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Stock-Price Forecasting Based on XGBoost and LSTM

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Computer Systems Science & Engineering 2022年第1期40卷 237-246页

作者： pham Hoang Vuong Trinh Tan Dat Tieu Khoi Mai pham Hoang Uyen pham The bao Information Science Faculty Sai Gon UniversityHo Chi Minh City700000Vietnam University of Economics and Law Ho Chi Minh City700000Vietnam

Using time-series data analysis for stock-price forecasting(SPF)is complex and challenging because many factors can influence stock prices(e.g.,inflation,seasonality,economic policy,societal behaviors).Such factors can be analyzed over time for *** learning and deep learning have been shown to obtain better forecasts of stock prices than traditional *** study,therefore,proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning ***,we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant ***,we fed selected features into a deep long short-term memory(LSTM)network to forecast stock *** deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future con-textual *** complex structure enables this network to capture more stochasticity within the stock *** method does not change when applied to stock data or Forex *** results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error,mean squared error,and root-mean-square error.

关键词： stock-price forecasting ARIMA XGBoost LSTM deep learning

An improved CRNN for Vietnamese Identity Card Information Recognition

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Computer Systems Science & Engineering 2022年第2期40卷 539-555页

作者： Trinh Tan Dat Le Tran Anh Dang Nguyen Nhat Truong pham Cung Le Thien Vu Vu Ngoc Thanh Sang pham Thi Vuong pham The bao Information Science Faculty Sai Gon UniversityHCM City700000Vietnam Faculty of Electrical&Electronics Engineering University of TechnologyHCM City700000Vietnam

This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity(CID)***,we apply Mask-RCNN to segment and align the CID card from the ***,we present two approaches to detect the CID card’s text lines using traditional image processing techniques compared to the EAST ***,we introduce a new end-to-end Convolutional Recurrent Neural Network(CRNN)model based on a combination of Connectionist Temporal Classification(CTC)and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions *** length of the CTC’s output label sequence is applied to the attention-based decoder prediction to make the final label *** process helps to decrease irregular alignments and speed up the label sequence estimation during training and inference,instead of only relying on a data-driven attention-based encoder-decoder to estimate the label sequence in long *** may directly learn the proposed model from a sequence of words without detailed *** evaluate the proposed system using a real collected Vietnamese CID card dataset and find that our method provides a 4.28%in WER and outperforms the common techniques.

关键词： Vietnamese text recognition OCR CRNN BLSTM attention mechanism joint CTC-Attention

Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers

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International Journal of Sediment Research 2023年第1期38卷 128-140页

作者： Duong Tran Anh Ahad Hasan Tanim Daniel Prakash Kushwaha Quoc bao pham Van Hieu Bui HUTECH University Center for River Harbor and Landslide Research Chittagong University of Engineering & Technology Govind Ballabh Pant University of Agriculture and Technology Institute of Applied Technology Thu Dau Mot University Faculty of Mechanical Engineering Thuyloi University

The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution(GSD), this study proposed a hybrid model of deep learning long Short-Term Memory(LSTM) combined with the Discrete Element Method(DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in threedimensional(3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination(R~2), root mean squared error(RMSE), and mean absolute error(MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples.

关键词： Numerical modeling Discrete element method LSTM Bed porosity Gravel-bed river

Characterization of Electrochromic Properties of Polyaniline Thin Films Electropolymerized in H₂SO₄ Solution

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Open Journal of Organic Polymer Materials 2016年第1期6卷 30-37页

作者： Dang Hai Ninh Tran Thi Thao pham duy long Nguyen Nang Dinh University of Engineering and Technology Vietnam National University Hanoi Hanoi Vietnam Institute of Materials Science Vietnamese Academy of Science and Technology Hanoi Vietnam

Polyaniline (PANI) onto indium-doped tin-oxide (ITO)-coated glass samples were prepared by electroopolymerization in 0.5 M H2SO4 solution. Structure and morphology characterization of the PANI films demonstrated that the films were grown onto ITO substrates in the form of polycrystalline microbelts separated by micropores. By analysing the UV-Vis absorption spectra of the PANI films, the energy bandgap was found to be approximately 2.75 eV. The PANI/ITO films exhibited a good reversible electrochromic display (ECD) performance when cycled in 0.1 M LiClO4 + pro-pylene carbonate. The response time of the ECD coloration was found to be as small as 15 s and the coloration efficiency was found to be 8.85 cm2 C-1. After 100 cycles of the ECD performance, the cyclic voltammetry curve of the working electrode maintained unchanged. This demonstrates that the electropolymerized PANI films can be served as a good candidate for ECD applications, taking advantage of their excellent properties in terms of chemical stability.

关键词： Polyaniline Thin Films UV-Vis Absorption Electrochromic Property

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression(SVR)with GWO,BAT and COA algorithms

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Geoscience Frontiers 2021年第3期12卷 384-398页

作者： Abdul-Lateef Balogun Fatemeh Rezaie Quoc bao pham Ljubomir Gigović Siniša Drobnjak Yusuf AAina Mahdi Panahi Shamsudeen Temitope Yekeen Saro Lee Geospatial Analysis and Modelling(GAM)Research Group Department of Civil&Environmental EngineeringUniversitit Teknologi PETRONS(UTP)Seri Iskandar 32610PerakMalaysia Geoscience Platform Research Division Korea Institute of Geoscience and Mineral Resources(KIGAM)124Gwahak-ro Yuseong-guDaejeon 34132Republic of Korea Department of Geophysical Exploration Korea University of Science and Technology217 Gajeong-ro Yuseong-guDaejeon 34113Republic of Korea Institute of Research and Development Duy Tan UniversityDanang 550000Vietnam Department of Geography University of Defence11000 BelgradeSerbia Military Geographical Institute 11000 BelgradeSerbia Department of Geomatics Engineering Technology Yanbu Industrial CollegeYanbuSaudi Arabia Division of Science Education College of Education#4–301Gangwondaehak-gil Chuncheon-siKangwon National UniversityGangwon-do 24341Republic of Korea Faculty of Environmental and Chemical Engineering Duy Tan UniversityDanang 550000Vietnam

In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility *** created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)*** landslide susceptibility map was produced using 14 causative *** applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive *** resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility ***-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of *** three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.

关键词： Landslide Machine learning Metaheuristic Spatial modeling Support vector regression

Flood susceptibility modelling using advanced ensemble machine learning models

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Geoscience Frontiers 2021年第3期12卷 60-77页

作者： Abu Reza Md Towfiqul Islam Swapan Talukdar Susanta Mahato Sonali Kundu Kutub Uddin Eibek Quoc bao pham Alban Kuriqi Nguyen Thi Thuy Linh Department of Disaster Management Begum Rokeya UniversityRangpurBangladesh Department of Geography University of Gour BangaMaldaWest BengalIndia Environmental Quality Atmospheric Science and Climate Change Research GroupTon Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Environment and Labour Safety Ton Duc Thang UniversityHo Chi Minh CityVietnam CERIS Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal Institute of Research and Development Duy Tan UniversityDanang 550000Vietnam Faculty of Environmental and Chemical Engineering Duy Tan UniversityDanang 550000Vietnam

Floods are one of nature's most destructive disasters because of the immense damage to land,buildings,and human *** is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash ***,earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood *** this study,we applied and assessed two new hybrid ensemble models,namely Dagging and Random Subspace(RS)coupled with Artificial Neural Network(ANN),Random Forest(RF),and Support Vector Machine(SVM)which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin,the northern region of *** application of these models includes twelve flood influencing factors with 413 current and former flooding points,which were transferred in a GIS *** information gain ratio,the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential *** the validation and the comparison of these models,for the ability to predict the statistical appraisal measures such as Freidman,Wilcoxon signed-rank,and t-paired tests and Receiver Operating Characteristic Curve(ROC)were *** value of the Area Under the Curve(AUC)of ROC was above 0.80 for all *** flood susceptibility modelling,the Dagging model performs superior,followed by RF,the ANN,the SVM,and the RS,then the several benchmark *** approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

关键词： Flood hazard Flood vulnerability Flash floods Debris flow Teesta River basin Bangladesh

Predicting soil erosion hazard in Lattakia Governorate(W Syria)

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International Journal of Sediment Research 2021年第2期36卷 207-220页

作者： Mohammed Safwan Khallouf Alaa Alshiehabi Omran bao pham Quoc Thi Thuy Linh Nguyen Nam Thai Van Tran Anh Duong Harsányi Endre Institution of Land Utilization Technology and Regional Planning Faculty of Agricultural and Food Sciences and Environmental Management University of Debrecen Administration of Natural Resources Management General Commission for Scientific Agricultural Research (GCSAR) 3. Department of Soil Sciences and Land Reclamation Faculty of Agriculture Al-Baath University Department of Soil and Environment Swedish University of Agricultural Sciences (SLU) Institute of Research and Development Duy Tan University Faculty of Environmental and Chemical Engineering Duy Tan University Faculty of Water Resource Engineering Thuyloi University Ho Chi Minh City University of Technology (HUTECH) Sustainable Management of Natural Resources and Environment Research Group Faculty of Environment and Labour Safety Ton Duc Thang University

The main objective of this study is to predict soil erosion in the Lattakia Governorate(W Syria) using the Water Erosion Prediction Project model(WEPP) and to compare the result with that of the RUSLE. Field survey and data collection were carried out, and 44 soil samples were analyzed. In addition, all the necessary input files were prepared for use in the WEPP model and RUSLE. Results show that more than of 80% of the locations studied experience slight to moderate erosion(less than 5 t/ha/y), whereas the rest of the locations experience severe soil erosion hazard. Moreover, the volume of runoff estimated by the WEPP model is in the range of 51 e321 mm, and the R~2 between the simulated soil erosion and the predicted runoff reached 0.68. Interestingly, the R~2 between the WEPP model and RUSLE is 0.56, which indicates a good correlation between the two models.

关键词： Soil erosion Run-off WEPP RUSLE Syria

GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms

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Geoscience Frontiers 2021年第2期12卷 857-876页

作者： Sk Ajim Ali Farhana Parvin Jana Vojteková Romulus Costache Nguyen Thi Thuy Linh Quoc bao pham Matej Vojtek Ljubomir Gigović Ateeque Ahmad Mohammad Ali Ghorbani Department of Geography Faculty of ScienceAligarh Muslim University(AMU)AligarhUP 202002India Department of Geography and Regional Development Faculty of Natural SciencesConstantine the Philosopher University in NitraTrieda A.Hlinku 194901 NitraSlovakia Research Institute of the University of Bucharest 90-92 Sos.Panduri5th DistrictBucharest 050663Romania National Institute of Hydrology and Water Management București-Ploiești Road97E1st DistrictBucharest 013686Romania Thuyloi University 175 Tay SonDong DaHanoiViet Nam Institute of Research and Development Duy Tan UniversityDanang 550000Viet Nam Faculty of Environmental and Chemical Engineering Duy Tan UniversityDanang 550000Viet Nam Department of Geography University of Defence11000 BelgradeSerbia Sustainable Management of Natural Resources and Environment Research Group Faculty of Environment and Labour SafetyTon Duc Thang UniversityHo Chi Minh CityViet Nam

Hazards and disasters have always negative impacts on the way of *** is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout *** present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,*** this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Naïve Bayes(NB)classifier,and random forest(RF)classifier were ***,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,*** geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover *** ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model ***,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Naïve Bayes(NB)classifier,and random forest(RF)classifier ***,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and *** results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).

关键词： Landslide susceptibility modeling Geographic information system Fuzzy DEMATEL Analytic network process Naïve Bayes classifier Random forest classifier

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越南Orgarnicare展示商铺

现代装饰 2019年第6期 36-41页

作者：无 Nguyēn Hai long Tran Thi Ngu Ngon Nguyēn Tuan Dang pham Khánh duy LêVan duy bao Saurav Shah Quang Dam(摄影) 范嘉苑(编译) Tropical Space 越南Tropical Space

Orgarnicare项目是一个为越南传统鱼露和有机产品而打造的展示空间。该项目由一座建于1975年之前的旧房子改造而成,其本身已历经过数次加固及扩建。对于此次的展示商铺改造,设计师旨在用具有越南特色的传统建材构建空间,让独具热带风情... 详细信息

Orgarnicare项目是一个为越南传统鱼露和有机产品而打造的展示空间。该项目由一座建于1975年之前的旧房子改造而成,其本身已历经过数次加固及扩建。对于此次的展示商铺改造,设计师旨在用具有越南特色的传统建材构建空间,让独具热带风情的空间元素与传统越南鱼露相互映衬,产生有趣的对话。

关键词：框架系统 Orgarnicare 热带风情