Authors: Yasik Sharma

 

 

Abstract: – Regenerative medicine aims to restore or replace damaged tissues and organs, offering new therapeutic strategies for conditions previously considered incurable. Nanotechnology, through the manipulation of materials at the nanoscale, has emerged as a transformative tool in this field by enabling precise control over cellular behavior and tissue microenvironments. Nanomaterials such as nanoparticles, nanofibers, and nanotubes exhibit unique physicochemical properties that facilitate enhanced scaffold design, targeted drug delivery, and real-time monitoring of tissue regeneration. This paper reviews current advancements in applying nanotechnology to regenerative medicine, focusing on its role in tissue engineering, stem cell modulation, and biomolecular delivery. Key challenges including biocompatibility, toxicity, and scalability are discussed, alongside future prospects that suggest integration of nanotechnology with biofabrication and personalized medicine could revolutionize regenerative therapies.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Kashish Srivastava, Prerana Kumari, Jatin Sharma, Nikhil Sharma, Dr. Hitesh Singh, Dr. Vivek Kumar

Abstract: The AI-Powered Personalized Cancer Treatment App "WeCan" is an innovative initiative that incorporates the latest technologies such as React.js, Vite, and Gemini AI to bring back personalized cancer treatment recommendations to patients. Our revolutionary approach leverages machine learning algorithms along with deep medical expertise to review individual patient profiles such as genetic data, medical history, and lifestyle factors to develop customized treatment plans optimized for efficacy and reduced side effects. WeCan's user-friendly interface allows patients to enter their medical details and gain personalized advice, while healthcare providers can view detailed reports and track patient progress in real-time. This enables data-driven decision-making and maximizes patient outcomes throughout the cancer care continuum. The core of WeCan's architecture revolves around an ultrasecure data management system that protects sensitive patient data through compliant storage and secure processing techniques. Sophisticated encryption technologies safeguard data in transit and at rest. The back end is engineered to integrate with existing healthcare infrastructure seamlessly, supporting effective data exchange with minimal administrative burden for providers, so they can spend more time on patient care and less on paperwork. With the fusion of AI-driven insights and predictive analytics, WeCan enables healthcare providers to detect high-risk patients and create customized treatment plans based on the individual characteristics, background, and genetic profile of each patient. Through this personalized strategy, decision-making is more informed, resources are optimized, and patient outcomes are maximized. The tool integrates patient data, such as treatment plans, prognosis factors, and probable outcomes, into a single, user-friendly interface. This simplifies the handling of cancer care and facilitates improved coordination among healthcare professionals, patients, and caregivers along the journey. Ultimately, this patientfocused and cooperative strategy revolutionizes the way healthcare providers interact with cancer patients by facilitating more efficacious and personalized treatments.

Published by: vikaspatanker

Authors: Ms. Monalisa Baral, Ms. Komal Bhamble, Dr. Jasbir Kaur, Ms. Ifrah Kampoo

Abstract: A variety of real-world problems are being addressed today by soft computing, but limited research has been found in the area of neuroimaging. Given the vast area of neuroimaging that can be explored through soft computing, researchers can work on and improve their work. The results of this research should advance soft-computing approaches to address neuroimaging problems. However, the human brain contains a large portion of the dataset, and the dataset is not homogeneous, so it is not easy to deal with the problem. A heuristic approach to soft computing can play an important role in addressing problems within limited time constraints. Furthermore, fuzzy logic is also an effective neural networks, fuzzy logic, statistics, and probabilistic inference. In addition, parts of machine learning and optimization are also included in this domain. However, Neuro imaging is primarily focused on brain data and the impact on behavioral and cognitive data by analyzing solution for identifying neurodivergent diseases. To combine all the fruitful solutions, this research topic aims to summarize all the solutions by covering a wide range of soft computing and neuroimaging methods

DOI: 10.61137/ijsret.vol.11.issue3.153

 

Published by: vikaspatanker

Authors: Manasa Jain

 

 

Abstract:

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Manasa Jain

 

 

Abstract:

DOI: http://doi.org/

 

 

Published by: vikaspatanker

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