Search results for: cluster centroids

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

Abstract:

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

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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

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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

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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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Authors: Md. Emaj Uddin

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This study compared socio-economic status attainment between the Muslim and Santal couples in rural Bangladesh. For this we hypothesized that socio-economic status attainment (occupation, education and income) of the Muslim couples was higher than the Santal ones in rural Bangladesh. In order to examine the hypothesis 288 couples (145 couples for Muslim and 143 couples for Santal) selected by cluster random sampling from Kalna village, Bangladesh were individually interviewed with semistructured questionnaire method. The results of Pearson Chi-Squire test suggest that there were significant differences in socio-economic status attainment between the two communities- couples. In addition, Pearson correlation coefficients also suggest that there were significant associations between the socio-economic statuses attained by the two communities- couples in rural Bangladesh. Further crosscultural study should conduct on how inter-community relations in rural social structure of Bangladesh influence the differences among the couples- socio-economic status attainment

Keywords: Bangladesh, Couple, Cross-Cultural Comparison, Muslim, Socio-Economic Status Attainment, Santal.

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Authors: Ajay Shekhar Pandey, D. Singh, S. K. Sinha

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A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and Fuzzy Inference Neural Network (FINN) for the data of the same system, for same time period. The fuzzy inference system has the network structure and the training procedure of a neural network which initially creates a rule base from existing historical load data. It is observed that the proposed clustering based model is giving better forecasting accuracy as compared to the other two methods. Test results also indicate that the RBFNN can forecast future loads with accuracy comparable to that of proposed method, where as the training time required in the case of FINN is much less.

Keywords: Load forecasting, clustering, fuzzy inference.

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Authors: Jagannath Aryal, Don Kulasiri, Dishi Liu

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This paper describes the optimization of a complex dairy farm simulation model using two quite different methods of optimization, the Genetic algorithm (GA) and the Lipschitz Branch-and-Bound (LBB) algorithm. These techniques have been used to improve an agricultural system model developed by Dexcel Limited, New Zealand, which describes a detailed representation of pastoral dairying scenarios and contains an 8-dimensional parameter space. The model incorporates the sub-models of pasture growth and animal metabolism, which are themselves complex in many cases. Each evaluation of the objective function, a composite 'Farm Performance Index (FPI)', requires simulation of at least a one-year period of farm operation with a daily time-step, and is therefore computationally expensive. The problem of visualization of the objective function (response surface) in high-dimensional spaces is also considered in the context of the farm optimization problem. Adaptations of the sammon mapping and parallel coordinates visualization are described which help visualize some important properties of the model-s output topography. From this study, it is found that GA requires fewer function evaluations in optimization than the LBB algorithm.

Keywords: Genetic Algorithm, Linux Cluster, LipschitzBranch-and-Bound, Optimization

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Authors: Takao Sakakura, Kazunori Fujimoto

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This paper proposes an analytical method for the dynamics of generating firms- alliance networks along with business phases. Dynamics in network developments have previously been discussed in the research areas of organizational strategy rather than in the areas of regional cluster, where the static properties of the networks are often discussed. The analytical method introduces the concept of business phases into innovation processes and uses relationships called prior experiences; this idea was developed in organizational strategy to investigate the state of networks from the viewpoints of tradeoffs between link stabilization and node exploration. This paper also discusses the results of the analytical method using five cases of the network developments of firms. The idea of Embeddedness helps interpret the backgrounds of the analytical results. The analytical method is useful for policymakers of regional clusters to establish concrete evaluation targets and a viewpoint for comparisons of policy programs.

Keywords: Regional Clusters, Alliance Networks, Innovation Processes, Prior Experiences, Embeddedness.

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Authors: Mohammad Akbarpour Sekeh, Mohd. Aizaini Maarof, Mohd. Foad Rohani, Malihe Motiei

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Duplicated region detection is a technical method to expose copy-paste forgeries on digital images. Copy-paste is one of the common types of forgeries to clone portion of an image in order to conceal or duplicate special object. In this type of forgery detection, extracting robust block feature and also high time complexity of matching step are two main open problems. This paper concentrates on computational time and proposes a local block matching algorithm based on block clustering to enhance time complexity. Time complexity of the proposed algorithm is formulated and effects of two parameter, block size and number of cluster, on efficiency of this algorithm are considered. The experimental results and mathematical analysis demonstrate this algorithm is more costeffective than lexicographically algorithms in time complexity issue when the image is complex.

Keywords: Copy-paste forgery detection, Duplicated region, Timecomplexity, Local block matching, Sequential block clustering.

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Authors: Mohamad Saleh Sangin Ostadi, Esmail Safari, Somayeh Akbari, Kaveh Qaderi Bagajan

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The aim of this study is to compare abused and normal male students in Tehran guidance schools with emphasis on the co-dependency of their mothers. The method of this study is based on survey method and comparison (Ex-Post Facto). The method of sampling is also multi-stage cluster. Accordingly, we did sampling from secondary schools of education and training in Tehran, including 12 schools with levels of first, second and third. Each of the schools represents the three – high, medium and low- economic and social conditions. In the following, three classes from every school and 20 students from each class were randomly selected. By (CTQ) abused and normal students were separated that 670 children were recognized as normal and 50 children as abused. Then, 50 children were randomly selected from normal group and compared with abused group. Using Spanned-Fischer Co-dependency Scale, we compared mothers of abused and normal students. The results showed that mothers of the abused children have higher co- dependency average comparing to the mothers of the normal children.

Keywords: Co-dependency, child abuse, abused children, parental psychological health.

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Authors: Pantip Kayee

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17α-ethinylestradiol (EE₂) is a recalcitrant micropollutant which is found in small amounts in municipal wastewater. But these small amounts still adversely affect for the reproductive function of aquatic organisms. Evidence in the past suggested that full-scale WWTPs equipped with nitrification process enhanced the removal of EE₂ in the municipal wastewater. EE₂ has been proven to be able to be transformed by ammonia oxidizing bacteria (AOB) via co-metabolism. This research aims to clarify the EE₂ degradation pattern by different consortium of ammonia oxidizing microorganism (AOM) including AOA (ammonia oxidizing archaea) and investigate contribution between the existing ammonia monooxygenase (AMO) and new synthesized AOM. The result showed that AOA or AOB of N. oligotropha cluster in enriched nitrifying activated sludge (NAS) from 2mM and 5mM, commonly found in municipal WWTPs, could degrade EE₂ in wastewater via co-metabolism. Moreover, the investigation of the contribution between the existing ammonia monooxygenase (AMO) and new synthesized AOM demonstrated that the new synthesized AMO enzyme may perform ammonia oxidation rather than the existing AMO enzyme or the existing AMO enzyme may has a small amount to oxidize ammonia.

Keywords: 17α-ethinylestradiol, nitrification, ammonia oxidizing bacteria, ammonia oxidizing archaea.

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

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Data clustering is an important data exploration technique with many applications in data mining. We present an enhanced version of the well known single link clustering algorithm. We will refer to this algorithm as DCBOR. The proposed algorithm alleviates the chain effect by removing the outliers from the given dataset. So this algorithm provides outlier detection and data clustering simultaneously. This algorithm does not need to update the distance matrix, since the algorithm depends on merging the most k-nearest objects in one step and the cluster continues grow as long as possible under specified condition. So the algorithm consists of two phases; at the first phase, it removes the outliers from the input dataset. At the second phase, it performs the clustering process. This algorithm discovers clusters of different shapes, sizes, densities and requires only one input parameter; this parameter represents a threshold for outlier points. The value of the input parameter is ranging from 0 to 1. The algorithm supports the user in determining an appropriate value for it. We have tested this algorithm on different datasets contain outlier and connecting clusters by chain of density points, and the algorithm discovers the correct clusters. The results of our experiments demonstrate the effectiveness and the efficiency of DCBOR.

Keywords: Data Clustering, Clustering Algorithms, Handling Noise, Arbitrary Shape of Clusters.

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Authors: Josip Visković, Josip Arnerić, Ante Rozga

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Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modeling time varying volatility are GARCH type models. When financial returns exhibit sudden jumps that are due to structural breaks, standard GARCH models show high volatility persistence, i.e. integrated behavior of the conditional variance. In such situations models in which the parameters are allowed to change over time are more appropriate. This paper compares different GARCH models in terms of their ability to describe structural changes in returns caused by financial crisis at stock markets of six selected central and east European countries. The empirical analysis demonstrates that Markov regime switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility when sudden switching occurs in response to financial crisis.

Keywords: Central and east European countries, financial crisis, Markov switching GARCH model, transition probabilities.

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Authors: Hmway Hmway Tar, Thi Thi Soe Nyaunt

Abstract:

Documents clustering become an essential technology with the popularity of the Internet. That also means that fast and high-quality document clustering technique play core topics. Text clustering or shortly clustering is about discovering semantically related groups in an unstructured collection of documents. Clustering has been very popular for a long time because it provides unique ways of digesting and generalizing large amounts of information. One of the issues of clustering is to extract proper feature (concept) of a problem domain. The existing clustering technology mainly focuses on term weight calculation. To achieve more accurate document clustering, more informative features including concept weight are important. Feature Selection is important for clustering process because some of the irrelevant or redundant feature may misguide the clustering results. To counteract this issue, the proposed system presents the concept weight for text clustering system developed based on a k-means algorithm in accordance with the principles of ontology so that the important of words of a cluster can be identified by the weight values. To a certain extent, it has resolved the semantic problem in specific areas.

Keywords: Clustering, Concept Weight, Document clustering, Feature Selection, Ontology

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Authors: Abder-Rahman Ali, Antoine Vacavant, Manuel Grand-Brochier, Adélaïde Albouy-Kissi, Jean-Yves Boire

Abstract:

In this paper, we present a new segmentation approach for liver lesions in regions of interest within MRI (Magnetic Resonance Imaging). This approach, based on a two-cluster Fuzzy CMeans methodology, considers the parameter variable compactness to handle uncertainty. Fine boundaries are detected by a local recursive merging of ambiguous pixels with a sequential forward floating selection with Zernike moments. The method has been tested on both synthetic and real images. When applied on synthetic images, the proposed approach provides good performance, segmentations obtained are accurate, their shape is consistent with the ground truth, and the extracted information is reliable. The results obtained on MR images confirm such observations. Our approach allows, even for difficult cases of MR images, to extract a segmentation with good performance in terms of accuracy and shape, which implies that the geometry of the tumor is preserved for further clinical activities (such as automatic extraction of pharmaco-kinetics properties, lesion characterization, etc.).

Keywords: Defuzzification, floating search, fuzzy clustering, Zernike moments.

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