Daily Archives: April 14, 2026

Authors: Mahe Bader Fatmi

Abstract: Industrial Training Institutes (ITIs) play a crucial role in developing a skilled workforce in India, yet many graduates face challenges in securing sustainable employment due to gaps between training and industry requirements. This paper explores how strategic management practices can enhance the employability of ITI-qualified students by aligning institutional objectives with dynamic labor market needs. It examines key strategies such as industry–institute partnerships, curriculum modernization, competency-based training, soft skills development, and the integration of digital technologies in vocational education. Drawing on selected Indian case studies, the study highlights successful models where collaboration with industry stakeholders, apprenticeship programs, and outcome-oriented training frameworks have significantly improved job placement rates. Institutions that adopted proactive management approaches—such as continuous skill mapping, faculty upskilling, and data-driven decision-making—demonstrated stronger employment outcomes for their students. The paper argues that adopting a strategic management perspective enables ITIs to transition from traditional training centers to agile, demand-driven skill hubs. It concludes by recommending policy-level support, strengthened public-private partnerships, and the institutionalization of monitoring and evaluation mechanisms to ensure long-term impact. These findings provide actionable insights for educators, policymakers, and administrators aiming to bridge the employability gap in India’s vocational education sector.

DOI: https://doi.org/10.5281/zenodo.19570413

Published by: vikaspatanker

Authors: Vivek Sharma

Abstract: The financialization of private markets represents a fundamental transformation in the architecture of global financial systems, characterized by the growing dominance of non- bank financial institutions and the expansion of market-based credit intermediation. Over the past two decades, private markets—including private equity, private credit, hedge funds, and venture capital—have evolved from niche investment segments into systemically important components of global finance. This transformation has been significantly accelerated by post-2008 regulatory reforms, particularly Basel III, which imposed stricter capital and liquidity requirements on traditional banking institutions. While these reforms enhanced banking sector resilience, they also constrained credit supply, thereby creating space for private markets to expand and assume a central role in financial intermediation. This study investigates the systemic risk implications of the financialization of private markets, with a particular focus on the role of private credit and leveraged investment strategies. The research adopts a mixed-method approach that integrates theoretical insights from financial instability theory with empirical analysis based on secondary data. Key data sources include the International Monetary Fund (IMF), Financial Stability Board (FSB), World Bank, and industry reports from Preqin and BlackRock. The empirical framework is built around a regression model that examines the relationship between systemic risk and its primary determinants, including leverage, default rates, and market volatility. The results reveal that leverage is the most significant driver of systemic risk within private markets. High leverage amplifies both returns and losses, increasing vulnerability to adverse economic shocks and contributing to financial instability. Default rates are also found to have a strong positive relationship with systemic risk, reflecting the deterioration of borrower credit quality during periods of economic stress. Market volatility further exacerbates risk by increasing uncertainty and triggering liquidity constraints, particularly in markets characterized by illiquid assets. The study also highlights the structural vulnerabilities associated with private markets, including opacity, liquidity mismatches, and interconnectedness with the broader financial system. Unlike traditional banks, private market institutions operate with limited regulatory oversight and disclosure requirements, making it difficult to assess risk exposure and monitor systemic threats. The increasing interconnectedness between private funds, banks, and institutional investors further amplifies the potential for contagion. Overall, the findings suggest that while private markets enhance financial efficiency and provide alternative sources of capital, they also pose significant systemic risks that extend beyond the traditional banking sector. The paper concludes by emphasizing the need for expanded macroprudential regulation, improved transparency, and enhanced data reporting frameworks to ensure financial stability in an increasingly financialized global economy.

DOI: https://doi.org/10.5281/zenodo.19570015

Published by: vikaspatanker

Authors: Satyam Sharma, Jakkala Sanjay Kumar, S.Harshvardhan, Ms. Pabbu Parameshwari

Abstract: Rapid growth in residential electricity consumption has created a strong need for intelligent and user-friendly monitoring solutions that can help users understand and control their energy usage. Traditional energy meters provide only cumulative readings and do not offer real-time insights into appliance-level consumption. This paper presents the design and implementation of an IoT-based Smart Power Monitoring System for home appliances that enables continuous monitoring and remote access to electrical parameters. The proposed system measures real-time voltage, current, power, and energy consumption using ZMPT101B voltage and ACS712 current sensors interfaced with an ESP32 microcontroller. The ESP32 processes sensor data and transmits it securely to cloud platforms such as ThingSpeak and Blynk via Wi-Fi, allowing users to visualize and analyze energy usage through mobile or web dashboards. In addition, the system incorporates a relay-based protection mechanism that automatically disconnects the load during abnormal conditions such as overcurrent, voltage fluctuations, or excessive power consumption. This feature helps prevent electrical damage and improves system safety. The proposed solution is low-cost, scal-able, and energy-efficient, making it suitable for residential envi-ronments as well as small industrial applications. By providing real-time monitoring, data analytics, and remote accessibility, the system promotes energy awareness, reduces electricity wastage, and supports the development of smarter and more sustainable energy management systems.

DOI: https://doi.org/10.5281/zenodo.19569819

Published by: vikaspatanker

Authors: Prof. Bhagwati Galande, Parth Bhosale, Sakshi Bathe, Aditi Gadhave, Prathamesh Amrutkar

Abstract: PulseSaver is designed to fix this. It’s an all-in-one digital network that acts as an emergency lifeline, instantly bringing together people who need help with people who can give it. We are building a single platform to manage donations of blood, organs, and money—all in real-time. We use smart AI technology to cut out the waiting. The system automatically and instantly matches the patient with the closest, most compatible, and fully verified donor or resource. Everything is secure and transparent: we rigorously verify all users and requests to build trust and ensure ethical donation practices. The main goal of PulseSaver is simple: to save lives by speeding up the emergency response and making sure critical resources get to the people who need them most, especially those in remote or underserved communities.

Published by: vikaspatanker

Authors: Nitesh Baranawal, Herambh Sakpal, Pranay Manoj, Kaustabh Kadam, Prof. Mohan Kumar

Abstract: The Steganography Hider System is a secure information-hiding solution designed to protect sensitive data by embedding it within digital images, making it imperceptible to unauthorized users. Unlike traditional encryption, which only disguises data, steganography conceals the very existence of the information, providing an additional layer of security. This system employs techniques such as Least Significant Bit (LSB) substitution, transform domain methods (e.g., DCT), or advanced neural network approaches to embed secret messages while maintaining the visual quality of the cover image. The proposed system allows users to securely hide and retrieve confidential information, ensuring data confidentiality, integrity, and robustness against common image processing operations such as compression, noise addition, and format conversion. This project serves as a practical demonstration of the importance of information security in today’s digital communication era, providing a user-friendly interface that can be applied in various fields such as secure communications, copyright protection, and digital forensics.

Published by: vikaspatanker

Authors: Pratiksha Shinde, Zishan Nadaf, Onkar Bhuse, Chaitanya Deshmukh

 

 

Abstract: Skin diseases constitute a major global healthcare challenge, particularly in regions with limited access to dermatological expertise. Early and accurate diagnosis is essential to reduce disease progression and associated healthcare costs. This research presents SkinSight, an advanced artificial intelligence–based clinical decision support system for automated skin disease detection using digital skin images. The proposed system is designed by aligning a real-world deployable application with state-of-the-art research methodologies reported in recent dermatology-focused deep learning literature. SkinSight integrates an advanced preprocessing pipeline for artifact removal and illumination normalization, a two-stage validation framework to ensure input reliability, and a dual-stream deep learning ensemble combining ResNet50 and InceptionV3 architectures. Additionally, the system incorporates explainable artificial intelligence (XAI) using Grad-CAM to provide visual interpretability of predictions. A comparative analysis between the initial deployment model and the research-grade pipeline is presented, followed by a structured roadmap for bridging implementation gaps. Experimental results demonstrate that aligning deployment architecture with research-level techniques significantly improves robustness, reliability, and clinical trustworthiness. The proposed framework highlights the importance of end- to-end consistency between research and deployment in AI-driven healthcare systems.

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Published by: vikaspatanker

Authors: Nupur Pradip Panchal, Sakshi Sanjay Shinde

Abstract: This research report presents the development and evaluation of a deep learning-based system for the automated detection of plant diseases through leaf image analysis. Aimed at addressing the significant economic impact of crop diseases in agriculture, the project leverages a Convolutional Neural Network (CNN) model trained on the publicly available Plant Village dataset. The implemented system processes leaf images through stages of pre-processing, augmentation, and feature extraction to classify diseases in crops such as tomato, potato, and maize. The model achieved a high classification accuracy of 96.5%, with supporting precision, recall, and F1-scores all above 95%. The study successfully demonstrates the technical feasibility of using image processing and deep learning for accurate, rapid disease identification. A key innovation proposed is the deployment of this model on a mobile application, which would provide farmers with an accessible tool for early disease detection and improved crop management, thereby enhancing agricultural productivity. The report also discusses the current limitations and potential future integrations with IoT and advanced imaging technologies for broader field application.

DOI: https://doi.org/10.5281/zenodo.19567125

Published by: vikaspatanker

Authors: Saif Chaudhary, Faizan Bari, Arbab Ansari, Fahim Shaikh, Prof. Anuja Kamat

Abstract: The rapid evolution of technology and the expanding digital economy have created significant complexity in career decision-making for students and fresh graduates. Traditional career counselling approaches are static, generalized, and fail to account for individual skill profiles, evolving industry demands, and personalized learning trajectories. This paper presents ElevateX, an AI-powered career guidance platform that leverages large language models (LLMs) and natural language processing (NLP) to deliver personalized career recommendations, skill gap analysis, dynamic learning roadmaps, project suggestions, resume insights, and mock interview simulations. ElevateX engages users through an intelligent questionnaire that assesses their skills, interests, and aspirations, and subsequently generates actionable guidance tailored to their profile. Experimental evaluations demonstrate high user satisfaction, improved career clarity, and measurable gains in skill awareness among participants. The platform addresses a critical gap in accessible, personalized, and data-driven career counselling for the student community.

DOI: https://doi.org/10.5281/zenodo.19566697

Published by: vikaspatanker

Authors: Ch. Saiharsha, D. Sathvika, J. Siddhi Haarika, P. Yuktha Laasya

Abstract: Braille literacy is very vital in facilitation of independent reading, education and communication among the visually impaired. Availability of affordable Braille learning and display systems is however low especially in environments with limited resources whereby commercial refreshable Braille displays are prohibitively expensive. In this paper, a low-cost assistive platform that is intended to aid in the learning of the Braille, visualization, and prototype of a system is presented. The offered system makes Grade-1 patterns of Braille represented in real-time, which lets the user comprehend the character mapping and sequencing. The design is focused on affordability, modularity and implementation ease, which fits the educational institutions and assistive technology development. The system has been proven to be reliable in converting textual input into relative Braille patterns. Even though the existing implementation offers a non-tactile representation, it is an effective training/validation tool of students, educators, and researchers. The suggested solution demonstrates the possibility of creating scalable and cost-efficient assistive solutions.

Published by: vikaspatanker

Authors: Himani Atul Khamkar, Riddhika Dattaram Zolage, Prof. Sanjay Eknath Gawli

Abstract: Social media platforms such as Instagram are widely used for communication, networking, and content sharing. How- ever, the rapid growth of these platforms has also led to a signifi- cant increase in fraudulent or fake accounts. These accounts are often involved in activities such as spamming, phishing, spreading misinformation, and manipulating engagement metrics. Due to the large number of users and the dynamic behavior of social media platforms, manual identification of fake accounts becomes difficult and inefficient. This research proposes a machine learning-based approach to detect fake Instagram accounts using profile-based features. Various attributes such as follower-following ratio, number of posts, engagement behavior, profile completeness, and other pro- file characteristics are analyzed. The Random Forest classification algorithm is used to distinguish between real and fake accounts. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate that the proposed approach can effectively identify fake accounts and contribute to improving the reliability and security of social media platforms.

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Published by: vikaspatanker