Vol 14 Issue 4 October 2026-December 2026

Abstract: Technology has become an essential part of modern organizations, supporting communication, financial activities, customer services, operations, data management and decision-making. At the same time, greater dependence on technology has created new risks. Cyberattacks, system failures, data loss, technology obsolescence, third-party service failures and human error can interrupt business activities and cause significant financial and operational consequences. Technology risk management is therefore becoming an important part of organizational risk management. This article examines the role of technology risk management in reducing operational disruption and strengthening organizational resilience. It discusses major categories of technology risk and considers how organizations can identify, assess, treat and monitor these risks. Particular attention is given to cybersecurity, data protection, cloud computing, third-party technology providers, human factors and emerging technologies. The article argues that technology risk management should not be viewed as the responsibility of the IT department alone. Effective management requires cooperation between senior management, technology specialists, risk professionals and employees across the organization. The article also highlights the importance of continuous monitoring, employee awareness, testing and organizational learning. A proactive and integrated approach to technology risk can help organizations reduce vulnerabilities while improving their ability to respond to unexpected events.

Keywords: technology risk management, modern organizations, supporting communication, financial activities, customer services, organizational learning.

Title: Technology Risk Management: Strengthening Organizational Resilience in a Digital Environment

Author: Zamil Alshammari

International Journal of Computer Science and Information Technology Research

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

Vol. 14, Issue 4, October 2026 - December 2026

Page No: 14-19

Research Publish Journals

Website: www.researchpublish.com

Published Date: 11-October-2026

Abstract: Today, the challenges in agriculture are so big and complicated that agricultural professionals can't just look at them and figure them out.  This study looks at the newest deep learning and computer vision techniques for finding and sorting mango leaf diseases. Leaf diseases cut the quality and quantity of mango crops by 30–80%, which costs the worldwide mango industry billions of dollars. We meticulously examine numerous scholarly articles on deep neural networks and image processing. We categorize current methodologies into three technical generations: conventional image processing systems, first deep learning applications, and advanced hybrid architectures that incorporate transfer learning, attention mechanisms, and explainable AI.  We systematically assess performance metrics, dataset characteristics, and implementation challenges across diverse geographical regions. Our comprehensive evaluation framework and prospective research trajectories underscore sustainable, scalable, and farmer-centric solutions.  This review is a complete guide for researchers studying tropical fruit and people who make agricultural technologies. This study fills in the gaps in research on dataset standardization, model interpretability, real-time deployment, and the integration of precision agriculture systems. 

Keywords: Smart Farming, Deep Learning, Mango Diseases, Computer Vision, Precision Agriculture, Transfer Learning.

Title: Deep Learning for Early Disease Identification in Leaves: A Pathway to Sustainable Mango Production

Author: Shivam Saini, Rohit Goyal, Rakesh Arya

International Journal of Computer Science and Information Technology Research

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

Vol. 14, Issue 4, October 2026 - December 2026

Page No: 8-13

Research Publish Journals

Website: www.researchpublish.com

Published Date: 05-October-2026

Abstract: The significant improvement in patient outcomes that can be achieved with the early identification of chronic kidney disease (CKD). This is because timely therapies can postpone the course of the disease to end-stage renal disease. Using clinical and laboratory characteristics that are routinely obtained, this study examines and analyzes a number of different machine learning algorithms for the early prediction and detection of chronic kidney disease (CKD). In this study, we examine the performance of a basic neural network, logistic regression, support vector machines, and random forest on a benchmark dataset for chronic kidney disease (CKD). Applications include feature selection, handling of missing values, and reduction of class imbalance. Through the utilization of stratified cross-validation, the model that performed the best was able to obtain an accuracy of (placeholder), an F1-score of (placeholder), and AUC-ROC (placeholder). With the help of SHAP values, we additionally offer the interpretability of the model in order to emphasize the clinical characteristics that have the most significant impact. The findings indicate that ensemble tree-based models with proper preprocessing not only offer good performance for early CKD prediction but also aid in clinical adoption through their interpretability.

Keywords: Chronic Kidney Disease, CKD prediction, machine learning, random forest, early prediction.

Title: A Review on Early Prediction and Identification of Chronic Kidney Disease Using Machine Learning Techniques

Author: Jyoti Kiran, Dr. Rohit Goyal, Mr. Rakesh Arya

International Journal of Computer Science and Information Technology Research

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

Vol. 14, Issue 4, October 2026 - December 2026

Page No: 1-7

Research Publish Journals

Website: www.researchpublish.com

Published Date: 01-October-2026