Neha Bharill

Bio: Neha Bharill is an academic researcher from École Centrale Paris. The author has contributed to research in topics: Cluster analysis & Fuzzy clustering. The author has an hindex of 9, co-authored 30 publications receiving 596 citations. Previous affiliations of Neha Bharill include Indian Institutes of Information Technology & Birla Institute of Technology and Science.

Fuzzy Based Scalable Clustering Algorithms for Handling Big Data Using Apache Spark

Abstract: A huge amount of digital data containing useful information, called Big Data, is generated everyday To mine such useful information, clustering is widely used data analysis technique A large number of Big Data analytics frameworks have been developed to scale the clustering algorithms for big data analysis One such framework called Apache Spark works really well for iterative algorithms by supporting in-memory computations, scalability etc We focus on the design and implementation of partitional based clustering algorithms on Apache Spark, which are suited for clustering large datasets due to their low computational requirements In this paper, we propose Scalable Random Sampling with Iterative Optimization Fuzzy c-Means algorithm (SRSIO-FCM) implemented on an Apache Spark Cluster to handle the challenges associated with big data clustering Experimental studies on various big datasets have been conducted The performance of SRSIO-FCM is judged in comparison with the proposed scalable version of the Literal Fuzzy c-Means (LFCM) and Random Sampling plus Extension Fuzzy c-Means (rseFCM) implemented on the Apache Spark cluster The comparative results are reported in terms of time and space complexity, run time and measure of clustering quality, showing that SRSIO-FCM is able to run in much less time without compromising the clustering quality

A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance

Abstract: This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.

Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer’s Disease

Abstract: Alzheimer’s disease (AD) is a neurodegenerative disorder resulting in memory loss and cognitive decline caused due to the death of brain cells. It is the most common form of dementia and accounts for 60-80% of all dementia cases. There is no single test for diagnosis of AD, the doctors rely on medical history, neuropsychological assessments, computed tomography (CT) or magnetic resonance imaging (MRI) scan of the brain, etc. to confirm a diagnosis. In terms of the treatment, currently, there is neither a cure nor any way to slow the progression of AD. However, for people with mild or moderate stages of this disease, there are some medications available to temporarily reduce symptoms and help to improve quality of life. Hence, early diagnosis of AD is extremely crucial for overall better management of the disease. The researches have shown some relation between neuropsychological scores and atrophies of the brain. This can be leveraged for the early diagnosis of AD. This paper makes use of feature selection techniques to extract the most important features in the diagnosis of AD. This paper demonstrates the need to combine neuropsychological scores like mini-mental state examination (MMSE) with MRI features to provide better decisional space for early diagnosis of AD. Through the experiments, including MMSE along with other features are found to improve the classification of AD, significantly.

A Novel Quantum-Inspired Fuzzy Based Neural Network for Data Classification

Abstract: The performance of the neural network (NN) depends on the various parameters such as structure, initial weight, number of hidden layer neurons, and learning rate. The improvement in classification performance of NN without changing its structure is a challenging issue. This paper proposes a novel learning model called Quantum-inspired Fuzzy Based Neural Network (Q-FNN) to solve two-class classification problems. In the proposed model, NN architecture is formed constructively by adding neurons in the hidden layer and learning is performed using the concept of Fuzzy c-Means (FCM) clustering, where the fuzziness parameter ( $m$ m ) is evolved using the quantum computing concept. The quantum computing concept provides a large search space for a selection of $m$ m , which helps in finding the optimal weights and also optimizes the network architecture. This paper also proposes a modified step activation function for the formation of hidden layer neurons, which handles the overlapping samples belong to different class regions. The performance of the proposed Q-FNN model is superior and competitive with the state-of-the-art methods in terms of accuracy, sensitivity, and specificity on 15 real-world benchmark datasets.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.