Authors: Bikku Kumar

Abstract: Mergers and acquisitions (M&A) have emerged as the most consequential instruments of corporate strategy in India's post-liberalisation growth narrative. This paper undertakes a rigorous multi-dimensional examination of India's ten largest M&A transactions — spanning banking, e-commerce, steel, telecommunications, cement, aluminium, pharmaceuticals, and automotive sectors — executed between 2007 and 2023 with a cumulative deal value exceeding USD 113 billion. Employing a descriptive-analytical framework grounded in secondary financial data, the study evaluates pre- and post-merger performance across key metrics including Return on Equity (ROE), Earnings Per Share (EPS), and Debt-to-Equity (D/E) ratio. A comparative matrix is deployed to assess strategic intent realisation, post-merger integration efficacy, and sector-specific determinants of M&A success or failure. Statistical analysis of financial ratios reveals a statistically significant divergence between successful and failed deals when measured against pre-merger benchmarks, with successful integrations yielding a mean ROE improvement of 2.1 percentage points. The findings unequivocally establish that strategic alignment, due diligence rigour, and cultural integration capacity are the decisive success factors — not deal size alone. The research contributes an empirically grounded, sector-comparative understanding of M&A dynamics in an emerging market context.

DOI: https://doi.org/10.5281/zenodo.20305623

Published by: vikaspatanker

Authors: Dr. Mohammad Shah Alam Chowdhury

Abstract: Since its independence in 1971, Bangladesh has transformed from one of the poorest nations in the world famously mischaracterized as a "basket case" to one of the fastest-growing economies in South Asia. This paper examines the trajectory of Bangladesh's economic growth, driven primarily by a booming ready-made garment (RMG) industry, robust remittance inflows, and significant advancements in social development indicators such as female labor force participation. However, despite reaching lower-middle-income status in 2015, the nation currently faces severe macroeconomic headwinds. Elevated inflation, banking sector vulnerabilities, low tax revenue mobilization, and external shocks have slowed recent GDP growth. This paper analyzes the historical drivers of growth, structural bottlenecks, and the urgent policy reforms required to ensure sustainable and inclusive economic development.

DOI: https://doi.org/10.5281/zenodo.20305441

Published by: vikaspatanker

Authors: Sai Raghu Ram Gummadidala

Abstract: The fast adoption of hybrid cloud ecosystems incorporating Software as a Service (SaaS), Infrastructure as a Service (IaaS) and on-premise infrastructures has increased significantly the complexity of enterprise networks. The integration between the components of this ecosystem creates serious security concerns associated with uncontrolled connectivity, shadow networking, lateral movement attacks, covert communications via APIs, and low visibility among other issues. Current perimeter-based security models cannot provide the required level of protection to current cloud infrastructures based on the principle of trust and lack of real-time monitoring. The objective of this paper is to propose a Zero Trust Shadow Networking Detection Framework to identify the risk of hidden communications within hybrid cloud ecosystems. The proposed framework relies on trust evaluation, adaptive anomaly detection, microsegmentation, behavior analysis, and threat monitoring leveraging machine learning for protecting communications in SaaS, IaaS and on-premise infrastructures. A dynamic connectivity graph is built to evaluate communication links and identify hidden channels. Mathematical trust modeling and risk propagation analysis have been introduced for the purpose of increasing threat detection efficiency and minimizing unauthorized access. Evaluation based on experiments conducted via simulation of hybrid cloud traffic conditions reveals that the presented framework is more effective than conventional firewalls, virtual private networks, and other Zero Trust frameworks in terms of detection efficiency, decreasing false positives, responding to threats, preventing lateral movement, and mitigating risks on the network.

DOI: http://doi.org/10.5281/zenodo.20305176

Published by: vikaspatanker

Authors: Ragula Rajesh, Potti Rakesh, Poojari Jayakrishna, Mrs.V. Elavenil

Abstract: This study describes the deployment of a cloud-native AI system designed to assist HR departments by offering automated resume screening and candidate-job matching. To provide precise and contextually aware responses, the system employs a Retrieval-Augmented Generation (RAG) technique, which combines a language model with a local knowledge store of job descriptions. We developed this totally with free and open-source technologies like AWS Lambda, BERT, and ChromaDB to make the solution more accessible for startups and SMEs. Open-source approaches, such as Tesseract for OCR, are used to add scanned resume capabilities. To ensure accuracy and validity, the data is also gathered from reputable sources like ESCO skill ontology and LinkedIn datasets. We used PDFs from these sources for RAG and stored them in vector databases for efficient document retrieval, as this system aims to bridge the information gap in high-volume hiring.

DOI: https://doi.org/10.5281/zenodo.20304132

Published by: vikaspatanker

Authors: Gajabheenkar Roshini, Dr.Lavanya Pamulaparty

Abstract: This research focuses on analyzing NFL offensive player statistics from 1999–2013 using Tableau visualization techniques. The dataset contains player demographics combine performance, draft information, and offensive statistics. Various visualizations such as bar charts, heat maps, dashboards, and highlight tables are used to identify player performance trends and statistical patterns. The analysis helps understand how player attributes and performance metrics contribute to offensive success in the NFL.

Published by: vikaspatanker

Authors: Malgaram Punith Teja, Srishti Singh, Baddula Sreeya Yadav, Mrs.Sumayya Samreen

Abstract: This research paper examines what makes an e-commerce business succeed. They use a data set on fifty thousand Amazon transactions to see the effect of all sorts of different variables on sales. What factors we considered was the sale price, discount percentage, ratings and amount of reviews. We examined the mechanics of pricing ,examined the level of consumer confidence in a product , we studied preferences we considered payment mode It shows how offs, and customer ratings influence earned money of the businessThis results from suggesting discounts might increase sales over a specific term but influence long term revenues via performing well on customer ratings and consumer trust. We find that both markets of North America and Middle-East have the revenue. The findings of this research may be accessed by merchants and marketers who wish to set their prices to helps consumers in their purchase decisions and at the same time, increase their revenues. Based on these results, they are able decide about their business.

DOI: https://doi.org/10.5281/zenodo.20302852

Published by: vikaspatanker

Authors: Eeshritha

Abstract: India generates millions of tonnes of plastic waste annually, much of which is non-biodegradable. This paper explores the feasibility of using recycled polypropylene (PP) to produce polymer currency notes. Drawing on Australia’s pioneering adoption of polymer banknotes, the study eval-uates technical, economic, policy, and social challenges, and proposes a phased roadmap for India to transition towards sustainable currency pro-duction. The findings suggest that recycled PP notes could simultaneously address waste management, enhance currency durability, and position India as a global leader in sustainable finance innovation.

Published by: vikaspatanker

Authors: Singareddy Saritha, M. Sivaparavathi

Abstract: A rising number of individuals are displaying signs such as excessive phone usage, loss of productivity, and even physical and psychological health concerns, making Smartphone addiction a major worry in recent years. The development of reliable instruments for the prediction of Smartphone addiction and the identification of those at risk is, hence, necessary. Using survey data on Smartphone use, we constructed a machine learning model to forecast Smartphone addiction in this research. There was a wide variety of mental health concerns addressed in the survey, including demographics, phone use patterns, and anxiety, despairs, and stress. The model was constructed using a well-liked and efficient machine learning technique. In this work, numerical variables are normalized and categorical variables are encoded as part of the data preprocessing to make sure the model can train properly. Also, we used measures like accuracy to measure the model's performance on the remaining data after training it on a subset of the data. The algorithm has successfully predicted Smartphone addiction with a high degree of accuracy, according to the findings. Use habits of mobile phones, including how often notifications were checked, how many hours spent on the phone daily, and the applications used most often, were the most critical variables for predicting addiction. Age, gender, and stress levels were other important factors. The constructed model has a number of possible uses. Healthcare providers might use it to identify patients at risk of Smartphone addiction and intervene accordingly. Also, app makers may utilize it to make their applications less addicting and more conducive to healthy phone habits. In a nutshell, the results show that machine learning algorithms can effectively predict Smartphone addiction. We need to conduct further studies to confirm our results on bigger and more varied datasets and to investigate other possible uses for this approach.

DOI: http://doi.org/10.5281/zenodo.20286754

Published by: vikaspatanker

Authors: Marella Maheswari, P ASHOKA

Abstract: Revolutionizing the verification of bank checks, this innovative technology simplifies the process by integrating deep learning, image processing, and an intuitive Django-based web interface. It streamlines the process with little human participation, making it easier than ever before. Our Convolutional neural network (CNN) trained on the IDRBT check dataset and executed in PyTorch has a 99.14% success rate in recognizing handwritten digits, as shown in the introductory article. Adaptive thresholding and Gaussian blurring are implemented in the source code to enhance the picture preparation. The optical character recognition (OCR) in MATLAB can recover machine-printed text with 97.7 percent accuracy, including IFSC codes and account numbers, when Pytesseract is used in the code for region-based text extraction. The approach uses SVM classification and SIFT feature extraction for real-time authenticity checks, allowing signature verification powered by SIFT and SVM to reach 98.1% accuracy. The web-based interface allows more users to upload photos of checks, train models, see datasets, and get immediate categorization results ("Genuine" or "Not Genuine"). The system complies with CTS-2010 standards for Indian banks and the extraction of critical details such as signatures, amounts, and check numbers is possible even if it supports formats from other countries. In order to automate the verification process and decrease processing time, operational expenditures, and fraud risks, it makes use of contour detection and region-based analysis. This scalable solution sets a new standard for secure, efficient financial transactions by combining the rigors approach from the paper with the actual code implementation. Future versions may support more than one language and format.

DOI: http://doi.org/10.5281/zenodo.20286653

Published by: vikaspatanker

Authors: Harshada Magar, Yash Bhalekar, Sarthak Belvalkar, Om Jadhav

Abstract: This paper presents ContractSphere AI, a system designed to help organizations manage their contracts more easily and securely. Managing contracts in companies involves many steps such as writing, reviewing, checking legal rules, and storing the final signed document. Doing all these steps manually takes a lot of time and often leads to mistakes. ContractSphere AI uses artificial intelligence to automate these steps and uses blockchain technology to make sure that signed contracts cannot be changed or faked. The system can understand contract language in multiple languages and can handle contracts from different countries with different legal rules. It uses a language model trained on legal documents together with a search system that finds relevant rules and contract examples. The final signed contract is stored securely by saving its unique hash on the blockchain, which proves the contract is genuine. This paper describes how the system works, explains the main processing steps, and discusses how well the system performs in terms of speed, security, and cost.

DOI: https://doi.org/10.5281/zenodo.20286629

Published by: vikaspatanker