Search results for: Linux Cluster

Authors: Mat Syai'in, Adi Soeprijanto

Abstract:

An Optimal Power Flow based on Improved Particle Swarm Optimization (OPF-IPSO) with Generator Capability Curve Constraint is used by NN-OPF as a reference to get pattern of generator scheduling. There are three stages in Designing NN-OPF. The first stage is design of OPF-IPSO with generator capability curve constraint. The second stage is clustering load to specific range and calculating its index. The third stage is training NN-OPF using constructive back propagation method. In training process total load and load index used as input, and pattern of generator scheduling used as output. Data used in this paper is power system of Java-Bali. Software used in this simulation is MATLAB.

Keywords: Optimal Power Flow, Generator Capability Curve, Improved Particle Swarm Optimization, Neural Network

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Authors: Nurul ‘Ulyani Mohd Najib, Nor’ Aini Yusof, Zulkifli Osman

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Studies on residential satisfaction have been actively discussed under family house setting. However, limited studies have been conducted on student residential satisfaction. This study is an attempt to fill the research gap. It focuses on the influence of socioeconomic on students- satisfaction with the universities- student housing facilities. The students who stayed at the on-campus student housing were the respondents. This study employed two-stage cluster sampling method in classifying the respondents. Self-administered questionnaires were distributed face-to-face to the students. In general, it is confirmed that students- socio-economic backgrounds have influence on the students- satisfaction with their housing facilities. The main influential factors were the students- economic status, sense of sharing, and ethnicity of their roommates. Furthermore, this study could also provide a useful feedback for the universities in order to improve their student housing facilities.

Keywords: Malaysia, Socio-economic, Student housing, Student residential satisfaction

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Authors: Mourad Moussa, Ali Douik, Hassani Messaoud

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In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.

Keywords: Image segmentation, objects tracking, Parzen window, singular value decomposition, target recognition.

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Authors: Guenther Schuh, Sebastian Woelk, Daniel Schraknepper, Anders Such

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The systematic evaluation of manufacturing technologies with regard to the potential for product designing constitutes a major challenge. Until now, conventional evaluation methods primarily consider the costs of manufacturing technologies. Thus, the potential of manufacturing technologies for achieving additional product design features is not completely captured. To compensate this deficit, final evaluations of new technologies are mainly intuitive in practice. Therefore, an additional evaluation dimension is needed which takes the potential of manufacturing technologies for specific realizable product designs into account. In this paper, we present the approach of an evaluation method for selecting manufacturing technologies with regard to their potential for product designing. This research is done within the Fraunhofer innovation cluster »AdaM« (Adaptive Manufacturing) which targets the development of resource efficient and adaptive manufacturing technology processes for complex turbomachinery components.

Keywords: Manufacturing, product design, production, technology assessment, technology management.

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Authors: A. Faro, D. Giordano, F. Maiorana

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Data mining uses a variety of techniques each of which is useful for some particular task. It is important to have a deep understanding of each technique and be able to perform sophisticated analysis. In this article we describe a tool built to simulate a variation of the Kohonen network to perform unsupervised clustering and support the entire data mining process up to results visualization. A graphical representation helps the user to find out a strategy to optmize classification by adding, moving or delete a neuron in order to change the number of classes. The tool is also able to automatically suggest a strategy for number of classes optimization.The tool is used to classify macroeconomic data that report the most developed countries? import and export. It is possible to classify the countries based on their economic behaviour and use an ad hoc tool to characterize the commercial behaviour of a country in a selected class from the analysis of positive and negative features that contribute to classes formation.

Keywords: Unsupervised classification, Kohonen networks, macroeconomics, Visual data mining, cluster interpretation.

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Authors: Amol Bapuso Rajmane, Pradeep M. Patil, Prakash J. Kulkarni

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The Information Retrieval community is facing the problem of effective representation of Web search results. When we organize web search results into clusters it becomes easy to the users to quickly browse through search results. The traditional search engines organize search results into clusters for ambiguous queries, representing each cluster for each meaning of the query. The clusters are obtained according to the topical similarity of the retrieved search results, but it is possible for results to be totally dissimilar and still correspond to the same meaning of the query. People search is also one of the most common tasks on the Web nowadays, but when a particular person’s name is queried the search engines return web pages which are related to different persons who have the same queried name. By placing the burden on the user of disambiguating and collecting pages relevant to a particular person, in this paper, we have developed an approach that clusters web pages based on the association of the web pages to the different people and clusters that are based on generic entity search.

Keywords: Entity resolution, information retrieval, graph based disambiguation, web people search, clustering.

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Authors: Shuhana Shamsuddin, Nur Rasyiqah Abu Hassan, Ahmad Bashri Sulaiman

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Liveable city is referred to as the quality of life in an area that contributes towards a safe, healthy and enjoyable place. This paper discusses the role of the streets- activities in making Kuala Lumpur a liveable city and the happiness level of the residents towards the city-s street activities. The study was conducted using the residents of Kuala Lumpur. A mixed method technique is used with the quantitative data as a main data and supported by the qualitative data. Data were collected using questionnaires, observation and also an interview session with a sample of residents of Kuala Lumpur. The sampling technique is based on multistage cluster data sampling. The findings revealed that, there is still no significant relationship between the length of stay of the resident in Kuala Lumpur with the happiness level towards the street activities that occurred in the city.

Keywords: Liveable city, activities, urban design quality, quality of life, happiness level.

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Authors: T. Niknam, M. Nayeripour, B.Bahmani Firouzi

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Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.

Keywords: Ant Colony Optimization (ACO), Data clustering, Hybrid evolutionary optimization algorithm, K-means clustering, Particle Swarm Optimization (PSO).

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Authors: Isari Pairoa, Proud Arunrangsiwed

Abstract:

Brand mascots are the cartoon characters, which are mainly designed for advertising or other related marketing purposes. Many brand mascots are extremely popular, since they were presented in commercial advertisements and Line Stickers. Brand Line Stickers could lead the users to identify with the brand and brand mascots, where might influence users to become loyal customers, and share the identity with the brand. The objective of the current study is to examine the effect of brand mascots on consumers’ decision and consumers’ intention to purchase the product. This study involved 400 participants, using cluster sampling from 50 districts in Bangkok metropolitan area. The descriptive analysis shows that using brand mascot causes consumers' positive attitude toward the products, and also heightens the possibility to purchasing the products. The current study suggests the new type of marketing strategy, which is brand fandom. This study has also contributed the knowledge to the area of integrated marketing communication and identification theory.

Keywords: Brand mascot, consumers’ behavior, marketing communication, purchasing.

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Authors: Nurul ‘Ulyani Mohd Najib, Nor’ Aini Yusof, Zulkifli Osman

Abstract:

Debates on residential satisfaction topic have been vigorously discussed in family house setting. Nonetheless, less or lack of attention was given to survey on student residential satisfaction in the campus house setting. This study, however, tried to fill in the gap by focusing more on the relationship between students- socio-economic backgrounds and student residential satisfaction with their on-campus student housing facilities. Two-stage cluster sampling method was employed to classify the respondents. Then, self-administered questionnaires were distributed face-to-face to the students. In general, it was confirmed that the students- socioeconomic backgrounds have significantly influence the students- satisfaction with their on-campus student housing facilities. The main influential factors were revealed as the economic status, sense of sharing, and the ethnicity of roommates. Likewise, this study could also provide some useful feedback for the universities administration in order to improve their student housing facilities.

Keywords: Malaysia, Socio-economic, Student housing, Studentresidential satisfaction

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Authors: Minami Ito, Akihiro Iijima

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On March 11, 2011, the Great East Japan Earthquake occurred off the coast of Sanriku, Japan. It is important to build a sustainable society through the reconstruction process rather than simply restoring the infrastructure. To compare the goals of reconstruction plans of quake-stricken municipalities, Japanese language morphological analysis was performed by using text mining techniques. Frequently-used nouns were sorted into four main categories of “life”, “disaster prevention”, “economy”, and “harmony with environment”. Because Soma City is affected by nuclear accident, sentences tagged to “harmony with environment” tended to be frequent compared to the other municipalities. Results from cluster analysis and principle component analysis clearly indicated that the local government reinforces the efforts to reduce risks from radiation exposure as a top priority.

Keywords: Eco-friendly reconstruction, harmony with environment, decontamination, nuclear disaster.

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Authors: Prakash G L, Thejaswini M, S H Manjula, K R Venugopal, L M Patnaik

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Wireless sensor network can be applied to both abominable and military environments. A primary goal in the design of wireless sensor networks is lifetime maximization, constrained by the energy capacity of batteries. One well-known method to reduce energy consumption in such networks is data aggregation. Providing efcient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present privacy-preserving data aggregation scheme for additive aggregation functions. The Cluster-based Private Data Aggregation (CPDA)leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.

Keywords: Aggregation, Clustering, Query Processing.

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Authors: Manasjyoti Bhuyan, Kandarpa Kumar Sarma, Hirendra Das

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Signature represents an individual characteristic of a person which can be used for his / her validation. For such application proper modeling is essential. Here we propose an offline signature recognition and verification scheme which is based on extraction of several features including one hybrid set from the input signature and compare them with the already trained forms. Feature points are classified using statistical parameters like mean and variance. The scanned signature is normalized in slant using a very simple algorithm with an intention to make the system robust which is found to be very helpful. The slant correction is further aided by the use of an Artificial Neural Network (ANN). The suggested scheme discriminates between originals and forged signatures from simple and random forgeries. The primary objective is to reduce the two crucial parameters-False Acceptance Rate (FAR) and False Rejection Rate (FRR) with lesser training time with an intension to make the system dynamic using a cluster of ANNs forming a multiple classifier system.

Keywords: offline, algorithm, FAR, FRR, ANN.

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Authors: Li Ni, Pen ManMan, Li KenLi

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Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.

Keywords: Chemical reaction optimization, expectation maximization, initial, objective function clustering.

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Authors: Meghna Maiti, Utpal Roy

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In India, till today, malaria is considered to be one of the significant infectious diseases. Most of the cases regional geographical factors are the principal elements to let the places a unique identity. The incidence and intensity of infectious diseases are quite common and affect different places differently across the nation. The present study aims to identify spatial clusters of hot spots and cold spots of malaria incidence and their seasonal variation during the three periods of 2012-2014, 2015-2017 and 2018-20 in the state of West Bengal in India. As malaria is a vector-borne disease, numbers of positive test results are to be reported by the laboratories to the Department of Health, West Bengal (through the National Vector Borne Disease Control Programme). Data on block-wise monthly malaria positive cases are collected from Health Management Information System (HMIS), Ministry of Health and Family Welfare, Government of India. Moran’s I statistic is performed to assess the spatial autocorrelation of malaria incidence. The spatial statistical analysis mainly Local Indicators of Spatial Autocorrelation (LISA) cluster and Local Geary Cluster are applied to find the spatial clusters of hot spots and cold spots and seasonal variability of malaria incidence over the three periods. The result indicates that the spatial distribution of malaria is clustered during each of the three periods of 2012-2014, 2015-2017 and 2018-20. The analysis shows that in all the cases, high-high clusters are primarily concentrated in the western (Purulia, Paschim Medinipur districts), central (Maldah, Murshidabad districts) and the northern parts (Jalpaiguri, Kochbihar districts) and low-low clusters are found in the lower Gangetic plain (central-south) mainly and northern parts of West Bengal during the stipulated period. Apart from this seasonal variability inter-year variation is also visible. The results from different methods of this study indicate significant variation in the spatial distribution of malaria incidence in West Bengal and high incidence clusters are primarily persistently concentrated over the western part during 2012-2020 along with a strong seasonal pattern with a peak in rainy and autumn. By applying the different techniques in identifying the different degrees of incidence zones of malaria across West Bengal, some specific pockets or malaria hotspots are marked and identified where the incidence rates are quite harmonious over the different periods. From this analysis, it is clear that malaria is not a disease that is distributed uniformly across the state; some specific pockets are more prone to be affected in particular seasons of each year. Disease ecology and spatial patterns must be the factors in explaining the real factors for the higher incidence of this issue within those affected districts. The further study mainly by applying empirical approach is needed for discerning the strong relationship between communicable disease and other associated affecting factors.

Keywords: Malaria, infectious diseases, spatial statistics, spatial autocorrelation, LISA.

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Authors: Emrah Bulut, Okan Duru, Shigeru Yoshida

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In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.

Keywords: C-means clustering, Fuzzy time series, Multi-variate design

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Authors: Y. Harold Robinson, E. Golden Julie

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Dynamic Routing in Wireless Sensor Networks (WSNs) has played a significant task in research for the recent years. Energy consumption and data delivery in time are the major parameters with the usage of sensor nodes that are significant criteria for these networks. The location of sensor nodes must not be prearranged. Clustering in WSN is a key methodology which is used to enlarge the life-time of a sensor network. It consists of numerous real-time applications. The features of WSNs are minimized the consumption of energy. Soft computing techniques can be included to accomplish improved performance. This paper surveys the modern trends in routing enclose fuzzy logic and Neuro-fuzzy logic based on the clustering techniques and implements a comparative study of the numerous related methodologies.

Keywords: Wireless sensor networks, clustering, fuzzy logic, neuro-fuzzy logic, energy efficiency.

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Authors: Nurcan Tuncbag, Turkan Haliloglu, Ozlem Keskin

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Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In the light of the clustering data, we have verified some interactions which were not identified as core interactions in DIP and also, we have characterized some functionally unknown proteins according to the interaction data and functional correlation. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins, also to predict new interactions and to characterize functions of unknown proteins.

Keywords: Pair-wise protein interactions, DIP database, functional correlations, biclustering.

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Authors: Setareh Shekarsaraei, Marjan Moridi, Nasser L. Hadipour

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In this study, nuclear magnetic resonance spectroscopy and nuclear quadrupole resonance spectroscopy parameters of 14N (Nitrogen in imidazole ring) in N–H…O hydrogen bonding for Histidine hydrochloride monohydrate were calculated via density functional theory. We considered a five-molecule model system of Histidine hydrochloride monohydrate. Also we examined the trends of environmental effect on hydrogen bonds as well as cooperativity. The functional used in this research is M06-2X which is a good functional and the obtained results has shown good agreement with experimental data. This functional was applied to calculate the NMR and NQR parameters. Some correlations among NBO parameters, NMR and NQR parameters have been studied which have shown the existence of strong correlations among them. Furthermore, the geometry optimization has been performed using M062X/6-31++G(d,p) method. In addition, in order to study cooperativity and changes in structural parameters, along with increase in cluster size, natural bond orbitals have been employed.

Keywords: Hydrogen bonding, Density Functional Theory (DFT), Natural bond Orbitals (NBO), cooperativity effects.

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Authors: Pooja Mishra, Chris Pollett

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This paper explores efficient ways to implement various media-updating features like news aggregation, video conversion, and bulk email handling. All of these jobs share the property that they are periodic in nature, and they all benefit from being handled in a distributed fashion. The data for these jobs also often comes from a social or collaborative source. We isolate the class of periodic, one round map reduce jobs as a useful setting to describe and handle media updating tasks. As such tasks are simpler than general map reduce jobs, programming them in a general map reduce platform could easily become tedious. This paper presents a MediaUpdater module of the Yioop Open Source Search Engine Web Portal designed to handle such jobs via an extension of a PHP class. We describe how to implement various media-updating tasks in our system as well as experiments carried out using these implementations on an Amazon Web Services cluster.

Keywords: Distributed jobs framework, news aggregation, video conversion, email.

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Authors: Myung-Won Lee, Keun-Chang Kwak

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This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.

Keywords: IT2-FCM, IT2-CFCM, context-based fuzzy clustering, adaptive neuro-fuzzy network, knowledge representation.

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Authors: Ashanie Guanathillake, Kithsiri Samarasinghe

Abstract:

Wireless Sensor Networks consist of inexpensive, low power sensor nodes deployed to monitor the environment and collect data. Gathering information in an energy efficient manner is a critical aspect to prolong the network lifetime. Clustering  algorithms have an advantage of enhancing the network lifetime. Current clustering algorithms usually focus on global re-clustering and local re-clustering separately. This paper, proposed a combination of those two reclustering methods to reduce the energy consumption of the network. Furthermore, the proposed algorithm can apply to homogeneous as well as heterogeneous wireless sensor networks. In addition, the cluster head rotation happens, only when its energy drops below a dynamic threshold value computed by the algorithm. The simulation result shows that the proposed algorithm prolong the network lifetime compared to existing algorithms.

Keywords: Energy efficient, Global re-clustering, Local re-clustering, Wireless sensor networks.

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Authors: Rita Riekstina-Dolge, Zanda Kruma, Daina Karklina, Fredijs Dimins

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Apple juice is the main raw material for cider production. In this study apple juices obtained from 14 dessert and crab variety apples grown in Latvia were investigated. For all samples soluble solids, titratable acidity, pH and sugar content were determined. Crab apples produce more dry matter, total sugar and acid content compared to the dessert apples but it depends on the apple variety. Total sugar content of crab apple juices was 1.3 to 1.8 times larger than in dessert apple juices. Titratable acidity of dessert apple juices is in the range of 4.1g L^-1 to 10.83g L^-1 and in crab apple juices titratable acidity is from 7.87g L^-1 to 19.6g L^-1. Fructose was detected as the main sugar whereas glucose level varied depending on the variety. The highest titratable acidity and content of sugars was detected in ‘Cornelia’ apples juice.

Keywords: Apple juice, hierarchical cluster analysis, sugars, titratable acidity.

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Authors: M. Staníčková

Abstract:

The paper deals with an application of quantitative analysis – the Data Envelopment Analysis (DEA) method to performance evaluation of the European Union Member States, in the reference years 2000 and 2011. The main aim of the paper is to measure efficiency changes over the reference years and to analyze a level of productivity in individual countries based on DEA method and to classify the EU Member States to homogeneous units (clusters) according to efficiency results. The theoretical part is devoted to the fundamental basis of performance theory and the methodology of DEA. The empirical part is aimed at measuring degree of productivity and level of efficiency changes of evaluated countries by basic DEA model – CCR CRS model, and specialized DEA approach – the Malmquist Index measuring the change of technical efficiency and the movement of production possibility frontier. Here, DEA method becomes a suitable tool for setting a competitive/uncompetitive position of each country because there is not only one factor evaluated, but a set of different factors that determine the degree of economic development.

Keywords: CCR CRS model, cluster analysis, DEA method, efficiency, EU, Malmquist index, performance.

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Authors: A.M. Fahim, A. M. Salem, F. A. Torkey, M. A. Ramadan

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Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, a density based clustering algorithm (DCBRD) is presented, relying on a knowledge acquired from the data by dividing the data space into overlapped regions. The proposed algorithm discovers arbitrary shaped clusters, requires no input parameters and uses the same definitions of DBSCAN algorithm. We performed an experimental evaluation of the effectiveness and efficiency of it, and compared this results with that of DBSCAN. The results of our experiments demonstrate that the proposed algorithm is significantly efficient in discovering clusters of arbitrary shape and size.

Keywords: Clustering Algorithms, Arbitrary Shape of clusters, cluster Analysis.

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Authors: K. Sakthivel, R. Nallusamy, C. Kavitha

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The goal of image segmentation is to cluster pixels into salient image regions. Segmentation could be used for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing, or image database lookup. In this paper, we present a color image segmentation using support vector machine (SVM) pixel classification. Firstly, the pixel level color and texture features of the image are extracted and they are used as input to the SVM classifier. These features are extracted using the homogeneity model and Gabor Filter. With the extracted pixel level features, the SVM Classifier is trained by using FCM (Fuzzy C-Means).The image segmentation takes the advantage of both the pixel level information of the image and also the ability of the SVM Classifier. The Experiments show that the proposed method has a very good segmentation result and a better efficiency, increases the quality of the image segmentation compared with the other segmentation methods proposed in the literature.

Keywords: Image Segmentation, Support Vector Machine, Fuzzy C–Means, Pixel Feature, Texture Feature, Homogeneity model, Gabor Filter.

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Authors: E. N. Sathishkumar, K. Thangavel, T. Chandrasekhar

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Feature selection is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that all genes are not important in gene expression data. Some of the genes may be redundant, and others may be irrelevant and noisy. Here a novel approach is proposed Hybrid K-Mean-Quick Reduct (KMQR) algorithm for gene selection from gene expression data. In this study, the entire dataset is divided into clusters by applying K-Means algorithm. Each cluster contains similar genes. The high class discriminated genes has been selected based on their degree of dependence by applying Quick Reduct algorithm to all the clusters. Average Correlation Value (ACV) is calculated for the high class discriminated genes. The clusters which have the ACV value as 1 is determined as significant clusters, whose classification accuracy will be equal or high when comparing to the accuracy of the entire dataset. The proposed algorithm is evaluated using WEKA classifiers and compared. The proposed work shows that the high classification accuracy.

Keywords: Clustering, Gene Selection, K-Mean-Quick Reduct, Rough Sets.

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Authors: A. Sarıkaya, A. Akın

Abstract:

This study was carried out to investigate effects of Control (C), 18 bud/vine, 23 bud/vine, 28 bud/vine, 18 bud/vine + TKI-Humas (soil), 23 bud/vine + TKI-Humas (soil), 28 bud/vine + TKI-Humas (soil) applications on yield and yield components of Alphonse Lavallee grape cultivar. The results were obtained as the highest cluster weight (302.31 g) with 18 bud/vine application; the highest berry weight (6.31 g) with 23 bud/vine + TKI-Humas (soil) and (6.79 g) with 28 bud/vine + TKI-Humas (soil) applications; the highest maturity index (36.95) with 18 bud/vine + TKI-Humas (soil) application; the highest L* color intensity (33.99) with 18 bud/vine + TKI-Humas (soil); the highest a* color intensity (1.53) with 23 bud/vine + TKI-Humas (soil) application. The effects of applications on grape fresh yield, grape juice yield and b* color intensity values were not found statistically significant.

Keywords: Alphonse Lavallee grape cultivar, crop load, TKI-Humas substances (soil), yield, quality.

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Authors: S. Sutin, M. Jaroensutasinee, K. Jaroensutasinee

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Water quality and freshwater fish diversity from nine waterfalls at Khao Luang National Park, Thailand was examined. Streams were shallow, fast flowing with clear water and rocky and sandy substrate. The mean water quality of waterfalls at Khao Luang National Park were as following pH 7.50, air temperature 24.27 °C, water temperature 26.37 °C, dissolved oxygen 7.88 mg/l, hardness 4.44-21.33 mg/l, alkalinity 3.55-11.88 mg/(as CaCO3). Twenty fish species were found at Khao Luang National Park belonging to nine families. A cluster analysis of water quality at Khao Luang National Park revealed that waterfalls at Khao Luang National Park were divided into two groups: A and B. Group A composed of two waterfalls (i.e. Aie Kaew and Wangmaipak) that flew to the Gulf of Thailand side. Group B composed of seven waterfalls (i.e. Promlok, Kalom, Nuafa, Suankun, Soidaw, Suanhai, and Thapae) that flew to the Andaman Sea side (Fig. 2) .The Cyprinids represented the major species in all the waterfalls comprising of 45%.

Keywords: Water quality, Freshwater fishes, National Park, Khao Luang, Thailand.

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Authors: Amir Sepasi Zahmati, Bahman Abolhassani, Ali Asghar Beheshti Shirazi, Ali Shojaee Bakhtiari

Abstract:

A wireless sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a novel hierarchical with static clustering routing protocol called Energy-Efficient Protocol with Static Clustering (EEPSC). EEPSC, partitions the network into static clusters, eliminates the overhead of dynamic clustering and utilizes temporary-cluster-heads to distribute the energy load among high-power sensor nodes; thus extends network lifetime. We have conducted simulation-based evaluations to compare the performance of EEPSC against Low-Energy Adaptive Clustering Hierarchy (LEACH). Our experiment results show that EEPSC outperforms LEACH in terms of network lifetime and power consumption minimization.

Keywords: Clustering methods, energy efficiency, routingprotocol, wireless sensor networks.

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