• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.26-32
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• 2023

Hajj is a fundamental pillar of Islam that all Muslims must perform at least once in their lives. However, Umrah can be performed several times yearly, depending on people's abilities. Every year, Muslims from all over the world travel to Saudi Arabia to perform Hajj. Hajj and Umrah pilgrims face multiple issues due to the large volume of people at the same time and place during the event. Therefore, a system is needed to facilitate the people's smooth execution of Hajj and Umrah procedures. Multiple devices are already installed in Makkah, but it would be better to suggest the data architectures with the help of machine learning approaches. The proposed system analyzes the services provided to the pilgrims regarding gender, location, and foreign pilgrims. The proposed system addressed the research problem of analyzing the Hajj pilgrim dataset most effectively. In addition, Visualizations of the proposed method showed the system's performance using data architectures. Machine learning algorithms classify whether male pilgrims are more significant than female pilgrims. Several algorithms were proposed to classify the data, including logistic regression, Naive Bayes, K-nearest neighbors, decision trees, random forests, and XGBoost. The decision tree accuracy value was 62.83%, whereas K-nearest Neighbors had 62.86%; other classifiers have lower accuracy than these. The open-source dataset was analyzed using different data architectures to store the data, and then machine learning approaches were used to classify the dataset.

• Journal of Korean Port Research
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• v.8 no.2
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• pp.37-56
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• 1994

The effects of wave energy focusing by a submerged berm type of structure is examined. The fundamental idea is based on the phenomenon of refraction by a lens-shaped crescent structure which results in the focusing of wave energy on the center line of the structure. The shape of the submerged structure is a complex curve combining circular with elliptical elements. Based on the design procedure, a special configuration of structure(termed herein as a triple crescent structure) is introduced. Next, some hydraulic model tests are performed to confirm the wave focusing effect in laboratory. In addition, in order to interpret the wave focusing performance behind the structure, a numerical procedure by the hybrid element method is used on the basis of the conventional mild slope equation but modified and extended to allow for steeper bottom slopes and higher curvature. The modified refraction and diffraction provide additional mechanism for wave height amplification and the maximum amplification for triple crescent structure is presented. It also allows for the possibility of wave energy scattering with the change of the incident wave direction. Comparisons with previous theoretical results involving a submerged crescent shape structure are described.

• Structural Monitoring and Maintenance
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• v.10 no.1
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• pp.43-62
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• 2023

Damage monitoring is a prerequisite step to ensure the safety and performance of concrete structures. Smart aggregate (SA) technique has been proven for its advantage to detect early-stage internal cracks in concrete. In this study, a 1-D CNN-based method is developed for autonomously classifying the damage feature in a concrete anchorage zone using the raw impedance signatures of the embedded SA sensor. Firstly, an overview of the developed method is presented. The fundamental theory of the SA technique is outlined. Also, a 1-D CNN classification model using the impedance signals is constructed. Secondly, the experiment on the SA-embedded concrete anchorage zone is carried out, and the impedance signals of the SA sensor are recorded under different applied force levels. Finally, the feasibility of the developed 1-D CNN model is examined to classify concrete damage features via noise-contaminated signals. The results show that the developed method can accurately classify the damaged features in the concrete anchorage zone.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.427-435
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• 2023

Cardinality estimation is used in wide range of applications and a fundamental problem processing a large range of data. While the internet moves into the era of big data, the function addressing cardinality estimation use only on-chip cache memory. To use memory efficiently, there have been various methods proposed. However, because of the noises between estimator, which is data structure per flow, loss of accuracy occurs in these algorithms. In this paper, we focus on minimizing noises. We propose multiple data structure that each estimator has the number of estimated value as many as the number of structures and choose the minimum value, which is one with minimum noises, We discover that the proposed algorithm achieves better performance than the best existing work using the same tight memory, such as 1 bit per flow, through experiment.

• The Journal of Economics, Marketing and Management
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• v.5 no.1
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• pp.1-20
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• 2017

An increasing number of researchers are finding that 'poor' CG is a leading factor in dismal performance, manipulated financial reports, and unhappy stakeholders. Undoubtedly, qualified, trained and mature Chartered Forensic Accountants (CFA's) can prove to be a valuable asset to the corporate sector, and gradually help to improve their CG system. The fundamental objective of this study is to find out "how can we integrate the expertise of the FA to improve the overall CG scenario prevalent in India?" This is a preliminary investigation of the necessary skills, educational and training requirements for CFA's to improve CG system. During 2011-12, a questionnaire-based survey was conducted in the NCR of India using a sample size of 120 practicing chartered accountants, accounting academics, and potential users of FA services. Results indicate that potential practitioners, academics and users agree that "critical thinking, written & oral communication, legal knowledge, auditing skills, deductive analysis, investigative flexibility, analytical proficiency and unstructured problem-solving are the most important skills required for the CFAs." Moreover, we found that all of the skills investigated in this study are 'potentially' important for the CFAs, which the educators at the Universities should use as an overall guide while designing their FA curriculum."

• Computers and Concrete
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• v.32 no.1
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• pp.1-13
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• 2023

This paper discusses a framework for predicting the flexural strength of prestressed and non-prestressed FRP reinforced T-shaped concrete beams using soft computing techniques. An analysis of 83 tests performed on T-beams of varying widths has been conducted for this purpose with different widths of compressive face, beam depth, compressive strength of concrete, area of prestressed and non-prestressed FRP bars, elasticity modulus of prestressed and non-prestressed FRP bars, and the ultimate tensile strength of prestressed and non-prestressed FRP bars. By analyzing the data using two soft computing techniques, named artificial neural networks (ANN) and gene expression programming (GEP), the fundamental parameters affecting the flexural performance of prestressed and non-prestressed FRP reinforced T-shaped beams were identified. The results showed that although the proposed ANN model outperformed the GEP model with higher values of R and lower error values, the closed-form equation of the GEP model can provide a simple way to predict the effect of input parameters on flexural strength as the output. The sensitivity analysis results revealed the most influential input parameters in ANN and GEP models are respectively the beam depth and elasticity modulus of FRP bars.

• Elastomers and Composites
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• v.58 no.3
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• pp.136-141
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• 2023

Polyacrylic rubber (ACM) is well known for its excellent heat resistance and chemical stability. Additionally, its performance can be readily manipulated by modifying its functional groups, rendering it highly attractive to various industries. However, extreme climate changes have necessitated an expansion of the operating temperature range and lifespan of ACM products. This requires the optimization of both the compounding process and functional-group design. Hence, we investigated the relationship between the cross-linking system and mechanical properties of an ACM with a carboxylic cure site. The crosslink density is determined by chemical kinetics according to the structure of additives, such as diamine crosslinkers and guanidine accelerators. This interaction enables the manipulation of the scotch time and mechanical properties of the compound. This fundamental study on the correlation analysis between cross-linking systems, physical properties, and storage stability can provide a foundation for material research aimed at satisfying the increasingly demanding service conditions of rubber products.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.1-11
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• 2023

Large language models seem promising for handling reasoning problems, but their underlying solving mechanisms remain unclear. Large language models will establish a new paradigm in artificial intelligence and the society as a whole. However, a major challenge of large language models is the massive resources required for training and operation. To address this issue, researchers are actively exploring compact large language models that retain the capabilities of large language models while notably reducing the model size. These research efforts are mainly focused on improving pretraining, instruction tuning, and alignment. On the other hand, chain-of-thought prompting is a technique aimed at enhancing the reasoning ability of large language models. It provides an answer through a series of intermediate reasoning steps when given a problem. By guiding the model through a multistep problem-solving process, chain-of-thought prompting may improve the model reasoning skills. Mathematical reasoning, which is a fundamental aspect of human intelligence, has played a crucial role in advancing large language models toward human-level performance. As a result, mathematical reasoning is being widely explored in the context of large language models. This type of research extends to various domains such as geometry problem solving, tabular mathematical reasoning, visual question answering, and other areas.

• Journal of Engineering Education Research
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• v.26 no.6
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• pp.40-50
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• 2023

This study is to examine the validity of assessing basic-level proficiency in physics among incoming engineering freshmen through item analysis and standard setting. For empirical analysis, we examined the physics subject taken by the freshman class of 2021 at K University, considering its significance for engineering students. In this study, we initially performed item analysis utilizing both classical test theory and item response theory. Subsequently, leveraging the item and test information, we employed a modified Angoff method and the Bookmark method for standard setting. Consequently, the difficulty level initially set during item development was found to be higher than the actual performance level exhibited by the students. This study highlights a discernible disparity between the expected university standard and the real proficiency level of incoming freshmen in terms of basic academic ability in physics. Based on these research findings, a comprehensive discussion on the fundamental academic competence of engineering students was conducted, underscoring the necessity for formulating a tailored learning approach leveraging the outcomes from the basic ability test.

• Journal of Korean Critical Care Nursing
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• v.17 no.1
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• pp.17-29
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• 2024

Purpose : This study sought to investigate novice nurses' communication education needs in the intensive care unit (ICU) using Importance-Performance Analysis (IPA) and Borich's need assessment model. This study identified communication challenges in clinical settings to develop a simulation program that enhances communication competencies based on educational requirements. Methods : A descriptive research design and a self-report questionnaire were used. The latter was developed and administered to 121 novice nurses with less than one year of experience in the ICU at various university hospitals in Korea. Data were collected via the online open chatroom from June 24th to July 28th, 2023. The communication education needs were identified using descriptive statistics, t-tests, IPA, and Borich's needs assessment model. Text analysis was used to categorize the participants' communication experience. Results : The results revealed that "communication with physicians," "communication with patients," and "communication with nurse on another shift" domains contained the most substantial educational needs for novice nurses working in the intensive care units. Conclusion : The results provide fundamental data for developing and enhancing customized communication education programs for novice ICU nurses. This valuable information could help ICU nurses and educators improve new nurses' communication skills, which would ultimately contribute to the advancement of nursing education and clinical practice.