Search

Vehicle pollution is a major concern in the current world. The technologies are improving day by day to reduce the pollution caused by the car. However, we are still lagging from addressing this critical issue completely. Therefore, road surveillance should be made stringent to capture those vehicles causing this serious problem. In this article, we have proposed a deep learning based framework which will identify the vehicle pollution from the images captured by the on-road surveillance camera. We have prepared an enriched large data set with significant variations which has been used in the training phase while deploying the deep model. The experiment has dealt with three base line Deep Learning CNN models, i.e. Inception-V3, MobileNet-V2 and InceptionResNet-V2. Transfer learning concept has been exploited to identify the on-road polluting vehicle. The experimental outcome has demonstrated the supremacy of the proposed approach in the traffic surveillance domain.

Graph Convolutional Networks have recently received a lot of attention for their capability of representation learning on non-Euclidian feature spaces. Graph convolutional networks aggregate the neighbouring node features and attribute to learn graph representations. Like traditional deep learning models, the representation power of graph convolutional networks also increases with the increasing number of layers. However, it also increases the difficulty associated with training such models. Deep graph convolutional networks suffer from issues like vanishing gradient or over-fitting. In this paper, we explored skip connections in graph convolutional networks and proposed a deep residual graph convolutional neural network for predicting node properties in a protein-protein interaction network. The proposed model is an improvement over traditional deep learning models and present state-of-the-art graph learning algorithms. The implementation of the algorithm, as well as the saved model, is available online for reproducibility at https://github.com/rangan2510/R-GCN.

According to the World Health Organization (WHO) Situation Reports of Corona Virus Disease(Covid-19), as on 15th May 2020, India has 81,970 totals confirmed cases, 2649 total deaths and is still within the limit of community transmission phase. In this study, we analyze the spread of the disease and the fatalities caused up to 15th May 2020, as per the data obtained. A granular computing based regression model, namely Granular Box Regression is used along with Linear Regression for comparative analysis to study the increase in the number of confirmed cases and deaths based on days and an increase in the number of samples tested per day. A separate analysis is also conducted to evaluate the performance of Polynomial Regression on the same dataset. The performance of the different models has been evaluated using R-squared, Mean Absolute Error, Root Mean Squared Error, and Mean Squared Error values.

Advancement of single-cell sequencing technology has made it possible to describe high throughput and low-cost genome-wide sequencing. There are several methods that utilized the single-cell sequencing techniques to determine the cell types that construct a complex tissue. Clustering followed by dimensionality reduction are used to determine cell type. Moreover, statistical analysis is performed to identify the uniqueness of each cell type. In this study, traditional hierarchical clustering is performed to identify the cell types present in peripheral blood cells. This classification reveals the morphologically and phonetically different cells present in peripheral blood cells of a healthy donor. Each cell type contains a specific marker that defines the individual character of the cell type. Furthermore, these markers play an important role in biological pathways. The association of markers with pathway helps in understanding the cell-to-cell heterogeneity. During the study, cell markers of each cell types are further considered to analysis the associated pathways. The analysis shows how the change in gene expression contributes to pathway shift due to several biological causes. This information provides new insight in the understanding of biology process from a single-cell perspective.

Dengue is currently the leading mosquito-borne viral disease of the world. Around 40% of the world’s population is at risk of dengue infection. This is caused by one of the four serotypes of dengue virus (DENV -1 to -4), which spreads between the humans by Aedes mosquitoes. Severe dengue is a life-threatening disease. Identification of critical human genes related to dengue infection leads to better understanding of the disease mechanisms, improved diagnostics and development of an effective treatment. Here, we introduced a novel systems biology approach to find crucial human genes for dengue infection. We have integrated a large volume of biological information on human genes throughout our analysis steps. We found three critical genes that presumably regulate the outcome of dengue infection from a pool of 244 human genes. Pathway enrichment analysis of these 3 genes also indicates infectious disease specific pathways. The proposed method may be useful for other viral diseases. Finally, the identification of crucial host genes for the virus can help the development of host-dependent antiviral drugs.

Identification of homogeneous regions in a satellite image is essentially the clustering of pixels in intensity space. Importantly remote sensing image like satellite images contain varieties of land cover types. Some of the covers are significantly large areas whereas some are relatively smaller regions. Therefore, automatically detecting such wide varying areas is a challenging task. Hence, this fact motivated us to propose an improved clustering technique viz. Ensemble based Differential Evolution for Fuzzy Clustering (EDEFC). For this purpose, very recently developed three variants of differential evolution (DE) are used in order to perform the clustering with different set of solutions. As a result, better clustering solution yields from the ensemble of DEs by exhaustive exploration of search space. The proposed EDEFC technique is applied on two numeric remote sensing datasets and Indian Remote Sensing (IRS) satellite image of Kolkata. The results of the EDEFC are shown quantitatively and visually by comparing with eight other clustering techniques. Moreover, the statistical significance test has also been performed in order to judge the superiority of EDEFC.

Kidney Renal Clear Cell Carcinoma (KIRC) is the common kidney cancer and ranks 8th among all other popular cancer types. Mostly adult humans are affected by this malignant disease. However, enough information have not been gathered for this special case of renal carcinoma. The experimentally validated data are also in a non uniformed form which makes it more difficult to decode the relation among miRNAs, genes and TFs. To address this obstacle in this current study, miRNAs, genes and TFs those associated with KIRC and played a significant role are considered as individual elements and a framework is proposed in order to find the connection among each entity. From this proposed framework some genes are identified those are not only targeted by miRNAs or TFs separately but also gene regulation has an impact due to the passive effect of TFs. From this network it is clear that gene regulation due to hsa-miR-146a-5p is indirectly controlled by those TFs also. For the further validation of these informative miRNAs and genes KEGG pathway and GO enrichment analysis are performed. In future study, these framework should be granted more attention in order to understand better prognosis about malignant diseases.

Link Prediction is an important problem in complex network analysis. The link prediction problem in dynamic network is a more difficult problem than its static counterpart, as dynamic networks allow links to be created or deleted over a period of time. We model the link prediction problem in dynamic network as a classification problem and use vector auto-regression based time series models and supervised learning approaches to predict the link status at future. The proposed method shows a significant improvement over the state-of-the-art.

The challenges faced in medical image processing makes image processing domain one of the active research areas nowadays. Brain tumors are aberrant growth formed by cells reproducing in an abnormal manner. Detecting the location of brain lesion (tumor) is essential as it helps in the diagnosis and cure of tumor. This paper proposes a novel clustering approach for the brain lesions in BRATS training 2017. This work is an advancement of a well-known Fuzzy C-Means (FCM) method to separate out different tissues of MR Brain images. Here, a novel membership function has been proposed. The evaluation of the adopted approach is compared using several validity functions with other state-of-the-art methods. The computational result on brain MR image shows that the proposed technique is powerful and precise than the standard FCM process. This proposed approach, based on entropy based variation of fuzzy membership function is able to handle uncertainty and vagueness in images by adopting new distance modification to calculate the optimum cluster for even mixed pixels. The method has been tested on BRATS 2017 training dataset. Compared to K-Means, K-Medoids and Fuzzy C-Means the proposed method significantly reduces the segmentation errors.

Demand side management of Energy in smart buildings often needs as a prerequisite (or benefits from) future appliance usage schedules of the occupants. In this work, a datadriven appliance usage prediction framework is proposed. The dependence of consumption trends on recent appliance usage history is explored through the use of temporally sensitive feature representations. Multi-label classification architectures, with appliance level adaptive thresholding are used as the predictive models. A data parallel distributed architecture is explored for our machine learning models to feasibly analyze larger data volumes generated by a Smart Building. The framework is extensively tested on the publicly available GREEND dataset. Appliance usage patterns are obtained at multiple temporal resolutions with encouraging results with lower training times demonstrated by our models on the distributed architecture.

An important problem in the analysis of complex networks is to mine the top k important nodes. The existing literature offers several metrics, also called centrality measures which estimates importance using the structural properties of node, namely, degree, closeness, betweenness, eigenvector centrality, Pagerank etc. Though there exists plenty of centrality measures, none of them emphasizes the non-linearity of data. In the current study we propose a non-linear principle component analysis based approach to identify the top k important nodes. The proposed method is evaluated based on the saturation time and the fraction of infected nodes during a susceptible-infected propagation. The experiment on synthetic as well as real life data sets show that the developed method is competitive with the state-of-the-art.

Over the past two decades, various research works have been going on Multiple Sequence Alignment (MSA) and it becomes an important domain in bioinformatics. This is an NPhard problem. For this purpose, various traditional, heuristics and metaheuristic methods have been applied. Among these methods, metaheuristics show an effective output to overcome the bottleneck of MSA problem. Different metaheuristic methods and software have been developed to overcome the speed and accuracy problem of MSA, while the number of sequences increases. In this article, we have surveyed widely used metaheuristic methods and alignment tools applied for solving MSA problem. However, after reviewing we can conclude that the time complexity is still a big challenge for MSA problem.

Understanding the propagation of human health behavior, such as smoking and obesity, and identification of the factors that control such phenomenon is an important area of research in recent years mainly because, in industrialized countries a substantial proportion of the mortality and quality of life is due to particular behavior patterns, and that these behavior patterns are modifiable. Predicting the individuals who are going to be overweight or obese in future, as overweight and obesity propagate over dynamic human interaction network, is an important problem in this area. However, the problem has received limited attention from the network analysis and machine learning perspective till date. In this work, we propose a scalable supervised prediction model based on convolutional regression framework that is particularly suitable for short time series data. We propose various schemes to model social influence for health behavior change. Further we study the contribution of the primary factors of overweight and obesity, like unhealthy diets, recent weight gains and inactivity in the prediction task. A thorough experiment shows the superiority of the proposed method over the state-of-the-art.

Over the years, numerous studies have been performed in order to identify messenger RNAs (mRNAs) that are differentially expressed at different biological conditions for various diseases including cancer. In this regard, getting complete and noiseless data were always very challenging in previous technological set-up. While the inception of Next-Generation Sequencing (NGS) technology revolutionized the genome research, especially in the field of mRNA expression profile analysis. Here such data of breast cancer is used from The Cancer Genome Atlas (TCGA) to identify the cancer biomarkers. For this purpose, data have been preprocessed using statistical test and fold change concepts so that significant number of differentially expressed up and down regulated mRNAs can be recognized. Thereafter, wrapper based feature selection approach using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) has been applied on such preprocessed dataset to identify the potential mRNAs as biomarkers. Identified top 10 biomarkers are COMP, LRRC15, CTHRC1, CILP2, FOXF1, FIGF, PRDM16, LMX1B, IRX5 and LEPREL1. The quantitative results of the proposed method have been demonstrated in comparison with other state-of-the-art methods. Finally, enrichment analysis and the KEGG pathway analysis have also been conducted for the selected mRNAs.

Next-generation sequencing allows high-throughput measurements of non-coding RNA expression levels in tissues. Analysis of microRNAs (miRNAs) is particularly effective in differentiation of cancerous tissue samples, based on patterns of their expression levels. The paper presents a wrapper feature selection approach based on t-Distributed Stochastic Neighbor Embedding (t-SNE), Covariance Matrix Adaptation Evolution Strategy (CMA-Es) and Support Vector Machine (SVM). The advantage of t-SNE is amplification of pairwise similarities by the means of t-Student neighborhood function. The attributes are embedded into 1-D space to reveal similarities between the features. Such information is used by CMA-ES through real-valued encoding in order to model pairwise relations between miRNAs with covariance matrices. Finally, the wrapper uses SVM to evaluate the objective, which expresses the tradeoff between classification quality and the desired number of features. The approach is tested on eight different cancer types from The Cancer Genome Atlas. It allows to find small sets of miRNAs to differentiate cancer types from a single tumor class to the normal one with high certainty.

MicroRNAs (miRNAs) are a class of ∼22-nucleotide endogenous noncoding RNAs which have critical functions across various biological processes. It is quite well-known that the miRNAs are playing a crucial role for regulating the expression of target gene via repressing translation or promoting messenger RNAs degradation. Therefore, identification of discriminative and differentially expressed miRNA as a signature is an important task for cancer therapy. In this regard, Next-Generation Sequencing (NGS) data of miRNAs, available at The Cancer Research Atlas (TCGA) repository, is analyzed here for prostate cancer. This cancer type is a serious threat to the health of men as found in the literature. Hence, finding miRNA signature using NGS based miRNA expression data for prostate cancer is an important research direction. Generally by motivating this fact, a new miRNA signature identification method for prostate cancer is proposed. The proposed method uses a global optimization technique, called Simulated Annealing (SA), Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifier. Here SA encodes L number of features, in this case miRNAs. Similar number of top L key principal components of the original dataset is extracted using PCA. Thereafter, such components are multiplied with the reduced subset of data so that the classification task can be done on diverse dataset using SVM. Here the classification accuracy of SVM is considered as an underlying objective to optimize using SA. The proposed method can be seen as feature section technique in order to find potential miRNA signature. Finally, the experimental results provide a set of miRNAs with optimal classification accuracy. However, due to the stochastic nature of this algorithm a list of miRNAs is prepared. From the top 15 miRNAs of that list, four miRNAs, hsa-mir-152, hsa-mir-23a, hsa-mir-302f and hsa-mir-101-1, are associated with prostate cancer. Moreover, the performance of the proposed method has also been compared with other widely used state-of-the-art techniques. Furthermore, the obtained results have been justified by means of statistical test along with biological significance tests for the selected miRNAs.

Biological networks such as protein-protein interaction networks (PPINs) play a crucial role in cell biology of an organism and are widely studied in bioinformatics and biostatistics. Scores of high throughput techniques are used to identify PPIs which are then used to compile PPINs of an organism. Detection of protein complexes that are associated with diseases is another ongoing challenge. Consequently, methods have been developed that use PPINs in order to predict or identify disease associated protein complexes. Here we have modeled this task as a multi-objective optimization problem and developed a multi-objective evolutionary framework called MODAPROC to detect protein complexes associated with human diseases. MODAPROC considered three objective functions viz., density, number of outward interactions, and disease associations of a protein complex. Density and disease associations are to be maximized while outward interactions are minimized here. We have analyzed the resulting protein complexes and their association with different disorders. Moreover, we have detected some key properties of the disease associated protein complexes as well as classified them according to different disorders. MODAPROC is fast, robust, and provides a good analysis tool for finding and discovering obscure disease specific gene clusters and can be extended using other domain specific objective functions.

In this paper a novel digital phase lock loop (DPLL) is introduced with Teager energy operator (TEO) incorporated discrete energy separation algorithm DESA2. The output of the DESA2 is controlled using genetic algorithm (GA) optimized fuzzy controller. The input and output membership functions are optimized using GA along with the input and output controller gains of the fuzzy controller. This kind of approach to design a DPLL is a new idea in the signal processing and DPLL design.

Smart grid technology is recognized as an important solution to the various challenges the age-old grid system is facing including but not limited to the increasing demand, high cost, environmental concerns. Smart grid incorporates distributed renewable energy sources and loads through low-voltage distribution network, microgrid. Determining the optimal operational capacity and configuration of the distributed energy resources (DERs) is a key problem in smart grid network. This paper presents a model for optimal scheduling of DERs and proposes its application in energy market. The proposed discrete time optimization model minimizes the operational cost assuming realistic parameters. The simulation results clearly indicate that the proposed approach is economically effective and profitable.

Clustering algorithms based on type-I fuzzy set theory have been used for handling overlapping partitioning area over the last few decades. However, these fail to deal with additional degree of fuzziness within the real life datasets, because the membership values of type-I fuzzy set are crisp real numbers. Therefore, since inception, the type-II fuzzy set theory has been studied to address the weakness of type-I fuzzy set theory as the membership value itself is fuzzy in type-II fuzzy set theory. On the other hand, rough set based clustering method helps in great extend to handle the inherent uncertainty and vagueness of the data with the concept of lower and upper approximation. However, in rough clustering, rough points are not so certain to a particular cluster. In that case, machine learning technique such as Support Vector Machine can be used to assign the rough points into proper clusters in order to get the better clustering result. Hence, in this article, Support Vector Machine integrated Rough type-II Fuzzy C-Means clustering technique using both the rough set and type-II fuzzy set theories, is proposed. The effectiveness of the proposed clustering technique has been demonstrated quantitatively and visually on several synthetic and real life datasets in comparison with other well-known clustering techniques. Finally, the superiority of the results produced by the proposed technique has been shown using statistical significance test.

In this paper, two quantum behaved multi-objective optimization techniques, based on Binary Particle Swarm Optimization and Ant Colony Optimization, have been introduced. The proposed approaches are used to search optimal threshold values of gray scale images, by optimizing the non-dominated solutions using Li's method as objective function. These approaches coalesce the meta-heuristic algorithms with the intrinsic features of quantum theory to make the techniques more effective. The best fitness values, the set of optimal thresholds and the computation times at different level of thresholding have been reported both for the proposed techniques and their equivalent classical counterparts. The superiority of the techniques presented in this paper, are established in terms of computational time. Thereafter, the mean fitness and the standard deviation of the objective values prove that the proposed techniques are more effectual of than others. Finally, the performance of each technique has been evaluated by determining the PSNR values of the test images. It was found that the proposed techniques have better PSNR values as compared to their corresponding components. Hence, quality of thresholding is established in favour of the proposed techniques.

Clustering is a simple technique to make partition of given data set into number of clusters. This paper presents an quantum inspired algorithm using GA to automatically find the number of clusters for image data set. The advantage lies in this technique is that no previous information about the data set used for classification is required before hand. The method decides the optimum cluster number on run. The popular evolutionary method called genetic algorithm has been used for generation wise improvement of clustering. CS measure is used as a fitness function in clustering. Effectiveness and accuracy of the proposed technique are demonstrated in terms of standard error found in computation. Finally, desired number of threshold values are heuristically taken from the input image to produce the image after thresholding.

In this article, a Quantum Inspired Tabu Search for Multi-level thresholding for Colour Image has been developed to boost the possible effectiveness than that of its classical counterpart. The proposed algorithm has been applied to two true colour images to determine optimal threshold values at different levels using Otsu's method as an objective function. The features of quantum mechanics are coupled with the basic constitution of a popular meta-heuristic algorithm, called tabu search to form the quantum inspired meta-heuristic algorithm. Between the participating algorithms, the proposed algorithm takes least time for execution. The usefulness of the proposed method is established in context of exactitude, resilience and computational time over its respective conventional method. In addition, a popular test, called one-tailed t-test, used for statistical measurement, demonstrates the efficiency of the proposed algorithm.

The problem of network partitioning into cohesive subgroups is of utmost interest in analysis of social networks. In this paper we use quasi clique based partitioning to study social cohesion. We propose a greedy algorithm based on the notion of strong Nash stability to determine the cohesive subgroups in a network. Through experimental results we show that the proposed algorithm yields promising results by identifying meaningful, compact and dense clusters in many real life social network data sets.

In this article, the particle swarm optimization and differential evolution algorithms inspired by the intrinsic principles of quantum mechanics are presented. These quantum versions of meta-heuristic algorithms, namely quantum inspired particle swarm optimization and quantum inspired differential evolution for multi-level thresholding have been designed to find optimal thresholds of colour images at different levels by exploiting Kapur's entropy as an objective function. The average fitness and the standard deviation of the fitness values are reported. The test results over two test images at different levels certify the efficacy of the proposed methods with reference to precision, computational time, and durability over their classical counterparts. At last, a statistical measure, t-test has been performed among the four methods (two quantum methods and two classical methods) taking two methods in a single grasp to ascertain the supremacy of the results.

This paper describes an approach aiming to automatically transform a model depicting a high level physical behavior into two different optimized building energy management application models. Basically, each application requires specific formalism and information therefore it is necessary to rewrite an initial physical behavior model to obtain application models. Up to now, these two application models should be rewritten manually, which is representing an significant work and possibly a source of error. In order to automatize this process, the MDE approach seems to be an appropriate solution. This paper presents core specifications of transformation of hinge model into application models. To illustrate this approach, transformations into both an acausal anticipative model based on a mixed integer linear programming problem and a nonlinear causal model for fast simulated annealing optimization are shown. These models are used for energy management of a smart building platform named “Monitoring and Habitat Intelligent” PREDIS in Grenoble, France.

Thresholding is a simple, effective and popular method for image segmentation. It can be bi-level or multi-level depending on number of segments in an image. Multi-level thresholding computationally takes more time than the bi-level thresholding. To reduce the computational complexity, here we propose two quantum inspired meta-heuristic methods, namely Quantum Inspired Ant Colony Optimization and Quantum Inspired Simulated Annealing for multi-level thresholding. The basic quantum principles are coalesced with meta-heuristic approaches to design the proposed methods. The performance of the proposed methods is demonstrated in comparison with its conventional versions for two test images in terms of optimal threshold values at different levels with the fitness measure, standard deviation of the fitness measure and the computational time. It has been noticed that the Quantum Inspired meta-heuristic methods are superior in terms of computational time compare to the other methods. Finally, statistical significance test, called t-test, has performed to establish the superiority of the results.

During the last one decade, rapid proliferation of categorical data attracts computer scientists and engineers to analyse the categorical data. In this regard, unsupervised technique, such as clustering has been used by inception of new algorithms or modification of the existing ones. These methods are basically optimizing single objective function to get the partitions. However, optimization of multiple conflicting objectives simultaneously may evolve better clustering results as the multiobjective optimization techniques have been successfully applied in various fields of engineering and science. Hence, in this article, Multiobjective Differential Evolution based Fuzzy Clustering for Categorical Data is proposed. For this purpose, differential evolution is used as an underlying optimization technique. Moreover, the index encoding scheme is used to encode the vector in differential evolution and after the mutation operation in differential evolution, scaling is introduced to adjust the encoded index value within the permissible range of categorical objects. The performance of the proposed method has been demonstrated by comparing it with the widely used state-of-the-art methods for two synthetic and two real life data sets. Finally, statistical test has been conducted to judge the superiority of the proposed method.

In this article a fuzzy rule-based classifier has been designed on the framework of multiobjective Particle Swarm Optimization. The proposed approach is applied on microarray gene expression data to obtain genes with significant expression with respect to two different classes. Two fuzzy sets are represented with the linguistic values “high” and “low”. On the training dataset, the proposed approach is applied for the purpose of deriving good classification rules. To be precise, the good rules are those that have less attributes in the antecedent part and provide maximum accuracy. Moreover we also consider the existing redundancy among the selected rules which should be minimized. Here the underlying structure is modeled using multiobjective PSO with the support of non-dominated sorting and crowding distance sorting. The first objective is to maximize the classification accuracy and second objective is to minimize the rule-base complexity (number of rules and average rule length) and the redundancy of the rules. The performance of the proposed algorithm is compared with that of single objective versions, Support Vector machine classifier and Bayes classifier on several real-life datasets.

Detection of protein complexes within protein-protein interaction networks (PPIN) is a valuable step toward the analysis of biological processes and pathways. Several high-throughput experimental techniques produce large number of PPIs that can be extensively utilized for constructing PPI network of a species. Decomposition of the whole PPI network into smaller and manageable modules is an ongoing challenge. Here we have developed a multi-objective algorithm for detecting human protein complexes by partitioning large human PPI network into clusters which serve as protein complexes. Some graphical properties like density, centrality etc., are utilized for building the objectives. Besides the graphical properties we have also exploited a fuzzy measure based semantic similarity approach to construct similarity based objective. The proposed technique is demonstrated in the human PPI network and the resulting complexes are analyzed in context of Gene Ontology (GO) and pathway enrichment. We have also compared our results with that of some state-of-the-art algorithms in context of different performance metrics. The biological relevance of our predicted complexes are also established here by linking them with 22 key disease classes.

Statistical analysis and association rule mining are two most efficient techniques, where the first one is used to identify differentially expressed/methylated genes across different types of samples or experimental conditions and the second one is used to determine expression/methylation relationships among them. In this article, we have performed an integrated analysis of statistical methods and association rule mining on mRNA expression and DNA methylation datasets for the prediction of Uterine Leiomyoma. Moreover, we have proposed a novel rule-base classifier. Depending on 16 different rule-interestingness measures, we have applied a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to determine its class-label (i.e. tumor or normal class-label) through weighted-sum method. We have run this classifier on the combined dataset using k-fold cross-validation and also performed a comparative performance analysis with other popular rule-base classifiers. Finally, we have predicted the status of some important genes (through frequency analysis in association rules for tumor and normal class-labels individually) that have a major role for tumor formation in Uterine Leiomyoma.

In this article, we have considered the problem of fuzzy clustering of categorical data. In this regard, the well-known fuzzy C-medoids algorithm for categorical data clustering is posed as a multiobjective optimization problem where the cluster medoids are encoded in the chromosomes of a multiobjective genetic algorithm. The chromosomes are of variable lengths to permit automatic evolution of the number of clusters. The chromosomes are updated through the medoid updating process of fuzzy C-medoids clustering. The fuzzy cluster variance and cluster separation are taken as the two objectives to be optimized simultaneously. The performance of the proposed algorithm has been compared with that of different well-known categorical data clustering algorithms and demonstrated for a variety of synthetic and real-life categorical data sets.

MicroRNAs (MiRNA's) are ∼22nt long; one of the types of non-coding RNA having an important role in the control of the gene regulation, either by termination of the translation procedure or sometimes activating the gene actively or passively. Host-Parasite relationship largely depends on the gene regulatory network as the parasite has to survive in the host cell against the immune response of the host. So miRNA may have an important role in the host parasite relationship. The malarial parasite needs to survive in the human host cell during its life cycle. As it is well known to us today that the gene regulatory network is largely controlled by the non coding miRNA, here we are interested to find out potential miRNA in the parasite genome computationally. We have identified few putative miRNA genes in the chromosome 1 & 2 of Plasmodium vivax which need to be validated by experimental procedures.

This article introduces a semisupervised support vector machine classification technique that exploits both labeled and unlabeled points for addressing the problem of pixel classification of remote sensing images. The proposed method is based on the transductive inference and in particular transductive SVM (TSVM). Transductive SVM progressively searches a reliable separating hyperplane in the high dimensional space through iterative method exploiting both labeled and unlabeled samples. In particular, a thresholding strategy and similarity in classification between successive transductive sets are exploited to select the reliable samples from the unlabeled set. The proposed technique is applied on two labeled datasets and one large unlabeled image dataset: IRS image of Mumbai and compared with the standard SVM and progressive TSVM (PTSVM). Experimental results confirm that employing this learning scheme removes unnecessary points to a great extent from the unlabeled set and increases the accuracy level on the other hand. Comparison is made in terms of accuracy for the numeric datasets and quantitative cluster validity indices as well as classified image quality for the image dataset.

MicroRNAs (miRNAs) are small non-coding RNAs that have been shown to play important roles in gene regulation and various biological processes. The abnormal expression of some specific miRNAs often results in the development of cancer. In this article, we have utilized a multiobjective genetic algorithm-based feature selection algorithm wrapped with support vector machine (SVM) classifier for selecting promising miRNAs having differential expression in benign and malignant tissue samples. Subsequently, the non-dominated sets of promising miRNAs are aggregated into a single most promising miRNA subset. Finally, the Signal-to-Noise Ratio (SNR) statistic has been applied on the obtained miRNA subset for identifying potential miRNA markers that distinguish the two classes (benign and malignant) of tissue samples. The performance has been demonstrated on four real-life miRNA expression datasets for different SVM kernel functions and the identified miRNA markers are reported.

Rank Aggregation, in layman's term, is a technique of inferring a consensus ranking when multiple ranked lists of a set objects are given. Rank Aggregation has importance in a wide spectrum of fields including spam reduction in meta search, social choice theory of welfare economics, microarray analysis in bioinformatics etc. Unfortunately an ample Rank Aggregation is computationally a hard task to do even for a small set of objects. Till the date several heuristic algorithms have been devised towards its improvement. Almost all these heuristics rely on certain notion of disagreement between two ranked lists. Kendall's tau distance is undoubtedly quite popular among them, for its various desirable features. Kendall's tau distance is often used by different heuristics for approximating the consensus list. We in this article point out an important drawback of the Kendall's tau distance and propose a modified measure by using Shanon's Entropy formula. We also explain its benefit through some artificial and real data.

Identifying possible viral-host protein-protein interactions is an important and useful approach in developing new drugs targeting those interactions. In this article, a recently published dataset containing records of interactions between a set of HIV-1 proteins and a set of human proteins has been analyzed using association rule mining. The main objective is to identify a set of association rules among the human proteins with high confidence. The well-known Apriori algorithm has been utilized for discovering the association rules. Moreover, we have predicted some new viral-human interactions based on the discovered association rules.

In this article, a Genetic Algorithm-based fuzzy clustering method (GOGA), which incorporates Gene Ontology (GO) knowledge in the clustering process, has been proposed for clustering microarray gene expression data. The proposed technique combines the expression-based and GO-based gene dissimilarity measures for this purpose. Both expression-based and GO-based clustering objectives have been incorporated in the fitness function. The performance of the proposed technique has been demonstrated on real-life Yeast Cell Cycle data set. KEGG pathway based enrichment studies have been conducted for validating the clustering results.

In this paper, a segmentation technique of multi-spectral magnetic resonance image of the brain using a new differential evolution based crisp clustering is proposed. Real-coded encoding of the cluster centres is used for this purpose. Here assignments of points to different clusters are made based on the Euclidean distance. The proposed method is applied on several simulated T1-weighted, T2-weighted and proton density for normal and MS lesion magnetic resonance brain images. Superiority of the proposed method over genetic algorithm based crisp clustering, simulated annealing based crisp clustering, K-means and average linkage are demonstrated quantitatively. Segmentation obtained by differential evolution based crisp clustering technique is also compared with the available ground truth information. Also statistical analysis has been conducted to judge the effectiveness. Matlab version of the software is available at http://bio.icm.edu.pl/∼darman/MRI.

Clustering methods are used for unsupervised classification of tumor subclasses in microarray gene expression data sets organized in a fashion where the rows represent the tumor samples and columns represent the genes. Clustering algorithms can be very sensitive with respect to the set of features (genes) considered in the clustering process. It is important to select the set of informative and relevant genes to be used for clustering. In this article, a multiobjective genetic algorithm based technique has been proposed for performing the tasks of gene selection and fuzzy clustering simultaneously. A novel encoding technique is developed in this regard and the algorithm searches for the best cluster centers while minimizing the number of selected genes. The number of clusters is evolved automatically. The performance of the proposed technique has been illustrated on an artificial data set and compared with that of several other related feature selection/clustering approaches. Moreover its performance is demonstrated on two real life multi-class gene expression data sets viz., Brain tumor and Lung tumor data sets.

Differential evolution has emerged as one of the fast, robust, and efficient global search heuristics of current interest. Besides its good convergence properties and suitability for parallelization, Differential evolution's main assets are its conceptual simplicity and ease of use. This paper describes an application of differential evolution to the fuzzy clustering for categorical data sets. The performance of the proposed method has been compared with the simulated annealing based fuzzy c-medoids clustering algorithm, fuzzy c-medoids, fuzzy c-modes and average linkage hierarchical clustering algorithm for two artificial and two real life categorical data sets. Statistical significance test has been carried out to establish the statistical significance of the proposed method.

Authentication of mobile subscribers and network are a challenging issue to the researchers due to increasing security threats and attacks with the enhanced population of wireless traffic. 3-G or advanced mobile communication system has been developed to speed up the data communication. 3-G mobile communications use two different switching techniques: circuit switching for voice and low speed data communications and packet switching mainly for data communication, but can afford voice communication like VoIP (Voice Over Internet Protocol), video telephony, multimedia service etc. Generally high speed data communication uses packet switching technique through PDSN (Packet Data Serving Node) servers. In circuit switching (3-G network), authentication is mutual where both MS and MSC or network authenticate each other, but in packet switching only network (servers in PDSN) examines the authenticity of MS. In this paper, we propose a mutual authentication technique for circuit and packet switching both that verifies the authenticity of the subscriber as well as the network by subscriber's password, SIM (expressed as identifier) and biometric property of the subscriber, termed as mutual authentication technique using three entities in 3-G mobile communications.

In this article, a novel multiobjective variable string length real coded genetic fuzzy clustering scheme for clustering microarray gene expression data has been proposed. The proposed technique automatically evolves the number of clusters along with the clustering result. The multiobjective variable string length clustering technique encodes the cluster centers in its chromosomes and simultaneously optimizes two fuzzy validity indices namely PBM index and Xie-Beni validity measure. In the final generation, it produces a set of non-dominated solutions, from which the best solution is selected using Silhouette index which is independent of the number of clusters. The corresponding chromosome length provides the number of clusters. The proposed method is applied on three publicly available real life gene expression data. Superiority of the proposed method over some other well known clustering algorithms has been demonstrated quantitatively.

In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic fuzzy clustering of the tissue samples. In this regard, coordinate of the cluster centers have been encoded in the chromosomes and three fuzzy cluster validity indices are simultaneously optimized. Each solution of the resultant Pareto-optimal set has been boosted by a novel technique based on Support Vector Machine (SVM) classification. Finally, the clustering information possessed by the non-dominated solutions are combined through a majority voting ensemble technique to produce the final clustering solution. The performance of the proposed multiobjective clustering method has been compared to several other microarray clustering algorithms for three publicly available benchmark cancer data sets, viz., Leukemia, Colon cancer and Lymphoma data to establish its superiority.

Authentication in sending information is a great research challenge. Many different authentication schemes have been invented to improve the security and efficiency due to increasing of threats and attacks with the increasing volume of traffic. There is a need for analysis the efficiency and security of authentication schemes to apply them in specific application. For this purpose, an analysis of few authentication schemes is made with several parameters of interest in this paper. We observe that no scheme is suitable for all applications neither any application is suitable invariably.