Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

A New Approach for Vehicle Number Plate Detection

Abstract: Identification of cars and their owners is a tedious and error prone job. The advent of automatic number plate detection can help tackle problems of parking and traffic control. The system is designed using image processing and machine learning. A new system is proposed to improve detection in low light and over exposure conditions. The image of vehicle is captured, which is preprocessed using techniques like grayscale, binarization. The resultant image is passed on for plate localization, for extracting the number plate using CCA (Connected Component Analysis) and ratio analysis. De-noising of number plate is done using various filters. The characters of the number plate are segmented by CCA and ratio analysis as well. Finally, the recognized characters are compared using techniques such as SVC (linear), SVC (poly), SVC (rbf), KNN, Extra Tree Classifier, LR+RF, and SVC+KNN. The proposed techniques help the system to detect well under dim light, over-exposed images and those in which the vehicle is angled.

Histopathological Image Classification by Optimized Neural Network Using IGSA

Abstract: The histopathological image classification is a vivid application for medical diagnosis and neural network has been successful in the image classification task. However, finding the optimal values of the neural network is still a challenging task. To accomplish the same, this paper considers a two-layer neural network which is optimized through intelligent gravitational search algorithm. Further, the optimized two-layer neural network is applied for the histopathological tissue classification into healthy and inflamed. The proposed method is validated on the publicly available tissue dataset, namely Animal Diagnostic Laboratory (ADL). The experimental results firm the better performance of the proposed method against state-of-the-art methods in terms of seven performance measures, namely recall, specificity, precision, false negative rate (FNR), accuracy, F1-score, and G-mean.

Recent Trends and Applications of Fuzzy Logic

Abstract: Classical set theory, which is based on dichotomy, is not applicable in cases where vagueness is involved. Fuzzy logic is based on the idea of relative graded membership. Fuzzy logic has all the strength to cope with vagueness, uncertainty, and imprecision. Fuzzy logic is a tool that connects human cognitive relations to computers, since computers are not at all good in reading imprecise and vague data. Fuzzy logic is gaining its popularity in various field of research. It found its application in decision making, identification, time series, pattern recognition, optimization, and control. This chapter discusses fuzzy logic, fuzzy sets, and major applications of fuzzy computing.

A Machine Learning Approach for Enhancing Defence Against Global Terrorism

Abstract: The objective of this work is to predict the region and country of a terrorist attack using machine learning approaches. The work has been carried out upon the Global Terrorism Database (GTD), which is an open database containing list of terrorist activities from 1970 to 2017. Six machine learning algorithms have been applied on a selected set of features from the dataset to achieve an accuracy of up to 82%. The results suggest that it is possible to train machine learning models in order to predict the region and country of terrorist attack if certain parameters are known. It is postulated that the work can be used for enhancing security against terrorist attacks in the world.