International Journal of Engineering Applied Science and Management

ISSN (Online): 2582-6948


Notice Board :

Call for Paper
Vol. 6 Issue 11

Submission Start Date:
Nov 01, 2025

Acceptence Notification Start:
Nov 10, 2025

Submission End:
Nov 25, 2025

Final MenuScript Due:
Nov 30, 2025

Publication Date:
Nov 30, 2025
                         Notice Board: Call for PaperVol. 6 Issue 11      Submission Start Date: Nov 01, 2025      Acceptence Notification Start: Nov 10, 2025      Submission End: Nov 25, 2025      Final MenuScript Due: Nov 30, 2025      Publication Date: Nov 30, 2025




Volume VI Issue XI

Paper Title
Stability Improvement in Hybrid Renewable Microgrid Using Intelligent STATCOM Control
Author Name
Alkesh Kumar Harode, Rahul Singh
Year Of Publication
2025
Volume and Issue
Volume 6 Issue 11
Abstract
The integration of solar photovoltaic (PV) and wind energy systems provides an eco-friendly solution for power generation, but their outputs are highly variable due to changing weather conditions. These fluctuations cause instability in voltage and reactive power in hybrid microgrids. To address these issues, this study proposes a Solar–Wind Hybrid Microgrid model incorporating a Static Synchronous Compensator (STATCOM) for dynamic reactive power compensation and voltage regulation. A Fuzzy Logic Controller (FLC) with 49 rules replaces the conventional Proportional–Integral (PI) controller to enhance the nonlinear control performance of STATCOM. The hybrid system consists of a 1.5 MW Doubly Fed Induction Generator (DFIG)-based wind turbine, a 0.1 MW solar PV system, and a 3 MVAR STATCOM connected at the point of common coupling (PCC). Simulation results in MATLAB/Simulink demonstrate that the fuzzy-controlled STATCOM significantly improves voltage stability and reduces bus voltage fluc
PaperID
2025/IJEASM/11/2025/3262

Paper Title
Federated Learning Models for Privacy-Preserving Healthcare Analytics
Author Name
K. Prasanth Kumar, N.Geetanjali
Year Of Publication
2025
Volume and Issue
Volume 6 Issue 11
Abstract
The proliferation of electronic health records (EHRs) and medical Internet of Things (IoT) data presents an unprecedented opportunity to advance healthcare through data-driven analytics, particularly with deep learning models. However, the sensitive nature of health data, coupled with stringent privacy regulations like HIPAA and GDPR, often isolates data in siloed institutions, creating a significant barrier to developing robust, generalized models. Federated Learning (FL) has emerged as a promising decentralized machine learning paradigm that enables model training across multiple data sources without sharing the raw data. This paper explores the application of FL in the healthcare domain, focusing on its role in preserving patient privacy. We provide a comprehensive literature survey of the current state-of-the-art. The core of this work involves a detailed methodology discussing six prominent federated learning models: Federated Averaging (FedAvg), Federated Averaging with Secure Ag
PaperID
2025/IJEASM/11/2025/3264

Paper Title
Federated Learning Models for Privacy-Preserving Healthcare Analytics
Author Name
Kaleemulla, Prakash Reddy. V
Year Of Publication
2025
Volume and Issue
Volume 6 Issue 11
Abstract
Emerging Markets (EMs) offer substantial growth opportunities but are simultaneously characterized by high financial volatility, institutional complexity, and structural risks inherent to developing economies. This paper analyzes the effectiveness of integrated risk governance structures in India, specifically examining the operational Financial Risk Management (FRM) frameworks of the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE)—collectively referred to as National Stock Exchanges (NSC)—and their alignment with Strategic Planning (SP) objectives, assessed through the lens of the Balanced Scorecard (BSC) framework. Utilizing secondary data from academic journals, SEBI, RBI, and market statistics spanning 2015 to 2025, the study adopts a descriptive and analytical approach. Findings confirm that Indian Market Infrastructure Institutions (MIIs) maintain robust, internationally compliant operational FRM systems, highlighted by dynamic margin regimes (SPAN, Extreme Loss Mar
PaperID
2025/IJEASM/11/2025/3265

Paper Title
A Robust Deep Learning Framework for Vehicle Speed Estimation and Intelligent Traffic Monitoring
Author Name
Mohit Tiwari, Vikas Sakalle
Year Of Publication
2025
Volume and Issue
Volume 6 Issue 11
Abstract
This work presents a deep learning framework designed to improve vehicle speed estimation and traffic monitoring in real-world environments. The system brings together a modern one stage detector, a reliable tracking module, automatic camera calibration and a hybrid speed estimation strategy. The goal is to offer a solution that works well across challenging situations such as occlusion, poor lighting and varied camera viewpoints. Recent results show that the proposed pipeline improves detection accuracy from about 85 percent to more than 93 percent when compared with earlier YOLO based detectors. Tracking also benefits from the updated design, with ByteTrack and BoTSORT reducing identity switches by almost half. The final speed estimation module produces an average error as low as 1.8 km/h when geometry based estimates are fused with optical flow. These improvements lead to a system that is both efficient and reliable for intelligent traffic monitoring. The framework can support enfor
PaperID
2025/IJEASM/11/2025/3267