Nitte Meenakshi Institute of Technology

About: Nitte Meenakshi Institute of Technology is a based out in . It is known for research contribution in the topics: Computer science & Ultimate tensile strength. The organization has 846 authors who have published 644 publications receiving 2702 citations.

DRAP: Decision Tree and Random Forest Based Classification Model to Predict Diabetes

Abstract: This paper proposes a method called "DRAP" for detecting Diabetes. DRAP uses recorded data of the existing patients and classifies the unknown instances into one of the two classes - yes or no (whether diabetic or not). We have used a hybrid of decision tree and random forest algorithms to construct DRAP.Features considered for the constructing DRAP are glucose level, blood pressure, insulin, body mass index, age, etc. The proposed algorithm is evaluated on real-life data set. Our seminal results show the accuracy of 72% for Decision Tree and 76.5% Random Forest, respectively.

Machine Learning based Restaurant Revenue Prediction

Abstract: Food industry has a crucial part in enhancing the financial progress of a country. This is very true for metropolitan cities than any small towns of our country. Despite the contribution of food industry to the economy, the revenue prediction of the restaurant has been limited. The agenda of this work is basically to detect the revenue for any upcoming setting of restaurant. There are three types of restaurant which have been encountered. They are inline, food court, and mobile. In our proposed solution, we take into consideration the various features of the datasets for the prediction. The input features were ordered based on their impact on the target attribute which was the restaurant revenue. Various other pre-processing techniques like Principal Component Analysis (PCA), feature selection and label encoding have been used. Without the proper analysis of Kaggle datasets pre-processing cannot be done. Algorithms are then evaluated on the test data after being trained on the training datasets. Random Forest (RF) was found to be the best performing model for revenue prediction when compared to linear regression model. The model accuracy does make a difference before pre-processing and after pre-processing. The accuracy increases after the applied methods of pre-processing.

Distributed arithmetic multiplier based Artificial Neural Network architecture for image compression

Abstract: In this paper we present FPGA implementation of Artificial Neural Networks for image compression. Image compression is a process which minimizes the size of an image file to an unacceptable level without degrading the quality of the image. The main components of an artificial neuron are adders and multipliers. In order to implement neural network, large number of adders and multipliers are required. The main constraint in the implementation of neural network is the area occupied by the multipliers. In order to overcome this area we propose the use of constant coefficient multiplier like distributed arithmetic (DA) based multiplier. The area efficiency is obtained by use of adders and shifters in Distributed arithmetic based multiplier architecture. The distributed arithmetic based multiplier is made use to implement 4∶4∶2∶2∶4 and 16∶16∶8∶8∶16 artificial neural network architecture for image compression. These architectures are implemented on FPGA and area efficiency is obtained.