National Institute of Technology, Meghalaya

About: National Institute of Technology, Meghalaya is a education organization based out in Shillong, India. It is known for research contribution in the topics: Control theory & Electric power system. The organization has 503 authors who have published 1062 publications receiving 6818 citations. The organization is also known as: NIT Meghalaya & NITM.

GA-Optimized Fuzzy-Feedforward-Bias Control of Motion by a Rugged Electrohydraulic System

Abstract: An electrohydraulic system with a proportional valve and industry-grade cylinder has been used to target the servoclass tracking performance. Such systems have a wide range of heavy-duty applications, where the environment could be quite dirty along with the demands becoming faster and more precise every day. High static friction in the cylinder and large deadband of the valve in the system pose control challenges that are more severe than in a system with a servovalve and a low-friction cylinder. A fuzzy-feedforward-bias controller has been developed and a genetic algorithm has been used to optimize the controller parameters. The real-time control experiments revealed excellent tracking throughout the cycle for sinusoidal displacements beyond 1.5 Hz.

Diabetes Disease Prediction Using Machine Learning Algorithms

Abstract: This paper deals with the prediction of Diabetes Disease by performing an analysis of five supervised machine learning algorithms, i.e. K-Nearest Neighbors, Naive Baye, Decision Tree Classifier, Random Forest and Support Vector Machine. Further, by incorporating all the present risk factors of the dataset, we have observed a stable accuracy after classifying and performing cross-validation. We managed to achieve a stable and highest accuracy of 76% with KNN classifier and remaining all other classifiers also give a stable accuracy of above 70%. We analyzed why specific Machine Learning classifiers do not yield stable and good accuracy by visualizing the training and testing accuracy and examining model overfitting and model underfitting. The main goal of this paper is to find the most optimal results in terms of accuracy and computational time for Diabetes disease prediction.