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| 1 | APASL consensus statements and management algorithms for hepatitis C virus infection显示文摘 | Masao Omata Tatsuo Kanda Ming-Lung Yu Osamu Yokosuka Seng-Gee Lim Wasim Jafri Ryosuke Tateishi Saeed S. Hamid Wan-Long Chuang Anuchit Chutaputti Lai Wei Jose Sollano Shiv Kumar Sarin Jia-Horng Kao Geoffrey W. McCaughan | 2012 | Hepatology International2012，，2： | 8 |
| 2 | APASL consensus statements and management algorithms for hepatitis C virus infection显示文摘 | Masao Omata Tatsuo Kanda Ming-Lung Yu Osamu Yokosuka Seng-Gee Lim Wasim Jafri Ryosuke Tateishi Saeed S. Hamid Wan-Long Chuang Anuchit Chutaputti Lai Wei Jose Sollano Shiv Kumar Sarin Jia-Horng Kao Geoffrey W. McCaughan | 2012 | Hepatology International2012，，2： | 3 |
| 3 | Successful recanalization with multimodality endovascular interventional therapy in acute ischemic stroke显示文摘Stroke is an important cause of death and disability in adults. However, effective treatments for patients with acute ischemic stroke are limited. Intravenous recombinant tissue plasminogen activator(iv rtPA) within 4.5 h after onset has been approved as a standard treatment for patients with acute ischemic stroke. However, due to time constraints, less than one percent of acute ischemic stroke patients in Thailand are able to obtain iv rtPA. Although endovascular interventional therapy has not yet been approved as standard treatment in acute ischemic stroke, it is the one of the potentially effective treatment options. There are several reliable methods of endovascular therapy for acute ischemic stroke patients. Endovascular interventional therapy has rarely been done in Thailand. We report seven patients with successful recanalization after endovascular treatment in acute large vessel stroke from a single stroke center in Thailand. Patient screening and selection with multimodal imaging protocol and multimodality methods of endovascular interventional therapy are described. | Amorn Jongsathapongpan Anuchit Raumthanthong Sombat Muengtaweepongsa | 2014 | World Journal of Clinical Cases2014，2，3： | 2 |
| 4 | Sustained response to peginterferon alfa-2a (40?kD) with or without lamivudine in Asian patients with HBeAg-positive and HBeAg-negative chronic hepatitis B显示文摘 | Teerha Piratvisuth George Lau You-Chen Chao Rui Jin Anuchit Chutaputti Q.-B. Zhang Tawesak Tanwandee Peter Button Matei Popescu | 2008 | Hepatology International2008，，1： | 1 |
| 5 | Epidemiologic and virologic characteristics of hepatitis B virus genotype B having the recombination with genotype C显示文摘 | Fuminaka Sugauchi Etsuro Orito Takafumi Ichida Hideaki Kato Hiroshi Sakugawa Shinichi Kakumu Takafumi Ishida Anuchit Chutaputti Ching-Ling Lai Robert G. Gish Ryuzo Ueda Yuzo Miyakawa Masashi Mizokami | 2003 | Gastroenterology2003，，4： | 1 |
| 6 | Baseline characteristics and early on-treatment response predict the outcomes of 2<ce:hsp sp='0.25'/>years of telbivudine treatment of chronic hepatitis B显示文摘 | Stefan Zeuzem Edward Gane Yun-Fan Liaw Seng G. Lim Adrian DiBisceglie Maria Buti Anuchit Chutaputti Jens Rasenack Jinlin Hou Christopher O’Brien Tuan T. Nguyen Jidong Jia Thierry Poynard Bruce Belanger Weibin Bao Nikolai V. Naoumov | 2009 | Journal of Hepatology2009，，1： | 1 |
| 7 | Baseline characteristics and early on-treatment response predict the outcomes of 2years of telbivudine treatment of chronic hepatitis B显示文摘 | Stefan Zeuzem Edward Gane Yun-Fan Liaw Seng G. Lim Adrian DiBisceglie Maria Buti Anuchit Chutaputti Jens Rasenack Jinlin Hou Christopher O’Brien Tuan T. Nguyen Jidong Jia Thierry Poynard Bruce Belanger Weibin Bao Nikolai V. Naoumov | 2009 | Journal of Hepatology2009，，1： | 1 |
| 8 | APASL consensus statements and management algorithms for hepatitis C virus infection显示文摘 | Masao Omata Tatsuo Kanda Ming-Lung Yu Osamu Yokosuka Seng-Gee Lim Wasim Jafri Ryosuke Tateishi Saeed S. Hamid Wan-Long Chuang Anuchit Chutaputti Lai Wei Jose Sollano Shiv Kumar Sarin Jia-Horng Kao Geoffrey W. McCaughan | 2012 | Hepatology International2012，，2： | 1 |
| 9 | An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch显示文摘Smoking is a major cause of cancer,heart disease and other afflictions that lead to early mortality.An effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment initiatives.Smoking activities often accompany other activities such as drinking or eating.Consequently,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR framework.The proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke dataset.Three different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%. | Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul | 2023 | Intelligent Automation & Soft Computing2023，，1： | 1 |
| 10 | Sustained response to peginterferon alfa-2a (40?kD) with or without lamivudine in Asian patients with HBeAg-positive and HBeAg-negative chronic hepatitis B显示文摘 | Teerha Piratvisuth George Lau You-Chen Chao Rui Jin Anuchit Chutaputti Q.-B. Zhang Tawesak Tanwandee Peter Button Matei Popescu | 2008 | Hepatology International2008，，1： | 1 |
| 11 | Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network显示文摘Falls are the contributing factor to both fatal and nonfatal injuries in the elderly.Therefore,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be essential.Recently,researchers have turned their attention from post-impact fall detection to pre-impact fall detection.Pre-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based methods.Moreover,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating time.In this work,we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial sensors.The proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm complexity.After training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,respectively.In addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LSTM),and a hybrid model(CNN-LSTM).The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models(CNN,LSTM,and CNN-LSTM)with significant improvements. | Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul | 2023 | Intelligent Automation & Soft Computing2023，，6： | 0 |
| 12 | A Modified-Simplified MPPT Technique for Three-Phase Single-State Grid-Connected PV Systems显示文摘Nowadays,the single state inverter for the grid-connected photovoltaic(PV)systems is becoming more and more popular as they can reduce circuit complexity resulting in less power losses of the inverter.This paper focuses on the use of model predictive control(MPC)to control a 3-phase and 2-level single-state grid-connected inverter in order to regulate the PV maximum power point(MPP).The algorithm of MPC scheme was done to measure the simultaneous current signal including predicting the next sampling current flow.The reference current(Id∗)was used to control the distribution of electrical power from the solar cell to the grid.To be able to control the maximum power point tracking(MPPT)when the sunlight suddenly changes,so that a developing MPPT based on estimation current perturbation and observation(ECP&O-MPPT)technique was used to control the reference current.This concept was experimented by using MATLAB/Simulink software package.The proposed technique was tested and compared with the old technique.The simulation results showed that the developed MPPT technique can track the MPP faster when the light changes rapidly under 1,000W/m2,25℃ standard climatic conditions.The MPPT time was 0.015 s.The total harmonic distortion(THD)was 2.17%and the power factor was 1. | Anuchit Aurairat Boonyang Plangklang | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 13 | Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors显示文摘The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new worker.Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques.Despite widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is problematic.Through the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction workers.The suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker tasks.The proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work activities.In addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed model.With an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalfindings revealed that the suggested model could accurately recognize the actions of construction workers.Furthermore,we examined the impact of window size and sensor position on the identification efficiency of the proposed method. | Sakorn Mekruksavanich Anuchit Jitpattanakul | 2023 | Intelligent Automation & Soft Computing2023，，5： | 0 |
| 14 | Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor显示文摘Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical impli-cations.Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments.Consequently,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts.This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection capability.The proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body parts.The experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor activity.Moreover,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and F1-score.Furthermore,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability. | Sakorn Mekruksavanich Narit Hnoohom Anuchit Jitpattanakul | 2023 | Intelligent Automation & Soft Computing2023，37，9： | 0 |
| 15 | Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors显示文摘Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road accidents.As a result,reckless driving behaviour can cause congestion and delays.Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem.Previous research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s face.On the other hand,driving a car is a complicated action that requires a wide range of body move-ments.In this work,we proposed a ResNet-SE model,an efficient deep learning classifier for driving activity clas-sification based on signal data obtained in real-world traffic conditions using smart glasses.End-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning model.Sensor data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this study.We performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its performance.According to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%. | Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul | 2023 | Intelligent Automation & Soft Computing2023，38，11： | 0 |
| 16 | A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification显示文摘In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation data.The classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS domain.Recently,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods.The ability to extract important features is vital in making RST classification more accurate.This work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction models.We used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification performance.Comparative experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art models.The proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt roads.Moreover,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively. | Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
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