Total Number Of Data Research Articles

지역 특산물을 활용한 관광자원화 연구

Food is considered one of the important factors among many tourist resources as it creates an image of tourism and induces tourists to visit again who are satisfied with the food. In terms of tourism, the importance of food experiences and visits to restaurants is increasing. Therefore, this study selected Jeju Island, Korea's leading tourist destination, as the research site. The purpose of the study was to explore the determinants of Jeju seafood's willingness to pay, noting that although Jeju Island's seafood was an important tourist resource, research conducted in this area was somewhat insufficient. The candidates to account for willingness to pay are price fairness, amount, poor sanitation, and healthiness. This study executed data collection for Koreans at Jeju Airport for 3 days from November 25 to 27, 2019. The total number of data used in the analysis is 252. Research hypotheses were tested using multiple linear regression. The results appears that willingness to pay is positively influenced by price fairness, amount, and healthiness. However, willingness to pay is negatively affected by poor sanitation. In terms of the control variables, consumers with higher monthly household income have higher willingness to pay for seafood. This study investigated the willingness of tourists to pay for seafood in Jeju Island to turn local specialties into tourism resources. Given the results, this research presents the implication to enhance the quality of Jeju seafood to develop tourism resources.

SVM ALGORITHM FOR PREDICTING RICE YIELDS

Agriculture in Indonesia is highly dependent on reservoir irrigation water sources and rain. Because some agricultural land in Indonesia is rainfed. Plants in Indonesia rely on water from rain and irrigation. Weather conditions greatly affect the number of farmers' harvest. Farmers often experience crop failures due to changing weather. From data from the Central Statistics Agency, it is stated that the number of rice yields in 2019 decreased by 7.76% compared to 2018. In order to avoid rice imports and rice food shortages, a breakthrough is needed that can help the government in making policies. One of the breakthroughs is creating a rice yield prediction system. The research process consisted of collecting data via the web: https://www.pertanian.go.id/home/?show=page&act=view&id=61. The data shows the variables of province, year, land area, production. The total number of data is 170 rows, with a division of 130 lines for training, and 40 for testing. Furthermore, the data is processed and processed and normalized. The results of data processing are then trained and predicted with a linear SVM kernel. The results of SVM prediction with original data without normalization of MAPE 6635.53%, and RMSE 1094810.74. The results of SVM prediction with normalized data first, the MAPE value was 9427.714%, and RMSE 0.017.

PENGIDENTIFIKASIAN SEGMENTASI PENGGUNA SISTEM MANAJEMEN PEMBELAJARAN SEBUAH UNIVERSITAS DENGAN METODE TWO-STEP CLUSTERING

This study aims to identify user segmentations of a learning management system at Universitas Negeri Malang using two-step clustering method. Data is collected through an on-line survey which is linked onto the academic information systems. Total number of data are 10.594 responses. This study runs three clustering methods, two-step, k-means, and kohonen clustering, in order to identify the user segmentation. Two-step clustering methods performs better than the other two clustering methods (k-means and kohonen) based on silhouette clustering index. The number of cluster resulted from two-step clustering method are three clusters. The top three of most important factors in identifying the user segmentation are the benefit of the learning management usage in helping to access information, increasing the effectiveness of accomplishing assignments, and students satisfaction on the learning management system services.

The Forms and Meanings of American Slangs Used inTED 2 Movie Scripts

The title of this paper is “ The Forms and Meanings of American Slang Use in ”TED 2” Movie Script. The aims of this study are to know and describe the forms and types of slang words and phrases used by the characters in TED 2 movie script, and to analyze the meanings of slangs found in TED 2 movie script. The data were collected from the TED 2 movie scripts. The data were collected through internet source. The data was analyzed by descriptive method. The theory used to analyze the problem was proposed by chapman (1998) about the types and forms of slang, while for the meaning of slang the theory used was proposed by Lyons (1981). Theresult of data analysis were presented with descriptive method. The findings of the research found that, there are two kind of slangs in the data, there are primary slang and secondary slang. The total number of data of primary slangs was found are twenty one, while for the secondary slangs they are found nineteen. As to the meaning of slang, there are two kind of meaning found in the data, they are literal meaning and figurative meaning.

A simple method for classifying data in relative growth analysis of crustaceans

Abstract Relative growth in crustaceans is a topic of quite high importance that allows, among other things, to estimate their size at the onset of morphometric sexual maturity. In this process, it is usual to start from a transition point and then generate regressions that reflect phases of the sexual development of the individuals (i.e., mature and immature). There are several statistical methods that allow data to be classified into subsets, but according to the specific growth pattern at issue, those methods may or may not be usable. This paper proposes a simple method to classify data into two subsets, viz., when they are overlapping over a wide range of sizes. The actual procedure consists of a linear regression that divides the data. Subsequently, a linear regression is applied to the groups generated by adjusting the parameters through maximum log-likelihood. The observed values will be classified according to the smallest residual difference generated with each regression line. The proposed method was tested by separating the sexes of Goyazana castelnaui, using real data. The efficiency of the method was analysed based on the percentage value between the number of total data and the number of correctly classified data. Additionally, the k-mean cluster was used as a conventional method, the results of which were reclassified by a linear discriminant analysis. The efficiency of the proposed method was >80% while that of the conventional method was >60%. The values misclassified by the proposed method were mixed with those of the opposite sex, so it was expected to fail in those cases. The proposed method is a simple alternative that can serve as a basis for subsequent morphometric analysis, especially for acquiring an initial insight in the structure of a dataset collected for a study of relative growth.

PHONOLOGY AND MORPHOLOGY INTERFERENCES IN BUKALAPAK ADVERTISEMENT JANUARI-JULY 2017 EDITION

This research is aimed at explaining the form of language interferences in bukalapak advertisement edisi during January - July 2017. This research used descriptive qualitative method through sociolinguistics approach. The object of the research were the utterances in the Bukalapak advertisement which display by official youtube during Januari up to July 2017. The total number of data used was 131 data. Those data were collected through recording and notetaking. Based on the data analysis, it is found that there is interferences in the Bukalapak advertisement which display by official youtube during Januari up to July 2017. The phonology interferences is divided into two types namely deletion of the sound and addition of the sound. While the morphology interference is the errors in affixation on some Indonesian words.

중국어 신어(新語)의 조어(造語) 형태에 대한 고찰 : 『現代漢語詞典(第7版)』의 신어를 중심으로

This study is about the neologism, which were newly included in Xiandai Hanyu cidian(7th edition)[Modern Chinese Dictionary]. It focuses on grasping the recent tendency of Mandarin new words by analyzing the ways of word-formation. The number of total data is 317, which can be classified into several types of words depending on the standards, such as syllable, part of speech, and combination of morphemes. According to the results of the study, various syllabic Mandarin words have been made from 2 syllables to 9 syllables, but still in Mandarin new words, disyllabic words have been formed most actively. Most of the new words are verbs and nouns. It is very interesting that many separable words, which are so called ‘liheci,’ appear in the data. Based on the ways of combing morphemes in words, the data can be divided into 5 types, such as mono-morphemic word, compound, derivative, abbreviation, and phrase. The most of the data are compounds and phrases, and they usually show modifying structures. It is interesting that there is no mono-syllabic word in the mono-morphemic words. Prefixes are used more often than suffixes in the derivatives. Various types of abbreviations such as acronym, blending, and clipping are shown in the data.

Achieving Efficient and Privacy-Preserving k-NN Query for Outsourced eHealthcare Data.

The boom of Internet of Things devices promotes huge volumes of eHealthcare data will be collected and aggregated at eHealthcare provider. With the help of these health data, eHealthcare provider can offer reliable data service (e.g., k-NN query) to doctors for better diagnosis. However, the IT facility in the eHealthcare provider is incompetent with the huge volumes of eHealthcare data, so one popular solution is to deploy a powerful cloud and appoint the cloud to execute the k-NN query service. In this case, since the eHealthcare data are very sensitive yet cloud servers are not fully trusted, directly executing the k-NN query service in the cloud inevitably incurs privacy challenges. Apart from the privacy issues, efficiency issues also need to be taken into consideration because achieving privacy requirement will incur additional computational cost. However, existing focuses on k-NN query do not (fully) consider the data privacy or are inefficient. For instance, the best computational complexity of k-NN query over encrypted eHealthcare data in the cloud is as large as [Formula: see text], where N is the total number of data. In this paper, aiming at addressing the privacy and efficiency challenges, we design an efficient and privacy-preserving k-NN query scheme for encrypted outsourced eHealthcare data. Our proposed scheme is characterized by integrating the k d-tree with the homomorphic encryption technique for efficient storing encrypted data in the cloud and processing privacy-preserving k-NN query over encrypted data. Compared with existing works, our proposed scheme is more efficient in terms of privacy-preserving k-NN query. Specifically, our proposed scheme can achieve k-NN computation over encrypted data with [Formula: see text] computational complexity, where l and N respectively denote the data dimension and the total number of data. In addition, detailed security analysis shows that our proposed scheme is really privacy-preserving under our security model and performance evaluation also indicates that our proposed scheme is indeed efficient in terms of computational cost.

The The Influence of Class Shift on Achieving Semantic Meaning in the English-Indonesian Translation of Yule’s Pragmatics

The important of translation can be seen in our daily life. Translation can be found in many aspects of our life such as books, newspaper, magazines, novel, comics, even movies. One important aspect in translation study is class shift. Class shift deals with the changing of word classes in source language to the target language. Hence one language and others are different, this study aimed to know the influence of class shift on achieving semantic meaning in the English-Indonesian translation of Yule’s pragmatics. Qualitative research is the type of this research and descriptive qualitative as research design. There are three chapters of the book which are chosen randomly as the data on this study. The total number of data is 98 from those three chapters. The results of the study show that there are seven types of class shift which are found in this study such as adjective to adverb, adjective to noun, adjective to verb, adverb to adjective, adverb to noun, adverb to verb, and verb to adjective. There are two types of class shift which often appear in this study. Those are adjective to noun and adverb to adjective. Class shift can be occurred anytime. The occurrence of class shifts does not guarantee anything in achieving meaning of a sentence.

Sexual Dimorphism Prediction of Darevskia bithynica (Méhely 1909) from Northwestern Anatolia, Turkey by Using Artificial Neural Network

The main aim of the manuscript is to predict the gender of Darevskia bithynica by using a feed-forward back-propagation multilayer artificial neural network (ANN). Nine morphological characters were used as an input parameters of the model. The gender type male or female is the output parameter. The total number of data is 115. In order to train, validate and test the ANN model 70%, 15% and 15% of the total data are randomly selected. The regression coefficient (R) values are evaluated as prediction performance. The network with 20 neurons in the hidden is the optimal structure of the ANN that predict the lizard gender with a high R values as 0.98, 0.97 and 0.96 for training, testing and all data, respectively. The lower mean square error (MSE) values for training and testing data are calculated as 0.0145 and 0.0161, respectively. The obtained results satisfactorily confirm the high ability of the ANNs in predicting the gender of Darevskia bithynica.

Minimax Normal Two-Armed Bandit with Indefinite Control Horizon

We consider the two-armed bandit problem as applied to data processing if there are two alternative processing methods available with different a priori unknown efficiencies. On should determine the most effective method and provide its dominating application. The total number of data, which is interpreted as a control horizon, is assumed to have a priori known probability distribution.The problem is considered in minimax (robust) setting. According to the main theorem of the theory of games minimax risk and minimax strategy are sought for as Bayesian ones corresponding to the worst-case prior distribution. We describe the properties of the worst-case prior and present a recursive Bellman-type equation for determination of both minimax strategy and minimax risk. Numerical results illustrating the proposed algorithm are given. The algorithm can be applied to optimization of parallel data processing if the number of processed data is not definitely known in advance.

Functions of Indirectness in American Idol Judges� Comments

Indirectness is one of the most efficient ways to communicate each other. People useindirectness in communication for different purposes. The most discussed and discovered purposeacross cultures is for politeness; people use language in indirect way to prevent hurting othersfeelings. In many cultures, indirectness is highly valued because saving face and harmony in socialrelationships are highly respected. This study is a descriptive study which is aimed at analyzing thefunctions or the purpose of using indirectness or indirect language in one of the rarely discussedrealm of language use, a TV show, specifically, American Idol. The findings also reveal the strategiesused in order to perform the functions. The data is limited to the audition of American Idol seasoneight (8) which was held in 2009. There were 7 recordings for the 8 cities of audition. The totalnumber of data used in this study was 50 indirect comments. The result shows that there were 3functions of the use of indirectness in the comments of the judges. The functions were (i) being ironic,(ii) being polite, and (iii) giving hints.Keywords: indirectness, politeness, judges comments

Performance Evaluation of Line Symmetry-Based Validity Indices on Clustering Algorithms

AbstractFinding the optimal number of clusters and the appropriate partitioning of the given dataset are the two major challenges while dealing with clustering. For both of these, cluster validity indices are used. In this paper, seven widely used cluster validity indices, namely DB index, PS index, I index, XB index, FS index, K index, and SV index, have been developed based on line symmetry distance measures. These indices provide the measure of line symmetry present in the partitioning of the dataset. These are able to detect clusters of any shape or size in a given dataset, as long as they possess the property of line symmetry. The performance of these indices is evaluated on three clustering algorithms: K-means, fuzzy-C means, and modified harmony search-based clustering (MHSC). The efficacy of symmetry-based validity indices on clustering algorithms is demonstrated on artificial and real-life datasets, six each, with the number of clusters varying from 2 to $\sqrt n ,$ where n is the total number of data points existing in the dataset. The experimental results reveal that the incorporation of line symmetry-based distance improves the capabilities of these existing validity indices in finding the appropriate number of clusters. Comparisons of these indices are done with the point symmetric and original versions of these seven validity indices. The results also demonstrate that the MHSC technique performs better as compared to other well-known clustering techniques. For real-life datasets, analysis of variance statistical analysis is also performed.

An Efficient Energy Clustering by Dynamic Multi-Chain Model in WSN

Effective and Efficient gathering data from different environments can be done by a large number of sensors are available in Wireless sensor networks currently for maximizing the network lifetime and minimizing the energy consumption and transmission delay. Because of sensors are working based on the power, it is compulsory to design an efficient protocol to save the energy as much as possible while routing. In this paper we propose an Energy Efficient Cluster-Dynamic Multi Chain Model (ECDMCM) model, efficient data aggregation and passing method in a routing protocol where the cluster Heads are connected dynamically in a chain manner. The chain construction occurs dynamically and the cluster head rotates locally inside the cluster over energy as well as distance between the cluster nodes and other cluster Heads. In the existing systems the cluster head elected over energy or distance. During the transmission of data re- formation of cluster does not exist. The outcome of results show that our suggested protocol significantly performs LEACH, CBRP and PEGASIS functions in terms of lifetime of network, period stability , instability period, energy consumption, transmission delay and the total number of data received at base station.

Isochronous information in a Greenland ice sheet radio echo sounding data set

AbstractThe evaluation of ice sheet models is one of the pressing problems in the study of ice sheet dynamics. Here we examine the question of how much isochronous information is contained within the publicly available Center for Remote Sensing of Ice Sheets (CReSIS) Greenland airborne radio echo soundings data set. We identify regions containing isochronous reflectors using automatic radio echo sounding processing (ARESP) algorithms. We find that isochronous reflectors are present within 36% of the CReSIS radio echo sounding englacial data by location and 41% by total number of data. Between 1000 and 3000 m in depth, isochronous reflectors are present along more than 50% of the data set flight path. Lower volumes of cold glacial period ice also correspond with more isochronous reflectors. We find good agreement between ARESP and continuity index results, providing confidence in these findings. Ice structure data sets, based on data identified here, will be of use in evaluating ice sheet simulations and the assessment of past rates of snow accumulation.

An Energy Efficient Cluster-chain Based Routing Protocol for Time Critical Applications in Wireless Sensor Networks

In this paper, we propose an Energy Efficient Cluster-Chain based Protocol for Time Critical applications (ECCPTC) in wireless sensor networks to maximize network lifetime and minimize energy consumption and transmission delay of time critical data. ECCPTC considers higher priority for time critical data than non-time critical data so that time critical data are immediately transmitted to the base station. ECCPTC uses a threshold value for reducing transmission delay of time critical data. ECCPTC organizes sensor nodes into clusters by using multiple metrics and constructs a chain among the sensor nodes within cluster so that each sensor node receives non-time critical data from a previous neighbor and transmits to a next neighbor. ECCPTC also adopts a chain based data transmission mechanism for sending non-time critical data packets from the cluster heads to the base station. Simulation results show that our proposed protocol significantly outperforms LEACH, CBRP and PEGASIS in terms of network lifetime, stability period, instability period, energy consumption, transmission delay of time critical data and the total number of data received at base station.

Choice of neighbor order in nearest-neighbor classification

The kth-nearest neighbor rule is arguably the simplest and most intuitively appealing nonparametric classification procedure. However, application of this method is inhibited by lack of knowledge about its properties, in particular, about the manner in which it is influenced by the value of k; and by the absence of techniques for empirical choice of k. In the present paper we detail the way in which the value of k determines the misclassification error. We consider two models, Poisson and Binomial, for the training samples. Under the first model, data are recorded in a Poisson stream and are “assigned” to one or other of the two populations in accordance with the prior probabilities. In particular, the total number of data in both training samples is a Poisson-distributed random variable. Under the Binomial model, however, the total number of data in the training samples is fixed, although again each data value is assigned in a random way. Although the values of risk and regret associated with the Poisson and Binomial models are different, they are asymptotically equivalent to first order, and also to the risks associated with kernel-based classifiers that are tailored to the case of two derivatives. These properties motivate new methods for choosing the value of k.

Performance evaluation and mathematical modelling of coliform die-off in tropical and subtropical waste stabilization ponds

The paper investigates the coliform removal in 33 facultative and maturation ponds in Brazil. The ponds were located in different parts of the country, with climates ranging from tropical to subtropical and latitude from 7 to 24°S. The ponds had different physical configurations, temperature and detention times. The total number of data used in the study, mostly comprising long-term averages, was 66. Two flow regimes were investigated: CSTR and dispersed flow. In the dispersed-flow model, the dispersion number was estimated using the formulae by Agunwamba et al. and Yanez, and both were found to give similar results. The coliform die-off coefficient K b was correlated with the pond depth and hydraulic detention time in the dispersed-flow model ( R 2=0.847). A regression equation, using theoretically generated data, was proposed, correlating K b for the CSTR model with K b for dispersed-flow model, using the length-to-breadth ratio and detention time of the pond ( R 2=0.997). Use of this equation yields virtually the same removal efficiency values as those obtained directly by the dispersed-flow model. Utilisation of the K b values calculated by the proposed regression models gave a very good prediction of the log effluent coliform concentration of the 33 ponds ( R 2=0.951 for the CSTR model and R 2=0.959 for the dispersed-flow model). Applying the proposed model for dispersed flow, it was concluded that, for a given removal efficiency, a shallow pond, due to its larger K b, requires less surface area, compared to a deep pond, even though the latter has a higher detention time.

1995年兵庫県南部地震における木造家屋の倒壊方向データの数値化,及び強震記録との関係

The database for the directions of collapsed/tilted wooden houses in the disaster belt during the 1995 Hyogo-ken-Nanbu earthquake, which were made by Matsuda and Takemura (1995), are digitized using a GIS software, and are made for the public use at a Web site: http://kouzou.cc.kogakuin.ac.jp. The total number of data is 2790, and the properties of each data consist of 1) the difference between collapse and tilt for the damaged house, 2) the location of the house (latitude and longitude), and 3) the direction of the collapse or tilt. The tolerances are estimated as 10 to 30 m for the locations, and as 5 to 10 degree for the directions. The directions of collapses are dominant at the south-east in the western part, the north in the central area, and the west in the eastern area of the disaster belt.

Optimal sampling interval for system identification based on decimation and interpolation

To improve the mean square error (MSE) of the least squares (LS) estimate of an impulse response, a new approach to signal sampling and measurement is proposed, based on decimation and interpolation. It is analytically shown that there exists an optimal sampling interval which can minimise the MSE, and this optimal sampling interval depends on the impulse response, the input power spectrum or the eigenvalues' distribution of the correlation matrix of the input signal, the noise variance and the total number of data. Furthermore, an effective data-based scheme to determine the optimal sampling interval is given by using the only accessible input-output data. The effectiveness of the presented algorithm is demonstrated through numerical examples.