Daily Archives: June 11, 2025

Authors: Assistant professor Satya Pavan Kumar Voleti, Madhu Babu Thadee, Sirisha Adari, Pala Vijayab, Satya Praksh

Abstract: Power system safety is a one of the major focused areas in recent days due to mismatch between power generation and power demand. Successful operation of a power system depends largely on the engineer's ability to provide reliable and uninterrupted service to the loads. Safety includes both the operation and planning of power system networks i.e both voltage and frequency at allloads must be held within acceptable tolerances so that the patron equipment will operate efficiently.Inordertoachieve safety the system generators should run synchronously andwith adequate capacity to meet the load power demand. Secondly, the integrity of the power network should 'be maintained to ensure continuity of service. Power systems occasionally suffer perturbations. These perturbations may be small originating from random changes in loads or they may be severe arising out of a fault on the network.This paper presents brief overview of differenttypesofinstabilities inpower systemandthe techniques used to overcome it. The paper also compares the applicabilityof different techniques on the basis of performance.

 

 

Published by: vikaspatanker

Authors: Faria Khan

 

 

Abstract: The integration of Artificial Intelligence (AI) into healthcare systems has revolutionized the landscape of medical diagnostics, treatment planning, patient care, and healthcare operations. With exponential growth in data generation and computational capabilities, AI-based health technologies are being rapidly adopted across clinical, administrative, and research domains. This paper provides a comprehensive market analysis of AI-based health technologies, exploring the current trends, key market drivers, challenges, regional developments, and future forecasts. As AI continues to evolve, its impact on healthcare systems is expected to increase significantly, transforming traditional healthcare models into more predictive, personalized, and efficient systems. Through data-driven insights and strategic foresight, this study aims to highlight the critical factors influencing the market trajectory and predict the future scope of AI in the global healthcare sector.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Nandini Bhatt

 

 

Abstract: Artificial Intelligence (AI) and Big Data Analytics are reshaping pharmaceutical supply chain management by enabling greater efficiency, transparency, and resilience. This paper examines the transformative impact of AI-driven big data technologies on pharmaceutical supply chains, highlighting their roles in demand forecasting, inventory management, quality control, and risk mitigation. It discusses the challenges of integrating AI and big data in complex, regulated environments and explores ethical and operational considerations. The study emphasizes how leveraging AI and big data analytics enhances supply chain agility, reduces costs, and improves patient access to medicines while addressing issues such as data security and regulatory compliance.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Manisha Wasnik, Shreya Sharma, Aamir Shaikh, Abhishek Shinde, Harsh Tagde

Abstract: CryptoNavigator is a cryptocurrency tracking application built with Flutter, Dart, and Supabase. It fetches live market data through the CoinGecko API, providing real-time price tracking, search, favorites, and deep insights per cryptocurrency. Secure user login through email verification and profile management are some of its security features. One of the main features is price prediction to assist investors in making knowledge-based decisions. With a user-friendly UI and cross-platform support, CryptoNavigator aims to assist both new and experienced investors in tracking and predicting cryptocurrency trends.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Professor Anita Mahajan, Ajaz Shaikh, Shubham Ghume, Neeraj Lonkar, Saif Shaikh

 

Abstract: Drug development research is traditionally a lengthy, resource-intensive, and expensive process, often relying on experimental approaches and iterative laboratory trials. the emergence of generative adversarial networks (gans) hasintroduced a novel and efficient approach to this field by facilitating the generation of new molecular structures. This research explores the application of molgan, a specialized gan framework tailored for generating molecular graphs in drug discovery. traditional methods struggle with inefficiencies and the vastness of the chemical space, making it challenging to identify molecules with specific pharmacological properties. molgan addresses these limitations by automating molecular generation while incorporating desired chemical characteristics. by leveraging reinforcement learning techniques, molgan fine- tunes the generation process to produce drug-like molecules, enhancing both the speed and effectiveness of drug discovery efforts.

DOI: http://doi.org/

 

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Authors: Anjali Gupta, Assistant Professor Pooja, Dr. Rajendra Singh

Abstract: In the modern era of rapid urbanization, gated communities and residential societies require an integrated, scalable, and secure management solution to handle day-to-day operations efficiently. This paper presents the design and development of Smart Society Solution (SSS)—a web-based platform engineered using React.js for the frontend, Strapi for headless backend services, and Tailwind CSS/Bootstrap for responsive UI/UX. The system provides a modular architecture, supporting essential community management functions like dashboard analytics, service request handling, resident and visitor management, billing, and reporting. RESTful APIs tested via Swagger ensure seamless client-server communication. The solution offers robust extensibility and has potential implications for smart city planning and digital governance

 

 

Published by: vikaspatanker

Authors: Raghu V Kaspa

Abstract: High Performing Organizations (HPOs) consistently outperform their peers in metrics such as innovation, agility, financial results, and employee engagement. This paper explores the critical attributes that characterize HPOs and applies these attributes to Tesla, Inc., as a case study. Through an analytical lens grounded in organizational theory, performance frameworks, and empirical evidence, Tesla’s rise as a global automotive and energy leader is examined to identify the drivers of its high performance.

 

 

Published by: vikaspatanker

Authors: Sonali Bahuguna, Professor Indu Sharma

Abstract: This research examines the connection between educational and vocational interests and the academic performance of secondary school students in Delhi. Considering the recent inclusion of vocational courses in more than 800 government schools as part of the samagra shiksha scheme, this study examines the correlation between students' interests and their academic achievements. The study employs a stratified random sample of 500 students from various socio-economic and institutional backgrounds, utilizing standardized interest inventories and academic data analysis. The findings indicate a strong positive relationship between educational and vocational interests and academic performance, with vocational interests having a greater predictive power. This supports the belief that incorporating student interests into educational content can enhance academic engagement and achievement.

 

 

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Authors: Sanjana Bhandare, Ajinkya Pokharkar, Prerna Bhandare, Dnyaneshwar Chaudhari, Dr. Manisha Wasnik

Abstract: In this era of the most popular social networking, Twitter has many users who express their thoughts in the form of tweets. This paper presents an idea to extract sentiments from tweets and a method to classify tweets as good, bad or neutral. This approach is beneficial in many ways for all organizations mentioned or tagged in a tweet. Generally speaking, tweets are in unstructured format and tweets need to be converted into structure first. In this paper, tweets are analyzed using predefined steps and accessed by the library using Twitter API. The data needs to be learned using algorithms that allow it to test tweets and extract the desired information from given tweets.

 

 

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Authors: Shanmukha Priya Kurre, Bapanapalli Naga Bhargavi, Guntur Pushpa, Dabbugottu Thirupathi Vani, Chinthabttina Meghamala, Dhulipudi Chinmayi Sai Sri.

 

Abstract: The Red Chilli Defect Detection and Removal System is a robust and automated solution designed to identify and remove defective red chillies on a conveyor belt. Utilizing a Raspberry Pi 3 B V1.2 and a Pi Camera module, the system captures real-time images of chillies as they move along the belt. Advanced image processing algorithms analyze these images to detect defects based on predefined parameters such as color, shape, and texture. Upon detection of a defective chilli, a control signal is sent to a DC motor-controlled ejection mechanism powered by an L293D motor driver to remove the defective chilli from the conveyor. This automated approach enhances the efficiency and precision of the quality control process in industries handling red chilli sorting, ensuring higher throughput and consistent product quality.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.275

 

Published by: vikaspatanker