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Journal articles on the topic 'Neural network MLP'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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1

Teja, G. Ravi, and M. R. Narasinga Rao. "Image Retrieval System using Fuzzy-Softmax MLP Neural Network." Indian Journal of Applied Research 3, no. 6 (2011): 169–74. http://dx.doi.org/10.15373/2249555x/june2013/57.

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Ziółkowski, Jarosław, Mateusz Oszczypała, Jerzy Małachowski, and Joanna Szkutnik-Rogoż. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles." Energies 14, no. 9 (2021): 2639. http://dx.doi.org/10.3390/en14092639.

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This publication presents a multi-faceted analysis of the fuel consumption of motor vehicles and the way human impacts the environment, with a particular emphasis on the passenger cars. The adopted research methodology is based on the use of artificial neural networks in order to create a predictive model on the basis of which fuel consumption of motor vehicles can be determined. A database containing 1750 records, being a set of information on vehicles manufactured in last decade, was used in the process of training the artificial neural networks. The MLP (Multi-Layer Perceptron) 22-10-3 netw

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El-Shafie, A., A. Noureldin, M. Taha, A. Hussain, and M. Mukhlisin. "Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia." Hydrology and Earth System Sciences 16, no. 4 (2012): 1151–69. http://dx.doi.org/10.5194/hess-16-1151-2012.

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Abstract. Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for

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Journal, Baghdad Science. "Using Neural Network with Speaker Applications." Baghdad Science Journal 7, no. 2 (2010): 1076–81. http://dx.doi.org/10.21123/bsj.7.2.1076-1081.

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In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification

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Mazher, Alaa noori, and Samira faris Khlibs. "Using Neural Network with Speaker Applications." Baghdad Science Journal 7, no. 2 (2010): 1076–81. http://dx.doi.org/10.21123/bsj.2010.7.2.1076-1081.

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In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification

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El-Shafie, A., A. Noureldin, M. R. Taha, and A. Hussain. "Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia." Hydrology and Earth System Sciences Discussions 8, no. 4 (2011): 6489–532. http://dx.doi.org/10.5194/hessd-8-6489-2011.

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Abstract. Rainfall is considered as one of the major component of the hydrological process, it takes significant part of evaluating drought and flooding events. Therefore, it is important to have accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting task such as Multi-Layer Perceptron Neural Networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and memoryless network architecture that is effective for compl

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Wongsathan, Rati, and Pasit Pothong. "Heart Disease Classification Using Artificial Neural Networks." Applied Mechanics and Materials 781 (August 2015): 624–27. http://dx.doi.org/10.4028/www.scientific.net/amm.781.624.

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Neural Networks (NNs) has emerged as an importance tool for classification in the field of decision making. The main objective of this work is to design the structure and select the optimized parameter in the neural networks to implement the heart disease classifier. Three types of neural networks, i.e. Multi-layered Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), and Generalized Regression Neural Network (GR-NN) have been used to test the performance of heart disease classification. The classification accuracy obtained by RBFNN gave a very high performance

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Kovács, László. "Classification Improvement with Integration of Radial Basis Function and Multilayer Perceptron Network Architectures." Mathematics 13, no. 9 (2025): 1471. https://doi.org/10.3390/math13091471.

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The radial basis function architecture and the multilayer perceptron architecture are very different approaches to neural networks in theory and practice. Considering their classification efficiency, both have different strengths; thus, the integration of these tools is an interesting but understudied problem domain. This paper presents a novel initialization method based on a distance-weighted homogeneity measure to construct a radial basis function network with fast convergence. The proposed radial basis function network is utilized in the development of an integrated RBF-MLP architecture. T

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Lazri, Mourad, Fethi Ouallouche, Karim Labadi, and Soltane Ameur. "Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG Data." E3S Web of Conferences 353 (2022): 01006. http://dx.doi.org/10.1051/e3sconf/202235301006.

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The application of artificial neural networks (ANN) in several fields has shown considerable success for classification or regression. Learning algorithms such as artificial neural networks must constantly readjust during the learning phase. This requires a relatively long learning time compared to the size and dimension of the data used. Contrary to these considerations, a new neural network, such as Extreme Learning Machine (ELM) has recently been implemented. The ELM does not care much about the size of the neural network, the hidden layer parameters are randomly generated and remain consta

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Mohmad Hassim, Yana Mazwin, and Rozaida Ghazali. "Using Artificial Bee Colony to Improve Functional Link Neural Network Training." Applied Mechanics and Materials 263-266 (December 2012): 2102–8. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2102.

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Artificial Neural Networks have emerged as an important tool for classification and have been widely used to classify non-linearly separable pattern. The most popular artificial neural networks model is a Multilayer Perceptron (MLP) that is able to perform classification task with significant success. However due to the complexity of MLP structure and also problems such as local minima trapping, over fitting and weight interference have made neural network training difficult. Thus, the easy way to avoid these problems is by removing the hidden layers. This paper presents the ability of Functio

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Fiqha, Iin, Gomal Juni Yandris, and Fitri Aini Nasution. "Implementation of Neural Network Algorithms in Predicting Student Graduation Rates." Sinkron 7, no. 1 (2022): 248–55. http://dx.doi.org/10.33395/sinkron.v7i1.11254.

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Higher education institutions are required to be providers of quality education. One of the instruments used by the government to measure the quality of education providers is the number of graduates. The higher the graduation rate, the better the quality of education and this good quality will positively affect the accreditation value given by BAN-PT. Therefore, in this study, researchers provide input for research conducted at Bhayangkara University, Greater Jakarta to predict student graduation rates using the Neural Network algorithm. Neural Network is a method in machine learning develope

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Sahu, Amaresh, and Sabyasachi Pattnaik. "Feature Selection Using Evolutionary Functional Link Neural Network for Classification." International Journal of Advances in Applied Sciences 6, no. 4 (2017): 359. http://dx.doi.org/10.11591/ijaas.v6.i4.pp359-367.

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<p>Computational time is high for Multilayer perceptron (MLP) trained with back propagation learning algorithm (BP) also the complexity of the network increases with the number of layers and number of nodes in layers. In contrast to MLP, functional link artificial neural network (FLANN) has less architectural complexity, easier to train, and gives better result in the classification problems. The paper proposed an evolutionary functional link artificial neural network (EFLANN) using genetic algorithm (GA) by eliminating features having little or no predictive information. Particle swarm

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Sultana, Zakia, Md Ashikur Rahman Khan, and Nusrat Jahan. "Early Breast Cancer Detection Utilizing Artificial Neural Network." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (March 18, 2021): 32–42. http://dx.doi.org/10.37394/23208.2021.18.4.

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Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs

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Firsov, Nikita, Evgeny Myasnikov, Valeriy Lobanov, et al. "HyperKAN: Kolmogorov–Arnold Networks Make Hyperspectral Image Classifiers Smarter." Sensors 24, no. 23 (2024): 7683. https://doi.org/10.3390/s24237683.

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In traditional neural network designs, a multilayer perceptron (MLP) is typically employed as a classification block following the feature extraction stage. However, the Kolmogorov–Arnold Network (KAN) presents a promising alternative to MLP, offering the potential to enhance prediction accuracy. In this paper, we studied KAN-based networks for pixel-wise classification of hyperspectral images. Initially, we compared baseline MLP and KAN networks with varying numbers of neurons in their hidden layers. Subsequently, we replaced the linear, convolutional, and attention layers of traditional neur

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Verma, Pratibha, Vineet Kumar Awasthi, and Sanat Kumar Sahu. "Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network." International Journal of Applied Evolutionary Computation 12, no. 3 (2021): 35–43. http://dx.doi.org/10.4018/ijaec.2021070103.

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Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neura

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Rudianto, Rudianto, Raden Kania, and Tifani Intan Solihati. "PREDIKSI KELULUSAN MAHASISWA TEKNIK INFORMATIKA UNIVERSITAS BANTEN JAYA MENGGUNAKAN ALGORITMA NEURAL NETWORK." Jurnal Sistem Informasi dan Informatika (Simika) 5, no. 2 (2022): 193–200. http://dx.doi.org/10.47080/simika.v5i2.2123.

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The university strives to provide relevant knowledge. One way the government can use it is to measure the quality of the institution by the number of graduates. The higher the pass rate, the higher the quality of training, which can have a positive impact on the certifications awarded by BAN-PT. This allows researchers to see how research is being conducted at the University of Banten Jaya. To predict graduation rates, students can use a type of artificial neural network algorithm commonly known as neural networks. Artificial neural networks are machine learning techniques developed from Multi

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Wang, Xuechun, and Vladimir L. Eliseev. "Methodology to improve the quality of neural network modeling of dynamic objects." Proceedings of Tomsk State University of Control Systems and Radioelectronics 27, no. 3 (2024): 92–99. https://doi.org/10.21293/1818-0442-2024-27-3-92-99.

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The problem of neural network modeling of nonlinear dynamic objects using recurrent neural networks is considered. An approach to improve the accuracy of modeling using a static neural network of the «multilayer perceptron» type, that processes correlation dependencies of a dynamic process and approximates the modeling error, is proposed. A technique for synthesis and application of the correlation neural network model CCF-MLP improving the quality of modeling of a conventional recurrent neural network, is formulated. Simulation experiments are carried out with a neural network recurrent netwo

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Mendes Júnior, José Jair Alves, Marcelo Bissi Pires, Mário Elias Marinho Vieira, Sérgio Okida, and Sergio Luiz Stevan Jr. "Neural Network to Failure Classification in Robotic Systems." Brazilian Journal of Instrumentation and Control 4, no. 1 (2016): 1. http://dx.doi.org/10.3895/bjic.v4n1.4663.

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A robotic system is a reconfigurable element, and inits programming, an algorithm can be implemented in order todetect and classify failures. This is an important step to ensurethat errors in actions do not cause damage or bring risks.Considering this, a Neural Network Multi Layer Perceptron(MLP) was used, in order to classify a set of failures in robotactuators, present in a database. This purpose is to analyze ifrobotic failures could be classified by MLP. The raw data aredivided in a temporal progression manner and torque in x, y andz axes. In total, five MLP neural networks were implemente

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Dejene, N. D., and D. W. Wolla. "Comparative analysis of artificial neural network model and analysis of variance for predicting defect formation in plastic injection moulding processes." IOP Conference Series: Materials Science and Engineering 1294, no. 1 (2023): 012050. http://dx.doi.org/10.1088/1757-899x/1294/1/012050.

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Abstract This study investigates the impact of plastic injection moulding process parameters on overflow defect formation. Experiments were conducted using a Taguchi L27 orthogonal array design. Multilayer Perceptron (MLP) artificial neural networks is explored and compared with ANOVA predictions. To assess model performance, the Root Mean Squared Error (RMSE) and the coefficient of determination (R2) is applied. The study considered temperature, speed, pressure, and packing force when constructing the MLP model using the back-propagation algorithm in Python. Results show that among the config

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LERNER, B., H. GUTERMAN, I. DINSTEIN, and Y. ROMEM. "HUMAN CHROMOSOME CLASSIFICATION USING MULTILAYER PERCEPTRON NEURAL NETWORK." International Journal of Neural Systems 06, no. 03 (1995): 359–70. http://dx.doi.org/10.1142/s012906579500024x.

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A multilayer perceptron (MLP) neural network (NN) has been studied for human chromosome classification. Only 10–20 examples were required for the MLP NN to reach its ultimate performance classifying chromosomes of 5 types. The empirical dependence of the entropic error on the number of examples was found to be highly comparable to the 1/t function. The principal component analysis (PCA) was used, both for network initialization and for feature reduction purposes. The PCA demonstrated the importance of retaining most of the image information whenever small training sets are used. The MLP NN cla

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Deme, C. Abraham. "Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using Artificial Neural Networks." International Journal of Trend in Scientific Research and Development 4, no. 2 (2020): 1119–23. https://doi.org/10.5281/zenodo.3855039.

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This study proposes Artificial Neural Network ANN based field strength prediction models for the rural areas of Abuja, the federal capital territory of Nigeria. The ANN based models were created on bases of the Generalized Regression Neural network GRNN and the Multi Layer Perceptron Neural Network MLP NN . These networks were created, trained and tested for field strength prediction using received power data recorded at 900MHz from multiple Base Transceiver Stations BTSs distributed across the rural areas. Results indicate that the GRNN and MLP NN based models with Root Mean Squared Error RMS

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H. Kashani, Mahsa, and Reza Soltangeys. "Comparison of Three Intelligent Techniques for Runoff Simulation." Civil Engineering Journal 4, no. 5 (2018): 1095. http://dx.doi.org/10.28991/cej-0309159.

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In this study, performance of a feedback neural network, Elman, is evaluated for runoff simulation. The model ability is compared with two other intelligent models namely, standalone feedforward Multi-layer Perceptron (MLP) neural network model and hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model. In this case, daily runoff data during monsoon period in a catchment located at south India were collected. Three statistical criteria, correlation coefficient, coefficient of efficiency and the difference of slope of a best-fit line from observed-estimated scatter plots to 1:1 line, were a

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LEHTOKANGAS, MIKKO. "FEEDFORWARD NEURAL NETWORK WITH ADAPTIVE REFERENCE PATTERN LAYER." International Journal of Neural Systems 09, no. 01 (1999): 1–9. http://dx.doi.org/10.1142/s0129065799000022.

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A hybrid neural network architecture is investigated for modeling purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers, the first hidden layer is selected to be an adaptive reference pattern layer. Each unit in this new layer incorporates a reference pattern that is located somewhere in the space spanned by the input variables. The outputs of these units are the component wise-squared differences between the elements of a reference pattern and the inputs. The reference pattern layer has some resemblance to the hidden layer of

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Ridwan, Ridwan, Hendarman Lubis, and Prio Kustanto. "Implementasi Algoritma Neural Network dalam Memprediksi Tingkat Kelulusan Mahasiswa." JURNAL MEDIA INFORMATIKA BUDIDARMA 4, no. 2 (2020): 286. http://dx.doi.org/10.30865/mib.v4i2.2035.

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Higher education institutions are demanded to be quality education providers. One of the instruments used by the government to measure the quality of education providers is the number of graduates. The higher the graduation level, the better the quality of education and this good quality will positively influence the value of accreditation given by BAN-PT. Therefore, in this study the researchers provided input for research conducted at Bhayangkara Jakarta Raya University to predict student graduation rates using the Neural Network algorithm. Neural Network is one method in machine learning de

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Milovanovic, Bratislav, Vera Markovic, Zlatica Marinkovic, and Zoran Stankovic. "Some applications of neural networks in microwave modeling." Journal of Automatic Control 13, no. 1 (2003): 39–46. http://dx.doi.org/10.2298/jac0301039m.

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This paper presents some applications of neural networks in the microwave modeling. The applications are related to modeling of either passive or active structures and devices. Modeling is performed using not only simple multilayer perception network (MLP) but also advanced knowledge based neural network (KBNN) structures.

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Mahmoudi, Jamal, Mohammad Ali Arjomand, Masoud Rezaei, and Mohammad Hossein Mohammadi. "Predicting the Earthquake Magnitude Using the Multilayer Perceptron Neural Network with Two Hidden Layers." Civil Engineering Journal 2, no. 1 (2016): 1–12. http://dx.doi.org/10.28991/cej-2016-00000008.

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Because of the major disadvantages of previous methods for calculating the magnitude of the earthquakes, the neural network as a new method is examined. In this paper a kind of neural network named Multilayer Perceptron (MLP) is used to predict magnitude of earthquakes. MLP neural network consist of three main layers; input layer, hidden layer and output layer. Since the best network configurations such as the best number of hidden nodes and the most appropriate training method cannot be determined in advance, and also, overtraining is possible, 128 models of network are evaluated to determine

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Yatmanov, Alexey N., Vasiliy Ya Apchel, Dmitrii V. Ovchinnikov, et al. "Use of value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment." Bulletin of the Russian Military Medical Academy 26, no. 4 (2024): 587–96. https://doi.org/10.17816/brmma635764.

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The paper demonstrates the potential for using value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment. A retrospective cohort study was conducted. For 2013–2021, 734 cadets of the Navy Military Training and Research Center “Soviet Union Fleet Admiral N.G. Kuznetsov Naval Academy” were examined, 48 of them were diagnosed with maladjustment. Neural networks were used for mathematical modeling of maladjustment prediction. The study included 8 cycles of neural network training and 7 cycles of neural network model testing. As the actual materi

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Bauer, EA. "Progressive trends on the application of artificial neural networks in animal sciences – A review." Veterinární Medicína 67, No. 5 (2022): 219–30. http://dx.doi.org/10.17221/45/2021-vetmed.

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In recent years, artificial neural networks have become the subject of intensive research in a number of scientific areas. The high performance and operational speed of neural models open up a wide spectrum of applications in various areas of life sciences. Objectives pursued by many scientists, who use neural modelling in their research, focus – among others – on intensifying real-time calculations. This study shows the possibility of using Multilayer-Perceptron (MLP) and Radial Basis Function (RBF) models of artificial neural networks for the future development of new methods for animal scie

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Channabassamma, N., Akhil Avchar, Vedala Rama Sastry, V. Sahas Swamy, and Ranjit Kolkar. "Predicting Burden Rock Velocity in Limestone Mines using Artificial Neural Network Models." Disaster Advances 18, no. 5 (2025): 133–38. https://doi.org/10.25303/185da1330138.

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The prediction of burden rock velocity is crucial in optimizing the efficiency of mining and excavation operations. This study presents a novel approach utilizing Artificial Neural Networks (ANNs) to accurately predict the velocity of burden rocks based on various input parameters such as rock property, geological property and bench properties. A comprehensive dataset was collected from field measurements and laboratory experiments to train the ANN models. The performance of the ANN models such as Multi-layered Perceptron (MLP), Deep Neural Network (DNN), simple MLP and Backpropagation Neural

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Lytvyn, Vasyl, Ivan Peleshchak, Roman Peleshchak, Oleksandr Mediakov, and Petro Pukach. "Development of a hybrid neural network model for mine detection by using ultrawideband radar data." Eastern-European Journal of Enterprise Technologies 3, no. 9 (123) (2023): 78–85. http://dx.doi.org/10.15587/1729-4061.2023.279891.

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The object of the study is the architecture of a hybrid neural network for mine recognition using ultra-wideband radar data. The work solves the problem of filtering reflected signals with interference and recognizing mines detected by ultra-wideband (UWB) radar. A hybrid neural network model in combination with the Adam learning algorithm is proposed. Filtering of reflected signals from mines is carried out using an MLP (multilayer perceptron) filter, which selects low-amplitude parts of signals that carry information about a hidden mine from the entire reflected signal. Mine recognition is c

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Galchonkov, Oleg, Oleksii Baranov, Oleh Maslov, Mykola Babych, and Illia Baskov. "MLP-KAN: implementation of the Kolmogorov-Arnold layer in a multilayer perceptron." Eastern-European Journal of Enterprise Technologies 3, no. 4 (135) (2025): 34–41. https://doi.org/10.15587/1729-4061.2025.328928.

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The object of this study is neural networks used for categorizing objects in images. The task addressed in the work is to identify options for building a multilayer perceptron architecture that apply the Kolmogorov-Arnold layer and are characterized by the best ratio of classification quality and computational effort. The paper proposes a modification to the multilayer perceptron (MLP) by replacing the first hidden layer with a Kolmogorov-Arnold layer. This allowed the use of the approximating properties of neurons and learning activation functions simultaneously. A feature of the designed MLP

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Du, Ke-Lin, Chi-Sing Leung, Wai Ho Mow, and M. N. S. Swamy. "Perceptron: Learning, Generalization, Model Selection, Fault Tolerance, and Role in the Deep Learning Era." Mathematics 10, no. 24 (2022): 4730. http://dx.doi.org/10.3390/math10244730.

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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 de

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Chen, Jing, Qi Liu, and Lingwang Gao. "Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis." Symmetry 13, no. 11 (2021): 2140. http://dx.doi.org/10.3390/sym13112140.

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Due to the benefits of convolutional neural networks (CNNs) in image classification, they have been extensively used in the computerized classification and focus of crop pests. The intention of the current find out about is to advance a deep convolutional neural network to mechanically identify 14 species of tea pests that possess symmetry properties. (1) As there are not enough tea pests images in the network to train the deep convolutional neural network, we proposes to classify tea pests images by fine-tuning the VGGNET-16 deep convolutional neural network. (2) Through comparison with tradi

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Warren Liao, T. "MLP neural network models of CMM measuring processes." Journal of Intelligent Manufacturing 7, no. 6 (1996): 413–25. http://dx.doi.org/10.1007/bf00122832.

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Jasim Saud, Laith, and Zainab Kudair Abass. "A Comparison between Multi-Layer Perceptron and Radial Basis Function Networks in Detecting Humans Based on Object Shape." Ibn AL- Haitham Journal For Pure and Applied Science 31, no. 2 (2018): 210. http://dx.doi.org/10.30526/31.2.1950.

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Human detection represents a main problem of interest when using video based monitoring. In this paper, artificial neural networks, namely multilayer perceptron (MLP) and radial basis function (RBF) are used to detect humans among different objects in a sequence of frames (images) using classification approach. The classification used is based on the shape of the object instead of depending on the contents of the frame. Initially, background subtraction is depended to extract objects of interest from the frame, then statistical and geometric information are obtained from vertical and horizonta

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Cho, Kar Mun, Nur Haizum Abd Rahman, and Iszuanie Syafidza Che Ilias. "Performance of Levenberg-Marquardt Neural Network Algorithm in Air Quality Forecasting." Sains Malaysiana 51, no. 8 (2021): 2645–54. http://dx.doi.org/10.17576/jsm-2022-5108-23.

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Levenberg-Marquardt algorithm and conjugate gradient method are frequently used for optimization in multi-layer perceptron (MLP). However, both algorithms have mixed conclusions in optimizing MLP in time series forecasting. This study uses autoregressive integrated moving average (ARIMA) and MLP with both Levenberg-Marquardt algorithm and conjugate gradient method. These methods were used to predict the Air Pollutant Index (API) in Malaysia's central region where represent urban and residential areas. The performances were discussed and compared using the mean square error (MSE) and mean absol

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Ismail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi, and A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network." IOP Conference Series: Materials Science and Engineering 1176, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.

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Abstract In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the ne

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Ismail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi, and A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network." IOP Conference Series: Materials Science and Engineering 1176, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.

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Abstract In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the ne

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López, Juan L., and José A. Vásquez-Coronel. "Congestive Heart Failure Category Classification Using Neural Networks in Short-Term Series." Applied Sciences 13, no. 24 (2023): 13211. http://dx.doi.org/10.3390/app132413211.

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Congestive heart failure carries immense importance in the realm of public health. This significance arises from its substantial influence on the number of lives lost, economic burdens, the potential for prevention, and the opportunity to enhance the well-being of both individuals and the broader community through decision-making in healthcare. Several researchers have proposed neural networks for classification of different congestive heart failure categories. However, there is little information about the confidence of the prediction on short-term series. Therefore, evaluating classification

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Leite Coelho da Silva, Felipe, Kleyton da Costa, Paulo Canas Rodrigues, Rodrigo Salas, and Javier Linkolk López-Gonzales. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector." Energies 15, no. 2 (2022): 588. http://dx.doi.org/10.3390/en15020588.

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Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multil

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Karlık, Bekir, and Kemal Yüksek. "Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System." Journal of Automated Methods and Management in Chemistry 2007 (2007): 1–6. http://dx.doi.org/10.1155/2007/38405.

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The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high

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Akbar Maulana and Enny Itje Sela. "The Implementation of Artificial Neural Networks for Stock Price Prediction." Journal of Engineering, Electrical and Informatics 3, no. 3 (2023): 34–44. http://dx.doi.org/10.55606/jeei.v3i3.2254.

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This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the

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Horák, Jakub, and Michaela Jannová. "Predicting the Oil Price Movement in Commodity Markets in Global Economic Meltdowns." Forecasting 5, no. 2 (2023): 374–89. http://dx.doi.org/10.3390/forecast5020020.

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The price of oil is nowadays a hot topic as it affects many areas of the world economy. The price of oil also plays an essential role in how the economic situation is currently developing (such as the COVID-19 pandemic, inflation and others) or the political situation in surrounding countries. The paper aims to predict the oil price movement in stock markets and to what extent the COVID-19 pandemic has affected stock markets. The experiment measures the price of oil from 2000 to 2022. Time-series-smoothing techniques for calculating the results involve multilayer perceptron (MLP) networks and

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Ramana, M. Venkata. "EmoTeluNet: A Deep Learning Architecture for Telugu Speech Emotion Recognition." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03765.

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Abstract—Speech Emotion Recognition (SER) is pivotal for advancing human-centric artificial intelligence, yet regional lan- guages like Telugu, spoken by over 80 million people, lack robust SER frameworks. This paper introduces Deep Telugu Emotion, a deep learning framework designed to recognize emotions in Telugu speech. We curated a novel dataset of Telugu emotional speech and evaluated six neural network models: Artificial Neural Network (ANN), Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (BiLSTM), Attention-based BiLSTM, Convolutional Recurrent Neural Network (CRNN),

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Khazin Kaman, Khairell, Mahdi Faramarzi, Sallehuddin Ibrahim, and Mohd Amri Md Yunus. "Artificial Neural Network for Non-Intrusive Electrical Energy Monitoring System." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 1 (2017): 124. http://dx.doi.org/10.11591/ijeecs.v6.i1.pp124-131.

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<p> This paper discusses non-intrusive electrical energy monitoring (NIEM) system in an effort to minimize electrical energy wastages. To realize the system, an energy meter is used to measure the electrical consumption by electrical appliances. The obtained data were analyzed using a method called multilayer perceptron (MLP) technique of artificial neural network (ANN). The event detection was implemented to identify the type of loads and the power consumption of the load which were identified as fan and lamp. The switching ON and OFF output events of the loads were inputted to MLP in o

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Atashfaraz, Navid. "SHORT-TERM WIND SPEED FORECASTING USING DEEP VARIATIONAL LSTM." Azerbaijan Journal of High Performance Computing 5, no. 2 (2022): 254–72. http://dx.doi.org/10.32010/26166127.2022.5.2.254.272.

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Wind speed and power at wind power stations affect the efficiency of a wind farm, so accurate wind forecasting, a nonlinear signal with high fluctuations, increases security and better efficiency than wind power. We are looking for wind speed for a wind farm in Iran. In this research, a combined neural network created from variational autoencoder (VAE), long-term, short-term memory (LSTM), and multilayer perceptron (MLP) for dimension Reduction and encoding is proposed for predicting short-term wind speeds. The data used in this research is related to the statistics of 10 minutes of wind speed

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Li, Xiao Jun, and Lin Li. "IP Core Based Hardware Implementation of Multi-Layer Perceptrons on FPGAs: A Parallel Approach." Advanced Materials Research 433-440 (January 2012): 5647–53. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5647.

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There’re many models derived from the famous bio-inspired artificial neural network (ANN). Among them, multi-layer perceptron (MLP) is widely used as a universal function approximator. With the development of EDA and recent research work, we are able to use rapid and convenient method to generate hardware implementation of MLP on FPGAs through pre-designed IP cores. In the mean time, we focus on achieving the inherent parallelism of neural networks. In this paper, we firstly propose the hardware architecture of modular IP cores. Then, a parallel MLP is devised as an example. At last, some conc

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Rezaeipanah, Amin, Rahmad Syah, Siswi Wulandari, and A. Arbansyah. "Design of Ensemble Classifier Model Based on MLP Neural Network For Breast Cancer Diagnosis." Inteligencia Artificial 24, no. 67 (2021): 147–56. http://dx.doi.org/10.4114/intartif.vol24iss67pp147-156.

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Nowadays, breast cancer is one of the leading causes of death women in the worldwide. If breast cancer is detected at the beginning stage, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of this cancer, however, efforts are still ongoing given the importance of the problem. Artificial Neural Networks (ANN) have been established as some of the most dominant machine learning algorithms, where they are very popular for prediction and classification work. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neur

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Pavić, Ivica, Frano Tomašević, and Ivana Damjanović. "Application of artificial neural networks for external network equivalent modeling." Journal of Energy - Energija 64, no. 1-4 (2022): 275–84. http://dx.doi.org/10.37798/2015641-4156.

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In this paper an artificial neural network (ANN) based methodology is proposed for determining an external network equivalent. The modified Newton-Raphson method with constant interchange of total active power between internal and external system is used for solving the load flow problem. A multilayer perceptron (MLP) with backpropagation training algorithm is applied for external network determination. The proposed methodology was tested with the IEEE 24-bus test network and simulation results show a very good performance of the ANN for external network modeling.

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HUSAINI, NOOR AIDA, ROZAIDA GHAZALI, NAZRI MOHD NAWI, LOKMAN HAKIM ISMAIL, MUSTAFA MAT DERIS, and TUTUT HERAWAN. "PI-SIGMA NEURAL NETWORK FOR A ONE-STEP-AHEAD TEMPERATURE FORECASTING." International Journal of Computational Intelligence and Applications 13, no. 04 (2014): 1450023. http://dx.doi.org/10.1142/s1469026814500230.

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The main purpose of this study is to employ Pi-Sigma Neural Network (PSNN) for one-step-ahead temperature forecasting. In this paper, we evaluate the performances of PSNN by comparing the network model with widely used Multilayer Perceptron (MLP). PSNN which is a class of Higher Order Neural Networks (HONN), has a highly regular structure, needs much smaller number of weights and less training time. The PSNN is use to overcome the drawbacks of MLP, which can easily trapped into local minima and prone to overfit. Both network models were trained with standard backpropagation algorithm. Through

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