Predictive performance of auto-aerated immobilized biomass reactor treating anaerobic effluent of cardboard wastewater enriched with bronopol (2-bromo-2-nitropropan-1,3-diol) via artificial neural network

Step-length measurement as a spatial gait parameter is useful for the physician and physical therapist for determining the patient’s gait condition. We hypothesized that this could be determined using ultrasonic sensors mounted on a shoe-type measurement device. For that purpose, we have developed a shoe-type measurement device to measure gait parameters. Our system was found to effectively measure step-length and pressure distribution. However, we found that the presence of shoes leads to perishable and fragile conditions for the sensors. Therefore, we redesigned the number, angle, and range of the ultrasonic sensors mounted on the shoes in order to clarify and improve the step-length prediction. This paper discusses the improvement of a shoe-type measurement device from the implementation with real shoes and the step-length prediction using an artificial neural network (ANN). The results of the experiment show that the number, angle, and positioning of ultrasonic sensors affect their ability to capture the human step region, that is, 50×70 cm under the experimental condition of foot progression angle up to 30 degrees. The results of the predictive performance of step-length using the proposed ANN architecture demonstrate an improvement.

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Wind power 24-h ahead forecast by an artificial neural network and an hybrid model: Comparison of the predictive performance

Renewable Energy ◽

10.1016/j.renene.2021.06.108 ◽

2021 ◽

Author(s):

Emanuele Ogliari ◽

Manfredo Guilizzoni ◽

Silvia Pretto ◽

Alessandro Giglio

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wind Power ◽

Hybrid Model ◽

Model Comparison ◽

Predictive Performance ◽

Artificial Neural

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Machine learning model development for predicting road transport GHG emissions in Canada

WSB Journal of Business and Finance ◽

10.2478/wsbjbf-2019-0022 ◽

2019 ◽

Vol 53 (2) ◽

pp. 55-72

Author(s):

Mohd Jawad Ur Rehman Khan ◽

Anjali Awasthi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Multilayer Perceptron ◽

Ghg Emissions ◽

Predictive Performance ◽

Road Transport ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Artificial Neural

Abstract Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.

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Measuring Predictive Performance of Data Mining and Artificial Neural Network Algorithms for Predicting Lactation Milk Yield in Indigenous Akkaraman Sheep

Pakistan Journal of Zoology ◽

10.17582/journal.pjz/2017.49.1.1.7 ◽

2016 ◽

Vol 49 (1) ◽

pp. 1-7 ◽

Cited By ~ 5

Author(s):

Koksal Karadas ◽

◽

Muhammad Tariq ◽

Mohammad Masood Tariq ◽

Ecevit Eyduran

Keyword(s):

Neural Network ◽

Data Mining ◽

Artificial Neural Network ◽

Milk Yield ◽

Predictive Performance ◽

Network Algorithms ◽

Artificial Neural

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Smart management of sample dilution using an artificial neural network to achieve streamlined processes and saving resources: the automated nephelometric testing of serum free light chain as case study

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2016-0263 ◽

2017 ◽

Vol 55 (2) ◽

Cited By ~ 1

Author(s):

Cristiano Ialongo ◽

Massimo Pieri ◽

Sergio Bernardini

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Light Chain ◽

Predictive Performance ◽

Free Light Chain ◽

Automated System ◽

Acceptable Result ◽

Serum Free ◽

Serum Free Light Chain ◽

Artificial Neural

AbstractBackground:Saving resources is a paramount issue for the modern laboratory, and new trainable as well as smart technologies can be used to allow the automated instrumentation to manage samples more efficiently in order to achieve streamlined processes. In this regard the serum free light chain (sFLC) testing represents an interesting challenge, as it usually causes using a number of assays before achieving an acceptable result within the analytical range.Methods:An artificial neural network based on the multi-layer perceptron (MLP-ANN) was used to infer the starting dilution status of sFLC samples based on the information available through the laboratory information system (LIS). After the learning phase, the MLP-ANN simulation was applied to the nephelometric testing routinely performed in our laboratory on a BN ProSpecResults:The MLP-ANN reduced the serum kappa free light chain (κ-FLC) and serum lambda free light chain (λ-FLC) wasted tests by 69.4% and 70.8% with respect to the naïve stepwise dilution scheme used by the automated analyzer, and by 64.9% and 66.9% compared to a “rational” dilution scheme based on a 4-step dilution.Conclusions:Although it was restricted to follow-up samples, the MLP-ANN showed good predictive performance, which alongside the possibility to implement it in any automated system, made it a suitable solution for achieving streamlined laboratory processes and saving resources.

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Assessing the predictive performance of artifIcial neural network-based classifiers based on different data preprocessing methods, distributions and training mechanisms

Intelligent Systems in Accounting Finance & Management ◽

10.1002/isaf.269 ◽

2005 ◽

Vol 13 (4) ◽

pp. 217-250 ◽

Cited By ~ 3

Author(s):

Adrian Costea ◽

Iulian Nastac

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Performance ◽

Data Preprocessing ◽

Artificial Neural ◽

And Training

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Measurement and calculation of calorific value of raw coal based on artificial neural network analysis method

Thermal Science ◽

10.2298/tsci191106087l ◽

2020 ◽

Vol 24 (5 Part B) ◽

pp. 3129-3137 ◽

Cited By ~ 1

Author(s):

Peng Liu ◽

Shuran Lv

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Rbf Neural Network ◽

Small Sample Size ◽

Calorific Value ◽

Predictive Performance ◽

Predictive Modelling ◽

Small Sample ◽

Rbf Network ◽

Artificial Neural

The calorific value of coal is the basic technical basis for calculating parameters such as boiler heat balance, thermal efficiency, and boiler output. The calorific value of coal has different meanings, such as the calorific value of the cartridge, the high calorific value of coal, and the low calorific value of coal to generate heat at a high level of constant humidity and no ash. This paper focuses on the analysis of the structure and algorithm characteristics of artificial neural network and RBF neural network. On this basis, the predictive modelling of the received low-level calorific value is carried out. Through the test summary, the predictiveness of the neural network is better than the empirical formula. For the prediction problem with small sample size, the RBF network has better prediction performance. In addition, the quality of the sample, including its quantity and comprehensiveness, has an important impact on the predictive performance and generalization ability of the model.

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