Daily Archives: March 7, 2026

Authors: Mr. Prathmesh M. Sadafale, Mr. Pratik S. Date, Mr. Raj S. Kharate, Prof. Ravindra R. Solanke

Abstract: Abstract – This research presents the design and development of an Embedded Intelligent System for Automatic Speed Regulation in sensitive areas such as school zones, hospitals, residential areas, and accident-prone locations. The main objective of the system is to improve road safety by automatically controlling vehicle speed without relying only on driver awareness. The proposed system uses embedded technology, sensors, and wireless communication to detect designated speed-control zones. When a vehicle enters a sensitive area, the system automatically limits its speed to a predefined safe level. Once the vehicle exits the zone, normal speed control is restored. The system operates in real time and reduces the risk of over-speeding. By minimizing human error and ensuring consistent speed regulation, the system enhances road safety, reduces accidents, and supports smarter transportation infrastructure.

 

 

Published by: vikaspatanker

Authors: Deepak Tomar, Kismat Chhillar

Abstract: Diabetes mellitus has emerged as a major global health concern, necessitating early detection and effective predictive mechanisms to support timely medical intervention. Machine learning techniques have increasingly been employed in healthcare analytics to improve diagnostic accuracy and assist clinicians in decision making. Among these techniques, the Support Vector Machine (SVM) algorithm has demonstrated strong performance in classification problems involving medical datasets. This study explores the application of SVM for predicting the likelihood of diabetes using patient health indicators such as body mass index, blood glucose level, age, and family medical history. By analyzing patterns within clinical data, the model classifies individuals into diabetic and non-diabetic categories. The predictive capability of SVM allows the identification of individuals who may be at risk of developing diabetes, thereby enabling preventive healthcare measures. Empirical findings from related studies indicate that SVM-based models can achieve high predictive accuracy, making them a reliable approach for diabetes prediction and early risk assessment in medical decision support systems.

DOI: https://doi.org/10.5281/zenodo.18899892

Published by: vikaspatanker

Authors: Rekha Agarwal, Rajesh Kumar Mishra, Divyansh Mishra

Abstract: Recent solar cycles, particularly the ascending and peak phases of Solar Cycle 25 (2020–2025), have been characterized by heightened solar activity, including X-class flares, fast coronal mass ejections (CMEs), and complex interplanetary shocks. These transient events strongly modulate galactic cosmic rays (GCRs) and produce solar energetic particles (SEPs), thereby altering the flux, energy spectrum, and anisotropy of high-energy charged particles in near-Earth space. This paper synthesizes observational and theoretical advances concerning the effect of recent solar events on high-energy cosmic ray particles (>100 MeV to multi-GeV), with emphasis on Forbush decreases, shock acceleration, magnetic cloud interactions, and ground level enhancements (GLEs). We discuss observations from neutron monitor networks and space-based detectors such as Parker Solar Probe, Solar Orbiter, ACE, and GOES, highlighting case studies from 2021–2024. Quantitative comparisons reveal cosmic ray depressions of 3–20% during major CME passages and episodic enhancements up to GeV energies during extreme SEP events. The broader implications for space weather, atmospheric ionization, and radiation risk are examined.

 

 

Published by: vikaspatanker

Authors: Jose Gnana Babu, Lata Khani Bisht, Visaga Perumal, Vineeth Chandy

Abstract: In recent years, artificial intelligence (AI) has emerged as a strategic catalyst in the field of drug discovery, revolutionizing one of the most complex and resource-intensive areas of the pharmaceutical industry. AI introduces innovative methodologies that enhance efficiency and precision across multiple stages of drug discovery and development, including—though not limited to—virtual screening, target identification, lead optimization, and clinical trials. This review provides an in-depth examination of current AI-driven tools, programs, and platforms that are reshaping modern drug discovery. Beyond presenting the present state of AI applications in this domain, it also explores future directions, existing challenges, and emerging opportunities. The traditional drug discovery process is often constrained by its high cost, long timelines, and substantial attrition rates. However, the integration of AI and machine learning (ML) has introduced transformative solutions, making drug development more rapid, cost-effective, and data-driven. Leveraging vast biological and chemical datasets, AI and ML employ advanced computational techniques—such as neural networks, natural language processing (NLP), and reinforcement learning—to enhance prediction accuracy and streamline decision-making throughout the drug discovery pipeline. These technologies facilitate the identification of novel therapeutic targets, accurate efficacy and safety predictions, and the optimization of clinical trial design, thereby significantly shortening development cycles and reducing overall expenditures. Real-world case studies further illustrate AI’s contribution to groundbreaking therapies in fields such as oncology, neurodegenerative disorders, and rare genetic diseases. Despite these remarkable advancements, notable challenges remain. Concerns surrounding data quality, model transparency, algorithmic bias, and regulatory compliance continue to pose barriers to widespread adoption. Moreover, ethical issues related to data privacy, accountability, and the interpretability of AI-driven decisions demand critical attention. Looking ahead, emerging paradigms such as multi-omics data integration, quantum computing, and precision medicine are expected to redefine the landscape of AI-assisted drug discovery. Achieving this vision will require interdisciplinary collaboration, technological innovation, and the establishment of robust ethical and regulatory frameworks. Collectively, these efforts will pave the way for a new era of patient-centric, precision-driven pharmaceutical development, fully harnessing the transformative potential of AI and ML in drug discovery.

DOI: https://doi.org/10.5281/zenodo.18899332

Published by: vikaspatanker

Sriram Ghanta
Affiliation	Senior Java Full Stack Developer
Email-Id:	shriram.gh@gmail.com
SUMMARY:- Results-oriented Senior Java Full Stack Developer with over 14 years of experience in designing, developing, and integrating enterprise-level applications across Retail, Finance, Healthcare, and Entertainment domains. Expertise in Java, Spring Boot, Microservices, RESTful APIs, and modern front-end technologies such as Angular. Proven ability to build scalable, cloud-native applications using AWS and containerized environments. Strong experience in system design, performance optimization, and implementing resilient architectures. Adept at working in Agile environments and delivering high-quality software solutions

Published by: vikaspatanker

Authors: Pranali Gavhane, Sapna Gaikwad, Sumit Ghogare, Abhay Ghalke

Abstract: This project proposes an Arduino-based automatic water distribution system designed to optimize water supply, minimize wastage, and reduce manual intervention in domestic or municipal contexts. Utilizing an Arduino microcontroller (such as Uno or Mega) as the central processing unit, the system integrates sensors to monitor water levels, flow rates, or soil moisture.

 

 

Published by: vikaspatanker

Authors: Pratik Bhosale, Vaishnavi Rathod, Kajal Zore, Parshuram Baile, Prof.Savita Biradar

Abstract: College Sports Management System is a modern web application designed to manage and organize sports activities within a college efficiently. In many educational institutions, sports events such as tournaments, team registrations, and match schedules are still managed manually using paper records or spreadsheets. This traditional approach can lead to errors, data loss, and difficulties in managing large amounts of information. To overcome these challenges, the College Sports Management System provides a digital platform that automates and simplifies the management of sports-related activities. The system is developed using React JS for the frontend and Python for the backend, providing a fast, interactive, and scalable web application. React JS helps in creating a dynamic and user-friendly interface where users can easily navigate through different sections such as sports events, match schedules, and team details. The backend developed using Python handles the application logic, data processing, and communication between the frontend and the database. This architecture ensures better performance, maintainability, and scalability of the system. The main objective of this system is to provide a centralized platform where administrators can efficiently manage players, teams, tournaments, and match schedules. The system includes several modules such as Admin Management, Player Registration, Team Management, Tournament Scheduling, and Match Result Management. Through the admin panel, administrators can add and manage player information, create teams, organize tournaments, schedule matches, and update match results. On the other hand, students and users can view sports event details, match schedules, and tournament results through the web interface. By implementing the College Sports Management System, the process of organizing and managing sports activities becomes more efficient and structured. The system reduces manual workload, improves data accuracy, and ensures that sports information is easily accessible. The use of modern web technologies like React JS and Python allows the application to provide better user experience, faster data handling, and improved system performance. In conclusion, the College Sports Management System offers an effective digital solution for managing sports events in colleges. It enhances the overall organization of sports activities and provides a convenient platform for administrators and students to access sports-related information. The system can also be extended in the future with additional features such as online player registration, live match score updates, and mobile application integration.

Published by: vikaspatanker

Authors: Prof. Atish Shriniwar, Ms.Vaishnavi Tikone, Ms.Akanksha Sawant, Prof. Badrinath Bulepatil

Abstract: The rapid expansion of India’s digital governance ecosystem under the Digital India initiative has positioned e- governance portals as critical platforms for delivering public services. However, the effectiveness of these platforms depends not only on service availability but also on ensuring accessibility, usability, and reliable performance. This study evaluates selected Indian e-governance portals to identify gaps affecting inclusive digital access. A mixed-method approach was adopted, combining automated accessibility testing based on WCAG 2.1 Level AA standards with a user perception survey conducted among 44 participants. The findings reveal a significant “Accessibility–Usability Gap.” Although most respondents were digitally proficient young adults, only 13.6% reported being very satisfied with their overall experience. Approximately 50% identified technical glitches and poor system performance as primary barriers, while 45.5% reported inadequate mobile compatibility. Furthermore, 27.3% indicated that accessibility support features, such as screen reader compatibility, were insufficient. The study concludes that digital accessibility must extend beyond technical compliance to incorporate mobile-first design principles, improved system performance, and user-centered interface development. Implementing these improvements can foster a more inclusive, efficient, and equitable digital governance framework in India.

Published by: vikaspatanker