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Author(s):
Author: Rachu Sharanappa Balegar Co- author: Sahana Gurusiddayya Hiremath.
Page No : 1-5
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Mergers, Acquisitions and the Changing Face of NBFCs in India:
Abstract
Non-banking financial companies (NBFCs) have shifted from the fringes of India’s financial system to its core, and from 2021 to 2026 a distinctive series of mergers and acquisitions (M&A) altered their ownership and structure. Using a multiple-case design built on secondary data, this paper studies nine landmark transactions: Piramal–DHFL, Poonawalla–Magma, the formation of Shriram Finance, the HDFC Ltd–HDFC Bank and IDFC–IDFC First Bank reverse mergers, Tata Motors Finance–Tata Capital, Reliance Capital–IIHL, Bain Capital–Manappuram and MUFG–Shriram Finance. The findings show that dealmaking has moved away from distress resolution under the Insolvency and Bankruptcy Code (IBC) and towards strategic consolidation and the entry of foreign capital, propelled by scale-based regulation, compulsory listing of upper-layer NBFCs, cheaper funding and investment in technology. Results after the deals vary: Shriram Finance and Poonawalla Fincorp grew their assets quickly while keeping asset quality steady, HDFC Bank endured sustained pressure on margins and low-cost deposits, and Tata Capital saw its asset quality weakened by the vehicle-finance book it absorbed. Approval delays and integration risk are the chief obstacles. It closes with policy suggestions on approvals, IBC implementation, prudential relief and disclosure.
| 2 |
Author(s):
Dr. Shakunthala B S, Kavana Y P, Namratha B L, Parvathi T D, Sahana P.
Page No : 1-7
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Civic Issue Reporter: A Web-Based Platform for Civic Complaint Management and Transparent Public Service Delivery
Abstract
Civic Issue Reporter is a web-based application that helps people report civic problems such as damaged roads, water shortages, garbage accumulation, and street-light failures. Citizens can register, submit complaints with photos and location details, and track their complaint status. Department users can view complaints and update their status, while administrators can manage complaints and department accounts. The system is developed using Node.js, Express, EJS, and MongoDB. It also includes optional AI-based complaint screening and email notifications. This application aims to make civic complaint management easier, improve communication between citizens and departments, and support better public service delivery. Further testing is required to check the system's performance, security, and reliability.
| 3 |
Author(s):
Pratik Hanvate,Roshan Nimaje, mohini nikam ,Rohit butle,sumit hate ,Harsh khadatkar.
Page No : 1-7
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DevOps Automation Platform for Deployment, Monitoring and Self-Healing of Cloud-Native Web Applications”
Abstract
The rapid growth of cloud computing and modern web applications has increased the need for reliable, scalable, and automated software deployment systems. Traditional deployment processes often involve manual configuration, time-consuming operations, deployment failures, and difficulties in monitoring application performance. To overcome these challenges, this project proposes a DevOps Automation Platform for Deployment, Monitoring and Self-Healing of Cloud-Native Web Applications.
The proposed system aims to automate the complete software development and deployment lifecycle using DevOps practices and cloud-native technologies. It integrates GitHub, Jenkins/GitHub Actions, Docker, Kubernetes, AWS, and Terraform to establish an automated Continuous Integration and Continuous Deployment (CI/CD) pipeline. Whenever developers commit or push code changes to the GitHub repository, the pipeline automatically triggers source code integration, application building, testing, containerization, and deployment.
| 4 |
Author(s):
Dr.D. Sivakumar, Manushree.M.S, Pavithra.S,Vinuthashree.G,Yamuna .Y.N,.
Page No : 1-7
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AI-Based Intelligent Crop Selection Using Market and Environmental Analysis
Abstract
Agriculture is one of the most important sectors, and selecting the right crop plays a major role in improving farmers' productivity and income. This paper presents an AI-based intelligent crop selection system that combines environmental conditions and market analysis to recommend suitable and profitable crops. The proposed system considers important environmental factors such as soil nutrients (Nitrogen, Phosphorus, and Potassium), soil pH, temperature, humidity, and rainfall. It also analyzes market factors such as crop demand, market prices, price trends, and estimated profitability. Machine learning algorithms, including Random Forest, XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN), are explored to improve crop recommendation. By combining environmental suitability with market conditions, the system helps farmers make informed decisions about crop selection, increase potential profits, and support sustainable agricultural practices.
