• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.17-25
• /
• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.177-189
• /
• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.

• Smart Structures and Systems
• /
• v.31 no.5
• /
• pp.485-500
• /
• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.4D
• /
• pp.423-432
• /
• 2010

This study suggested CRMS (construction risk management system) which is a new risk analysis model after analyzing existing risk management process for to guarantee a successful performance at the construction planning and work phase. CRMS is risk management procedures in order that the contractor identify, analyze and administrate the risk during performing construction project. This model may give much help to quantify and be ready the right managing methods about identified risk by the contractor. Especially, the most important and difficult things of all risk management may be to identify risk in the project. This study make more focusing on the developing a procedure that can identify risk more easily in the construction project. The risk is divided into global risk and local risk of a project. Also, this study suggests methods which are using the RBS (risk breakdown structure) related with WBS. This result will be useful as basic materials for developing computerizing system for risk management.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.960-966
• /
• 2022

Mobility is an essential human need. Human survival and societal interaction depend on the ability to move people and goods. Efficient mobility systems are essential facilitators of economic development. Cities could not exist and global trade could not occur without systems to transport people and goods cheaply and efficiently. Rail has been considered as one of the important components of the transportation infrastructure required to service and improve the performance and productivity of an economy. In Nigeria, the rail infrastructure built by the colonial master several decades ago has been left in a state of total deterioration. This long neglect was occasioned by the failure of the government to pay adequate attention to infrastructure development. There is a vital and urgent need for rail infrastructure development in Nigeria. This study presents a systematic review of the evolution of rail, the current nature of railway infrastructure delivery in Nigeria, and offers possible suggestions on how to achieve an effective and sustainable rail infrastructure delivery in Nigeria. A thorough literature search of academic databases was conducted on current research trends on the subject of railway infrastructure by systematically reviewing selected published articles from reputable research domains. The analysis of the selected articles revealed the following among others (1) the existing railway infrastructure is in a state of mess and not sustainable, and (2), Government's investment/commitment in rail infrastructure seems inadequate compared to what is obtainable in other developed countries. Rail infrastructure development cannot be left to the Federal government of Nigeria to solve on its own; collaboration and participation are required. Government as a matter of priority should devote considerable attention to the development of rail infrastructure to harness the economic potential and transformation that sustainable rail infrastructural projects will provide.

• Nuclear Engineering and Technology
• /
• v.56 no.3
• /
• pp.973-979
• /
• 2024

In the framework of the Generation IV research and development project, in which the French Commission of Alternative and Atomic Energies (CEA) is involved, a main objective for the design of Sodium-cooled Fast Reactor (SFR) is to meet the safety goals for severe accidents. Among the severe ones, the Unprotected Transient OverPower (UTOP) accidents can lead very quickly to a global melting of the core. UTOP accidents can be considered either as slow during a Control Rod Withdrawal (CRW) or as fast. The paper focuses on fast UTOP accidents, which occur in a few milliseconds, and three different scenarios are considered: rupture of the core support plate, uncontrolled passage of a gas bubble inside the core and core mechanical distortion such as a core flowering/compaction during an earthquake. Several levels and rates of reactivity insertions are also considered and the thermal-mechanical behavior of an ASTRID fuel pin from the ASTRID CFV core is simulated with the GERMINAL code. Two types of fuel pins are simulated, inner and outer core pins, and three different burn-up are considered. Moreover, the feedback from the CABRI programs on these type of transients is used in order to evaluate the failure mechanism in terms of kinetics of energy injection and fuel melting. The CABRI experiments complete the analysis made with GERMINAL calculations and have shown that three dominant mechanisms can be considered as responsible for pin failure or onset of pin degradation during ULOF/UTOP accident: molten cavity pressure loading, fuel-cladding mechanical interaction (FCMI) and fuel break-up. The study is one of the first step in fast UTOP accidents modelling with GERMINAL and it has shown that the code can already succeed in modelling these type of scenarios up to the sodium boiling point. The modeling of the radial propagation of the melting front, validated by comparison with CABRI tests, is already very efficient.

• Journal of the Korean earth science society
• /
• v.45 no.4
• /
• pp.304-317
• /
• 2024

This study proposes a soil moisture retrieval method from ground reflection signals received by Global Positioning System (GPS) antenna modules consisting of an up-looking (UP) right-hand circular polarization (RHCP) and two down-looking (DW) RHCP and left-hand circular polarization (LHCP) signals. Field experiments at four different surface types (asphalt, grassland, dry soil, and moist soil) revealed that the DW RHCP and LHCP signals are affected by antenna height and multipath interference signals. The strength differences between the DW LHCP and UP RHCP signals were in good agreement with the DW LHCP signals. Methodologically, this study applied a spectrum analysis to the detrended surface-reflected signals for RHCP and LHCP. The study indicated that the down-looking antenna exhibited greater sensitivity to reflected GPS signals than the up-looking antenna. We demonstrated the feasibility of estimating soil moisture using GPS signals, by comparing LHCP signals received by the down-looking antenna with theoretical values. This study presents a novel method for estimating soil moisture in vegetated areas, leveraging the advantage of cross-polarization comparisons to achieve stronger signal strength than single-polarization reflection signals. With further research, including long-term observations and detailed analysis, the proposed method has the potential to enhance performance significantly.

• Journal of Korea Port Economic Association
• /
• v.40 no.2
• /
• pp.137-155
• /
• 2024

To achieve the national goal of "2050 Carbon Neutrality" in the era of the climate crisis, it is important to support the decarbonization of ports, which are the vital node of the global supply chain. Following the establishment of the concept of port's decarbonzation, this study reviewed the obstacles and solutions to port decarbonization through literature research. Furthermore, the goals and strategies for decarbonization implementation of world major ports were examined through case analysis, and the level of decarbonization implementation of the five Korean major ports was quantitatively evaluated using a performance-based score measurement method. As a result of the analysis, the level of decarbonization of Korean ports is generally far behind that of advanced countries. In particular, measures for environment-friendly inland transportation, future alternative fuel bunkering facilities, and various market-based incentive policies are needed. As a policy task for the decarbonization of Korean ports, first, the necessity of establishing a emission inventory, monitoring, and reporting system and the disclosure of related information, second, the mixing strategy of various greenhouse gas reduction measures, and third, the increase in the proportion of renewable energy at ports were suggested.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.894-901
• /
• 2024

The construction industry has been recognized as one of the most high-risk industries globally, promoting a shift towards enhancing safety culture to mitigate accident rates. With a notable good safety performance in Australia, this study therefore compares its advanced safety culture with the evolving safety culture in China through a systematic review of literature published over the last two decades. The aim of the research is to explore the influence of differing societal cultural contexts on the development of safety culture. The study covers various aspects of safety culture, including leadership and management commitment, regulatory environments, safety communication, workers'involvement, and organizational safety systems. Findings indicate a strong commitment from industry participants in both countries. However, there are notable differences in safety culture conceptualization and implementation. Australia showcases a mature safety culture, deeply integrated with stringent regulations and fostering individual proactive engagement. Conversely, China's safety culture, marked by rapid evolution, emphasizes regulatory compliance, with challenges in achieving broad worker participation. The analysis highlights that Australian construction workers' inclination towards a proactive approach in managing safety, in contrast to Chinese construction workers who tend to focus more on adhering to safety regulations than actively participating in safety initiatives. These findings emphasize the significant role societal culture plays in shaping construction safety cultures. The study's insights are instrumental for practitioners across the global construction industry, advocating for the adoption of nuanced, culturally sensitive safety management strategies to enhance safety outcomes.

• Journal of Navigation and Port Research
• /
• v.46 no.4
• /
• pp.338-343
• /
• 2022

As the revitalization of the global maritime industry continues, the number of foreign ships navigating the maritime territories of maritime neighboring countries has rapidly increased. However, large-scale marine accidents have occurred, caused by the insufficient establishment of a system for management and operation relative to vessels' safety-condition. To address that, the IMO has granted the right to exercise port state control, especially for foreign vessels, to countries with jurisdiction over maritime territories with strengthening regulations and guidelines. In particular, the Republic of Korea, as a member of the TOKYO MOU, is conducting PSC, but as of 2020, the proportion of foreign ships was three times higher than that of national ships that called in domestic ports. However, the inspection rate was low at 9% which has not met the recommended level by the TOKYO MOU. Thus, this study conducted an IPA analysis as well as content analysis, by collecting the practical opinions and views of PSCO through objective questionnaires and written expert interviews, for improving the efficiency and effectiveness of domestic PSC. As a result, it was derived that the importance and performance related to human factors such as life on board, working environment, and response to safety accidents should be improved in to raise the quality of PSC inspection. Additionally, the work environment and performance of PSC in domestic ports for foreign vessels could be improved, if multifaceted support bases are established, for administrative unification of related tests for PSC, recruitment of PSCO, activation of the defection-reporting system, reorganization of the PSC execution group, etc.