Bharati Vidyapeeth's College of Engineering

About: Bharati Vidyapeeth's College of Engineering is a based out in . It is known for research contribution in the topics: Deep learning & Computer science. The organization has 709 authors who have published 622 publications receiving 3550 citations.

Breast Cancer Detection Using Decision Tree, Naïve Bayes, KNN and SVM Classifiers: A Comparative Study

Abstract: The breast cancer remains as the major cause for the fatality in the women. To predict and classify breast cancer at early stage, researchers have used various machine learning procedures. Various classification algorithms like decision tree, SVM, KNN have been used on breast cancer dataset to categorize a cancer stage as either nonthreatening or threatening. In this paper, the four basic models of classification i.e. decision tree, Naive Bayes, KNN, SVM have been used to classify the cancer stage. Then, a comparative study is done on these classifier using different accuracy measures like precision, recall and f1-score. This study shows that decision tree classifiers are best among all classifiers in terms of accuracy.

Hermitian–Toeplitz and Hankel determinants for certain starlike functions

Abstract: The sharp lower and upper estimates on the second- and third-order Hermitian–Toeplitz determinants for the classes of starlike functions associated with the modified sigmoid function and a related ...

Impact of gray hole attack in VANET

Abstract: Vehicular Ad Hoc Network(VANET) is a technology which accommodate the vehicle to interconnect with each other through a wireless network. So that it can track and locate other vehicles to provide road safety. Any fixed infrastructure is missing so effective route for transporting data communication is established. Security is a major issue in VANET as it can be life threatening. VANET is a subclass of ad hoc network and it is almost same as Mobile Ad Hoc Networks (MANET) but in VANET nodes are vehicles. It is a challenging topic because of frequent link disruptions caused by vehicle mobility. We have used AODV routing protocol in VANET for proper communication between nodes by forwarding data packets. We have implemented the gray hole attack on routing protocol AODV and shown its impact on implementation of VANET. We have analyzed variable parameters like a packet delivery ratio (PDR), normalized routing load (NRL), delay and throughput.

Sentiment classification using hybrid feature selection and ensemble classifier

Abstract: This paper presents a Hybrid Feature Selection Technique for Sentiment Classification. We have used a Genetic Algorithm and a combination of existing Feature Selection methods, namely: Information Gain (IG), CHI Square (CHI), and GINI Index (GINI). First, we have obtained features from three different selection approaches as mentioned above and then performed the UNION SET Operation to extract the reduced feature set. Then, Genetic Algorithm is applied to optimize the feature set further. This paper also presents an Ensemble Approach based on the error rate obtained different domain datasets. To test our proposed Hybrid Feature Selection and Ensemble Classification approach, we have considered four Support Vector Machine (SVM) classifier variants. We have used UCI ML Datasets of three domains namely: IMDB Movie Review, Amazon Product Review and Yelp Restaurant Reviews. The experimental results show that our proposed approach performed best in all three domain datasets. Further, we also presented T-Test for Statistical Significance between classifiers and comparison is also done based on Precision, Recall, F1-Score, AUC and model execution time.