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Journal articles on the topic 'Hidden layers'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Zahara, Soffa, Yanuarini Nur Sukmaningtyas, Ronny Makhfuddin Akbar, and Muhammad Zainul Abidin. "Pengaruh Jumlah Hidden Layer dan Neuron pada Model Multilayer Perceptron untuk Prediksi Emas." Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika 8, no. 2 (2025): 269–75. https://doi.org/10.47324/ilkominfo.v8i2.309.

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Abstrak: Pemilihan kombinasi banyaknya hidden layer dan neuron sangat menentukan performansi model deep learning. Terlalu sedikit hidden layer dan neuron yang digunakan dapat menyebabkan rendahnya akurasi, sedangkan jika terlalu banyak maka dapat meningkatkan kompleksitas pemrosesan sehingga sampai saat ini masih belum ada pedoman kombinasi tetap untuk menentukan jumlah hidden layer dan neuron. Penelitian ini bertujuan untuk mengobservasi pengaruh variasi hidden layer dan neuron, dalam kinerja akurasi metode Multilayer Perceptron dengan variasi dari 1 sampai 6 lapis hidden layer. Data yang dig

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PATRICK VINCENT, ASSUNTA MALAR, and HASSILAH SALLEH. "AN INVESTIGATION INTO THE PERFORMANCE OF THE MULTILAYER PERCEPTRON ARCHITECTURE OF DEEP LEARNING IN FORECASTING STOCK PRICES." Universiti Malaysia Terengganu Journal of Undergraduate Research 3, no. 2 (2021): 61–68. http://dx.doi.org/10.46754/umtjur.v3i2.205.

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A wide range of studies have been conducted on deep learning to forecast time series data. However, very few researches have discussed the optimal number of hidden layers and nodes in each hidden layer of the architecture. It is crucial to study the number of hidden layers and nodes in each hidden layer as it controls the performance of the architecture. Apart from that, in the presence of the activation function, diverse computation between the hidden layers and output layer can take place. Therefore, in this study, the multilayer perceptron (MLP) architecture is developed using the Python so

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Bachtiar, Bachtiar, Tarmizi Tarmizi, and Ramzi Adriman. "Analysis of the Best Neural Network Configuration for Predicting Household Customer Kwh Sales in Banda Aceh City." Circuit: Jurnal Ilmiah Pendidikan Teknik Elektro 8, no. 2 (2024): 154. http://dx.doi.org/10.22373/crc.v8i2.22017.

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Energy consumption (kWh) is critical to the operation of electrical systems. Predictive modeling optimizes energy usage, increasing power system efficiency. This study created an artificial neural network (ANN) architecture to estimate energy consumption (kWh) for home users in Banda Aceh. The ANN topology consisted of 5 input layers, 5-25 hidden layers, and one output layer. This study used two scenarios: first, the ANN topology was trained using the logsig activation function, and then the tansig activation function was used for training. Based on training simulations, the ANN architecture w

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Samann, Fars, and Thomas Schanze. "Multiple parallel hidden layers autoencoder for denoising ECG signal." Current Directions in Biomedical Engineering 8, no. 2 (2022): 161–64. http://dx.doi.org/10.1515/cdbme-2022-1042.

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Abstract Deep learning with multiple hidden layers denoising autoencoders (MHL-DAE) is commonly used to denoise images and signals through dimension reduction. Here, we explore the potential of multiple parallel hidden layers denoising autoencoder (MPHL-DAE) to denoise complex bio-signals, like electrocardiogram (ECG). A merge layer, e.g., average layer is considered as the output of the proposed model by combining the outputs of the parallel hidden layers. The parallel hidden layers in the coding layer with activation function of different scale a, e.g., are considered to capture distinct fea

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Xiao, Dong, Beijing Li, and Yachun Mao. "A Multiple Hidden Layers Extreme Learning Machine Method and Its Application." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/4670187.

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Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output

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Ross, Matt, Nareg Berberian, Albino Nikolla, and Sylvain Chartier. "Dynamic multilayer growth: Parallel vs. sequential approaches." PLOS ONE 19, no. 5 (2024): e0301513. http://dx.doi.org/10.1371/journal.pone.0301513.

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The decision of when to add a new hidden unit or layer is a fundamental challenge for constructive algorithms. It becomes even more complex in the context of multiple hidden layers. Growing both network width and depth offers a robust framework for leveraging the ability to capture more information from the data and model more complex representations. In the context of multiple hidden layers, should growing units occur sequentially with hidden units only being grown in one layer at a time or in parallel with hidden units growing across multiple layers simultaneously? The effects of growing seq

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Mustafidah, Hindayati, and Suwarsito Suwarsito. "Performance of Levenberg-Marquardt Algorithm in Backpropagation Network Based on the Number of Neurons in Hidden Layers and Learning Rate." JUITA: Jurnal Informatika 8, no. 1 (2020): 29. http://dx.doi.org/10.30595/juita.v8i1.7150.

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One of the supervised learning paradigms in artificial neural networks (ANN) that are in great developed is the backpropagation model. Backpropagation is a perceptron learning algorithm with many layers to change weights connected to neurons in hidden layers. The performance of the algorithm is influenced by several network parameters including the number of neurons in the input layer, the maximum epoch used, learning rate (lr) value, the hidden layer configuration, and the resulting error (MSE). Some of the tests conducted in previous studies obtained information that the Levenberg-Marquardt

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Peleshchak, Ivan, and Diana Koshtura. "Evaluation of Classification Accuracy Using Feedforward Neural Network for Dynamic Objects." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 15 (July 15, 2024): 260–72. http://dx.doi.org/10.23939/sisn2024.15.260.

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This paper investigates the impact of the number of hidden layers, the number of neurons in these layers, and the types of activation functions on the accuracy of classifying projectiles of six types (A – (artillery); A/M – (artillery/missile); A/R – (armor-piercing); A/RC – (armor-piercing- incendiary); M – (missile); R – (armor-piercing shells)) using a multi-layer neural network, evaluated by a confusion matrix. Specifically, confusion matrices were constructed to assess the accuracy of classifying projectiles of six types (A – (artillery); A/M – (artillery/missile); A/R – (armor-piercing),

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Alhassan, Seiba, Gaddafi Abdul-Salaam, Michael Asante, Yaw Missah, and Ernest Ganaa. "Analyzing Autoencoder-Based Intrusion Detection System Performance." Journal of Information Security and Cybercrimes Research 6, no. 2 (2023): 105–15. http://dx.doi.org/10.26735/ylxb6430.

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The rise in cyberattacks targeting critical network infrastructure has spurred an increased emphasis on the development of robust cybersecurity measures. In this context, there is a growing exploration of effective Intrusion Detection Systems (IDS) that leverage Machine Learning (ML) and Deep Learning (DL), with a particular emphasis on autoencoders. Recognizing the pressing need to mitigate cyber threats, our study underscores the crucial importance of advancing these methodologies. Our study aims to identify the optimal architecture for an Intrusion Detection System (IDS) based on autoencode

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Alghamdi, Mostafa, Tami Alwajeeh, Fahad Aljabeer, Setiawan Assegaff, and Rahmat Budiarto. "Experimenting Hand-Gesture Image Recognition using Simple Deep Neural Network." International Journal of Engineering & Technology 7, no. 3.32 (2018): 103. http://dx.doi.org/10.14419/ijet.v7i3.32.18403.

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Traditionally human interacts with a computer by using keyboard and mouse. Considering person with handicapped from the wrist to the fingertip or amputated wrists or fingertips need alternative way; using voice or hand gesture. This work focuses on the use of hand-gesture image recognition. There are two main issues should be considered; less interactivity in static hand gesture recognition, and less accuracy in dynamic hand gesture recognition. This paper attempts to improve the accuracy of hand-gesture image recognition by experimenting simple deep learning neural network (DLNN). As this wor

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Ratu Perwira Negara, Habibi, Irzani Irzani, and Ripai Ripai. "Konstruksi Model Matematika Pola Curah Hujan Menggunakan Artificial Neural Network (ANN) dengan Metode Backpropagation." Justek : Jurnal Sains dan Teknologi 1, no. 1 (2018): 10. http://dx.doi.org/10.31764/justek.v1i1.400.

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Abstrak: Penelitian ini bertujuan untuk menentukan model pola curah hujan menggunakan Artificial Neural Network (ANN) dengan metode Backpropagatiaon. Sampel dalam penelitian ini adalah 5 pos pencataan hujan di daerah Lombok Tengah bagian Selatan dan 2 pos pencatatan hujan di daerah Lombok Timur bagian Selatan. Data penelitian berupa data setengah bulanan yang dicatat dari tahun 1973 sampai tahun 2010 untuk daerah Lombok Tengah bagian Selatan dan data dari tahun 1974 sampai tahun 2010 untuk daerah Lombok Timur bagian Selatan. Metode penilitian dilakukan dengan melakukan pembelajaran data curah

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Wen, Zhangxin, Sihan Duan, and Hong Liu. "Revealing the Hidden Layers." Journal of the American College of Cardiology 84, no. 22 (2024): e315. http://dx.doi.org/10.1016/j.jacc.2024.07.060.

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Yang, Linrang. "Predicting consumer acceptance of automobiles based on deep learning and traditional machine learning algorithms." Applied and Computational Engineering 27, no. 1 (2023): 30–37. http://dx.doi.org/10.54254/2755-2721/27/20230119.

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Researchers have made significant progress in machine learning in recent years. Machine learning can learn and predict large and complex data sets. Researchers have divided machine learning algorithms into two categories: deep learning and traditional machine learning. Every problem can be predicted in both ways. This paper uses the "Car Data" dataset to investigate deep learning and traditional machine learning. In order to find a machine learning algorithm that is more conducive to analyzing and predicting consumers' acceptance of different cars, this paper mainly explores the differences in

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Yang, Linrang. "Predicting consumer acceptance of automobiles based on deep learning and traditional machine learning algorithms." Applied and Computational Engineering 27, no. 9 (2023): 30–37. http://dx.doi.org/10.54254/2755-2721/27/ojs/20230119.

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 Researchers have made significant progress in machine learning in recent years. Machine learning can learn and predict large and complex data sets. Researchers have divided machine learning algorithms into two categories: deep learning and traditional machine learning. Every problem can be predicted in both ways. This paper uses the "Car Data" dataset to investigate deep learning and traditional machine learning. In order to find a machine learning algorithm that is more conducive to analyzing and predicting consumers' acceptance of different cars, this paper mainly explores the differe

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Baek, Jieun, and Yosoon Choi. "Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine." Applied Sciences 9, no. 19 (2019): 4180. http://dx.doi.org/10.3390/app9194180.

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A new method using a deep neural network (DNN) model is proposed to predict the ore production and crusher utilization of a truck haulage system in an underground mine. An underground limestone mine was selected as the study area, and the DNN model input/output nodes were designed to reflect the truck haulage system characteristics. Big data collected on-site for 1 month were processed to create learning datasets. To select the optimal DNN learning model, the numbers of hidden layers and hidden layer nodes were set to various values for analyzing the training and test data. The optimal DNN mod

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PAUGAM-MOISY, HÉLÈNE. "HOW TO MAKE GOOD USE OF MULTILAYER NEURAL NETWORKS." Journal of Biological Systems 03, no. 04 (1995): 1177–91. http://dx.doi.org/10.1142/s0218339095001064.

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This article is a survey of recent advances on multilayer neural networks. The first section is a short summary on multilayer neural networks, their history, their architecture and their learning rule, the well-known back-propagation. In the following section, several theorems are cited, which present one-hidden-layer neural networks as universal approximators. The next section points out that two hidden layers are often required for exactly realizing d-dimensional dichotomies. Defining the frontier between one-hidden-layer and two-hidden-layer networks is still an open problem. Several bounds

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Lawrence, E., E. J. Garba, Y. M. Malgwi, and M. A. Hambali. "An Application of Artificial Neural Network for Wind Speeds and Directions Forecasts in Airports." European Journal of Electrical Engineering and Computer Science 6, no. 1 (2022): 53–59. http://dx.doi.org/10.24018/ejece.2022.6.1.407.

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Wind speed patterns are highly dynamic and non-linear and thus cannot be accurately forecasted using conventional linear regression models. In this work, Artificial Neural Network (ANN) technique was applied to forecast wind speeds and directions in airports. Monthly data of maximum temperature, minimum temperature, wind speed, wind direction, relative humidity and wind run for Yola International Airport were collected from 1995 to 2021 from Nigerian Meteorological Agency (NIMET) Abuja-Nigeria. Six Neural Network models were built. ANN with no hidden layers, ANN model with one hidden layer and

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Dureja, Aman, and Payal Pahwa. "Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks." Recent Patents on Computer Science 12, no. 3 (2019): 156–61. http://dx.doi.org/10.2174/2213275911666181025143029.

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Background: In making the deep neural network, activation functions play an important role. But the choice of activation functions also affects the network in term of optimization and to retrieve the better results. Several activation functions have been introduced in machine learning for many practical applications. But which activation function should use at hidden layer of deep neural networks was not identified. Objective: The primary objective of this analysis was to describe which activation function must be used at hidden layers for deep neural networks to solve complex non-linear probl

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Carpenter, William C., and Margery E. Hoffman. "Guidelines for the selection of network architecture." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, no. 5 (1997): 395–408. http://dx.doi.org/10.1017/s0890060400003322.

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AbstractThis paper is concerned with presenting guidelines to aide in the selection of the appropriate network architecture for back-propagation neural networks used as approximators. In particular, its goal is to indicate under what circumstances neural networks should have two hidden layers and under what circumstances they should have one hidden layer. Networks with one and with two hidden layers were used to approximate numerous test functions. Guidelines were developed from the results of these investigations.

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Sugiarto, Dedy, Dimmas Mulya, Abdul Rochman, and Is Mardianto. "PERBANDINGAN WAKTU EKSEKUSI PERAMALAN HARGA KOMODITAS PANGAN MENGGUNAKAN SPARKR DAN R STUDIO." Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika 16, no. 1 (2022): 73–80. http://dx.doi.org/10.47111/jti.v16i1.3911.

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The arrival of the big data era with characteristics such as large volumes of data makes the calculation of execution time a concern when carrying out data analytics processes, such as forecasting food commodity prices. This study aims to examine the effect of the big data framework through the use of sparkR. The test is carried out by varying several deep learning forecasting models, namely the multi-layer perceptron model and by using the price of one food commodity from 2018 to 2020. The results show that sparkR is significantly shorter its execution time when compared to R studio. The resu

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Prasetyo, Simeon Yuda. "Prediksi Gagal Jantung Menggunakan Artificial Neural Network." Jurnal SAINTEKOM 13, no. 1 (2023): 79–88. http://dx.doi.org/10.33020/saintekom.v13i1.379.

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Cardiovascular disease or heart problems are the leading cause of death worldwide. According to WHO (World Health Organization) every year there are more than 17.9 million deaths worldwide. In previous studies, there have been many studies related to the application of machine learning to predict heart failure and obtained quite good results, ranging from 85 percent to 90 percent, with sophisticated models optimized using neural networks. In this research, experiments were carried out using similar architectures based on the state of the art from previous research, namely Artificial Neural Net

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Sengstock, Brian. "Hidden layers: neurodiversity in paramedicine." Journal of Paramedic Practice 16, no. 4 (2024): 164–65. http://dx.doi.org/10.12968/jpar.2024.16.4.164.

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Singh, Priyanka, Samir Kumar Borgohain, Achintya Kumar Sarkar, Jayendra Kumar, and Lakhan Dev Sharma. "Feed-Forward Deep Neural Network (FFDNN)-Based Deep Features for Static Malware Detection." International Journal of Intelligent Systems 2023 (February 20, 2023): 1–20. http://dx.doi.org/10.1155/2023/9544481.

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The portable executable header (PEH) information is commonly used as a feature for malware detection systems to train and validate machine learning (ML) or deep learning (DL) classifiers. We propose to extract the deep features from the PEH information through hidden layers of a feed-forward deep neural network (FFDNN). The extraction of deep features of hidden layers represents the dataset with a better generalization for malware detection. While feeding the deep feature of one hidden layer to the succeeding layer, the Gaussian error linear unit (GeLU) activation function is applied. The FFDN

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Shilovskii, G. V., and V. M. Yulkova. "IMPLEMENTATION POSSIBILITY OF A LIKE DEEP LEARNING ALGORITHMS ON NON-DEEP NETWORKS WITH HIDDEN LAYERS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 198 (December 2020): 14–19. http://dx.doi.org/10.14489/vkit.2020.12.pp.014-019.

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Learning deep neural networks using the backpropagation algorithm is considered implausible from a biological point of view. Numerous recent publications offer sophisticated models for biologically plausible deep learning options that typically define success as achieving a test accuracy of around 98 % in the MNIST dataset. Here we examine how far we can go in the classification of numbers (MNIST) with biologically plausible rules for learning in a network with one hidden layer and one reading layer. The weights of the hidden layer are either fixed (random or random Gabor filters), or are trai

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Liu, Jingyi, and Ba Tuan Le. "Incremental Multiple Hidden Layers Regularized Extreme Learning Machine Based on Forced Positive-Definite Cholesky Factorization." Mathematical Problems in Engineering 2019 (April 24, 2019): 1–15. http://dx.doi.org/10.1155/2019/6740523.

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The theory and implementation of extreme learning machine (ELM) prove that it is a simple, efficient, and accurate machine learning method. Compared with other single hidden layer feedforward neural network algorithms, ELM is characterized by simpler parameter selection rules, faster convergence speed, and less human intervention. The multiple hidden layer regularized extreme learning machine (MRELM) inherits these advantages of ELM and has higher prediction accuracy. In the MRELM model, the number of hidden layers is randomly initiated and fixed, and there is no iterative tuning process. Howe

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Elansari, Taoufyq, Mohammed Ouanan, and Hamid Bourray. "A novel Mathematical Modeling for Deep Multilayer Perceptron Optimization: Architecture Optimization and Activation Functions Selection." Statistics, Optimization & Information Computing 12, no. 5 (2024): 1409–24. http://dx.doi.org/10.19139/soic-2310-5070-1990.

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The Multilayer Perceptron (MLP) is an artificial neural network composed of one or more hidden layers. It has found wide use in various fields and applications. The number of neurons in the hidden layers, the number of hidden layers, and the activation functions employed in each layer significantly influence the convergence of MLP learning algorithms. This article presents a model for selecting activation functions and optimizing the structure of the multilayer perceptron, formulated in terms of mixed-variable optimization. To solve the obtained model, a hybrid algorithm is used, combining sto

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Pellegrino, Eric, Theo Brunet, Christel Pissier, et al. "Deep Learning Architecture Optimization with Metaheuristic Algorithms for Predicting BRCA1/BRCA2 Pathogenicity NGS Analysis." BioMedInformatics 2, no. 2 (2022): 244–67. http://dx.doi.org/10.3390/biomedinformatics2020016.

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Motivation, BRCA1 and BRCA2 are genes with tumor suppressor activity. They are involved in a considerable number of biological processes. To help the biologist in tumor classification, we developed a deep learning algorithm. The question when we want to construct a neural network is how many hidden layers and neurons should we use. If the number of inputs and outputs is defined by the problem, the number of hidden layers and neurons is difficult to define. Hidden layers and neurons that make up each layer of the neural network influence the performance of system predictions. There are differen

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Deng, Tiancheng. "Effect of the Number of Hidden Layer Neurons on the Accuracy of the Back Propagation Neural Network." Highlights in Science, Engineering and Technology 74 (December 29, 2023): 462–68. http://dx.doi.org/10.54097/nbra6h45.

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Back propagation neural network (BPNN) is one of the most basic and commonly used models in machine learning. Hidden layers play a crucial function in maximizing the performance of neural networks, especially when solving complicated issues that demand strict adherence to accuracy and time complexity requirements. The only reliable ways at the moment are just experience and attempting each situation, as the process of determining the amount of Hidden Layer neurons is still unclear. To investigate this relationship, this article conducted extensive experiments involving designing and training t

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Zhang, Zhisheng. "Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNN." Mathematical Problems in Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/3485182.

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Short-term load forecasting (STLF) model based on the fusion of Phase Space Reconstruction Theory (PSRT) and Quantum Chaotic Neural Networks (QCNN) was proposed. The quantum computation and chaotic mechanism were integrated into QCNN, which was composed of quantum neurons and chaotic neurons. QCNN has four layers, and they are the input layer, the first hidden layer of quantum hidden nodes, the second hidden layer of chaotic hidden nodes, and the output layer. The theoretical basis of constructing QCNN is Phase Space Reconstruction Theory (PSRT). Through the actual example simulation, the simu

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Krotov, Dmitry, and John J. Hopfield. "Unsupervised learning by competing hidden units." Proceedings of the National Academy of Sciences 116, no. 16 (2019): 7723–31. http://dx.doi.org/10.1073/pnas.1820458116.

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It is widely believed that end-to-end training with the backpropagation algorithm is essential for learning good feature detectors in early layers of artificial neural networks, so that these detectors are useful for the task performed by the higher layers of that neural network. At the same time, the traditional form of backpropagation is biologically implausible. In the present paper we propose an unusual learning rule, which has a degree of biological plausibility and which is motivated by Hebb’s idea that change of the synapse strength should be local—i.e., should depend only on the activi

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Osigbemeh, Michael, Augustine Azubogu, Michael Ayomoh, and Alpheus Okahu. "Efficacy of Two Hidden Layers Artificial Neural Network Synapticity for Deep Learning: A Case of Pattern Recognition." Journal of Applied Artificial Intelligence 6, no. 1 (2025): 24–38. https://doi.org/10.48185/jaai.v6i1.1408.

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Most research works in Artificial Neural Network (ANN) are accustomed with the use of single hidden layer (SHL) topology without giving considerations to the problem type, its complexity and desired depth of supervised or unsupervised learning. This could be partly due to the inherent complexities associated with the use of more than one hidden layer which in turn affects solution efficiency. However, the trade-off occasionally is between efficiency and effectiveness of result. When effectiveness is prioritized perhaps for sensitive or mission critical systems, then multiple hidden layers can

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Muhammad Ridwan Ali and Ario Yudo Husodo. "Pengenalan Plat Kendaraan Bermotor Menggunakan Metode Gradien Karakter dan BPNN (Backpropagation Neural Network)." Journal of Computer Science and Informatics Engineering (J-Cosine) 4, no. 2 (2020): 169–78. http://dx.doi.org/10.29303/jcosine.v4i2.328.

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License plates are a unique feature to identify a vehicle in the combination between letters and numbers. Feature extraction needed to identify each letter and number in a digital image. There are several methods in feature extraction, one of them uses a gradient feature extraction. In this research, an application program to identify the license plate is a character gradient method and backpropagation neural network (BNN). First, the digital image is cropped to get a license plate then segmented to generate each character. The next step is the extraction feature using Character gradient to ge

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Tran, Trang Thi Kieu, Taesam Lee, and Jong-Suk Kim. "Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?" Atmosphere 11, no. 10 (2020): 1072. http://dx.doi.org/10.3390/atmos11101072.

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Weather forecasting, especially that of extreme climatic events, has gained considerable attention among researchers due to their impacts on natural ecosystems and human life. The applicability of artificial neural networks (ANNs) in non-linear process forecasting has significantly contributed to hydro-climatology. The efficiency of neural network functions depends on the network structure and parameters. This study proposed a new approach to forecasting a one-day-ahead maximum temperature time series for South Korea to discuss the relationship between network specifications and performance by

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Ji, Miaomiao, Peng Liu, and Qiufeng Wu. "Feasibility of Hybrid PSO-ANN Model for Identifying Soybean Diseases." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–16. http://dx.doi.org/10.4018/ijcini.290328.

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Soybean disease has become one of vital factors restricting the sustainable development of high-yield and high-quality soybean industry. A hybrid artificial neural network (ANN) model optimized via particle swarm optimization (PSO) algorithm, which is denoted as PSO-ANN, is proposed in this paper for soybean diseases identification based on categorical feature inputs. Augmentation dataset is created via Synthetic minority over-sampling technique (SMOTE) to deal with quantitative insufficiency and categorical unbalance of the dataset. PSO algorithm is used to optimize the parameters in ANN, inc

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Siswanto, Joko, Benny Daniawan, Haryani Haryani, and Pipit Rusmandani. "Testing Of Deep Learning-Based LSTM Model For Number Of Road Accidents Predicting." Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi 3, no. 3 (2024): 95–103. http://dx.doi.org/10.20885/snati.v3.i3.37.

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Many have used the prediction of the number of road accidents, but it is still rare to find those who use and test prediction models that are not suitable. Predictive models that have been used to predict road accidents have proven successful, but have not provided model testing with data that is different from the deep learning approach. The LSTM model test is proposed to be tested with 5 different datasets from Kaggle and 3 hidden layer variations. The test results of the LSTM model are that with variations of 4 hidden layers it can achieve higher accuracy results than those without hidden l

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Lankston, Robert W. "The seismic refraction method: A viable tool for mapping shallow targets into the 1990s." GEOPHYSICS 54, no. 12 (1989): 1535–42. http://dx.doi.org/10.1190/1.1442621.

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Geometrical considerations show that first arrivals can be recorded from below hidden layers. A certain minimum amount of data must be collected in order to resolve lateral versus vertical subsurface changes and thereby to determine the interpretation method. Field procedures, therefore, are independent of the interpretation method. The optimum XY parameter in the generalized reciprocal method (GRM) of processing refraction seismic data is significant as a quality control factor in refraction data interpretation. By comparison of the optimum XY value that is recovered through velocity analysis

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Lala, Timotei. "Stability Analysis of Batch Offline Action-Dependent Heuristic Dynamic Programming Using Deep Neural Networks." Mathematics 13, no. 2 (2025): 206. https://doi.org/10.3390/math13020206.

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In this paper, the theoretical stability of batch offline action-dependent heuristic dynamic programming (BOADHDP) is analyzed for deep neural network (NN) approximators for both the action value function and controller which are iteratively improved using collected experiences from the environment. Our findings extend previous research on the stability of online adaptive ADHDP learning with single-hidden-layer NNs by addressing the case of deep neural networks with an arbitrary number of hidden layers, updated offline using batched gradient descend updates. Specifically, our work shows that t

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Dheyaa, Shaheed Al-Azzawi. "Application and evaluation of the neural network in gearbox." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 1 (2020): 19–29. https://doi.org/10.12928/TELKOMNIKA.v18i1.13760.

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We developed old designed of a Back-Propagation neural network (BPNN), which it was designed by other researchers, and we made modification in their structure. The 1st velocity ratio was discriminated by lowest speed, and highest twist. The 6<sup>th </sup>velocity ratio was discriminated by highest speed, and lowest twist. The aim of this paper is to design neural structure get best performance to control an electrical automotive transportation six-speed gearbox of the vehicle. We focus on the evaluation of the BPNN to select the suitable number of layers and neurons. Experimentally, the struc

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Boltayev, Sunnat, Bobomurod Rakhmonov, Obid Muhiddinov, Aziz Saitov, and Zohid Toshboyev. "A block model development for intelligent control of the switches operating apparatus position in the electrical interlocking system." E3S Web of Conferences 264 (2021): 05043. http://dx.doi.org/10.1051/e3sconf/202126405043.

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In this scientific article, we developed a model of block C that controls the position of the switches. A mathematical model of the block was calculated using a two-layer neural network along with the blocks of the block. An imitation model of the switch control block using the SoDeSys program is presented. In the modeling process, it was studied that a multilayer neural network consists of one or more hidden layers of neurons-the entrance, exit, and the neurons located between them. And block C was determined that it was possible to model with the help of a 2-layer neural network and was expr

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Fung, Hon-Kwok, and Leong Kwan Li. "Minimal Feedforward Parity Networks Using Threshold Gates." Neural Computation 13, no. 2 (2001): 319–26. http://dx.doi.org/10.1162/089976601300014556.

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This article presents preliminary research on the general problem of reducing the number of neurons needed in a neural network so that the network can perform a specific recognition task. We consider a single-hidden-layer feedforward network in which only McCulloch-Pitts units are employed in the hidden layer. We show that if only interconnections between adjacent layers are allowed, the minimum size of the hidden layer required to solve the n-bit parity problem is n when n ≤ 4.

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Pant, Aruna, and Adesh Kumar. "Design and implementation of deep neural network hardware chip and its performance analysis." IAES International Journal of Robotics and Automation (IJRA) 13, no. 4 (2024): 485. http://dx.doi.org/10.11591/ijra.v13i4.pp485-494.

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The artificial neural network (ANN) with a single layer has a limited capacity to process data. Multiple neurons are connected in the human brain, and the actual capacity of the brain lies in the interconnectedness of multiple neurons. As a specified generalization of ANN deep learning makes use of two or more hidden layers, which implies that a greater number of neurons are required to construct the model. A network that has more than one hidden layer, also known as two or more hidden layers, is referred to as a deep neural network, and the process of training such networks is referred to as

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Pant, Aruna, and Adesh Kumar. "Design and implementation of deep neural network hardware chip and its performance analysis." IAES International Journal of Robotics and Automation 13, no. 4 (2024): 485–94. https://doi.org/10.11591/ijra.v13i4.pp485-494.

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The artificial neural network (ANN) with a single layer has a limited capacity to process data. Multiple neurons are connected in the human brain, and the actual capacity of the brain lies in the interconnectedness of multiple neurons. As a specified generalization of ANN deep learning makes use of two or more hidden layers, which implies that a greater number of neurons are required to construct the model. A network that has more than one hidden layer, also known as two or more hidden layers, is referred to as a deep neural network, and the process of training such networks is referred to as

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Wan, Jie, Jinfu Liu, Guorui Ren, Yufeng Guo, Daren Yu, and Qinghua Hu. "Day-Ahead Prediction of Wind Speed with Deep Feature Learning." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 05 (2016): 1650011. http://dx.doi.org/10.1142/s0218001416500117.

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Day-ahead prediction of wind speed is a basic and key problem of large-scale wind power penetration. Many current techniques fail to satisfy practical engineering requirements because of wind speed's strong nonlinear features, influenced by many complex factors, and the general model's inability to automatically learn features. It is well recognized that wind speed varies in different patterns. In this paper, we propose a deep feature learning (DFL) approach to wind speed forecasting because of its advantages at both multi-layer feature extraction and unsupervised learning. A deep belief netwo

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Mohammad, Abdulrahman Th, Hasanen M. Hussen, and Hussein J. Akeiber. "Prediction the output power of photovoltaic module using artificial neural networks model with optimizing the neurons number." International Journal of Renewable Energy Development 12, no. 3 (2023): 478–87. http://dx.doi.org/10.14710/ijred.2023.49972.

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Artificial neural networks (ANNs) is an adaptive system that has the ability to predict the relationship between the input and output parameters without defining the physical and operation conditions. In this study, some queries about using ANN methodology are simply clarified especially about the neurons number and their relationship with input and output parameters. In addition, two ANN models are developed using MATLAB code to predict the power production of a polycrystalline PV module in the real weather conditions of Iraq. The ANN models are then used to optimize the neurons number in the

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Stathakis, D. "How many hidden layers and nodes?" International Journal of Remote Sensing 30, no. 8 (2009): 2133–47. http://dx.doi.org/10.1080/01431160802549278.

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Kawaji, Hideya, Mari Nakamura, Yukari Takahashi, et al. "Hidden layers of human small RNAs." BMC Genomics 9, no. 1 (2008): 157. http://dx.doi.org/10.1186/1471-2164-9-157.

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AISYAH, SITI, SRI WAHYUNINGSIH, and FDT AMIJAYA. "PERAMALAN JUMLAH TITIK PANAS PROVINSI KALIMANTAN TIMUR MENGGUNAKAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK." Jambura Journal of Probability and Statistics 2, no. 2 (2021): 64–74. http://dx.doi.org/10.34312/jjps.v2i2.10292.

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Radial Basis Function Neural Network (RBFNN) is a neural that uses a radial base function in hidden layers for classification and forecasting purposes. Neural Network is developed into a radial function base with an information processing system that has characteristics similar to biological neural networks, consisting of input layers, hidden layers, and output layers. The data used in this study is data on the number of hotspots in East Kalimantan Province obtained from the official website of the National Aeronautics and Space Administration (NASA). The purpose of this research is to obtain

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Kartika, Nadia Dwi, I. Wayan Astika, and Edi Santosa. "Oil Palm Yield Forecasting Based on Weather Variables Using Artificial Neural Network." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 3 (2016): 626. http://dx.doi.org/10.11591/ijeecs.v3.i3.pp626-633.

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Forecasting of oil palm yield has become a main factor in the management of oil palm industries for proper planning and decision making in order to avoid monthly high cost in harvesting. Predicting future value of oil palm yield with minimum error becomes an important issue recently. A lot of factors determine the productivity of oil palm and weather variables play an important role that affect plant growth and development that may reduce yield significantly. This research used secondary data of yield and weather variables available in company administration. It proposed feed forward neural ne

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Salakhutdinov, Ruslan, and Geoffrey Hinton. "An Efficient Learning Procedure for Deep Boltzmann Machines." Neural Computation 24, no. 8 (2012): 1967–2006. http://dx.doi.org/10.1162/neco_a_00311.

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We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer p

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Yu, C. L., H. Yang, D. C. Zhao, C. C. Liu, T. Zhang, and H. T. Jiang. "Prediction of the sintering shrinkage of glass-alumina functionally graded materials by a BP artificial neural network." Science of Sintering 41, no. 3 (2009): 257–66. http://dx.doi.org/10.2298/sos0903257y.

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The shrinkage of the glass-alumina functionally graded materials (G-A FGMs) as a function of sintering temperature, layers, and the alumina content was predicted by a back propagation artificial neural network (BP-ANN). The BP-ANN was composed of an input layer, a hidden layer, and an output layer. 21 sets of experimental data were trained, in which the temperature, layers, and the alumina content as input parameters whereas the shrinkage as the output parameter. 5 sets of experimental data were used to identify the accuracy of the BP-ANN. From the prediction, selection of the hidden layer neu

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