Volume 12 | Issue 4

 Abstract

The advancement of technology in today's education has completely changed traditional education. This brief describes an education technology (EdTech) platform designed to enhance educational technology by integrating a variety of digital tools and resources. Our platform offers a variety of pathways that meet the diverse needs of students, teachers, and institutions.

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Web Development, Ed-tech platform

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Ensuring buildings can withstand earthquakes is crucial, requiring careful material selection, weight calculations, and structural planning. Techniques like special braces and walls are tailored to the seismic risk of the location. Strong concrete, flexible floors, and retrofitting existing structures are also vital. This research presents a comprehensive evaluation of 36 structural models across six seismic scenarios, focusing on 8, 10, and 12-story buildings in Seismic Zones IV and V, with and without seismic enhancements. Using STAAD.Pro Connect Edition software, the study assesses different bracing systems and shear walls under earthquake loads, presenting findings in tables. Results show shear walls consistently reduce bending and increase shear force, while diagonal and inverted V-shaped bracings also effectively reduce bending. The study emphasizes project-specific requirements when selecting earthquake-resistant designs, weighing trade-offs between bending moment, shear force, and deflection. Location and height significantly impact seismic performance, with shear walls performing well in Zone V but potentially inducing more bending. In Zone IV, diagonal or inverted V-shaped bracings exhibit less deflection and shear force. In conclusion, the optimal earthquake-resistant design depends on specific needs. This research provides insights for developing earthquake-resistant buildings in high-risk areas, considering both structural performance and regional seismic characteristics.

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Seismic, Multistorey building, Bracing system, Structural analysis, Shear wall

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PDFs have pervaded modern life, acting as vital documentation tool in various contexts. Yet, the aspiration for "Data-On-Demand" presents a novel challenge: immediate and accessible data availability. Large Language Models (LLMs) offer a promising resolution. It is a type of machine learning model that can perform a variety of natural language processing (NLP) tasks such as generating and classifying text, answering questions in a conversational manner, and translating text from one language to another. By harnessing potent algorithms, LLMs reconfigure PDF interaction dynamics. Transforming PDFs into interactive conversations augments document processing and enables real-time engagement, effectively transcending the confines of conventional documents. LLMs, trained on extensive textual datasets, simulate human-like interactions. This transformative approach empowers users to interact with PDFs using natural language, seamlessly extracting pertinent information. To realize this concept, a web platform is proposed, allowing users to upload or scan PDFs and make inquiries in natural language. The LLM-powered system extracts and presents information, ushering in a paradigm shift in document interaction and data accessibility.

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Chatbots, Large Language Models, Natural Language Processing, Information Retrieval , Artificial Intelligence

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Sentiment analysis is a branch of natural language processing that attempts to automatically categorize these viewpoints as neutral, negative, or positive. We investigate the efficacy of several sentiment analysis methods on Twitter data in this research article. We examine how feature selection, pre-processing methods, and machine learning algorithms affect sentiment analysis performance. Our findings demonstrate that the accuracy of sentiment analysis on Twitter data is increased when feature selection and machine learning methods are combined. We also point out the difficulties and restrictions associated with sentiment analysis on Twitter data, including the usage of irony and sarcasm in tweets. The field of sentiment analysis of Twitter data has seen a great deal of activity. The primary topic of this survey is sentiment analysis of Twitter data, which is useful for analyzing information found in tweets that contain highly unstructured, heterogeneous opinions that can be neutral, positive, or negative.

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This paper explores the legal landscape surrounding talent representation agreements, focusing on the entertainment industry. It examines the nuances of such agreements, their significance, and the legal considerations inherent in drafting and executing them. Through a comprehensive review of existing literature, analysis of materials and experimental techniques, and discussions on results, this paper aims to provide a thorough understanding of talent representation agreements and their implications.

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Talent Representation Agreements

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The Aim of the study is know about safety and security's impact on Employees Performance in Railway Industry. Railway make big impact in whole world so they have to make sure safety for their employees at workplace. Railway always put their employees safety on top priority. safety and security is always important factor in any industry. in this Research paper shows effect of safety of employees on employees perfoamence. For this information Questionnarie survey has conducted from Railway employees. with sample size 82 this survey was conducted in Railway industry. for finding releationship among variables there is used chi-square test for categorieal question which included in questionnarie. this survey helps to understand how Railway give safety to their employees. And hoe employees safety and security make impact on employees performance at their workplace in Railway Industry.

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Railway Industry, Employees Safety, Employees Security, Employees Perfoamence.

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The website project offers a user-friendly platform with a carefully selected collection of courses from reliable learning providers, with the goal of revolutionizing access to online education. Its main mission is to make it easier for users with a variety of interests and learning goals to find and access instructional information. Through the consolidation of courses from several providers, the website aims to promote accessibility to online learning materials and cultivate a community of lifelong learners. The project's primary goal is to provide consumers with a seamless browsing experience by using an intuitive and well-organized interface. The website makes sure users can easily and quickly access the information they need by following user experience design concepts like clear labeling, intuitive navigation menus, and responsive layout. The user's browsing experience is further improved by features like search capabilities and filtering options, which improve the user's ability to find relevant content. A crucial component of the project is customization and personalization, which acknowledges that users have different tastes and learning methods. By providing "Paid" and "Free" options, consumers are able to select courses that fit their commitment level and financial constraints. With the help of dynamic filtering features like toggle buttons, users may customize their learning experience to meet their unique needs by narrowing down their search results according to factors like price, subject matter, or degree of difficulty. The project's core value is accessibility, which guarantees that users of all backgrounds and skill levels can take advantage of its resources. The website seamlessly adjusts to various screen sizes and devices, such as desktops, laptops, tablets, and smartphones, thanks to responsive design approaches. Furthermore, the website is inclusive and suitable for all users by incorporating factors like color contrast, readability, and assistive technologies. The project uses interactive components and visual feedback systems to improve user engagement and learning outcomes. An immersive atmosphere that promotes exploration and active engagement is created by elements like hover effects, interactive buttons, and multimedia material. In order to encourage user participation and information exchange, the website may also incorporate social features like discussion boards or collaborative learning applications. The project involves an extensive development process that includes analysis, planning, design, development, testing, deployment, and maintenance from a technological perspective. With the use of technologies such as HTML, CSS, JavaScript the project seeks to create a stable and expandable framework that can support further development and expansion. The project's technical design incorporates important elements such as content management, performance optimization, and data security. To construct a complete online learning platform, the website project is a complex undertaking that integrates components of user experience design, accessibility, customization, and technological development. Its main objective is to enable people to reach their educational objectives and realize their full potential by giving them access to top-notch instructional materials and creating a welcoming learning environment.

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E-learning, Art, Website, Front-end, Back-end, etc.

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ABSTRACT Human life is precious and irreplaceable. Literature records human lives for centuries, assisting humans to live and to know about character and characterization, and it has various phases of life. Literature plays an important role, and it educates, guides, leads, and makes people happier. Modernism changes the lives of humans to the utmost level of advancement. Literature has the greatest virtues, and it has also not failed to appreciate the original characteristics of humans through fictional characters. This paper aims to bring about the famous writer Sir Kinsley William Amis, a famous comic novelist of the twentieth century. As a great writer, Kinsley Amis satirizes hypocritical society through his novels. This paper discusses Kinsley's two novels, Lucky Jim and The Green Man, as a comparative study of society and how people's characters reflect on different situations. It is apparent how the people forget their virtue and self-respect, which proves to be an unsound mind that prevails within the characters and society.

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Kinsley William, Lucky Jim, virtue and self-respect, comparative study.

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Although the impact of Generative Artificial Intelligence (GAI) such as Chat-GPT is great, it must be used carefully and cautiously to avoid problems arising from assumptions. In this article, I show an example where Chat-GPT produces incorrect results in calculating the transaction number. Therefore, I advise smart students to use Chat-GPT or any other GAI as study partners and not as a substitute for their hard work This report explores the ability of Chat-GPT as an automatic assistant A variety of tasks including research writing, mathematics, education, programming and therapy. We are investigating Chat-GPT's ability to increase productivity, improve problem-solving processes, and improve typing. We also highlight the risks of over-reliance on Chat-GPT in these areas. These limitations include factors such as false positives and negative responses, coding errors, limited judgment capacity, reliability, and serious legal and ethical concerns. We explain the areas and purposes for which Chat-GPT is useful, applications in which it should be used with caution, and situations in which its reliability is limited. Considering the limitations described and that error-prone tools can pose certain problems for non-professionals, Chat-GPT should be used in conjunction with appropriate methods. We provide guidelines and graphics for using Chat-GPT effectively through scientific research. Our proposal concerns interaction with GPT and independent verification of output. Considering the importance of using Chat-GPT intelligently and intelligently, we recommend its use by experts in the f i eld. Chat-GPT is the result of many years of hard work and development, resulting from generations of intellectual expertise. Early research can be traced back to experts, and success in natural language processing (NLP) has increased in recent years.

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CHAT-GPT, AI COMPANION, IR SENSORS, LED, ESP-32 .

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In this paper, a new kind of sets called F-generalized closed (briefly Fg-closed) sets are introduced, which is a generalization of F-closed as well as g-closed sets and also studied some basic properties of Fg-closed sets in topological spaces. Further by utilizing Fg-closed sets, we obtained a characterization of normal spaces. Moreover, we also introduced a new class of normal spaces is called F-normal spaces in topological spaces and investigated some properties of F-normal spaces in the terms of Fg-open sets.

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F-closed set, Fg-closed set, Fg-open set, F-normal Space, almost Fg-closed function, almost Fg-continuous function etc.

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A common disease that affected the larger section of the society is Cancer. It is of many types like breast cancer, Lung cancer etc. This paper involves the Machine Learning in the prediction of cancer at initial stages. Cancer is a large set of diseases that can affect various body parts. It starts when cells in the body start to grow uncontrollable manner and spread to other parts. These abnormal behaviors of cells are termed as cancerous cells which ultimately lead to cancer. In this Disease a cell divides continuously and abnormally and it is a Deadly Disease. Aim of this document is to generate the Machine Learning (ML) models which can predict the cancer in the initial stages so that we can improve the chances of survival. Patients who are suffering from this disease can only be saved if it is identified at initial stages like stage I and stage II. If it is detected in stages like stage III and stage IV also known as advance stages, then the chance of survival of patient will be minimal. ML and the new technologies evolve around us can be helpful to tackle this kind of healthcare problem. Cancer symptoms depending on its type, its location, and its stages. Some general symptoms that may indicate the presence of cancer like Unexplained weight loss, Fatigue, Persistent cough, Unexplained pain, Changes in skin moles or growths, Persistent indigestion or difficulty swallowing etc. Different Model provides some statistics and understanding which can be useful in the assessment of model and provide a better way for the analysis.

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Machine Learning, model, Cancer, Prediction, Cancerous, Deadly Disease, stages, healthcare, Symptoms, abnormal, analysis.

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SWAYAM is a programme initiated by the Government of India and designed to achieve the three cardinal principles of education policies etc.., access equity and quality. The objective of this effort is to take the best teaching learning resources to all, including the most disadvantaged. This is done through online platforms that facilitates hosting of all the courses. SWAYAM conduct many online courses like NPTEL National Programme on Technology Enhanced Learning (NPTEL),MOOCS(Massive Open Online Course) offered on SWAYAM platforms changes every courses and seeks to bridge the digital divide for students who have hitherto remained untouched by the digital for revolution and have not been able to join the mainstream of the knowledge economy.

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Accessing quality and equity, learning resources, digital divide for students.

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The main aim of the firm should aim at maximizing the wealth of its shareholders. A firm works hard in order to earn sufficient return in its operation. Earning a steady amount of profit requires successful sales activity of the company.

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Cash flow forecasting, cash inflow and outflow

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The subjects of this research study are Indigenous literature, human values, and activism. This paper examines activism, human values, and Indigenous writing from 2008 to 2015 concerning several novels, including Amitav Ghosh's Sea of Poppies, Jhumpa Lahiri's The Lowland, Arundhati Roy's The Ministry of Utmost Happiness, Viet Thanh Nguyen's The Sympathizer, Madhuri Vijay's The Far Field. By examining and promoting indigenous literature, the project seeks to increase activism related to human values. The goal of this research is to promote a greater understanding of common human values like justice, equality, respect, and environmental stewardship by utilising the rich cultural histories and distinctive viewpoints found in indigenous literature.

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Activism, Indeginious literature, Human Values

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In today's digital age, personalized music recommendation systems play a crucial role in enhancing user experience on music streaming platforms. This research presents a novel approach to song recommendation by integrating facial emotion detection technology. By leveraging deep learning models and computer vision techniques, the proposed system aims to analyze users' facial expressions in real-time and recommend songs that align with their emotional states. Through rigorous experimentation and analysis, this paper evaluates the effectiveness and feasibility of the proposed system in enhancing user engagement and satisfaction in music streaming platforms.

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Key Words: Facial Emotion Detection, Song Recommendation, Deep Learning, Computer Vision, Music Streaming Platforms

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The rapid advancements in Artificial Intelligence (AI) and image processing have led to a surge in manipulated images, Deepfake technology allows for the creation of highly realistic, yet entirely fabricated, images, posing significant ethical and security challenges While deep learning models show potential in detecting these manipulations, their complexity and computational demands can hinder practical use. Alternative computer vision methods offer new approaches. One such method is Tamper Trace, a novel tool designed to identify highlight manipulated areas within images. This tool utilizes a unique combination of hash-based analysis and pixel-wise iteration making it robust in detecting various image manipulation techniques. Notably, Tamper Trace goes beyond traditional photo editing detection and identifies AI generated Deepfakes as well.

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Hash-based analysis, image manipulation, Tamper Trace, Deepfake.

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The environment of financial forecasting and investing strategies is undergoing a transformation as a result of the rapidly developing field of stock market trend research in conjunction with machine learning (ML). This research paper looks into the thorough overview and synthesis of current breakthroughs in machine learning approaches applied to stock market patterns. It highlights key achievements, obstacles, and prospective directions in the field. It performs a thorough analysis of a number of machine learning algorithms, including but not limited to deep learning, reinforcement learning, and hybrid models, highlighting the different ways in which these algorithms can be applied, as well as their strengths and limits, in predicting changes in stock prices. A comprehensive study of data preparation techniques, feature selection, and the incorporation of alternative data sources is included in the analysis. The goal of this analysis is to improve the accuracy of predictions. Additionally, the practical implications of machine learning in stock market trend analysis are discussed in this work. These implications include risk management, algorithmic trading, and portfolio optimisation. At the end of the paper, some insights into potential future research paths are presented. These trajectories have the potential to bridge existing gaps and encourage the creation of machine learning models that are more robust, transparent, and ethical for financial market analysis.

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Machine Learning, Stock Market Trends, Financial Forecasting, Deep Learning, Reinforcement Learning, Hybrid Models, Algorithmic Trading, Feature Selection, Data Preprocessing, Portfolio Optimization.

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Breast cancer is one of the most common types of cancer among women globally. Early detection of breast cancer is crucial for successful treatment and improved patient outcomes. Traditional methods of breast cancer diagnosis rely on imaging techniques and biopsy, which can be invasive and time-consuming. Machine learning, particularly deep learning, has shown promise in predicting breast cancer using clinical data. This project aims to develop a deep learning model to predict breast cancer using clinical , genetics, imaging data and other relevant factors. Various machine learning algorithms, including logistic regression, support vector machines, random forests, and deep neural networks, are employed to analyze and extract meaningful patterns from the dataset. The dataset used in this project will be a large and diverse collection of breast cancer patient data. The deep learning model will be trained and evaluated using a variety of performance metrics, including accuracy, sensitivity, and specificity. The results of this study could have significant implications for breast cancer diagnosis and treatment. A reliable and accurate deep learning model could help improve early detection and reduce the need for invasive procedures, leading to better patient outcomes. Additionally, this project could contribute to the growing field of medical machine learning and pave the way for future research in the area of cancer prediction and treatment.

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Breast cancer, Deep learning, Clinical data, CNN, Early detection.

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 Abstract

Adhunik Parivesh Mein Ramayana Ke Naari Patron Ki Bhoomika

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Adhunik Parivesh Mein Ramayana Ke Naari Patron Ki Bhoomika

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Golden Therapy: Crafting and Assessing Turmeric Emulgel Formulation is a comprehensive study aimed at formulating and evaluating the efficacy of a turmeric-based emulgel for potential therapeutic applications. Turmeric, renowned for its medicinal properties, particularly its anti-inflammatory and antioxidant effects attributed to curcumin, serves as the key ingredient in this formulation. The emulgel combines the benefits of both emulsion and gel systems, providing enhanced skin permeability and prolonged drug release. The research begins with the development of the emulgel formulation using a combination of hydrophilic and lipophilic ingredients to optimize stability, texture, and drug release characteristics. Various emulsifiers, gelling agents, and penetration enhancers are meticulously selected and incorporated to achieve the desired rheological properties and skin permeation profile. Furthermore, the study evaluates the physicochemical properties, such as pH, viscosity, spreadability, and drug content uniformity, to ensure the formulation's suitability for topical application. In vitro assessments, including skin irritation studies and skin permeation studies using Franz diffusion cells, provide valuable insights into the safety and efficacy of the emulgel. Moreover, the formulation undergoes comprehensive in vivo evaluations to assess its therapeutic potential, including anti-inflammatory activity using carrageenan-induced paw Edema model and antioxidant activity utilizing appropriate assays. These evaluations aim to validate the effectiveness of the turmeric emulgel in alleviating inflammation and oxidative stress, thereby supporting its potential use in various dermatological conditions. Physicochemical characterization of the emulgel includes assessments of pH, viscosity, spreadability, and drug content uniformity to ensure quality and consistency. In vitro studies, such as skin irritation tests and skin permeation studies using Franz diffusion cells, provide insights into the formulation's safety and efficacy profile. In vivo evaluations focus on assessing the therapeutic potential of the turmeric emulgel, including anti-inflammatory and antioxidant activities. Animal models are utilized to investigate its efficacy in mitigating inflammation and oxidative stress, thereby supporting its potential for dermatological applications. The findings of this study contribute to the growing body of research on turmeric-based formulations and highlight the promising prospects of turmeric emulgel as a novel therapeutic agent in skincare and dermatology. Overall, this study presents a systematic approach to the development and assessment of a turmeric-based emulgel formulation, offering promising prospects for its application in skincare and dermatological therapy.

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 Keywords

Golden Therapy, Turmeric, Emulgel formulation, Anti-inflammatory, Antioxidant, Dermatological applications, Excipients, Physicochemical characterization, In vitro studies, In vivo evaluations, Skin permeation, Rheological properties, Skin compatibility, Drug release kinetics, Animal models.

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