Category Archives: Uncategorized

Authors: Hemanth Kumar, Madhu Gowda

Abstract: Artificial intelligence (AI) is rapidly transforming the biotechnology sector by enabling startups to accelerate research and development, optimize clinical trials, and develop personalized medicine approaches. This paper explores the strategic implementation of AI in biotech startups, examining both the remarkable opportunities AI offers and the significant challenges these emerging companies face in adopting such advanced technologies. We discuss the role of AI in drug discovery, diagnostics, and therapeutic innovation, while highlighting barriers related to data management, regulatory compliance, funding, and talent acquisition. The paper concludes by providing insights into overcoming these challenges through interdisciplinary collaboration, ethical practices, and strategic partnerships. Ultimately, successful AI integration is poised to revolutionize healthcare by enabling biotech startups to deliver groundbreaking treatments and improve patient outcomes.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.564

 

 

Published by: vikaspatanker

Authors: Selva Murugan

Abstract: Chronic diseases such as diabetes, cardiovascular disorders, cancer, and neurodegenerative conditions represent a major global health burden. Early diagnosis and personalized treatment strategies significantly improve patient outcomes, and the identification of reliable biomarkers is central to these efforts. Machine learning (ML), a subset of artificial intelligence, has emerged as a powerful tool to analyze complex biomedical data and discover novel biomarkers that traditional statistical methods may overlook. This paper explores the application of machine learning techniques in identifying novel biomarkers for chronic diseases by integrating multi-omics data, clinical records, and imaging datasets. It discusses various ML algorithms, challenges in data preprocessing and interpretation, and the translational potential of ML-driven biomarker discovery for precision medicine.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.563

 

 

Published by: vikaspatanker

Authors: Shalini Bhandar

Abstract: The traditional process of vaccine development is often lengthy, costly, and complex, involving multiple stages from antigen discovery to clinical trials. The integration of artificial intelligence (AI) in vaccine research has the potential to revolutionize this field by accelerating the design, testing, and production of vaccines. AI-powered tools and machine learning algorithms facilitate rapid antigen identification, prediction of immune responses, optimization of vaccine candidates, and streamlined clinical trial management. This paper explores how AI is transforming vaccine development by reducing timelines, enhancing precision, and improving safety and efficacy. Challenges such as data availability, model reliability, and ethical considerations are discussed, alongside future perspectives on AI-driven vaccine innovation, especially highlighted by the COVID-19 pandemic.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.562

 

 

Published by: vikaspatanker

Authors: Shruthi Singh

Abstract: Electronic Health Records (EHRs) have revolutionized healthcare by digitizing patient information, enabling comprehensive data capture across clinical settings. The integration of machine learning (ML) techniques with EHR data holds immense potential for predictive healthcare, facilitating early diagnosis, risk stratification, personalized treatment, and improved patient outcomes. This paper explores how machine learning algorithms applied to EHR datasets can transform healthcare delivery by enabling predictive analytics, clinical decision support, and population health management. Key challenges such as data quality, interoperability, privacy, and model interpretability are discussed alongside emerging solutions. The future of predictive healthcare lies in harnessing the synergy of EHRs and AI to advance precision medicine, reduce costs, and enhance healthcare accessibility.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.561

 

 

Published by: vikaspatanker

Authors: Nisha Prabhakar

Abstract: The human microbiome, consisting of trillions of microorganisms inhabiting various body sites, plays a critical role in health and disease. Recent advances in high-throughput sequencing and metagenomics have generated vast datasets characterizing the complex microbial communities and their functional capabilities. However, the intricate interactions between microbiota, host physiology, and environmental factors pose significant challenges to data interpretation and the extraction of actionable insights. Artificial intelligence (AI), particularly machine learning, offers powerful computational tools to analyze complex, high-dimensional microbiome data, identify novel patterns, predict disease associations, and inform personalized therapeutic strategies. This paper explores AI-driven approaches to deciphering the human microbiome, including data integration techniques, predictive modeling, challenges in microbiome research, and future perspectives for leveraging AI to transform microbiome science and precision medicine.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.560

 

 

Published by: vikaspatanker

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