Simple Artificial Neural Network Model Research Articles

Comparative Analysis of Artificial Neural Network (ANN) and Wavelet Integrated Artificial Neural Network (W-ANN) Approaches for Rainfall Modeling of Southern Rajasthan, India

This paper addresses the challenge of predicting erratic rainfall in Rajasthan state of India, particularly in southern regions. Reliable rainfall predictions are crucial for water resource management and agriculture planning. The research involved selecting 58 stations across seven districts of southern Rajasthan and identifying the best fit computational neural (ANN) and wavelet integrated computational neural (W-ANN) architectures based on performance metrics. Different combinations of input characters, hidden layer neurons, learning algorithms, and training cycles were tested to determine optimal models. Hybrid models, combining wavelet analysis with ANN, were explored to tackle non-stationary hydrologic signals effectively. Results showed that ANN Model C with ten input layer neurons performed best for 74% of stations, followed by Model B (21% of stations) and Model A (5% of stations). Models with increased input and hidden layer neurons performed better. Among the selected stations, 81% of stations demonstrated improved performance using W-ANN models due to effective signal decomposition and information extraction. The hybrid W-ANN models outperformed simple ANN models for rainfall prediction. Both ANN and W-ANN models accurately forecasted weekly rainfall, as observed in the comparison of actual and forecasted values.

Ultraviolet Erythemal Irradiance (UVER) under Different Sky Conditions in Burgos, Spain: Multilinear Regression and Artificial Neural Network Models

Different strategies for modeling Global Horizontal UltraViolet Erythemal irradiance (GHUVE) based on meteorological parameters measured in Burgos (Spain) have been developed. The experimental campaign ran from September 2020 to June 2022. The selection of relevant variables for modeling was based on Pearson’s correlation coefficient. Multilinear Regression Model (MLR) and artificial neural network (ANN) techniques were employed to model GHUVE under different sky conditions (all skies, overcast, intermediate, and clear skies), classified according to the CIE standard on a 10 min basis. ANN models of GHUVE outperform those based on MLR according to the traditional statistical indices used in this study (R2, MBE, and nRMSE). Moreover, the work proposes a simple all-sky ANN model of GHUVE based on usually recorded variables at ground meteorological stations.

Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India.

The intensity and frequency of diverse hydro-meteorological disasters viz., extreme droughts, severe floods, and cyclones have increasing trends due to unsustainable management of land and water resources, coupled with increasing industrialization, urbanization and climate change. This study focuses on the forecasting of drought using selected Artificial Neural Network (ANN)-based models to enable decision-makers to improve regional water management plans and disaster mitigation/reduction plans. Four ANN models were developed in this study, viz., one conventional ANN model and three hybrid ANN models: (a) Wavelet based-ANN (WANN), (b) Bootstrap based-ANN (BANN), and (c) Wavelet-Bootstrap based-ANN (WBANN). The Standardized Precipitation Evapotranspiration Index (SPEI), the best drought index identified for the study area, was used as a variable for drought forecasting. Three drought indices, such as SPEI-3, SPEI-6 and SPEI-12 respectively representing "short-term", "intermediate-term", and "long-term" drought conditions, were forecasted for 1-month to 3-month lead times for six weather stations over the study area. Both statistical and graphical indicators were considered to assess the performance of the developed models. For the hybrid wavelet model, the performance was evaluated for different vanishing moments of Daubechies wavelets and decomposition levels. The best-performing bootstrap-based model was further used for analysing the uncertainty associated with different drought forecasts. Among the models developed for drought forecasting for 1 to 3 months, the performances of the WANN and WBANN models are superior to the simple ANN and BANN models for the SPEI-3, SPEI-6, and SPEI-12 up to the 3-month lead time. The performance of the WANN and WBANN models is the best for SPEI-12 (MAE = 0.091-0.347, NSE = 0.873-0.982) followed by SPEI-6 (MAE = 0.258-0.593; NSE = 0.487-0.848) and SPEI-3 (MAE = 0.332-0.787, NSE = 0.196-0.825) for all the stations up to 3-month lead time. This finding is supported by the WBANN analyze uncertainties as narrower band width for SPEI-12 (0.240-0.898) as compared to SPEI-6 (0.402-1.62) and SPEI-3 (0.474-2.304). Therefore, the WBANN model is recommended for the early warning of drought events as it facilitates the uncertainty analysis of drought forecasting results.

Neurosymbolic artificial intelligence (NSAI) based algorithm for predicting the impact strength of additive manufactured polylactic acid (PLA) specimens

In this study, we introduce application of Neurosymbolic Artificial Intelligence (NSAI) for predicting the impact strength of additive manufactured polylactic acid (PLA) components, representing the first-ever use of NSAI in the domain of additive manufacturing. The NSAI model amalgamates the advantages of neural networks and symbolic AI, offering a more robust and accurate prediction than traditional machine learning techniques. Experimental data was collected and synthetically augmented to 1000 data points, enhancing the model’s precision. The Neurosymbolic model was developed using a neural network architecture comprising input, two hidden layers, and an output layer, followed by a decision tree regressor representing the symbolic component. The model’s performance was benchmarked against a Simple Artificial Neural Network (ANN) model by assessing mean squared error (MSE) and R-squared (R2) values for both training and validation datasets. The results reveal that the Neurosymbolic model surpasses the Simple ANN model, attaining lower MSE and higher R2 values for both training and validation sets. This innovative application of the Neurosymbolic approach in estimating the impact strength of additive manufactured PLA components underscores its potential for optimizing the additive manufacturing process. Future research could investigate further refinements to the Neurosymbolic model, extend its application to other materials and additive manufacturing processes, and incorporate real-time monitoring and control for enhanced process optimization.

A simple ANN-MLP model for estimating 60-GHz PDP inside public and private vehicles

Radio wave propagation in an intra-vehicular (IV) environment is markedly different from other well-studied indoor scenarios, such as an office or a factory floor. While millimetre wave (mmWave)-based intra-vehicular communications promise large bandwidth and can achieve ultra-high data rates with lower latency, exploiting the advantages of mmWave communications largely relies on adequately characterising the propagation channel. Channel characterisation is most accurately done through an extensive channel sounding, but due to hardware and environmental constraints, it is impractical to test channel conditions for all possible transmitter and receiver locations. Artificial neural network (ANN)-based channel sounding can overcome this impediment by learning and estimating the channel parameters from the channel environment. We estimate the power delay profile in intra-vehicular public and private vehicle scenarios with a high accuracy using a simple feedforward multi-layer perception-based ANN model. Such artificially generated models can help extrapolate other relevant scenarios for which measurement data are unavailable. The proposed model efficiently matches the taped delay line samples obtained from real-world data, as shown by goodness-of-fit parameters and confusion matrices.

Klasifikasi Kendaraan Berbasis Suara Di Lalu Lintas: Implementasi TinyML

Driving is deeply embedded in Indonesian culture. All Indonesian citizens (WNI) who meet the qualifications can obtain a driver's license (SIM). As a result, the number of drivers in Indonesia is relatively significant, resulting in a high rate of traffic accidents. One of the reasons leading to the rising number of road accidents is hearing loss. This study focuses on building a tool that can classify vehicles based on the audio of the vehicle while it is running in order to assist drivers with hearing impairments. This study employs the Arduino Nano 33 BLE Sense, which includes an embedded microphone, and the Edge Impulse open platform. A simple artificial neural network model with four 1-D convolution layers and one hidden layer was utilized. The dataset was obtained manually using the YouTube open platform and was captured directly on the Edge Impulse platform using the Arduino Nano's microphone. The model training procedure was repeated 80 epochs. During training and testing, the model is regarded to be accurate, with accuracy scores of 96.3% and 90.9%, respectively. With these adequate results, the model is judged eligible for implementation, despite the fact that it still requires further refinement.

Modeling relational responding with artificial neural networks

Relational responding refers to behavior that conforms to a rule for com- paring stimuli. Lazareva et al. (2014) trained pigeons to choose either the smaller or the larger of two circles, using 1–3 pairs of circles for training and 17–19 new pairs for testing. The pigeons showed relational responding on some test pairs and systematic failures on others. We present a simple artificial neural network model that reproduces the animals’ behavior well, similarly to Lazareva et al.’s (2014) statistical model based on stimulus features and stimulus relationships. We analyze how the network model gener- alizes from training to test stimuli, and show that it can reconcile contrasting ideas about relational responding from the seminal works by Köhler (1929, 1918/1938, 1924), positing that animals can learn relational rules such as “choose the larger stimulus,” and Spence (1937), positing that relational re- sponding can be explained based on stimulus generalization.

Application of Artificial Neural Network to Nucleic Acid Analysis: Accurate Discrimination for Untypical Real-Time Fluorescence Curves With High Specificity and Sensitivity

Abstract As a division of polymerase chain reaction (PCR), convective PCR (CPCR) is able to achieve highly efficient thermal cycling based on free thermal convection with pseudo-isothermal heating, which could be beneficial to point-of-care (POC) nucleic acid analysis. Similar to traditional PCR or isothermal amplification, due to a couple of issues, e.g., reagent, primer design, reactor, reaction dynamics, amplification status, temperature and heating condition, and other reasons, in some cases of CPCR tests, untypical real-time fluorescence curves with positive or negative tests will show up. Especially, when parts of the characteristics between untypical low-positive and negative tests are mixed together, it is difficult to discriminate between them using traditional cycle threshold (Ct) value method. To handle this issue which may occur in CPCR, traditional PCR or isothermal amplification, as an example, instead of using complicated mathematical modeling and signal processing strategy, an artificial intelligence (AI) classification method with artificial neural network (ANN) modeling is developed to improve the accuracy of nucleic acid detection. It has been proven that both the detection specificity and sensitivity can be significantly improved even with a simple ANN model. It can be estimated that the developed method based on AI modeling can be adopted to solve similar problem with PCR or isothermal amplification methods.

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple steatosis, steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Genetic, nutritional factors, obesity, insulin resistance, gut microbiota are among the risk factors for NAFLD. The genetic variant Patatin-like phospholipase domain-containing protein 3 (PNPLA3) plays an important role in the development of a number of liver diseases ranging from steatosis, chronic hepatitis, cirrhosis and HCC. Due to the increase in the prevalence of NAFLD, new models are being developed with machine learning, deep learning, artificial neural network (ANN) algorithms in the field of artificial intelligence (AI) to determine low-cost, noninvasive diagnostic methods. Models developed with ANN from AI modules are important in order to examine biochemical and genomic information in detail in the diagnosis of NAFLD. The aim of this study is to develop a simple ANN model using biochemical and genotypic parameters in the diagnosis of NAFLD. A total of 300 patients followed up with the diagnosis of NAFLD and 100 controls were included in the study. The data set was divided into two as training and test set. Genotyping of PNPLA3 (CC, CG, GG) as genomic analysis was performed with real time PCR device. The algorithm used for the diagnosis of NAFLD was designed using age, body mass index (BMI), mean platelet volume (MPV), insulin resistance (IR), alanine aminotransferase (ALT), genotype PNPLA3 (CC, CG, GG) parameters. MLP Classifier algorithm from ANN was used in the development of the model. ANN algorithms are used in python programming language. Statistical analyzes were made in SPSS program. Percent accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall, and f1-score results were determined. The accuracy percentage was determined as 0.979 in the train set and 0.970 in the test set. The Log Loss value was set to 0.09. The developed neural network achieved an accuracy percentage of 97.0% during testing, with an area under the ROC curve value of 0.95. We think that the ANN model developed with genomic and biochemical parameters can be used as a cost-effective, noninvasive new predictive diagnostic model in clinical practice in the diagnosis of NAFLD.

Efficient calculation of optical properties of suspended-core fiber via a machine learning algorithm.

Growing nonlinearity demands in mid-infrared applications place more outstanding requirements on fiber structure design. Chalcogenide suspended-core fibers (SCFs) are considered excellent candidates for mid-infrared applications due to their significant advantages in nonlinearity and dispersion management. However, traditional numerical methods for accurate modeling and optimization of SCFs often rely on the performance of computing devices and have many limitations when dealing with complex models. A machine learning algorithm is applied to calculate the optical properties of chalcogenide SCFs, including effective mode area, nonlinear coefficient, and dispersion. The established artificial neural network (ANN) model enables accurate prediction of the above optical properties of As2S3 SCF, for which in the wavelength range of 1.0 to 4.0 µm, the radius of the fiber core is 0.4 to 0.6 µm, and width of the cantilever is 0.06 to 0.09 µm. We demonstrate that this simple ANN model has considerable advantages over the traditional numerical calculation model in computational speed and resource utilization. In summary, the proposed model can quickly provide more accurate optical property predictions, providing a cost-effective solution for precise modeling and optimization of chalcogenide SCFs.

Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.

Prophecy of Stock Price of Bharat Immunological & Biological Corporation Ltd using Hybrid ANN and PSO Model

The prediction of the time series has always attracted much interest from investors and researchers to evaluate financial risk. Stock market movements are extremely complex and are influenced by different factors. Hence it is very important to find the most important factors for the stock market. But the high level of noise and complexity of the financial data makes this job very difficult. Many authors have already used artificial neural network for this kind of forecasting tasks, but hybridization model of artificial neural network is considered to be widely used and better performing forecasting model among others. The dormant high noises data mess up the performance, so to enhance the prediction accuracy. We considered a set of seven technical attribute of stock market to perform the hybrid model of Artificial Neural Network (ANN) and Particle Swarm Optimization algorithms. The efficiency of the proposed method is measured by the stock price of Bharat Immunological & Biological Corporation Ltd with 3945 number of daily transactional data. Empirical prediction analysis shows that the proposed model enhances the performance in comparison to simple ANN model.

Forecast of the outbreak of COVID-19 using artificial neural network: Case study Qatar, Spain, and Italy

The present study illustrates the outbreak prediction and analysis on the growth and expansion of the COVID-19 pandemic using artificial neural network (ANN). The first wave of the pandemic outbreak of the novel Coronavirus (SARS-CoV-2) began in September 2019 and continued to March 2020. As declared by the World Health Organization (WHO), this virus affected populations all over the globe, and its accelerated spread is a universal concern. An ANN architecture was developed to predict the serious pandemic outbreak impact in Qatar, Spain, and Italy. Official statistical data gathered from each country until July 6th was used to validate and test the prediction model. The model sensitivity was analyzed using the root mean square error (RMSE), the mean absolute percentage error and the regression coefficient index R2, which yielded highly accurate values of the predicted correlation for the infected and dead cases of 0.99 for the dates considered. The verified and validated growth model of COVID-19 for these countries showed the effects of the measures taken by the government and medical sectors to alleviate the pandemic effect and the effort to decrease the spread of the virus in order to reduce the death rate. The differences in the spread rate were related to different exogenous factors (such as social, political, and health factors, among others) that are difficult to measure. The simple and well-structured ANN model can be adapted to different propagation dynamics and could be useful for health managers and decision-makers to better control and prevent the occurrence of a pandemic.

Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network

Rock compressive strength is an important mechanical parameter for the design, excavation, and stability analysis of rock mass engineering in cold regions. Accurate and rapid prediction of rock compressive strength has great engineering value in guiding the efficient construction of rock mass engineering in a cold regions. In this study, the prediction of triaxial compressive strength (TCS) for sandstone subjected to freeze-thaw cycles was proposed using a genetic algorithm (GA) and an artificial neural network (ANN). For this purpose, a database including four model inputs, namely, the longitudinal wave velocity, porosity, confining pressure, and number of freeze-thaw cycles, and one output, the TCS of the rock, was established. The structure, initial connection weights, and biases of the ANN were optimized progressively based on GA. After obtaining the optimal GA-ANN model, the performance of the GA-ANN model was compared with that of a simple ANN model. The results revealed that the proposed hybrid GA-ANN model had a higher accuracy in predicting the testing datasets than the simple ANN model: the root mean square error (RMSE), mean absolute error (MAE), and R squared ( R 2 ) were equal to 1.083, 0.893, and 0.993, respectively, for the hybrid GA-ANN model, while the corresponding values were 2.676, 2.153, and 0.952 for the simple ANN model.

Integrated deep learning neural network and desirability analysis in biogas plants: A powerful tool to optimize biogas purification

Performing anaerobic digestion is affected by different slurry properties, and its optimization poses many practical constraints. In high-dimensional input parameters with small sample size data, regression models and simple artificial neural network models may not be good enough at estimating responses. Therefore, a deep learning neural network (DNN) model was developed to estimate the responses (biogas compounds) according to the slurry properties. This model was able to predict the biogas compounds with high accuracy in comparison with regression models and back propagation neural network models. The DNN model was integrated with desirability analysis to determine optimum amounts of the slurry properties, and thus, increase biogas purification. Accordingly, the optimum ranges of C/N (15.04–18.95), BOD/COD (0.763–0.818), TS (8.1–10.6%) and T.VS (38.19–49.46%) were more precise than the ranges reported in the literature. The results indicated that large amounts of BOD/COD had a deterrent effect on desirability values, and therefore had an inhibitory effect on biogas purification. Further, pH amounts slightly above neutral could improve biogas purification. Suitable amounts of the slurry properties for the second repetition of experiments were all in the determined optimum ranges, indicating that the optimum ranges were practical to be used in biogas plants.

Regression and ANN models for predicting MOR and MOE of heat-treated fir wood

Modulus of rupture (MOR) and modulus of elasticity (MOE) of heat-treated fir wood (abies sp) were predicted by simple and multiple regression and artificial neural network (ANN) models based on ΔE (total color difference), ΔL (lightness difference), contact angle, and mass loss. The results showed that ANN provided better performance compared to regression models. Predicted values of MOR and MOE by ANN and regression models concur well with measured values. ΔL was the best predictor for predicting MOR in both simple ANN and regression models. The cubic function was the best simple regression model for the prediction of both MOR and MOE. The lowest mean absolute percentage error (MAPE) by simple ANN model for the prediction of MOR and MOE were 2.21% and 0.61%, respectively and their corresponding MAPE values for the simple linear regression model were 3.99% and 0.91%. MAPE for prediction of MOR by multiple regression and ANN models were 2.75% and 0.7% respectively, and their corresponding MAPE for MOE were 1.80% and 0.55%, which were comparable with those of simple ANN and regression models. Since the difference between simple and multiple models is small, simple models based on predictors which could be easily and in a short time measured in-line and be inexpensive to predict MOR and MOE with low MAPE, are recommended. Therefore, from this point of view, the color change could be the best option, which can also be measured inline. Contact angle and mass loss are the next options. Mass loss is measured at the beginning and at the end of the heat treatment, and the contact angle can be measured in 30–60 s after heat treatment.

Prediction of Flood in Barak River using Hybrid Machine Learning Approaches: A Case Study

Abstract Flooding causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Therefore, flood prediction is of prime importance for reducing loss of life and devastation to property. To model complex nature of hydrologic processes artificial neural network (ANN) tool is effectively being utilized for modelling different nonlinear relationships, and has proved to be an appropriate method for flood prediction. Present study investigated relative accuracy of radial basis function neural network (RBFNN) and support vector machine (SVM) models combined with Firefly Algorithm (FA) in predicting river flood discharge and contrasted with that of regular ANN, RBFNN and SVM models. Monthly river flow data of Silchar and Dholai stations located in Cachar district of Assam, India are utilized for the present study. For assessing model performance, coefficient of determination (R2), mean square error (MSE) and root mean square error (RMSE) were measured. Evaluation of outcomes shows that both RBF-FA (radial basis function - firefly algorithm) and SVM-FA (support vector machine - firefly algorithm) hybrid models give more precise forecasting results than RBFNN, FFBPNN (feed forward back propagation neural network) and SVM models. Yet, it can be observed that SVM-FA model give better forecasting outputs with R2 value 0.9818 than RBF-FA model. Results also reveal that simple SVM model performs marginally better than simple ANN model.

Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models

Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh.
 Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)

Valorization of Vegetable Food Waste and By-Products Through Fermentation Processes.

There is a general interest in finding new ways of valorizing fruit and vegetable processing by-products. With this aim, applications of industrial fermentation to improve nutritional value, or to produce biologically active compounds, have been developed. In this sense, the fermentation of a wide variety of by-products including rice, barley, soya, citrus, and milling by-products has been reported. This minireview gives an overview of recent fermentation-based valorization strategies developed in the last 2 years. To aid the designing of new bioprocesses of industrial interest, this minireview also provides a detailed comparison of the fermentation conditions needed to produce specific bioactive compounds through a simple artificial neural network model. Different applications reported have been focused on increasing the nutritional value of vegetable by-products, while several lactic acid bacteria and Penicillium species have been used to produce high purity lactic acid. Bacteria and fungi like Bacillus subtilis, Rhizopus oligosporus, or Fusarium flocciferum may be used to efficiently produce protein extracts with high biological value and a wide variety of functional carbohydrates and glycosidases have been produced employing Aspergillus, Yarrowia, and Trichoderma species. Fermentative patterns summarized may guide the production of functional ingredients for novel food formulation and the development of low-cost bioprocesses leading to a transition toward a bioeconomy model.

A SIMPLE ARTIFICIAL NEURAL NETWORK FOR FIRE DETECTION USING LANDSAT-8 DATA

Abstract. Fixed threshold models have been widely used in active fire detection products. However, its accuracy is limited due to the complexity of setting up thresholds. Artificial neural network (ANN) is capable of learning from data and can decide weights automatically. Given enough data, an ANN model is able to optimize itself and quickly find an optimal solution. In this work, a simple ANN model is implemented to classify fire pixels from Landsat-8 data. Experimental results show that our ANN model effectively achieves fire detection and performs better than fixed threshold model in certain circumstances.