Category Archives: Uncategorized

Authors: Manasa Jain

 

 

Abstract:

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Keerthi Kumar

 

 

Abstract: The convergence of Artificial Intelligence (AI) and nanomedicine has sparked a transformative wave in the biomedical and pharmaceutical industries, opening new pathways for disease diagnosis, treatment, and drug delivery at the nanoscale. As AI technologies enhance the design, functionality, and application of nanomaterials, nanomedicine ventures have become highly attractive to investors seeking long-term value and breakthrough innovations. This paper presents a comprehensive analysis of investment strategies in AI-driven nanomedicine ventures, focusing on the unique technological, financial, and regulatory dynamics of this rapidly growing domain. From venture capital and private equity to public funding and strategic partnerships, the investment landscape surrounding AI-nanomedicine is evolving, driven by innovation potential, patient demand, and the promise of market disruption. By examining investment trends, risk management techniques, key success factors, and emerging market opportunities, this paper offers a strategic framework for stakeholders aiming to capitalize on this cutting-edge intersection of technology and healthcare.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Kusum Kumari, Goutam Shaw, Debosmita Sukul, Anurima Majumdar, Antara Ghosal, Koushik Pal

Abstract: This paper discusses the advancement, challenges, and future of machine unlearning with emphasis on its significance in enhancing data privacy, security, and compliance with regulatory requirements. The review process began in 2015 and is ongoing to the current year. As privacy has become the focal point within the machine learning community, along with regulations like the General Data Protection Regulation (GDPR), machine unlearning—removing specific data from machine learning models—has attracted significant attention. The process of deleting such data is naturally timeconsuming, considering that it requires a complete retraining of the entire model; hence, traditional models have a dilemma because the process of erasing data is technically challenging and usually impractical considering the associated costs of computation. Machine unlearning enhances data privacy by facilitating selective erasure of specific data points without the need for total model retraining. It also improves model responsiveness and compliance with regulations like GDPR, hence encouraging the ethical application of artificial intelligence. The advantages of machine unlearning are enhanced data privacy, enhanced model performance, efficient utilization of resources, reduction of bias, quicker updates, and ensured compliance with ethics and laws. Through out the extensive literature survey a significant gap is observed to be that there are no reproducible, standardized procedures confirming the complete and effective elimination of data without compromising model efficiency and scalability. Areas of latent application in sectors like healthcare, finance, personalized services, and federated learning are identified, particularly in situations where unlearning is required to ensure privacy and compliance with regulations.

DOI: DOI: http://doi.org/10.5281/zenodo.15783199

 

Published by: vikaspatanker

Authors: Mamata Gowda

 

 

Abstract: Maharani’s CollegeAs Artificial Intelligence (AI) transforms the biomedical industry, regulatory bodies face the critical task of ensuring that these innovations are safe, ethical, and effective for public use. From diagnostic algorithms and AI-enhanced drug development to robotic surgeries and personalized medicine, AI technologies are redefining clinical practices and research methodologies. However, their rapid integration raises significant regulatory challenges, particularly in areas concerning data privacy, algorithmic transparency, clinical validation, and liability. This paper provides an in-depth exploration of the current regulatory landscape governing AI in biomedical applications. It analyzes the roles of major regulatory agencies such as the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), and others in shaping guidelines for AI deployment. Furthermore, it highlights the complexities involved in classifying AI tools, updating compliance frameworks for adaptive algorithms, and harmonizing international standards. By dissecting case studies and emerging trends, this paper offers insights into how regulatory frameworks can evolve to balance innovation with patient safety and public trust in the age of AI-powered healthcare.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

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/

 

Published by: vikaspatanker

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