StuA: An Intelligent Student Assistant

With advanced innovation in digital technology, demand for virtual assistants is arising which can assist a person and at the same time, minimize the need for interaction with the human. Acknowledging the requirement, we propose an interactive and intelligent student assistant, StuA, which can help new-comer in a college who are hesitant in interacting with the seniors as they fear of being ragged. StuA is capable of answering all types of queries of a new-comer related to academics, examinations, library, hostel and extra curriculum activities. The model is designed using CLIPS which allows inferring using forward chaining. Nevertheless, a generalized algorithm for backward chaining for CLIPS is also implemented. Validation of the proposed model is presented in five steps which show that the model is complete and consistent with 99.16% accuracy of the knowledge model. Moreover, the backward chaining algorithm is found to be 100% accurate

WQVP: An API enabled Open Data Machine Learning based Solution for Water Quality Visualization and Prediction

Water is an essential component required by living bodies for their survival. In today’s world, most of the water utilization is done by human beings. Due to this, there is a lot of adverse impact on water bodies. As human consumption of water increases, their pollution also increases. In order to control pollution impact and take measures to reduce water pollution, several methods have been proposed by researchers. Water Quality Index measures are one such method being adopted and used to measure harmful constituents of water. In recent times initiatives have been taken by international and national governing bodies to provide data through Open Data Initiatives that can be publicly made available. This data fetched in real time through APIs can be used for providing data analysis to naïve natives of the place with better understanding features like visualizations. Machine learning based techniques have proved to be a great tool for providing unsupervised learning in this area. We have implemented an API enabled Open Data Machine Learning based Solution for Water Quality Visualization and Prediction for Australian Rivers

StuA: An Intelligent Student Assistant

With advanced innovation in digital technology, demand for virtual assistants is arising which can assist a person and at the same time, minimize the need for interaction with the human. Acknowledging the requirement, we propose an interactive and intelligent student assistant, StuA, which can help new-comer in a college who are hesitant in interacting with the seniors as they fear of being ragged. StuA is capable of answering all types of queries of a new-comer related to academics, examinations, library, hostel and extra curriculum activities. The model is designed using CLIPS which allows inferring using forward chaining. Nevertheless, a generalized algorithm for backward chaining for CLIPS is also implemented. Validation of the proposed model is presented in five steps which show that the model is complete and consistent with 99.16% accuracy of the knowledge model. Moreover, the backward chaining algorithm is found to be 100% accurate

StuA: An Intelligent Student Assistant

With advanced innovation in digital technology, demand for virtual assistants is arising which can assist a person and at the same time, minimize the need for interaction with the human. Acknowledging the requirement, we propose an interactive and intelligent student assistant, StuA, which can help new-comer in a college who are hesitant in interacting with the seniors as they fear of being ragged. StuA is capable of answering all types of queries of a new-comer related to academics, examinations, library, hostel and extra curriculum activities. The model is designed using CLIPS which allows inferring using forward chaining. Nevertheless, a generalized algorithm for backward chaining for CLIPS is also implemented. Validation of the proposed model is presented in five steps which show that the model is complete and consistent with 99.16% accuracy of the knowledge model. Moreover, the backward chaining algorithm is found to be 100% accurate