Suraiya Jabin

Authentic mobile-biometric signature verification system

This is an undeniable fact that in the coming years a considerable percentage of organisations are drifting toward mobile devices for authentication. Banking sector as an additional offshoot has shifted to mobile devices with their applications for e-banking and mobile-banking, giving rise to an emergent requirement of a foolproof and authentic mobile-biometric system. This study presents an authentic mobile-biometric signature verification system and a comparative analysis of the performance of the proposed system for the two datasets; one using the standard device that is used for capturing biometric signatures and the other one is a mobile database taken from a smart phone for biometric signature authentication. The results presented demonstrate that the proposed system outperforms existing mobile-biometric signature verification systems based on dynamic time warping and hidden Markov model. Moreover, this study presents a comprehensive survey of mobile-biometric systems, different devices and hardware needed to support mobile biometrics along with open issues and challenges faced by the mobile-biometric systems. The experiments presented establish that the performance of mobile devices is low as compared with normal biometric signature capturing devices and the major reason the authors found is the absence of pen-tilt angle information in the mobile device datasets.

Biometric signature verification

In recent years, biometric signature verification BSV has been considered with renewed interest with increasing need of security and individual verification and authentication whether in banks, offices, institutions or other commercial organisations. Biometric signature verification is a behavioural biometric technique as a signature signifies unique behaviour of an individual. It can upgrade online banking using online digital systems for signing which cannot be altered or manipulated. Digital signature pads use algorithms to record the features of the signature, which is used to authenticate a signer during a transaction. This paper aims to present a comprehensive literature survey of the most recent research papers on biometric signature verification. It highlights the most important methods and addresses variations in the methods and features that are being taken up in the most recent research in this field along with the possible extensions.

A comparative study of the recent trends in biometric signature verification

Signature verification is a widely and commonly accepted practice for authentication of an individual. Whereas off-line signature verification contributes very less to accurate identification, on-line signature verification has been successfully implemented in recent researches to achieve 80%-98% of accuracy. Various approaches have been used to implement biometric signature recognition some of which are dynamic time warping (DTW), Bayesian Learning, Hidden Markov model (HMM), Neural Networks, Support Vector machine (SVM) etc. This paper presents a comparative and qualitative study of these methods used for biometric signature verification.

Evolving weight matrices to increase the capacity of Hopfield neural network associative memory using hybrid evolutionary algorithm

This paper describes the implementation of a hybrid evolutionary technique to increase the capacity of associative memory in Hopfield type of neural network. Various operators of genetic algorithm (mutation, crossover, elitism etc) are used to evolve the population of optimal weight matrices for the purpose of recall of the prototype input patterns with induced noise. The optimal weight matrix found during the training is used as seed for starting the GA, instead starting with random weight matrix. It has been observed that for Hopfield neural networks of various sizes the recalling is successful if number of patterns stored is within 40% of the total number of nodes in the network which is towards the higher side than the earlier reported capacity.

Learning Classifier Systems Approach for Automated Discovery of Hierarchical Censored Production Rules

This article presents Learning Classifier Systems (LCS) approach for automated discovery of Hierarchical Censored Production Rules (HCPR). A LCS is an adaptive system that learns to perform the best action given its input. By best is generally meant the action that will receive the most reward or reinforcement from the system’s environment. A classifier system consists of three main components: rule and message system, apportionment of credit system, genetic algorithm (GA). In the proposed LCS, concatenate of the Hierarchical Censored Production Rule-trees form the genotype, and therefore the GA operates on a population of HCPR-trees. More recently, LCSs have proved efficient at solving automatic classification tasks. Hierarchical Censored Production Rules is a system of knowledge representation that exhibited variable certainty as well as variable specificity and offered mechanisms for handling the trade off between the two. An appropriate chromosome representation scheme, suitable genetic operators, appropriate fitness function and also appropriate credit assignment scheme is proposed to evolve the best HCPR-trees. Experimental results are presented to demonstrate the performance of the proposed system.

User emotion analysis in conflicting versus non-conflicting regions using online social networks

Abstract Society plays a vital role in maintaining the emotional health of an individual. People living in conflicting zones are emotionally degraded and often hold more negativity than the people living in serene areas. Nowadays, analyzing the emotions shared on social networking websites is an ongoing topic of research. In this paper, we presented the potential of user data available on the Facebook website in distinguishing the emotions of netizens in conflicting versus non-conflicting areas. We collected the Facebook posts of the users living in Kashmir (conflicting region) and Delhi (non-conflicting) with the help of two source accounts. Plutchik’s eight basic emotions, namely, fear, anger, sadness, joy, surprise, disgust, trust and anticipation have been used to determine the emotion state of a user. Based on two well-known lexicons, namely, EmoLex and Empath, a new dictionary called MoodBook is designed to determine the user emotions from their posts. After analyzing the data, we found that violence in the conflicting region has badly affected the psychology of the citizens as most of the people in Kashmir fall under three negative emotion categories, namely, fear, anger, and sadness, whereas the joy mood has been found more in the posts of Delhi-based users. Furthermore, a mood-vector is created for each user and used as an input to k-means clustering where it has been found that the citizens of two regions form separate groups based on their psychological state. The study of the difference between emotions expressed online by the citizens of conflicting and non-conflicting has not been seen in the literature till date.

Big Data: Issues, Challenges, and Techniques in Business Intelligence

During the last decade, the most challenging problem the world envisaged was big data problem. The big data problem means that data is growing at a much faster rate than computational speeds. And it is the result of the fact that storage cost is getting cheaper day by day, so people as well as almost all business or scientific organizations are storing more and more data. Social activities, scientific experiments, biological explorations along with the sensor devices are great big data contributors. Big data is beneficial to the society and business but at the same time, it brings challenges to the scientific communities. The existing traditional tools, machine learning algorithms, and techniques are not capable of handling, managing, and analyzing big data, although various scalable machine learning algorithms, techniques, and tools (e.g., Hadoop and Apache Spark open source platforms) are prevalent. In this paper, we have identified the most pertinent issues and challenges related to big data and point out a comprehensive comparison of various techniques for handling big data problem.

Poker hand classification

Artificial Neural Network (ANN) forms a useful tool in pattern recognition tasks. Collection of five, eight or more cards in a cards game are normally called poker hands. There are various poker variations, each with different poker hands ranking. In the present paper, an attempt is made to solve poker hand classification problem using different learning paradigms and architectures of artificial neural network: multi-layer feed-forward Backpropagation (supervised) and self-organizing map (un-supervised). Poker data set is touted to be a difficult dataset for classification algorithms. Experimental results are presented to demonstrate the performance of the proposed system. The paper also aims to suggest about training algorithms and training parameters that must be chosen in order to solve poker hand classification problem using neural network model. As neural networks are the most convenient tools for handling complicated data sets with real values, one of the most important objectives of the paper is to explain how a neural network can also be used successfully for classification kind of problems involving categorical attributes. The proposed model succeeded in classification of poker hands with 94% classification accuracy.