• The Journal of Asian Finance, Economics and Business
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• 제8권2호
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• pp.643-655
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• 2021

This study investigated the effect of the Amanah Ikhtiar Malaysia (AIM) microfinancing on the economic, social, and household empowerment of women borrowers in Malaysia. The study used a quantitative approach based on primary data. For this study, the participants comprised 384 AIM borrowers from Terengganu, Kelantan, and Pahang in the east coast region of Malaysia. Purposive stratified random sampling was used as well as the Krejcie and Morgan method to count the number of samples. Descriptive statistics and the Women Empowerment Index (WEI) were used in the analysis. The study findings reveal that AIM microfinancing affects the economic, social, and household empowerment of women borrowers in Malaysia. However, in comparing the three categories, women enjoyed more freedom in social and household decision-making than in economic decision-making. The present study recommends policies for the successful and effective operation of microfinance programs by providing the necessary guidelines for the control of AIM loan for women borrowers; increasing income-generating activities, sufficient access of credit, and proper education for the borrowers; and giving economic freedom of choice with necessary skill training policymaking options for the government and NGOs with the aim to improve the total household income and empowerment of the microcredit borrowers in Malaysia.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1891-1908
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• 2021

Cyber deception defense mitigates Advanced Persistent Threats (APTs) with deploying deceptive entities, such as the Honeyfile. The Honeyfile distracts attackers from valuable digital documents and attracts unauthorized access by deliberately exposing fake content. The effectiveness of distraction and trap lies in the enticement of fake content. However, existing studies on the Honeyfile focus less on this perspective. In this work, we seek to improve the enticement of fake text content through enhancing its readability, indistinguishability, and believability. Hence, an enticing deceptive-content generator, EDGE, is presented. The EDGE is constructed with three steps: extracting key concepts with a semantics-aware K-means clustering algorithm, searching for candidate deceptive concepts within the Word2Vec model, and generating deceptive text content under the Integrated Readability Index (IR). Furthermore, the readability and believability performance analyses are undertaken. The experimental results show that EDGE generates indistinguishable deceptive text content without decreasing readability. In all, EDGE proves effective to generate enticing deceptive text content as deception defense against APTs.

• Structural Engineering and Mechanics
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• 제80권6호
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• pp.677-688
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• 2021

The building design codes and standards in many countries usually are either fully or partially adopted from the international codes. However, regional conditions like the quality of construction industry and different statistical parameters of load and resistance have essential roles in the code calibration of building design codes. This paper presents a probabilistic approach to assess the reliability level of adopted national building codes by simulating design situations and considering all load combinations. The impact of the uncertainty of wind and earthquake loads, which are entirely regional condition dependent and have a high degree of uncertainty, are quantified. In this study, the design situation is modeled by generating thousands of numbers for load effect ratios, and the reliability level of steel elements for all load combinations and different load ratios is established and compared to the target reliability. This approach is applied to the Iranian structural steel code as a case study. The results indicate that the Iranian structural steel code lacks safety in some load combinations, such as gravity and earthquake load combinations, and is conservative for other load combinations. The present procedure can be applied to the assessment of the reliability level of other national codes.

• International Journal of Contents
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• 제17권4호
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• Journal of Forest and Environmental Science
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• 제39권2호
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• pp.65-72
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• 2023

The endangered species of Cyathea spinulosa (tree ferns) are among the least concerned ferns of Nepal that bring threats to them and their habitat. A way to reduce such threats is by maintaining a database of species' whereabouts and generating a scientific understanding the habitat preferences. This will eventually help in the formulation of conservation plans for the species. This research aimed to characterize the suitable habitat of C. spinulosa by enumerating the location of species in the Panchase Forests of central Nepal. The statistical index method was applied to relate the occurrence locations of species with various environmental factors for the development of indices. The suitable habitat of C. spinulosa (more and most suitable categories) covered 119 km² and accounted for 43% of the total area studied. 74.4% of occurrence locations of C. spinulosa were recorded from these habitats. The habitat characteristics suitable for C. spinulosa were: proximity to streams (high moisture), land covered by forested area (shady area), mid-elevations of hills about 1,000 m to 2,000 m (sub-tropical climate), slope gradient of 20° to 40° (steep slopes), and northern to eastern aspects. These habitat characteristics could be considered for in-situ protection of tree ferns and designating the conservation plots.

• 대한치과교정학회지
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• 제52권2호
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• pp.102-111
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• 2022

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Journal of Acupuncture Research
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• 제41권2호
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• pp.129-134
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• 2024

Knee osteoarthritis (KOA) is a prevalent degenerative joint disease causing significant pain and dysfunction. This case report presents the use of electromagnetic acupuncture utilizing a Whata 153 device generating a magnetic field to enhance acupuncture stimulation for the treatment of KOA. A 69-year-old female diagnosed with KOA experienced a reduction in pain (numerical rating scale score from 7 to 4), improved gait, and decreased stiffness and swelling after daily electromagnetic acupuncture treatments during hospitalization. In addition, the Korean Western Ontario and McMaster Universities Osteoarthritis Index scoreimproved from 20 to 14, and the patient rated her overall improvement as "significantly improved" on the patient's global impression of change scale. Although these findings suggest potential benefits of electromagnetic acupuncture for KOA, the case report design limits its generalizability. More controlled trials are warranted to confirm the efficacy and safety of electromagnetic acupuncture as a treatment of KOA.

• 대한원격탐사학회지
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• 제40권2호
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• pp.123-139
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• 2024

Forest vertical structure is vital for comprehending ecosystems and biodiversity, in addition to fundamental forest information. Currently, the forest vertical structure is predominantly assessed via an in-situ method, which is not only difficult to apply to inaccessible locations or large areas but also costly and requires substantial human resources. Therefore, mapping systems based on remote sensing data have been actively explored. Recently, research on analyzing and classifying images using machine learning techniques has been actively conducted and applied to map the vertical structure of forests accurately. In this study, Sentinel-2 and digital surface model images were obtained on two different dates separated by approximately one month, and the spectral index and tree height maps were generated separately. Furthermore, according to the acquisition time, the input data were separated into cases 1 and 2, which were then combined to generate case 3. Using these data, forest vetical structure mapping models based on random forest, support vector machine, and extreme gradient boost(XGBoost)were generated. Consequently, nine models were generated, with the XGBoost model in Case 3 performing the best, with an average precision of 0.99 and an F1 score of 0.91. We confirmed that generating a forest vertical structure mapping model utilizing bi-seasonal data and an appropriate model can result in an accuracy of 90% or higher.

• 한국산학기술학회논문지
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• 제12권1호
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• pp.545-551
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• 2011

최근 고품질의 수돗물에 대한 소비자의 요구가 상승함에 따라 상수도 배급수관의 내부 부식에 의한 수질악화 및 부식제어 연구에 대한 관심이 높아지고 있다. 이에 따라 노후 관 교체 사업을 대신하여 수질 관리를 위한 부식 제어 수단을 강구하지 않고서는 근본적인 문제 해결이 이루어질 수 없는 실정이다. 본 연구에서는 수질안정화 약품 주입에 의한 상수도관 내부 부식제어 효율을 평가하기 위해 Pilot Plant 실험을 실시하였으며, 부식성제어 효율은 물의 부식성을 나타내는 LSI(Langelier Saturation Index)값에 의해 평가되었다. 실험결과, Pilot Plant에 의해 제조된 반응수는 수질안정화 약품인 액상소석회($Ca(OH)_2$, liquid lime)의 주입으로 부식성이 개선되어 철 용출이 억제되는 효과가 확인되었다. 강관과 동관을 절단하여 제작한 시편의 부식도 측정을 통해 각각 35.4, 44.5%의 부식제어 효과가 있음을 확인하였고 수질안정화 약품이 주입된 Sample관이 더 두터운 부식 생성물 층을 갖고 있는 것으로 밝혀졌으며, 결과적으로 수질안정화 약품을 투입한 배관이 부식 방지 측면에서 안정한 수질을 갖고 있음을 알 수 있었다.