• International Journal of Knowledge Content Development & Technology
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• v.9 no.4
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• pp.7-29
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• 2019

The study was carried out to investigate the influence of demographics and social media use on perceived academic stress of secondary school students in St. Thomas Aquinas College, Akure, Nigeria. This was achieved using five research questions and four null hypotheses. The population of the study consisted of 1,107 students at ISCED level 3, out of which 286 were selected based on the Israel (2003) model for determining sample size. The Ex-Post Facto (EPT) research design of the correlational type was employed to investigate the study while questionnaire was used for data elicitation. Out of the 286 copies distributed, 192 (67% response rate) were retrieved and analyzed using descriptive statistics (frequency, percentage, mean and standard deviation) and inferential statistics (correlation and multiple regression). The findings revealed that there is high frequency of social media use among secondary school students, majorly for meeting new friends and chatting. The perceived academic stress (PAS) of the students was found to be moderate. Of the three demographics considered, only class had significant influence on PAS. Use of social media did not have significantly influence on PAS of the students. When combined, demographics and social media use predicted PAS; and when considered relatively, of the independent variables, only class as an aspect of demographics predicted PAS. Therefore, the authors concluded that PAS of secondary school students is not directly influenced by demographics (except for class) and social media use. Based on the study's conclusion, recommendations were made.

• Nutrition Research and Practice
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• v.13 no.6
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• pp.521-528
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• 2019

BACKGROUND/OBJECTIVES: The aim of this study was to develop Korean food image detection and recognition model for use in mobile devices for accurate estimation of dietary intake. MATERIALS/METHODS: We collected food images by taking pictures or by searching web images and built an image dataset for use in training a complex recognition model for Korean food. Augmentation techniques were performed in order to increase the dataset size. The dataset for training contained more than 92,000 images categorized into 23 groups of Korean food. All images were down-sampled to a fixed resolution of $150{\times}150$ and then randomly divided into training and testing groups at a ratio of 3:1, resulting in 69,000 training images and 23,000 test images. We used a Deep Convolutional Neural Network (DCNN) for the complex recognition model and compared the results with those of other networks: AlexNet, GoogLeNet, Very Deep Convolutional Neural Network, VGG and ResNet, for large-scale image recognition. RESULTS: Our complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ms) than those of the other networks. CONCLUSION: The results showed that K-foodNet achieved better performance in detecting and recognizing Korean food compared to other state-of-the-art models.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.5
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• pp.432-442
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• 2021

Purpose: The aim of this study was to evaluate the nutrient content consumed by children and adolescents on home-prepared versus chef-prepared specific carbohydrate diets (SCD) as therapy for inflammatory bowel disease (IBD). Methods: Dietary intake of two cohorts with active IBD initiating the SCD over 12 weeks was assessed. The home-prepared cohort received detailed guidance from dietitians on implementation of the SCD. The chef in the other cohort was knowledgeable in the SCD and prepared meals from a fixed set of recipes. Data from 3-day diet diaries at 4 different time points were collected. US Recommended Daily Allowances (RDA) were calculated for macronutrients, vitamins, and minerals. Results: Eight participants on the homemade SCD and 5 participants on the chef-prepared SCD were included in analysis. Mean % RDA for energy intake was 115% and 87% for homemade and chef-prepared groups (p<0.01). Mean % RDA for protein intake was 337% for homemade SCD and 216% for chef-prepared SCD (p<0.01). The homemade SCD group had higher mean % RDA values for vitamin A and iron, while the chef-prepared SCD group had higher intake of vitamins B1, B2, D, phosphorus and zinc (p<0.01 for all). Conclusion: The SCD implemented homemade versus chef-prepared can result in significantly different intake of nutrients and this may influence efficacy of this dietary therapy. Meal preparation dynamics and the motivation of families who pursue dietary treatment may play an important role on the foods consumed and the outcomes on dietary therapy with the SCD.

• Animal Bioscience
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• v.35 no.2
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• pp.177-183
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• 2022

Objective: The present study evaluated the efficiency of embryo development and pregnancy of somatic cell nuclear transfer (SCNT) embryos using different source-matured oocytes in Camelus dromedarius. Methods: Camelus dromedarius embryos were produced by SCNT using in vivo- and in vitro- matured oocytes. In vitro embryo developmental capacity of reconstructed embryos was evaluated. To confirm the efficiency of pregnancy and live birth rates, a total of 72 blastocysts using in vitro- matured oocytes transferred into 45 surrogates and 95 blastocysts using in vivo- matured oocytes were transferred into 62 surrogates by transvaginal method. Results: The collected oocytes derived from ovum pick up showed higher maturation potential into metaphase II oocytes than oocytes from the slaughterhouse. The competence of cleavage, and blastocyst were also significantly higher in in vivo- matured oocytes than in vitro- matured oocytes. After embryo transfer, 11 pregnant and 10 live births were confirmed in in vivo- matured oocytes group, and 2 pregnant and 1 live birth were confirmed in in vitro- matured oocytes group. Furthermore, blastocysts produced by in vivo-matured oocytes resulted in significantly higher early pregnancy and live birth rates than in vitro-matured oocytes. Conclusion: In this study, SCNT embryos using in vivo- and in vitro-matured camel oocytes were successfully developed, and pregnancy was established in recipient camels. We also confirmed that in vivo-matured oocytes improved the development of embryos and the pregnancy capacity using the blastocyst embryo transfer method.

• Safety and Health at Work
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• v.13 no.1
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• pp.40-50
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• 2022

Background: Underground mines have several hazards that could lead to serious consequences if they come into effect. Acquiring, evaluating, and using the real-time data from the atmospheric monitoring system and miner's positional information is crucial in deciding the best course of action. Methods: A graphical user interface-based software is developed that uses an AutoCAD-based mine map, real-time atmospheric monitoring system, and miners' positional information to guide on the shortest route to mine exit and other locations within the mine, including the refuge chamber. Several algorithms are implemented to enhance the visualization of the program and guide the miners through the shortest routes. The information relayed by the sensors and communicated by other personnel are collected, evaluated, and used by the program in proposing the best course of action. Results: The program was evaluated using two case studies involving rescue relating to elevated carbon monoxide levels and increased temperature simulating fire scenarios. The program proposed the shortest path from the miner's current location to the exit of the mine, nearest refuge chamber, and the phone location. The real-time sensor information relayed by all the sensors was collected in a comma-separated value file. Conclusion: This program presents an important tool that aggregates information relayed by sensors to propose the best rescue strategy. The visualization capability of the program allows the operator to observe all the information on a screen and monitor the rescue in real time. This program permits the incorporation of additional sensors and algorithms to further customize the tool.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.9-15
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• 2023

Metaverse is a modern new technology that is advancing quickly. The goal of this study is to investigate this technique from the perspective of computer vision as well as general perspective. A thorough analysis of computer vision related Metaverse topics has been done in this study. Its history, method, architecture, benefits, and drawbacks are all covered. The Metaverse's future and the steps that must be taken to adapt to this technology are described. The concepts of Mixed Reality (MR), Augmented Reality (AR), Extended Reality (XR) and Virtual Reality (VR) are briefly discussed. The role of computer vision and its application, advantages and disadvantages and the future research areas are discussed.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.455-462
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• 2020

Noise which is unwanted sound is a serious pollutant that can affect human health, as well as the working and living environment if exposed to humans. However, current noise management on the construction project is generally conducted after the noise exceeds the regulation standard, which increases the conflicts with inhabitants near the construction site and threats to the safety and productivity of construction workers. To overcome the limitations of the current noise management methods, the activities of construction equipment which is the main source of construction noise need to be managed throughout the construction period in real-time. Therefore, this paper proposed a framework for automatically detecting noise sources in construction sites in real-time based on convolutional neural networks (CNNs) according to the following four steps: (i) Step 1: Definition of the noise sources; (ii) Step 2: Data preparation; (iii) Step 3: Noise source classification using the audio CNN; and (iv) Step 4: Noise source detection using the visual CNN. The short-time Fourier transform (STFT) and temporal image processing are used to contain temporal features of the audio and visual data. In addition, the AlexNet and You Only Look Once v3 (YOLOv3) algorithms have been adopted to classify and detect the noise sources in real-time. As a result, the proposed framework is expected to immediately find construction activities as current noise sources on the video of the construction site. The proposed framework could be helpful for environmental construction managers to efficiently identify and control the noise by automatically detecting the noise sources among many activities carried out by various types of construction equipment. Thereby, not only conflicts between inhabitants and construction companies caused by construction noise can be prevented, but also the noise-related health risks and productivity degradation for construction workers and inhabitants near the construction site can be minimized.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1132-1141
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• 2021

Objective: To investigate changes in the median nerve, retinaculum, and carpal tunnel on ultrasound after successful endoscopic carpal tunnel release (ECTR). Materials and Methods: This prospective study involved 37 wrists in 35 patients (5 male, 30 female; mean age ± standard deviation [SD], 56.9 ± 6.7 years) with primary carpal tunnel syndrome (CTS). An in-house developed scoring system (0-3) was used to gauge the clinical improvement after ECTR. Ultrasound was performed before ECTR, and at 1, 3, and 12 months post-ECTR. Changes in the median nerve, flexor retinaculum, and carpal tunnel morphology on ultrasound after ECTR were analyzed. Ultrasound parameters for different clinical improvement groups were compared. Results: All patients improved clinically after ECTR. The average clinical improvement score ± SD at 12 months post-ECTR was 2.2 ± 0.7. The median nerve cross-sectional area proximal and distal to the tunnel decreased at all time intervals post-ECTR but remained swollen compared to normal values. Serial changes in the median nerve caliber and retinacular bowing after ECTR were more pronounced at the tunnel outlet than at the tunnel inlet. The flexor retinaculum had reformed in 25 (68%) of 37 wrists after 12 months. Conclusion: Postoperative changes in median nerve and retinaculum parameters were most pronounced at the tunnel outlet. Even in patients with clinical improvement after ECTR, nearly all ultrasound parameters remain abnormal at one year post-ECTR. These ultrasound parameters should not necessarily be relied upon to diagnose persistent CTS after ECTR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.126-146
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• 2024

Caladium bicolor is one of the most popular plants in Thailand. The original species of Caladium bicolor was found a hundred years ago. Until now, there are more than 500 species through multiplication. The classification of Caladium bicolor can be done by using its color and shape. This study aims to develop a model to classify Caladium bicolor using a transfer learning technique. This work also presents a proof of concept, GUI design, and web application deployment using the user-design-center method. We also evaluated the performance of the following pre-trained models in this work, and the results are as follow: 87.29% for AlexNet, 90.68% for GoogleNet, 93.59% for XceptionNet, 93.22% for MobileNetV2, 89.83% for RestNet18, 88.98% for RestNet50, 97.46% for RestNet101, and 94.92% for InceptionResNetV2. This work was implemented using MATLAB R2023a.