Authors: Tanira Poddar

Abstract: The exponential growth of healthcare data, driven by electronic health records (EHRs), medical imaging, wearable sensors, genomic sequencing, and real-time monitoring systems, has resulted in unprecedented opportunities for transforming medical care. Big data frameworks provide the computational backbone to store, process, analyze, and visualize these massive, heterogeneous datasets. Their applications extend from early disease prediction and personalized medicine to hospital workflow optimization, fraud detection, and population health management. However, integrating big data solutions in healthcare presents challenges such as data privacy, fragmented systems, interoperability issues, and resource-intensive infrastructure requirements. This review comprehensively explores the evolution and impact of big data frameworks in healthcare IT, evaluating critical technologies, architectures, applications, and implementation strategies. It also highlights the barriers and future directions for leveraging big data to improve clinical practice, research, and administration. Insights are drawn from recent studies, practical use cases, and emerging trends in artificial intelligence, predictive analytics, and real-time decision support. The review ultimately provides a roadmap for stakeholders—clinicians, technologists, administrators, and researchers—to harness big data for better outcomes, operational efficiency, and patient-centric care.

DOI: http://doi.org/10.5281/zenodo.16982564

Published by: vikaspatanker

Authors: Ashwani sagar, Assistant Professor Khemraj Beragi

Abstract: Shell-and-tube heat exchangers remain vital in industrial applications, and baffle design plays a key role in their thermal performance. This review paper provides a comprehensive comparison of four baffle configurations – segmental, helical, flower, and hybrid – highlighting their impact on heat transfer and pressure drop characteristics. Conventional segmental baffles generate strong cross-flow and high heat transfer but suffer from flow stagnation regions and significant pressure losses. Helical baffles have been developed to promote a smoother shell-side flow, yielding comparable heat transfer with markedly reduced pressure drop and fouling tendency. Flower baffles, a newer biomimetic design, employ petal-like baffle plates to induce swirling flows, achieving an excellent compromise between enhanced convective heat transfer and lower pumping power requirements. Hybrid baffles combine features of segmental and helical designs to further intensify heat transfer, albeit with some pressure drop penalty. The paper synthesizes findings from recent experimental and computational studies (including the author’s thesis work) to quantitatively compare performance metrics of these baffle types. A comparative analysis is presented with a summary table of key metrics and a graphical illustration of heat transfer vs. pressure drop trade-offs. The review also discusses practical considerations, such as manufacturing complexity and fouling behavior, and identifies research gaps. Overall, advanced baffle configurations demonstrate significant potential for improving energy efficiency in heat exchangers, and ongoing innovations in baffle design offer promising opportunities for future thermal performance enhancements.

DOI: http://doi.org/10.5281/zenodo.16981738

Published by: vikaspatanker

Authors: Devraj Singh, Professor Vinit Kumar Sharma, Assistant Professor Kamal Kumar

 

Abstract: Intuitionistic fuzzy soft set (IFSS) theory offers an effective and comprehensive algorithm to handle uncertainty by incorporating parameterized elements which makes it a strong technique for decision-making (DM). For the purpose to aggregate IFS numbers (IFSNs), we propose new operation rules for IFSNs. Then, by utilizing the proposed operations, we propose intuitionistic fuzzy soft Yager weighted averaging (IFSYWA) and geometric (IFSYWG) aggregation operator (AO). Further, we thoroughly examine the mathematical characteristics of the proposed IFSYWA AO and IFSYWG AO such as idempotency and monotonicity. By using the proposed IFSYWA and IFSYWG AO, we develop a multi-attribute group decision-making (MAGDM) algorithm for IFSNs environment. Usefulness of proposed MAGDM algorithm is illustrated by a real-world MAGDM problem focussed on selecting the best renewable energy project for investment.Lastly, the results confirm that the suggested AOs can be used to solve MAGDM difficulties.

DOI: https://doi.org/10.5281/zenodo.16982006

Published by: vikaspatanker

Authors: Riyan Dastoor

Abstract: Digital transformation embodies the fundamental integration of digital technology into all facets of business, revolutionizing how organizations operate and deliver value to customers. At the core of this transformation lies Customer Relationship Management (CRM) systems, with Salesforce CRM being a leading platform that harnesses cloud technology to empower businesses. Salesforce’s cloud-based CRM eliminates traditional IT burdens by offering scalable, flexible, and seamlessly integrated solutions that centralize customer data and automate essential processes. This unification fosters enhanced collaboration, data-driven decision-making, and personalized customer experiences. As companies face rising customer expectations and increasing competitive pressure, digital transformation fueled by Salesforce CRM provides a strategic advantage by enabling agility, efficiency, and innovation. Through advanced AI capabilities, automation, and a robust cloud infrastructure, Salesforce CRM transcends simple contact management and becomes the backbone of customer-centric business models. This article explores the multifaceted role Salesforce CRM and cloud systems play in driving digital transformation, discussing its impact on operational processes, customer engagement, scalability, and organizational success.

DOI: https://doi.org/10.5281/zenodo.16980036

 

Published by: vikaspatanker

Authors: Ishira Venkatesh

Abstract: Bioinformatics has become a pivotal force in transforming neuroscience research, enabling deep insights into the structure and function of the brain. By integrating computational approaches with experimental data, neuroscientists can now analyze complex neural networks, decipher molecular mechanisms, and unravel the genetic underpinnings of neurological disorders. The surge in large-scale data—from genomics and transcriptomics to neuroimaging and electrophysiology—has created both opportunities and challenges, necessitating advanced analytical tools capable of processing and interpreting vast datasets. Bioinformatics methods have empowered the identification of novel biomarkers, the understanding of brain development, and the discovery of therapeutic targets, bringing precision and efficiency to neuroscience studies. Moreover, bioinformatics facilitates interdisciplinary collaborations, connecting computer scientists, biologists, and clinicians to resolve intricate questions related to cognition, behavior, and disease. The application of machine learning, network analysis, and data mining techniques has enhanced the predictive accuracy for diagnosis and treatment strategies. As neural data repositories expand, bioinformatics supports the harmonization and sharing of information, promoting reproducibility and fostering the growth of open science. Despite these advances, challenges remain, including data standardization, the need for high computational power, and the integration of multi-modal data. Continuous development of bioinformatics tools is required to address these challenges while ensuring ethical considerations are met in data management. Ultimately, bioinformatics is reshaping neuroscience, fueling discoveries that have the potential to transform our understanding of the brain, mental health, and neurological diseases.

DOI: https://doi.org/10.5281/zenodo.16979834

 

Published by: vikaspatanker

Authors: Arhaan Madavi

Abstract: Cloud-native security has become an essential paradigm in modern computing, aligning security strategies with the dynamic and scalable architecture of cloud-native applications. As enterprises transition from traditional on-premises environments to distributed, containerized, and microservices-based infrastructure, the security landscape shifts dramatically. This review synthesizes current research and best practices in cloud-native security, outlining critical challenges, innovative solutions, and industry trends. Cloud-native environments are characterized by their reliance on containers, Kubernetes, service meshes, and serverless functions, which bring new opportunities alongside new threats. The paper discusses how traditional perimeter-based security approaches are being replaced by identity-driven, zero-trust models, embedding security into every layer of application design and deployment. Topics such as secure software supply chains, runtime protection, compliance automation, and infrastructure-as-code security are explored. This review aims to provide a single resource for researchers, DevSecOps practitioners, and enterprise architects seeking a comprehensive understanding of cloud-native security, emphasizing the importance of collaboration between development, operations, and security teams. Through an in-depth analysis of technologies, frameworks, and strategies, the article clarifies how organizations can address the unique risks present in modern cloud-native ecosystems while enabling agility and continuous delivery. By surveying academic literature and industry reports prior to 2014, we situate key advancements in their historical context, revealing the trajectory toward the current state of cloud-native security. The findings underscore the necessity for proactive, automated, and scalable security practices that evolve with cloud-native application lifecycles.

DOI: https://doi.org/10.5281/zenodo.16979595

 

Published by: vikaspatanker

Authors: Samaira Lodh

Abstract: Artificial Intelligence (AI) is revolutionizing healthcare diagnostics by providing unprecedented capabilities in data analysis, pattern recognition, and predictive modeling. AI-powered tools have demonstrated potential in increasing diagnostic accuracy, reducing diagnostic errors, optimizing treatment pathways, and ultimately improving patient outcomes. The integration of AI with healthcare diagnostics stands at the forefront of digital transformation, leveraging advancements in machine learning, deep learning, and natural language processing. These technologies enable precise identification of diseases from various forms of medical data, including imaging, genomics, and patient records. Despite remarkable progress, the field faces challenges such as data privacy concerns, ethical dilemmas, integration with existing healthcare workflows, and the need for transparency and explainability in AI-driven decisions. Emerging trends like explainable AI, federated learning, and the use of AI for point-of-care diagnostics are shaping the future of healthcare diagnostics. This article explores these trends, evaluates their potential impact, and discusses the implications for practitioners, patients, and policymakers. The ultimate aim is to provide an in-depth understanding of how AI is redefining healthcare diagnostics, the directions in which the field is evolving, and the unresolved questions that must be addressed to leverage the full potential of AI while safeguarding ethical and clinical standards.

DOI: https://doi.org/10.5281/zenodo.16979367

 

Published by: vikaspatanker

Authors: Praveen bodana, Assistant Professor Khemraj Beragi

Abstract: Thermal power plants (TPPs) are a critical component of global electricity generation, yet they often suffer from efficiency loss and unplanned outages due to equipment faults. Traditional maintenance strategies (reactive or preventive) are often too slow or costly. In contrast, machine learning (ML) methods can analyze large historical and real-time sensor data to detect anomalies and predict failures early. This paper surveys supervised methods (SVM, random forests, neural networks), unsupervised models (autoencoders, clustering, PCA), and hybrid physics-ML approaches for TPP monitoring. It also examines sensor optimization and IoT-enabled real-time monitoring. Case examples from the literature show that ML-based predictive maintenance can greatly reduce unplanned downtime and maintenance costs (e.g., cutting costs by roughly 20–40%) while improving equipment availability. The findings indicate that optimized sensor networks, integrated IoT data, and advanced ML models can substantially enhance fault detection accuracy and overall plant efficiency.

DOI: http://doi.org/10.5281/zenodo.16964139

Published by: vikaspatanker

Authors: Uchenna Evans-Anoruo

Abstract: The escalating complexity of healthcare delivery in the United States, coupled with increasing costs and demand for services, necessitates sophisticated analytical approaches to optimize system performance. This article presents a comprehensive framework for implementing data-driven decision-making in healthcare systems through the integration of operations research techniques and statistical modeling. By leveraging queuing theory, simulation modeling, and decision analysis, healthcare organizations can significantly improve resource allocation, patient flow management, and service delivery efficiency. The integration of advanced IT systems enables real-time data collection and analysis, supporting continuous optimization of healthcare operations. This research demonstrates how systematic application of these methodologies can address critical challenges in US healthcare delivery while maintaining quality standards and improving patient outcomes.

DOI: https://doi.org/10.5281/zenodo.16964206

 

Published by: vikaspatanker

Authors: Bhupesh Paliwal, Professor Amit Thakur

 

Abstract: A Mobile Ad hoc Network (MANET) is a dynamic wireless network that can be formed infrastructure less connections in which each node can act as a router. The nodes in MANET themselves are responsible for dynamically discovering other nodes to communicate. Although the ongoing trend is to adopt ad hoc networks for commercial uses due to their certain unique properties, the main challenge is the vulnerability to security attacks. In the presence of malicious nodes, one of the main challenges in MANET is to design the robust security solution that can protect MANET from various routing attacks. Different mechanisms have been proposed using various cryptographic techniques to countermeasure the routing attacks against MANET. As a result, attacks with malicious intent have been and will be devised to exploit these vulnerabilities and to cripple the MANET operations. Attack prevention measures, such as authentication and encryption, can be used as the first line of defense for reducing the possibilities of attacks. However, these mechanisms are not suitable for MANET resource constraints, i.e., limited bandwidth and battery power, because they introduce heavy traffic load to exchange and verifying keys. In this paper, we identify the existent security threats an ad hoc network faces, the security services required to be achieved and the countermeasures for attacks in routing protocols. To accomplish our goal, we have done literature survey in gathering information related to various types of attacks and solutions. Finally, we have identified the challenges and proposed solutions to overcome them. In our survey, we focus on the findings and related works from which to provide secure protocols for MANETs. However, in short, we can say that the complete security solution requires the prevention, detection and reaction mechanisms applied in MANET.

DOI: https://doi.org/10.5281/zenodo.16962628

 

Published by: vikaspatanker