@article{roadside accidents in india and image preprocessing for autonomous cars_2020, url={http://www.ijstr.org/final-print/mar2020/Roadside-Accidents-In-India-And-Image-Preprocessing-For-Autonomous-Cars.pdf}, journal={INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH}, year={2020}, month={Mar} }
 @article{sam isenberg_2019, title={Evolution of Different Music Genres}, url={https://www.ijeat.org/wp-content/uploads/papers/v9i1/A1674109119.pdf}, DOI={10.35940/ijeat.A1674.109119}, abstractNote={This paper aims to study the evolution of five different music genres: Hip-Hop, Rock, Pop, Country and Metal over the last five decades. Each style of music has been classified into three-time slots: Before the 2000s, between 2000 to 2009 and from 2010 to present. Length of lyrics for each genre over the three different time slots has been compared. Similarly, most frequently used words were studied to understand the mood and expression of emotions which each style carries and how they have evolved. Then the lexical diversity and sentimental analysis over time of each music type have been studied to analyze how the songs have evolved to satisfy the ever-changing taste of listeners and the dynamic underlying tone which they carry. We have also studied the various classification models to predict genres with lyrics based on numerous parameters like most frequently words, word- length, lyrics length. At last, we present our opinion on what can we expect from each music-genre in the near future.}, note={Available:}, journal={International Journal of Engineering and Advanced Technology}, author={Sam Isenberg, „Lyrics Classifier‟}, year={2019}, month={Oct} }
 @inbook{mishra_kumawat_selvakumar_2019, title={Performance Analysis of Flappy Bird Playing Agent Using Neural Network and Genetic Algorithm}, ISBN={9789811513831 9789811513848}, ISSN={1865-0929 1865-0937}, url={http://dx.doi.org/10.1007/978-981-15-1384-8_21}, DOI={10.1007/978-981-15-1384-8_21}, abstractNote={The aim of this paper is to develop and study an artificial intelligence based game-playing agent using genetic algorithm and neural networks. We first create an agent which learns how to optimally play the famous “Flappy Bird” game by safely dodging all the barriers and flapping its way through them and then study the effect of changing various parameters like number of neurons on the hidden layer, gravity, speed, gap between trees has on the learning process. The gameplay was divided into two level of difficulty to facilitate study on the learning process. Phaser Framework was used to facilitate HTML5 programming for introducing real-life factors like gravity, collision and Synaptic Neural Network library was used to implement neural network so as to avoid creating a neural network from scratch. Machine Learning Algorithm which we have adopted in this project is based on the concept of Neuro-Evolution and this form of machine learning uses algorithms which can evolve and mature over time such as a genetic algorithm to train artificial neural networks.}, booktitle={Communications in Computer and Information Science}, publisher={Springer Singapore}, author={Mishra, Yash and Kumawat, Vijay and Selvakumar, K.}, year={2019}, pages={253–265} }
 @inproceedings{performance analysis of flappy bird playing agent using neural network and genetic algorithm_2019, booktitle={4th International Conference Information, Communication & Computing Technology}, year={2019} }