Bayesian-Assisted Multilayer Neural Network Structure Adaptation Method for Microwave Design

Monkeypox virus is gaining attention due to its severity and spread among people. This study sheds light on the modeling and forecasting of new monkeypox cases. Knowledge about the future situation of the virus using a more accurate time series and stochastic models is required for future actions and plans to cope with the challenge. We conduct a side-by-side comparison of the machine learning approach with the traditional time series model. The multilayer perceptron model (MLP), a machine learning technique, and the Box-Jenkins methodology, also known as the ARIMA model, are used for classical modeling. Both methods are applied to the Monkeypox cumulative data set and compared using different model selection criteria such as root mean square error, mean square error, mean absolute error, and mean absolute percentage error. With a root mean square error of 150.78, the monkeypox series follows the ARIMA (7,1,7) model among the other potential models. Comparatively, we use the multilayer perceptron (MLP) model, which employs the sigmoid activation function and has a different number of hidden neurons in a single hidden layer. The root mean square error of the MLP model, which uses a single input and ten hidden neurons, is 54.40, significantly lower than that of the ARIMA model. The actual confirmed cases versus estimated or fitted plots also demonstrate that the multilayer perceptron model has a better fit for the monkeypox data than the ARIMA model. When it comes to predicting monkeypox, the machine learning method outperforms the traditional time series. A better match can be achieved in future studies by applying the extreme learning machine model (ELM), support vector machine (SVM), and some other methods with various activation functions. It is thus concluded that the selected data provide a real picture of the virus. If the situations remain the same, governments and other stockholders should ensure the follow-up of Standard Operating Procedures (SOPs) among the masses, as the trends will continue rising in the upcoming 10 days. However, governments should take some serious interventions to cope with the virus. In the ARIMA models selected for forecasting, we did not incorporate the effect of covariates such as the effect of net migration of monkeypox virus patients, government interventions, etc.

This research investigated and optimized the separation of carbon dioxide (CO2) from natural gas in an adsorption column filled with grafted-beam nanofiber adsorbent. The main purpose of using ANN and RSM models in this manuscript is to compare these two methods in predicting the CO2 adsorption capacity. In other words, it was made to find a suitable model that has the highest agreement with the experimental data. Also, the other purpose of using the RSM model is to detect the optimized empirical conditions. Moreover, two common ANN models are applied in this work, including a multilayer perceptron (MLP) and radial basis function (RBF). The novelties of this work are explained as follows: (1) detecting the optimized synthesis factors of NF-PAN/PUGMA sorbent which possess the highest CO2 adsorption capacity with the help of response surface methodology (RSM), (2) studying the simultaneous interaction of synthesis parameters on the CO2 adsorption capacity with the help of both RSM and artificial neural networks (ANNs), (3) testing two types of ANN models including multilayer perceptron (MLP) and radial basis function (RBF) to predict the effect of monomer volume percentage and irradiation dose on the CO2 adsorption capacity. Indeed, finding the best model (ANN or RSM) can help engineers in practical applications predict CO2 adsorption capacity using NF-PAN/PUGMA under different conditions without incurring high-priced chemical materials, electricity, or human resources. The validation results were examined using correlation coefficients (R2) of RSM, RBF, and MLP models. The correlation coefficients for the RSM, RBF, and MLP models were 0.9910, 0.9949, and 0.9968, respectively. Additionally, the average absolute relative deviation (AARD) values for the RBF and MLP models were 0.00046512 and 0.00045511, respectively, indicating that the MLP model is better than the RBF model. To identify the optimal network structure, the trial-and-error method was conducted for MLP and RBF models. The number of neurons was found at 12 and 45 for MLP and RBF, respectively. The optimized effective parameters were obtained using RSM: 25.80% GMA, 66.45% amine, and an irradiation intensity of 28kGy.

In solar thermal systems, solar air heater (SAH) is an important device for heating air using energy of the sun. The solar collector is the most important part of SAH which collects solar radiations as thermal energy (heat) and transmits it to the air. For efficient utilization of solar energy, the system performance needs to be optimized. There are many techniques that are used for optimization but Artificial Intelligence (AI) technique is very effective. Artificial neural networks (ANN) are coming up as valuable options in place of traditional statistical modeling techniques in many scientific disciplines. ANN technique is the most used one for modeling, prediction, and control. This technique is computes faster results and it can solve the complex and nonlinear problems which cannot be solved by other conventional approaches. Also this technique requires large data sets but not separate programming for solving the problems. In this paper, all the published research works from year 2009 to 2021 related to MLP model for performance prediction has been reviewed, which are applied on solar air heating systems. The main aim of current study is to review research work related to multi-layer perceptron (MLP) model applied in SAH and to find a gap for future research. This paper encompasses many research articles related to neural networks which focus particularly on MLP model. Review of some research papers has been included in detail for the proposed work. Also the present review article reported that MLP structure of neural model is the most commonly applicable model used for prediction of SAHs performances with satisfactory results.

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Mercury is a highly poisonous metal which is mostly found in environment. It is considered at the top of the parameters of water quality that requires investigations for planning and management. To understand the status of mercury in the groundwater of Naameh Landfill, Artificial Neural Network (ANN) models were used as indicators of water quality and for the prediction of Mercury. Two types of feed forward networks have been used including multilayer perceptron (MLP) and radial basis function (RBF). A number of different MLP neural networks algorithms and RBF networks trained and developed with reference to pH, EC, TDS, TON, calcium and magnesium to predict Mercury concentration in groundwater. Six scenarios were used to train MLP and RBF networks for choosing the best-fit model for predicting water quality parameters in groundwater of Naameh Landfill. The performances of MLP and RBF models were evaluated by utilizing the coefficient of determination (R2). The results showed that the computed values of R2 for MLP and RBF were 0.791 and 0.881respectively. In addition, the prediction results showed that both types of networks are very good for predicting Mercury concentration in the ground water of our study area. Moreover, the results showed that there are mercury residues for 2 years ahead even if there is no discharge in this place. As a matter of fact, there are no studies that encompass status of heavy metals in municipal solid waste landfills in Lebanon or neighboring countries using ANN models. Thus, this study can be described as unique as it demonstrated a 9 year groundwater data (2011-2019), presented data and projected data for two upcoming years. This is crucial especially in the continual waste crisis that Lebanon is facing and the absence of sustainable disposal practices. This data is a rigid base and a solid reference for developing adequate solutions to prevent future contamination of groundwater with its associated negative impacts on the health and wellbeing of individuals.

This study considers the usage of multilinear regression and artificial neural network modelling to forecast ozone concentrations with regard to weather-related indicators (wind speed, wind direction, relative humidity and temperature). Initial data were obtained by measuring the meteorological parameters using the PC Radio Weather Station. Ozone concentrations near high-voltage lines were measured using RS1003 and at a 220 m distance using ML9811. Neural network models such as the multilayer perceptron and radial basis function neural networks were constructed. The prognostic capacities of the designed models were assessed by comparing the result data by way of the square of the coefficient of multiple correlations (R2) and mean square error (MSE) values. The number of hidden neurons was optimised by decreasing an error function that recorded the number of units in the hidden layers to the precision of the expanded networks. The neural software IBM SPSS 26v was used for artificial neural network (ANN) modelling. The study demonstrated that the linear regression modelling approach was lacking in its capacity to predict the investigated ozone concentrations by used parameters, whereas the use of an ANN offered more precise outcomes. The conducted tests’ results established the strength of the designed artificial neural network models with irrelevant differences between detected and forecasted data.

The artificial neural networks (ANN) by their capacities of training, classification, and decision, give a solution to bearing diagnosis problem by the automatic classification of the vibratory signals corresponding to the various states the machines. They are intended to increase the precision(accuracy) and to reduce errors caused by subjective human judgments. However it is important to note that the ANNs in the aids to diagnosis must be set for optimum performance. The non-existence of predefined rules for ANNs parameters setting (number of hidden neurons in each hidden layers etc…) obstruct the achievement of optimal performances. The use of genetic algorithm (GA) can solve this problem by the parameters and structure optimization of ANN. This paper discusses the use of the ANN multilayer Perceptron (MLP), for the diagnosis of electric motor bearings, by the automatic classification of the various operating conditions the machine .The signals taken from the experimental test rig are processed by using various methods of signal processing. The calculated indicators were used to build the patterns vector, which is used for the following to train and test of the network. The GA are used to search(optimize) the structure and the various parameters of the network, which simplifies the neural network structure and makes the training process more efficient and giving the best performances of the network.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

In this work, with the aim of accurate prediction of water content, H2S and CO2 absorption capacity of diethanolamine (DEA) solvent in Khangiran gas sweetening plant, an artificial neural network (ANN) model of feed-forward multilayer perceptron, with the learning algorithm of Levenberg–Marquardt has been developed. The training, validation, and testing of the ANN model, respectively, was performed using 70, 15, and 15% of all of the gathered operation data. An optimization procedure on the basis of the genetic algorithm (GA) was implemented to select the optimum ANN architecture. Accordingly, a three layer feed-forward neural network with Levenberg–Marquardt back-propagation training algorithm was designed and developed. The structure of the model comprised of 12 variables as inputs and three as outputs, 13 neurons in the hidden layer, the log-sigmoid transfer function in the hidden layer, and the output layer containing linear transfer function. The results, based on statistical analysis, showed very little difference between the predicted and actual operation data with a remarkably low mean square error (MSE) value and a coefficient of determination (R2) value approaching one. The mentioned factors are strong indicators of the proposed model’s high accuracy in predicting the output variables.

The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and simplest neural network models. However, it is incapable of classifying linearly inseparable patterns. A new era of neural network research started in 1986, when the backpropagation (BP) algorithm was rediscovered for training the multilayer perceptron (MLP) model. An MLP with a large number of hidden nodes can function as a universal approximator. To date, the MLP model is the most fundamental and important neural network model. It is also the most investigated neural network model. Even in this AI or deep learning era, the MLP is still among the few most investigated and used neural network models. Numerous new results have been obtained in the past three decades. This survey paper gives a comprehensive and state-of-the-art introduction to the perceptron model, with emphasis on learning, generalization, model selection and fault tolerance. The role of the perceptron model in the deep learning era is also described. This paper provides a concluding survey of perceptron learning, and it covers all the major achievements in the past seven decades. It also serves a tutorial for perceptron learning.

We aim to estimate the depth of subsurface cavities from gravity data by a new method of 2D inversion through a Multi Layer Perceptron(MLP) neural network.Infact, this method is an intelligent way to interpret gravity data and gain an estimation of depth and shape. The MLP neural network was trained for two main models of cavities: sphere and cylinder in a domain of radius and depth. We tested different MLP’s with different number of neurons in the hidden layer and obtained the optimum value for number of neurons in the hidden layer. Then it was tested in present of 10% noise(S/N=.1), and also tested for real data. It presented good results for depth estimation of subsurface cavities. Keywords: gravity, 2D inversion, subsurface cavities, artificial neural networks, Multi Layer Perceptron

In the present study, deep learning neural network model has been employed in many engineering problems including heat transfer prediction. The main consideration of this document is to predict the performance of the boiling heat transfer in helical coils under terrestrial gravity conditions and compare with actual experimental data. Total of 877 data sample has been used in the present neural model. Artificial new Neural Network (ANN) model developed in Python environment with Multi-layer Perceptron (MLP) using four parameters (helical coils dimensions, mass flow rate, heating power, inlet temperature) and one parameter (outlet temperature) has been used in the input layer and output layer in order. Levenberg-Marquardt (LM) algorithm using L2 Regularization to find out the optimal model. A typical feed-forward neural network model composed of three layers, with 30 numbers of neurons in each hidden layer, has been found as optimal based on statistical error analysis. The 4-30-30-1 neural model predicts the characteristics of the helical coil with the accuracy of 98.16 percent in the training stage and 96.68 percent in the testing stage. The result indicated that the proposed ANN model successfully predicts the heat transfer performance in helical coils and can be applied for others operation concerned with heat transfer prediction for future works

Fly ash and phosphogypsum are industrial by-products requiring a cost to get rid of. Their potential use in the synthesis of geopolymer bricks provides great benefits such as the saving use of natural resources and the solid by-product waste management. Compressive strength is the most important parameter for geopolymer bricks design. In this study, two artificial neural networks, namely the multilayer perceptron (MLP) and the radial basis function (RBF) networks, have been investigated to predict the compressive strength. While developing the MLP or RBF models, 99 experimental observations were used for training and testing. Two evaluation steps were performed: The first step determined the effective number of hidden layers and neurons in each hidden layer as well as the appropriate activation function in predicting the compressive strength. The second evaluation step evaluated the accuracy with which the model would predict the compressive strength of geopolymers. The MLP neural network with two hidden layers having 8 and 10 neurons and the hyperbolic tangent activation function was the best model for predicting the compressive strength. Artificial neural networks can be used as a reliable and accurate technique for estimating the parameters of geopolymer materials.

In this study an artificial neural network, multi-layer perceptron (MLP), and Peleg's mathematical model were used to find the best model for the prediction of rehydration kinetic of air dried potato cubes. For rehydration, samples were immersed in water during different periods of time and temperatures (23±2 °C and 100±2 °C). Rehydration kinetic was monitored by measuring samples weights at regular intervals. In MLP neural network, water temperature, soaking time and potato varieties (Agria, Satina and Kenebek) were used as input parameters and the moisture content was used as output parameter. The results were compared with experimental data. Both Peleg's model and MLP had a proper correlation coefficient for each variety and water temperature but the correlation coefficient of the generalized Peleg's model was lower than of MLP.

The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T—number. The result of the study reveals that the forecast error with MLP model is minimum (4.70 %) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62 %. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8 %, respectively. The models provide the forecast beyond 72 h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models.