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.

Wireless hand gesture controlled multiple musical instruments

Abstract: We live in two worlds: the physical and the digital. We can touch, see, feel, hear and taste the physical world. The digital represents the information, data, and software. The basic idea of our system is to take a part of the physical word, filled with music and drop it into the digital word by replacing a guitar, a drum set or any other kind of musical instrument with merely two hand gloves. Each of the gloves will embed a micro controller, an Inertial Measurement Unit (IMU) and a transmitter. The motion of the IMU will be processed in each of the micro controller and will be transmitted via respective transmitters. The data will be received by the third micro controller, consisting of two receiving modules. The signals will be transmitted on the respective movements of one’s hand and the bend experienced in the fingers (the same situation occurs while playing the actual guitars). The received data will be processed at the receivers end and henceforth converted to musical sound. The system will be light weighted, easy for a learner and can be used to play multiple types of sounds. It is working efficiently as soothing amplified sound produced. The system will be easier to carry and may replace big clumsy musical instruments. It will be much easier to play for everyone especially for people with disabilities.

A Comprehensive Review on Design of Autonomous Robotic Boat for Rescue Applications

Abstract: New technologies are advancing and emerging day by day to improve the safety of humans by making use of various autonomous technologies. The continuous utilization of autonomous vehicles/systems in search and rescue (SAR) operations is a challenging research area particularly for marine-based activities. A comprehensive systematic literature review (SLR) providing an overview of improvisations that have been done in the field of autonomous technologies for search and rescue operation over the last five years has been compiled in this paper. A methodology for using autonomous vehicles in water for SAR operations has been incorporated and demonstrated. The focus of this study is to look at the various techniques and address different challenges faced for human beings’ safety during rescue operation. The comparison of results achieved for various technologies and algorithms is highlighted in this paper. This literature survey proves to be a good source of information for fellow researchers to precisely analyze the study results.

Exploration of Mars Using Augmented Reality

Abstract: We describe our preliminary work on the scope for augmented reality being used in the exploration of Mars. This would be the potential use case of augmented reality and can also help the better understanding of the Mars surface and its environment. The application could be a practical use case for education and research on Mars. We also focus on providing this system to mass people which could help in evolving and bringing out efficient ways for exploration and colonization of Mars.

Optimisation of C5.0 Using Association Rules and Prediction of Employee Attrition

Abstract: IBM Watson Human Resource Employee Attrition Dataset is analysed to predict the employee attrition based on five selected attributes which are Gender, Distance from Home, Environment Satisfaction, Work Life Balance and Education Field out of 36 variables present in the dataset. Association Rule Algorithm ‘Apriori’ along with Decision Tree Algorithm ‘C5.0’ is used. The processing time taken to predict an attrition using the selected attributes using C5.0 with association is 0.02 ms while using traditional C5.0 is 2 ms. RAM consumption for C5.0 with association is 30.89 MB while for traditional C5.0, it is 48 MB. This is a new approach to predict the employee attrition which is better in efficiency than simply applying decision tree algorithms.

Categorizing Text Documents Using Naïve Bayes, SVM and Logistic Regression

Abstract: Categorizing Text documents is the method of arranging different types of documents into labelled data. The field of this paper is to combine the Data mining Technology, Data extraction and Artificial Intelligence for text categorization. This paper will showcase the features of the technologies involved. There are three machine learning algorithms (SVM, Multinomial Naive Bayes and Logistic Regression) used in this paper for text categorization, i.e. arrange documents into different categories of dataset 20 news groups. In the evaluation of the above classification techniques, SVM classifier outperforms other classifiers for text categorization.