Natural language processing (NLP) is a major area of artificial intelligence research, which in its turn serves as a field of application and interaction of a number of other traditional AI areas. Until recently, the focus in AI applications in NLP was on knowledge representation, logical reasoning, and constraint satisfaction - first applied to semantics and later to the grammar. In the last decade, a dramatic shift in the NLP research has led to the prevalence of very large-scale applications of statistical methods, such as machine learning and data mining. Naturally, this also opened the way to the learning and optimization methods that constitute the core of modern AI, most notably genetic algorithms and neural networks. In this paper we give an overview of the current trends in NLP and discuss the possible applications of traditional AI techniques and their combination in this fascinating area.

The performance prediction of a student in different subjects is important so that the professors as well as the students can gain an insight into their future performance and thus improve before failing. Lately, the number of dropouts has increased in the context of advanced education, affecting the image of schools and their occupation. Different machine learning algorithms and models can be used for the prediction and forecasting purpose. The existing system explores the capacity to send and collect knowledge from undergraduates but, in addition, it is impossible to find useful information. The proposed system will predict the performance of the student. This paper presents the study of different available algorithms, which will be suited for the proposed system. The paper concludes with a new system which can overcome the shortcomings of the present system that only stored the student information and various algorithms suited for it.

This paper presents an educational application for Nursery Kids and Illiterate from Rural India using Augmented Reality (AR) technology, which makes easy, interactive, informative, enjoyable, and interesting to the kids and Illiterate People. Augmented Reality (AR) is an interactive experience or a combination of real-world and digital data. It simplifies and enhances user knowledge of interaction with physical world by imposing virtual images on real ones. This is the new way of manipulating how we interact with that world, without replacing the real world. Augmented Reality has manifested in various fields today like Education, Navigation, Games, Industry, Medical, Advertisements, Training Classes and Architecture.AR in education will soon affect the conventional learning process. Our approach has the potential to speed up the design of 3D views of view of objects related to that letter and help kids to learn and realize concepts in a better and interactive way. The idea presented in this report is to show how AR can be used to enhance the learning experience of kids the app can be used to reduce irreverence of kids who not interesting to learning.

Data scientists are challenged with more data than they will ever be able to analyse as data collection and storage costs continue to drop. The most fascinating developments in machine learning require vast amounts of data. But it also creates a new challenge for the machine learning community, as all supervised learning-based machine learning applications remain practically useless without labelled data. Labelling large datasets has become a vital challenge. A specific instance of Supervised Machine Learning is Active Learning. By actively choosing the important data points, this method builds a highperformance classifier thus minimizing the size of the training dataset.

Style transfer is an optimization technique used to create a new image out of a content image and a style image (as in an artistic work by a well-known painter) as a result of blending, the content image is altered to be painted in the same style as the content image but to resemble it of the style image. The essential method for creating style transfer is the convolutional neural network that allows for transfer (CNN). This paper will analyse key methods for performing style transfer photos and quickly compare multiple models and their outcomes. We primarily contrast one pioneering technique, developed by Leon Gatys in 2015, with the most recent technology in the High-Resolution Network from 2019 for the transfer of photorealistic style. Gatys's Neural Algorithm of Artistic Style produces excellent results, but the resulting data doesn't maintain the content image's characteristics and the fact that the paper gives no concise description of the internal mechanisms gram matrix. The enhancement is the conversion to a photorealistic style transfer from Gatys' neural style transfer. It aids in maintaining the structure and defining characteristics within the content image. Both methods make use of VGG, a network that was trained using the ImageNet dataset for conducting image categorization. We made our own data set with five types or groups and used transfer learning in a VGG network to do a style transfer using transfer learning.

This project deals with the design and manufacture of a mechanism to carry load on staircase. Our project consist of a rover with a Rocker bogie mechanism on which load is transverse over stairs. Rocker bogie plays important function in rough and steep hills terrain. The primary mechanical feature of the rocker bogie design is its drive train simplicity and efficient high degree of mobility suspension system capable of traversing rough terrain, which is accomplished by using six motors for mobility. A series of mobility experiment in the land, rough roads, inclined, stairs and obstacle surface concluded that bogie can achieve some distance transverse field.

This work investigates the result of an experimental study carried out to determine the performance of domestic refrigerator when a propane-butane mixture is liquefied petroleum gas(LPG) which is locally available and comprises 24.4% propane, 56.4% butane and 17.2% isobutene which is very from company to company. The LPG is cheaper and possesses an environmental friendly nature with no ozone depletion potential (ODP). It is used in world for cooking purposes. The various methods of refrigeration on the basis of standard refrigerant discussed. He refrigerator used in the present study is of medium size with a gross capacity of 125litre and is designed to work on LPG. The performance parameters investigated is the refrigeration effect in certain time. The refrigerator worked efficiently when LPG was used as refrigerant instead of CFC 12. The evaporator temperature reached -5 ºC with and an ambient temperature of12 ºC. Also from the experiment which done in atmospheric condition, we can predict the optimum value of cooling effect with the suitable operating condition of regulating valve and capillary tube of the system. The results of the present work indicate the successful use of this propane butane mixture as an alternative refrigerant to CFC 12 in domestic refrigerant.

Development gathering is an expansive characteristic in true courts all around the planet. It maintains quick association in value association from case enlistment through case decision. The result shows a basic improvement to the extent that court work processes the chiefs, court information and records the board and getting together with various associations. All the while, different imaginative, utilitarian and individual issues arise out of this advancement gathering. No current framework is accessible to give court administration on web-based stages. In this task we will proposed web application framework utilizing django system and google cloud administration to store information.

This development aims to contribute treatments (medicines) that are either fresh or stale in nature. The new treatments can be handed to a poor individual who will be able to use them in the future. This programme assists the user in donating any unused medicines to a non-profit organisation. There are three people that make up this body: the administrator, the non-governmental organisation, and the user. A system administrator will log in and manage users, including deleting and barring users who are giving incorrect or expired drugs. In addition, the administration has a consultant, similar to the appointment requested by the NGO. The administration receives a monthly noise of remedies that have been contributed. Authorizations are used to register and login for non-profit organisations. They can promote a request for an appointment, which will be added to a more appropriate and comprehensive list by the administrator. The standard is managed by an NGO, which aids in the preservation of the history of the current medicines. In the event of a safety issue, an NGO can also modify their PIN. Authorizations are used to allow users to register and log in. They can provide remedies by providing remedy details and generating demand; once this has been agreed upon by the administration and the NGO, they will plan a date for the donation to take place. Also available to consumers is a history of their previous remedial transactions.

Machine learning is mainly divided into two sections, specifically supervised and unsupervised learning. Supervised learning is additionally divided into two fundamental parts, that is, the regression type and classification type. Unsupervised learning additionally has its types like clustering, association, PCA, and so on. Here, we consider the use case from the supervised machine learning approach. We consider the dataset of the wheat kernel, used to classify 3 types of wheat, specifically Kama, Rosa, and Canadian, consisting of properties of the wheat kernel like area, compactness, perimeter, and so on. So, as this dataset comprises 3 classes or 3 distinct results, it is called multi-class data, which is multiple classes. Various machine learning algorithms can assist us in solving this multi-class classification problem. Some of the algorithms from Bagging [Ensemble Technique] are Random Forest, Boosting [Ensemble Technique] are LightGBM, XGBoost, Gradient Boosting, Support Vector Machine Classifier, Decision Tree Classifier, Logistic Regression, and so on. The above-mentioned algorithms work in different ways to find the solution to multi-class classification problems. In a large number of problems, we use an SVM classifier to solve the wheat kernel identifier use case. The SVM classifier is essential in solving binary classification problems. Nonetheless, here, in the wheat kernel identification case, multiple classes are three types of classes of items in the original dataset. This work focuses on learning the approaches for improving the results by using the support vector classifier and applying legitimate hyper-parameter tuning to get generalized output.