Search results for: Multidimensional Sequence Data

Authors: Rosaline Govender

Abstract:

This research study is an exploration of the selfdirected professional development of teachers who teach in public schools in an era of democracy and educational change in South Africa. Amidst an ever-changing educational system, the teachers in this study position themselves as self-directed teacher-learners where they adopt particular learning practices which enable change within the broader discourses of public schooling. Life-story interviews were used to enter into the private and public spaces of five teachers which offer glimpses of how particular systems shaped their identities, and how the meanings of self-directed teacher-learner shaped their learning practices. Through the Multidimensional Framework of Analysis and Interpretation the teachers’ stories were analysed through three lenses: restorying the field texts - the self through story; the teacher-learner in relation to social contexts, and practices of self-directed learning. This study shows that as teacherlearners learn for change through self-directed learning practices, they develop their agency as transformative intellectuals, which is necessary for the reworking of South African public schools.

Keywords: Professional development, professionality, professionalism, self-directed learning.

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Authors: Miloje S. Radenkovic, Tamal Bose

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This paper presents the convergence analysis of a prediction based blind equalizer for IIR channels. Predictor parameters are estimated by using the recursive least squares algorithm. It is shown that the prediction error converges almost surely (a.s.) toward a scalar multiple of the unknown input symbol sequence. It is also proved that the convergence rate of the parameter estimation error is of the same order as that in the iterated logarithm law.

Keywords: Adaptive blind equalizer, Recursive leastsquares, Adaptive Filtering, Convergence analysis.

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Authors: Sanaa Chafik, ImaneDaoudi, Mounim A. El Yacoubi, Hamid El Ouardi

Abstract:

Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbour search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect structure and query performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. In this paper, we propose a new hashing algorithm to overcome the storage space problem and improve query time, while keeping a good accuracy as similar to that achieved by the original Euclidean LSH. The Experimental results on a real large-scale dataset show that the proposed approach achieves good performances and consumes less memory than the Euclidean LSH.

Keywords: Approximate Nearest Neighbor Search, Content based image retrieval (CBIR), Curse of dimensionality, Locality sensitive hashing, Multidimensional indexing, Scalability.

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Authors: V. R. Asghari, M. Ardebilipour

Abstract:

In the context of spectrum surveillance, a method to recover the code of spread spectrum signal is presented, whereas the receiver has no knowledge of the transmitter-s spreading sequence. The approach is based on a genetic algorithm (GA), which is forced to model the received signal. Genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems. Experimental results show that the method provides a good estimation, even when the signal power is below the noise power.

Keywords: Code estimation, genetic algorithms, spread spectrum.

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Authors: Bremananth R, Radhika.V, Thenmozhi.S

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Sequences of execution of algorithms in an interactive manner using multimedia tools are employed in this paper. It helps to realize the concept of fundamentals of algorithms such as searching and sorting method in a simple manner. Visualization gains more attention than theoretical study and it is an easy way of learning process. We propose methods for finding runtime sequence of each algorithm in an interactive way and aims to overcome the drawbacks of the existing character systems. System illustrates each and every step clearly using text and animation. Comparisons of its time complexity have been carried out and results show that our approach provides better perceptive of algorithms.

Keywords: Algorithms, Searching, Sorting, Visualization.

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Authors: Cristina G. Dascâlu, Corina Dima Cozma, Elena Carmen Cotrutz

Abstract:

The medical data statistical analysis often requires the using of some special techniques, because of the particularities of these data. The principal components analysis and the data clustering are two statistical methods for data mining very useful in the medical field, the first one as a method to decrease the number of studied parameters, and the second one as a method to analyze the connections between diagnosis and the data about the patient-s condition. In this paper we investigate the implications obtained from a specific data analysis technique: the data clustering preceded by a selection of the most relevant parameters, made using the principal components analysis. Our assumption was that, using the principal components analysis before data clustering - in order to select and to classify only the most relevant parameters – the accuracy of clustering is improved, but the practical results showed the opposite fact: the clustering accuracy decreases, with a percentage approximately equal with the percentage of information loss reported by the principal components analysis.

Keywords: Data clustering, medical data, principal components analysis.

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Authors: Rui Wu, Tapani Ristaniemi

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In this paper, we propose a new algorithm for joint time-delay and direction-of-arrival (DOA) estimation, here called two-dimensional code acquisition, in an asynchronous directsequence code-division multiple-access (DS-CDMA) array system. This algorithm depends on eigenvector-eigenvalue decomposition of sample correlation matrix, and requires to know desired user-s training sequence. The performance of the algorithm is analyzed both analytically and numerically in uncorrelated and coherent multipath environment. Numerical examples show that the algorithm is robust with unknown number of coherent signals.

Keywords: Two-Dimensional Code Acquisition, EV-t, DSCDMA

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Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

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The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-Doppler features.

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Authors: Leong Lee, Cyriac Kandoth, Jennifer L. Leopold, Ronald L. Frank

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Protein 3D structure prediction has always been an important research area in bioinformatics. In particular, the prediction of secondary structure has been a well-studied research topic. Despite the recent breakthrough of combining multiple sequence alignment information and artificial intelligence algorithms to predict protein secondary structure, the Q3 accuracy of various computational prediction algorithms rarely has exceeded 75%. In a previous paper [1], this research team presented a rule-based method called RT-RICO (Relaxed Threshold Rule Induction from Coverings) to predict protein secondary structure. The average Q3 accuracy on the sample datasets using RT-RICO was 80.3%, an improvement over comparable computational methods. Although this demonstrated that RT-RICO might be a promising approach for predicting secondary structure, the algorithm-s computational complexity and program running time limited its use. Herein a parallelized implementation of a slightly modified RT-RICO approach is presented. This new version of the algorithm facilitated the testing of a much larger dataset of 396 protein domains [2]. Parallelized RTRICO achieved a Q3 score of 74.6%, which is higher than the consensus prediction accuracy of 72.9% that was achieved for the same test dataset by a combination of four secondary structure prediction methods [2].

Keywords: data mining, protein secondary structure prediction, parallelization.

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: Data Estimation, link data, machine learning, road network.

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Authors: Youkyung Park, SeungYup Kang, SungHo Kim, SimKyun Yook

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IMCS is Integrated Monitoring and Control System for thermal power plant. This system consists of mainly two parts; controllers and OIS (Operator Interface System). These two parts are connected by Ethernet-based communication. The controller side of communication is managed by CNet module and OIS side is managed by data server of OIS. CNet module sends the data of controller to data server and receives commend data from data server. To minimizes or balance the load of data server, this module buffers data created by controller at every cycle and send buffered data to data server on request of data server. For multiple data server, this module manages the connection line with each data server and response for each request from multiple data server. CNet module is included in each controller of redundant system. When controller fail-over happens on redundant system, this module can provide data of controller to data sever without loss. This paper presents three main features – separation of get task, usage of ring buffer and monitoring communication status –of CNet module to carry out these functions.

Keywords: Ethernet communication, DCS, power plant, ring buffer, data integrity

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Authors: F. Moghtaderi, M. Burke, J. Troelsen

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There has been a significant decline in active travel and a massive increase in the use of car dependent travel in many countries during the past two decades. Evidential risks for people’s physical and mental health problems are correlated with this increased use of motorized travel. These health related problems range from overweight and obesity to increased air pollution. In response to these rising concerns health professionals, traffic planers, local authorities and others have introduced a variety of initiatives to counterbalance the dominance of cars for daily journeys. However, the nature of travel behavior change interventions, which aim to reduce car use, are very complex and challenging regarding their interactions with human behavior. To change travel behavior at least two aspects have to be taken into consideration. First, how to alter attitudes and perceptions toward the sustainable and healthy modes of travel, in competition with experiences of private car use. And second, how to make these behavior change processes irreversible and sustainable. There are no comprehensive models available to guide policy interventions to increase the level of success of travel behavior change interventions across both these dimensions. A comprehensive theoretical framework is required in the effort to optimize how to facilitate and guide the processes of data collection and analysis to achieve the best possible guidelines for policy makers. Regarding the gaps in the travel behavior change research literature, this paper attempted to identify and suggest a multidimensional framework in order to facilitate planning the implemented travel behavior change interventions. A structured mixed-method model is suggested to improve the analytic power of the results according to the complexity of human behavior. In order to recognize people’s attitudes towards a specific travel mode, the Theory of Planned Behavior (TPB) was operationalized. But in order to capture decision making processes the Transtheoretical model of Behavior Change (TTM) was also used. Consequently, the combination of these two theories (TTM and TPB) has resulted in a synthesis with appropriate concepts to identify and design an implemented travel behavior change interventions.

Keywords: Behavior change theories, Theoretical framework, Travel behavior change interventions.

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Authors: A. Alexandru, C. A. Alexandru, D. Coardos, E. Tudora

Abstract:

Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented.

Keywords: Big data, big data Analytics, Hadoop framework, cloud computing.

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Authors: V. Hepsiba Mabel, K. Alagarsamy, Justus S.

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Chord formation in western music notations is an intelligent art form which is learnt over the years by a musician to acquire it. Still it is a question of creativity that brings the perfect chord sequence that matches music score. This work focuses on the process of forming chords using a custom-designed knowledgebase (KB) of Music Expert System. An optimal Chord-Set for a given music score is arrived by using the chord-pool in the KB and the finding the chord match using Jusic Distance (JD). Conceptual Graph based knowledge representation model is followed for knowledge storage and retrieval in the knowledgebase.

Keywords: Knowledge, Music, Representation, Knowledgebase, Chord-Set.

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Authors: Alvaro Rodriguez, Maria Bermudez, Juan R. Rabuñal, Jeronimo Puertas

Abstract:

One of the main issues in Computer Vision is to extract the movement of one or several points or objects of interest in an image or video sequence to conduct any kind of study or control process. Different techniques to solve this problem have been applied in numerous areas such as surveillance systems, analysis of traffic, motion capture, image compression, navigation systems and others, where the specific characteristics of each scenario determine the approximation to the problem. This paper puts forward a Computer Vision based algorithm to analyze fish trajectories in high turbulence conditions in artificial structures called vertical slot fishways, designed to allow the upstream migration of fish through obstructions in rivers. The suggested algorithm calculates the position of the fish at every instant starting from images recorded with a camera and using neural networks to execute fish detection on images. Different laboratory tests have been carried out in a full scale fishway model and with living fishes, allowing the reconstruction of the fish trajectory and the measurement of velocities and accelerations of the fish. These data can provide useful information to design more effective vertical slot fishways.

Keywords: Computer Vision, Neural Network, Fishway, Fish Trajectory, Tracking

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Authors: Tomy Prihananto, Damar Apri Sudarmadi

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Indonesia recognizes the right to privacy as a human right. Indonesia provides legal protection against data management activities because the protection of personal data is a part of human rights. This paper aims to describe the arrangement of data management and data management in Indonesia. This paper is a descriptive research with qualitative approach and collecting data from literature study. Results of this paper are comprehensive arrangement of data that have been set up as a technical requirement of data protection by encryption methods. Arrangements on encryption and protection of personal data are mutually reinforcing arrangements in the protection of personal data. Indonesia has two important and immediately enacted laws that provide protection for the privacy of information that is part of human rights.

Keywords: Indonesia, protection, personal data, privacy, human rights, encryption.

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Authors: I. Habi, M. Bouguerra, D. Ouahdi, H. Meglouli

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In this paper, we apply the PQ theory with shunt active power filter in an unbalanced and distorted power system voltage to compensate the perturbations generated by non linear load. The power factor is also improved in the current source. The PLL system is used to extract the fundamental component of the even sequence under conditions mentioned of the power system voltage.

Keywords: Converter, power filter, harmonies, non-linear load, pq theory, PLL, unbalanced voltages, distorted voltages.

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Authors: Vernie C. Convicto, Henry P. Aringa, Wilson I. Barredo

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This study presents a conformational model of the helical structures of globular protein particularly ferritin in the framework of white noise path integral formulation by using Associated Legendre functions, Bessel and convolution of Bessel and trigonometric functions as modulating functions. The model incorporates chirality features of proteins and their helix-turn-helix sequence structural motif.

Keywords: Globular protein, modulating function, white noise, winding probability.

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Authors: X. Gumara Rigol, I. Martínez de Apellaniz Anzuola, A. Garcia Serrano, A. Franzi Cros, O. Vidal Calbet, A. Al Maruf

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Traditional federated architectures for data warehousing work well when corporations have existing regional data warehouses and there is a need to aggregate data at a global level. Schibsted Media Group has been maturing from a decentralised organisation into a more globalised one and needed to build both some of the regional data warehouses for some brands at the same time as the global one. In this paper, we present the architectural alternatives studied and why a custom federated approach was the notable recommendation to go further with the implementation. Although the data warehouses are logically federated, the implementation uses a single database system which presented many advantages like: cost reduction and improved data access to global users allowing consumers of the data to have a common data model for detailed analysis across different geographies and a flexible layer for local specific needs in the same place.

Keywords: Data integration, data warehousing, federated architecture, online analytical processing.

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Authors: Martin Lnenicka

Abstract:

Governments collect and produce large amounts of data. Increasingly, governments worldwide have started to implement open data initiatives and also launch open data portals to enable the release of these data in open and reusable formats. Therefore, a large number of open data repositories, catalogues and portals have been emerging in the world. The greater availability of interoperable and linkable open government data catalyzes secondary use of such data, so they can be used for building useful applications which leverage their value, allow insight, provide access to government services, and support transparency. The efficient development of successful open data portals makes it necessary to evaluate them systematic, in order to understand them better and assess the various types of value they generate, and identify the required improvements for increasing this value. Thus, the attention of this paper is directed particularly to the field of open data portals. The main aim of this paper is to compare the selected open data portals on the national level using content analysis and propose a new evaluation framework, which further improves the quality of these portals. It also establishes a set of considerations for involving businesses and citizens to create eservices and applications that leverage on the datasets available from these portals.

Keywords: Big data, content analysis, criteria comparison, data quality, open data, open data portals, public sector.

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Authors: Abhijit Mitra

Abstract:

The paper provides an in-depth tutorial of mathematical construction of maximal length sequences (m-sequences) via primitive polynomials and how to map the same when implemented in shift registers. It is equally important to check whether a polynomial is primitive or not so as to get proper m-sequences. A fast method to identify primitive polynomials over binary fields is proposed where the complexity is considerably less in comparison with the standard procedures for the same purpose.

Keywords: Finite field, irreducible polynomial, primitive polynomial, maximal length sequence, additive shift register, multiplicative shift register.

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Authors: S. Madhavi, S. Abirami, C. Bharathi, B. Ekambaram, T. Krishna Sankar, A. Nattudurai, N. Vijayarangan

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The high utilization rate of Automated Teller Machine (ATM) has inevitably caused the phenomena of waiting for a long time in the queue. This in turn has increased the out of stock situations. The ATM utilization helps to determine the usage level and states the necessity of the ATM based on the utilization of the ATM system. The time in which the ATM used more frequently (peak time) and based on the predicted solution the necessary actions are taken by the bank management. The analysis can be done by using the concept of Data Mining and the major part are analyzed based on the predictive data mining. The results are predicted from the historical data (past data) and track the relevant solution which is required. Weka tool is used for the analysis of data based on predictive data mining.

Keywords: ATM, Bank Management, Data Mining, Historical data, Predictive Data Mining, Weka tool.

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Authors: Youngpo Lee, Seokho Yoon

Abstract:

In this paper, we propose a distance estimation scheme for radar systems using direct sequence ultra wideband (DS-UWB) signals. The proposed distance estimation scheme averages out the noise by accumulating the correlator outputs of the radar, and thus, helps the radar to employ a short-length DS-UWB signal reducing the correlation processing time. Numerical results confirm that the proposed distance estimation scheme provides a better estimation performance and a reduced correlation processing time compared with those of the conventional DS-UWB radars.

Keywords: Radar, DS-UWB, distance estimation, correlation accumulation.

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Authors: N. Fujisaka

Abstract:

This sociological case study analyzes a sequence of student maltreatment in an elementary school in Japan, based on narratives from former students. Among various forms of student maltreatment, emotional maltreatment has received less attention. One reason for this is that emotional maltreatment is often considered part of education and is difficult to capture in surveys. To discuss the challenge of recognizing emotional maltreatment, it is necessary to consider the social background in which student maltreatment occurs. Therefore, from the perspective of the sociology of education, this study aims to clarify the process through which emotional maltreatment was embraced by students within a Japanese classroom. The research employs retrospective narrative data collected through interviews and autoethnography. The research results imply a pattern of emotional maltreatment that is challenging to differentiate from education. The finding is analyzed and discussed in conjunction with the cycle of violence theory and the deschooling theory. The cycle of violence theory explains how violence in a specific relationship can be tolerated. The deschooling theory provides a sociological explanation for how emotional maltreatment can be overlooked in society. Analyzing the case in association with these two theories highlights the characteristics of teachers’ behaviors that rationalize maltreatment as education and hinder students from escaping emotional maltreatment. This study deepens our understanding of the causes of student maltreatment and provides a perspective for future qualitative and quantitative research.

Keywords: Emotional maltreatment, education, student maltreatment, Japan.

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Authors: Jaechun No

Abstract:

As data to be stored in storage subsystems tremendously increases, data protection techniques have become more important than ever, to provide data availability and reliability. In this paper, we present the file system-based data protection (WOWSnap) that has been implemented using WORM (Write-Once-Read-Many) scheme. In the WOWSnap, once WORM files have been created, only the privileged read requests to them are allowed to protect data against any intentional/accidental intrusions. Furthermore, all WORM files are related to their protection cycle that is a time period during which WORM files should securely be protected. Once their protection cycle is expired, the WORM files are automatically moved to the general-purpose data section without any user interference. This prevents the WORM data section from being consumed by unnecessary files. We evaluated the performance of WOWSnap on Linux cluster.

Keywords: Data protection, Protection cycle, WORM

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Authors: Pavel Vazan, Pavol Tanuska, Michal Kebisek

Abstract:

The paper gives the pilot results of the project that is oriented on the use of data mining techniques and knowledge discoveries from production systems through them. They have been used in the management of these systems. The simulation models of manufacturing systems have been developed to obtain the necessary data about production. The authors have developed the way of storing data obtained from the simulation models in the data warehouse. Data mining model has been created by using specific methods and selected techniques for defined problems of production system management. The new knowledge has been applied to production management system. Gained knowledge has been tested on simulation models of the production system. An important benefit of the project has been proposal of the new methodology. This methodology is focused on data mining from the databases that store operational data about the production process.

Keywords: data mining, data warehousing, management of production system, simulation

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Authors: Yan Ren, Jun Qiao, Xianxia Liu, Pengyan Wang, Qiang Fu, Huijun Shi, Fei Guo, Yuanzhi Wang, Hui Zhang, Jinliang Sheng, Xinli Gu, Xiao-Jun Liu, Chuangfu Chen

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As part of national epidemiological survey on bovine viral diarrhea virus (BVDV), a total of 274 dejecta samples were collected from 14 cattle farms in 8 areas of Xinjiang Uygur Autonomous Region in northwestern China. Total RNA was extracted from each sample, and 5--untranslated region (UTR) of BVDV genome was amplified by using two-step reverse transcriptase-polymerase chain reaction (RT-PCR). The PCR products were subsequently sequenced to study the genetic variations of BVDV in these areas. Among the 274 samples, 33 samples were found virus-positive. According to sequence analysis of the PCR products, the 33 samples could be arranged into 16 groups. All the sequences, however, were highly conserved with BVDV Osloss strains. The virus possessed theses sequences belonged to BVDV-1b subtype by phylogenetic analysis. Based on these data, we established a typing tree for BVDV in these areas. Our results suggested that BVDV-1b was a predominant subgenotype in northwestern China and no correlation between the genetic and geographical distances could be observed above the farm level.

Keywords: bovine viral diarrhea virus, molecular epidemiology, phylogenetic analysis.

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Authors: S. Sarumathi, N. Shanthi, S. Vidhya, M. Sharmila

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There have been a lot of efforts and researches undertaken in developing efficient tools for performing several tasks in data mining. Due to the massive amount of information embedded in huge data warehouses maintained in several domains, the extraction of meaningful pattern is no longer feasible. This issue turns to be more obligatory for developing several tools in data mining. Furthermore the major aspire of data mining software is to build a resourceful predictive or descriptive model for handling large amount of information more efficiently and user friendly. Data mining mainly contracts with excessive collection of data that inflicts huge rigorous computational constraints. These out coming challenges lead to the emergence of powerful data mining technologies. In this survey a diverse collection of data mining tools are exemplified and also contrasted with the salient features and performance behavior of each tool.

Keywords: Business Analytics, Data Mining, Data Analysis, Machine Learning, Text Mining, Predictive Analytics, Visualization.

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Authors: Dennis A. Apuan

Abstract:

Categorical data based on description of the agricultural landscape imposed some mathematical and analytical limitations. This problem however can be overcome by data transformation through coding scheme and the use of non-parametric multivariate approach. The present study describes data transformation from qualitative to numerical descriptors. In a collection of 103 random soil samples over a 60 hectare field, categorical data were obtained from the following variables: levels of nitrogen, phosphorus, potassium, pH, hue, chroma, value and data on topography, vegetation type, and the presence of rocks. Categorical data were coded, and Spearman-s rho correlation was then calculated using PAST software ver. 1.78 in which Principal Component Analysis was based. Results revealed successful data transformation, generating 1030 quantitative descriptors. Visualization based on the new set of descriptors showed clear differences among sites, and amount of variation was successfully measured. Possible applications of data transformation are discussed.

Keywords: data transformation, numerical descriptors, principalcomponent analysis

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Authors: Ali Salem Bin Samma, Rosalina Abdul Salam

Abstract:

Representation and description of object shapes by the slopes of their contours or borders are proposed. The idea is to capture the essence of the features that make it easier for a shape to be stored, transmitted, compared and recognized. These features must be independent of translation, rotation and scaling of the shape. A approach is proposed to obtain high performance, efficiency and to merge the boundaries into sequence of straight line segments with the fewest possible segments. Evaluation on the performance of the proposed method is based on its comparison with established method of object shape description.

Keywords: Shape description, Shape representation and Slope.

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