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

Authors: Dr. Zimora Kaldu

Abstract: Personalized medicine, also known as precision medicine, seeks to tailor medical treatment to the individual characteristics of each patient, including their genetic makeup, lifestyle, and environment. Nanotechnology provides innovative tools such as nanocarriers, nanosensors, and nanorobots that enable targeted drug delivery, sensitive diagnostics, and real-time monitoring. Deep learning, a subset of artificial intelligence, has demonstrated remarkable success in analyzing complex biomedical data and extracting meaningful insights. The integration of deep learning with nanotechnology holds great promise for advancing personalized medicine by optimizing therapeutic strategies, enhancing diagnostic accuracy, and improving patient outcomes. This paper explores the convergence of these fields, reviewing current applications, challenges, and future prospects in developing personalized healthcare solutions.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.547

 

 

Published by: vikaspatanker

Authors: Dr. Zirika Temba

Abstract: Early detection of diseases significantly improves patient outcomes by enabling timely intervention and effective treatment. Smart nanosensors, leveraging advances in nanotechnology, offer remarkable sensitivity and specificity in detecting biomarkers associated with various diseases at their earliest stages. However, the complexity of the signals generated by these sensors and the vast amount of data involved require advanced computational techniques for accurate interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, plays an increasingly vital role in processing nanosensor data, identifying patterns, and enhancing diagnostic accuracy. This paper reviews the integration of AI with nanosensor technology for early disease detection, discusses key design considerations, presents notable applications, and explores the challenges and future opportunities in this interdisciplinary field.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.546

 

 

Published by: vikaspatanker

Authors: Mitali Vashishtha

Abstract: This paper explores Unified Payments Interface (UPI), a digital payment system developed in India. It examines how it connects with banks, reduces environmental impact through digital transactions, and promotes financial inclusion. The paper examines the role of the Unified Payments Interface (UPI) in promoting sustainability in finance. It highlights how UPI has transformed digital payments by integrating seamlessly with banks, fostering financial inclusion, and reducing reliance on physical infrastructure. The research explores its contributions to economic sustainability, by lowering transaction costs and enabling cashless ecosystems, and environmental sustainability , through paperless transactions and reduced carbon footprints. Additionally, the paper discusses UPI’s integration with the banking ecosystem, its challenges, and its potential as a model for sustainable digital finance.

Published by: vikaspatanker

Authors: Dr. Halifu Zenbe

Abstract: Nanoparticles play a pivotal role in modern biomedical applications, particularly in targeted drug delivery, imaging, and diagnostics. Understanding the complex interactions between nanoparticles and cellular systems is crucial to ensure efficacy, minimize toxicity, and enhance the overall performance of nanomedicine. However, the multifaceted nature of nanoparticle-cell interactions, influenced by numerous physicochemical parameters and cellular heterogeneity, poses a significant challenge for traditional experimental approaches. Machine learning (ML), a subset of artificial intelligence, provides powerful tools for analyzing complex datasets and predicting biological responses to nanoparticles. This paper explores various machine learning methodologies applied to predict nanoparticle-cell interactions, discusses key applications and case studies, addresses the challenges in data acquisition and model validation, and outlines future perspectives to improve predictive accuracy and accelerate nanomedicine development.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.545

 

Published by: vikaspatanker

Authors: Tando Kulesi

Abstract: Targeted drug delivery represents a transformative approach in modern therapeutics, aiming to precisely deliver pharmaceutical agents to specific tissues, cells, or intracellular compartments. This approach significantly improves therapeutic efficacy while minimizing off-target side effects commonly associated with conventional systemic drug administration. Nanocarriers—engineered nanoscale vehicles such as liposomes, polymeric nanoparticles, dendrimers, and metallic nanostructures—have become central to targeted drug delivery due to their tunable physicochemical properties and ability to navigate complex biological environments. Despite their promise, designing nanocarriers that achieve optimal targeting, stability, and controlled release remains a challenging task involving multifaceted biological and physicochemical interactions. Artificial Intelligence (AI), especially through machine learning and deep learning, is revolutionizing this design process by enabling the analysis and interpretation of complex datasets, predicting nanocarrier behavior in biological systems, and optimizing their design parameters for improved performance. This paper thoroughly reviews the current advances in applying AI for the design of nanocarriers, explores successful case studies, discusses inherent challenges, and envisions future directions that could dramatically accelerate nanomedicine development and personalized healthcare.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.544

 

Published by: vikaspatanker

Authors: Harshita Shukla, Archit Budiyal, Assistant Professor Er. Vivek Agnihotri

Abstract: The global rise in energy demand and environmental degradation, largely due to industrialization and rapid population growth, has highlighted the urgent need for sustainable waste management and alternative energy solutions. Plastic waste, in particular, poses a serious ecological challenge because of its non-biodegradable nature and the drawbacks of traditional disposal methods like landfilling, incineration, and mechanical recycling, which often result in harmful environmental and health impacts. This study focuses on pyrolysis, a thermochemical process conducted in the absence of oxygen, to convert plastic waste—specifically polypropylene—into valuable fuel products such as pyrolytic oil, non- condensable gases, and char. The pyrolytic oil obtained from this process has calorific properties similar to conventional fossil fuels, making it a promising substitute. Pyrolysis is also capable of processing mixed and unwashed plastics, reducing the need for pre- treatment and minimizing toxic emissions compared to incineration. This research explores the efficiency and fuel yield of plastic waste pyrolysis under controlled conditions, demonstrating its potential as a sustainable waste-to-energy method. The findings support the integration of pyrolysis into circular economy strategies by addressing plastic pollution and contributing to clean energy generation.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.125

 

 

Published by: vikaspatanker

Authors: MA. Michelle V. Valles

Abstract: This study investigates the implementation of the Revised Computerization Program (D.O. No. 16, s. 2023) and its impact on organizational performance within the San Pedro City Schools Division. Utilizing a descriptive research design, data were collected from 345 respondents (supervisors, school administrators, teachers, and non-teaching personnel) through surveys and documentary analysis. The study employed statistical tools such as weighted mean, ANOVA, Pearson correlation, and t-test to analyze the data. Findings reveal that while the program is generally implemented, significant challenges persist, particularly concerning inadequate hardware infrastructure, insufficient teacher training, and unreliable internet connectivity. Despite these challenges, a strong positive correlation was found between the program's implementation and organizational performance. Based on these findings, a proposed intervention scheme (PACTS: Program for Advancing Computerization and Technologies in Schools) is presented and deemed highly acceptable by respondents. This scheme, which includes targeted strategies for hardware upgrades, enhanced teacher training, and improved internet access, is recommended for implementation to further enhance the effectiveness of the Revised Computerization Program and optimize organizational performance within the San Pedro City Schools Division.

 

 

Published by: vikaspatanker

Authors: Research Scholar Aman Malik

Abstract: The rapid deployment of Artificial Intelligence (AI) across diverse sectors—including healthcare, transportation, finance, and governance—has prompted pressing legal and ethical concerns, especially in technologically emerging economies like India. This paper critically examines the legal and ethical challenges surrounding the integration of AI systems in practical applications, with a focus on the Indian regulatory landscape. While AI promises efficiency and innovation, it also raises fundamental questions of accountability, privacy, bias, and transparency. Key issues such as the attribution of liability for autonomous decisions, the ethical implications of algorithmic discrimination, and the lack of a clear legal framework for AI-generated data and actions are discussed. The paper further explores the limitations of existing Indian laws, including the Information Technology Act, 2000 and the absence of a dedicated AI or data protection statute (pre-GDPR adaptation). Drawing on global standards and domestic case studies, this study proposes a need for robust regulatory mechanisms, ethical design protocols, and sector-specific governance to ensure responsible AI deployment. The findings aim to contribute to the evolving discourse on AI governance and serve as a foundational reference for future legal reforms in India.

 

Published by: vikaspatanker

Authors: Raghu V Kaspa, Ramya K Cherukuvada

 

Abstract: Simulated Annealing (SA) is a probabilistic technique used for approximating the global optimum of a given function, with origins in statistical mechanics. It has found widespread utility in optimization problems central to machine learning (ML), particularly where the solution space is large and complex. This paper investigates the theoretical underpinnings of SA, explores its applications within ML domains, compares it with other optimization algorithms, and evaluates its performance. The work concludes with a discussion on SA's strengths and limitations in the context of modern ML challenges. The purpose of this research is to position SA as a viable tool in the ML optimization toolkit, particularly for tasks involving large, multi-modal search spaces where deterministic methods may falter.

DOI: 10.61137/ijsret.vol.11.issue3.124

 

Published by: vikaspatanker