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These days everyone will change their life style to search the movies on the internet. They will provide the information of their preferences which the y like to watch. There are many recommended and popular system is applied to search their favorite things like books, articles music videos, movies etc. these paper we are proposed a movie recommendation system. It will be working on the various filter or collaborate filter method that will collect and gave the information to the user and it will also analyzes the user and gave them the best movies to there users at that time. We sorted the movies according to the user recommendation of the previous users preferences so for these purpose we use k-means algorithm. Movie recommendation system can also help the user to find the choices of there movies are based on there previous experiences and manners without wasting its useless time on the browser to search their best movies. It will give us various type of previous recommendation using customized the database. then the user can browse it easily and choice his best movie

The dream house price predictor project aims to build a machine learning model that can predict the selling price of a house based on various features such as location, number of bedrooms, square footage, and other relevant factors. The model will be trained on a dataset of historical housing prices and features, and will use regression techniques to make predictions on new, unseen data. The project will also explore the impact of different features on house prices and provide insights into which factors are the most important in determining the value of a property. The goal of the project is to provide a tool for homebuyers, sellers, and real estate professionals to better understand the market and make informed decisions. The Dream House Price Predictor project is aimed at predicting the prices of residential properties based on various features such as location, size, number of bedrooms, and other amenities. The project uses a dataset of real estate transactions and employs machine learning algorithms to build a predictive model. The model is trained on the historical data and tested on a validation set to ensure its accuracy. The results of the project can be used by real estate agents, property buyers, and sellers to make informed decisions about pricing and investment opportunities. This project demonstrates the potential of machine learning to assist in the real estate market and provides a valuable tool for predicting property prices.

Generative Adversarial Networks (GANs) have rapidly become a focal point of research due to their ability to generate realistic images. First introduced in 2014, GANs have been applied in a multitude of fields such as computer vision and natural language processing, yielding impressive results. Image synthesis is among the most thoroughly researched applications of GANs, and the results thus far have demonstrated the potential of GANs in image synthesis. This paper provides a taxonomy of the methods used in image synthesis, reviews various models for text-to-image synthesis and image-to-image translation, discusses evaluation metrics, and highlights future research directions for image synthesis using GANs..

The healthcare industry is currently undergoing a digital transformation, and blockchain technology has evolved as a budding remedy because of some of its previous challenges. Blockchain, a distributed ledger technology, can securely, safely and transparently record and store data. Electronic Health Records (EHRs) are electronically stored health information in a digital format. This paper discusses the possible uses of blockchain technology in healthcare and its potential advantages and benefits. The discussion of the paper begins with the challenges faced by the healthcare industry in present time, such as the lack of interoperability, data security concerns, and inefficiencies in healthcare delivery. The discussion then turns to how blockchain technology can help avoid these challenges by offering a secure, immutable, and transparent platform for healthcare data sharing. The paper also discusses how of blockchain technology can be used in healthcare in present time, such as patient identity management, Electronic Health Records (EHRs), clinical trials, and supply chain management. For each use-case, the paper discusses the advantages and disadvantages of using blockchain technology and provides some examples of blockchainbased solutions. Also, the paper describes some of the technical factors, such as scalability, privacy, interoperability, and regulatory compliance, that must be taken into account while using blockchain in the healthcare industry. The paper also looks over the challenges and restrictions of using blockchain technology in healthcare, such as the high computational costs and the requirement for standardization. Finally, the paper concludes by discussing the impact of blockchain technology on the healthcare sector, like improving patient outcomes, cutting costs, and enabling new business models. The spotlight of the paper is that in spite of the fact that blockchain technology does not offer a fix for every issue plaguing the healthcare sector, the study argues that it has the capability to be an important tool for improve healthcare outcomes and delivery. In conclusion, blockchain technology has the capacity to modify healthcare by strengthening patient privacy, streamlining data management, and managing clinical trials and the supply chain management. The healthcare sector must address a number of technological, organisational, and regulatory obstacles to fully achieve these advantages. To overcome the above obstacles and enable the wider application of blockchain technology in healthcare, more study and development are required

The aim of paper is to prognosticating the online social network of people for chancing their eventuality, chancing fake information and reality. The study considers the popular social networking sites like Facebook, Twitter, and LinkedIn which are available for people to share there point of views and where they may unintentionally or intentionally provide personal information about themselves, such as their interests, comments, and likes and dislikes. The work that has been done to date to gather data on a specific person in order to profile that person's introductory information is addressed by machine learning. These media profiles are helpful to identify the personality t of a person and their original information, which is essential for chancing the Government to identify people involved in spreading fake news in social network communities. Introduce the latest machine learning trend used in this study to test each person's uniqueness as they interact with the social networking community. Millions of individuals use social media platforms to communicate with friends, family, and companies, and its use has expanded significantly in recent years. Social media firms are continuously looking for techniques to forecast user behavior on their platforms, such as likes, shares, and comments, in order to enhance user experience. To study user behavior and create predictions based on prior encounters, machine learning techniques can be employed. This study investigates how machine learning may be used to forecast user behavior on social networks.

Neutron depth profiling (NDP) has been employed to examine manufacturing processes and starting materials for several high-technology applications. NDP combines nuclear and atomic physics processes to determine the concentration profile of several light elements in the near surface region (∼1–8 μm) of smooth surfaces. The method is both quantitative and non-destructive. Analyses are performed at the Center for Neutron Research at NIST on samples prepared at Corning Incorporated. Two types of samples have been analyzed: (1) Boron profiles are measured in glasses to determine B loss due to its volatilization during manufacturing. Surface depletion of B is a key characteristic of borosilicate materials for both chemical vapor deposition and conventional melting processes. (2) For lithium niobate, a quantitative measure of Li concentration can differentiate congruent and stoichiometric compositions and any surface depletion in commercial wafers.