Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.

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A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm is comparable to those of the mentioned algorithms and can be efficiently employed to solve engineering problems with high dimensionality.

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On the performance of artificial bee colony (ABC) algorithm

A comparative study of Artificial Bee Colony algorithm

Biomedical datasets pose a unique challenge for machine learning and data mining techniques to extract accurate, comprehensible and hidden knowledge from them. In this paper, we comprehensively investigate the role of a biomedical dataset on the classification accuracy of an algorithm. To this end, we quantify the complexity of a biomedical dataset in terms of its missing values, imbalance ratio, noise and information gain. We have performed our experiments using six well-known evolutionary rule learning algorithms: XCS, UCS, GAssist, cAnt-Miner, SLAVE and Ishibuchi, on 31 publicly available biomedical datasets. The results of our experiments show that GAssist gives better classification accuracy among the compared schemes. However, the nature of a biomedical dataset -- not the selection of evolutionary algorithm -- plays a major role in determining the classification accuracy of a dataset. We further show that noise is a dominating factor in determining the complexity of a dataset and it is inversely proportional to the classification accuracy of all the algorithms. The complexity of biomedical dataset will prove useful to researchers in evaluating the classification potential of their dataset for automatic knowledge extraction.

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Performance evaluation of evolutionary algorithms in classification of biomedical datasets

This paper analyzes the security vulnerabilities and requirements of IP Multimedia Subsystem (IMS), particularly the impact of Denial-of-Service (DoS) and Distributed DoS (DDoS)attacks on the IMS. We propose and develop an intelligent Bio-inspired, self-defending security framework for the IMS and Next Generation all-IP Networks. Our proposed framework will complement the existing authentication and encryption mechanisms to protect infrastructure nodes and subscribers against the attacks launched by the malicious nodes in the network. This framework is expected to become a cardinal component which can be integrated into any IMS converged network infrastructure to provide defense against wide variety of attacks particularly DoS and DDoS attacks.

Attack analysis & bio-inspired security framework for IP multimedia subsystem

Recently, content-based Spam detection frameworks are receiving a significant amount of attention by academic researchers and industrial practitioners. However, the anticipated wide scale proliferation is limited (mainly) because of two important shortcomings: (1) high false alarm rate that results in moving legitimate messages into Spam folders, and (2) inability to self-learn a user's profile, as a result, they are unable to identify useful Spam (we call it Good Spam) that might be of great interest to a user's personal or business aspirations. In this paper, we propose USpam, a system that uses ontologies to model features that are extracted rom a user's profile. The features are given to machine learning classifiers J48 and Naive Bayes -- that learn a user centric model of Good Spam or Bad Spam. As a result, the system puts a message into a user's inbox if its contents are relevant to his interests. The USpam is evaluated on NRON Spam datasets, and the results of experiments reveal that false alarms are reduced by 10% to 30% compared with existing prior art without compromising the detection accuracy.

USpam -- A User Centric Ontology Driven Spam Detection System

From the wisdom of the hive to intelligent routing in telecommunication networks

The electrocardiogram (ECG) is the most clinically accepted diagnostic tool used by physicians for interpreting the functional activity of the heart. The existing ECG machines require an expert-in-the-loop for identifying abnormalities in cardiac activity - commonly referred to as Arrhythmia - of a patient. The accuracy of diagnosis is directly dependent on the skill set of the physician; as a result, in rural and remote places, where no ECG specialist wants to relocate, the patients are unable to get any help in case of life threatening arrhythmias. In this paper, we investigate the suitability of evolutionary algorithms to discriminate a normal ECG from an abnormal one with minimum user intervention. Consequently, the human dependent errors are minimized. The intelligent framework is efficient and can be used for realtime ECG analysis to complement the diagnostic efficiency and accuracy of ECG specialists. Moreover, the system could also be used to raise early alarms for patients where no ECG specialist is available. In this paper, we aim at autonomously detecting six types of Arrhythmia: (1) Tachycardia, (2) Bradycardia, (3) Right Bundle Branch Block, (4) Left Bundle Branch Block, (5) Old Inferior Myocardial Infarction, and (6) Old Anterior Myocardial Infarction. We evaluate the accuracy of our system by selecting the best back end classifier from a set of 8 evolutionary classifiers. The results of our experiments show that our system is able to achieve more than 98% accuracy in detecting most types of Arrhythmia.

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Muddassar Farooq

Papers

Performance evaluation of evolutionary algorithms in classification of biomedical datasets

Attack analysis & bio-inspired security framework for IP multimedia subsystem

USpam -- A User Centric Ontology Driven Spam Detection System

From the wisdom of the hive to intelligent routing in telecommunication networks

Using evolutionary algorithms for ECG Arrhythmia detection and classification