Search results for: mesh clustering

Authors: Amani Tahat, Serdal Kirmizialtin

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Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

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Authors: Tooraj Karimi, Mohammadreza Sadeghi Moghadam

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Grey set theory has the advantage of using fewer data to analyze many factors, and it is therefore more appropriate for system study rather than traditional statistical regression which require massive data, normal distribution in the data and few variant factors. So, in this paper grey clustering and entropy of coefficient vector of grey evaluations are used to analyze energy consumption in buildings of the Oil Ministry in Tehran. In fact, this article intends to analyze the results of energy audit reports and defines most favorable characteristics of system, which is energy consumption of buildings, and most favorable factors affecting these characteristics in order to modify and improve them. According to the results of the model, ‘the real Building Load Coefficient’ has been selected as the most important system characteristic and ‘uncontrolled area of the building’ has been diagnosed as the most favorable factor which has the greatest effect on energy consumption of building. Grey clustering in this study has been used for two purposes: First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, grey clustering with variable weights has been used to classify all buildings in three categories named ‘no standard deviation’, ‘low standard deviation’ and ‘non- standard’. Entropy of coefficient vector of Grey evaluations is calculated to investigate greyness of results. It shows that among the 38 buildings surveyed in terms of energy consumption, 3 cases are in standard group, 24 cases are in ‘low standard deviation’ group and 11 buildings are completely non-standard. In addition, clustering greyness of 13 buildings is less than 0.5 and average uncertainly of clustering results is 66%.

Keywords: energy audit, grey set theory, grey incidence matrixes, grey clustering, Iran oil ministry

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Authors: Enver Kucukkulahli, Pakize Erdogmus, Kemal Polat

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The 2D image segmentation is a significant process in finding a suitable region in medical images such as MRI, PET, CT etc. In this study, we have focused on 2D MRI images for image segmentation process. We have designed a GUI (graphical user interface) written in MATLABTM for 2D MRI images. In this program, there are two different interfaces including data pre-processing and image clustering or segmentation. In the data pre-processing section, there are median filter, average filter, unsharp mask filter, Wiener filter, and custom filter (a filter that is designed by user in MATLAB). As for the image clustering, there are seven different image segmentations for 2D MR images. These image segmentation algorithms are as follows: PSO (particle swarm optimization), GA (genetic algorithm), Lloyds algorithm, k-means, the combination of Lloyds and k-means, mean shift clustering, and finally BBO (Biogeography Based Optimization). To find the suitable cluster number in 2D MRI, we have designed the histogram based cluster estimation method and then applied to these numbers to image segmentation algorithms to cluster an image automatically. Also, we have selected the best hybrid method for each 2D MR images thanks to this GUI software.

Keywords: image segmentation, clustering, GUI, 2D MRI

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Authors: Amal Machtalay

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In this survey, we aim to review the rapidly growing literature on numerical methods to solve different forms of mean field problems, namely mean field games (MFG), mean field controls (MFC), potential MFGs, and master equations, as well as their corresponding recent applications. Here, we distinguish two families of numerical methods: iterative methods based on mesh generation and those called mesh-free, normally related to neural networking and learning frameworks.

Keywords: mean-field games, numerical schemes, partial differential equations, complex systems, machine learning

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Authors: Caio Marcelo Nunes, Anderson da Silva Soares, Gustavo Teodoro Laureano, Clarimar Jose Coelho

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This paper presents a methodology to pixel recognition in aerial images using fuzzy $c$-means algorithm. This algorithm is a alternative to recognize areas considering uncertainties and inaccuracies. Traditional clustering technics are used in recognizing of multispectral images of earth's surface. This technics recognize well-defined borders that can be easily discretized. However, in the real world there are many areas with uncertainties and inaccuracies which can be mapped by clustering algorithms that use fuzzy sets. The methodology presents in this work is applied to multispectral images obtained from Landsat-5/TM satellite. The pixels are joined using the $c$-means algorithm. After, a classification process identify the types of surface according the patterns obtained from spectral response of image surface. The classes considered are, exposed soil, moist soil, vegetation, turbid water and clean water. The results obtained shows that the fuzzy clustering identify the real type of the earth's surface.

Keywords: aerial images, fuzzy clustering, image processing, pattern recognition

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Authors: Rishabh Srivastav, Divyam Sharma

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We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic.

Keywords: categorical data, fuzzy logic, genetic algorithm, K modes clustering, rough sets

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Authors: Ritu Vashistha, Jitender Kumar

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Clustering using the K-means algorithm is a very common way to understand and analyze the obtained output data. When a similar object is grouped, this is called the basis of Clustering. There is K number of objects and C number of cluster in to single cluster in which k is always supposed to be less than C having each cluster to be its own centroid but the major problem is how is identify the cluster is correct based on the data. Formulation of the cluster is not a regular task for every tuple of row record or entity but it is done by an iterative process. Each and every record, tuple, entity is checked and examined and similarity dissimilarity is examined. So this iterative process seems to be very lengthy and unable to give optimal output for the cluster and time taken to find the cluster. To overcome the drawback challenge, we are proposing a formula to find the clusters at the run time, so this approach can give us optimal results. The proposed approach uses the Euclidian distance formula as well melanosis to find the minimum distance between slots as technically we called clusters and the same approach we have also applied to Ant Colony Optimization(ACO) algorithm, which results in the production of two and multi-dimensional matrix.

Keywords: ant colony optimization, data clustering, centroids, data mining, k-means

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Authors: Gaurav Bisht, Rahul Rathnakumar, Ravikumar Duggirala

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Predictive simulations are one of the key focus areas in safety-critical industries such as aerospace and high-performance automotive engineering. The stone-chipping study is one such effort taken up by the industry to predict and evaluate the damage caused due to gravel impact on vehicles. This paper describes a finite elements based method that can simulate the ejection of gravel chips from a vehicle tire. The FE simulations were used to obtain the initial ejection velocity of the stones for various driving conditions using a computational contact mechanics approach. To verify the accuracy of the tire model, several parametric studies were conducted. The FE simulations resulted in stone loft velocities ranging from 0–8 m/s, regardless of tire speed. The stress on the tire at the instant of initial contact with the stone increased linearly with vehicle speed. Mesh convergence studies indicated that a highly resolved tire mesh tends to result in better momentum transfer between the tire and the stone. A fine tire mesh also showed a linearly increasing relationship between the tire forward speed and stone lofting speed, which was not observed in coarser meshes. However, it also highlighted a potential challenge, in that the ejection velocity vector of the stone seemed to be sensitive to the mesh, owing to the FE-based contact mechanical formulation of the problem.

Keywords: abaqus, contact mechanics, foreign object debris, stone chipping

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

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We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Shahida Khatoon, Mohd. Faisal Jalil, Vaishali Gautam

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The Photovoltaic (PV) generated power is mainly dependent on environmental factors. The PV array’s lifetime and overall systems effectiveness reduce due to the partial shading condition. Clustering the electrical connections between solar modules is a viable strategy for minimizing these power losses by shade diffusion. This article comprehensively evaluates various PV array clustering/reconfiguration models for PV systems. These are static and dynamic reconfiguration techniques for extracting maximum power in mismatch conditions. This paper explores and analyzes current breakthroughs in solar PV performance improvement strategies that merit further investigation. Altogether, researchers and academicians working in the field of dedicated solar power generation will benefit from this research.

Keywords: static reconfiguration, dynamic reconfiguration, photo voltaic array, partial shading, CTC configuration

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Authors: Haowei Chen, Kaiqi Xiong

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This paper aims to answer how malware will propagate in Wireless Mesh Networks (WMNs) and how communication radius and distributed density of nodes affects the process of spreading. The above analysis is essential for devising network-wide strategies to counter malware. We answer these questions by developing an improved dynamical system that models malware propagation in the area where nodes were uniformly distributed. The proposed model captures both the spatial and temporal dynamics regarding the malware spreading process. Equilibrium and stability are also discussed based on the threshold of the system. If the threshold is less than one, the infected nodes disappear, and if the threshold is greater than one, the infected nodes asymptotically stabilize at the endemic equilibrium. Numerical simulations are investigated about communication radius and distributed density of nodes in WMNs, which allows us to draw various insights that can be used to guide security defense.

Keywords: Bluetooth security, malware propagation, wireless mesh networks, stability analysis

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Authors: M. Nekri, A. Khelladi

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The world wide web network is a network with a complex topology, the main properties of which are the distribution of degrees in power law, A low clustering coefficient and a weak average distance. Modeling the web as a graph allows locating the information in little time and consequently offering a help in the construction of the research engine. Here, we present a model based on the already existing probabilistic graphs with all the aforesaid characteristics. This work will consist in studying the web in order to know its structuring thus it will enable us to modelize it more easily and propose a possible algorithm for its exploration.

Keywords: clustering coefficient, preferential attachment, small world, web community

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Authors: Michele C. Mesomo, Madeline de Souza Correa, Roberta L. Kruger, Luis R. S. Kanda, Marcos L. Corazza

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Natural extracts of plants have been used for many years for different purposes and recently they have been screened for their potential use as alternative remedies and food preservatives. Inflorescences of M. paradisiaca L., also known as the heart of the banana, have great economic interest due to its fruit. All parts of the banana are used for many different purposes, including use in folk medicine. The use of extraction via supercritical technology has grown in recent years, though it is still necessary to obtain experimental information for the construction of industrial plants. This work reports the extraction of Musa paradisiaca L. using compressed propane as solvent. The effects of the supercritical extraction conditions, pressure and temperature on the yield were evaluated. The raw material, inflorescences banana, was dried at 313.15 K and milled. The particle size used for the packaging of the extraction cell was 12 mesh (23.5%), 16 mesh (23.5%), 32 mesh (34.5%), 48 mesh (18.5%). The extractions were performed in a laboratory scale unit at pressures of 3.0 MPa, 6.5 MPa and 10.0 MPa and at 308.15 K, 323.15 K and 338.15 K. The operating conditions tested achieved a maximum yield of 2.94 wt% for the CO2 extraction at 10.0 MPa and 338.15 K, higher pressure and temperature. The lower yield, 2.29 wt%, was obtained in the condition of lower pressure and higher temperature. Temperature presented significant and positive effect on the extraction yield with supercritical CO2, while pressure had no effect on the yield. The overall extraction curves showed typical behavior obtained for the supercritical extraction procedure and and reached a constant extraction rate of about 80 to 100 min. The largest amount of extract was obtained at the beginning of the process, within 10 to 60 min.

Keywords: banana, natural products, supercritical extraction, temperature

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Authors: Pradeep Kumar

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The present work is based on application of cement mortar with natural sand and discontinuous steel fiber through which bending behavior of skinny beam was evaluated. This research is to study the effects of combining reinforcing steel meshes (continuous steel reinforcement) with discontinuous fibers as reinforcement in skinny walled Portland cement based cement mortar with crushed stone dust as a fine aggregate. The term ‘skinny’ means thickness of the beams is less than 25 mm. The main idea behind this combination is to satisfy the ultimate strength limit state through the steel mesh reinforcement (as a main reinforcement) and to control the cracking under service loads through fiber (Recron 3s) reinforcement (as secondary reinforcement). The main object of this study is to carry out the bending behavior of mortar reinforced thin beam with only one layer of steel mesh (with various transfer wire spacing) and with a recron 3s (Reliance) fifers. The wide experimental program with bending tests is undertaken. The following variables are investigated: (a) the reference mesh size - 25.4 x 25.4 mm and 50.8 x 50.8 mm; (b) the transverse wire spacing - 25.4 mm, 50.8 mm, and no transverse wires; (c) the type of fibers – Reliance (Recron 3s, 6mm length); and (d) the fiber volume fraction – 0.1% and 0.25%. Some of the main conclusions are: (a) the use of recron 3s fibers leads to a little better overall performance than that with no fiber; (b) an increase in equivalent stress is observed when 0.1% RF,0.25% R Fibers are used; (c) when 25.4 x 50.8 size steel mesh is used, no noticeable change in behavior is observed in comparison to specimens without fibers; and (d) for no fibers 0.1% and o.1% RF the transverse wire spacing has some little effect on the equivalent stress for RF fibers, the transverse wire has no influence but the equivalent stress are increased.

Keywords: cement mortar, crushed stone dust, fibre, steel mesh

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Authors: Mansir Abubakar, Hazlina Hamdan, Norwati Mustapha, Teh Noranis Mohd Aris

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In order to build a matching pattern for each class correspondences of ontology, it is required to specify a set of attribute correspondences across two corresponding classes by clustering. Clustering reduces the size of potential attribute correspondences considered in the matching activity, which will significantly reduce the computation workload; otherwise, all attributes of a class should be compared with all attributes of the corresponding class. Most existing ontology matching approaches lack scalable attributes discovery methods, such as cluster-based attribute searching. This problem makes ontology matching activity computationally expensive. It is therefore vital in ontology matching to design a scalable element or attribute correspondence discovery method that would reduce the size of potential elements correspondences during mapping thereby reduce the computational workload in a matching process as a whole. The objective of this work is 1) to design a clustering method for discovering similar attributes correspondences and relationships between ontologies, 2) to discover element correspondences by classifying elements of each class based on element’s value features using K-medoids clustering technique. Discovering attribute correspondence is highly required for comparing instances when matching two ontologies. During the matching process, any two instances across two different data sets should be compared to their attribute values, so that they can be regarded to be the same or not. Intuitively, any two instances that come from classes across which there is a class correspondence are likely to be identical to each other. Besides, any two instances that hold more similar attribute values are more likely to be matched than the ones with less similar attribute values. Most of the time, similar attribute values exist in the two instances across which there is an attribute correspondence. This work will present how to classify attributes of each class with K-medoids clustering, then, clustered groups to be mapped by their statistical value features. We will also show how to map attributes of a clustered group to attributes of the mapped clustered group, generating a set of potential attribute correspondences that would be applied to generate a matching pattern. The K-medoids clustering phase would largely reduce the number of attribute pairs that are not corresponding for comparing instances as only the coverage probability of attributes pairs that reaches 100% and attributes above the specified threshold can be considered as potential attributes for a matching. Using clustering will reduce the size of potential elements correspondences to be considered during mapping activity, which will in turn reduce the computational workload significantly. Otherwise, all element of the class in source ontology have to be compared with all elements of the corresponding classes in target ontology. K-medoids can ably cluster attributes of each class, so that a proportion of attribute pairs that are not corresponding would not be considered when constructing the matching pattern.

Keywords: attribute correspondence, clustering, computational workload, k-medoids clustering, ontology matching

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Authors: M. A. I Perera, C. R. Wijesinghe, A. R. Weerasinghe

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his study was conducted to review and identify the unsupervised techniques that can be employed to analyze gene expression data in order to identify better subtypes of tumors. Identifying subtypes of cancer help in improving the efficacy and reducing the toxicity of the treatments by identifying clues to find target therapeutics. Process of gene expression data analysis described under three steps as preprocessing, clustering, and cluster validation. Feature selection is important since the genomic data are high dimensional with a large number of features compared to samples. Hierarchical clustering and K Means are often used in the analysis of gene expression data. There are several cluster validation techniques used in validating the clusters. Heatmaps are an effective external validation method that allows comparing the identified classes with clinical variables and visual analysis of the classes.

Keywords: cancer subtypes, gene expression data analysis, clustering, cluster validation

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Authors: Thibaud Berthomier, Ali Mansour, Luc Bressollette, Frédéric Le Roy, Dominique Mottier, Léo Fréchier, Barthélémy Hermenault

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Deep Venous Thrombosis (DVT) occurs when a thrombus is formed within a deep vein (most often in the legs). This disease can be deadly if a part or the whole thrombus reaches the lung and causes a Pulmonary Embolism (PE). This disorder, often asymptomatic, has multifactorial causes: immobilization, surgery, pregnancy, age, cancers, and genetic variations. Our project aims to relate the thrombus epidemiology (origins, patient predispositions, PE) to its structure using ultrasound images. Ultrasonography and elastography were collected using Toshiba Aplio 500 at Brest Hospital. This manuscript compares two classification approaches: spectral clustering and scattering operator. The former is based on the graph and matrix theories while the latter cascades wavelet convolutions with nonlinear modulus and averaging operators.

Keywords: deep venous thrombosis, ultrasonography, elastography, scattering operator, wavelet, spectral clustering

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Authors: Tamanna Goyal, Divya Bansal, Sanjeev Sofat

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In world-threatening terrorist attacks, where early detection, distinction, and prediction are effective diagnosis techniques and for functionally accurate and precise analysis of terrorism data, there are so many data mining & statistical approaches to assure accuracy. The computational extraction of derived patterns is a non-trivial task which comprises specific domain discovery by means of sophisticated algorithm design and analysis. This paper proposes an approach for similarity extraction by obtaining the useful attributes from the available datasets of terrorist attacks and then applying feature selection technique based on the statistical impurity measures followed by clustering techniques on the basis of similarity measures. On the basis of degree of participation of attributes in the rules, the associative dependencies between the attacks are analyzed. Consequently, to compute the similarity among the discovered rules, we applied a weighted similarity measure. Finally, the rules are grouped by applying using hierarchical clustering. We have applied it to an open source dataset to determine the usability and efficiency of our technique, and a literature search is also accomplished to support the efficiency and accuracy of our results.

Keywords: association rules, clustering, similarity measure, statistical approaches

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Authors: Sadia Basar, Naila Habib, Awais Adnan

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The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

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Authors: Qiming Zhang, Youda Ye, Qinxue Jiang

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Accurate prediction of external flowfield and aero-heating at the wall of hypersonic vehicle is very crucial for the design of aircrafts. Unstructured/hybrid meshes have more powerful advantages than structured meshes in terms of pre-processing, parallel computing and mesh adaptation, so it is imperative to develop high-resolution numerical methods for the calculation of aerothermal environment on unstructured/hybrid meshes. The inviscid flux scheme is one of the most important factors affecting the accuracy of unstructured/ hybrid mesh heat flux calculation. Here, a new hybrid flux scheme is developed and the approach of interface type selection is proposed: i.e. 1) using the exact Riemann scheme solution to calculate the flux on the faces parallel to the wall; 2) employing Sterger-Warming (S-W) scheme to improve the stability of the numerical scheme in other interfaces. The results of the heat flux fit the one observed experimentally and have little dependence on grids, which show great application prospect in unstructured/ hybrid mesh.

Keywords: aero-heating prediction, computational fluid dynamics, hybrid meshes, hybrid schemes

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Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: K. Gomathi

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Mobile Ad hoc Networks (MANET) is defined as collection of routable wireless mobile nodes with no centralized administration and communicate each other using radio signals. Especially MANETs deployed in hostile environments where hackers will try to disturb the secure data transfer and drain the valuable network resources. Since MANET is battery operated network, preserving the network resource is essential one. For resource constrained computation, efficient routing and to increase the network stability, the network is divided into smaller groups called clusters. The clustering architecture consists of Cluster Head(CH), ordinary node and gateway. The CH is responsible for inter and intra cluster routing. CH election is a prominent research area and many more algorithms are developed using many different metrics. The CH with longer life sustains network lifetime, for this purpose Secondary Cluster Head(SCH) also elected and it is more economical. To nominate efficient CH, a Enhanced Distributed Weighted Clustering Algorithm (EDWCA) has been proposed. This approach considers metrics like battery power, degree difference and speed of the node for CH election. The proficiency of proposed one is evaluated and compared with existing algorithm using Network Simulator(NS-2).

Keywords: MANET, EDWCA, clustering, cluster head

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Authors: Chahrazed Zouaoui

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In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

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Authors: Hae-Yeoun Lee

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Quantification of cardiac function is performed by calculating blood volume and ejection fraction in routine clinical practice. However, these works have been performed by manual contouring,which requires computational costs and varies on the observer. In this paper, an automatic left ventricle segmentation algorithm on cardiac magnetic resonance images (MRI) is presented. Using knowledge on cardiac MRI, a K-mean clustering technique is applied to segment blood region on a coil-sensitivity corrected image. Then, a graph searching technique is used to correct segmentation errors from coil distortion and noises. Finally, blood volume and ejection fraction are calculated. Using cardiac MRI from 15 subjects, the presented algorithm is tested and compared with manual contouring by experts to show outstanding performance.

Keywords: cardiac MRI, graph searching, left ventricle segmentation, K-means clustering

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Authors: C. Román, M. Guevara, P. Salas, D. Duclap, J. Houenou, C. Poupon, J. F. Mangin, P. Guevara

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Diffusion Magnetic Resonance Imaging (dMRI) allows the non-invasive study of human brain white matter. From diffusion data, it is possible to reconstruct fiber trajectories using tractography algorithms. Our previous work consists in an automatic method for the identification of short association bundles of the superficial white matter (SWM), based on a whole brain inter-subject hierarchical clustering applied to a HARDI database. The method finds representative clusters of similar fibers, belonging to a group of subjects, according to a distance measure between fibers, using a non-linear registration (DTI-TK). The algorithm performs an automatic labeling based on the anatomy, defined by a cortex mesh parcelated with FreeSurfer software. The clustering was applied to two independent groups of 37 subjects. The clusters resulting from both groups were compared using a restrictive threshold of mean distance between each pair of bundles from different groups, in order to keep reproducible connections. In the left hemisphere, 48 reproducible bundles were found, while 43 bundles where found in the right hemisphere. An inter-hemispheric bundle correspondence was then applied. The symmetric horizontal reflection of the right bundles was calculated, in order to obtain the position of them in the left hemisphere. Next, the intersection between similar bundles was calculated. The pairs of bundles with a fiber intersection percentage higher than 50% were considered similar. The similar bundles between both hemispheres were fused and symmetrized. We obtained 30 common bundles between hemispheres. An atlas was created with the resulting bundles and used to segment 78 new subjects from another HARDI database, using a distance threshold between 6-8 mm according to the bundle length. Finally, a laterality index was calculated based on the bundle volume. Seven bundles of the atlas presented right laterality (IP_SP_1i, LO_LO_1i, Op_Tr_0i, PoC_PoC_0i, PoC_PreC_2i, PreC_SM_0i, y RoMF_RoMF_0i) and one presented left laterality (IP_SP_2i), there is no tendency of lateralization according to the brain region. Many factors can affect the results, like tractography artifacts, subject registration, and bundle segmentation. Further studies are necessary in order to establish the influence of these factors and evaluate SWM laterality.

Keywords: dMRI, hierarchical clustering, lateralization index, tractography

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Authors: Samira-Sadat JamaliDinan, Haidar Almohri, Mohammad-Reza Nazem-Zadeh

Abstract:

Lateralization of temporal lobe epilepsy (TLE) is very important for positive surgical outcomes. We propose a machine learning framework to ultimately identify the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Unlike most studies that use classification algorithms, we propose an effective clustering approach to distinguish between normal and TLE cases. We apply the famous Minkowski weighted K-Means (MWK-Means) technique as the clustering framework. To overcome the problem of poor initialization of K-Means, we use particle swarm optimization (PSO) to effectively select the initial centroids of clusters prior to applying MWK-Means. We demonstrate that compared to K-means and MWK-means independently, this approach is able to improve the result of a benchmark data set.

Keywords: temporal lobe epilepsy, machine learning, clustering, magnetoencephalography

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Authors: Filiz Ersoz, Taner Ersoz, Tugrul Bayraktar

Abstract:

Renewable energy is referred to as "clean energy" and common popular support for the use of renewable energy (RE) is to provide electricity with zero carbon dioxide emissions. This study provides useful insight into the European Union (EU) RE, especially, into electricity generation obtained from renewables, and their targets. The objective of this study is to identify groups of European countries, using multivariate statistical analysis and selected indicators. The hierarchical clustering method is used to decide the number of clusters for EU countries. The conducted statistical hierarchical cluster analysis is based on the Ward’s clustering method and squared Euclidean distances. Hierarchical cluster analysis identified eight distinct clusters of European countries. Then, non-hierarchical clustering (k-means) method was applied. Discriminant analysis was used to determine the validity of the results with data normalized by Z score transformation. To explore the relationship between the selected indicators, correlation coefficients were computed. The results of the study reveal the current situation of RE in European Union Member States.

Keywords: share of electricity generation, k-means clustering, discriminant, CO2 emission

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

Abstract:

Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks

Procedia PDF Downloads 361

Authors: Nathalie Bolding, Marta Montero, Joaquim Cevallos, Juan F. Martin-Lazaro

Abstract:

Background: Inhaled epoprostenol (iEPO) have been shown to improve PaO2:FiO2 (PF) in combination with bronchodilators (BD). However, there is not an available device to deliver these two therapies concomitantly. We describe a new method to provide this therapy successfully. Objective: To evaluate the response to continuous nebulization of iEPO and intermittent nebulization of Salbutamol/Ipratropium bromide in adults with severe respiratory failure through a double mesh nebulisation in tandem. Methods: This observational study included two mechanical ventilated adults under hourly ventilatory, gasometrical and clinical measurements during 48h. Both had severe respiratory failure treated with continuous iEPO (50 – 200 micrograms/h) and BD (Salbutamol 2.5 mg and Ipratropium bromide 500 mcg every 6 hours) through double mesh nebulisation (Aerogen solo®) placed in tandem in the dry side of the humidificator. The primary endpoints were the variables associated with a positive response to this tandem nebulised therapy (PaFiO2 index, ROX index). Secondary endpoints were laboratory (ABG) clinical and ventilatory variables. Statistical analysis (SPSS v29) included linear regression and ANOVA. Results: The patients included (n=2) survived, both extubated, one after ECMO therapy. Severe acute respiratory failure had a positive response rate to continuous iEPO and intermittent BD: PaFiO2 increased (7.40 to 30.91; P75: 27%) as well as ROX index (2.91 to 11.43; P75: 33%). There was a linear correlation of improvement between iEPO with PaFiO2 (ANOVA, r=0.393, p<0.002) and ROX (r=0.419, p<0.001). iEPO+BD therapy did not show any complications. Conclusion: Continuous and intermittent mesh tandem nebulisation can be effectively delivered with this method with a positive effect in ventilatory parameters without observed complications. Randomised studies will be able to provide reassurance in this new therapy.

Keywords: tandem, mesh, nebulisers, pulmonary, vasoldilators, bronchodilators, respiratory, failure

Procedia PDF Downloads 48