Category Archives: Uncategorized

Authors: R.L Aarthi, Dr. S. Selvi

Abstract: The Vedic Multiplier, derived from the ancient Urdhva-Tiryakbhyam sutra, provides an efficient and structured approach to perform high-speed multiplication, which is a fundamental operation in digital signal processing, image processing, embedded systems, and VLSI applications. Conventional multipliers such as array or Wallace tree multipliers, although accurate, often require large hardware resources and suffer from increased delay due to complex carry propagation paths, limiting their suitability for low-power and small-scale designs. In this project, a Vedic multiplier is designed and implemented in Verilog HDL, incorporating Ripple Carry Adder (RCA) optimization for the accumulation stage to reduce design complexity and ensure consistent performance. The design process covers the implementation of basic modules including AND, OR, Half Adder (HA), Full Adder (FA), and Ripple Carry Adder (RCA), which are then combined to form 2-bit and 4- bit Vedic multipliers. By leveraging the RCA for final addition, the architecture minimizes hardware overhead while maintaining reliable accuracy across test cases. Simulation and functional verification were carried out using industry-standard EDA tools, and results validate the correctness of multiplication operations for various inputs with low area utilization and moderate delay. The optimized Vedic multiplier demonstrates efficient trade-offs in terms of area and delay, establishing it as a simple yet effective solution for arithmetic-intensive applications in energy-constrained embedded systems and FPGA-based platforms, with scalability potential for higher bit-width multipliers. Furthermore, the simplicity of the RCA- based approach makes the proposed architecture highly adaptable for classroom learning, research, and prototyping environments where clarity and resource efficiency are essential. While advanced adders such as Carry Lookahead or Carry Save adders may provide lower propagation delay in large-scale multipliers, the Ripple Carry Adder offers a favourable balance of low power consumption, reduced complexity, and straightforward implementation, making it especially effective for small-to-medium bit-width operations. This highlights the practicality of the proposed design as a baseline for further optimization, with the potential to extend towards pipelined or parallel Vedic multiplier architectures suitable for real-time signal processing and embedded computing applications.

DOI: https://doi.org/10.5281/zenodo.19276670

Published by: vikaspatanker

Authors: Mr. Anne Murali Krishna, Dr. M. A. Rasheed, Dr. N.Y. Raju

Abstract: Healthcare and pharmaceutical organizations depend on robust and efficient supply chains to ensure continuous access to essential medicines and medical products. Recent global disruptions have exposed critical vulnerabilities in conventional supply chain structures, underscoring the urgent need for strategic transformation. This study investigates the influence of supply chain resilience, risk management practices, and collaborative strategies on operational efficiency within healthcare and pharmaceutical organizations. A quantitative research approach was employed, with primary data collected from 180 supply chain professionals; this sample also served as a pilot study to validate the research instrument. Reliability and validity were established through appropriate statistical tests. Data were analyzed using descriptive statistics, correlation analysis, and multiple regression techniques. The findings reveal that resilience-focused supply chain strategies have a significant positive impact on operational efficiency and overall performance. The study offers empirical evidence and practical insights to support the development of more resilient healthcare and pharmaceutical supply chains in increasingly uncertain and dynamic environments.

DOI: https://doi.org/10.5281/zenodo.19276170

Published by: vikaspatanker

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