A SURVEY ON ANT COLONY OPTIMIZATION ALGORITHM

A novel Ant Colony Optimization algorithm (ACO) combined for the hierarchical multi- label classification problem of protein function prediction. This kind of problem is mainly focused on biometric area, given the large increase in the number of uncharacterized proteins available for analysis and the importance of determining their functions in order to improve the current biological knowledge. Because it is known that a protein can perform more than one function and many protein functional-definition schemes are organized in a hierarchical structure, the classification problem in this case is an instance of a hierarchical multi-label problem. In this classification method, each class might have multiple class labels and class labels are represented in a hierarchical structure—either a tree or a directed acyclic graph (DAG) structure. A more difficult problem than conventional flat classification in this approach, given that the classification algorithm has to take into account hierarchical relationships between class labels and be able to predict multiple class labels for the same example. The proposed ACO algorithm discovers an ordered list of hierarchical multi-label classification rules

ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA

Improving a Particle Swarm Optimization-based Clustering Method

This thesis discusses clustering related works with emphasis on Particle Swarm Optimization (PSO) principles. Specifically, we review in detail the PSO clustering algorithm proposed by Van Der Merwe & Engelbrecht, the particle swarm clustering (PSC) algorithm proposed by Cohen & de Castro, Szabo’s modified PSC (mPSC), and Georgieva & Engelbrecht’s Cooperative-Multi-Population PSO (CMPSO). In this thesis, an improvement over Van Der Merwe & Engelbrecht’s PSO clustering has been proposed and tested for standard datasets. The improvements observed in those experiments vary from slight to moderate, both in terms of minimizing the cost function, and in terms of run time

Improving a Particle Swarm Optimization-based Clustering Method

This thesis discusses clustering related works with emphasis on Particle Swarm Optimization (PSO) principles. Specifically, we review in detail the PSO clustering algorithm proposed by Van Der Merwe & Engelbrecht, the particle swarm clustering (PSC) algorithm proposed by Cohen & de Castro, Szabo’s modified PSC (mPSC), and Georgieva & Engelbrecht’s Cooperative-Multi-Population PSO (CMPSO). In this thesis, an improvement over Van Der Merwe & Engelbrecht’s PSO clustering has been proposed and tested for standard datasets. The improvements observed in those experiments vary from slight to moderate, both in terms of minimizing the cost function, and in terms of run time

Embedding Feature Selection for Large-scale Hierarchical Classification

Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-scale HC problem. It speeds up the training process, reduces the prediction time and minimizes the memory requirements by compressing the total size of learned model weight vectors. Majority of the studies have also shown feature selection to be competent and successful in improving the classification accuracy by removing irrelevant features. In this work, we investigate various filter-based feature selection methods for dimensionality reduction to solve the large-scale HC problem. Our experimental evaluation on text and image datasets with varying distribution of features, classes and instances shows upto 3x order of speed-up on massive datasets and upto 45% less memory requirements for storing the weight vectors of learned model without any significant loss (improvement for some datasets) in the classification accuracy. Source Code: https://cs.gmu.edu/~mlbio/featureselection.Comment: IEEE International Conference on Big Data (IEEE BigData 2016

Water filtration by using apple and banana peels as activated carbon

Water filter is an important devices for reducing the contaminants in raw water. Activated from charcoal is used to absorb the contaminants. Fruit peels are some of the suitable alternative carbon to substitute the charcoal. Determining the role of fruit peels which were apple and banana peels powder as activated carbon in water filter is the main goal. Drying and blending the peels till they become powder is the way to allow them to absorb the contaminants. Comparing the results for raw water before and after filtering is the observation. After filtering the raw water, the reading for pH was 6.8 which is in normal pH and turbidity reading recorded was 658 NTU. As for the colour, the water becomes more clear compared to the raw water. This study has found that fruit peels such as banana and apple are an effective substitute to charcoal as natural absorbent