Category Archives: Uncategorized

Abstract-This project introduces the YouTube Video Transcribe Summarizer, a tool designed to automatically extract transcripts and generate concise summaries from YouTube videos. By leveraging the YouTube Transcript API, the system retrieves accurate video transcripts and utilizes Google Gemini Pro’s advanced text-based model to create coherent summaries.
Users can input a YouTube video URL, which displays the video thumbnail for context. The application features a customizable prompt template to tailor the summary generation process, ensuring relevance to individual needs. Built on a user-friendly Streamlit interface, this tool aims to enhance content accessibility and engagement. Additionally, the project explores the possibility of executing local models for improved performance and user control. By streamlining the summarization of video content, the YouTube Video Transcribe Summarizer facilitates more efficient information consumption, empowering users to navigate the vast landscape of online video more effectively.

Abstract-Phishing is a category of cyberattack that includes the theft of credit card numbers, passwords, and other private data. We have employed machine learning algorithms to identify phishing websites in order to prevent phishing fraud. The availability of several services, including social networking, software downloads, online banking, entertainment, and education, has sped up the development of the Web in recent years. Consequently, enormous volumes of data are downloaded and uploaded to the Internet on a regular basis. Attackers can now obtain private information, including social security numbers, account numbers, passwords, and usernames, as well as financial information. This is one of the most important problems with web security and is referred to as a “phishing” attack on the internet. To identify these malicious websites, we employ a variety of machine learning methods, including KNN, Naive Bayes, Gradient Boosting, and Decision Trees. The study is broken down into the following sections. The introduction outlines the tools, methods, and concentrated zones that are employed. The process of gathering the data needed to proceed is described in depth in the preliminary section. Subsequently, the paper highlights the thorough examination of the information sources.

Abstract-The COVID-19 pandemic has had an unprecedented impact on health systems in most countries, and in particular, on the mental health and well-being of health workers on the frontlines of pandemic response efforts. The purpose of this study is to provide an evidence-based overview of the adverse mental health impacts on healthcare workers during times of crisis and other challenging working conditions and to highlight the importance of prioritizing and protecting the mental health and well-being of the healthcare workforce, particularly in the context of the emotional intelligence.

Abstract-Cardiovascular diseases (CVDs) are a leading cause of death worldwide, posing a significant health threat not only in India but across the globe. This highlights the critical need for a dependable, precise, and accessible system to diagnose such conditions promptly, enabling timely treatment. Machine learning algorithms have become invaluable tools in healthcare, automating the analysis of extensive and complex datasets. Recent studies demonstrate that various machine learning techniques can aid healthcare professionals in diagnosing heart-related conditions. The heart, second only to the brain in importance, plays a vital role in circulating blood throughout the body. Predicting heart disease occurrence is thus essential in the medical field. Data analytics enhances the prediction accuracy by analysing large volumes of patient data, often maintained on a monthly basis, which could be utilized to anticipate potential future diseases. Techniques such as Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM) are widely applied to predict heart conditions. Diagnosing and predicting heart diseases remain a considerable challenge for both doctors and hospitals globally. To mitigate the high mortality rate associated with these diseases, efficient and rapid detection methods are essential. Machine learning and data mining techniques hold a crucial role in this context. Researchers are accelerating efforts to develop machine learning-based software that can assist doctors in both predicting and diagnosing heart diseases. This research project aims to leverage machine learning algorithms to predict the likelihood of heart disease in patients.

Abstract-This study explores the potential of Kraft lignin and wood-based additives to mitigate rutting in porous asphalt concrete (PAC), a material widely used for its water permeability and noise-reducing properties. PAC, however, suffers from rutting, a type of pavement distress that leads to deformations and reduced performance under traffic loads. The research evaluates the impact of incorporating Kraft lignin and wood-based modifiers into PAC to enhance its rutting resistance. Experimental investigations, including wheel-tracking and Marshall stability tests, were conducted on asphalt samples with varying concentrations of these modifiers. Results indicated that both Kraft lignin and wood-based additives significantly improved rutting resistance, with lignin contributing to greater binder stiffness and wood additives enhancing aggregate bonding. These findings suggest that bio-based modifiers could offer a sustainable solution to improving the durability of porous asphalt pavements, reducing maintenance costs and environmental impact.

Abstract-Medical image analysis is a critical component in modern healthcare, enabling more accurate and timely diagnoses. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown impressive capabilities in automating medical image interpretation. This paper reviews the latest advancements in deep learning methods for medical image analysis, covering key applications such as image classification, segmentation, and object detection. We discuss the challenges in applying deep learning models to medical imaging, such as the need for large annotated datasets, generalization to diverse datasets, and model interpretability. Additionally, we provide an overview of state-of-the-art architectures and their performance in different medical imaging tasks. Finally, we address the future directions and potential clinical applications of these techniques.