Deep Learning Neural Networks with Metaheuristic Optimization Algorithms for Groundwater Contamination Vulnerability Mapping in Miryang Aquifer, South Korea

2020 ◽  
Author(s):  
Hussam Eldin Elzain ◽  
Sang Yong Chung ◽  
Venkatramanan Senapathi ◽  
Kye-Hun Park
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
South Korea ◽  
Groundwater Contamination ◽  
Mean Square Error ◽  
Hybrid Models ◽  
Vulnerability Mapping ◽  
Mean Square ◽  
Artificial Neural

<p>This study aims to use an integration of genetic algorithm (GA) model and particle swarm optimization (PSO) with the Deep Learning Neural Networks (DLNN) for groundwater contamination vulnerability. Miryang, a city in the northeastern portion of Gyeongnam Province, South Korea was selected as a case study since it showed urban and rural functions and had undergone groundwater pollution. To initialize the modeling purposes, parameters such as depth to water, net recharge, topographic slope, aquifer type, impact to vadose zone, hydraulic conductivity and land use were classified into numerical classes and used as input variables. Two-hybrid models of DLNN-GA and DLNN-PSO were implemented using 95 measured nitrate concentration from monitoring wells for the training and testing of artificial neural networks. The performance of the hybrid models was evaluated by several statistical criteria of error: Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Average Error (MAE). The hybrid vulnerability models were also validated by the Area Under the curve (AUC). DLNN-PSO showed the highest (AUC=0.974) performance in comparison with DLNN-GA (AUC=0.954) and Shallow Artificial Neural Networks model (AUC=0.70). The results showed that the proposed hybrid models were more superior than the benchmarked shallow artificial neural networks model used for groundwater contamination vulnerability mapping as a good alternative several years ago.</p>

scholarly journals Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling

2017 ◽  
Vol 23 (2) ◽  
pp. 251-258 ◽  
Author(s):  
Mohsen Beigi ◽  
Mehdi Torki-Harchegani ◽  
Mahmood Mahmoodi-Eshkaftaki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Mean Square Error ◽  
Back Propagation ◽  
Drying Kinetics ◽  
Mean Square ◽  
Rough Rice ◽  
Artificial Neural ◽  
Drying Curves

The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

scholarly journals Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4572
Author(s):  
Ioannis O. Vardiambasis ◽  
Theodoros N. Kapetanakis ◽  
Christos D. Nikolopoulos ◽  
Trinh Kieu Trang ◽  
Toshiki Tsubota ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sewage Sludge ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Food Waste ◽  
Hydrothermal Carbonization ◽  
Mean Square ◽  
Artificial Neural

In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN3 (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R2 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN1, R2 0.897, root mean square error 1.289).

GIS-based Seismic Vulnerability Mapping: A Comparison of Artificial neural networks Hybrid Models

2021 ◽  
pp. 1-21
Author(s):  
Peyman Yariyan ◽  
Rahim Ali Abbaspour ◽  
Alireza Chehreghan ◽  
MohammadReza Karami ◽  
Artemi Cerdà
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Seismic Vulnerability ◽  
Hybrid Models ◽  
Vulnerability Mapping ◽  
Artificial Neural

scholarly journals Comparison of neural networks and genetic algorithms to determine missing precipitation data (Case study: the city of Sari)

2020 ◽  
Vol 11 (29) ◽  
pp. 114-128
Author(s):  
Ali Mahdavi ◽  
Mohsen Najarchi ◽  
Emadoddin Hazaveie ◽  
Seyed Mohammad Mirhosayni Hazave ◽  
Seyed Mohammad Mahdai Najafizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Artificial Neural ◽  
The City

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

scholarly journals Application of Artificial Neural Networks for Producing an Estimation of High-Density Polyethylene

Polymers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2319
Author(s):  
Akbar Maleki ◽  
Mostafa Safdari Shadloo ◽  
Amin Rahmat
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square Error ◽  
Superior Performance ◽  
Copolymer Composition ◽  
Mean Square ◽  
Multi Layer Perceptron ◽  
Hydrogen Flow ◽  
Generalized Regression Neural Networks ◽  
Artificial Neural

Polyethylene as a thermoplastic has received the uppermost popularity in a vast variety of applied contexts. Polyethylene is produced by several commercially obtainable technologies. Since Ziegler–Natta catalysts generate polyolefin with broad molecular weight and copolymer composition distributions, this type of model was utilized to simulate the polymerization procedure. The EIX (ethylene index) is the critical controlling variable that indicates product characteristics. Since it is difficult to measure the EIX, estimation is a problem causing the greatest challenges in the applicability of production. To resolve such problems, ANNs (artificial neural networks) are utilized in the present paper to predict the EIX from some simply computed variables of the system. In fact, the EIX is calculated as a function of pressure, ethylene flow, hydrogen flow, 1-butane flow, catalyst flow, and TEA (triethylaluminium) flow. The estimation was accomplished via the Multi-Layer Perceptron, Radial Basis, Cascade Feed-forward, and Generalized Regression Neural Networks. According to the results, the superior performance of the Multi-Layer Perceptron model than other ANN models was clearly demonstrated. Based on our findings, this model can predict production levels with R2 (regression coefficient), MSE (mean square error), AARD% (average absolute relative deviation percent), and RMSE (root mean square error) of, respectively, 0.89413, 0.02217, 0.4213, and 0.1489.

SỬ DỤNG DEEP NEURAL NETWORKS PHÁT HIỆN GAI ĐỘNG KINH TRONG BẢN GHI EEG

2020 ◽  
pp. 77-84
Author(s):  
Xuyến
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Artificial Neural

Deep Neural Networks là một thuật toán dạy cho máy học, là phương pháp nâng cao của mạng nơ-ron nhân tạo (Artificial Neural Networks) nhiều tầng để học biểu diễn mô hình đối tượng. Bài báo trình bày phương pháp để phát hiện spike tự động, giải quyết bài toán cho các bác sỹ khi phân tích dữ liệu khổng lồ được thu thập từ bản ghi điện não để xác định một khu vực của não gây ra chứng động kinh. Hàng triệu mẫu được phân tích thủ công đã được đào tạo lại để tìm các gai liêp tiếp phát ra từ vùng não bị ảnh hưởng. Để đánh giá phương pháp đề xuất, tác giả đã xây dựng hệ thống trong đó sử dụng một số mô hình deep learning đưa vào thử nghiệm hỗ trợ các bác sỹ khám phát hiện và chẩn đoán sớm bệnh.

scholarly journals KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

2020 ◽  
Vol 9 (1) ◽  
pp. 7-10
Author(s):  
Hendry Fonda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
18Th Century ◽  
The Public ◽  
Artificial Neural ◽  
The Difference ◽  
Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese  batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning   ABSTRAK   Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi    fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks

Energy ◽  
2018 ◽  
Vol 151 ◽  
pp. 347-357 ◽  
Author(s):  
Henrique do Nascimento Camelo ◽  
Paulo Sérgio Lucio ◽  
João Bosco Verçosa Leal Junior ◽  
Paulo Cesar Marques de Carvalho ◽  
Daniel von Glehn dos Santos
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid Models ◽  
Wind Generation ◽  
Time Series Models ◽  
Artificial Neural

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

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