Authors: Dr.M. Lalithamigai, Harshini S, Srinithi A

Abstract: An AI studies team has taken artificial intelligence as a way for robots to enter into a new realm of technology in which they are no longer programmed with hard rules and can be adaptive, learn based on their surroundings, and therefore have the ability to evolve through their experiences. Robots that are being created using AI technology will provide robots with the ability to learn via machine learning (ML), computer vision, fusing sensor data through sensor fusion algorithms, and making decisions using algorithms suitable for the individual use cases. The work done by researching teams in these areas has been explored in this publication, including how this technology has changed and improved due to AI, as well as how it has changed the way we think of Robots and how they can manage tasks without requiring human input. The main body of research focuses on how Autonomy in Artificial Intelligence will change multiple industries, which include but are not limited to—(i.e.) Healthcare & Medical, Manufacturing, Transportation, Space Exploration, etc.

DOI: https://doi.org/10.5281/zenodo.19275480

 

Published by: vikaspatanker

Authors: Mamata Ramesh Patil

Abstract: So in India, with technology's widespread, the agricultural industry of India has been in the process of transformation due to rapid introduction of new-age technology in the agricultural ecosystem like digital platforms, automation and precision farming tooling, artificial intelligence and decision-making analytics. Many private agribusiness firms and agri-tech startups are already using this new technology to boost productivity, lower costs and deliver services to farmers. But the challenges facing those workers are new as well, and it’s hard to tell you who the employees working in such organizations will face. Technology-dependent firms employ agricultural graduates who need to learn new tools instantly, upgrade skills constantly and work under high pressures to perform. The current study provides a reflection on the current human resource problems experienced by graduates of agriculture working at technology-intensive agribusiness companies, with especial attention to their motivation for work and job satisfaction. Data were obtained from 21 agrarian graduates who worked at private agribusiness companies and agri-tech firms using a structured questionnaire as a primary source. Key components addressed in the study include technological applicability, training support, support from the organization itself, perceived satisfaction with their salary, career progress prospects and work social life balance. The results suggest that the majority of the respondents are willing and comfortable with new technologies and have a moderate to high level of motivation. But there are issues of fairness, career advancement, and work pressure. There were positive associations of good training and supportive management with satisfaction level. The research suggests that companies cannot simply promote technological success, unless they properly address the issues with human resource. Which indicates that agribusinesses need to pay attention to worker development, the supportive work environment for employees and fair salaries to retain the motivated workforce in a tech-oriented agricultural environment.

Published by: vikaspatanker

Authors: Mrs. A. Sangeetha Priya, Ms. Aneesha Barveen.S, Ms. Shahar Banu.M

Abstract: The communication between hearing impaired individuals and the general public still remains a challenge due to the lack of real time sign language interpretation systems. This paper presents a real time webcam-based sign language translation system using computer vision to facilitate efficient communication. The proposed system analyses live video feed from a standard webcam using a vision-based pipeline for hand gesture recognition. The proposed system employs hand landmarks to analyse the video feed using a strong computer vision framework, which assists in extracting precise spatial information from sign language gestures. The extracted information is then analysed and categorized to identify corresponding sign language symbols, which are then translated into readable text output in real time. The end of this research work reveals that the proposed approach is a cost effective and efficient solution for sign language translation. The solution will focus on processing, latency, and usability, making it useful for real world assistive communication problems. The experimental analysis proves the accuracy of the recognition in controlled lighting conditions and various orientations of the hand. The solution will prove that the proposed solution is cost effective and scalable for sign language translation. This research work validates the application of computer vision based assistive technology to enhance communication accessibility and inclusivity. The proposed system can be further extended to support the translation of a broader vocabulary set, dynamic signs, and multiple languages.

DOI: https://doi.org/10.5281/zenodo.19254689

 

Published by: vikaspatanker

Authors: Dr.S. Thilagavathi, Mohammed Safi TJ, Ms. Diyana Fathima H

Abstract: PLANEXA is a hierarchical reasoning system that aims to assist in medical diagnostic decision-making in a complex clinical environment. PLANEXA structures medical knowledge into multiple levels of reasoning, from basic patient information such as symptoms, vital signs, lab results, and medical history. It progresses to higher-level tasks such as forming diagnostic hypotheses and assisting in clinical decision-making. PLANEXA employs rule-based reasoning, probabilistic inference, and knowledge-driven models to effectively address diagnostic uncertainty and interdependencies among clinical variables. The system's design enables it to decompose complex diagnostic problems into smaller, more tractable sub-problems. This strategy enables efficient reasoning, hypothesis refinement, and learning from new patient data as it becomes available. PLANEXA is also concerned with explainability, as it develops well-defined diagnostic pathways that help clinicians understand why particular diagnoses and recommendations are made. This helps to establish trust, usability, and its integration into the clinical workflow. Results from experimental evaluations conducted on representative clinical cases and standard benchmark problems demonstrate that PLANEXA enhances diagnostic performance, reduces reasoning complexity, and improves decision consistency relative to traditional flat or single-layer models. PLANEXA has immense potential for scalability across multiple domains of medicine and evolving with changes in clinical knowledge. PLANEXA marks an important advancement toward smart, understandable, and dependable AI-driven medical diagnostic support systems that aim to reduce diagnostic errors and improve patient outcomes.

DOI: https://doi.org/10.5281/zenodo.19254441

Published by: vikaspatanker

Authors: Harris Frank DJ, Thansil Ahamed S, Ms. B. Vinitha

Abstract: Integrating the Internet into many applications has made securing users’ data and maintaining their privacy a significant concern. In recent years, blockchains (BC) have garnered much attention due to their distinctive properties, which include decentralization, immutability, anonymity, security, and auditability. BC technology was utilized in various non- financial applications, like the Internet of Things (IoT), wireless sensor networks (WSN), and cloud computing. The objective of this study is to conduct an analysis of previously published research and provide a summary of the efforts put into researching BC applications for network security. In this study, many networking technologies, including IoT, Industrial IoT, Cloud, WSN, VANET, and MANET, were used in conjunction with BC technology to investigate applications for network security. This study presents an analysis of network security, along with its limitations and contributions, with an overview of the BC evolution, BC architecture, its working principle, and its application, as well as the advantages and disadvantages associated with BC. In this study, recently published articles on BC-based solutions for network security and privacy preservation that were published between 2018 and 2022 are analyzed. The surveyed articles are categorized according to the network application, methodology, and contribution. In conclusion, an analysis of the implementation of BC technology across various networks and their issues and challenges are presented.

DOI: https://doi.org/10.5281/zenodo.19253455

Published by: vikaspatanker

Authors: Mr.P.M.Mohammed Sarjun, Mr.S.Sanjay Aravinth, Ms.B.Vinitha

Abstract: The fast development of Information and Communication Technology (ICT) has changed digital infrastructures into connected, smart, and data-focused systems. Today’s ICT environments produce large amounts of different data from Internet of Things (IoT) devices, business systems, cloud platforms, mobile networks, and spread-out communication setups. While new technologies like Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics, Cloud Computing, and improved Cybersecurity methods have shown significant progress in automation and scalability, using them separately often leads to fragmented structures, issues with compatibility, and security risks. This research proposes a detailed multi-layer framework for smart and secure Information and Communication Systems using new ICT technologies. The framework combines real-time data collection, distributed data handling, AI-driven predictive analytics, encryption-based communication methods, anomaly detection systems, and hybrid cloud orchestration into one architecture. The proposed model focuses on modularity, scalability, interoperability, and built-in security features to ensure resilience against changing cyber threats. Experimental validation using simulated distributed ICT datasets shows notable performance improvements. These include a 32% reduction in latency, a 34% boost in throughput, and a 96.4% accuracy rate in detecting anomalies. The framework can be applied in smart cities, healthcare systems, enterprise automation, and intelligent transportation systems. This study offers a clear plan for future ICT architectures that support sustainable and secure digital change.

DOI: https://doi.org/10.5281/zenodo.19252668

Published by: vikaspatanker

Authors: Ms. Joshika.J, Ms. Mangayarkkarasi.G, Dr. P. Jayasheelan

Abstract: Water scarcity is one of the major global challenges affecting human life, agriculture, and industrial development. Rapid population growth, climate change, and inefficient water usage have intensified this problem. This paper presents a smart mobile application for water scarcity prediction and management. The proposed system collects real-time data on water usage, weather conditions, and water availability through sensors and user input. The application analyzes this data using machine learning techniques to predict future water shortages. It also provides alerts, usage reports, and conservation suggestions to users. The system aims to promote efficient water utilization and create awareness about water conservation. The experimental results show that the proposed solution is cost-effective, user-friendly, and suitable for real-world implementation.

DOI: https://doi.org/10.5281/zenodo.19252024

Published by: vikaspatanker

Authors: Ebormi S Langshiang, Ambiangmiki S Langshiang

Abstract: Background: The South West Khasi Hills District of Meghalaya, India, is among the most disaster-prone regions in Northeast India, regularly exposed to earthquakes, landslides, flash floods, and cyclonic winds due to its complex geomorphology and geological settings. Despite heightened vulnerability, systematic Disaster Risk Reduction (DRR) education within formal school settings remains critically underdeveloped. Objectives: This study examines the effectiveness of school-based DRR familiarization programs in enhancing disaster preparedness knowledge among secondary school students in the district. Methods: Using a quasi-experimental pre-test/post-test research design, data were collected from 376 students across eight purposively selected schools. Structured questionnaires, direct observation, and focus group discussions constituted primary data collection instruments. Paired sample t-tests, one-way ANOVA, chi-square tests, and multiple linear regression analyses were employed. Results: Post-program DRR knowledge scores increased significantly (pre-mean = 2.12; post-mean = 3.76; t = 22.47, p < 0.001). The familiarization program demonstrated statistically significant improvements across all six knowledge domains, including hazard identification, evacuation procedures, first aid basics, early warning systems, risk mapping, and community response. Grade level (F = 19.84, p < 0.001) and school type were significant moderating variables. Multiple regression revealed that pre-program knowledge (β = 0.38), grade level (β = 0.22), and participation duration (β = 0.19) were the strongest predictors of post-program learning outcomes (R² = 0.579). Conclusion: Structured DRR familiarization programs embedded within the school curriculum are highly effective in building resilience competencies among students in disaster-prone hill districts. Policy recommendations include institutionalizing DRR modules within the formal curriculum, training teachers as DRR facilitators, and establishing school disaster management committees.

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Published by: vikaspatanker

Nagender Yamsani
Affiliation	Inspire Brands as Lead MDM Engineer.
Email-Id:	nyamsani@gmail.com
Professional Summary; Nagender Yamsani is an accomplished Global Master Data Architect and Senior Salesforce Developer at Inspire Brands, bringing more than 23 years of experience in enterprise data management, digital transformation, and large scale systems engineering. He specializes in Master Data Management (MDM), data governance, metadata strategy, and AI enabled automation, with deep expertise in architecting multi domain MDM solutions using TIBCO EBX. At Inspire Brands, Nagender leads the development of enterprise‑grade data models, governance frameworks, and intelligent stewardship workflows that ensure trusted, harmonized, and analytics‑ready master data across a global portfolio of brands. His advanced command of TIBCO EBX enables him to design scalable, policy‑driven governance structures that significantly improve data quality, regulatory compliance, and operational efficiency.Beyond his enterprise leadership, Nagender actively contributes to the broader research and technology community through peer review, editorial engagements, and scholarly writing in data engineering, AI governance, and enterprise systems. His work has earned multiple international recognitions for innovation and impact in digital transformation.With a strong blend of technical depth, architectural vision, and academic engagement, Nagender is committed to advancing global best practices in MDM, explainable AI, and enterprise data governance, making him a valuable contributor to editorial boards seeking authoritative expertise in these domains.

Published by: vikaspatanker

Authors: Miss Priyanka A.Narad, Prof. Rahul Bhandekar, Prof.Vijayata Dalwankar

Abstract: Document forgery has become a serious concern in digital services such as banking, education, recruitment, and government verification systems. Manual verification is time-consuming, error-prone, and not scalable. This research proposes an AI-based document verification system that combines Optical Character Recognition (OCR), Convolutional Neural Networks (CNN), and metadata analysis to verify the authenticity of digital documents. The system performs image forgery detection, text consistency verification, and metadata anomaly checking to generate a final authenticity score. By integrating visual, textual, and hidden metadata features, the proposed approach improves reliability, reduces false verification, and supports automated decision-making. Experimental analysis demonstrates that the hybrid model outperforms traditional single-technique verification methods and is suitable for real-world document authentication systems.

DOI: https://doi.org/10.5281/zenodo.19250764

 

Published by: vikaspatanker