Category Archives: Uncategorized

Authors: Tabassum Begum

Abstract: Personalized medicine, which aims to tailor healthcare interventions to individual patients, is revolutionizing modern healthcare. Predictive analytics, powered by machine learning algorithms, plays a pivotal role in this transformation by extracting valuable insights from vast and heterogeneous healthcare data. This paper explores the application of predictive analytics in personalized medicine, focusing on the machine learning methodologies that enable disease prognosis, patient stratification, and treatment optimization. We discuss the types of healthcare data utilized, challenges such as data quality and interpretability, and highlight case studies across various disease domains. Finally, we examine future prospects for integrating predictive analytics into routine clinical workflows to enhance patient outcomes.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.556

 

 

Published by: vikaspatanker

Authors: Satish Swamy

Abstract: The advent of high-throughput sequencing technologies has revolutionized genomics by generating massive volumes of data, uncovering the genetic basis of complex diseases. However, the sheer complexity and dimensionality of genomic data pose substantial challenges for traditional analytical methods. Artificial intelligence (AI), particularly machine learning and deep learning, provides powerful tools to analyze, interpret, and integrate genomic data to unravel the intricate genetic architecture of complex diseases. This paper explores AI methodologies applied in genomic data analysis, focusing on variant calling, functional annotation, gene-gene interactions, and disease risk prediction. It examines current applications, challenges such as data heterogeneity and model interpretability, and discusses future perspectives in advancing precision medicine.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.555

 

 

Published by: vikaspatanker

Authors: Priya Deshmukh

Abstract: Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS), impose a significant burden on public health worldwide. These diseases typically develop insidiously over years, with symptoms becoming apparent only after substantial neuronal loss has occurred. Early and accurate diagnosis is paramount to implementing interventions that could delay progression, improve patient quality of life, and optimize healthcare resources. In recent years, machine learning (ML) has emerged as a revolutionary approach for processing complex biomedical data to assist in early diagnosis and prognosis of neurodegenerative conditions. This paper comprehensively explores the diverse machine learning methodologies applied to early ND diagnosis, emphasizing the role of neuroimaging, molecular biomarkers, genetic data, and clinical assessments. It discusses the entire diagnostic pipeline from data acquisition to model deployment, addresses challenges such as data heterogeneity and interpretability, and outlines future directions to integrate ML-based systems into clinical practice effectively.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.554

 

 

Published by: vikaspatanker

Authors: Deepshikha Jain, Associate Professor R.K.Bhatia

 

Abstract: The purpose is to investigate the sources and physio-chemical characteristics of effluent generated by the galvanizing industry. This study aims to highlight the potential environmental impacts of such effluents and to identify the specific metallic pollutants present.Study Design/Methodology/ApproachA comprehensive analysis was conducted on effluent samples collected from various galvanizing facilities. The study employed standard analytical techniques to measure key physio-chemical parameters, including pH, biochemical oxygen demand (BOD), and chemical oxygen demand (COD). Finding.The analysis revealed that the effluent exhibited an acidic pH, indicating a significant deviation from neutral conditions. High levels of BOD and COD were detected, suggesting a substantial organic load that could negatively impact aquatic ecosystems. These findings underscore the pressing need for effective treatment and management strategies to mitigate the environmental risks associated with galvanizing industry effluents. Originality This study contributes to the existing body of knowledge by providing a detailed characterization of galvanizing industry effluents. The identification of specific metallic pollutants offers valuable insights for regulatory agencies and industry stakeholders. The findings serve as a foundation for future research aimed at enhancing effluent treatment processes and promoting sustainable practices within the galvanizing sector.

DOI: 10.61137/ijsret.vol.11.issue3.126

 

Published by: vikaspatanker

Authors: Manoj Sekhar

Abstract: Nanomedicine has revolutionized therapeutic strategies by enabling targeted drug delivery, controlled release, and improved bioavailability. However, the complexity of biological systems and variability among patients often limits the efficacy of nanomedicine therapies. Machine learning (ML), a subset of artificial intelligence, offers powerful tools for analyzing large datasets, predicting therapeutic outcomes, and optimizing nanomedicine design and administration protocols. This paper explores how machine learning techniques can enhance the efficacy of nanomedicine therapies by improving nanoparticle design, personalizing treatment regimens, predicting patient responses, and monitoring treatment progress in real time. It discusses recent advances, challenges, ethical considerations, and future prospects, emphasizing the critical role of ML in transforming nanomedicine from a one-size-fits-all approach to precision medicine.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.553

 

 

Published by: vikaspatanker

Authors: Prabhu Prasad

Abstract: The integration of artificial intelligence (AI) with nanotechnology is rapidly transforming the landscape of drug delivery systems, enabling the creation of smart, responsive platforms capable of adapting to dynamic biological environments. Responsive drug delivery systems use nanocarriers that can detect specific physiological cues and release therapeutic agents accordingly, improving efficacy and minimizing side effects. This paper delves into the role of AI in designing and optimizing these nanocarriers, discussing machine learning models for predicting carrier behavior, AI-driven synthesis, and personalized drug release strategies. It also examines biomedical applications, challenges, ethical considerations, and future directions, highlighting how this synergy paves the way for precision medicine tailored to individual patients' needs.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.552

 

 

Published by: vikaspatanker

Authors: Dr. Shafiq Ruslan

Abstract: The integration of artificial intelligence (AI) with nanotechnology has led to the emergence of AI-powered nanodevices capable of real-time monitoring of physiological parameters. These innovative devices offer unprecedented sensitivity, accuracy, and miniaturization, enabling continuous health monitoring at the molecular and cellular levels. This paper explores the development, functioning, and biomedical applications of AI-enabled nanodevices designed to monitor vital physiological signals in real time. It further discusses the challenges, recent advancements, and future directions in the field, emphasizing the transformative potential of these technologies in personalized healthcare and disease management.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.551

 

 

Published by: vikaspatanker

Authors: Dr. Nazrin Hidayat

Abstract: The rapid advancement of nanotechnology has led to the widespread development and application of nanomaterials in diverse fields, including medicine, electronics, and environmental science. Despite their numerous benefits, nanomaterials pose potential risks to human health and the environment due to their unique physicochemical properties. Accurate assessment of nanomaterial toxicity is therefore crucial to ensure safe usage and regulatory compliance. Machine learning (ML), a subset of artificial intelligence, offers powerful predictive modeling techniques that can analyze complex datasets to forecast nanomaterial toxicity effectively. This paper explores the role of machine learning in predicting the toxicological effects of nanomaterials, reviews common ML algorithms employed, discusses data challenges, and highlights future prospects for integrating ML-driven toxicity prediction into nanomaterial safety assessment frameworks.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.550

 

 

Published by: vikaspatanker

Authors: Dr. Hafizul Ramzee

Abstract: Nanorobotics, a cutting-edge field at the crossroads of nanotechnology and robotics, is poised to revolutionize minimally invasive surgery by enabling interventions at a scale previously unimaginable. The integration of artificial intelligence (AI) with nanorobotics significantly enhances the capability of these tiny machines to navigate complex biological environments, perform precise therapeutic actions, and adapt to dynamic physiological conditions. This paper provides a comprehensive exploration of how AI supports the development, control, and application of nanorobots for minimally invasive surgical procedures. It discusses current state-of-the-art technologies, specific biomedical applications, inherent challenges, ethical considerations, and future research directions. The convergence of AI and nanorobotics represents a paradigm shift towards highly personalized, safer, and more effective surgical techniques, potentially transforming patient care and outcomes in the years to come.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.549

 

 

Published by: vikaspatanker

Authors: Dr. Enobi Qwama

Abstract: Nanoparticles have become a cornerstone in the field of biomedicine due to their unique physicochemical properties and ability to interact at the cellular and molecular levels. Efficient synthesis of nanoparticles with precise control over size, shape, and surface characteristics is critical for their successful application in drug delivery, imaging, and therapeutic interventions. Artificial intelligence (AI), particularly machine learning and deep learning techniques, has emerged as a powerful tool to optimize nanoparticle synthesis processes by analyzing complex experimental data and predicting ideal synthesis parameters. This paper explores how AI-driven methodologies enhance nanoparticle synthesis, discusses current applications in biomedicine, and addresses challenges and future perspectives for integrating AI into nanomanufacturing workflows.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.548

 

 

Published by: vikaspatanker