Campus Interactive Chatbot for Students

Chatbots are the Bots where user gets the information which he needed from the Bot in natural language without getting help from the third party or a person. In this paper Campus interactive chatbot uses an artificial Intelligence that analyses the user query and understand the user message later provide a response based on the user query. Students should individually need to go to college if he need any information like courses offered by the college, college timings, admission process, etc. from help desk. This process is timing consuming and requires manpower to provide information to the students. Hence, Interactive chatbots can developed to provide information to the use

Asynchronous Execution Platform for Edge Node Devices

A Asynchronous distributed execution platform which enables efficient and seamless task submissions on a remote node from a cluster of edge node devices using a reactive framework and provide real-time metrics of execution persisted on elastic database. Queues are used for job submission along with different compute units delivering the infrastructure for execution of submitted jobs. The proposed system is a generic framework that can be used in any enterprise web application where execution on a remote node is required. Through this we aim to provide an enterprise grade solution for task submission and management on a remote machine, using new, efficient technologies like SpringBoot and RabbitMQ. There is a demand in remote computing and huge workloads that cannot be executed on small local machines, our system can be used directly or indirectly by incorporated in other solutions

Classification of Diabetic Retinopathy using Convolutional Neural Network

Diabetic Retinopathy is a scenario in medical field which leads to the rise of damage of blood vessels in the retina which is due to diabetes mellitus. The suitable detection for this kind of problems and care to be done immediately in order to prohibit loss of sight in a person. Presently, diagnosing Diabetic Retinopathy manually is a time- consuming process where they require experienced clinicians to examine the digital-colored fundus images. Here, we have proposed a machine learning technology using Convolutional Neural Network (CNN) approach which has emerged as an operative productive tool in medical image examination for the classification and detection of Diabetic Retinopathy (DR) in real-world. The different layers which are used to detect the brain tumor are conv2D, Activation, MaxPooling2D, Dense and Flatten. The set used here considers 750 retinal images, with 600 training images and the test set considers 150 images with the accuracy of 82.75% which ran for 80 epochs