Authors: Narasimhamurthy K N, Ashok G V, Ashwini S

Abstract: In view of this, Indoor as well as outdoor radon concentration measurement has been carried out in specific residential and schools located in Mandya, Karnataka using well known SSNTD technique. The indoor radon level is predicted in the same selected dwellings using the suitable model which is based on the mass balance equation and the results are compared with the measured values. Annual mean values of 222Rn in selected houses and schools were found to be 19.68 Bq m-3 respectively. Annual mean values in some other survey for 222Rn and 220Rn concentrations was found to be 22.4 and 24.1 Bq m-3 respectively. The total annual effective dose received by the general public due to radon and thoron is found to be 1.1 mSv y-1, which is close to the Indian average value of 1.11 mSv y-1. The doses to different organs and tissues were calculated using the ICRP model of the respiratory tract and inter comparison was discussed.

DOI: https://doi.org/10.5281/zenodo.18228616

 

Published by: vikaspatanker

Authors: Pranesh Mudiraj

Abstract: The integration of advanced machine learning models into SAP ERP systems has revolutionized the traditional landscape of financial planning and analysis by shifting organizational focus from reactive reporting to proactive forecasting. This review article evaluates the transition from manual, spreadsheet-based accounting toward automated predictive frameworks that leverage the in-memory computing power of SAP S/4HANA. We examine a diverse taxonomy of algorithms, ranging from classical time-series analysis and ensemble methods to sophisticated deep learning architectures, and their specific applications in revenue projection, cash flow management, and risk mitigation. The study details the technical synergy between the SAP Business Technology Platform and embedded analytical engines, emphasizing the importance of data preprocessing and feature engineering in a complex enterprise environment. Furthermore, we provide a comparative analysis between traditional and machine-learning-based forecasting, highlighting improvements in accuracy, cycle time, and scalability. The paper concludes by discussing emerging trends such as generative AI and real-time predictive accounting, offering a strategic roadmap for financial leaders aiming to implement data-driven decision-making processes. By synthesizing current methodologies and practical use cases, this study demonstrates how predictive analytics serves as a cornerstone for the modern intelligent enterprise.

DOI: http://doi.org/10.5281/zenodo.18221566

Published by: vikaspatanker

Authors: Nivaan Varma

Abstract: As global business environments become increasingly volatile, traditional enterprise risk management frameworks struggle to keep pace with high-velocity, interconnected disruptions. This review article investigates the integration of artificial intelligence into risk management lifecycles to enhance business resilience. We examine how machine learning, natural language processing, and predictive analytics transform the stages of risk identification, assessment, and mitigation from reactive to proactive processes. The study highlights the role of AI in critical domains such as cybersecurity, supply chain elasticity, and financial stability, while also addressing the theoretical shift toward the anticipate-absorb-recover-adapt cycle of resilience. Furthermore, the article explores the significant challenges associated with AI adoption, including model opacity, data bias, and the urgent need for explainable AI and human-in-the-loop governance. By synthesizing current research with emerging trends like generative AI and quantum-resistant modeling, we provide a strategic roadmap for organizations aiming to build antifragile systems. This study concludes that the synergy between human strategic judgment and machine intelligence is the fundamental requirement for maintaining long-term survivability in the digital age.

DOI: http://doi.org/10.5281/zenodo.18221560

Published by: vikaspatanker

Authors: Omkar Walunj, Pranav Hole, Sarthak Thigale, Sohan SandbhorD

Abstract: With the rapid advancement of Artificial Intelligence (AI), healthcare systems are shifting from reactive to proactive models capable of predicting, diagnosing, and preventing diseases. This paper presents Smarthealth, a cloud-based predictive healthcare system that utilizes machine learning algorithms to analyze patient data, anticipate potential health issues, and generate timely alerts. The system integrates AI models for disease prediction and employs Firebase for real-time synchronization and secure data storage. The objective of this work is to develop an efficient, scalable, and secure AI-driven healthcare prediction platform that assists doctors and patients in early diagnosis and informed medical decision-making.

Published by: vikaspatanker

Authors: Gayathri S, Varshameena. M

Abstract: Cloud computing has emerged as a revolutionary technology that enables on-demand access to shared computing resources such as storage, applications, and processing power through the internet. In recent years, educational institutions have increasingly adopted cloud computing to modernize teaching, learning, and administrative processes. This shift is driven by the growing demand for flexible learning environments, digital collaboration, remote accessibility, and cost-effective infrastructure management. Traditional educational systems rely heavily on physical hardware and locally installed software, which often leads to high maintenance costs, limited scalability, and restricted access to learning resources. Cloud computing overcomes these limitations by offering scalable, reliable, and affordable solutions tailored to academic needs. This paper explores the adoption of cloud computing in educational institutions, focusing on its architecture, service models, and practical applications. Cloud-based platforms such as Learning Management Systems (LMS), virtual classrooms, digital libraries, and online assessment tools have transformed the educational ecosystem by enabling anytime-anywhere learning. The study highlights key benefits of cloud adoption, including reduced operational costs, improved collaboration among students and faculty, enhanced data storage and backup capabilities, and increased institutional efficiency. Additionally, cloud computing supports innovation in education by integrating emerging technologies such as artificial intelligence, big data analytics, and smart learning environments. Despite its advantages, the adoption of cloud computing in education also presents challenges such as data security, privacy concerns, internet dependency, and vendor lock-in. This paper discusses these challenges and emphasizes the importance of implementing strong security policies, data protection mechanisms, and regulatory compliance to ensure safe and effective cloud usage. The study concludes that cloud computing plays a vital role in the digital transformation of educational institutions and has the potential to significantly improve the quality, accessibility, and sustainability of education. With proper planning and governance, cloud computing can serve as a powerful enabler for the future of education.

Published by: vikaspatanker

Authors: K. Sai Teja, M.Surya Teja, S.Bharath Simha Rao, Y.Hemanth Kumar

Abstract: Face morphing attacks represent a critical vulnerability in biometric authentication sys- tems, where two or more facial images are digitally blended to create a forged identity. Such morphed images can successfully deceive automated face verification systems, leading to severe risks in applications like passport issuance, border control, and iden- tity management. Traditional detection techniques, relying on handcrafted features or differential meth- ods, often fail to generalize across diverse morphing techniques and image qualities. To overcome these limitations, we propose MorphDetect, a deep learning-based Single- Image Morphing Attack Detection (S-MAD) system powered by the EfficientNet-B7 model. The system first preprocesses face images for normalization and then extracts high- dimensional features using EfficientNet-B7’s advanced convolutional blocks. These features are passed through a classification layer that determines whether an input is genuine or morphed, producing a reliable confidence score for decision-making. MorphDetect eliminates the need for a trusted reference image and provides a scal- able, real-time solution for morph detection. By leveraging a strong pretrained back- bone, it ensures robustness against unseen morphing techniques and diverse imaging conditions. This makes the system well-suited for deployment in high-security appli- cations such as e-passport verification, financial KYC procedures, and secure access systems.

Published by: vikaspatanker

Authors: Tushar Kadam, Dhiraj Gadhave, Nirzara Sarole, Shital Shinde

Abstract: Landfills are a potential threat to human health and the environment, especially from the detrimental and toxic heavy metals. This study focuses on the assessment of heavy metals contamination in leachate and surface soils from different landfills in Pune. The impacted soils showed high heavy metal concentrations especially at non-sanitary unlined landfills, as compared to background values, and natural soil nearby the landfills. Leachate possesses potential risk to surface and groundwater aquifer within the area surrounding the landfill site. The aim of this chapter is to assess the physical parameters and heavy metal levels in leachate. Heavy metals are one of the important pollutants in landfill leachate. Plants and soil near the landfill may be contaminated by leachate. In this study, by evaluating the heavy metals in the leachate of three landfills, the amount of pollution caused by the leachate in the environment around the landfills in Pune was investigated.

Published by: vikaspatanker

Authors: Angel Lalu, Dr Prakash R, Shreyas Sunil, Nandhakumar S, Divya Bharti

Abstract: Self-healing distribution systems are one of the foundational requirements for future smart grids that are built to withstand disturbances, accommodate bidirectional power flow, and also maintain reliability despite the threat of in- creasing renewable penetration. Traditional FLISR (Fault Location, Isolation, and Service Restoration) solutions used currently depend mostly on SCADA, PMUs, and other high- cost protection relays. This infrastructure is usually not un- available in low-voltage networks, microgrids, and academic environments for teaching purposes. Our work proposes a novel low-cost, microcontroller-based self-healing grid pro- totype that uses ACS712 current sensors, ESP32/ESP8266 wireless sensing nodes communicating via ESP-NOW, and an STM32 Nucleo 64 (F446RE) microcontroller executing an embedded Random Forest classifier through the Eloquent- TinyML library. This system automatically and autonomously detects, classifies, and isolates faults based on a real-time multi- feature current signature. Our experimental setup and further validation shows an overall classification accuracy of 92.76%, ESP-NOW latency of 12-to 18 ms over 22 metres, and a pro- tection response time under 200 ms. Compared to other con- ventional schemes, our proposed architecture provides an inex- pensive yet robust platform similar to SCADA-like self-healing behaviours.

Published by: vikaspatanker

Authors: Mrs, Seema Amol More, Professor Dr. Swati Nitin Sayankar

Abstract: Education 5.0 represents a paradigm shift toward human-centric, ethical, and sustainable learning ecosystems by synergizing advanced digital technologies with societal needs. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers in transforming higher education through personalized learning, predictive analytics, and intelligent decision support. However, the absence of a unified and scalable framework often leads to fragmented adoption and ethical concerns. This paper proposes a comprehensive AI–ML framework tailored for higher education institutions from an Education 5.0 perspective. The framework integrates data-driven learning analytics, adaptive instructional systems, student performance prediction, and automated academic administration while emphasizing transparency, inclusivity, and data privacy. The proposed architecture consists of layered modules encompassing data acquisition, intelligent processing, decision intelligence, and stakeholder interaction. A conceptual case study demonstrates the applicability of the framework in a university environment. Comparative analysis highlights improvements in academic outcomes, operational efficiency, and learner engagement. The proposed framework provides a structured pathway for institutions seeking sustainable and ethical AI adoption, contributing to the evolving discourse on next-generation higher education systems.

DOI: http://doi.org/10.5281/zenodo.18204804

Published by: vikaspatanker

Authors: Ms Ruchi Natekar

Abstract: In Mumbai’s fast-growing private real estate construction sector, persistent challenges—cost overruns, schedule delays, and inconsistent quality—continue to limit project performance despite rising demand and increasing urban pressures. Artificial Intelligence (AI) has emerged globally as a transformative tool capable of reshaping construction planning, execution, and monitoring. Yet, in Mumbai, AI adoption remains at a formative stage, shaped by a complex interplay of technological limitations, cultural resistance, and organisational readiness. This study explores how AI is currently being used, where it creates value, and what barriers must be overcome for meaningful transformation. A mixed-methods research design was employed to capture both the breadth and depth of AI adoption. Quantitative insights were gathered through a structured survey of 99 construction professionals, spanning developers, engineers, consultants, and project managers. To complement this, qualitative interviews and focus group discussions were conducted with industry experts to understand their lived experiences, perceptions, and concerns regarding AI-enabled practices. Data were analysed using descriptive statistics, factor analysis, and thematic coding to produce an integrated, evidence-based understanding of AI’s real-world impact within Mumbai’s construction environment. Findings reveal that while AI adoption is still emerging, its footprint is steadily expanding. The most recognised and frequently applied AI tools include predictive analytics for cost estimation, automated scheduling systems, and computer-vision-based quality inspections. Respondents involved in AI-enabled projects reported heightened confidence in the technology’s potential to enhance efficiency, reduce rework, and improve decision-making. However, this optimism exists alongside significant obstacles. The study identifies notable barriers such as low digital literacy, fragmented data systems, regulatory ambiguity, and organisational cultural resistance. Many firms struggle to integrate AI into legacy workflows, and small and medium-sized enterprises face higher financial and technical hurdles. The discussion highlights that successful AI-enabled transformation requires more than just technological investment—it demands structural, cultural, and behavioural shifts within organisations. AI’s impact is therefore as socio-technical as it is operational, requiring alignment across people, processes, and platforms. This research confirms that AI holds strong promise for reducing chronic inefficiencies in Mumbai’s real estate construction sector. Yet, the gap between theoretical potential and on-ground performance remains wide. To bridge this divide, organisations must adopt a phased, context-appropriate strategy that prioritises digital literacy, data standardisation, regulatory clarity, and targeted workforce upskilling. The study offers a practical implementation roadmap tailored to Mumbai’s unique ecosystem, serving as a valuable resource for developers, project managers, policymakers, and technology providers. Ultimately, AI is positioned not as a replacement for human expertise, but as a powerful enabler of smarter, safer, and more resilient urban development.

DOI: https://doi.org/10.5281/zenodo.18204579

 

Published by: vikaspatanker