Authors: Mr. Battu Rajesh, Associate Professor Mr. M. Satyanarayana

Abstract: In this study, a content-based image retrieval (CBIR) system was built as an efficient image retrieval tool, allowing the user to send a query to the system, which then retrieves the user's desired image from the image database. We wanted to present a quick overview of the novel coronavirus (SARS-CoV-2) and a better knowledge of the coronavirus illness (COVID-19) in diabetics and its therapy. In this study, we use the COVID-19 dataset to train machine learning algorithms, which subsequently predict whether a person has type diabetes. If type 2 diabetes is detected in a person's test record, he is more prone to COVID-19 disease, heart disease, or renal disease.

DOI: https://doi.org/10.5281/zenodo.15789601

Published by: vikaspatanker

Authors: Mr.Gurudas jadhav, Mr. Mayur Shinde, Dr. Jasbir Kaur, Assistant Professor Mr. Suraj Kana

Abstract: With the explosion of data in recent years, the methods used for extracting, transforming, and loading (ETL) or extracting, loading, and transforming (ELT) data have become critical in the design of modern data pipelines. These methodologies are pivotal in ensuring that raw data from disparate sources is cleansed, structured, and made analytics-ready for business decision-making and operational insight. The efficiency and effectiveness of these processes directly impact the performance of data warehouses and the value extracted from data analytics initiatives. This paper presents a comprehensive comparison of ETL and ELT paradigms in terms of architecture, performance, scalability, cost efficiency, governance, and use-case suitability. Through an in-depth exploration of their underlying technologies, application scenarios, and industry adoption patterns, we aim to clarify the decision-making process for choosing the right approach in different organizational contexts. We consider technical, operational, and business dimensions that influence the selection between ETL and ELT, including data volume, regulatory compliance, tool ecosystems, and team skillsets. Moreover, we delve into the role of emerging cloud-native platforms that support ELT’s rise, and how modern engineering practices such as version control, CI/CD, and modular design are redefining data transformation workflows. Case studies from leading technology firms illustrate practical implementations and benefits of these approaches, highlighting real-world trade-offs. We also explore the future trends and hybrid architectures that aim to harness the strengths of both paradigms in increasingly complex data environments, particularly in light of advancements in artificial intelligence, real-time processing, and decentralized data ownership models such as data mesh. By synthesizing insights from academic research, industry white papers, and technical documentation, this paper provides a strategic framework to guide enterprises in architecting resilient, scalable, and future-ready data integration solutions. The paper concludes with references to academic research, industry white papers, and technical documentation.

 

 

Published by: vikaspatanker

Authors: Rutik Jadyar, Hritik Acharya, Dr. Jasbir Kaur, Assistant Professor Ms. Ifra kampoo

Abstract: In recent years, the use of digital health data has grown rapidly. However, sharing sensitive medical information can lead to serious privacy concerns. Traditional data analysis methods require centralizing data, which poses a risk of exposing private information. Federated Learning (FL) is a new method that allows hospitals and healthcare institutions to collaborate on machine learning models without sharing actual patient data. Instead, the model is trained across different devices or servers holding local data. This paper explains how FL works, its benefits for healthcare, and how it can be applied to protect patient privacy while still enabling powerful data analysis.

 

 

Published by: vikaspatanker

Authors: Ms Merlin Steffy M, Professor Dr. F. Ramesh Dhanaseelan, Associate Professor Dr. M. Jeya Sutha

Abstract: This research presents histotripsy is a non-invasive ultrasound technique used for precise tissue ablation, showing promise in treating conditions like kidney tumors. This project proposes a deep learning-based segmentation system using a Convolutional Neural Network (CNN) with a ResNet-18 backbone to identify ablated regions in ultrasound images automatically. The system is trained on phantom images and uses digital photographs as ground truth. In addition to image segmentation, the system overlays segmented zones, counts treatment pulses, and supports real-time monitoring significantly improving speed, accuracy, and clinical decision- making.

DOI: http://doi.org/10.5281/zenodo.15833637

Published by: vikaspatanker

Authors: Ms. Muskan Shaikh, Dr.Jasbir Kaur,, Mr.Suraj Kanal

 

 

Abstract: – This paper discusses the importance and advantages of big data analysis and application in social media marketing. With the popularity of social media platforms, big data analysis provides enterprises with opportunities to deeply understand user needs, optimize marketing strategies and improve marketing effects. This paper introduces the current situation of social media marketing, and expounds in detail the application of big data analysis in user portrait analysis, user behavior analysis and marketing effect evaluation. Through big data analysis, enterprises can formulate more accurate marketing strategies, improve marketing accuracy, optimize user experience and improve marketing efficiency. However, big data analysis also faces challenges such as data quality and privacy protection, which requires enterprises to pay attention to data security and compliance in the process of application.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Ms J. Alfreena, Professor Dr. F. Ramesh Dhanaseelan, Associate Professor Dr. M. Jeya Sutha

Abstract: Fingerprints are widely used in security, healthcare, and criminal investigations for identification. Slap fingerprint images, which capture multiple fingerprints in one scan, improve accuracy but are hard to process due to different angles, background noise, and small fingerprint sizes. This system includes Clarkson Rotated Fingerprint Segmentation that accurately detects and labels fingerprints using bounding boxes. It performs better than traditional systems like National Fingerprint Segmentation, handling rotated images effectively and feature extraction with the Canny edge detection algorithm to accurately detect fingerprint edges. These advancements reduce errors, improve real-time scanning, and enhance fingerprint security systems. This makes fingerprint recognition more accurate and adaptable across different conditions.

DOI: https://doi.org/10.5281/zenodo.15833678

Published by: vikaspatanker

Authors: Assistant Professor Ajay Kumar

Abstract: CRISPR/Cas9 genome editing has revolutionized plant biotechnology by enabling precise, efficient modifications to target genes associated with stress tolerance. This paper reviews current advances in CRISPR/Cas9 applications for enhancing abiotic (drought, salinity) and biotic (pathogen) stress resistance in major crops. We first outline the molecular mechanism of the CRISPR/Cas9 system and delivery strategies in plants. Next, we examine key case studies: OsERA1 and OsDST edits for drought resilience in rice (Ogata et al.), ARGOS8 modification in maize (Shi et al.), SlHyPRP1 disruption for salt tolerance in tomato (Tran et al.), and powdery mildew resistance via TaMLO and PMR4 edits in wheat and tomato (Wang et al.; Santillán Martínez et al.). We then discuss methodological challenges—off-target effects, regeneration efficiency—and regulatory frameworks governing genome-edited crops. Finally, we explore future directions, including multiplex editing, transgene‐free approaches, and integration with computational tools to accelerate breeding programs. Our synthesis highlights CRISPR/Cas9’s transformative potential for sustainable agriculture under climate change.

Published by: vikaspatanker

Authors: Assistant Professor Ajay Kumar

Abstract: This study aims to document and analyze the traditional use of medicinal plants among three indigenous communities, integrating ethnobotanical knowledge into broader conservation and pharmacological frameworks. Field surveys were conducted in each community’s natural habitat, complemented by ninety semi-structured interviews with traditional healers and elders. Guided transect walks facilitated in-situ identification and GPS mapping of specimens, which were then authenticated and deposited as herbarium vouchers. Quantitative analyses employed Use Value (UV), Informant Consensus Factor (ICF), and Fidelity Level (FL) indices to assess species importance and consensus. In total, 212 medicinal plant species across 78 botanical families were recorded. The most-valued taxa, notably members of Fabaceae and Lamiaceae, exhibited high UV scores (≥0.65), while gastrointestinal remedies showed the strongest agreement among informants (ICF = 0.89). Five flagship species demonstrated fidelity levels above 80 percent, indicating specialized therapeutic roles. These findings underscore the richness and specificity of indigenous pharmacopoeias, offering critical insights for targeted phytochemical investigations. By highlighting culturally salient species and consensus patterns, this research contributes to in situ conservation planning, supports community-led knowledge preservation, and identifies promising candidates for drug-development pipelines.

Published by: vikaspatanker

Authors: Dr. Manish Singh

Abstract: B2B content marketing is the practice of using content to promote markerters'product or services to a business audience. It involves producing high-quality content that appeals to and/or addresses major pain points of B2B consumers. This may be in the form of blog posts, infographics, case studies, white papers, tutorials and educational videos among many others. Additionally, B2B content marketing requires strategically distributing content to reach marketers’' target audience. It goes hand in hand with your social media content strategy where you share links and visuals to attract the right people. Email, organic search and paid ads are other popular distribution channels included in a B2B content marketing strategy.

DOI: https://doi.org/10.5281/zenodo.15783227

Published by: vikaspatanker

Authors: Assistant Professor Ajay Kumar

Abstract: The accelerating rise in atmospheric carbon dioxide (CO₂) concentrations has intensified efforts to enhance terrestrial carbon sinks, particularly through strategic deployment of C₃ and C₄ photosynthetic pathways. This study synthesizes current knowledge on the carbon sequestration potential of C₃ plants, which benefit markedly from CO₂ enrichment but suffer from photorespiration and nutrient constraints, and C₄ plants, which maintain efficiency under heat, drought, and low CO₂ conditions due to their biochemical CO₂-concentrating mechanism. We review field-based flux measurements, remote sensing classification, and genome-scale metabolic models to quantify net primary production responses, soil carbon inputs, and distributional shifts under projected climate scenarios. Findings indicate that C₃ afforestation can maximize sequestration in temperate regions when nutrient limitations are managed, while C₄ bioenergy crops offer robust carbon capture and water-use advantages in warmer, water-limited biomes. We recommend region-specific species selection, integrated methodological frameworks combining eddy-covariance, high-resolution imagery, and mechanistic models, and exploration of synthetic biology and machine-learning tools to refine sequestration estimates. This comprehensive approach informs land-management and policy strategies aimed at mitigating climate change through optimized carbon-negative land uses.

Published by: vikaspatanker