• Journal of Multimedia Information System
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• 제4권4호
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• pp.163-170
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• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.

• Journal of Multimedia Information System
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• 제7권1호
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• pp.87-96
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• 2020

Parents' desire for daily English practice for students and the urge for a etter English education has led to a demand for a highly qualified educational curriculum. In addition to getting deep-dive insights into parents' decision-making process, the increasingly important role of STEM education method has been proved in accordance with plenty of evidenced-based many of previous research. This study was conducted to examine the five independent factors affecting parents' intentions when designating English centers for their children, respectively named: Facility, Teachers, Brands, Social factors and Tuition. Through a preliminary approach, 235 samples were analyzed. Analysis of the sample responses reveals the importance level of five independent factors affecting Parents' intention to choose English centers in descending order: Facilities, Teachers, Brands, Tuition and Social factors. Furthermore, a positive correlation was found between STEM and Intention to choose English centers, indicating that whether the center applies STEM-related curriculum in English courses or not, in fact, has a significant impact on parents' decision. In terms of attitude towards STEM, further statistical tests revealed an interesting finding, which is the fact that how STEM affects Intention of parents is directly proportional to the Attitude towards STEM. With the help of SPSS 20 and AMOS 20, the SEM analysis was processed in order to draw conclusions about factors impacting Intentions to choose English centers. Combining with Multi-group analysis, the major role of STEM education has been assessed in relation to plenty of correlations. In light of recent major and massive growth of English teaching market in Vietnam, several suggestions and recommendations in terms of sustainable developments have been displayed in this research.

• Journal of Multimedia Information System
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• 제7권1호
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• pp.1-10
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• 2020

The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.

• 경영정보학연구
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• 제13권2호
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• pp.73-87
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• 2011

본 연구는 국내 대학 및 소속을 두고 있는 한국 IS 연구자의 연구실적을 측정하였다. 본 연구자들은 선행연구에 기반해 MIS Quarterly(MISQ), Information Systems Research(ISR), Journal of Management Information Systems(JMlS), Journal of the Association for Information Systems(JAIS), European Journal of Information Systems(EJIS), Information Systems Journal(ISJ), Decision Support Systems(DSS), Communications of the Association for Information Systems(CAIS), Information and Management(I&M), Journal of Strategy Information Systems(JSlS), Journal of Information Technology(JIT) 11 개 선도 저널을 선정하였고, 2007년부터 2009년까지 출판된 한국 IS 연구자의 논문을 싸이언토메트릭 기법을 통해 측정하였다. 본 연구결과는 한국 IS 연구자의 우수한 연구실적에 대한 정보를 제공함으로써 우수 연구자의 파악 및 채용 정보 획득에 도움을 줄 것이고, 더불어 신입생 유치에 있어 학교와 학과의 우수성을 홍보하는데 자료를 제공해 줄 것이다. 본 연구는 한국 IS 연구자의 연구실적 측정에 대한 연구가 드믄 실정에서 연구의 독창성이 존재하며, 한국 IS 연구자의 논문 출판 추세를 이해하는데 도움을 제공할 것이다.

• Journal of Multimedia Information System
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• 제5권4호
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• pp.283-290
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• 2018

Smarthome is one of the remarkable fields of the industrial revolution 4.0, which are popular in many countries such USA, EURO, Japan and Korea. However, how Vietnamese people are considering and having intension to use smarthome devices is still a question for marketers and researchers. This study develops a comprehensive research model that can explain potential customers' behavioral intentions to adopt and use Smarthome services in Viet Nam and figure which key factors affect the intention. This study proposes and validates a new theoretical model that extends the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). The model consists of eight factors that affect one element (Behavioral Intention), specifically: Habits, Perceived usefulness, Perceived ease of use, Trust, Innovativeness Personal, Perceived value, Hedonic motivations and Social influences. Data were collected from 304 Vietnamese citizens by sending the email to survey questionnaire, direct contact with the experienced smarthome users. Through regression analysis, data from samples were analysed with SPSS 20 software and verification of hypotheses, the results show the key determinants affecting the intention to use smarthome appliances in Vietnam: Perceived Value, Social Influence, Perceived Usefulness, Perceived Ease of Use and Trust. From this result, several recommendations have been suggested to to smarthome devices vendors and marketer to improve products as well as marketing approaches to meet customer needs.

• Journal of Minimally Invasive Surgery
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• 제21권4호
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• pp.148-153
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• 2018

Purpose: Single incision laparoscopic appendectomy (SILA) is a widely used surgical procedure for treatment of appendicitis with better cosmesis. However, many surgeons generally tend to choose conventional multiport laparoscopic appendectomy regarding with complicated appendicitis. The aim of this study is to demonstrate the safety and feasibility of SILA for treatment of complicated appendicitis by comparison with 3-ports conventional laparoscopic appendectomy (CLA). Methods: Retrospective chart review of patients diagnosed appendicitis at single hospital during January 2015 to May 2017 collected 500 patients. Among 134 patients with complicated appendicitis, we compared outcomes for 29 patients who got SILA and 105 patients who got CLA. Results: 179 and 321 patients were treated by SILA and CLA, respectively. 134 (26.8%) patients were treated for complicated appendicitis, 29 patients by SILA and 105 patients by CLA, respectively. There was no case converted to open or added additional trocar in both groups. There were no differences in demographics with regard to age, sex, body mass index (BMI), and American society of anesthesiologists (ASA) scores. There was no difference in mean operating time ($58.97{\pm}18.53$ (SILA) vs. $57.57{\pm}21.48$ (CLA), p=0.751). The drain insertion rate (6.9% vs 37.1%, p=0.001) and the length of hospital stay ($2.76{\pm}1.41$ vs. $3.97{\pm}2.97$, p=0.035) were lower in SILA group with significance. There was no significant difference in the rate of surgical site infection (6.9% vs. 6.7%, p=1.000). Conclusion: This study demonstrates that SILA is a feasible and safe procedure for treatment of complicated appendicitis.

• Journal of Minimally Invasive Surgery
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• 제21권4호
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• pp.154-159
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• 2018

Purpose: Intussusception is a common cause of intestinal obstruction in children. While most patients can be treated by enema reduction, about 20% require surgery. We investigated the usefulness and feasibility of laparoscopic surgery and the intraoperative risk of bowel resection. Methods: We retrospectively reviewed pediatric patients who underwent surgery for intussusception from 2010 to 2017. We collected data for age, gender, body weight, associated symptoms, duration of symptoms, white blood cell count, operating time, and postoperative complications. Results: Of 155 patients, 37 (23.8%) underwent surgery due to enema reduction failure in 29 (78.3%), recurrence in 6 (16.3%), a suspicious lead point in 1, and suspicious ischemic change observed on ultrasonography in 1. The mean age was $26.8{\pm}18.9$ months (range, 3.5~76.7 months), and the mean body weight was $12.9{\pm}3.9kg$ (range, 5.4~22.2 kg). Laparoscopic surgery was successful in 29 patients (78.4%), and 7 (18.9%) needed bowel resection and anastomosis. The mean operating time was $56.7{\pm}32.8min$. A lead point was found in 3 patients in the bowel resection group (p=0.005); in addition, the operating time and hospital stay were longer in this group. There were no intra- or postoperative complications. Conclusion: Laparoscopic surgery was successful in 78.4% of the patients with a short hospital stay and early oral intake. The only predictive factor for bowel resection was the presence of a lead point. Laparoscopic surgery may be an optimal treatment intervention for children with intussusception, except for those who show initial peritonitis.

• Journal of Multimedia Information System
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• 제7권4호
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• pp.239-248
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• 2020

The performance of the facial recognition system is determined by many technical factors. Further, most of the technical factors have been realized or are still in continued research. The recognition rate has a great influence on performance not only by technical factors but also by other factors. However, researchers are trying to improve the recognition rate by focusing only on technical factors. The mechanism of recognizing is to compare a face image obtained by photography to an already stored face image and determine the score of the similarity. However, if the photographed image is damaged by external light, even a system with a good algorithm will fail to recognize it. Therefore, it is important to prevent the disturbance of light entering from the outside, so it should be blocked, but the camera will not work without light. Thus, it is proposed that a method to secure the external light but block the disturbance of light that affects photography. A method of blocking disturbance light is to use a polarization filter. There are three polarization methods: circular polarization, linear polarization, and elliptical polarization. In this paper, an experiment was performed to overcome disturbance of light using only a circularly polarized filter. In addition, a lighting system that reproduces disturbance light was provided for the experiment, and light of varying intensities and angles was installed to affect the face recognition camera. As a result of actual application, it was determined that a very improved recognition performance in various disturbance light environments.

• Journal of Multimedia Information System
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• 제9권2호
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• pp.113-120
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• 2022

Mongolian folk songs are inspired by Mongolian labor songs and are classified into long and short songs. Mongolian long songs have ancient origins, are rich in legends, and are a great source of folklore. So it was inscribed by UNESCO in 2008. Mongolian written literature is formed under the direct influence of oral literature. Mongolian long song has 3 classes: ayzam, suman, and besreg by their lyrics and structure. In ayzam long song, the world perfectly embodies the philosophical nature of world phenomena and the nature of human life. Suman long song has a wide range of topics such as the common way of life, respect for ancestors, respect for fathers, respect for mountains and water, livestock and animal husbandry, as well as the history of Mongolia. Besreg long songs are dominated by commanded and trained characters. In this paper, we proposed a method to classify their 3 types of long songs using machine learning, based on their lyrics structures without semantic information. We collected lyrics of over 80 long songs and extracted 11 features from every single song. The features are the name of a song, number of the verse, number of lines, number of words, general value, double value, elapsed time of verse, elapsed time of 5 words, and the longest elapsed time of 1 word, full text, and type label. In experimental results, our proposed features show on average 78% recognition rates in function type machine learning methods, to classify the ayzam, suman, and besreg classes.

• Journal of Multimedia Information System
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• 제9권2호
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• pp.145-154
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• 2022

The authors are making a prototype flexible board of a radio-frequency transmitter for measuring an electromyogram (EMG) of a flying moth and plan to apply for an experimental station license from the Ministry of Internal Affairs and Communications of Japan in the summer of 2022. The goal is to create a continuous low-dose exposure standard that incorporates scientific and physiological functional assessments to replace the current standard based on lethal dose 50. This paper describes the technical evaluation of the hardware. The signal of a bipolar EMG electrode is amplified by an operational amplifier. This potential is added to a voltage-controlled crystal oscillator (27 MHz, bandwidth: 4 kHz), frequency-converted, and transmitted from an antenna about 10 cm long (diameter: 0.03 mm). The power source is a 1.55-V wristwatch battery that has a total weight of about 0.3 g (one dry battery and analog circuit) and an expected operating time of 20 minutes. The output power is -7 dBm and the effective isotropic radiated power is -40 dBm. The signal is received by a dual-whip antenna (2.15 dBi) at a distance of about 100 m from the moth. The link margin of the communication circuit is above 30 dB within 100 m. The concepts of this hardware and the measurement data are presented in this paper. This will be the first biological data transmission from a moth with an official license. In future, this telemetry system will improve the detection of physiological abnormalities of moths.