Microarray datasets have a wide application in bioinformatics research. Analysis to measure the expression level of thousands of genes of this kind of high-throughput data can help for finding the... Show More

Microarray datasets have a wide application in bioinformatics research. Analysis to measure the expression level of thousands of genes of this kind of high-throughput data can help for finding the cause and subsequent treatment of any disease. There are many techniques in gene analysis to extract biologically relevant information from inconsistent and ambiguous data. In this paper, the concepts of functional dependency and closure of an attribute of database technology are used for finding the most important set of genes for cancer detection. Firstly, the method computes similarity factor between each pair of genes. Based on the similarity factors a set of gene dependency is formed from which closure set is obtained. Subsequently, conditional probability based interestingness measurements are used to determine the most informative gene for disease classification. The proposed method is applied on some publicly available cancerous gene expression dataset. The result shows the effectiveness and robustness of the algorithm.

Among some of the competent optimization algorithms, nature inspired algorithms are quite popular due to their flexibility and ease of use in diversified domains. Moreover, balancing between... Show More

Among some of the competent optimization algorithms, nature inspired algorithms are quite popular due to their flexibility and ease of use in diversified domains. Moreover, balancing between exploration and exploitation is one of the important aspects of nature inspired optimizations. In this paper, a recently developed nature inspired algorithm such as black hole algorithm has been used with the functional link neural network for handling the nonlinearity nature of system identification. Specifically, the proposed hybrid approach is used to solve classification problem. The results of the hybrid approach are compared with some of the other popular competent nature based approaches and found the superiority of the proposed method over others. Also, a brief discussion on the working principles of the black hole algorithm and its available literatures are discussed.

Social network analysis has gained much importance these days. Social network analysis is the process of recording various patterns of interactions between a set of social entities. An important... Show More

Social network analysis has gained much importance these days. Social network analysis is the process of recording various patterns of interactions between a set of social entities. An important phenomenon that draws the attention of analysis is the emergence of communities in these networks. The understanding and detection of communities in these networks is a challenging research problem. However, approaches to detect communities have largely focused on identifying communities in static social networks. But real-world social networks are not always static. In fact, many social networks in reality (such as Facebook, Bebo and Twitter) are dynamic networks that frequently change over time. In this paper, a framework is proposed for community detection in dynamic social networks, which explores self-organizing maps (SOM) for cluster selection and modularity measure for community strength identification. Experimental results on synthetic network datasets show the effectiveness of the proposed approach.

This article proposes a unique optimization algorithm like Adaptive Cuckoo Search (AdCS) algorithm followed by an Intrinsic Discriminant Analysis (IDA) to design an intelligent object classifier for... Show More

This article proposes a unique optimization algorithm like Adaptive Cuckoo Search (AdCS) algorithm followed by an Intrinsic Discriminant Analysis (IDA) to design an intelligent object classifier for inspection of defective object like bottle in a manufacturing unit. By using this methodology the response time is very faster than the other techniques. The projected scheme is authenticated using different bench mark test functions along with an effective inspection procedure for identification of bottle by using AdCS, Principal-Component-Analysis (PCA) and IDA. Due to this the projected procedures terms as PCA+IDA for dimension reduction in addition to this AdCS-IDA for classification or identification of defective bottles. The analyzed response obtained from by an application of AdCS algorithm followed by IDA and compared to other algorithm like Least-Square-Support-Vector-Machine (LSSVM), Linear Kernel Radial-Basic-Function (RBF) to the proposed model, the earlier applied scheme reveals the remarkable performance.

Rough set model introduced by Pawlak in 1982 was dependent upon equivalence relations (equivalently on partitions), which had restricted its application due to its stringent requirements. The notion... Show More

Rough set model introduced by Pawlak in 1982 was dependent upon equivalence relations (equivalently on partitions), which had restricted its application due to its stringent requirements. The notion of covering is an extension of that of a partition and is generated by relations much less restricted than equivalence relations. Several covering based rough sets are found in literature. As the notion of rough sets, basic or otherwise are unigranular from the granular computing point of view, in order to handle more than one granular structures on a universe simultaneously, optimistic and pessimistic multigranular computing were introduced by Qian et al in 2006 and 2010 respectively. Combining the two concepts of covering and multigranulation, covering based multigranular models were introduced by Liu et al in 2012. The notion of mathematical equality of concepts is too stringent and less applicable in real life situations. In order to incorporate human knowledge into it, four types of approximate equalities basing upon rough sets were introduced by Novotny and Pawlak in 1985 and by Tripathy in 2011. In this paper, we study the covering based pessimistic multigranular approximate rough equalities and establish several of their properties and provide suitable examples for illustration and in constructing counter examples in the proofs. This is an attempt to generalize the notion of approximate equalities by one more level in order to extend their applicability.

This paper deals with an EOQ (Economic Order Quantity) model for deteriorating items having the following characteristics: 1) Deteriorating items follow a three-parameter Weibull distribution... Show More

This paper deals with an EOQ (Economic Order Quantity) model for deteriorating items having the following characteristics: 1) Deteriorating items follow a three-parameter Weibull distribution deterioration rate; 2) Shortages are allowed and are partially backlogged; 3) Salvage value of items is incorporated; 4) Demand is deterministic and a time-dependent quadratic function of time. The principal objective of the introduced model is to minimize the average total inventory cost by finding an optimal replenishment policy. The effectiveness of the model is validated with a numerical example and the sensitivity analysis of the optimal solutions to changes in the values of the various parameters associated with the model has been performed.

Wireless Sensor Networks (WSNs) are the focus of considerable research for different applications. This paper proposes a Fuzzy Knowledge based Artificial Neural Network Routing (ANNR) fault... Show More

Wireless Sensor Networks (WSNs) are the focus of considerable research for different applications. This paper proposes a Fuzzy Knowledge based Artificial Neural Network Routing (ANNR) fault tolerance mechanism for WSNs. The proposed method uses an exponential Bi-directional Associative Memory (eBAM) for the encoding and decoding of data packets and application of Intelligent Sleeping Mechanism (ISM) to conserve energy. A combination of fuzzy rules is used to identify the faulty nodes in the network. The Cluster Head (CH) acts as the data aggregator in the network. It applies the fuzzy knowledge based Node Appraisal Technique (NAT) in order to identify the faulty nodes in the network. The performance of the proposed ANNR is compared with that of Low-Energy Adaptive Clustering Hierarchy (LEACH), Dual Homed Routing (DHR) and Informer Homed Routing (IHR) through simulation.

In this paper, the main concern is to evaluate the web-sources, which are to be selected as an external source for web-warehousing. In order to identify the web sources, they are evaluated on the... Show More

In this paper, the main concern is to evaluate the web-sources, which are to be selected as an external source for web-warehousing. In order to identify the web sources, they are evaluated on the basis of their multiple features. For it, Multi-Criteria Decision Making (MCDM) approach is used. In this paper, among all the MCDM approach, the focus is on “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) approach and proposing an enhancement in this method. The traditional TOPSIS approach uses Euclidean Distance to measure the similarity. Here, Jeffrey Divergence has been proposed instead of Euclidean Distance to compute the similarity measure which includes asymmetric and symmetric distances during computation. Experimental analysis of both the variations of TOPSIS approach have been conducted and the result shows the enhancement in the selection of web sources.