Vol 13 Issue 4 October 2025-December 2025

Abstract: Pawn broking is one of the oldest and most stable financial services in Sri Lanka, offering short-term, collateral-backed credit primarily against gold jewellery. Despite its resilience, the sector faces growing uncertainty driven by fluctuations in gold prices, changing interest rate environments, and inconsistent repayment behaviours. These factors increase operational risk and make accurate forecasting difficult, especially for small and medium pawn-broking businesses that rely heavily on manual or semi-digital systems. This study introduces an Excel-based Decision Support Model (DSM) designed to simulate loan portfolio behaviour, assess exposure to price and rate volatility, and support more systematic decision-making. By integrating automated calculations, scenario analysis, and risk visualization tools, the DSM enhances the ability of pawn brokers to anticipate financial outcomes.

Keywords: Pawn broking, Decision Support System, Gold price volatility, Interest rate risk, Excel modelling.

Title: An Excel-Based Decision Support Model for Pawn Broking Businesses: A Case Study in Sri Lanka

Author: N.D.Gunasekara, M.A.C.N.Perera

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 13, Issue 4, October 2025 - December 2025

Page No: 31-40

Research Publish Journals

Website: www.researchpublish.com

Published Date: 13-December-2025

Abstract: The implementation of a security diagnostic tool on computer systems involves preparing the system, choosing the right tool, running the scan and analyzing the results. The major challenge involves reduced performance, downtime or even data loss which leads to reduced performance of a system. The methodology adopted included setting up the required software and hardware environment, installing and configuring the diagnostic tool, and testing its performance under different load conditions. Real-time data was collected, analyzed, and presented in graphical and textual formats, while alerts were triggered whenever thresholds were exceeded. This project highlights the importance of implementing security diagnostic tools as a proactive approach to system management. It recommends that future work should enhance such tools with advanced visualization features, remote alerting options, and broader applicability across enterprise environments.

Keywords: security diagnostic, computer systems, hardware environment, security diagnostic tools, enterprise environments.

Title: IMPLEMENTATION OF A SECURITY DIAGNOSTIC TOOL ON COMPUTER SYSTEMS

Author: Joda S.C, Olokode S.M, Olubo H.O, Bamidele A.D, Jinadu R.A, Felemu O.D.

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 13, Issue 4, October 2025 - December 2025

Page No: 23-30

Research Publish Journals

Website: www.researchpublish.com

Published Date: 04-November-2025

Abstract: Innovation one of the most valuable aspect which the word is focusing on, with it we can create a simple life, logic and easy protecting our lives as much as possible, the aim is well defined, we are focusing on the creation of smart devices which they can handle all our routine tasks.

In this research paper, we are going to focus on one of the most useful objects, which more than 1 billion people use it daily and because of it, we can move and execute faster our tasks – “the car or automobile”.

We present a study in the Artificial Intelligence field, in it we will show how we can create a system, which explores our environment’s events and migrate them to the car sensors for executing some instructions.

We will show how we can determine functions and procedures to create them and launch in the right moment by the exploitation of the environment events, which they represent the logic parameters passed in any function or procedure.

Keywords: Car, Automobile, AI, Events, Predicates, Parameters.

Title: How to Make Your Car Smart by Exploring the Environment’s Events

Author: Abdellah Redjaimia, Artem Avakiants, Gu Qing, Liu Ruiying, Li Yafei

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 13, Issue 4, October 2025 - December 2025

Page No: 12-22

Research Publish Journals

Website: www.researchpublish.com

Published Date: 10-October-2025

Abstract: One of the primary information technologies for antenatal management is the utilization of a machine learning system for healthcare interventions.  A machine learning tool is a vital resource for antenatal care and the monitoring of pregnant women, offering services that support healthcare providers, pregnant women, and their family members in predicting and providing adequate healthcare. One of the major challenges faced by pregnant women is the issue of gestational diabetes during pregnancy. It comes with complications such as spontaneous abortion, secondary infections, fetal malformation, hypertensive disorder, cholestasis, obstructed vaginal delivery, and kidney disease. This study aims to develop a machine learning model for predicting gestational diabetes mellitus (GDM) using medical history and socioeconomic features of pregnant women, employing three machine learning models: Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM). The results of the implementation show that XGBoost achieved an F-score of 0.6234 and an accuracy of 83.11%, RF achieved an F-score of 0.6053 and an accuracy of 82.79%, while SVM obtained a performance accuracy of 83.44% with an F-score of 0.5731. The developed machine learning approach will guide healthcare practitioners on the deployment of machine learning models for gestational diabetes mellitus prediction.

Keywords: machine learning, disease prediction, gestational diabetes mellitus, Extreme Gradient Boosting, Artificial Intelligence.

Title: Machine Learning-Based Extreme Gradient Boost Algorithm for the Prediction of Gestational Diabetes Mellitus among Pregnant Women

Author: Ogechukwu Emmanuella Ndukwe, Laeticia Nneka Onyejegbu, Friday E. Onuodu

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 13, Issue 4, October 2025 - December 2025

Page No: 1-11

Research Publish Journals

Website: www.researchpublish.com

Published Date: 04-October-2025