Artificial Neural Network Analysis Research Articles

Role of Mothers’ Expressed Emotions in Psychopathology and Social-Emotional Competence in Adolescents

Objective: To study the role of mothers’ expressed emotions in psychopathology and social-emotional competence in adolescents. Study Design: Cross sectional study. Place and Duration of Study: Department of Psychology, University of Gujrat, Gujrat Pakistan, from Jul 2020 to Aug 2021 Methodology: Five hundred mothers and their five hundred adolescents were approached to measure the role of mothers’expressed emotions in psychopathology and social-emotional competence in adolescents. For this purpose, parental Expressed Emotions Scale-1 was used, and to assess the psychopathology and social-emotional competence in adolescents, Social Anxiety Scale-2 and Social-Emotional Competence Scale-3 were used. Results: Artificial neural network (ANN) analysis was used to identify the mothers' expressed emotions as predictors of social anxiety and social-emotional competence in adolescents. Results showed incorrect predictions was for training data (60%) and for testing data (28%), which signify the current results that around 72% of right decision about mothers’ expressed emotions as a predictor of social anxiety and social-emotional competence in adolescents. Conclusion: In the teenage period, youth face an increasing number of psychological, social, and developmental challenges. Mothers are the first teachers of their children. When they fail to support them and display ways of negatively expressing emotions, it leads to overall mental health issues, in which social anxiety is the most common challenge.

Identifying digital leadership's role in fostering competitive advantage through responsible innovation: A SEM-Neural Network approach

Contemporary research suggests that digital leadership (DL) plays a pivotal role in harnessing digital transformation to drive digital development, impacting organisations, society, and the environment. Nonetheless, empirical studies on the role of DL in fostering responsible innovation (RI) for a competitive advantage (CA) remain scarce despite its significance. Drawing on the upper echelons theory, this research examines how DL, operationalised as a reflective-formative higher-order construct, influences CA with RI as the mediator and social orientation (SO) as the moderator. Data were gathered through an online survey from 150 innovative firms, and a dual-stage analysis, combining Partial Least Squares Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN) techniques, was performed. The PLS results indicated that DL positively influences RI, which in turn, positively contributes to CA. Additionally, SO strengthens the link between DL and RI. The subsequent ANN analysis, in alignment with the PLS findings, predicted that the role of “digital innovator” holds the greatest significance, followed by the “digital manager” and “digital enabler” roles within the spectrum of DL. Overall, these findings hold substantial implications for the fields of leadership and RI within digital organisations, shedding light on the crucial relationship between DL, RI, SO and CA.

Evaluating machine utilization times for roadheaders used in coal mines: Regression and artificial neural network analyses

Roadheaders are extensively utilized for tunnel heading rock engineering applications all over the world. To create a work plan and calculate costs, it is critical to forecast roadheader performance as precisely as possible. Machine utilization time (MUT) is required for the calculation of daily advance rate of roadheaders. This paper investigates the values of MUT for roadheaders used in underground coal mines. The performance measurements were conducted on fifty different locations for axial machines and thirty-nine different locations for transverse machines. MUT values vary from 15 % to 37.5 % with an average of 26.3 % for axial roadheaders, and vary from 6.9 % to 37.9 % with an average of 18.4 % for transvers roadheaders. The average MUT is 25.4% for all measurements. The percentage of average support time approximately equals to the average MUT. Multiple regression and artificial neural network models were also developed for estimating MUT. Concluding remark is that the determined MUT values and the derived estimation models for roadheaders will be very useful for coal miners.

Analysis and prediction of older adult sports participation in South Korea using artificial neural networks and logistic regression models

BackgroundKorea’s aging population and the lack of older adult participation in sports are increasing medical expenses.AimsThis study aimed to segment older adult sports participants based on their demographic characteristics and exercise practice behavior and applied artificial neural network and logistic regression models to these segments to best predict the effect of medical cost reduction. It presents strategies for older adult sports participation.MethodsA sample comprising data on 1,770 older adults aged 50 years and above was drawn from the 2019 National Sports Survey. The data were analyzed through frequency analysis, hierarchical and K-means clustering, artificial neural network, logistic regression, cross-tabulation analyses, and one-way ANOVA using SPSS 23 and Modeler 14.2.ResultsThe participants were divided into five clusters. The artificial neural network and logistic analysis models showed that the cluster comprising married women in their 60s who participated in active exercise had the highest possibility of reducing medical expenses.DiscussionTargeting women in their 60s who actively participate in sports, the government should expand the supply of local gymnasiums, community centers, and sports programs. If local gymnasiums and community centers run sports programs and appoint appropriate sports instructors, the most effective medical cost reduction effect can be obtained.ConclusionsThis study contributes to the field by providing insights into the specific demographic segments to focus on for measures to reduce medical costs through sports participation.

Examining the two‐dimensional perceived marketplace influence and the role of financial incentives by SEM and ANN

AbstractIn recent years, research on sustainable consumption has been particularly relevant, highlighting the importance of the collective over the individual to reduce pollution. This study focuses on the study of the perceived marketplace influence (PMI) concept in its organizational and consumer dimensions, together with the financial incentives that exist in the adoption of electric cars and their effect on green customer engagement. A sample of 382 potential buyers of electric vehicles was obtained. A new hybrid analytical approach was taken structural equation modelling and artificial neural network. The research found the most significant variables affecting purchase intention were financial incentives, followed by PMI Organization and finally PMI Consumer. The results of artificial neural network analysis confirmed all the findings of the structural equation modelling, although the importance of each PMI dimension is different for each technique used. The conclusions point to new business opportunities that can be exploited by companies selling this green technology.

Co-combustion performances of biomass pyrolysis semi-coke and rapeseed cake: PCA, 2D-COS and full range prediction of M-DAEM via machine learning

Biomass pyrolysis semi-coke (PC) is a refractory byproduct of biomass refinery system, and its efficient downstream disposal is of significance to improve the bioenergy utilization efficiency. Herein, we proposed an environmentally friendly co-combustion strategy to explore the synergistic conversion of PC with agricultural waste rapeseed cake (RC). The co-combustion interaction, kinetics, prediction and emission responses were determined through principal component analysis (PCA), multiple distributed activation energy model (M-DAEM), artificial neural network (ANN) and two-dimensional correlation spectroscopy (2D-COS) analysis. The results indicated that the P/R (mixed) ratio in 1:1 strengthened the reaction rate for main incineration stage, whereas lower P/R ratio advanced the peak temperature and cooperated with temperature range in 730–790 K to facilitate co-combustion synergies. The prediction of the kinetic distribution under all mixed ratios was successfully conducted (R2 = 0.999863) through M-DAEM coupling with ANN, from which the activation energy distribution centers E0 and standard deviation σ were in the ranges of 115.23–270.84 kJ/mol and 2.89–42.11 kJ/mol, respectively. Co-combustion resulted in centralized activation energy distribution, and successfully lowered the reaction energy barriers while augmenting the instantaneous energy release. Meanwhile, the temperature dependency responses of flue gas were varied significantly as a function of mixed ratios and temperatures.

S899 Predictors of Major Adverse Cardiac and Cerebrovascular Events in Inflammatory Bowel Disease Using Artificial Neural Network Analysis in a Nationally Representative US Cohort

S899 Predictors of Major Adverse Cardiac and Cerebrovascular Events in Inflammatory Bowel Disease Using Artificial Neural Network Analysis in a Nationally Representative US Cohort

Numerical modeling and forecasting temperature distribution by neural network and regression analysis

Environmental changes occur due to various parameters, and global warming is one of those parameters. It is observed that the daily mean temperature has constantly been increasing as time passes. The knowledge of temperature distribution allows us to decide the stuff that strongly depends upon temperature variation. An attempt has been made to model and forecast temperature distributions for 2018-2020. Artificial Neural Network (ANN) and multiple regression analyses have been used to forecast daily mean temperatures for one of Pakistan's cities of Sindh (Nawabshah). Environmental data from 2010 to 2020 has been used to predict daily mean temperature. The statistical errors such as RMSE, MABE and MAPE and coefficient of determination R2 are calculated to check the results' validity. Both models are suitable for predicting temperature distribution; however, ANN gives the best result. Two different regression models (linear & non-linear) are employed for the numerical fitting of temperature data; the non-linear model shows the better fitting.

Improving plastic manufacturing processes with the integration of Six Sigma and machine learning techniques: a case study

ABSTRACT This research integrates machine learning (ML) and Six Sigma’s Define, Measure, Analyze, Improve, and Control (DMAIC) methodology to address these issues. The study details the selection and utilization of ML techniques, including Linear Regression (LR), Artificial Neural Network (ANN), Decision Tree (DT), K-nearest neighbors (KNN), and Cluster Analysis (CA). Implemented at the Innovative Plastic Manufacturing Company in Egypt, this research enhances the consistency of plastic bottle production by addressing issues such as surface marks, flashes, bubbles, and variations in liter capacity. Integrating Six Sigma with ML techniques reduces the average defect rate from approximately 67.8%. It elevates the Sigma level from 3.14 to 4.30, reducing material over-consumption costs from 5% to 1.7% of total manufacturing expenses. Notably, the KNN model achieves the best results for defect testing, with an R-squared value of 98.8%. These methodologies lead to cost reduction, increased competitiveness, and improved product quality when implemented.

The crisis effect in TPB as a moderator for post-pandemic entrepreneurial intentions among higher education students: PLS-SEM and ANN approach

This research examines college students' entrepreneurial inclinations using TPB, self-efficacy, and the crisis effect. It also examines the crisis effect's moderating influence post-pandemic. A unique analytical technique using Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) was used to evaluate the model's resilience. 310 Indian university students were surveyed online. Self-efficacy is a crucial predictor of entrepreneurial tendencies among higher education students. ANN analysis confirms SEM findings that self-efficacy and perceived behaviour control shape entrepreneurial desires. Despite its negative impact, the crisis effect doesn't appear to affect entrepreneurs' objectives. The crisis impact moderates all exogenous and endogenous factors except subjective norms and entrepreneurial goals, the research finds. The research also shows that students' education and geography affect their entrepreneurial inclinations. Gender, however, has little control. Policymakers and higher education administrators could boost entrepreneurial ambitions by fostering students' self-efficacy and perceived behaviour control. Understanding these elements allows higher education stakeholders to create targeted interventions and support systems to foster college student entrepreneurship.

Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression

This study investigates the surface parameters and environmental factors affecting the energy production of a 500 kWp photovoltaic (PV) solar power plant in Igdir province. Using both the PV panel characteristics and the weather conditions specific to the power plant location, a total of 7 detailed features were included. The estimation of the power plant efficiency, a novel contribution not found in previous studies, is also a major focus. The performance evaluation of different models, including feed-forward neural networks and multiple linear regression, demonstrates the effectiveness of artificial neural networks in capturing the complex relationships between features and efficiency despite limited data availability. Principal Component Analysis (PCA) was used to reduce feature dimensions, showing that even with a reduced feature set, accurate efficiency prediction is still achievable. Prediction using PCA is one of the novelties of the paper. The effects of solar irradiation, module power, and module temperature on power plant efficiency are revealed. The results provide valuable insights for optimizing energy investments in the Igdir region and highlight the potential of artificial neural networks in energy forecasting, demonstrating their suitability for capturing complex patterns in solar power plant efficiency.

Standard Setting with Artificial Neural Networks: TIMSS 2015 Mathematics Case

This study aims to demonstrate the optimal way to determine the cut-off score to be used to interpret the total scores obtained from an achievement test or scale using the Artificial Neural Networks method. To this end, the multiple-choice item responses in the Booklet-11 Mathematics subtest at the 8th grade level in the TIMSS 2015 Turkey sample dataset were used to determine the cut-off score for the achievement test. The item responses in the “Students Like Learning Mathematics Scale” in the TIMSS 2015 8th grade Mathematics Student Questionnaire were used to determine the cut-off score for the scale. The data were accessed from the TIMSS international database and the data were analyzed in MATLAB R2017b software. As a result of the study, the most appropriate cut-off score to be used for the evaluation of the total scores obtained from the TIMSS 2015 8th grade level Booklet-11 Mathematics subtest was determined as 45.5 out of 0-100 points with the Artificial Neural Network analysis method. The overall level of agreement between the cut-off score and the pass/fail classification based on 400 points, which is the lowest level of the TIMSS International Benchmark, was determined as 81%. The most appropriate cut-off score to be used for the evaluation of the scores obtained from the Students Like Learning Mathematics Scale (SLLSS) in the TIMSS 2015 8th grade student survey was determined as 19.6 out of 9-36 points. The overall level of agreement between the cut-off score and the classification of students who like/don’t like learning mathematics using the criterion based on the expression given in the original scale description was found to be 83%. The results concluded that the validity of the standard-setting studies conducted with the artificial neural network method was high. As a result, researchers are recommended to use the Artificial Neural Networks method to determine the cut-off score to be used in the interpretation of the total scores obtained from the achievement test or the total scale scores obtained from the scales.

Estimation of conventional and innovative models for Rwanda's GDP per capita: A comparative analysis of artificial neural networks and Box–Jenkins methodologies

This study aims to model the time series data of Rwanda's GDP per capita using the Box–Jenkins (BJ) approach as a conventional approach and artificial neural networks (ANNs) as an innovative approach. The BJ technique, which utilizes autoregressive moving average - ARMA or an integrated version of ARMA - ARIMA, has been applied, and it is found that the ARIMA (0,2,1) model is the best fit for Rwanda's GDP per capita. Moreover, three ANNs models (the long short-term model- LSTM, the generalized regression neural network- GRNN, and the multilayer perception - MLP) have been utilized for the dataset. It is shown that the MLP and GRNN models have outperformed the LSTM model and BJ approach in terms of statistical criteria. In addition, the LSTM model is not superior to the BJ model, except for the Mean Absolute Percentage Error (MAPE) value. Therefore, any inference that ANNs models will always be superior to traditional approaches would be misleading. It is necessary to evaluate the results cumulatively using both traditional and innovative methods and then come to a conclusion. Additionally, the study provides insights into the use of both BJ and ANNs approaches for modeling time series data of GDP per capita and highlights the potential of ANNs models for improving the prediction accuracy of GDP per capita. Decision-makers and policymakers may benefit in the future from these analyses and results including recommendations.

Cross-grain fracture characterization in softwood using artificial neural network analysis of acoustic emissions

Cross-grain fracture characterization in softwood using artificial neural network analysis of acoustic emissions

Assessing and detection of multiple bruises in peaches based on structured hyperspectral imaging

This study aimed to detect various types of postharvest damages in peaches based on structured hyperspectral imaging (S-HSI), including impact, falling, and compression damage, which can lead to bruising. The research involved three different spatial frequencies (60, 100, and 150 m−1) and used a 2π/3 phase shift interval to capture S-HSI images. These images were then processed using a mathematical demodulated model to create high-resolution image cubes that included both image and spectral information from the S-HSI data. Artificial neural network and principal component analysis were applied to develop bruise detection models using S-HSI spectra, which showed better discriminating effects compared with the ordinary hyperspectral spectra. The best performing discriminating models for healthy and three kinds of bruised samples were developed using the spectra of spatial frequency with 100 + 150 m−1, respectively. This study demonstrated the potential of S-HSI as an effective optical technique for bruise detection of peach.

Artificial neural networks for predicting mechanical properties of Al2219-B4C-Gr composites with multireinforcements

Artificial neural networks (ANNs) have gained prominence as a reliable model for clustering, grouping, and analysis in various domains. In recent times, machine learning (ML) models such as ANNs have proved to be on par with traditional regression and statistical models in terms of performance and usability. This study focuses on the fabrication of multicomponents-reinforced composites (Boron carbide (B4C) and Graphite (Gr)) using the stir casting technique. The addition of Magnesium to the melt enhances the wettability of B4C and Gr particles within the matrix. The microstructure and mechanical properties of the resulting Al-Mg-metal matrix composites (MMCs) are analyzed. Scanning electron micrographs reveal that B4C and Gr particles were uniformly dispersed in the matrix. X-Ray diffraction analysis confirmed the dispersion of the strengthening. The mechanical properties, including hardness, tensile, compressive, and impact strength, increased with the increase in B4C and Gr wt.%. As the percentage of B4C and Gr reinforcement wt.% increased, the load on the matrix reduced and its load-bearing capacity improved. The strain field generation rate also increased with an increase in B4C and Gr in the matrix, resulting in enhanced mechanical properties. The ANN analysis further confirmed that B4C was the more significant contributor to the mechanical properties of the composites.

Artificial neural networks based on segmented model for adaptability and stability evaluation of soybean genotypes

Unlike models based on simple linear regressions, segmented models can better assess the adaptability and stability of genotypes, demonstrating a nonlinear response pattern over environmental variation. However, these methods can be under statistical limitations, such as the Type Error II increase and biased estimates. Therefore, this work aimed to transpose the concepts of adaptability and stability from the statistical analysis of a segmented model to the discriminatory potential of an artificial neural network (ANN) and use it to classify soybean (Glycine max (L.) Merr.) genotypes. An ANN training was carried out with the grain yield of 7,200 soybean genotypes simulated in 15 different environments. The ANN topology chosen was the one that had less than 1% of error in the testing phase with 1,800 simulated genotypes. A total of 9,000 simulated soybean genotypes were previously arranged in 18 different classes, which represented the combination of nine classes of adaptability by the Verma and collaborators (VCM) method and two classes of stability (invariability concept) by the Finlay & Wilkinson (FW) method. Finally, the grain production of ten real soybean genotypes was inputted into the ANN-trained model, and the classification regarding adaptability and stability was obtained. There was 90% agreement between the ANN and VCM analyses regarding the adaptability classification and 20% regarding stability. With the methods presented in this work, it was demonstrated that the potential of using ANNs to assess the adaptability of genotypes is strong. In addition, since stability was introduced in the ANN as a different concept from that used to classify the genotypes by the statistical method, such classification needs to be reviewed and further improved

Experimental and Artificial Neural Network Analysis of Geocell Reinforcement on Deformation and Vertical Stress Distribution of Quarry Waste Base

Experimental and Artificial Neural Network Analysis of Geocell Reinforcement on Deformation and Vertical Stress Distribution of Quarry Waste Base

Comparative analysis of artificial neural network (ANN) models: CO2 loading in MDEA and blended MDEA/PZ solvents

CO2 loading plays a vital role in gauging a solvent's capability to absorb CO2 from combustion gases and natural gas flow. Yet, the customary approaches for measuring CO2 loading hinge on laborious and costly laboratory experiments. Therefore, researchers employed artificial neural network (ANN) approaches to estimate CO2 loading using various datasets, although case-specific methods and approaches restricted their attempts. This study employed two datasets with 303 (CO2 absorption via MDEA) and 361 (CO2 absorption via MDEA/PZ) acceptable experimental data to create ANN models to estimate CO2 loading in aqueous amine solvents. The CO2 loading is evaluated using a wide range of input parameters, including temperature, CO2 partial pressure, Methyldiethanolamine (MDEA) solvent, and Piperazine (PZ) solvent for cases A and B, respectively. Twelve distinct training algorithms, four distinct activation functions, and three hidden layers with neuron variations created the models. ANN models trained with a blend of PZ and MDEA solvents generated more accurate predictions compared to models trained with solely MDEA solvent. The sigmoidal activation functions (Tansig and Logsig) generated more precise predictions than the linear activation functions (Poslin and Purelin). In addition, “Levenberg-Marquardt (lm)” with the highest coefficient of determination (R2) was selected as the best training algorithm among the twelve training functions. The best average coefficients of determination were determined to be 0.964 for the tansig activation function with [25 18 10] number of neurons in case A and 0.967 for the logsig activation function with [30 20 12] number of neurons in case B. The statistical analysis (R2, MSE, RMSE, AAD, AARD, and run time) confirmed that the network provided accurate estimates of CO2 loading for both datasets. The prediction potential of the network is then further indicated by model generalization inside the training data set.

Ultrasound characteristics of follicular and parafollicular thyroid neoplasms: diagnostic performance of artificial neural network

BackgroundUltrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain features observable by ultrasound have recently been equated with potential malignancy. This retrospective cohort study was conducted to test the hypothesis that radiomics of the four categorical divisions (medullary [MTC], papillary [PTC], or follicular [FTC] carcinoma and follicular thyroid adenoma [FTA]) demonstrate distinctive sonographic characteristics. Using an artificial neural network model for proof of concept, these sonographic features served as input.MethodsA total of 148 patients were enrolled for study, all with confirmed thyroid pathology in one of the four named categories. Preoperative ultrasound profiles were obtained via standardized protocols. The neural network consisted of seven input neurons; three hidden layers with 50, 250, and 100 neurons, respectively; and one output layer.ResultsRadiomics of contour, structure, and calcifications differed significantly according to nodule type (p = 0.025, p = 0.032, and p = 0.0002, respectively). Levels of accuracy shown by artificial neural network analysis in discriminating among categories ranged from 0.59 to 0.98 (95% confidence interval [CI]: 0.57–0.99), with positive and negative predictive ranges of 0.41–0.99 and 0.78–0.97, respectively.ConclusionsOur data indicate that some MTCs, PTCs, FTCs, and FTAs have distinctive sonographic characteristics. However, a significant overlap of these characteristics may impede an explicit classification. Further prospective investigations involving larger patient and nodule numbers and multicenter access should be pursued to determine if neural networks of this sort are beneficial, helping to classify neoplasms of the thyroid gland.