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Journal of China University of Geosciences 2003年第2期14卷 161-165页

作者： Zhou Pinggen China Institute of Geo environment Monitoring, Beijing 100081 China Institute of Geo-environment Monitoring Beijing 100081

The factors of geomorphology, geological setting, effect of ground water and environment dynamic factors (e.g. rainfall and artificial water recharge) should be integrated in the discrimination of the stability of the ancient landslide. As the criterion of landslide stability has been studied, the artificial neural network model was then applied to discriminate the stability of the ancient landslide in the impounding area of the Three Gorges project on the Yangtze River, China. The model has the property of self adaptive identifying and integrating complex qualitative factors and quantitative factors. The results of the artificial neural network model are coincided well with what were gained by classical limit equilibrium analysis (the Bishop method and Janbu method) and by other comprehensive discrimination methods.

关键词： ancient landslide stability artificial neural network model the Three Gorges.

artificial neural network model of constitutive relations for shock-prestrained copper

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中国有色金属学会会刊：英文版 2001年第2期11卷 210-212页

作者：杨扬朱远志李正华张新明杨立斌陈志永中南大学材料系湖南长沙 410083

Data from the deformation on Split Hopkinson Bar were used for constructing an artificial neural network model. When putting the thermodynamic parameters of the metals into the trained network model, the corresponding yielding stress can be predicted. The results show that the systematic error is small when the objective function is 0.5 , the number of the nodes in the hidden layer is 6 and the learning rate is about 0.1 , and the accuracy of the rate error is less than 3%. [

关键词： shock prestrain constitutive relations artificial neural network model

An Intelligent Prediction model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN model

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Computer modeling in Engineering & Sciences 2022年第10期133卷 31-46页

作者： G.Naveen Sundar Stalin Selvaraj D.Narmadha K.Martin Sagayam A.Amir Anton Jone Ayman A.Aly Dac-Nhuong Le Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatore641114India Centre for Nanotechnology&Advanced Biomaterials(CeNTAB) School of Chemical&BiotechnologySASTRA Deemed to be UniversityThanjavur613401India Department of ECE Karunya Institute of Technology and SciencesCoimbatore641114India Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

Hepatocellular carcinoma(HCC)is one major cause of cancer-related mortality around the ***,at advanced stages of HCC,systematic treatment options are currently *** a result,new pharmacological targetsmust be discovered regularly,and then tailored medicines against HCC must be *** this research,we used biomarkers of HCC to collect the protein interaction network related to ***,DC(Degree Centrality)was employed to assess the importance of each *** an improved Graph Coloring algorithm was used to rank the target proteins according to the interaction with the primary target protein after assessing the top ranked proteins related to ***,physio-chemical proteins are used to evaluate the outcome of the top ranked *** proposed graph theory and machine learning techniques have been compared with six existing *** the proposed approach,16 proteins have been identified as potential therapeutic drug targets for Hepatic *** is observable that the proposed method gives remarkable performance than the existing centrality measures in terms of Accuracy,Precision,Recall,Sensitivity,Specificity and F-measure.

关键词： Drug target detection hepatic carcinoma degree centrality graph coloring artificial neural network model

Prediction of tensile strength of friction stir welded 6061 Al plates

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China Welding 2019年第3期28卷 1-6页

作者： Farghaly Ahmed A El-Nikhaily Ahmed E Essa A R S Mechanical Department Faculty of Industrial EducationSuez University43527Egypt Mechanical Engineering Department Egyptian Academy for Engineering&Advanced TechnologyAffiliated to Ministry of Military Production3056Egypt

The present paper investigates the prediction of tensile strength after friction stir welding(FSW)using artificial neural network(ANN)in the MATLAB *** experimental results are used to develop the mathematical *** combined influence of welding speed,rotation speed,and axial force on the tensile strength of 6061 Al plates is *** of the tensile test are used to train and test the ANN model.A multi-layer solution is developed using the ANN model to predict tensile *** propagation(BP)method is initially trained using 80%of the experimental data,then,testing is performed with the rest of the *** indicate that predicted values are close to the corresponding measured values.

关键词： prediction friction stir welding 6061 aluminum alloy artificial neural network model

Identification and validation of prognostic factors in patients with COVID-19: A retrospective study based on artificial intelligence algorithms

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Journal of Intensive Medicine 2021年第2期1卷 103-109页

作者： Sheng Zhang Sisi Huang Jiao Liu Xuan Dong Mei Meng Limin Chen Zhenliang Wen Lidi Zhang Yizhu Chen Hangxiang Du Yongan Liu Tao Wang Dechang Chen Department of Critical Care Medicine Ruijin HospitalShanghai Jiao Tong University School of MedicineNo.197Ruijin 2nd RoadShanghai 200025China Tuberculosis and Respiratory Department Wuhan Jinyin-tan HospitalNo.1 Yintan RoadWuhan 430023China

Background:Novel coronavirus disease 2019(COVID-19)is an ongoing global pandemic with high *** several studies have reported different risk factors for mortality in patients based on traditional analytics,few studies have used artificial intelligence(AI)*** study investigated prognostic factors for COVID-19 patients using AI ***:COVID-19 patients who were admitted in Wuhan Infectious Diseases Hospital from December 29,2019 to March 2,2020 were *** whole cohort was randomly divided into training and testing sets at a 6:4 *** and clinical data were analyzed to identify predictors of mortality using least absolute shrinkage and selection operator(LASSO)regression and LASSO-based artificial neural network(ANN)*** predictive performance of the models was evaluated using receiver operating characteristic(ROC)curve ***:A total of 1145 patients(610 male,53.3%)were included in the *** the 1145 patients,704 were assigned to the training set and 441 were assigned to the testing *** median age of the patients was 57 years(range:47-66 years).Severity of illness,age,platelet count,leukocyte count,prealbumin,C-reactive protein(CRP),total bilirubin,Acute Physiology and Chronic Health Evaluation(APACHE)II score,and Sequential Organ Failure Assessment(SOFA)score were identified as independent prognostic factors for *** these nine factors into the LASSO regression model yielded a correct classification rate of 0.98,with area under the ROC curve(AUC)values of 0.980 and 0.990 in the training and testing cohorts,*** the same factors into the LASSO-based ANN model yielded a correct classification rate of 0.990,with an AUC of 0.980 in both the training and testing ***:Both the LASSO regression and LASSO-based ANN model accurately predicted the clinical outcome of patients with *** of illness,age,platelet count,leukocyte count,pr

关键词： COVID-19 Least absolute shrinkage and selection operator(LASSO)regression model artificial neural network model artificial intelligence Prognosis

GIS and ANN model for landslide susceptibility mapping

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Journal of Geographical Sciences 2001年第3期11卷 374-381页

作者： XU Zeng-wang (State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China) State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources Research CAS

Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage Une are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.

关键词： GIS artificial neural network model landslide susceptibility mapping