Revealing prediction of perched cum off-centered wick solar still performance using network based on optimizer algorithm

Currently, internet of things (IoT) devices like environmental sensors are used to capture real-time data that can be viewed and interpreted via a visual format supported by a server computer. However, to facilitate modeling and forecasting, artificial intelligence (AI) techniques are effective in statistically analyzing complex non-linear systems and a large amount of historical data series within a short period. This present review article covers selected research journals published from 2014 to 2020. The findings from previous research indicate that despite the limitations of artificial neural network (ANN) tools, ANN has proved to be useful and powerful techniques that can be used in the field of hydrology. Similarly, ANN tools have the ability to evaluate historical data collected from different river stations and wastewater treatment plants with minimum errors within a short time. Therefore, based on the selected past literature used for this review we found that different types of ANN algorithm such as feed-forward backpropagation (FFBP) algorithm, gradient descent, Broyden-Fletcher-Goldfarb-Shanno (BFGS), conjugate gradient, radial basis function neural networks (RBFNN), neural network fitting (NNF), cascade forward back propagation (CFBP), ensemble ANN (EANN) and single AAN (SANN) have been employed in the prediction and monitoring of water quality parameters with satisfactory outcome. Furthermore, modeling alongside forecasting of water quality parameters would act as a big leap for government agencies and independent organisations in monitoring, decision making and regulating waste discharged into natural water bodies in order to achieve a safe and improved water quality for users.

This paper reports the study of groundwater quality assessment in Boluwaduro community, Ofatedo in Osun State. In addition, it utilized the Artificial Neural Network (ANN) tool in MATLAB Software to simulate the water quality parameters/contaminants. Water samples were taken from 18 randomly selected dugwells and subjected to physico-chemicals and microbiological analysis. The mean concentrations of nitrate, nitrite, lead and iron are 20.12 mg/L, 0.78 mg/L, 0.159 mg/L and 0.35 mg/L respectively. Total plate counts range between 27 – 96 cfu/mL with growth in all the water samples. The ANN structure was trained in several rounds till satisfactory output was obtained with correlation value of R2 = 0.97. Simulation of the pH using ANN provides a good match at 10% increment of chloride, nitrate and iron and the pH value of the water sources increased with the corresponding increase in the concentrations of the parameters. The generated model for TDS gave a good prediction with total hardness and magnesium respectively. The concentrations of some metals in the wells are not safe for drinking; it could pose danger to users of the water sources. It is therefore recommended that the wells in the community should be subjected to routine monitoring and treatment of the contaminants should be enforced.

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.

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 paper, we study a new model that represents the symmetric connection between capacitance–voltage and Schottky diode. This model has a symmetrical shape towards the horizontal direction. In recent times, works conducted on artificial neural network structure, which is one of the greatest actual artificial intelligence apparatuses used in various fields, stated that artificial neural networks are apparatuses that proposal very high forecast performance by the side of conventional structures. In the current investigation, an artificial neural network structure has been generated to guess the capacitance voltage productions of the Schottky diode with organic polymer edge, contingent on the frequency with a symmetrical shape. Of the dataset, 130 were grouped for training, 28 for validation, and 28 for testing. In order to evaluate the effect of the number of neurons on the prediction accuracy, three different models with different neuron numbers have been developed. This study, in which an artificial neural network model, although well-trained, could not predict the output values correctly, is a first in the literature. With this aspect, the study can be considered as a pioneering study that brings a novelty to the literature.

The main aim of this study is to estimate the bearing strength on pin loaded composite plates using test data of different ambient conditions depending on time. Artificial neural network (ANN) tool is used for prediction purpose. Artificial neural network program is developed by MATLAB software. The composite plates were divided into nine groups, each one contains 60 specimens. These groups were kept in different environment conditions. To obtain the optimum geometrical dimensions of specimens, the distance from hole axis to the side of the test sample of the pin diameter ratio (E/D) and the sample width to pin diameter ratio (W/D) were changed systematically in the experimental samples. Data from fatigue test results obtained from the multi-layered, feedforward and backpropagation algorithm were used to train the artificial neural network model. In modeling of artificial neural network, the test conditions, the test periods, the day intervals, the distance from hole axis to the side of test sample of the pin diameter ratio (E/D) and the sample width to pin diameter ratio (W/D) were used as input parameters and the bearing strength data was used as output parameter. The values obtained from the artificial neural network training and testing were evaluated by applying statistical analyses that are widely used in ANN models. It is widely known that difficult experimental studies and complexity of the analytical expression could be solved by ANN models as it was seen in many studies. This study has also shown that artificial neural network is a convenient method for predicting the bearing strength on pin loaded composite plates.

Several researchers have investigated the relationships among different physical attributes of the fruits. For proper design and operation of grading systems, important relationships among the mass and other properties of fruits such as length, width, thickness, arithmetic mean diameter, geometric mean diameter, sphericity, surface area, volume, projected area, shape index, aspect ratio and elongations must be known. Recent researches have focused on artificial neural network (ANN) approaches to predict hard-to-find attributes of the fruits from easily-determined and readily available values. In this study, Modular Neural Network (MNN) and Radial Basis Neural Network (RBNN) structures of Artificial Neural Network (ANN) were employed to predict walnut mass from the physical attributes of the walnuts. Root mean square errors (RMSE) of MNN structure ranged from 0.60 to 0.89, while RMSE of RBNN structure were found to be very low (0.0002) in all of walnut varieties. These results showed that RBNN structures of Artificial Neural Network could potentially be used to estimate mass of walnuts and various physical attributes of walnuts were sufficient to predict the mass characteristics of a walnut.

Review of <i>Environmental and Hydrological Systems Modeling</i> by A. W. JayawardenaCRC Press Taylor &amp; Francis Group, Boca Raton, FL 33487; 2014; ISBN 978-0-415-46532-8; 516 pp.; $99.95.

An artificial neural network (ANN) is an information processing modelling of the human brain inspired by the way biological nervous systems behave. There are about 100 billion neurons in the human brain. Each neuron has a connection point between 1,000 and 100,000. The key element of this paradigm is the novel structure of the information processing system. In the human brain, information is stored in such a way as to be distributed, and we can extract more than one piece of this information when necessary from our memory in parallel. We are not mistaken when we say that a human brain is made up of thousands of very powerful parallel processors. It is composed of a large number of highly interconnected processing elements (neurons) working in union to solve specific problems. ANN, like people, learns by example. The chapter includes characteristics of artificial neural networks, structure of ANN, elements of artificial neural networks, pros and cons of ANN.

Soil Coefficient of Consolidation (Cv) is a crucial mechanical parameter and used to characterize whether the soil undergoes consolidation or compaction when subjected to pressure. In order to define such a parameter, the experimental approaches are costly, time-consuming, and required appropriate equipment to perform the tests. In this study, the development of an alternative manner to estimate the Cv, based on Artificial Neural Network (ANN), was conducted. A database containing 188 tests was used to develop the ANN model. Two structures of ANN were considered, and the accuracy of each model was assessed using common statistical measurements such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In performing 600 simulations in each case, the ANN structure containing 14 neurons was statistically superior to the other one. Finally, a typical ANN result was presented to prove that it can be an excellent predictor of the problem, with a satisfying accuracy performance that yielded of RMSE = 0.0614, MAE = 0.0415, and R2 = 0.99727. This study might help in quick and accurate prediction of the Cv used in civil engineering problems.

An artificial neural network (ANN) is a network designed with adaptation to a computer system. The developed computer system will perform functions oriented to the way the brain works (neuron concept). This study is an extension to the study of the suitability of ANN to be applied in numbers of applications, especially in the field of medical engineering. ANN has been widely being used in medicine, ANN is widely applied in education, research, and even decision making. In this study, ANN will be trained for pre-testing to predict the cardiac abnormalities symptom based on selected reference parameters. This reference parameter is better known as the input parameter to the ANN to detect cardiac abnormalities, among which are the of the height of peak/wave (amplitude) and time occurrence of peak/wave (duration of time) extracted from the electrocardiogram (ECG) signal. A complete ECG complex contains a P peak, a QRS wave, and a T peak. For each P peak, QRS wave, and T peak, amplitude height and duration will be measured to serve as input parameters. This makes six parameters defined as inputs to the ANN. This study has used a Multilayer Perceptron (MLP) network as ANN structure by being trained using three different training algorithms namely Backpropagation (BP), Lavenberg Marquardt (LM) and Bayesian Regularization (BR). At the end of the study, it showed the MLP network which by BR training algorithm gave the highest accuracy prediction (94.04%), followed by LM (92.95%) and BP (88.77%). In this study all MLP networks were activated using the Tansig activation function.

This study intended to use optimized artificial neural network (ANN) for the design of pure cohesive slopes (by means of considering sufficient safety factors (SF) of stability). A total of 630 finite element limit equilibrium analysis were completed to provide datasets of training and testing for preparation of the ANN-based predictive models. The database consisted of 504 training datasets (80% of the database) and 126 testing datasets (20% of the database). To construct an optimized ANN structure, variables of ANN algorithm such as a number of neurons in hidden layer and the number of hidden layers were changed (e.g. with a series of trial and error process). The input parameters that used in the analysis include setback distance ratio (b/B), the undrained shear strength of the cohesive soil (Cu), applied stresses on the slope (Fy) and slope angle (β) while the final output was the value of SF. The predicted results for datasets from ANN models were assessed based on several well-known statistical indices namely, R2, RMSE and VAF. Also, in order to evaluate the performance of predicted network two ranking systems of (1) colour intensity rating (CIR) and (2) total ranking method (TRM), i.e. based on the result of statistical indices, were utilized. The optimal architecture of 4 × 6 × 1 was found for the ANN structure. After the performance of the optimized ANN model, the results are provided as a simple tansig formula which can be used for further application of engineering purposes. In the optimized ANN network and based on R2, RMSE and VAF, values of (0.999, 0.0350 and 99.9978) and (0.999, 0.0379 and 99.998) were found, respectively, for both of the normalized training and testing datasets. This proves the excellent performance of the proposed ANN model in estimating the SF of the slope. As the final outputs and to show the reliability of the proposed ANN formula, a series of optimized ANN design solution charts are compared to a similar example of measured charts.

Influence of random topology in artificial neural networks: A survey

Thanks to their convex formulation, kernel regressions have shown an improved accuracy with respect to artificial neural network (ANN) structures in regression problems where a reduced set of training samples are available. However, despite the above interesting features, kernel regressions are inherently less flexible than ANN structures since their implementations are usually limited to scalar-output regression problems. This article presents a vector-valued (multioutput) formulation of the kernel ridge regression (KRR) aimed at bridging the gap between multioutput ANN structures and scalar kernel-based approaches. The proposed vector-valued KRR relies on a generalized definition of the reproducing kernel Hilbert space (RKHS) and on a new multioutput kernel structure. The mathematical background of the proposed vector-valued formulation is extensively discussed together with different matrix kernel functions and training schemes. Moreover, a compression strategy based on the Nystrom approximation is presented to reduce the computational complexity of the model training. The effectiveness and the performance of the proposed vector-valued KRR are discussed on an illustrative example consisting of a high-speed link and on the optimization of a Doherty amplifier.

Wheat is a stable food raw material for the majority of people around the world as wheat-based products provide an important part of the daily energy intake in many countries. Wheat is generally milled into flour prior to use in the bakery industry. Flour yield is one of the major quality criteria in wheat milling. Flout yield detection requires large amount of sample, costly machinery, longer time of tempering and milling practices requiring substantial workload. In this study, artificial neural network (ANN) approach has been employed to predict flour milling yield. The ANN was designed in the MATLAB software using such wheat physical properties as hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness. Flour yields and four different kernel physical features (hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness) were first collected from 2400 wheat samples through the conventional methods. The ANN was trained using 85% of 2400 flour yield data and tested with 15% of the remaining data. In the training of the ANN, various models have been investigated to find the best ANN structure. Additionally, two data sets with and without grain hardness have been employed to determine the effect of grain hardness on the prediction performance of the ANN model. It was found that grain hardness which reduced the MAE values from 2.3333 to 2.2611 and RMSE values from 3.0775 to 2.9146 gave better result. The results proved that the developed ANN model can be used to estimate flour yield using wheat kernel physical properties.