Artificial Neural Network Method Research Articles

Improving support vector machine and backpropagation performance for diabetes mellitus classification

Diabetes mellitus is a glucose disorder disease in the human body that contributes significantly to the high mortality rate. Various studies on early detection and classification have been conducted as a diabetes mellitus prevention effort by applying a machine learning model. The problems that may occur are weak model performance and misclassification caused by imbalanced data. The existence of dominating (majority) data causes poor model performance in identifying minority data. This paper proposed handling the problem of imbalanced data by performing the synthetic minority oversampling technique (SMOTE) and observing its effect on the classification performance of the support vector machine (SVM) and Backpropagation artificial neural network (ANN) methods. The experiment showed that the SVM method and imbalanced data achieved 94.31% accuracy, and the Backpropagation ANN achieved 91.56% accuracy. At the same time, the SVM method and balanced data produced an accuracy of 98.85%, while the Backpropagation ANN method and balanced data produced an accuracy of 94.90%. The results show that oversampling techniques can improve the performance of the classification model for each data class.

Assessment of meteorological parameters in predicting seasonal temperature of Dhaka city using ANN

In this study, an attempt has been made to investigate the possibility of a machine learning model, Artificial Neural Network (ANN) for seasonal prediction of the temperature of Dhaka city. Prior knowledge of temperature is essential, especially in tropical regions like Dhaka, as it aids in forecasting heatwaves and implementing effective preparedness schemes. While various machine learning models have been employed for the prediction of hot weather across the world, research specially focused on Bangladesh is limited. Additionally, the application of machine learning models needs to be curated to suit the particular weather features of any region. Therefore, this study approaches ANN method for prediction of the temperature of Dhaka exploring the underlying role of related weather variables. Using the daily data for the months of February to July collected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (0.25° × 0.25° global grid) for the years 2011–2020, this study focuses on finding the combination of weather variables in predicting temperatures. The densely populated city, Dhaka, has faced severe consequences due to extreme climate conditions in recent years, and this study will pave a new dimension for further research regarding the topic.

Water Consumption Using Artificial Neural Network Modelling in Commercial Aircraft

Amidst the current period of increased environmental consciousness and the aviation industry's dedication to sustainability, the delicate balance between water-carrying and fuel usage in commercial aircraft has become a crucial issue due to the rapid development of aviation technology. The primarypurpose of this study is to evaluate thewater usage of commercial aeroplanes and to optimise the aeroplanes' usage of water. Moreover, the study focuses on analysing the comparison between water consumption estimated using the Artificial Neural Networks (ANN) andhistorical data collected precisely. By referring to the actual operational data of water tank usage of the Boeing 787-9 fleet in a commercial airline, the flight hours, total passengers, and also actual portable water tank wereobtained. The data weregathered over twoyears,starting from January 2022 until December 2023,for day and night flight time. After all the collected data werecompleted, the optimisation of the water consumption was conducted and analysed using ANN. The optimised results were then compared using two conventional methods,IATA and Boeing. In conclusion, the ANNmethod has the least errorscompared to IATA and Boeing.The ANN method is suggested as a method to optimise thewater tank volume for commercial aircraft.

Application of artificial neural network method for early detection of dengue fever

Application of artificial neural network method for early detection of dengue fever

FORECASTING THE SUCCESS OF EDUCATION SEEKERS FROM A SEPARATE EDUCATIONAL COMPONENT BASED ON THE RESULTS OF THE PRELIMINARY MASTERY OF SUBJECT COMPETENCIES

The paper examines the main concepts related to the quality of education in general and the assimilation of educational material by higher education seekers. The task of predicting a seeker's grade in any discipline is formulated with data on his assimilation of program learning outcomes that also correspond to this discipline. The available specialized information system of own development is described which applies a number of methods (multivariate linear regression, artificial neural networks, k-nearest neighbors) and determines which method will be the most effective for the analysis of specific data. It is noted that during the further improvement of the quality system of knowledge assessment, it is important to determine at what level the student of education possesses the acquired competences, i.e. to calculate the success of seekers in terms of general and professional competences and program learning outcomes, determined by the standards of higher education and educational programs developed on their basis. The developed algorithm for calculating the success rate of higher education applicants in terms of program learning outcomes is presented; according to this algorithm, data were prepared on the acquisition of software creation competencies by 78 seekers of the first level of higher education of the educational and professional program Intelligent Decision Support Systems specialty 124, Systems analysis, of the DSEA. To solve the problem of forecasting by the method of artificial neural networks, the programming and data analysis language R is proposed. A script for finding the optimal neural network architecture is created. It was found that the best result (correlation is 0.9599, average absolute reduced error is 0.1132, percentage of correctly predicted points on the Ukrainian scale is 79.2) provides a perceptron with two hidden layers and five neurons in each one. This network was applied to predict the success of the new academic group: correlation is 0.923, the average absolute reduced error is 0.0654, the percentage of correctly predicted points on the Ukrainian scale is 82.4. The obtained results can be used to assess the quality of the structural and logical scheme of the EPP and in the work of the department during the analysis of seekers' success, etc.

Electronic Nose Based on Sensor Array for Classification of Beef and Rat Meat Using Backpropagation Artificial Neural Network Method

Electronic Nose Based on Sensor Array for Classification of Beef and Rat Meat Using Backpropagation Artificial Neural Network Method

Total organic carbon (TOC) estimation using ensemble and artificial neural network methods; a case study from Kazhdumi Formation, NW Persian Gulf

Total organic carbon (TOC) estimation using ensemble and artificial neural network methods; a case study from Kazhdumi Formation, NW Persian Gulf

Determinants of Students’ Satisfaction with AI Tools in Education: A PLS-SEM-ANN Approach

The emergence of Artificial Intelligence (AI) technology has significantly disrupted the educational landscape. The latest development in AI, generative AI that can generate new and tailored to specific content, has significantly impacted education. Given the value of AI technology in general and generative AI specific to users in education, such as students, the adaptability of these technologies has significantly increased. However, continuing and productive usage of AI tools depends upon students’ satisfaction with these tools. Drawing from the existing research, the present research has developed factors that affect students’ general satisfaction with AI tools. The research collected the data using a survey questionnaire from a Saudi Arabian university. The two-stage method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) have been employed. The two-stage method is applied in a way that PLS-SEM is used for testing the hypothesis and significance of the factor’s influence on satisfaction, and ANN is used to determine the relevant importance of the factor. The PLS-SEM results have shown that factors such as content quality, emotional wellbeing and perceived utility determine student satisfaction with AI tools. The ANN results show that emotional wellbeing is the most critical factor in satisfaction, followed equally by content quality and perceived utility.

Mangrove Species Classification from Unmanned Aerial Vehicle Hyperspectral Images Using Object-Oriented Methods Based on Feature Combination and Optimization.

Accurate and timely acquisition of the spatial distribution of mangrove species is essential for conserving ecological diversity. Hyperspectral imaging sensors are recognized as effective tools for monitoring mangroves. However, the spatial complexity of mangrove forests and the spectral redundancy of hyperspectral images pose challenges to fine classification. Moreover, finely classifying mangrove species using only spectral information is difficult due to spectral similarities among species. To address these issues, this study proposes an object-oriented multi-feature combination method for fine classification. Specifically, hyperspectral images were segmented using multi-scale segmentation techniques to obtain different species of objects. Then, a variety of features were extracted, including spectral, vegetation indices, fractional order differential, texture, and geometric features, and a genetic algorithm was used for feature selection. Additionally, ten feature combination schemes were designed to compare the effects on mangrove species classification. In terms of classification algorithms, the classification capabilities of four machine learning classifiers were evaluated, including K-nearest neighbor (KNN), support vector machines (SVM), random forests (RF), and artificial neural networks (ANN) methods. The results indicate that SVM based on texture features achieved the highest classification accuracy among single-feature variables, with an overall accuracy of 97.04%. Among feature combination variables, ANN based on raw spectra, first-order differential spectra, texture features, vegetation indices, and geometric features achieved the highest classification accuracy, with an overall accuracy of 98.03%. Texture features and fractional order differentiation are identified as important variables, while vegetation index and geometric features can further improve classification accuracy. Object-based classification, compared to pixel-based classification, can avoid the salt-and-pepper phenomenon and significantly enhance the accuracy and efficiency of mangrove species classification. Overall, the multi-feature combination method and object-based classification strategy proposed in this study provide strong technical support for the fine classification of mangrove species and are expected to play an important role in mangrove restoration and management.

Neural connectivity patterns explain why adolescents perceive the world as moving slow

That younger individuals perceive the world as moving slower than adults is a familiar phenomenon. Yet, it remains an open question why that is. Using event segmentation theory, electroencephalogram (EEG) beamforming and nonlinear causal relationship estimation using artificial neural network methods, we studied neural activity while adolescent and adult participants segmented a movie. We show when participants were instructed to segment a movie into meaningful units, adolescents partitioned incoming information into fewer encapsulated segments or episodes of longer duration than adults. Importantly, directed communication between medial frontal and lower-level perceptual areas and between occipito-temporal regions in specific neural oscillation spectrums explained behavioral differences between groups. Overall, the study reveals that a different organization of directed communication between brain regions and inefficient transmission of information between brain regions are key to understand why younger people perceive the world as moving slow.

Comparison of data-driven stochastic window operation models for residential buildings

In residential buildings, window opening is typically one of the most significant parameters that affects indoor air temperature and thus the energy required for heating and cooling. While several field measurement campaigns have been undertaken, and datasets have been used to calibrate data-driven stochastic models based on different techniques, a critical comparison between these window operation models, analysing their suitability for building energy simulation, is needed. This study compares seven different modelling approaches including Gaussian distribution function, logistic regression, Markov chain, Markov-logit hybrid, classification tree, artificial neural network and random forest, trained and tested using measurements of 21 living rooms and 10 bedrooms from residential buildings located in Australian subtropical and temperate climates. Two types of modelling approaches were tested: individual, where each window operation was modelled individually, and cohort, where a common model was developed considering all the windows in the dataset. The stochastic nature of window opening was introduced to the models by Monte Carlo methods. True Positive Rate (TPR) and True Negative Rate (TNR), quantified using the Area Under Curve (AUC) method, were used to benchmark the performance of the different models. The classification trees and random forest were identified as more accurate methods (>0.74 median AUC) for cohort modelling, while artificial neural network and Markov-logit hybrid methods were identified as more accurate methods (>0.7 median AUC) for individual window operation modelling in building simulation applications.

Landslide Susceptibility Assessment by Machine Learning and Frequency Ratio Methods Using XRAIN Radar-Acquired Rainfall Data

This study is an efficiency comparison between four methods for the production of landslide susceptibility maps (LSMs), which include random forest (RF), artificial neural network (ANN), and logistic regression (LR) as the machine learning (ML) techniques and frequency ratio (FR) as a statistical method. The study area is located in the Southern Hiroshima Prefecture in western Japan, a locality known to suffer from rainfall-induced landslide disasters, the most recent one in July 2018. The landslide conditioning factors (LCFs) considered in this study are lithology, land use, altitude, slope angle, slope aspect, distance to drainage, distance to lineament, soil class, and mean annual precipitation. The rainfall LCF data comprise XRAIN (eXtended RAdar Information Network) radar records, which are novel in the task of LSM production. The accuracy of the produced LSMs was calculated with the area under the receiver operating characteristic curve (AUROC), and an automatic hyperparameter tuning and result comparison system based on AUROC scores was utilized. The calculated AUROC scores of the resulting LSMs were 0.952 for the RF method, 0.9247 for the ANN method, 0.9016 for the LR method, and 0.8424 for the FR. It is also noteworthy that the ML methods are substantially swifter and more practical than the FR method and allow for multiple and automatic experimentations with different hyperparameter settings, providing fine and accurate outcomes with the given data. The results evidence that ML techniques are more efficient when dealing with hazard assessment problems such as the one exemplified in this study. Although the conclusion that the RF method is the most accurate for LSM production as found by other authors in the literature, ML method efficiency may vary depending on the specific study area, and thus the use of an automatic multi-method LSM production system with hyperparameter tuning such as the one utilized in this study is advised. It was also found that XRAIN radar-acquired mean annual precipitation data are effective when used as an LCF in LSM production.

Optimisation of MHD flow within trapezoidal cavity containing hybrid nanofluid by artificial neural network

PurposeThis study aims to numerically investigate heat transport in a trapezoidal cavity using hybrid nanoparticles (Ag-$Al_2O_3$). Unlike previous studies, this one covers magnetohydrodynamics, joule heating with viscous dissipation, heat absorption and generation. The left and right sides of the chasm are frigid. The upper wall heats, whereas the bottom wall remains adiabatic.Design/methodology/approachAfter reducing the system of dimensional equations to dimensionless equations, the authors use the Galerkin finite element method to solve them numerically. Geometric parameters affect heating efficiency; thus, the authors use flow metrics such as the Reynold number Re, magnetic parameter M, volume fraction coefficient, heat absorption and Eckert number Ec. The authors use the finite volume method to solve the governing equations after converting them to dimensionless form. The authors also try the artificial neural network method to predict the innovative cavity’s heat response in future scenarios. Transition state charts, regression analysis, MSE and error histograms accelerate, smooth and accurately converge solutions.FindingsAs the magnetic parameter and Eckert number increase, the enclosure emits more heat. As Reynold and volume fraction coefficients rise, the Nusselt number falls. It rose as magnetic, Eckert and heat absorption characteristics increased. The average Nusselt number rises with Reynolds and volume fraction coefficients. The magnetic, Eckert and heat absorption characteristics have inverse values.Originality/valueThis study numerically investigates heat transport in a trapezoidal cavity using hybrid nanoparticles (Ag-$Al_2O_3$). Unlike previous studies, this one covers MHD, joule heating with viscous dissipation, heat absorption and generation. The left and right sides of the chasm are frigid. The upper wall heats, whereas the bottom wall remains adiabatic.

Analysis of Dynamical Assisting and Opposing Flow Characteristics of Darcy Surface-Filled Ternary Nanoparticles and Fourier Flux: Artificial Neural Network and Levenberg Method

In this study, the effects of suction and buoyancy are used to analyze the Ternary hybrid nanofluid flow on a stretching sheet through a porous media. Ternary hybrid nanofluid is (Carbon nanotubes) CNT[Formula: see text]Graphene[Formula: see text] with base fluid as Water. Case-1 Buoyancy Assisting flow and Case-2 Buoyancy Opposing flow. Hybrid nanofluids have been used to speed up the heat transfer process. Nonlinear partial differential equations (PDEs) have been converted to ordinary differential equations (ODEs) using Lie group transformations. The ODE45, an algorithmic approach, has been using the aid of this built-in solver, and the resulting Ordinary differential equations were resolved. The general relationship between temperature, velocity, heat transfer rate, and shear stress on a stretchy surface is shown for a range of values of the significant factors. The temperature profiles have been rising with the impact of [Formula: see text]. Using streamlines to examine the flow pattern of a fluid and a method of machine learning, in terms of modern language, an artificial neural network (ANN) made up of artificial neurons or nodes is known as a neural network. A neural network is a network or circuit of genetic neurons.

A Railway Roadbed Deformation Monitoring System Using Deep Learning and AI Intelligent Technology

The author has designed an automated monitoring system for settlement and deformation of high-speed railway subgrade. Firstly, an automated monitoring system is designed based on sensors, data collection and transmission, client tracking and querying, monitoring result processing, automated warning, manual monitoring data analysis, monitoring data analysis and evaluation. Secondly, the system software and hardware are designed, the author calculated and analyzed engineering examples from the perspective of practical applications using artificial neural network methods, obtained corresponding deformation analysis models, and predicted deformation. Finally, the practical application of the system was analyzed for its effectiveness. The experimental results indicate that, the BP artificial neural network method is used to model and predict the Deformation monitoring data. On the premise of 20 learning samples and 4 prediction samples, the Root-mean-square deviation of the prediction is 0.32mm, which shows that the deformation prediction using BP model is feasible and effective in a certain precision range. It has been proven that the application of artificial neural network methods in monitoring and prediction of practical engineering has certain practical significance.

The employment of domain adaptation strategy for improving the applicability of neural network-based coke quality prediction for smart cokemaking process

Precise coke quality prediction is essential for coke production process optimization to achieve the reduction in energy consumption and CO2 emissions, thus moving toward carbon neutrality in the coking industry. However, the complexity of coal molecular structures and the chemical reactions in the cokemaking process pose significant challenges to the applicability of existing coke quality prediction models. Based on the recently widely employed Artificial Neural Network (ANN) method, this study is the first to introduce domain adaptation strategies for improving the applicability of ANN in predicting coke quality including Coke Strength after Reaction (CSR) and Coke Reactivity Index (CRI). 649 Chinese coal samples with properties including Mad, Ad, Vdaf, St,d, G, X and Y along with coke quality were collected. They were initially categorized into source and target domains characterized by different distributions. Subsequently, two scenarios were independently evaluated based on coal samples in the target domain, which either lacked any information or contained partial information of actual coke quality. The results suggested that the proposed approach can significantly enhance the predictive performance for coal samples across various distributions. Moreover, a comprehensive investigation was also conducted to determine key factors influencing the effectiveness of coke quality prediction with this approach.

Implementation of Backpropagation Artificial Neural Network for Electricity Load Forecasting in Jember District

The increase in population and various kinds of human activities in the world has made it possible for changes to increase the need for electrical power with demand that is not the same at any time. Based on this description, this research will propose research on the theme of electricity load forecasting as a preventive measure to determine future electricity load needs. Research was assisted using MATLAB data processing software to process research data. Three forecasting models were carried out, namely day, night and day-night conditions. From these three forecasting models, parameters such as epoch, number of input layers, number of hidden layers, activation function, and etc. The data is divided into two parts, training data and test data with a ratio of 70: 30. Test results using the backpropagation artificial neural network method show the highest MSE values for the three forecasting models, day, night, and day-night, are, 0.0039, 0.0041, and 0.002 while the lowest MSE values were in the three models are, 6.77E-04, 0.001, and 0.0011.

Wearable Loops for Dynamic Monitoring of Joint Flexion: A Machine Learning Approach

We present a machine learning driven system to monitor joint flexion angles during dynamic motion, using a wearable loop-based sensor. Our approach uses wearable loops to collect transmission coefficient data and an Artificial Neural Network (ANN) with fine-tuned parameters to increase accuracy of the measured angles. We train and validate the ANN for sagittal plane flexion of a leg phantom emulating slow motion, walking, brisk walking, and jogging. We fabricate the loops on conductive threads and evaluate the effect of fabric drift via measurements in the absence and presence of fabric. In the absence of fabric, our model produced a root mean square error (RMSE) of 5.90°, 6.11°, 5.90°, and 5.44° during slow motion, walking, brisk walking, and jogging. The presence of fabric degraded the RMSE to 8.97°, 7.21°, 9.41°, and 7.79°, respectively. Without the proposed ANN method, errors exceeded 35.07° for all scenarios. Proof-of-concept results on three human subjects further validate this performance. Our approach empowers feasibility of wearable loop sensors for motion capture in dynamic, real-world environments. Increasing speed of motion and the presence of fabric degrade sensor performance due to added noise. Nevertheless, the proposed framework is generalizable and can be expanded upon in the future to improve upon the reported angular resolution.

Study on hot deformation behavior and recrystallization mechanism of an Al-6.3Zn-2.5Mg-2.6Cu-0.11Zr alloy based on machine learning

The data from thermal simulation plays a significant role in understanding the deformation behavior and deformation mechanisms of materials, and provides guidance for the design of hot deformation processes. In this paper, thermal simulation experiments were performed on an Al-6.3Zn-2.5Mg-2.6Cu-0.11Zr alloy, and the microstructure after hot deformation were characterized by Electron Backscatter Diffraction (EBSD) technique. The thermal simulation data and microstructural characteristics were analyzed and studied using machine learning technique: the Backpropagation Artificial Neural Network (BP-ANN) method, correlation analysis and K-means clustering analysis. A model for predicting not only flow stress but also dislocation density was established using the BP-ANN method, which exhibits good prediction accuracy. The R² and AARE values are 90.19 % and 6.87 % for the prediction of dislocation density, and are 98.67 % and 6.96 % for the prediction of flow stress. Subsequently, correlation analysis was performed to investigate the relationship between energy dissipation efficiency η and deformation parameters as well as microstructural characteristics, revealing that energy dissipation rate is correlated positively with the content of dynamical recrystallization (DRX) and high angle grain boundary (coefficients of 0.90 and 0.82) and negatively with the dislocation density and the Zener-Hollomon parameter (coefficients of −0.98 and −0.97). Additionally, K-means clustering analysis was applied to study the relationship between the energy dissipation efficiency and softening mechanisms, it was found that the softening mechanisms changed with the variation of η value, dynamic recovery (DV), geometric DRX (GDRX) and continuous DRX (CDRX) dominated when η>0.38; DV, CDRX and discontinuous DRX(DDRX) dominated when 0.28<η<0.38; DV dominated when η<0.28. The results show that machine learning techniques are useful tools in mining thermal simulation data, and more information can be obtained than traditional data mining methods.

Identification of the Structure of Liquid–Gas Flow in a Horizontal Pipeline Using the Gamma-Ray Absorption and a Convolutional Neural Network

Knowledge of the liquid–gas flow regime is important for the proper control of many industrial processes (e.g., in the mining, nuclear, petrochemical, and environmental industries). The latest publications in this field concern the use of computational intelligence methods for flow structure recognition, which include, for example, expert systems and artificial neural networks. Generally, machine learning methods exploit various characteristics of sensors signals in the value, time, frequency, and time–frequency domain. In this work, the convolutional neural network (CNN) VGG-16 is applied for analysis of histogram images of signals obtained for water–air flow by using gamma-ray absorption. The experiments were carried out on the laboratory hydraulic installation fitted with a radiometric measurement system. The essential part of the hydraulic installation is a horizontal pipeline made of metalplex, 4.5 m long, with an internal diameter of 30 mm. The radiometric measurement set used in the investigation consists of a linear Am-241 radiation source with an energy of 59.5 keV and a scintillation detector with a NaI(Tl) crystal. In this work, four types of water–air flow regimes (plug, slug, bubble, and transitional plug–bubble) were studied. MATLAB 2022a software was used to analyze the measurement signal obtained from the detector. It was found that the CNN network correctly recognizes the flow regime in more than 90% of the cases.