Volume 12 | Issue 4 |

 Abstract

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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 Keywords

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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 Abstract

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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 Keywords

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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 Keywords

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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 Keywords

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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 Keywords

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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