Type 2 diabetes is a persistent disorder that affects millions of individuals globally. It is characterised by the excessive levels of glucose within the blood due to insulin resistance or the incapability to supply insulin. Early detection and prediction of type 2 diabetes can improve patient outcomes. K-Nearest Neighbor (KNN) is used in the present model to predict type 2 diabetes. The KNN set of rules is a simple but powerful machine learning set of rules used for categorization and regression. It's far a non-parametric approach that makes predictions based totally on the nearest k-neighbours in a dataset. KNN is widely used in healthcare and scientific studies to expect and classify sicknesses primarily based on the affected person’s data. The intention of this work is to predict the threat of growing type 2 diabetes using the KNN set of rules. Data has been collected from electronic medical records of patients diagnosed with type 2 diabetes and healthy individuals. The dataset consists of various patient attributes, such as age, gender, body mass index, blood pressure, cholesterol levels, and glucose levels. Information has also been collected about lifestyle habits, such as physical activity, smoking status, and alcohol consumption. Data have been pre-processed by removing missing values and outliers, and normalization of the data has been done to ensure that all features have the same scale. Splitting the dataset into training and test sets, with training sets using 80% of the data and test sets using 20% of the data is performed. KNN algorithm have been used to classify the patients into two groups: those at high risk of developing type 2 diabetes and those at low risk. The model's performance has been assessed using a variety of metrics, including accuracy, precision, recall, and F1-score.

Healthcare service has huge demand these days as it really helps in managing a hospital or a medical office. The scope of Healthcare service systems is increasing by each day and it is true for the entire world. Some of these solutions include improved awareness about Healthcare services and health policies. The objective of this system is to provide medical assistance to people instantly with the help of technology. This system eradicates the cultural sensitivity that prevails in many hospitals and improvises the quality of medical assistance. The captivating features of this system are online doctor, medicines at doorstep, bulletin of awareness. The users can also navigate and choose among various insurance schemes that are displayed.Unified Modeling language (UML) is a standardized modeling language enabling developers to specify, visualize, construct and document artifacts of a software system. It uses graphic notation to create visual models of software systems. This paper contains the UML diagrams for better understanding of the system with the help of Star UML tool.Usecase diagrams are used during the analysis phase of a project to identify system functionalities. Class diagram represents the static view of an application.The class diagrams are the only UML diagrams, which can be mapped directly with object-oriented languages.Activity diagram is an important behavioral diagram in UML diagram to describe dynamic aspects of the system. Activity diagram is essentially an advanced version of flow chart that modeling the flow from one activity to another activity.The state machine diagram shows the different states of an entity and focuses more on how it responds to various events by changing from one state to another. Statechart diagram is used to capture the dynamic aspect of a system. State machine diagrams are used to represent the behavior of an application. The sequence diagram focuses on the messages that are passed during an interaction in a time based perspective.A Communication diagram models the interactions between objects or parts in terms of sequenced messages. It describes both the static structure and dynamic behavior of a system. Component diagrams are used to model the physical aspects of a system. It does not describe the functionality of the system but it describes the components used to make those functionalities. Deployment Diagram is a type of diagram that specifies the physical hardware on which the software system will execute. It also determines how the software is deployed on the underlying hardware. UML is a modeling language used by software developers.UML can be used to develop diagrams and provide users with ready-to-use, expressive modeling examples. Some UML tools generate program language code from UML.UML can be used for modeling a system independent of a platform language. UML is a graphical language for visualizing, specifying, constructing, and documenting information about software-intensive systems.UML gives a standard way to write a system model, covering conceptual ideas.

Computational Linguistics involves the techniques of Computer Science which play a vital role in recognizing written or printed characters such as numbers or letters to change them into a form that the computer can use it efficiently. Convolutional Neural Network differs from other approaches by extracting the features automatically. The proposed approach is capable of recognizing characters in a variety of challenging conditions using the Convolutional Neural Network, where traditional character recognition systems fail, notably in the presence of low resolution, substantial blur, low contrast, and other distortions. Intellectual Character Recognition System is an application that uses Convolutional Neural Network (CNN) to recognize the Tamil character dataset accurately developed by HP Labs India. The novelty of this system is that, it recognizes the characters of the Predominant Tamil language. With the help of suitable datasets consisting of the Tamil Scripts, the model is trained efficiently. This work has produced a training accuracy of 99.16% which is far better compared to the traditional approaches.

Computational linguistics is the branch of linguistics in which the techniques of computer science are applied to the analysis and synthesis of language and speech. The main goals of computational linguistics include: Text-to- speech conversion, Speech-to-text conversion and Translating from one language to another. A part of Computational Linguistics is the Character recognition. Character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. Character recognition methodology mainly focuses on recognizing the characters irrespective of the difficulties that arises due to the variations in writing style. The aim of this project is to perform character recognition for of one of the complex structures of south Indian language ‘Tamil’ using a supervised algorithm that increases the accuracy of recognition. The novelty of this system is that it recognizes the characters of the Predominant Tamil Language. The proposed approach is capable of recognizing text where the traditional character recognition systems fails, notably in the presence of blur, low contrast, low resolution, high image noise, and other distortions. This system uses Convolutional Neural Network Algorithm that are able to exact the local features more accurately as they restrict the receptive fields of the hidden layers to be local. Convolutional Neural Networks are a great kind of multi-layer neural networks that uses back-propagation algorithm. Convolutional Neural Networks are used to recognize visual patterns directly from pixel images with minimal preprocessing. This trained network is used for recognition and classification. The results show that the proposed system yields good recognition rates.

This study aims to research on immersive and interactive experience to users by using augmented reality and GPS technology. The system will be built using the Vuforia engine or ARcore support, and will be able to detect the user's location and device location using GPS. Once the system is initiated, it will check for GPS support in the user's device and ask for camera access. Then, using Vuforia's markerless tool and AR+GPS package, the system will detect the user's location and search for the rendered scene for the respective location. Each building will be labelled with AR objects for better understanding of the place and tracking where the user is. The AR objects will provide information about the building, such as its name, purpose, and history. This will enhance the user's experience and enable them to navigate the campus more easily. The application will create a scene that renders the real environment image and the rendered AR scene image, providing an interactive output. Users will be able to interact with the AR objects by clicking on them to access more information, such as photos and videos. Overall, the proposed system will be a useful tool for navigating the campus, providing users with a unique and immersive experience.