• 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.

• Biomedical Science Letters
• /
• v.29 no.1
• /
• pp.11-25
• /
• 2023

In hepatocellular carcinoma (HCC), chromosome 4 open-reading frame 47 (C4orf47) has not been so far investigated for its prognostic value or association with infiltrating immune cells. We performed bioinformatics analysis on HCC data and analyzed the data using online databases such as TIMER, UALCAN, Kaplan-Meier plotter, LinkedOmics, and GEPIA2. We found that C4orf47 expression in HCC was higher compared to normal tissues. High C4orf47 expression was associated with a worse prognosis in HCC. The correlation between C4orf47 and infiltrating immune cells is positively associated with CD4+T cells, B cells, neutrophils, macrophages, and dendritic cells in HCC. Moreover, high C4orf47 expression was correlated with a poor prognosis of infiltrating immune cells. Analysis of C4orf47 gene co-expression networks revealed that 12501 genes were positively correlated with C4orf47, whereas 7200 genes were negatively correlated. The positively related genes of C4orf47 are associated with a high hazard ratio in different types of cancer, including HCC. Regarding the biological functions of C4orf47 gene, it mainly regulates RNA metabolic process, DNA replication, and cell cycle. The C4orf47 gene may play a prognostic role by regulating the global transcriptome process in HCC. Our findings demonstrate that high C4orf47 expression correlates with poor prognosis and tumor-infiltrating immune cells in HCC. We suggest that C4orf47 is a novel prognostic biomarker and potential immune therapeutic target for HCC.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.7
• /
• pp.1759-1772
• /
• 2023

Chinese named entity recognition (NER) is a challenging work that seeks to find, recognize and classify various types of information elements in unstructured text. Due to the Chinese text has no natural boundary like the spaces in the English text, Chinese named entity identification is much more difficult. At present, most deep learning based NER models are developed using a bidirectional long short-term memory network (BiLSTM), yet the performance still has some space to improve. To further improve their performance in Chinese NER tasks, we propose a new NER model, IDCNN-BiLSTM-Highway, which is a combination of the BiLSTM, the iterated dilated convolutional neural network (IDCNN) and the highway network. In our model, IDCNN is used to achieve multiscale context aggregation from a long sequence of words. Highway network is used to effectively connect different layers of networks, allowing information to pass through network layers smoothly without attenuation. Finally, the global optimum tag result is obtained by introducing conditional random field (CRF). The experimental results show that compared with other popular deep learning-based NER models, our model shows superior performance on two Chinese NER data sets: Resume and Yidu-S4k, The F1-scores are 94.98 and 77.59, respectively.

• Computers and Concrete
• /
• v.32 no.2
• /
• pp.217-232
• /
• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• Journal of Korean Library and Information Science Society
• /
• v.55 no.1
• /
• pp.215-237
• /
• 2024

This study proposes a selection plan for research entities and PIDs and a strategy for building and operating a PID consortium based on a survey of advanced cases of research entities and PID operations in major countries such as the United Kingdom, Germany, Canada, Japan, China, and Australia. The criteria for selecting research entities and PIDs are 'research life cycle' and 'PID infrastructure maturity'. Based on the two selection criteria, it is proposed to prioritize research entity-PID pairs such as 'Researcher-ORCID', 'Publication-DOI', 'Data-DOI', 'Institution-ROR', 'Grant-DOI', and 'Project-RAiD' and expand to other research entities and PIDs in the emerging stage. The strategy for establishing and operating a PID consortium should encourage the participation of various PID stakeholders, identify the latest trends through collaborative networks with domestic and international PID organizations, lead education and outreach activities to raise awareness and increase utilization of PID, and secure policy support and financial stability. This is expected to lay the foundation for domestic research entities to gain visibility and accessibility at the global level.

• Journal of Drive and Control
• /
• v.21 no.2
• /
• pp.53-64
• /
• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.7
• /
• pp.1968-1985
• /
• 2024

At present, the world is witnessing a rapid change in all the fields of human civilization business interests and goals of all the sectors are changing very fast. Global changes are taking place quickly in all fields - manufacturing, service, agriculture, and external sectors. There are plenty of hurdles in the emerging technologies in agriculture in the modern days. While adopting such technologies as transparency and trust issues among stakeholders, there arises a pressurized necessity on food suppliers because it has to create sustainable systems not only addressing demand-supply disparities but also ensuring food authenticity. Recent studies have attempted to explore the potential of technologies like blockchain and practices for smart and sustainable agriculture. Besides, this well-researched work investigates how a scientific cum technological blockchain architecture addresses supply chain challenges in Precision Agriculture to take up challenges related to transparency traceability, and security. A robust registration phase, efficient authentication mechanisms, and optimized data management strategies are the key components of the proposed architecture. Through secured key exchange mechanisms and encryption techniques, client's identities are verified with inevitable complexity. The confluence of IoT and blockchain technologies that set up modern farms amplify control within supply chain networks. The practical manifestation of the researchers' novel blockchain architecture that has been executed on the Hyperledger network, exposes a clear validation using corroboration of concept. Through exhaustive experimental analyses that encompass, transaction confirmation time and scalability metrics, the proposed architecture not only demonstrates efficiency but also underscores its usability to meet the demands of contemporary Precision Agriculture systems. However, the scholarly paper based upon a comprehensive overview resolves a solution as a fruitful and impactful contribution to blockchain applications in agriculture supply chains.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.1294-1294
• /
• 2024

Flood risk maps are used in urban flooding to understand the spatial extent and depth of inundation damage. To construct these maps, hydrodynamic modeling capable of simulating flood waves is necessary. Flood waves are typically fast, and inundation patterns can significantly vary depending on the terrain, making it essential to accurately represent the terrain of the flood source in flood wave analysis. Recently, methods using UAVs for terrain data construction through Structure-from-Motion or LiDAR have been utilized. These methods are crucial for UAV operations, and thus, still require a lot of time and manpower, and are limited when UAV operations are not possible. Therefore, for efficient nationwide monitoring, this study developed a model that can automatically generate terrain data by estimating depth information from a single image using c-GAN (Conditional Generative Adversarial Networks) and BBDM (Brownian Bridge Diffusion Model). The training, utilization, and validation datasets employed images from the ISPRS (2018) and directly aerial photographed image sets from five locations in the territory of the Republic of Korea. Compared to the ground truth of the test data set, it is considered sufficiently usable as terrain data for flood wave analysis, capable of generating highly accurate and precise terrain data with high reproducibility.

• International Journal of Knowledge Content Development & Technology
• /
• v.14 no.4
• /
• pp.65-84
• /
• 2024

The study was conducted on the awareness and application of internet of things in universities libraries in Kwara State, Nigeria. the study formulated used four research questions and used eighty five (85) samples as the population using total enumerative sampling techniques. A survey method was used in undertaking the study, in which answers were sought on the level of awareness of the internet of things in universities libraries in Kwara State, the extent of application of the internet of things in universities libraries in Kwara State, the benefit of internet of things in universities libraries in Kwara State, the challenges faced in the application of internet of things in universities libraries in Kwara State. The data collected from the study were analyzed using frequency tables and percentage. The study discovered that there the students are aware of the internet of things in universities libraries in Kwara State and the benefit of internet of a things include: Device in the IoT platforms are heterogeneous and are based on different hardware platforms and networks, It gives the high level of interoperability and interconnectivity, IoT platform has sensors which detect or measure any changes in the environment to generate data that can report on their status or even interact with the environment, IoT comes with the combination of algorithms and computation, software & hardware that makes it smart and Anything can be interconnected with the global information and communication infrastructure and the study identified data interpretation problem, Lack of skilled and specialized workers, Cost and Challenges in online security as well as Software complexity are major challenges faced in the application of internet of things in universities libraries in Kwara State. In conclusion the study made some recommendations which include that: Future libraries should be equipped with new technologies and networking devices as soon as possible. As this will be essential for users and librarians to have sufficient knowledge about IOT technologies.

• Journal of the Korean Geographical Society
• /
• v.47 no.4
• /
• pp.522-540
• /
• 2012

The main aim of the paper is to identify the position or status of Korean economic geography in changing global economic geography by reviewing papers published in Korean geographical journals since the mid-1950s. Since the late 20th century as economic geography has developed significantly with the introduction of new research issues, methodologies, and theory and concepts, economic geography in Korea also has gone through rapid development in terms of both quantitative and qualitative perspectives. The paper attempts to analyze trends in Korean economic geography by reviewing agricultural, industrial, commercial geographies, and others since the mid-1950s. The review of economic geography in Korea would be based on four periods classified by research issues and approaches; foundation (~1950s), positioning (1960s and 1970s), jump and rush (1980s and mid-1990s), and transitional period (late 1990s~). Agricultural geography in Korea has decreased due to increases of the interests in industrial geography since the 1980s. In particular, since the late 1990s industrial geography has undergone a significant transition in accordance with the emergence of new theories of institutional perspectives, centering around issues on value chains, innovative cluster, cooperative and competitive networks, foreign direct investment, flexible specialization and venture ecology. Along with this, there has been changes in the interest of commercial geography in Korea from researches on periodical markets, the structure of store formats, and distributions by commodity, to researches on producer services and retailer's locational behaviors and commercial supremacy according to the emergence of new store formats. Since the late 1990s, many researches and discussions associated with the new economic geography began to emerge in Korea. Various research issues are focused on analyzing changes of local, regional and global economic spaces and their processes in relation to institutional perspectives, knowledge and innovation, production chain and innovative networks, industrial clusters and RIS, and geographies of service. Although economic geography in Korea has developed significantly both in quantitative and qualitative perspectives, we pointed out that it has still limited in some specific scope and issues. Therefore, it is likely to imply that its scope and issues should be diversified with new perspectives and approaches.