Prediction of clothing comfort sensation of an undershirt using artificial neural networks with psychophysiological responses as input data

The clothing comfort sensation is a combination of complex components, including psychological and physiological responses. General linear analysis is not always sufficient for the evaluation of the clothing comfort sensation. The current study sought to predict the clothing comfort sensation of wearing an undershirt using an artificial neural network (ANN). We constructed ANN models with psychological sensation data and physiological response data as inputs, including electrocardiogram and thermo-physiological indicators, and the clothing comfort sensation as the output. For the input layer of the model, three conditions were used: the psychological response data only, the physiological response data only, and both the psychological and physiological data. The number of hidden layers in the models ranged from one to three, and the number of units in each hidden layer was changed when fixed values of 30, 60, and 90 were used, or according to the number of data points in the input conditions. The results revealed that, among the three conditions, the accuracy rate was higher when both psychological and physiological response data were used as input. The prediction results exhibited an accuracy rate of up to 85% for unknown test data. The results suggest that the method of evaluating the state of clothing comfort sensation when wearing an undershirt using psychophysiological response measurement was effective and that neural networks are useful for predicting the clothing comfort sensation.

Download Full-text

Jaringan Syaraf Tiruan dalam Memprediksi Penjualan Air Minum dalam Kemasan dengan Metode Backpropogation

Prosiding Seminar Nasional Riset Information Science (SENARIS) ◽

10.30645/senaris.v1i0.57 ◽

2019 ◽

Vol 1 ◽

pp. 512

Author(s):

Hafiz Pratama ◽

Poningsih Poningsih ◽

Jalaluddin Jalaluddin

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Network Architecture ◽

Bottled Water ◽

Accuracy Rate ◽

Matlab Software ◽

Artificial Neural

This study predicts the sale of bottled water by applying Artificial Neural Networks. The application uses the Backpropogation Algorithm where the data entered is the number of sales, then Artificial Neural Networks are formed by determining the number of each layer. After the network is formed training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, output units and network architecture. Testing is done with Matlab software. Predictions with the best accuracy use 3-10-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using architecture 3-40-1 with an accuracy rate of 33%.

Download Full-text

Fundamental Categories of Artificial Neural Networks

Applications of Artificial Neural Networks for Nonlinear Data - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-4042-8.ch003 ◽

2021 ◽

pp. 30-64

Author(s):

Arunaben Prahladbhai Gurjar ◽

Shitalben Bhagubhai Patel

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Mathematical Operation ◽

Artificial Neural ◽

Classification Prediction ◽

Artificial Neural Network Ann ◽

Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Download Full-text

Fundamental Categories of Artificial Neural Networks

10.4018/978-1-6684-2408-7.ch001 ◽

2022 ◽

pp. 1-30

Author(s):

Arunaben Prahladbhai Gurjar ◽

Shitalben Bhagubhai Patel

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Mathematical Operation ◽

Artificial Neural ◽

Classification Prediction ◽

Artificial Neural Network Ann ◽

Fundamental Categories

Download Full-text

Artificial Neural Networks and Learning Techniques

Psychology and Mental Health ◽

10.4018/978-1-5225-0159-6.ch004 ◽

2016 ◽

pp. 89-112

Author(s):

Pushpendu Kar ◽

Anusua Das

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Research Work ◽

The Neural Network ◽

Learning Techniques ◽

Learning Procedure ◽

Processing Component ◽

Artificial Neural ◽

Artificial Neural Network Ann

The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

Download Full-text

Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening

Molecules ◽

10.3390/molecules25245942 ◽

2020 ◽

Vol 25 (24) ◽

pp. 5942

Author(s):

Maciej Przybyłek

Keyword(s):

Neural Networks ◽

Molecular Descriptors ◽

Matthews Correlation Coefficient ◽

Model Development ◽

Operating Characteristics ◽

Glucosidase Inhibitors ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Bioassay Data ◽

Beta Glucosidase

Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models’ accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs.

Download Full-text

Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks

Journal of Electrical Engineering ◽

10.2478/v10187-010-0033-4 ◽

2010 ◽

Vol 61 (4) ◽

pp. 235-240 ◽

Cited By ~ 5

Author(s):

Perumal Chandrasekar ◽

Vijayarajan Kamaraj

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Power Quality ◽

Wavelet Transforms ◽

Back Propagation ◽

Voltage Sag ◽

Artificial Neural ◽

Artificial Neural Network Ann

Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks In this paper, the modified wavelet based artificial neural network (ANN) is implemented and tested for power signal disturbances. The power signal is decomposed by using modified wavelet transform and the classification is carried by using ANN. Discrete modified wavelet transforms based signal decomposition technique is integrated with the back propagation artificial neural network model is proposed. Varieties of power quality events including voltage sag, swell, momentary interruption, harmonics, transient oscillation and voltage fluctuation are used to test the performance of the proposed approach. The simulation is carried out by using MATLAB software. The simulation results show that the proposed scheme offers superior detection and classification compared to the conventional approaches.

Download Full-text

A neural network model for predicting maximum shear capacity of concrete beams without transverse reinforcement

Canadian Journal of Civil Engineering ◽

10.1139/l05-003 ◽

2005 ◽

Vol 32 (4) ◽

pp. 644-657 ◽

Cited By ~ 8

Author(s):

Ayman Ahmed Seleemah

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Experimental Work ◽

Concrete Beams ◽

Shear Capacity ◽

Transverse Reinforcement ◽

Artificial Neural ◽

Artificial Neural Network Ann

Different relationships have been proposed by codes and researchers for predicting the shear capacity of members without transverse reinforcement. In this paper, the applicability of the artificial neural network (ANN) technique as an analytical alternative to existing methods for predicting this shear capacity is investigated using a critically reviewed and agreed upon database of experimental work that serves as a basis of comparison and (or) assessment of existing and new relationships. Both ANN and eight different codes and researcher's predictions of the shear capacity of the specimens of the database were compared. The ANN predictions are much superior to those of any of the current available relationships.Key words: artificial neural networks, shear capacity, transverse reinforcement, beams.

Download Full-text

Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction

International Journal of Photoenergy ◽

10.1155/2012/946890 ◽

2012 ◽

Vol 2012 ◽

pp. 1-7 ◽

Cited By ~ 21

Author(s):

Tamer Khatib ◽

Azah Mohamed ◽

K. Sopian ◽

M. Mahmoud

Keyword(s):

Neural Networks ◽

Solar Radiation ◽

Mean Bias Error ◽

Percentage Error ◽

Bias Error ◽

Climate Conditions ◽

The Neural Networks ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Radiation Conditions

This paper presents an assessment for the artificial neural network (ANN) based approach for hourly solar radiation prediction. The Four ANNs topologies were used including a generalized (GRNN), a feed-forward backpropagation (FFNN), a cascade-forward backpropagation (CFNN), and an Elman backpropagation (ELMNN). The three statistical values used to evaluate the efficacy of the neural networks were mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Prediction results show that the GRNN exceeds the other proposed methods. The average values of the MAPE, MBE and RMSE using GRNN were 4.9%, 0.29% and 5.75%, respectively. FFNN and CFNN efficacies were acceptable in general, but their predictive value was degraded in poor solar radiation conditions. The average values of the MAPE, MBE and RMSE using the FFNN were 23%, −.09% and 21.9%, respectively, while the average values of the MAPE, MBE and RMSE using CFNN were 22.5%, −19.15% and 21.9%, respectively. ELMNN fared the worst among the proposed methods in predicting hourly solar radiation with average MABE, MBE and RMSE values of 34.5%, −11.1% and 34.35%. The use of the GRNN to predict solar radiation in all climate conditions yielded results that were highly accurate and efficient.

Download Full-text

Estimation of liquefaction-induced horizontal displacements using artificial neural networks

Canadian Geotechnical Journal ◽

10.1139/t00-087 ◽

2001 ◽

Vol 38 (1) ◽

pp. 200-207 ◽

Cited By ~ 13

Author(s):

M Chiru-Danzer ◽

C H Juang ◽

R A Christopher ◽

J Suber

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Field Performance ◽

Horizontal Displacement ◽

Lateral Spreading ◽

Ann Model ◽

Modeling And Analysis ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Horizontal Displacements

In the present study, artificial neural network (ANN) models based on field performance data are developed for predicting liquefaction-induced horizontal displacements. A database consisting of 443 measurements of horizontal displacements forms the basis for ANN modeling and analysis. The ANN model resulted in predictive capabilities that surpass those of published methods. A sensitivity analysis of the ANN model is conducted to evaluate the effect of each individual input variable on the calculated horizontal displacement. The newly developed ANN model is compared with and shown to be more accurate than other existing methods in predicting liquefaction-induced horizontal displacements.Key words: liquefaction, artificial neural networks, lateral spreading.

Download Full-text

ClassiPhages 2.0: Sequence-based classification of phages using Artificial Neural Networks

10.1101/558171 ◽

2019 ◽

Cited By ~ 1

Author(s):

Cynthia Maria Chibani ◽

Florentin Meinecke ◽

Anton Farr ◽

Sascha Dietrich ◽

Heiko Liesegang

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Markov Models ◽

International Committee ◽

Biological Data ◽

Sequencing Technologies ◽

Artificial Neural ◽

Taxonomic Markers ◽

Artificial Neural Network Ann

AbstractBackground/ MotivationIn the era of affordable next generation sequencing technologies we are facing an exploding amount of new phage genome sequences. This requests high throughput phage classification tools that meet the standards of the International Committee on Taxonomy of Viruses (ICTV). However, an accurate prediction of phage taxonomic classification derived from phage sequences still poses a challenge due to the lack of performant taxonomic markers. Since machine learning methods have proved to be efficient for the classification of biological data we investigated how artificial neural networks perform on the task of phage taxonomy.ResultsIn this work, 5,920 constructed and refined profile Hidden Markov Models (HMMs), derived from 8,721 phage sequences classified into 12 well known phage families, were used to scan phage proteome datasets. The resulting Phage Family-proteome to Phage-derived-HMMs scoring matrix was used to develop and train an Artificial Neural Network (ANN) to find patterns for phage classification into one of the phage families. Results show that using the 100 fold cross-validation test, the proposed method achieved an overall accuracy of 84.18 %. The ANN was tested on a set of unclassified phages and resulted in a taxonomic prediction. The ANN prediction was benchmarked against the prediction resulting of multi-HMM hits, and showed that the ANN performance is dependent on the quality of the input matrix.ConclusionsWe believe that, as long as some phage families on public databases are underrepresented, multi-HMM hits can be used as a classification method to populate those phage families, which in turn will improve the performance and accuracy of the ANN. We believe that the proposed method is an effective and promising method for phage classification. The good performance of the ANN and HMM based predictor indicates the efficiency of the method for phage classification, where we foresee its improvement with an increasing number of sequenced viral genomes.

Download Full-text