Salabat Khan

Solution of n-Queen problem using ACO

In this paper, a solution is proposed for n-Queen problem based on ACO (Ant Colony Optimization). The n-Queen problem become intractable for large values of ‘n’ and thus placed in NP (Non-Deterministic Polynomial) class problem. The n-Queen problem is basically a generalized form of 8-Queen problem. In 8-Queen problem, the goal is to place 8 queens such that no queen can kill the other using standard chess queen moves. So, in this paper, the proposed solution will be applied to 8-Queen problem. The solution can very easily be extended to the generalized form of the problem for large values of ‘n’. The paper contains the detail discussion of problem background, problem complexity, Ant Colony Optimization (Swarm Intelligence) and a fair amount of experimental graphs.

A novel ant colony optimization based single path hierarchical classification algorithm for predicting gene ontology

There exist numerous state of the art classification algorithms that are designed to handle the data with nominal or binary class labels. Unfortunately, less attention is given to the genre of classification problems where the classes are organized as a structured hierarchy; such as protein function prediction (target area in this work), test scores, gene ontology, web page categorization, text categorization etc. The structured hierarchy is usually represented as a tree or a directed acyclic graph (DAG) where there exist IS-A relationship among the class labels. Class labels at upper level of the hierarchy are more abstract and easy to predict whereas class labels at deeper level are most specific and challenging for correct prediction. It is helpful to consider this class hierarchy for designing a hypothesis that can handle the tradeoff between prediction accuracy and prediction specificity. In this paper, a novel ant colony optimization (ACO) based single path hierarchical classification algorithm is proposed that incorporates the given class hierarchy during its learning phase. The algorithm produces IF-THEN ordered rule list and thus offer comprehensible classification model. Detailed discussion on the architecture and design of the proposed technique is provided which is followed by the empirical evaluation on six ion-channels data sets (related to protein function prediction) and two publicly available data sets. The performance of the algorithm is encouraging as compared to the existing methods based on the statistically significant Students t-test (keeping in view, prediction accuracy and specificity) and thus confirm the promising ability of the proposed technique for hierarchical classification task.

Optimized Gabor features for mass classification in mammography

Display Omitted Key idea is optimizing Gabor filters such that they respond stronger to features that best discriminate normal and abnormal tissues.Contribution is about a strategy based on PSO and incremental clustering for optimizing a Gabor filter bank for accurate detection.Optimized Gabor filter bank is applied on overlapping blocks of ROIs to collect moment-based features from the magnitudes of Gabor responses. Gabor filter bank has been successfully used for false positive reduction problem and the discrimination of benign and malignant masses in breast cancer detection. However, a generic Gabor filter bank is not adapted to multi-orientation and multi-scale texture micro-patterns present in the regions of interest (ROIs) of mammograms. There are two main optimization concerns: how many filters should be in a Gabor filter band and what should be their parameters. Addressing these issues, this work focuses on finding optimizing Gabor filter banks based on an incremental clustering algorithm and Particle Swarm Optimization (PSO). We employ an SVM with Gaussian kernel as a fitness function for PSO. The effect of optimized Gabor filter bank was evaluated on 1024 ROIs extracted from a Digital Database for Screening Mammography (DDSM) using four performance measures (i.e., accuracy, area under ROC curve, sensitivity and specificity) for the above mentioned mass classification problems. The results show that the proposed method enhances the performance and reduces the computational cost. Moreover, the Wilcoxon signed rank test over the significance level of 0.05 reveals that the performance difference between the optimized Gabor filter bank and non-optimized Gabor filter bank is statistically significant.

Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization Based Classifiers

The primary objective of this research is to propose and investigate a novel ant colony optimization-based classification rule discovery algorithm and its variants. The main feature of this algorithm is a new heuristic function based on the correlation between attributes of a dataset. Several aspects and parameters of the proposed algorithm are investigated by experimentation on a number of benchmark datasets. We study the performance of our proposed approach and compare it with several state-of-the art commonly used classification algorithms. Experimental results indicate that the proposed approach builds more accurate models than the compared algorithms. The high accuracy supplemented by the comprehensibility of the discovered rule sets is the main advantage of this method.

A Comparison of Different Gabor Features for Mass Classification in Mammography

Masses are among the early signs of breast cancer, which is the second major cause of death in women. For mass detection, a mammogram is segmented into regions of interest (ROIs) that contain masses as well as suspicious normal tissues, which lead to false positives. The problem is to reduce the false positives by classifying ROIs as masses and normal tissues. Further, the detected masses are needed to be discriminated as benign and malignant. We investigate the performance of six different Gabor feature extraction approaches for these mass classification problems. These techniques employ Gabor filter banks for extracting multiscale and multiorientation texture features which represent structural properties of masses and normal dense tissues in mammograms. The feature extraction approaches are evaluated over the ROIs extracted from MIAS database. Successive Enhancement Learning based weighted Support Vector Machine (SELwSVM) is used to efficiently classify the generated unbalanced datasets. The best performance in terms of area under ROC curve (Az = 1.0) is obtained by the Gabor features extracted using first order statistics of the Gabor responses and LDA.

CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET

A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.

Cryptanalysis of Four-Rounded DES Using Ant Colony Optimization

It is hard for the cryptanalysts to apply traditional techniques and brute-force attacks against feistel ciphers due to their inherent structure based on high nonlinearity and low autocorrelation. In this paper, we propose a technique for the cryptanalysis of four-rounded Data Encryption Standard (DES) based on Binary Ant Colony Optimization (BACO). A known plaintext attack is used to recover the secret key of the DES cipher. The environment for the ants is a directed graph, which we call search space, is constructed for efficiently searching the secret key. We also develop a heuristic function which measures the quality of a constructed solution. Several optimum keys are computed over different runs on the basis of routes completed by the ants. These optimum keys are then used to find each individual bit of the 56 bit secret key used by DES. The results of our experiments show that ACO is an effective technique for the cryptanalysis of four-rounded DES. To the best of our knowledge, this is the first time that BACO has been used for this specific problem.

Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks

Abstract In vehicular ad-hoc network (VANETs), frequent topology changes occur due to fast moving nature of mobile nodes. This random topology creates instability that leads to scalability issues. To overcome this problem, clustering can be performed. Existing approaches for clustering in VANETs generate large number of cluster-heads which utilize the scarce wireless resources resulting in degraded performance. In this article, grey wolf optimization based clustering algorithm for VANETs is proposed, that replicates the social behaviour and hunting mechanism of grey wolfs for creating efficient clusters. The linearly decreasing factor of grey wolf nature enforces to converge earlier, which provides the optimized number of clusters. The proposed method is compared with well- known meta-heuristics from literature and results show that it provides optimal outcomes that lead to a robust routing protocol for clustering of VANETs, which is appropriate for highways and can accomplish quality communication, confirming reliable delivery of information to each vehicle.

Ant colony optimization based hierarchical multi-label classification algorithm

Display OmittedAn example search space of hmAntMiner-C for constructing rule antecedent. This paper presents a hierarchical multi-label classification algorithm (hmAntMiner-C).It uses correlation of attribute-value pairs for constructing IF-THEN rule list.Comparison is provided with some other state of the art algorithms with promising results. There exist numerous state of the art classification algorithms that are designed to handle the data with nominal or binary class labels, where a sample belongs to only a single class label. In these problems, known as flat classification problems, class labels are independent of each other. Unfortunately, on the other hand, less attention is given to the genre of classification problems where samples may belong to several classes and at the same time the class labels are organized based on a structured hierarchy; such as gene ontology, protein function prediction, test scores, web page categorization, text categorization etc. This article presents a novel Ant Colony Optimization based hierarchical multi-label classification algorithm that can handle such a complex instance of classification problems and can incorporates the given class hierarchy during its learning phase. The algorithm produces IF-THEN ordered rule list to learn a comprehensible model which can easily be verified by experts. It exploits positive correlation between the domain values of two related attributes to improve the discrimination power of resultant classification model, up to a significant level. The paper contains rich details regarding hierarchical single label (or single path) and multi-label classification problems and different categories of corresponding solutions. The proposed method is evaluated on sixteen most challenging bioinformatics datasets; some of these containing hundreds of attributes and thousands of class labels. At the end, the proposed method is compared with four recent state of the art hierarchical multi-label classification algorithms. The empirical evaluation confirms the promising ability of the proposed technique for hierarchical multi-label classification task.

A solution to bipartite drawing problem using genetic algorithm

Crossing minimization problem in a bipartite graph is a well-known NP-Complete problem. Drawing the directed/undirected graphs such that they are easy to understand and remember requires some drawing aesthetics and crossing minimization is one of them. In this paper, we investigate an intelligent evolutionary technique i.e. Genetic Algorithm (GA) for bipartite drawing problem (BDP). Two techniques GA1 and GA2 are proposed based on Genetic Algorithm. It is shown that these techniques outperform previously known heuristics e.g., MinSort (M-Sort) and BaryCenter (BC) as well as a genetic algorithm based level permutation problem (LPP), especially when applied to low density graphs. The solution is tested over various parameter values of genetic bipartite drawing problem. Experimental results show the promising capability of the proposed solution over previously known heuristics.