's longevity, and facilitate smart building automation in hospitals. The EECANC protocol amalgamates wearable medical monitoring (oxygen saturation, body temperature, heart rate, and motion tracking) with intelligent hospital building automation (HVAC regulation, lighting management, and security surveillance) through a hierarchical Wireless Sensor Network-based clustering system. By reducing routing and data redundancy, cluster heads (CHs) and agent nodes (ANs) reduce redundant transmissions and extend the life of sensor batteries. EECANC limits direct interaction with the hospital's Smart Building Management System, thereby reducing emergency response times and improving energy efficiency throughout the hospital. The efficiency of EECANC was proven by comparing its performance with other existing clustering protocols, including EECAS, ECRRS, EA-DB-CRP, and IEE-LEACH. The protocol achieved a successful packet delivery rate of 83.33% to the base station, exceeding the performance of EECAS (83.33%), ECRRS (48.45%), EA-DB-CRP (54.37%), and IEE-LEACH (59.13%). The system demonstrated better energy utilization, resulting in a longer network longevity and lower transmission costs especially during high-traffic medical events. It is clear from the first and last node death rates that EECANC is the most energy-efficient protocol, significantly better than the other methods available. The EECANC model supports hospital automation, enhances patient safety, and promotes sustainability, providing a cost-effective and energy-efficient solution for future smart healthcare facilities
Privacy-preserving fitness recommendation system using modified seagull monarch butterfly optimized deep learning model Gupta, Esmita; Shinde, Shilpa
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp393-404

Abstract

This paper presents a novel modified seagull monarch butterfly optimization (MSMBO) algorithm, with a multi-objective focus on privacy and personalization in the fitness recommender system using a refined three-tier deep learning structure. The method is divided into three phases. In the first phase, fitness data from wearable devices undergoes preprocessing to eliminate noise and standardize features. The second phase incorporates improved elliptic curve cryptography (IECC) alongside the MSMBO to encrypt user data securely, ensuring privacy in cloud storage. This phase also enhances neural network performance by optimizing weights and hyperparameters through feature selection, effectively reducing data complexity while boosting accuracy. In the third phase, ConvCaps extracts spatial data features, while Bi-LSTM identifies temporal dependencies. The proposed system balances multiple objectives like novelty, accuracy, and precision, while safeguarding user data through robust encryption. With the experimental findings, our suggested method performs better than current existing models, especially in heart rate prediction and fitness pattern identification. The overall outcome makes the system ideal for privacyconscious, personalized fitness recommendations. The model’s shows significant improvement in mean squared error (MSE), normalized mean squared error (NMSE), and mean absolute percentage error (MAPE), thus verifying its effectiveness in secure, real-time fitness tracking.
Co-Authors Gupta, Esmita Mirkar, Sulalah Qais