Hajira Jabeen

Opposition based initialization in particle swarm optimization (O-PSO)

Particle Swarm Optimization, a population based optimization technique has been used in wide number of application areas to solve optimization problems. This paper presents a new algorithm for initialization of population in standard PSO called Opposition based Particle Swarm Optimization (O-PSO). The performance of proposed initialization algorithm is compared with the existing PSO variants on several benchmark functions and the experimental results reveal that O-PSO outperforms existing approaches to a large extent.

Word length based zero-watermarking algorithm for tamper detection in text documents

Copyright protection and authentication of digital content has become a major concern in the current digital era. Plain text is the widely used means of information exchange on the Internet and it is essential to verify the authenticity of information in any form of communication. There are very limited techniques available for plain text watermarking, authentication, and tamper detection. This paper presents a novel zero-watermarking algorithm for tamper detection in plain text documents. The algorithm generates a watermark based on the text contents which can be extracted later using extraction algorithm to identify the status of tampering in the text document. Experimental results demonstrate the effectiveness of the algorithm against random tampering attacks. Watermark pattern matching and watermark distortion rate are used as evaluation parameters on multiple text samples of varying length.

Two-stage learning for multi-class classification using genetic programming

Abstract This paper introduces a two-stage strategy for multi-class classification problems. The proposed technique is an advancement of tradition binary decomposition method. In the first stage, the classifiers are trained for each class versus the remaining classes. A modified fitness value is used to select good discriminators for the imbalanced data. In the second stage, the classifiers are integrated and treated as a single chromosome that can classify any of the classes from the dataset. A population of such classifier-chromosomes is created from good classifiers (for individual classes) of the first phase. This population is evolved further, with a fitness that combines accuracy and conflicts. The proposed method encourages the classifier combination with good discrimination among all classes and less conflicts. The two-stage learning has been tested on several benchmark datasets and results are found encouraging.

Opposition based PSO and mutation operators

Particle Swarm Optimization (PSO) algorithm has shown good performance in many optimization problems, but PSO suffers from the problem of early convergence into a local minima. Introduction of opposition based initialization and mutation operators have played an important role to overcome the convergence problem in function optimization. In this study we have reviewed different variants of PSO for function optimization. Researchers have proposed different modifications in PSO to prevent it from getting stuck in local optima. At the end, we have proposed a variant of PSO for better conversion.

The BigDataEurope Platform – Supporting the Variety Dimension of Big Data

The management and analysis of large-scale datasets – described with the term Big Data – involves the three classic dimensions volume, velocity and variety. While the former two are well supported by a plethora of software components, the variety dimension is still rather neglected. We present the BDE platform – an easy-to-deploy, easy-to-use and adaptable (cluster-based and standalone) platform for the execution of big data components and tools like Hadoop, Spark, Flink, Flume and Cassandra. The BDE platform was designed based upon the requirements gathered from seven of the societal challenges put forward by the European Commission in the Horizon 2020 programme and targeted by the BigDataEurope pilots. As a result, the BDE platform allows to perform a variety of Big Data flow tasks like message passing, storage, analysis or publishing. To facilitate the processing of heterogeneous data, a particular innovation of the platform is the Semantic Layer, which allows to directly process RDF data and to map and transform arbitrary data into RDF. The advantages of the BDE platform are demonstrated through seven pilots, each focusing on a major societal challenge.

A Novel Tournament Selection Based Differential Evolution Variant for Continuous Optimization Problems

Differential evolution (DE) is a powerful global optimization algorithm which has been studied intensively by many researchers in the recent years. A number of variants have been established for the algorithm that makes DE more applicable. However, most of the variants are suffering from the problems of convergence speed and local optima. A novel tournament based parent selection variant of DE algorithm is proposed in this research. The proposed variant enhances searching capability and improves convergence speed of DE algorithm. This paper also presents a novel statistical comparison of existing DE mutation variants which categorizes these variants in terms of their overall performance. Experimental results show that the proposed DE variant has significance performance over other DE mutation variants.

Two layered Genetic Programming for mixed-attribute data classification

The important problem of data classification spans numerous real life applications. The classification problem has been tackled by using Genetic Programming in many successful ways. Most approaches focus on classification of only one type of data. However, most of the real-world data contain a mixture of categorical and continuous attributes. In this paper, we present an approach to classify mixed attribute data using Two Layered Genetic Programming (L2GP). The presented approach does not transform data into any other type and combines the properties of arithmetic expressions (using numerical data) and logical expressions (using categorical data). The outer layer contains logical functions and some nodes. These nodes contain the inner layer and are either logical or arithmetic expressions. Logical expressions give their Boolean output to the outer tree. The arithmetic expressions give a real value as their output. Positive real value is considered true and a negative value is considered false. These outputs of inner layers are used to evaluate the outer layer which determines the classification decision. The proposed classification technique has been applied on various heterogeneous data classification problems and found successful.

Distributed Semantic Analytics Using the SANSA Stack

A major research challenge is to perform scalable analysis of large-scale knowledge graphs to facilitate applications like link prediction, knowledge base completion and reasoning. Analytics methods which exploit expressive structures usually do not scale well to very large knowledge bases, and most analytics approaches which do scale horizontally (i.e., can be executed in a distributed environment) work on simple feature-vector-based input. This software framework paper describes the ongoing Semantic Analytics Stack (SANSA) project, which supports expressive and scalable semantic analytics by providing functionality for distributed computing on RDF data.

A framework for optimization of genetic programming evolved classifier expressions using particle swarm optimization

Genetic Programming has emerged as an efficient algorithm for classification It offers several prominent features like transparency, flexibility and efficient data modeling ability However, GP requires long training times and suffers from increase in average population size during evolution The aim of this paper is to introduce a framework to increase the accuracy of classifiers by performing a PSO based optimization approach The proposed hybrid framework has been found efficient in increasing the accuracy of classifiers (expressed in the form of binary expression trees) in comparatively lesser number of function evaluations The technique has been tested using five datasets from the UCI ML repository and found efficient.

Classifying Data Heterogeneity within Budget and Spending Open Data

Open data has gained momentum for the past few years, but not much consumption was done over published open budget and spending datasets. Many challenges to consume open budget and spending data are still open. One of the challenges is the heterogeneity of these datasets. We analyze more than 75 different budget and spending datasets released by different public administrations from various levels of administrations and locations. We select five datasets, then present and illustrate several types of budget and spending heterogeneities. We compare these heterogeneities with state of the art fiscal data models, the OpenBudgets.eu (OBEU) data model and Fiscal Data Package (FDP) which are designed specifically for representing budget and spending datasets. The comparison provides hints for both datasets publishers and technical/research communities that deal with open data in budget and spending domain.