• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.942-948
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• 2021

The aim of this work is to help inform the decision for choosing a convenient material for the PGAA (Prompt Gamma Activation Analysis) collimator plug to be installed at the tangential channel of the Moroccan Triga Mark II Research Reactor. Two families of materials are usually used for collimator construction: a mixture of high-density polyethylene (HDPE) with boron, which is commonly used to moderate and absorb neutrons, and heavy materials, either for gamma absorption or for fast neutron absorption. An investigation of two different collimator designs was performed using N-Particle Monte Carlo MCNP6.2 code with the ENDF/B-VII.1 and MCLIP84 libraries. For each design, carbon steel and lead materials were used separately as collimator heavy materials. The performed study focused on both the impact on neutron beam quality and the neutron-gamma background at the exit of the collimator beam tube. An analysis and assessment of the principal findings is presented in this paper, as well as recommendations.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.359-364
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• 2021

In recent years Hadoop usage has been increasing day by day. The need of development of the technology and its specified outcomes are eagerly waiting across globe to adopt speedy access of data. Need of computers and its dependency is increasing day by day. Big data is exponentially growing as the entire world is working in online mode. Large amount of data has been produced which is very difficult to handle and process within a short time. In present situation industries are widely using the Hadoop framework to store, process and produce at the specified time with huge amount of data that has been put on the server. Processing of this huge amount of data having small files & its storage optimization is a big problem. HDFS, Sequence files, HAR, NHAR various techniques have been already proposed. In this paper we have discussed about various existing techniques which are developed for accessing and storing small files efficiently. Out of the various techniques we have specifically tried to implement the HDFS- HAR, NHAR techniques.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3944-3951
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• 2021

Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.770-802
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• 2022

With the development of deep learning techniques, text mining is producing breakthrough performance improvements, promising future applications, and practical use cases across many fields. Likewise, even though several attempts have been made in the field of financial information, few cases apply the current technological trends. Recently, companies and government agencies have attempted to conduct research and apply text mining in the field of financial information. First, in this study, we investigate various works using text mining to show what studies have been conducted in the financial sector. Second, to broaden the view of financial application, we provide a description of several text mining techniques that can be used in the field of financial information and summarize various paradigms in which these technologies can be applied. Third, we also provide practical cases for applying the latest text mining techniques in the field of financial information to provide more tangible guidance for those who will use text mining techniques in finance. Lastly, we propose potential future research topics in the field of financial information and present the research methods and utilization plans. This study can motivate researchers studying financial issues to use text mining techniques to gain new insights and improve their work from the rich information hidden in text data.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• The Research Journal of the Costume Culture
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• v.32 no.3
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• pp.438-451
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• 2024

The interlacing technique has a long history of use as a means of creative expression and persists in modern society to satisfy the individual pursuit of pastimes. This method has the developmental potential to create new trends in the future. Interlacing techniques (e.g., basketry, plaiting, braiding, and knotting) are closely related to plastic arts fields, architecture, art, and industrial design where the various interlacing practices are applied. This research uses case analysis to study the types of expression found in the formative art field wherein the interlacing technique is applied. Results reveal several expressions, including optical illusion visual type, relief surface type, porous perspective type, and object borrowing type, all of which appeared in fashion bags. The aesthetic formativeness, which appeared in the fashion bags that applied interlacing techniques, was then classified according to geometric formative beauty in a process based on rules and order. Nature-friendly formative beauty reflecting handicraft locality and omnidirectional formative beauty by disordered deconstruction and heterogeneous combination were determined. The use of interlacing techniques that show creative, unique combinations and variations is expected to inspire the development and application of bag design that suits individual and original modern fashion trends. One limitation of this thesis is that it only studied cases appearing in modern fashion bags.

• Journal of the Korean Society of Floral Art and Design
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• no.43
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• pp.123-138
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• 2020

This study found Flower Jewelry works in the monthly magazine specializing in flower decoration for nine years from 2011 to 2019. Based on the analysis of the type of expression, method of expression, type and number of plant materials used, and the type and number of non-plant materials used for the flower jewelry found, it was conducted to find out the trend of flower jewelry in Korea. By expression type, a total of 96 works were analyzed as 20.83% for headdresses, 57.29% for necklaces, 5.21% for earrings, 6.25% for lists, and 10.42% for other works(χ²=94.833, p<.001). According to the analysis of the frequency of use of expression techniques, headdresses, necklaces, and lists for each work were produced using five to six different expression techniques and earrings were produced using two to four expression techniques. Material coupling techniques 34.43%, flower and leaf utilization 30.17%, visual techniques 16.63%, collectivization techniques 14.12%, technical highlighting techniques 4.26%, and other 0.39% (χ²=455.222, p<.001). The most frequently used techniques were framing techniques 16.63% and knotting techniques 16.44%. Plant materials used in flower jewelry were found to be 22.61% for Phalaenopsis spp., 13.48% for Gomphrena globosa, 9.57% for Gloriosa rothschildiana, 7.39% for Epidendrum cinnabarinum, 6.96% for Chamelaucium uncinatum and 4.78% for Craspedia globosa (χ²=718.104, p<.001). In the case of branch, the most common was used with 70.00% of the Cornus walteri, and 10.00% of Actinidia arguta, Celastrus orbiculatus, and Salix pseudolasiogyne were used respectively (χ²=10.800, p=.013). In the case of foliage, 24.65% Aspidistra elatior, 24.62% Asparagus asparagoides, 11.54% Senecio rowleyanus, and 6.15% Ceropegia woodii (χ²=269.385, p<.001). In the case of berries, 44.44% of the fruits of the Smilax china, 33.33% of the Hypericum patulum, and 11.11% of the Phytolacca americana were found (χ²=11.444, p =.022). Non-planting materials used in the manufacture of flower jewelry were found to be 47.34% of 2mm aluminium wire, 33.73% of copper wire and 10.06% of 1mm aluminum wire (χ²=186.704, p<.001). The figure was 53.57% for pearls, 12.50% for ribbons, and 4.14% for spangles and feathers.

• Journal of the Chungcheong Mathematical Society
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• v.8 no.1
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• pp.1-10
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• 1995

For analyzing systems of multi-variate nonlinear equations, the linearized modelling techniques are elaborated. The technique applies Newton-Raphson iteration, partial differentiation and matrix operation providing solvable solutions to complicated problems. Practical application examples are given in; determining the zero point of functions, determining maximum (or minimum) point of functions, nonlinear regression analysis, and solving complex co-efficient polynomials. Merits and demerits of linearized modelling techniques are also discussed.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.433-443
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• 2003

In this study, we provide a comparison of multivariate Q-techniques in the up-to-date versions of SAS, SPSS, Minitab and S-plus well known to those who study statistics. We can analyze data through the direct Input method(command) in SAS and use of menu method in SPSS, Minitab and S-plus. The analysis performance method is chosen by the high frequency of use. Widely we compare with each Q-techniques form according to input data, input option, statistical chart and statistical output.