Category Archives: Uncategorized

Authors: Akula Manasa, Pasula Chandu, Bada Abhinay, Mrs. Y. Ashwini

Abstract: This study analyzes the air quality index (AQI) of Bangalore city over seven years on time period (2018-2024) covered 2,556 days, by using TABLEAU as the primary visualization software, through tableau, the huge and complex datasets will be turn like charts, graphs and more. It focuses on 8 key components of AQI, PM 2.5, PM10, NO2, SO2, CO, NH3, Pb and O3. It’s analyzing that the air quality was changes according to the seasons, where most pollutant air was recorded in the winter months (December-February) and the cleanest air was recorded at Monsoon season (June-August). The year of 2020 the AIR QUALITY recorded lowest average over (AQI-64.47), due to the reason of covid 19 pandemic Lockdown occurred. Approximately 66.8% of days were falls under “Moderate” category, while only 17.4% fall are considered as “Good”. These results share a clear vision to make a good plan for urban developers, city planners to analyse the conditions and improve AIR QUALITY on Bangalore.

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Authors: K. Vigneshwar, G.Bharath Simha Reddy, G.Shashidhar, A.Uday Kiran

Abstract: Public safety in public areas has become a significant concern for governments and businesses globally. Video surveillance systems are being increasingly integrated to ensure public safety, with deep learning techniques enhancing their ability to detect potential threats. Traditional video surveillance often relies on passive monitoring, but with advancements in AI, surveillance systems can now actively detect risks such as weapons (guns and knives) in real- time. This paper presents a deep learning-based system for weapon detection using MobileNet- V2, a CNN model known for its computational efficiency. MobileNet-V2 has shown an improvement of approximately 35% in processing speed compared to its predecessor, MobileNet-V1, while maintaining similar accuracy levels. This increase in speed is crucial for real-time weapon detection, where quick identification and response are vital to preventing threats. The study compares two approaches to weapon detection using CNNs, evaluating MobileNet-V1 and MobileNet-V2. The results indicate that MobileNet-V2 outperforms MobileNet-V1 not only in terms of speed but also in its ability to maintain high accuracy, marking a significant advancement in the field of weapon detection through deep learning. These improvements are vital in practical applications, such as public spaces, where large amounts of video data must be processed rapidly. The proposed system demonstrates a clear enhancement over prior methods in detecting guns and knives, offering a reliable, fast solution for real-time surveillance. This research highlights the effectiveness of MobileNet-V2 in improving public safety through advanced AI technology, providing a scalable solution for detecting threats in urban environments.

 

 

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Authors: Lagusani Yashwanth Goud, K. Susmitha

Abstract: Generic medications are becoming an essential part of contemporary healthcare due to the growing demand for reasonably priced medications. Despite efforts by the European Medicines Agency (EMA) to harmonize regulations, different European countries have different requirements for marketing authorization of generics. The regulatory framework for the approval of generic drugs in a few European nations, such as Germany, France, the United Kingdom, Spain, and Italy, is compared in this thesis. It draws attention to variations in bioequivalency standards, dossier submission requirements, approval schedules, and review processes. The results highlight the need for additional harmonization to improve patient access to reasonably priced medications and expedite generic drug market access.

DOI: http://doi.org/10.5281/zenodo.20442010

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Authors: Shivani, Prof. Seema Kohli

Abstract: Ayurveda is an ancient Indian system of medicine that explains health as a dynamic balance between the body, mind, and the natural environment. According to Ayurvedic philosophy, human life is deeply interconnected with environmental elements such as air, water, land, vegetation, and climate. In the present era, environmental science highlights growing concerns including pollution, climate change, deforestation, and biodiversity loss, all of which pose serious threats to both ecological stability and human health. These challenges emphasize the urgent need for sustainable and preventive approaches to healthcare and environmental protection. Ayurvedic concepts such as Panchamahabhuta (five fundamental elements), Ritucharya (seasonal regimen), and Desha (influence of geographical and environmental factors) explain how changes in the environment directly affect human health and disease patterns. Seasonal variations, climatic conditions, and ecological imbalance play a crucial role in disturbing bodily harmony, leading to the development of various disorders. These principles closely align with environmental science, which also focuses on maintaining ecological balance for healthy living. This paper aims to correlate Ayurvedic principles with environmental science to develop an integrated framework for sustainable healthcare and ecosystem conservation. The study is based on a review of classicalAyurvedic texts, current environmental challenges, and modern scientific research related to medicinal plants, pollution, and ecosystem health. Integrating traditional Ayurvedic knowledge with modern environmental management offers a holistic approach to disease prevention, health promotion, biodiversity conservation, and sustainable development.

DOI: https://doi.org/10.5281/zenodo.20441818

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Authors: Gourani Shruthi, K. Someshwar

Abstract: This study focuses on the formulation and characterization of water-in-oil-in-water (W/O/W) multiple emulsions of Azilsartan Medoxomil to enhance its solubility, stability, and oral drug delivery performance. A total of nine formulations (AZL1–AZL9) were developed using Span 20, Span 40, and Span 80 at varying concentrations as primary emulsifiers, while Tween 80 was employed as the secondary emulsifier. The prepared multiple emulsions were evaluated for various physicochemical and morphological parameters including visual appearance, organoleptic properties, microscopic examination, globule size, polydispersity index (PDI), zeta potential, viscosity, pH, conductivity, drug content, entrapment efficiency, in vitro drug release, and stability studies. Among all formulations, AZL6 exhibited superior characteristics with a mean globule size of 2.6 μm, uniform droplet distribution, high entrapment efficiency of 98.2 ± 1.1%, optimum viscosity, and cumulative drug release of 94.3% over 12 hours. The formulation also demonstrated good colloidal stability with a zeta potential value of −29.5 mV. Drug release kinetic studies revealed that the optimized formulation followed the Higuchi diffusion model and Super Case II transport mechanism, indicating both diffusion- and erosion-controlled release behavior. Furthermore, post-formulation stability studies, including centrifugation stress testing, confirmed the physical stability of the emulsion system. The findings of this investigation suggest that multiple emulsions represent a promising and effective delivery approach for poorly water-soluble drugs such as Azilsartan Medoxomil, with potential to improve oral bioavailability and therapeutic efficacy.

DOI: http://doi.org/10.5281/zenodo.20441639

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Authors: Sherla Prasanna, B. Swathi

Abstract: By offering reasonably priced substitutes for name-brand drugs, the generic drug market in the United States plays a vital part in guaranteeing accessible healthcare. The U.S. Food and Drug Administration (FDA), through the Abbreviated New Drug Application (ANDA) procedure, is the primary regulatory body that oversees the process of bringing a generic medication to market. In order to help pharmaceutical businesses effectively negotiate the challenging development and approval process for generic pharmaceuticals, this thesis examines project management techniques. In order to provide an organized strategy for cost management, time efficiency, risk reduction, and regulatory compliance, the study incorporates concepts from the Project Management Body of Knowledge (PMBOK), pharmaceutical R&D, regulatory science, and quality systems. Important topics like bioequivalency research, intellectual property issues, risk-based quality management, and cross-functional team communication frameworks are emphasized. The effect of strategic project management on cutting time-to-market without sacrificing quality or compliance is illustrated through real-world case studies and industry best practices.

DOI: http://doi.org/10.5281/zenodo.20441301

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Authors: Anpe Fut Micheal, Prof. Kagoro L. A Dayil, Prof. Dahak A Dayil, Prof. Adelakun A. Esther

Abstract: In a world grappling with malnutrition and food insecurity, Guizotia abyssinica, or niger, emerges as a beacon of hope, offering substantial nutritional benefits. This study meticulously evaluates the nutritional composition and amino acid profile of G. abyssinica across various growth stages, aiming to address protein-energy malnutrition prevalent in Nigerian populations. Conducted in the Benue State region, the research involved comprehensive analyses of chemical composition, digestibility, and fatty acid profiles in niger seeds harvested at different developmental phases. Findings revealed a significant decline in crude protein from 163 g/kg at the early vegetative stage to 86 g/kg at the grain fill stage, alongside a notable increase in fiber content, indicating the complex interplay between growth stage and nutritional quality. The fatty acid profile predominantly featured essential fatty acids such as α-linolenic acid (C18:3 n-3) and linoleic acid (C18:2 n-6), underscoring the oil's potential health benefits. The study advocates for the strategic use of G. abyssinica in dietary interventions to combat malnutrition, emphasizing its role in enhancing food security and promoting sustainable agricultural practices. Overall, the research contributes vital insights into the nutritional value of niger seeds, positioning them as a sustainable solution for addressing dietary deficiencies in vulnerable populations.

DOI: https://doi.org/10.5281/zenodo.20440373

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Authors: Kavade Shirisha, K. Someshwar

Abstract: Combination products, which are a new class of treatments that present difficult regulatory issues, are made up of a combination of medications, devices, and/or biological products. The United States Food and Drug Administration's (USFDA) regulatory framework for the categorization, approval, and supervision of combination products is examined in this thesis. Key provisions under 21 CFR Part 3 are highlighted, along with the Office of Combination Products' (OCP) function, the principal mode of action (PMOA) determination procedure, and premarket submission paths such as NDA, BLA, and PMA/510(k). To demonstrate how recommendations are applied in practical situations, case studies and regulatory precedents are examined. In order to expedite product development and guarantee compliance, the study emphasizes the significance of interdisciplinary cooperation and early regulatory engagement.

DOI: http://doi.org/10.5281/zenodo.20440187

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Authors: E. Priyadharshini

Abstract: Medical image analysis plays a significant role in the early detection and diagnosis of cancer. Accurate segmentation and classification of malignant tissues are essential for improving clinical decision-making and patient outcomes. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly neural networks, have demonstrated remarkable success in biomedical image processing applications. However, the performance of conventional Multi-Layer Perceptron (MLP) networks is highly dependent on optimal parameter tuning, which remains a challenging task due to the complexity and high dimensionality of medical image data. This paper proposes an optimized MLP model using the Salp Swarm Algorithm (SSA) for malignant tissue segmentation and classification in biomedical images. SSA is a nature-inspired metaheuristic optimization technique modeled on the swarming behavior of salps in ocean environments. The algorithm offers strong global search capability, faster convergence, and improved avoidance of local optima compared with traditional optimization methods. By integrating SSA with the MLP network, the proposed model enhances feature selection, weight optimization, and classification accuracy. The proposed SSA-MLP framework is evaluated using publicly available biomedical image datasets. Performance assessment is carried out using standard evaluation metrics including Accuracy, Sensitivity, Specificity, Precision, F1-Score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Experimental results demonstrate that the SSA-tuned MLP model achieves superior performance when compared with conventional machine learning and neural network approaches. The model shows improved segmentation quality, enhanced classification capability, and greater robustness in detecting malignant tissues. This study contributes to the advancement of intelligent medical imaging systems by presenting a reliable and efficient optimization-based neural network model for cancer diagnosis. The findings indicate that SSA can significantly improve neural network performance in medical image analysis, thereby supporting accurate diagnosis and effective clinical decision support systems.curve Receiver Operating Characteristic (AUC-ROC)

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Authors: K. Vigneshwar, P. Shruthi, J. Rahul Naik, P. Khaleel Basha

Abstract: Deoxyribonucleic acid (DNA) is a biological macromolecule. Its main function is information storage. At present, the advancement of sequencing technology had caused DNA sequence data to grow at an explosive rate, which has also pushed the study of DNA sequences in the wave of big data. Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in DNA sequence data analysis and obtained a lot of research achievements. Firstly, the review introduces the development process of sequencing technology, expounds on the concept of DNA sequence data structure and sequence similarity. Then we analyze the basic process of data mining, summary several major machine learning algorithms like Multinomial NB Classifier & Random Forest, and put forward the challenges faced by machine learning algorithms in the mining of biological sequence data and possible solutions in the future. Then we review four typical applications of machine learning in DNA sequence data: DNA sequence alignment, DNA sequence classification, DNA sequence clustering, and DNA pattern mining. We analyze their corresponding biological application background and significance, and systematically summarized the development and potential problems in the field of DNA sequence data mining using Multinomial NB Classifier & Random Forest. Finally, we summarize the content of the review and look into the future of some research directions for the next step.

DOI: https://doi.org/10.5281/zenodo.20425270

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