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

Published by: vikaspatanker

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

Published by: vikaspatanker

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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Authors: P. Anusha, Syed Mannan Uddin, T.Sree Chandana, V.Vaishnavi

Abstract: Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition, flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but overfitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy (90.2% for the binary classification) and can overcome the overfitting problem.

DOI: http://doi.org/10.5281/zenodo.20423275

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Authors: P. Anusha, E. Naveen Kumar, G. Sravanthi, E. Rohitha

Abstract: Sentiment analysis is a crucial task in natural language processing (NLP) that aims to determine the overall sentiment or opinion expressed by a reviewer towards a movie. This study focuses on the sentiment analysis of IMDB movie reviews using various machine learning and NLP techniques. The findings indicate that feature selection can enhance the accuracy of sentiment-based classification, but the effectiveness depends on the specific method and number of features selected. The paper also presents a comprehensive comparison of traditional machine learning techniques and advanced transformer-based models for sentiment analysis of IMDB movie reviews. The results provide insights into choosing appropriate methods for accurate and timely sentiment analysis on IMDB data. The study employs feature extraction techniques such as bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), and word2vec. Feature selection using methods like chi-square is shown to improve classification performance.

DOI: http://doi.org/10.5281/zenodo.20423234

Published by: vikaspatanker

Authors: S Jayashree Ananth, Naveen V S

Abstract: The digitization of the healthcare industry has resulted in massive collection of personal health information among hospitals, clinics, and research institutions. But strict privacy laws (HIPAA, GDPR), along with other institutional obstacles, hinder data collection in a centralized manner, resulting in data silos that prevent the construction of efficient machine learning models for predicting diseases, estimating treatment, and managing public health issues. In this paper, we introduce a framework for privacy-preserving federated learning (PPFL) in healthcare. Our proposed framework includes three techniques: (1) Federated Averaging with differential privacy (DP-FedAvg) for model privacy, (2) Secure Multi-Party Computation (SMPC) for private aggregation of gradients, and (3) Homomorphic Encryption (HE) for performing computations on encrypted data. Our PPFL framework is evaluated on three real-life datasets of healthcare applications (mortality prediction from ICU records, diabetic retinopathy classification, and diagnosing COVID-19 patients) and outperforms federated learning with centralization in terms of model accuracy (within 3.2%) and provides differential privacy guarantees with ε=1.0 and δ=10⁻⁵.

DOI: https://doi.org/10.5281/zenodo.20415139

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Authors: Dr. Ashish Saxena, Priya Kumari

Abstract: Indias financial world has changed a lot because of technology and digitalization. Now digital payments are a part of our daily transactions. They are fast, easy, convenient and safe. The Unified Payments Interface or UPI is a leading platform for payments. It was developed by the National Payments Corporation of India. UPI helps people transfer money instantly using their phones. They do not need to share their bank details for each transaction. This is possible because many people have smartphones, internet access and use banking. This study looks at how UPI affects users, businesses and Indias digital payment system. It explores how people are changing their behavior to use transactions more. UPI plays a role in helping India become a cashless economy. We look at how UPI simplifies money transfers, bill payments, online shopping and transactions with merchants. We also check how satisfied customers are with UPI, how easy it's to use how fast it is, how secure it is and how reliable it is. The research uses data from questionnaires and surveys well as information from other sources like journals, government reports and websites. We use methods to understand this data and see how UPI affects financial transactions. We found that UPI makes payments more efficient and convenient. It helps reduce the use of cash and increases the use of services. The reasons for this are that UPI is accessible, cost, fast and has many apps. However, there are still some challenges. These include cyber threats, connectivity problems, technical issues and a lack of knowledge. In conclusion UPI has changed Indias payment systems for the better. It has helped include people in the financial system and created opportunities, for businesses, consumers and institutions. As UPI continues to grow it promises to create a secure and cashless financial system that supports a digitally empowered economy.

DOI: http://doi.org/10.5281/zenodo.20412253

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Authors: Mr. Shrikant Karampuri, Dr. P. Jeyanthi

Abstract: The fast development of technologies, automation, and digitalization processes have led to a considerable disruption of how workforce planning is traditionally performed and have created a significant gap between the skills of the existing workforce and the skills that will be necessary in the future for the success of organizations. The process of competency mapping which includes the identification, evaluation, and alignment of skills with strategic goals is extremely important to ensure future readiness via skill development. This paper introduces a new approach to the competency mapping using artificial intelligence, which utilizes NLP algorithms for skill extraction from unstructured sources (resumes, job descriptions, and performance evaluations), GNNs for skill adjacency and competency modeling, and BKT for prediction of the evolution of individual skills. When applied to a database of 50,000 employees in a multinational technology company, our approach yields an accuracy of 89.7% for skill extraction, 82% for skill adjacencies, and 76% for the prediction of future skill gaps. The proposed approach allows us to create personalized learning paths and reduce time-to-competency by 34% in six months.

DOI: https://doi.org/10.5281/zenodo.20411976

Published by: vikaspatanker