Prediction Model of Commercial Economic Index Based on BPNN Optimization Algorithm

Missing data occurs in all research, especially in medical studies. Missing data is the situation in which a part of research data has not been reported. This will result in the incompatibility of the sample and the population and misguided conclusions. Missing data is usual in research, and the extent of it will determine how misinterpreted the conclusions will be. All methods of parameter estimation and prediction models are based on the assumption that the data are complete. Extensive missing data will result in false predictions and increased bias. In the present study, a novel method has been proposed for the imputation of medical missing data. The method determines what algorithm is suitable for the imputation of missing data. To do so, a multiobjective particle swarm optimization algorithm was used. The algorithm imputes the missing data in a way that if a prediction model is applied to the data, both specificity and sensitivity will be optimized. Our proposed model was evaluated using real data of gastric cancer and acute T-cell leukemia (ATLL). First, the model was then used to impute the missing data. Then, the missing data were imputed using deletion, average, expectation maximization, MICE, and missForest methods. Finally, the prediction model was applied for both imputed datasets. The accuracy of the prediction model for the first and the second imputation methods was 0.5 and 16.5, respectively. The novel imputation method was more accurate than similar algorithms like expectation maximization and MICE.

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Hydrate formation prediction model for binary gases of methane + ethane and methane + propane by using optimization algorithm

Petroleum Science and Technology ◽

10.1080/10916466.2019.1655447 ◽

2019 ◽

Vol 38 (1) ◽

pp. 36-42 ◽

Cited By ~ 1

Author(s):

Aijaz Abbasi ◽

Fakhruldin Mohd Hashim

Keyword(s):

Prediction Model ◽

Optimization Algorithm ◽

Hydrate Formation

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Multi-objective Optimization Algorithm for Multimedia English Teaching (MOAMET) Based on Computer Network Traffic Prediction Model

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v13i03.8372 ◽

2018 ◽

Vol 13 (03) ◽

pp. 58 ◽

Cited By ~ 1

Author(s):

Wei Wei Feng

Keyword(s):

Time Series ◽

Prediction Model ◽

Optimization Algorithm ◽

Network Traffic ◽

Computer Network ◽

Reference Points ◽

Traffic Prediction ◽

Multi Objective Optimization ◽

English Teaching ◽

Multi Objective

In order to solve the problem of multi-objective optimization for multimedia English teaching, this paper proposes a multi-objective optimization algorithm for multimedia English teaching (MOAMET) based on computer network traffic prediction model, which is based on the computer network traffic prediction model strategy. This algorithm establishes time series for individuals correlated to same reference points, and for such time series through computer network traffic model optimizes multimedia English teaching objectives. Meanwhile, it feeds back the prediction error of the historical moment to the current prediction to improve the accuracy of the optimization, and adds disturbance in each optimized individual to increase the diversity of initial multimedia English teaching so as to speed up the convergence speed of the algorithm in the new environment. Through experiments it teats the algorithm, also makes comparison and analysis with two existing algorithms, the results show that the proposed algorithm can maintain good performance in dealing with multi-objective optimization for multimedia English teaching.

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A novel weather prediction model using a hybrid mechanism based on MLP and VAE with fire-fly optimization algorithm

Evolutionary Intelligence ◽

10.1007/s12065-021-00589-8 ◽

2021 ◽

Author(s):

Veera Ankalu Vuyyuru ◽

G. Appa Rao ◽

Y. V. Srinivasa Murthy

Keyword(s):

Prediction Model ◽

Optimization Algorithm ◽

Weather Prediction ◽

Hybrid Mechanism

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Construct the prediction model for China agricultural output value based on the optimization neural network of fruit fly optimization algorithm

Future Generation Computer Systems ◽

10.1016/j.future.2018.04.058 ◽

2018 ◽

Vol 86 ◽

pp. 663-669 ◽

Cited By ~ 12

Author(s):

Shi-Zhuan Han ◽

Wen-Tsao Pan ◽

Ying-Ying Zhou ◽

Zong-Li Liu

Keyword(s):

Neural Network ◽

Prediction Model ◽

Optimization Algorithm ◽

Fruit Fly ◽

Fruit Fly Optimization Algorithm ◽

Agricultural Output ◽

Fruit Fly Optimization

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Firefly Optimization Algorithm for the Prediction of Uplift due to High-Pressure Jet Grouting

Advances in Civil Engineering ◽

10.1155/2020/8833784 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Wukui Dai ◽

Li Liang ◽

Bingji Zhang

Keyword(s):

High Pressure ◽

Prediction Model ◽

Optimization Algorithm ◽

Target Function ◽

In Situ Monitoring ◽

Single Pile ◽

Stochastic Medium ◽

Jet Grouting ◽

Firefly Optimization ◽

Firefly Optimization Algorithm

A project that needs to be uplifted by high-pressure jet grouting (HPJG) is exposed to particular geological and engineering circumstances; meanwhile, HPJG has intense subjectivity, short of the theoretical base, to ascertain the influence angle β and enlarged radius Δ a , which are the main parameters that affect the uplift effect. Therefore, we proposed a new method based on the firefly optimization algorithm to search for the optimal solution for the target function. Stochastic medium theory (SMT) was used in this article, in which the effect of single-pile HPJG was simulated as the superposition effect of the foam slurry at the same distance, to construct a stochastic medium prediction model of the effect of uplift due to multi-HPJG. In accordance with the range of the prediction results of single-pile HPJG and combined with in situ monitoring data to define the target function, the optimal parameters are substituted into the prediction model to predict the subsequent uplift effect due to HPJG. As a result of the global optimization capacity and by comparison with the genetic algorithm, the FOA has a greater advantage in terms of effectiveness and precision. Finally, it is proven that the prediction result meets the requirement of the prediction in advance by statistical data.

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Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v3i3.1086 ◽

2019 ◽

Vol 3 (3) ◽

pp. 357-363

Author(s):

Soffa Zahara ◽

Sugianto ◽

M. Bahril Ilmiddafiq

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Optimization Algorithm ◽

Price Index ◽

Short Term Memory ◽

Wholesale Price ◽

Stochastic Gradient Descent ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome RNN’s lact about maintaining long period of memories. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Currently, machine learning is a burning issue in economic world, abundant studies such predicting macroeconomic and microeconomics indicators are emerge. Inflation rate has been used for decision making for central banks also private sector. In Indonesia, CPI (Consumer Price Index) is one of best practice inflation indicators besides Wholesale Price Index and The Gross Domestic Product (GDP). Since CPI data could be used as a direction for next inflation move, we conducted CPI prediction model using LSTM method. The network model input consists of 28 variables of staple price in Surabaya and the output is CPI value, also the entire development of prediction model are done in Amazon Web Service (AWS) Cloud. In the interest of accuracy improvement, we used several optimization algorithm i.e. Stochastic Gradient Descent (sgd), Root Mean Square Propagation (RMSProp), Adaptive Gradient(AdaGrad), Adaptive moment (Adam), Adadelta, Nesterov Adam (Nadam) and Adamax. The results indicate that Nadam has 4,008 RMSE’s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.

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