Notice Board :

Call for Paper
Vol. 7 Issue 5

Submission Start Date:
May 01, 2026

Acceptence Notification Start:
May 10, 2026

Submission End:
May 25, 2026

Final MenuScript Due:
May 31, 2026

Publication Date:
May 31, 2026
                         Notice Board: Call for PaperVol. 7 Issue 5      Submission Start Date: May 01, 2026      Acceptence Notification Start: May 10, 2026      Submission End: May 25, 2026      Final MenuScript Due: May 31, 2026      Publication Date: May 31, 2026




Volume VII Issue III

Author Name
Anil Tiwari, Trilok Singh, Suresh A. Shan
Year Of Publication
2026
Volume and Issue
Volume 7 Issue 3
Abstract
This research explores the role of Agentic Artificial Intelligence (Agentic AI) in enhancing Cybersecurity Governance, Risk, and Compliance (GRC) within telecom and enterprise systems. Agentic AI is characterized by autonomous decision-making, continuous learning, contextual reasoning, and goal-oriented task execution that offers transformative potential for real-time risk assessment, automated compliance enforcement, predictive threat mitigation, and intelligent security orchestration. The study examines how Agentic AI can integrate with existing cybersecurity architectures, security operations centers (SOCs), and regulatory frameworks to enable adaptive governance, proactive risk management, and continuous compliance assurance. Using a qualitative research approach grounded in a systematic literature review and documented industry case studies from telecom operators and large enterprises. The analysis highlights improvements in automated risk scoring, real-time policy enforcement, re
PaperID
2026/IJEASM/3/2026/3343

Author Name
Nitin Narayan Wandre, Bhairo Singh, Rajesh Ahirwal
Year Of Publication
2026
Volume and Issue
Volume 7 Issue 3
Abstract
Bolted flange joints are critical components in piping and pressure vessel systems, where maintaining joint integrity and preventing leakage are primary concerns. While the strength of metallic joints has been well studied since the 1920s, leakage analysis remains a challenging issue due to the complex interactions between bolt load, internal pressure, flange stiffness, and gasket material properties. Gaskets play a crucial role in sealing performance, yet their nonlinear behavior and susceptibility to permanent deformation add complexity to joint analysis. Flange rotation and varying contact stresses further influence leakage, necessitating rigorous evaluation methods. This study presents a finite element (FE) model for analyzing gasket contact stresses under different loading conditions. The model accounts for variations in compression due to flange rotation and examines the sealing performance of different gasket materials. The findings provide insights into optimizing bolted flange
PaperID
2026/IJEASM/3/2026/3345

Author Name
V. Ramanujam, S. Muralitharan
Year Of Publication
2026
Volume and Issue
Volume 7 Issue 3
Abstract
The study examines a quantitative cross sectional research design to investigate the underlying dimensions of digital financial adoption. A structured 60 item questionnaire with a five-point Likert Scale was used to collect the data from 270 respondents. The technology Acceptance Model is used as the foundation for the instrument development, and which was expanded to incorporate, social inclusion and environmental sustainability perspective. Exploratory Factor Analysis was conducted to determine the latent factor structure using Principal Component Analysis with Varimax rotation, Bartlett’s Test of Sphericity (<0.001) and a KMO value of 0.980 indicates the outstanding data suitability. Five different factors with eigen value larger than 1.0 were extracted, collectively accounted for 23.32 of the total variances. These factors include perceived usefulness, trust, and convenience, digital financial knowledge and technical competence, social & financial inclusion results, environmental s
PaperID
2026/IJEASM/3/2026/6686

Author Name
Rozina Naz, Nidhi Tijare, Shruti Lanjewar
Year Of Publication
2026
Volume and Issue
Volume 7 Issue 3
Abstract
As employee attrition become a serious problem for companies as it affects productivity, disrupts work, and increases hiring costs. To come out of this problem, this paper brings a machine learning – based system which can predicts employee attrition using HR data. This system analyses historical employee records to identify trends such as is employee is willing to leave the company or voluntary resignation. The dataset should be clean and organized before applying the machine learning model, this also contain the algorithm such as logistic regression and random forest. This system works better than the traditional system as it predicts the risk of employee who is going to leave. performs better than traditional methods. This helps HR teams in planning effective retention strategies
PaperID
P.97