检索结果-内蒙古大学图书馆

Positional Approach to the Voting Function Formation of Random Forest Trees as an Example of Solving the Differentiating Tuberculosis Forms Problem 17

Positional Approach to the Voting Function Formation of Rand...

引用

IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT)

作者： Matviichuk, Oleksandr Horodetska, Olena Linnik, Mykola Biloshytska, Oksana Pavlov, Vladimir Nastenko, Ievgen Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Dept Biomed Cybernet Kiev Ukraine NAVE Ukraine FG Yanovsky Natl Inst Phthisiol & Pulmonol Dept Epidemiol & Org Problems Phthisiol Kiev Ukraine

ISBN: (纸本)9798350334319

The paper proposes a new voting technology for random forest trees - the Positional Approach to the Voting Function Formation (PAVFF). In contrast to existing forms of organizing the voting of random forest trees, the paper proposes to change the subjects of voting and to use as such individual finite elements of the tree, with weights determined in accordance with the competences of new voting units. Each forest tree in the voting function is represented by its individual branch (voting unit) with the corresponding competence level assigned at the stage of tree finite element verification. Furthermore, we propose different mechanisms for organizing the received units in the voting process. The effectiveness of the new mechanism is shown by the example of the differentiation problem of drug-sensitive and drugresistant forms of tuberculosis. The task feature space is formed by the ROI (regions of interest) textural characteristics on the patient's lungs CT scan. The initial feature space was composed of the elements of a few textural characteristic matrices. From over half a million input features, a few optimal ensembles were selected to form random forest trees. We used intra- and inter-class variance selection techniques for this purpose, with the final selection made by a genetic algorithm using a combined correlation criterion. After verification of voting units (finite elements of trees) 3 variants of voting by competence were formed: by the most competent unit, weighted average participation of all participants, and group voting with coefficient revaluation by the group method of data handling. The results were compared with similar voting by random forest trees. A 5% improvement in classification quality is shown.

关键词： Random Forest Voting Function voting unit classification trees union metrics of confidence group method of data handling drug-sensitive drug-resistant form of tuberculosis

来源：评论

学校读者我要写书评

暂无评论

Multiple graph kernel learning based on GMDH-type neural network

引用

INFORMATION FUSION 2021年 66卷 100-110页

作者： Xu, Lixiang Bai, Lu Xiao, Jin Liu, Qi Chen, Enhong Wang, Xiaofeng Tang, Yuanyan Hefei Univ Coll Artificial Intelligence & Big Data Hefei 230601 Anhui Peoples R China Cent Univ Finance & Econ Sch Informat Beijing 100081 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Sichuan Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Univ Macau Zhuhai UM Sci & Technol Res Inst Macau Peoples R China UOW Coll Hong Kong Fac Sci & Technol Hong Kong Peoples R China

Multiple kernel learning (MKL), as a principled classification method, selects and combines base kernels to increase the categorization accuracy of Support Vector Machines (SVMs). The group method of data handling neural network (GMDH-NN) has been applied in many fields of optimization, data mining, and pattern recognition. It can automatically seek interrelatedness in data, select an optimal structure for the model or network, and enhance the accuracy of existing algorithms. We can utilize the advantages of the GMDH-NN to build a multiple graph kernel learning (MGKL) method and enhance the categorization performance of graph kernel SVMs. In this paper, we first define a unitized symmetric regularity criterion (USRC) to improve the symmetric regularity criterion of GMDH-NN. Second, a novel structure for the initial model of the GMDH-NN is defined, which uses the posterior probability output of graph kernel SVMs. We then use a hybrid graph kernel in the H-1-space for MGKL in combination with the GMDH-NN. This way, we can obtain a pool of optimal graph kernels with different kernel parameters. Our experiments on standard graph datasets show that this new MGKL method is highly effective.

关键词： Support vector machine group method of data handling Ensemble selection Regularity criterion Probabilistic output

来源：评论

学校读者我要写书评

暂无评论

Predicting beach profile evolution with group method data handling-type neural networks on beaches with seawalls

引用

Frontiers of Structural and Civil Engineering 2013年第2期7卷 117-126页

作者： M.A.LASHTEH NESHAEI M.A.MEHRDAD N.ABEDIMAHZOON N.ASADOLLAHI Department of Civil Engineering Guilan UniversityP.O.BOX:3756RashtIran

A major goal of coastal engineering is to develop models for the reliable prediction of short-and longterm near shore *** most successful coastal models are numerical models,which allow flexibility in the choice of initial and boundary *** the present study,evolutionary algorithms(EAs)are employed for multi-objective Pareto optimum design of group method data handling(GMDH)-type neural networks that have been used for bed evolution modeling in the surf zone for reflective beaches,based on the irregular wave experiments performed at the Hydraulic Laboratory of Imperial College(London,UK).The input parameters used for such modeling are significant wave height,wave period,wave action duration,reflection coefficient,distance from shoreline and sand *** this way,EAs with an encoding scheme are presented for evolutionary design of the generalized GMDH-type neural networks,in which the connectivity configurations in such networks are not limited to adjacent ***,multi-objective EAs with a diversity preserving mechanism are used for Pareto optimization of such GMDH-type neural *** most important objectives of GMDH-type neural networks that are considered in this study are training error(TE),prediction error(PE),and number of neurons(N).Different pairs of these objective functions are selected for two-objective optimization ***,optimal Pareto fronts of such models are obtained in each case,which exhibit the trade-offs between the corresponding pair of the objectives and,thus,provide different non-dominated optimal choices of GMDH-type neural network model for beach profile *** results showed that the present model has been successfully used to optimally prediction of beach profile evolution on beaches with seawalls.

关键词： beach profile evolution genetic algorithms group method of data handling Pareto reflective beaches

来源：评论

学校读者我要写书评

暂无评论

Design of a virtual test cell based on GMDH-type neural network for a heavy-duty diesel engine

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2021年第2-3期235卷 436-445页

作者： Ghanaati, Ali Sjoblom, Jonas Faghani, Ethan Chalmers Univ Technol Div Combust & Prop Syst Mech & Maritime Sci S-41296 Gothenburg Sweden Volvo AB Gothenburg Sweden

The engine development process faces big challenges from new strict emission regulations in addition to the need for fuel efficiency improvements. The Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) environments decreases the required time during engine development, calibration, verification, and validation of the product. An accurate and easy to build dyno-engine model with real-time operational ability is required for this purpose. Artificial Neural Networks (ANN) have shown ability to model dynamic and complex systems like internal combustion engines. In this paper, the group method of data handling (GMDH) algorithm was utilized to build an ANN model of a heavy-duty diesel engine. One objective is to reduce the amount of manual labor on the results during the ANN model development process. The GMDH algorithm is a self-organizing process that will find the system laws and optimize the model structure automatically in one iteration. The GMDH model results were compared with a model developed by Levenberg-Marquardt Backpropagation (LM-BP) algorithm. The ANN models used actuator signals from an Engine Management System (EMS) to simulate the engine operation parameters. As revealed by the simulation results, the ANN models successfully predicted engine torque, fuel flow, and NOx concentration. The GMDH model as a self-organized model reduced lead time, had slightly higher transient cycle accuracy, had fewer inconsistent predictions, and demonstrated better extrapolation capability. The prediction accuracy for transient operation was improved by shifting the predicted value by calculating time delay and a decrease of 76.66% for fuel flow and 66.51% for NOX concentration in model accuracy were achieved. The GMDH dyno-engine model can be effectively applied as a virtual test cell instrument for testing, calibration, and optimization purposes.

关键词： Virtual test cell hardware-in-the-loop artificial neural network group method of data handling self-organized system

来源：评论

学校读者我要写书评

暂无评论

A hybrid model to assess the hydropower potential for rivers

引用

INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2021年第1期11卷 88-113页

作者： Al-Juboori, Anas Mahmood Guven, Aytac Univ Mosul Dams & Water Resources Res Ctr Mosul Iraq Gaziantep Univ Civil Engn Dept TR-27310 Gaziantep Turkey

A computer program called hydropower plant site assessment program (HPSAP) was developed by using Visual Basic programming to assess the hydropower potential in river basins. A methodology to select the best site to install the hydropower plant was developed according to meteorological, hydrological and economical design criteria. HPSAP has several options to analyse watersheds for different head or power criteria. HPSAP estimates the locations coordinate and upstream drainage area for powerhouse and weir locations, elevation of powerhouse and weir, the distance between weir site and powerhouse site, penstock length, design head and power for proposed plants. The design discharge is estimated by using a flow duration curve regression equation developed by group method of data handling (GMDH) based on rainfall-runoff model outputs. The hydrological engineering centre-hydrological modelling system (HEC-HMS) model was used as the hydrological rainfall-runoff model in this study. The results have proven the ability of the program to assess the hydropower potential in river basins.

关键词： run-of-river hydropower plant flow duration curve FDC Visual Basic programming hydropower program hydrological engineering centre-hydrological modelling system HEC-HMS group method of data handling GMDH

来源：评论

学校读者我要写书评

暂无评论

Deep learning integrated approach for hydrocarbon source rock evaluation and geochemical indicators prediction in the Jurassic - Paleogene of the Mandawa basin, SE Tanzania

引用

ENERGY 2023年 284卷

作者： Mkono, Christopher N. Chuanbo, Shen Mulashani, Alvin K. Mwakipunda, Grant Charles China Univ Geosci Key Lab Tecton & Petr Resources Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Sch Earth Resources Dept Petr Engn Wuhan 430074 Peoples R China Mbeya Univ Sci & Technol Dept Geosci & Min Technol Coll Engn & Technol Box 131 Mbeya Tanzania

The world's energy demands are growing at an unprecedented rate, and the exploration of new hydrocarbon sources is more important than ever. Therefore, the objective of this study was first to quantitatively analyze hydrocarbon source rock potentiality of the Triassic-Jurassic of Mandawa Basin based on the generalized group method of data handling neural network (g-GMDH), Machine learning, and Geochemical using well logs data. Then a novel g-GMDH was presented to predict a continuous geochemical log profile of TOC, Tmax, S1, and S2. It was observed that the basin's hydrocarbon source rocks are classified as fair to very good source rocks with TOC contents ranging from 0.5 to 8.7 wt%. The source rocks contain mixed kerogen type II and III, which are oil and gas-prone, ranging from immature to mature source rocks. The results of the predictive models indicated that the g-GMDH model trained better whilst generalizing well throughout the testing data than both GPR and SVM models. Specifically, the g-GMDH when tested on unseen data had the least value of MSE = 0.18, 2.35, 0.08, and 61.74 for TOC, Tmax, S1, and S2 respectively, and MAE = 0.45, 1.37, 0.17 and 11.55 for TOC, Tmax, S1 and S2 respectively. The g-GMDH model was further applied to assess the source rock and predict the geochemical information in the East Lika well, which lacks core data. The proposed model can offer rapid and real-time values of geochemical indicators and are independent of laboratory-dependent parameters therefore, can be adopted as an improved technique for evaluating source rocks in frontier basins.

关键词： Total organic carbon group method of data handling Hydrocarbon potentials Thermal maturity Source rocks Energy

来源：评论

学校读者我要写书评

暂无评论

Development of novel optimized deep learning algorithms for wildfire modeling: A case study of Maui, Hawai'i

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期125卷

作者： Rezaie, Fatemeh Panahi, Mahdi Bateni, Sayed M. Lee, Saro Jun, Changhyun Trauernicht, Clay Neale, Christopher M. U. Korea Inst Geosci & Mineral Resources KIGAM Geosci Data Ctr 124 Gwahak Ro Daejeon 34132 South Korea Korea Univ Sci & Technol Dept Geophys Explorat 217 Gajeong Ro Daejeon 34113 South Korea Univ Hawaii Manoa Dept Civil & Environm Engn Honolulu HI 96822 USA Univ Hawaii Manoa Water Resources Res Ctr Honolulu HI 96822 USA Chosun Univ Dept Comp Engn Gwangju 61452 South Korea Chung Ang Univ Coll Engn Dept Civil & Environm Engn Seoul 06974 South Korea Univ Hawaii Manoa Dept Nat Resources & Environm Management Honolulu HI 96822 USA Univ Nebraska Daugherty Water Food Global Inst Lincoln NE USA

To address the growing global concern regarding increased wildfire occurrences and their widespread socio-ecological impacts, cost-effective and practical approaches must be urgently identified to accurately predict the probability of wildfire incidents. The objective of this study was to develop deep learning models to estimate the likelihood of wildfire incidents and compare the predictive capability of the methods. To this end, the group method of data handling (GMDH) and convolutional neural network (CNN) algorithms were coupled with the biogeography-based optimization (BBO) and ant colony optimization (ACO) algorithms to enhance the predictive performance of the models. Overall, 1745 historical wildfires were identified in the island of Maui, Hawai'i. Among them, 1221 events (70%) were randomly selected to generate the models, and the remaining 524 (30%) were used for validation. The frequency ratio, information gain ratio (IGR), and variance inflation factor methods were used to select the optimal number of predictor variables for model development. 13 influencing factors: elevation, aspect, slope, plan curvature, slope length, valley depth, topographic wetness index, mean annual wind speed, mean annual air temperature, mean monthly rainfall, distance to the road, distance to the river, and normalized difference vegetation index were selected to generate the proposed models and detect fire-prone areas. The IGR values indicated that the key parameters for predicting wildfire susceptibility were the distance to the road, mean annual air temperature, elevation, and slope. Finally, the area under the receiver operating characteristic curve (AUROC) was computed to verify the reliability and accuracy of the wildfire susceptibility maps. Both the optimization algorithms enhanced the performances of the GMDH and CNN models. The ACO most notably enhanced the CNN performance compared with the models (AUROCTraining = 0.889 and AUROCTest ing = 0.885). The findings demonst

关键词： Wildfire susceptibility map Convolutional neural network group method of data handling Ant colony optimization Hawai'i

来源：评论

学校读者我要写书评

暂无评论

Investigation into the nonlinear Kalman filter to correct the INS/GNSS integrated navigation system

引用

GPS SOLUTIONS 2023年第2期27卷 91页

作者： Neusypin, Konstantin Kupriyanov, Andrey Maslennikov, Andrey Selezneva, Maria Bauman Moscow State Tech Univ Moscow Russia Moscow State Univ Geodesy & Cartog Moscow Russia

The integrated navigation system is the inertial navigation system (INS), corrected by global navigation satellite system (GNSS) data. The correction could be done algorithmically by utilizing nonlinear Kalman filtering (NKF). In practice, the NKF uses an INS error model as an a priori model that is not always adequate to handle the dynamics of the true and unknown INS error model. To eliminate such modeling errors, we propose a new INS/GPS correction approach with modified adaptive NKF. In the proposed NKF, instead of the a priori model, the model constructed during the pre-flight test for a particular INS is used. To realize this, the full algorithm includes an INS error model construction algorithm, a way of reduced measurement generation, and criteria for divergence detection. INS error model construction both during pre-flight test and during flight is done by the group method of data handling (GMDH). Flight experiments were performed for an empirical study of the INS error model and its effect on the total accuracy of computed navigational data. The navigational equipment was installed on the balloon-an airborne radio-transparent object. The results of the experiments validate the effectiveness and accuracy of the proposed INS/GPS correction approach.

关键词： Inertial navigation systems GNSS Nonlinear Kalman filter Error model Adaptation group method of data handling GPStation6 RTK SPAN

来源：评论

学校读者我要写书评

暂无评论

Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran

引用

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2021年第8期35卷 1615-1635页

作者： Aghelpour, Pouya Bahrami-Pichaghchi, Hadigheh Varshavian, Vahid Bu Ali Sina Univ Fac Agr Dept Water Engn Hamadan Hamadan Iran Sari Agr Sci & Nat Resources Univ Fac Agr Engn Dept Water Engn Sari Iran

Hydrological drought is an environmental event that affects surface water resources such as surface runoff and reservoir levels and its prediction, can help the water managers to be aware of the future status of the region. This study aims to predict and evaluate hydrological drought in the southwestern margin of the Caspian Sea. For this, Streamflow Drought Index (SDI) is used as a multi-scalar hydrological drought indicator and calculated in time windows of 1, 3, 6, 9 and 12-month. In this study, time series stochastic models were used for the first time to predict SDI and compared with two black-box machine learning (ML) methods including Adaptive Neuro-Fuzzy Inference System (ANFIS) and group method of data handling (GMDH). The used data belongs to two rivers named Khalkaei and Pasikhan in Guilan province, and the period of 1986-2015 on a monthly scale. Autocorrelation and Partial Autocorrelation Functions (ACF and PACF) was used for selecting inputs among the series' monthly time lags. The results showed that there are seasonal trends in SDI's 1, 3, 6 and 9-month time windows;therefore, these time windows have better adaptability with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. But there is no seasonal trend in the 12-month time window (SDI12) series and the non-seasonal Autoregressive Moving Average (ARMA) model was found as the best choice for this time window. Among the models, the MLs (GMDH and ANFIS) had approximately similar prediction accuracies. Comparing the models indicate that the linear stochastic models in addition to the simplicity of use were significantly more accurate than the complex non-linear ML models. The current results suggest using the stochastic models for hydrological drought forecasting, in catchment areas.

关键词： Hydrological drought Streamflow drought index group method of data handling ARIMA ANFIS

来源：评论

学校读者我要写书评

暂无评论

Application of inclusive multiple model for the prediction of saffron water footprint

引用

AGRICULTURAL WATER MANAGEMENT 2023年 277卷

作者： Moshizi, Zahra Gerkani Nezhad Bazrafshan, Ommolbanin Etedali, Hadi Ramezani Esmaeilpour, Yahya Collins, Brain Univ Hormozgan Dept Nat Resources Engn Bandar Abbas Iran Imam Khomeini Int Univ Dept Water Sci & Engn Qazvin Iran James Cook Univ Coll Sci & Engn Townsville Qld 4811 Australia

Applying new approaches in the management of water resources is a vital issue, especially in arid and semi-arid regions. The water footprint is a key index in water management. Therefore, it is necessary to predict its changes for future durations. The soft computing model is one of the most widely used models in predicting and estimating agroclimatic variables. The purpose of this study is to predict the green and blue water footprints of saffron product using the soft computing model. In order to select the most effective variables in prediction water footprints, the individual input was eliminated one by one and the effect of each on the residual mean square error (RMSE) was measured. In the first stage, the group method of data handling (GMDH) and evolutionary algorithms have been applied. In the next stage, the output of individual models was incorporated into the Inclusive Multiple Model (IMM) as the input variables in order to predict the blue and green water footprints of saffron product in three homogenous agroclimatic regions. Finally, the uncertainty of the model caused by the input and parameters was evaluated. The contributions of this research are introducing optimized GMDH and new ensemble models for predicting BWF, and GWF, uncertainty analysis and investigating effective inputs on the GWF and BWF. The results indicated that the most important variables affecting green and blue water footprints are plant transpiration, evapotranspiration, and yield, since removing these variables significantly increased the RMSE (range=11-25). Among the GMDH models, the best performance belonged to NMRA (Naked Mole Ranked Algorithm) due to the fast convergence and high accuracy of the outputs. In this regard, the IMM has a better performance (FSD=0.76, NSE=0.95, MAE) = 8, PBIAS= 8) than the alternatives due to applying the outputs of several individual models and the lowest uncertainty based on the parameters and inputs of the model (p = 0.98, r = 0.08).

关键词： Water footprint Saffron Crop and climate variables group method of data handling Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：