• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.147-152
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• 2023

Mostly in motor fault detection the instantaneous values 3 axis vibration and 3phase current in time domain are acquired and converted to frequency domain. Vibrations are more useful in diagnosing the mechanical faults and motor current has remained more useful in electrical fault diagnosis. With having some experience and knowledge on the behavior of acquired data the electrical and mechanical faults are diagnosed through signal processing techniques or combine machine learning and signal processing techniques. In this paper, a single-layer LSTM based condition monitoring system is proposed in which the instantaneous values of three phased motor current are firstly acquired in simulated motor in in health and supply imbalance conditions in each of three stator currents. The acquired three phase current in time domain is then used to train a LSTM network, which can identify the type of fault in electrical supply of motor and phase in which the fault has occurred. Experimental results shows that the proposed single layer LSTM algorithm can identify the electrical supply faults and phase of fault with an average accuracy of 88% based on the three phase stator current as raw data without any processing or feature extraction.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.375-386
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• 2022

As information asset protection is recognized as an important management factor for organizations, organizations are increasing their investments in information security(IS) policies and technologies. However, strict application of IS may cause non-compliance behavior through IS stress on employees of the organization. Accordingly, this study suggests a mechanism by which employee stress affects IS compliance intentions through self-determination, and a method to reinforce IS compliance intentions through person-organization fit. We conducted an online survey of employees working at companies that adopted IS policies and tested hypotheses using 475 samples. First, as a result of analyzing the main effects of applying the structural equation model, role stress affected IS compliance intention through self-determination. Second, as a result of analyzing the moderating effect of applying Process 3.1, personal organization fit strengthened the relationship between self-determination and IS compliance intention. The research suggests a direction for achieving internal IS goals by confirming the influence of IS stress and behavioral causes of employees.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.241-247
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• 2022

This study examines teachers' implementation of strategies to modify the aggressive behavior of students with intellectual disabilities in Saudi Arabia, to determine the obstacles to their real-world execution. In addition, it presents potential approaches to overcome the obstacles to implementing strategies with this group of students. The research employed a qualitative design using semi-structured interviews as a data collection tool and applied a thematic analysis. The study population comprised 35 teachers of students with intellectual disabilities and the study sample numbered six teachers. The interviews were conducted via different methods: three by phone, two face-to face, and one using the Zoom platform. The results revealed inadequate understanding among teachers of intellectual disability and behaviour modification strategies, and this affected their capacity to develop plans that were compatible with the needs of students with intellectual disability. The findings also identified multiple obstacles that impede teachers' implementation of strategies to modify aggressive behaviour among students with intellectual disabilities; the most important being the lack of input from a psychological specialist when developing programs to modify aggressive behaviour. In general, it is apparent that programs for modifying aggressive behaviour are neither structured nor complementary, due to the scarcity of administrators with sufficient knowledge and familiarity with the characteristics and personalities of students with intellectual disabilities. This study presents several recommendations, the most important of which is that teachers of students with intellectual disability should develop themselves through training courses to enable them to deal with these students and create treatment plans that include strategies and clear steps to modify the aggressive behaviour of students with intellectual disabilities. To support teachers, it is also necessary to remove the obstacles facing education centres by providing financial support to create an environment in which they can access the required devices and equipment in their classes.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.65-73
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• 2023

Fishways are essential structures that must be installed in rivers to facilitate the movement of fish between upstream and downstream areas. However, the efficiency of fish passage varies depending on the flow conditions within the fishway. Therefore, this study examined the fish-friendly flow characteristics within a stepped fishway at different overflow depths using FLOW-3D, and conducted experiments for model validation. The key parameters affecting fish swimming ability include velocity, turbulent kinetic energy, and energy dissipation rate. These factors were assessed using a simulated fish species, the zacco platypus, to evaluate the suitability of fish-friendly flow condition. It was confirmed that overflow depth significantly influences fish behavior, and an appropriate overflow depth is required for stepped fishway design. The results of this study are expected to serve as fundamental data for the design of stepped fishways in the future.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.216-222
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• 2024

Objectives: To compare the levels of anxiety on GAD-7 scale among undergraduates of dental, medical and engineering students during COVID-19. The secondary objectives were to correlate the factors influencing level of anxiety and to assess the coping strategies practiced by undergraduates' students of Karachi duri.ng COVID-19 outbreak. Methodology: The cross-sectional based survey was conducted online among the medical, dental and engineering undergraduates' university students of private sector in Karachi through purposive sampling technique during COVID-19 lock down period. The GAD-7 validated tool was used along with the demographic variables, related stress factors and the coping skills practiced during this outbreak. Total 571 questionnaires were found completed in all sections. The data was analyzed on SPSS version 23. P-value <0.05 was considered as statistically significant. Results: The mental health of the students was assessed on GADS-7 scale as normal, mild, moderate and severe levels. From the total (n=18-3.2%) were normal, (n=132-23.1%) had mild, (n=343-60.1%) had moderate and (n=78-13.7%) had severe anxiety level on GADS-7. The levels of anxiety on GAD-7 scale were all positively associated with the related stressors at p-value of 0.000. Moreover the results depicted that there was a moderate and positive correlation found (0.456, 0.447, 0.512 and 0.452) for all related stressors and GAD-7 scale. Taking breaks from watching, reading news regarding the outbreak of COVID-19, meditation and engaging in some other activities were the most frequently used coping strategies for all levels of anxiety among three cohorts of undergraduates'. Conclusion: Undergraduates has shown 96.9% drastically increased level of anxiety during the outbreak of COVID 19 pandemic. Taking breaks from watching, reading news regarding the outbreak of COVID-19 was the most frequent behavior practiced by the students.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.49-65
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• 2023

Purpose - In the Korean and Chinese social landscape, it is vital to appreciate the significance of the Japanese history problem. The current study investigated whether the perception of the Japanese history problem affects decisions regarding technology adoption in organizations by comparing South Korea and China. Design/methodology/approach - The study involved 305 Korean and 379 Chinese participants who responded to scenarios and surveys regarding the adoption of workplace surveillance cameras supplied by a Japanese company. Findings - Using a moderated mediation model based on protection motivation theory (PMT), we found that past experiences of privacy invasion significantly reduced trust in the adoption of surveillance cameras at work. This relationship was mediated by respondents' perceptions of security vulnerability. The current study, however, did not confirm any significant moderating effect of the Japanese history problem priming on trust in the adoption of workplace surveillance cameras. Research implications - This suggests that the Japanese history problem may have a limited impact on organizational technology adoption decisions, different from the political consumerism behavior driven by public anti-Japanese affectivity. The current study reaffirms the validity and applicability of PMT and provides both theoretical insights and practical recommendations.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.11-20
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• 2024

While artificial neural networks are adept at identifying patterns, they can struggle to distinguish between actual correlations and false associations between extracted facial features and criminal behavior within the training data. These associations may not indicate causal connections. Socioeconomic factors, ethnicity, or even chance occurrences in the data can influence both facial features and criminal activity. Consequently, the artificial neural network might identify linked features without understanding the underlying cause. This raises concerns about incorrect linkages and potential misclassification of individuals based on features unrelated to criminal tendencies. To address this challenge, we propose a novel region-based training approach for artificial neural networks focused on criminal propensity detection. Instead of solely relying on overall facial recognition, the network would systematically analyze each facial feature in isolation. This fine-grained approach would enable the network to identify which specific features hold the strongest correlations with criminal activity within the training data. By focusing on these key features, the network can be optimized for more accurate and reliable criminal propensity prediction. This study examines the effectiveness of various algorithms for criminal propensity classification. We evaluate YOLO versions YOLOv5 and YOLOv8 alongside VGG-16. Our findings indicate that YOLO achieved the highest accuracy 0.93 in classifying criminal and non-criminal facial features. While these results are promising, we acknowledge the need for further research on bias and misclassification in criminal justice applications

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.255-274
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• 2024

Purpose: Non-Fungible Token (NFT) ART, based on NFT technology, represents a novel form of art that has recently garnered significant attention in the art market. NFT technology enables the assertion of ownership over digital data, introducing the concept of ownership into the digital realm. As digital data gains ownership, NFT ART is anticipated to be positively viewed as an investment and expected to become more active due to the characteristics of this new technology. Therefore, this study aims to verify the influence of NFT ART characteristics on perceived value. This study contributes to extracting the distinctive characteristics of NFT ART compared to other forms of art and to understanding the perceived value of NFT ART among consumers with purchasing experience. Methods: This study applied structural equation modeling to explore the relationships among the variables using SPSS 26.0 and R program version 4.2.3. A total of 320 questionnaires were retrieved, all of which were adopted as valid analytical samples without missing values. Results: The findings indicate that the decentralization, transparency, and scarcity of NFT ART positively influence the perceived usefulness and enjoyment among consumers, while security does not have a significant impact. This suggests that higher levels of decentralization, transparency, and scarcity in NFT ART enhance perceived usefulness and enjoyment for consumers, significantly influencing the perceived value. Furthermore, it was confirmed that these characteristics are considered important values and perceptions from the consumer's perspective. Conclusion: The research presents positive factors for the activating of purchases among consumers considering buying NFT ART. It emphasizes the necessity of benefits for all participants to activate the art market. Additionally, the perceived value provides crucial insights for inducing active purchasing behavior in the NFT ART market and serves as a foundational study for further research.

• Computers and Concrete
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• v.34 no.4
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• pp.379-391
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• 2024

The ultra-high performance concrete (UHPC) mixed with hybrid fibers has excellent mechanical properties and durability, and the hybrid fibers have a certain impact on the bearing capacity, deformation capacity, and crack propagation of beams. Many scholars have conducted a series of studies on the bending performance of prestressed UHPC beams, but there are few studies on prestressed UHPC beams mixed with hybrid fibers. In this study, five bonded post-tensioned partially prestressed UHPC beams mixed with steel fibers and macro-polyolefin fibers were poured and subjected to four-points symmetric loading bending tests. The effects of different prestressing degrees and prestressing levels on the load-deflection curves, crack propagation, failure modes and ultimate bearing capacity of beams were discussed. The results showed that flexural failure occurred in the prestressed UHPC beams with hybrid fibers, and the integrity of specimens was good. When the prestressing degree was the same, the higher the prestressing level, the better the crack resistance capacity of UHPC beams; When the prestressing level was 90%, increasing the prestressing degree was beneficial to improve the crack resistance and ultimate bearing capacity of UHPC beams. When the prestressing degree increased from 0.41 to 0.59, the cracking load and ultimate load increased by 66.0% and 41.4%, respectively, but the ductility decreased by 61.2%. Based on the plane section assumption and considering the bridging effect of short fibers, the cracking moment and ultimate bearing moment were calculated, with good agreement between the test and calculated values.