BP neural network prediction model for suicide attempt among Chinese rural residents

Lyu, Juncheng; Zhang, Jie

doi:10.1016/j.jad.2018.12.111

Cited by 68 publications

(43 citation statements)

References 38 publications

(40 reference statements)

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“…In fact, the method of determining the number of the hidden layer neurons based solely on the input and the output is inaccurate in many cases [37]. This is because the factors affecting the network structure are mainly the number of the training samples, the size of the sample noise, and the complexity of the function or classification problem to be simulated.…”

Section: Bp Neural Network Prediction Algorithmmentioning

confidence: 99%

Combined Prediction for Vehicle Speed with Fixed Route

Zhang

Liu

2020

Chin. J. Mech. Eng.

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Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles. Nowadays, people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning, but the prediction accuracy still needs to be improved. The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy; problems, such as over fitting, occur in the process of improving prediction accuracy. The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction. By combining the two prediction algorithms, the fusion of prediction performance is achieved, the limit of the single prediction performance is crossed, and the goal of improving vehicle speed prediction performance is achieved. In this paper, an extraction method suitable for fixed route vehicle speed is designed. The application of Markov and back propagation (BP) neural network in predictions is introduced. Three new combined prediction methods, all named Markov and BP Neural Network (MBNN) combined prediction algorithm, are proposed, which make full use of the advantages of Markov and BP neural network algorithms. Finally, the comparison among the prediction methods has been carried out. The results show that the three MBNN models have improved by about 19%, 28%, and 29% compared with the Markov prediction model, which has better performance in the single prediction models. Overall, the MBNN combined prediction models can improve the prediction accuracy by 25.3% on average, which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

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Section: Bp Neural Network Prediction Algorithmmentioning

confidence: 99%

Combined Prediction for Vehicle Speed with Fixed Route

Zhang

Liu

2020

Chin. J. Mech. Eng.

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“…Artificial neural networks have recorded breakthroughs in the diagnosis of mental health problems such as depression, schizophrenia, bipolar, etc., [33] used the DCNN to diagnose seizure using EEG signal data collected from the Boon University Germany which yielded an accuracy rate and sensitivity rate of 88.67% and 95.00% respectively. In another study, [42] developed a diagnostic model using the BPNN for suicide attempt prediction with an accuracy rate for positive prediction value as 86.0% and negative predictive value as 84.1%. Huang, Wu, and Su [43] in their research proposed a domain adaptation technique that was based on a hierarchical spectral clustering algorithm.…”

Section: 5mental Disordersmentioning

confidence: 99%

Untitled

2020

IJCSES

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Artificial Intelligence systems (especially computer-aided diagnosis and artificial neural networks) are increasingly finding many uses in medical diagnosis application in recent times. These methods are adaptive learning algorithms that are capable of handling multiple and heterogeneous types of clinical data with a view of integrating them into categorized outputs. In this study, we briefly review and discuss the concept, capabilities, and applicability of artificial neural network techniques to medical diagnosis, through consideration of some selected physical and mental diseases. The study focuses on scholarly researches within the years, 2010 to 2019. Findings show that no electronic online clinical database exists in Nigeria and the Sub-Saharan countries, most review researches in this area focused mainly on physical diseases without considering mental illnesses, the application of ANN in mental and comorbid disorders have not been thoroughly studied, ANN models and algorithms consider mainly homogeneous input data sources and not heterogeneous input data sources, and ANN models on multi-objective output systems are few as compared to single output ANN models.

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“…Qin et al [26] put forward an improved GA optimizer, which was used to solve key technical problems in conceptual design stage of automobiles. Lyu et al [27] and Wang et al [28] proposed an improved GA and optimize the BPNN model. Su and Lin [29] designed an algorithm combining GA and BPNN to realize maximum power point tracking in photovoltaic power generation.…”

Section: Optimization Problems Using Ga For Demand Predictionmentioning

confidence: 99%

Demand Prediction of Emergency Supplies under Fuzzy and Missing Partial Data

Zhang

Qiu

et al. 2019

Discrete Dynamics in Nature and Society

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An accurate demand prediction of emergency supplies according to disaster information and historical data is an important research subject in emergency rescue. This study aims at improving supplies demand prediction accuracy under partial data fuzziness and missing. The main contributions of this study are summarized as follows. (1) In view that it is difficult for the turning point of the whitenization weight function to determine fuzzy data, two computational formulas solving “core” of fuzzy interval grey numbers were proposed, and the obtained “core” replaced primary fuzzy information so as to reach the goal of transforming uncertain information into certain information. (2) For partial data missing, the improved grey k-nearest neighbor (GKNN) algorithm was put forward based on grey relation degree and K-nearest neighbor (KNN) algorithm. Weights were introduced in the filling and logic test conditions were added after filling so that filling results were of higher truthfulness and accuracy. (3) The preprocessed data are input into the improved algorithm based on the genetic algorithm and BP neural networks (GABP) to obtain the demand prediction model. Finally the calculation presents that the prediction accuracy and its stability are improved at the five-group comparative tests of calculated examples of actual disasters. The experiments indicated that the supplies demand prediction model under data fuzziness and missing proposed in this study was of higher prediction accuracy.

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BP neural network prediction model for suicide attempt among Chinese rural residents

Cited by 68 publications

References 38 publications

Combined Prediction for Vehicle Speed with Fixed Route

Combined Prediction for Vehicle Speed with Fixed Route

Untitled

Demand Prediction of Emergency Supplies under Fuzzy and Missing Partial Data

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