Daily Archives: May 31, 2025

Authors: Ritu Jain

Abstract: Immunotherapy has revolutionized cancer treatment by harnessing the immune system to recognize and eliminate malignant cells. However, despite its promising outcomes, patient responses to immunotherapy are highly heterogeneous, with many experiencing minimal benefits or adverse reactions. Accurately predicting which patients will respond positively is a critical challenge for clinicians aiming to tailor treatments effectively. Machine learning (ML), a branch of artificial intelligence capable of analyzing complex, high-dimensional datasets, has emerged as a powerful tool to develop predictive models that can forecast patient responses to immunotherapy. This paper explores the diverse ML techniques applied to immunotherapy response prediction, the integration of multi-omics and clinical data, the challenges faced in clinical translation, and future opportunities for advancing personalized cancer therapy through ML-driven insights.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.559

 

 

Published by: vikaspatanker

Authors: Madhvi Sharma, Professor Amit Thakur

Abstract: The revolution of Internet of Things (IoT) is pervading many facets of our everyday life. Among the multiple IoT application domains, well-being is becoming one of the popular scenarios in IoT which aims to offer new services including smart fitness. This paper focuses on smart fitness covering IoT-based solutions for this domain as well as the impacts of artificial intelligence and social-IoT. IoT-based smart fitness is divided into three categories: Fitness trackers (including wearable and non-wearable sensors), movement analysis and fitness applications. Data collected from IoT-based smart fitness and users could be used for enhancing training performance by Artificial Intelligence (AI)-based algorithms. Sensor to sensor relationship is another notable topic which can be implemented by social-IoT that can share data, information and experiences of users’ training from different places and times. In this his study a comprehensive review on different types of fitness trackers and fitness applications in provided and followed by a review of AI algorithms used in smart fitness scenarios. Lastly detail discussions on the benefits and the potential problems of smart fitness are presented and a shortlist of existing gaps and potential future work have been identified and proposed.

 

 

Published by: vikaspatanker

Authors: Prabhu Nagrajan

 

 

Abstract: Rare diseases, affecting a small percentage of the population, present significant challenges in drug development due to limited patient numbers and scarce resources. Drug repurposing, which identifies new therapeutic uses for existing drugs, offers a promising approach to accelerate treatment availability and reduce costs. Artificial intelligence (AI), with its ability to analyze vast biomedical datasets and uncover hidden patterns, is transforming drug repurposing efforts. This paper explores how AI techniques such as machine learning, natural language processing, and network analysis are utilized to identify repurposing candidates for rare diseases. We discuss data sources, computational strategies, successful case studies, challenges in implementation, and the future outlook of AI-driven drug repurposing to enhance rare disease therapy development.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.558

 

 

Published by: vikaspatanker

Authors: Shalini Nair

Abstract: Histopathological image analysis is a critical process in diagnosing a wide range of diseases, particularly cancers. Traditionally, it relies heavily on the expertise of pathologists to interpret tissue samples under a microscope. However, this manual approach is time-consuming, subject to inter-observer variability, and limited by human fatigue. Deep learning (DL), a subset of artificial intelligence, offers transformative potential in histopathology by automating image interpretation with high accuracy and consistency. This paper explores the applications of deep learning in histopathological image analysis, focusing on convolutional neural networks (CNNs), segmentation techniques, classification models, and recent advances in digital pathology. Challenges, such as data heterogeneity, annotation bottlenecks, and model interpretability, are discussed alongside future prospects for integrating DL into routine clinical workflows to improve diagnostic precision and patient outcomes.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.557

 

 

Published by: vikaspatanker

Authors: Aakansha Deshmukh, Professor Amit Thakur

 

 

Abstract: Internet-of-Things (IoT)-based Heterogeneous Wireless Sensor Network (HWSN) has emerged as a prevalent technology that plays a significant role in developing various human-centric applications. Like in a wireless sensor network (WSN), energy is also the most crucial resource in IoT-based HWSN. The researchers have proposed many works to achieve energy-efficient network operations by minimizing energy usage. A vast proportion of these works emphasize using the clustering approach, which has proved its worth to a great extent. However, most schemes require the repeated formation of clusters incurring a significant amount of nodes’ energy in the clustering process. The protocol design of such schemes also varies with the changing levels of heterogeneity. In this work, a hybrid clustering scheme- An Energy-Efficient Hybrid Clustering Technique (EEHCT) has been proposed for IoT-based HWSN that minimizes the energy consumption in clusters’ formation and distributes the network load evenly irrespective of the heterogeneity level to prolong network lifetime. It appropriately utilizes dynamic and static clustering strategies to formulate the load-balanced clusters in the network.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

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