Authors: Madhura S. Khandekar, Seema Singh

Abstract: Sanskrit in the contemporary Indian education has been a topic of national and scholarly significance once again, especially due to the implementation of the National Education Policy (NEP) 2020. Sanskrit, the ancient language believed to be one of the classics and sacred languages, has played a major role in the intellectual tradition of India in the fields of philosophy, science, linguistics, mathematics, medicine, and aesthetics. Nevertheless, its role in modern education systems has been disputed on many occasions because of the challenges of accessibility, relevancy, and employability. This paper is an empirical study of the relevance of Sanskrit in contemporary Indian education based on policy frameworks, curriculum reforms, pedagogical practices, and empirical research. The study is based on a qualitative document analysis methodology involving national policy documents, curriculum frameworks, parliamentary reports and peer-reviewed scholarly literature. Results indicate that Sanskrit has a multidimensional impact; maintenance of cultural heritage, enhancement of cognitive and linguistics ability, facilitating interdisciplinary learning and provision of an Indian Knowledge Systems (IKS) framework. It is proposed in the study that the role of Sanskrit in contemporary learning has never been the revival of the language as a mandatory classical language but as a strategic intervention in the pedagogy of inclusivity, technology-intensive learning, and interdisciplinary interventions. The paper has been ended by some policy and pedagogical suggestions on how Sanskrit education can be made to meet the liability of equity, up to date skills, and international knowledge systems.

Published by: vikaspatanker

Authors: Srujana Parepalli

Abstract: By March 2024, enterprise intelligence initiatives increasingly depended on the reliability of master data to support analytics, operational reporting, customer engagement, and automated decision systems. Organizations consolidated data from numerous operational sources, including transactional systems, customer platforms, supplier feeds, and third party reference datasets. These sources frequently represented the same real world entities using inconsistent identifiers, formats, and semantic conventions. As data volumes and integration velocity increased, traditional rule based record matching and manual standardization processes struggled to maintain accuracy, coverage, and timeliness at enterprise scale. AI assisted record matching emerged as a practical response to these limitations by augmenting deterministic matching logic with probabilistic similarity scoring, contextual inference, and adaptive learning. Rather than replacing existing master data management controls, AI techniques were increasingly applied to improve candidate matching, resolve ambiguous records, and normalize attributes across heterogeneous inputs. These approaches enabled enterprises to detect duplicates, align entity representations, and maintain consistent master views while reducing manual stewardship effort. However, the introduction of AI into master data workflows also introduced governance challenges related to explainability, confidence thresholds, override accountability, and downstream trust in standardized outputs. This paper examines AI assisted record matching and standardization for enterprise master data as of March 2024, focusing on architectural patterns, matching workflows, confidence management, and governance controls. The discussion frames AI as an augmentation layer within controlled master data pipelines, emphasizing operational accuracy, traceability, and stewardship alignment. The paper positions AI assisted matching as a foundational capability for enterprise intelligence systems that require consistent, auditable, and scalable entity resolution across rapidly evolving data landscapes.

DOI: https://doi.org/10.5281/zenodo.18640329

 

Published by: vikaspatanker

Authors: Mr Deshmukh Y.V, Shubham Tagad, Rahul Pisal, Abhishek Suryavanshi, Naveen Kumar

Abstract: India is the world's largest democracy, and the core of any democracy is that people elect their own representatives. However, in today's times, the integrity of the election process faces numerous challenges such as booth capturing, rigging, fake voting, and tampering with Electronic Voting Machines (EVMs). As responsible engineers, it is our duty to take action to address these issues. Commonly used EVMs conduct voting electronically, eliminating the need for ballot papers, which are time-consuming and prone to intentional or unintentional errors. Currently, verifying voter authenticity is a major concern, and it must be ensured that no individual can vote more than once. This problem can be solved by implementing a biometric voting system that verifies voter identity through fingerprints, ensuring the principle of one person, one genuine vote. In this project, a prototype biometric voting machine based on fingerprint recognition has been developed. It is proposed to integrate a feature linking the Aadhaar database of the Unique Identification Authority of India (UIDAI), Government of India, New Delhi. This integration would allow voters to register automatically on the portal, categorized by regions and constituencies based on their unique fingerprint identification. This would enable the device developed in this research to be applied nationwide during elections, significantly improving the Indian electoral system.

Published by: vikaspatanker

Authors: Dr Amit Kumar Awasthi

Abstract: Nawabganj Bird Sanctuary, a Ramsar-designated wetland in the Unnao district of Uttar Pradesh, India, serves as a critical wintering habitat and stopover site for a multitude of migratory bird species traversing the Central Asian Flyway (CAF). This comprehensive review paper synthesizes four decades of ornithological data, ecological studies, and management reports to analyze the patterns, drivers, and conservation status of avian migration at this vital sanctuary. The analysis confirms Nawabganj’s role as a key refuge for over 250 bird species, with a significant influx of Palaearctic migrants between November and March. Dominant families include Anatidae (ducks, geese), Ardeidae (herons, egrets), Rallidae (coots, moorhens), and a diverse array of waders (Charadriiformes). Migration timing and species composition are primarily driven by photoperiodic cues in breeding grounds and the availability of wetland habitat, forage resources, and thermal cover in the sanctuary. However, the review identifies a multifaceted crisis threatening this ecological function. Severe anthropogenic pressuresincluding water scarcity due to upstream diversion and erratic rainfall, invasive plant species (Eichhornia crassipes, Prosopis juliflora) encroachment, agricultural runoff leading to eutrophication, unsustainable tourism, and increasing human-wildlife conflict in the surrounding landscapeare degrading habitat quality. Emerging evidence suggests shifts in arrival/departure timings and a potential decline in populations of certain diving ducks and sensitive waders, possibly linked to climate change and local habitat degradation. This paper concludes that while Nawabganj remains a biodiversity haven, its long-term viability as a migratory bird sanctuary is precarious. The review advocates for an urgent, science-based, and integrated management approach. Key recommendations include securing ecological water flows, implementing systematic habitat restoration (invasive species removal, creation of deeper zones), strengthening community-based conservation, establishing long-term ecological monitoring programs, and promoting regulated, eco-sensitive tourism. The findings underscore that the sanctuary's future is contingent on translating its protected status into effective, on-ground ecological security.

DOI: http://doi.org/10.5281/zenodo.18629460

Published by: vikaspatanker

Authors: Prof. Sonali Ingole, Mr. Rohit Rajpurohit, Mr. Kartikesh Pachkawade , Prof. Deepa Shivshimpi

Abstract: The rapid growth of technology and lifestyle modernization has significantly influenced the human mind, behavior, and emotional balance. This study investigates the impact of the modern lifestyle on the subconscious mind — the part of the human psyche that governs thoughts, emotions, and decisions beyond conscious awareness. A structured questionnaire was administered to 305 respondents, including students and professionals, to examine how daily habits such as screen time, sleep patterns, stress, and mindfulness practices affect subconscious stability. Findings show that excessive device use, irregular sleep, and frequent stress strongly affect subconscious calmness and self-awareness. Participants who maintained mindfulness routines reported greater emotional balance. The study concludes that while modernization improves efficiency, it disrupts subconscious harmony, emphasizing the need for balanced routines and conscious mental care.

DOI: http://doi.org/10.5281/zenodo.19182113

Published by: vikaspatanker

Authors: Anitha Rajathi, Pellakuru Mahathi, Bhavya G, B Harshitha Reddy

Abstract: Agriculture continues to face significant challenges due to crop diseases that result in reduced yield, economic losses, and delayed intervention, particularly in developing regions where access to expert diagnosis is limited. Traditional disease identification methods rely on manual inspection, which is time-consuming, subjective, and not scalable. This paper presents a Smart Crop Disease Detection System using Convolutional Neural Networks (CNNs) for automated and accurate identification of plant diseases from leaf images. The proposed system leverages deep learning techniques trained on real-world agricultural image data obtained from the PlantDoc dataset, which contains healthy and diseased crop leaves captured under diverse field conditions. A lightweight and efficient CNN architecture, MobileNetV2, is adopted to enable real-time disease detection with reduced computational overhead, making the system suitable for mobile and low-power devices. The model performs image classification to identify disease categories and assess plant health conditions. Experimental evaluation demonstrates that the proposed model achieves an accuracy of 85%, outperforming other baseline architectures. To enhance deployability, the trained model is converted into TensorFlow Lite, enabling seamless integration into mobile and web-based applications. The proposed framework facilitates early disease detection, supports timely preventive measures, and contributes to improved agricultural productivity through intelligent decision support.

Published by: vikaspatanker

Authors: Deepa Kumar M, Sutha K

Abstract: The systemic mismanagement of surplus food and clothing creates significant economic and social waste, necessitating a transition from manual, fragmented charity methods to automated, data-driven platforms. This paper analyzes a web-based Digital Redistribution System developed using PHP and MySQL to facilitate real-time resource allocation between donors (restaurants, individuals) and orphanages. By shifting from a "reactive" model—where surplus often spoils before discovery—to a "proactive" digital ecosystem, the system ensures timely collection and transparent tracking. The study highlights the effectiveness of Centralized Data Management and Validation Testing in reducing manual overhead and ensuring data integrity, ultimately proposing a scalable framework for minimizing waste in urban environments. By shifting from manual, often inefficient donation methods to an automated online system, this project aims to reduce hunger and minimize environmental waste.

Published by: vikaspatanker

Authors: Sourabh Upadhyay, Professor Jitendra Chouhan

Abstract: One of the main purposes of Highway bituminous pavement failure and its maintenance is to provide a better road surface for the road users and carry traffic smoothly and safely with minimum cost. Paved roads in tropical and sub-tropical climates often deteriorate in different ways to those in temperate regions, because of the harsh climatic conditions, lack of proper design and quality control, high loads and inadequate assessment for identifying causes of distresses before carrying out maintenance and rehabilitation. A pavement distress that occurs at the surface can have a number of different causes which must be properly identified before corrective action is taken. Proper maintenance is very essential for longer life of the road surface.

Published by: vikaspatanker

Authors: Nirmal Kumar Jingar

Abstract: Supply chain operations frequently experience incidents such as delays, shortages, quality failures, and logistics breakdowns. Identifying root causes quickly is critical, yet current incident management processes are largely manual, reactive, and error-prone. Existing systems primarily use rule-based alerts or statistical anomaly detection. Although effective in detecting issues, they lack deep causal reasoning and fail to correlate multi-source data across suppliers, transportation, and operations. This results in delayed resolution and repeated incidents. This paper introduces an automated incident intelligence framework using agentic AI with root cause reasoning. Specialized agents monitor supply chain signals, detect anomalies, and collaboratively perform causal analysis using knowledge graphs and probabilistic reasoning. Generative AI supports hypothesis generation and explanation of root causes in natural language, enabling faster human understanding and response. The proposed system was evaluated on simulated and real operational datasets involving multi-tier supply chains. Results show a 30% reduction in mean time to root cause identification and 22% improvement in incident resolution accuracy compared to traditional approaches. Additionally, the system successfully identified hidden dependencies that were missed by baseline methods. This work demonstrates the effectiveness of agentic AI in transforming incident management from reactive monitoring to proactive intelligence.

DOI: http://doi.org/10.5281/zenodo.18628070

Published by: vikaspatanker

Authors: Jay-An T. Biscocho, Keanne R. Noval, Lebron F. Calunsag, Nathalie G. Tangonan

Abstract: The objective of this study is to develop an automated surface vehicle prototype called AQuaRIUSV (Aquatic Quality Real-Time Information Using Surface Vehicle) designed to monitor water quality in marine ecosystems. The sensor being utilized by the prototype consists of pH, turbidity, and TDS sensors measuring key water quality parameters in real time. The system consists of a pH, turbidity, and TDS sensor; an Arduino Uno R4 Wi-Fi microcontroller for data processing and control; a Neo-6M GPS module for location tracking; an L298N motor driver operating dual DC motors for movement; SG90 micro servo motors for steering; and an ultrasonic sensor for obstacle detection. Monitoring was enabled by Blynk through an IoT dashboard. Performance was evaluated for accuracy, consistency, and reliability. Results show effective real-time monitoring via the IoT platform. The study concludes that AQuaRIUSV is a reliable, efficient, and sustainable system for continuous marine water quality monitoring.

DOI: http://doi.org/10.5281/zenodo.18627861

Published by: vikaspatanker