http://nopr.niscpr.res.in/handle/123456789/67794 2026-10-10T16:41:26Z http://nopr.niscpr.res.in/handle/123456789/67802 Title: Real-Data based Economic Emission Load Dispatch with Renewable Energy and Electric Vehicle Integration using Artificial Ecosystem-based Optimization Authors: Soni, Jatin Abstract: This paper presents a data-driven approach to the Economic Emission Load Dispatch (EELD) problem that integrates Renewable Energy Sources (RES) and Plug-in Electric Vehicles (PEVs). The Artificial Ecosystem-Based Optimization (AEO) algorithm is used to address the stochastic nature of the power system of the future by including real wind and solar data from the ‘Renewable.ninja’ platform for Gujarat, India. This data-driven framework not only captures essential uncertainties in RES generation but also the arrival, departure, and waiting times for PEVs. The method uses the AEO algorithm to simulate ecosystem-like interactions that help the system achieve a good balance between exploration and exploitation, thereby minimizing both total generation costs and environmental emissions. The study has been conducted on 10-unit and 20-unit thermal generation systems, including practical Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) operations. The main results reveal that the AEO algorithm is instrumental in improving system performance by offering a good balance of trade-off between economic and environmental goals. Also, a performance comparison of the AEO algorithm with the latest optimization methods shows that AEO is more effective and stable under complex dispatch scenarios. The paper argues that combining real-world data with ecosystem-based optimization not only provides a scalable, sustainable solution but also offers a way out of modern grid management. This study stands out for combining site-specific meteorological data with high-fidelity PEV behavioural modelling to provide a practical, field-ready strategy for utility operators to manage the volatility of green energy and electric mobility transitions Page(s): 91-103 2026-02-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/67801 Title: Twin Transformation in SMEs: Integrating Digital Technologies and Circular Economy Principles Authors: Yashwant Waware, Shital; Biradar, Ramdas; Maruti Waware, Madhukar; Baban Ghunake, Komal; Sadashiv Kore, Sandeep; Mache, Ashok; Murali, Govindarajan; Prakashrao Jadhav, Shraddha; Sidhappa Kurhade, Anant Abstract: Industry 4.0 (I4.0) based on the proliferation of emerging digital technologies like Internet of Things (IoT), AI, CPS, big data analytics and cloud is transforming manufacturing & production around the globe. Meanwhile, the Circular Economy (CE) paradigm has emerged as a driving force of sustainable industrial sectors which focuses on the improvement of resource optimization and waste generation minimization to ensure economy developed for sustainable benefit. Though significant industry has done a lot of work on the integration of I4.0 – the reality is that from a CE perspective, Small and Medium Enterprises (SMEs) are constrained by financial, structural as well as operational restrictions implicated in engaging with the concept. This paper explores literature regarding emerging diagnostics and assessment models for the nexus of Industry 4.0 with circular economy readiness in SMEs. The paper reviews common research methods, discusses critical gaps in the extant literature, and suggests a multi-level readiness framework based on technological, organizational, and environmental levels. The model is underpinned by fuzzy logic and MCDM methods. By integrating sustainability targets with digital transformation, the paper provides actionability for SME managers, policy makers and industry practitioners in search of competitive, agile and environmentally friendly production systems. Page(s): 104-114 2026-02-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/67800 Title: An Insight from A Programmer’s Perspective on Cloud Container Security Architecture Authors: Jovanović, Aleksandar; Simjanović, Dušan; Lukić, Vladimir; Milić, Petar; Savić, Dragan; Zdravković, Nemanja Abstract: In today’s cloud solution deployments, an important question to consider is whether your development and delivery infrastructure is genuinely secure when using standard security scanning methods and depending on the security protocols of major data providers like AWS, Google, Microsoft Azure, and Kubernetes for application deployment. The references analyzed on common threats in cloud environments and existing solutions, instantiated a need for a survey among 50 software professionals with varied experience in cloud and traditional security to be conducted. The findings highlighted the need for the ranking of common cloud threats in software development for cloud platforms by using Analytic Hierarchy Process (AHP) analysis. The results indicate that safeguarding cloud environments demands a multifaceted approach that addresses the nuanced challenges posed by out-of-date applications, operating system vulnerabilities, and third-party apps, all while remaining vigilant against other miscellaneous threats. Notably, third-party applications, while speeding up software delivery, pose significant security risks. This supports the “shifting left” paradigm, which emphasizes integrating security early in the development cycle. Additionally, the importance of a protective layer between hosts and containers through a common response protocol is determined. Docker accounts for 54.7% of the total deployment, showing that more than half of the respondents deployed container images using Docker. With values of 0.526 with λ = 0.5 of FAHP (Fuzzy Analytic Hierarchy Process), experts were either not sure or declared containers as non-repudiable, showing that programmers do not know if the container is the one it poses to be. Page(s): 115-127 2026-02-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/67799 Title: A Novel Hybrid Stacked Ensemble Model for Breast Cancer Classification Authors: Arya, Suraj; Anju Abstract: The healthcare industry also leverages technology to address various problems. The medical industry continues to adopt artificial intelligence, deep learning, Machine Learning (ML), and big data solutions to automate tasks, improve workflows, and enhance decision-making. Currently, various AI−based solutions are available in the healthcare industry, for example, the analysis of medical images and the identification of patterns in patient data. Thus, these emerging techniques provides many solutions, such as predictive healthcare, and automated drug discovery. The present study proposes the earlier cancer−detection and prediction model to resolve this real-life problem. This study proposed an improved ML model using Synthetic Minority Oversampling Technique (SMOTE) to detect and predict breast cancer at an earlier stage. The four machine learning algorithms achieved the highest test-set accuracy. These algorithms include a novel Hybrid Stacked Ensemble Model (HSEM) and Random Forest (RF), achieving accuracies of 99.12% and 98.59%, respectively; logistic regression, achieving 98.59%; and the support vector classifier, achieving 98.25%. The Area under curve (AUC) for the Breast Cancer (BC) dataset with the HSEM and RF classifier is 99.90%, indicating the model's accuracy. Secondly, cancer treatment exists in expensive types of treatments, and cost has an important role. Therefore, a low-cost solution is required and would be beneficial for the healthcare industry. Thus, this paper developed a novel, low-cost model for cancer prediction for the healthcare industry, enabling people to estimate their cancer risk earlier Page(s): 128-139 2026-02-01T00:00:00Z