College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Computer science & Sliding mode control. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

An Empirical Comparison of Supervised Machine Learning Algorithms for Internet of Things Data

Abstract: Internet of Things(IoT) is one of the rapidly growing fields andn has a wide range of applications such as smart cities, smart homes, connected wearable, connected health-care, and connected automobiles, etc. These IoT applications generate tremendous amounts of data which needs to be analyzed to draw useful inferences required to optimize the performance of IoT applications. The artificial intelligence(AI) and machine learning (ML) play the significant role in building the smart IoT systems. The main objective of the paper is a comprehensive analysis of five well-known supervised machine learning algorithms on IoT datasets. The five classifiers are K-Nearest Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and Logistic Regression (LR). The feature reduction is performed using PCA algorithm. The performance of these five classifiers has been compared on the basis of six characteristics of IoT dataset such as size, number of features, number of classes, class imbalance, missing values and execution time. The classifiers have also been compared on various performance metrics such as precision, recall, f1-score, kappa, and accuracy. As per our results, the DT classifier gives the best accuracy of 99% among all the algorithms for all datasets. The results also show the performance of RF and KNN as almost similar and the NB and LR perform the worst among all the classifiers

Robust Automatic Pectoral Muscle Segmentation from Mammograms Using Texture Gradient and Euclidean Distance Regression

Abstract: In computer-aided diagnosis (CAD) of mediolateral oblique (MLO) view of mammogram, the accuracy of tissue segmentation highly depends on the exclusion of pectoral muscle. Robust methods for such exclusions are essential as the normal presence of pectoral muscle can bias the decision of CAD. In this paper, a novel texture gradient-based approach for automatic segmentation of pectoral muscle is proposed. The pectoral edge is initially approximated to a straight line by applying Hough transform on Probable Texture Gradient (PTG) map of the mammogram followed by block averaging with the aid of approximated line. Furthermore, a smooth pectoral muscle curve is achieved with proposed Euclidean Distance Regression (EDR) technique and polynomial modeling. The algorithm is robust to texture and overlapping fibro glandular tissues. The method is validated with 340 MLO views from three databases-including 200 randomly selected scanned film images from miniMIAS, 100 computed radiography images and 40 full-field digital mammogram images. Qualitatively, 96.75 % of the pectoral muscles are segmented with an acceptable pectoral score index. The proposed method not only outperforms state-of-the-art approaches but also accurately quantifies the pectoral edge. Thus, its high accuracy and relatively quick processing time clearly justify its suitability for CAD.

Adaptive Neuro-fuzzy Inference System with Subtractive Clustering: A Model to Predict Fiber and Yarn Relationship:

Abstract: The spinning process is an important process in the Textile Industry. The yarn (output) coming out of the spinning process has a unique relationship with the input fibers. The input and output prop...