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Journal articles on the topic 'Neural network classification algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

MANGAL, MANISH, and MANU PRATAP SINGH. "ANALYSIS OF MULTIDIMENSIONAL XOR CLASSIFICATION PROBLEM WITH EVOLUTIONARY FEEDFORWARD NEURAL NETWORKS." International Journal on Artificial Intelligence Tools 16, no. 01 (2007): 111–20. http://dx.doi.org/10.1142/s0218213007003229.

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This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the e

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Tarasova, Anna E., Evgeny M. Tarasov, Alexander L. Zolkin, Vyacheslav A. Pestin, and Alexey S. Bityutskiy. "Computational approach based on use of multilayer neural networks in classification of rail line states." Journal of Physics: Conference Series 2697, no. 1 (2024): 012030. http://dx.doi.org/10.1088/1742-6596/2697/1/012030.

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Abstract The article considers the issue of classification of the states of rail lines using multilayer neural networks. It is shown that with significant external disturbing influences, the most promising direction is the retraining of classification models using neural networks. The representativeness of the sample is proposed to be realized by applying the scaling of informative features. It is proposed to minimize classification errors by gradient optimization methods. In the work during classification of rail lines states by neural networks, at the first stage, artificial classes of state

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Chakraborty, Manomita. "Symbolic Interpretation of Trained Neural Network Ensembles." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 32, no. 05 (2024): 695–719. http://dx.doi.org/10.1142/s0218488524500168.

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Symbolically representing the knowledge acquired by a neural network is a profound endeavor aimed at illuminating the latent information embedded within the network. The literature offers a multitude of algorithms dedicated to extracting symbolic classification rules from neural networks. While some excel in producing highly accurate rules, others specialize in generating rules that are easily comprehensible. Nevertheless, only a scant few algorithms manage to strike a harmonious balance between comprehensibility and accuracy. One such exemplary technique is the Rule Extraction from Neural Net

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CAIFENG, Zhao. "MULTI-SCALE NEURAL NETWORK-BASED CLASSIFICATION METHOD FOR SKIN PATHOLOGICAL IMAGES." MEASURING AND COMPUTING DEVICES IN TECHNOLOGICAL PROCESSES, no. 4 (November 28, 2024): 348–54. https://doi.org/10.31891/2219-9365-2024-80-42.

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Skin pathological images contain essential diagnostic information across various scales. To effectively utilize multi-scale features, this study proposes a classification method based on multi-scale neural networks. The method involves a variable multi-scale neural network structure with a backbone network and multiple scale input branches inserted at different layers, facilitating feature extraction and fusion. Two search algorithms – a minimum cost-based search algorithm and a hill-climbing search algorithm – are introduced to identify the optimal network structure. Experimental results demo

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Luo, Lihua. "Research on Image Classification Algorithm Based on Convolutional Neural Network." Journal of Physics: Conference Series 2083, no. 3 (2021): 032054. http://dx.doi.org/10.1088/1742-6596/2083/3/032054.

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Abstract Nowadays, we are in the information age. Pictures carry a lot of information and play an indispensable role. For a large number of images, it is very important to find useful image information within the effective time. Therefore, the excellent performance of the image classification algorithm has certain influence factors on the result of image classification. Image classification is to input an image, and then use a certain classification algorithm to determine the category of the image. The main process of image classification: image preprocessing, image feature extraction and clas

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Reuben, B., and K. Ambujam. "Neural network approach to detect renal calculi." i-manager’s Journal on Image Processing 10, no. 4 (2023): 31. http://dx.doi.org/10.26634/jip.10.4.20329.

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Back Propagation Network is the most commonly used algorithm in training neural networks. It is employed in processing the images and data to implement an automated kidney stone classification. The conventional technique for classifying medical resonance kidney images and detecting stones relies on human examination. This method is not accurate since it is impractical to handle large amount of data. Magnetic Resonance (MR) Images may inherently possess noise caused by operator errors. This causes earnest inaccuracies in classification features and diseases in image processing. However, the usa

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Ge, Kao, Jian-Qiang Zhao, and Yan-Yong Zhao. "GR-GNN: Gated Recursion-Based Graph Neural Network Algorithm." Mathematics 10, no. 7 (2022): 1171. http://dx.doi.org/10.3390/math10071171.

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Under an internet background involving artificial intelligence and big data—unstructured, materialized, network graph-structured data, such as social networks, knowledge graphs, and compound molecules, have gradually entered into various specific business scenarios. One problem that urgently needs to be solved in the industry involves how to perform feature extractions, transformations, and operations in graph-structured data to solve downstream tasks, such as node classifications and graph classifications in actual business scenarios. Therefore, this paper proposes a gated recursion-based gra

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Bredikhin, Arsentiy Igorevich. "Training algorithms for convolutional neural networks." Yugra State University Bulletin 15, no. 1 (2019): 41–54. http://dx.doi.org/10.17816/byusu20190141-54.

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In this article we consider one of the most used classes of neural networks convolutional neural networks (hereinafter CNN). In particular, the areas of their application, algorithms of signal propagation by CNN and CNN training are described and the methods of CNN functioning algorithms implementation in MATLAB programming language are given. The article presents the results of research on the effectiveness of the CNN learning algorithm in solving classification problems with its help. In the course of these studies, such a characteristic of the neural network as the dynamics of the network e

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Azlah, Muhammad Azfar Firdaus, Lee Suan Chua, Fakhrul Razan Rahmad, Farah Izana Abdullah, and Sharifah Rafidah Wan Alwi. "Review on Techniques for Plant Leaf Classification and Recognition." Computers 8, no. 4 (2019): 77. http://dx.doi.org/10.3390/computers8040077.

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Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extract

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Tchórzewski, Jerzy, and Tomasz Mielcarz. "Selection of an algorithm for classifying data quoted on the Day Ahead Market of TGE S.A. in MATLAB and Simulink using Deep Learning Toolbox." Studia Informatica. System and information technology 28, no. 1 (2023): 83–108. http://dx.doi.org/10.34739/si.2023.28.05.

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The article contains an analysis leading to the selection of an algorithm for classifying data listed on the Day-Ahead Market of TGE S.A. in MATLAB and Simulink using Deep Learning Toolbox. In this regard, an introduction to deep learning methods, classification methods, and classification algorithms is provided first. Particular attention was paid to the essence of three important deep learningmethods in the classification, i.e. the methods called: Stochastic Gradient Descent Momentum, Root Mean Square Prop and Adaptive Moment Estimation. Then, three architectures of artificial neural network

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Varsha, Bhosale, and Archana Patankar Dr. "Classification of Landsat 8 Images using Neural Networks." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 8 (2022): 23–28. https://doi.org/10.35940/ijitee.H9133.0711822.

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<strong>Abstract</strong>: Several geospatial applications use Classification. Classification is useful in identifying change detection. The Changes amongst various classes are identified at different time periods thus helping to analyze the changes happening in Land Use Land cover (LULC) of the area under consideration over a period. Neural networks have shown its command in majority of fields in solving complex problems, and geospatial field is also benefited by the Neural Networks. Several effective and efficient mechanisms are suggested for supervised satellite image classification. The Ne

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Cui, Jiacheng. "Research on application of model ensemble in sports image classification based on environmental information." Journal of Physics: Conference Series 2634, no. 1 (2023): 012035. http://dx.doi.org/10.1088/1742-6596/2634/1/012035.

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Abstract In recent years, a deep convolutional neural network has been widely used in the field of image classification. However, training a satisfactory network is very arduous, not only to tune the hyper-parameters in the network but also to avoid the overfitting problem caused by a deep neural network. Another point is that it is difficult for a neural network to learn subtle details without human annotation. Therefore this paper proposed a preferable classification algorithm for sports classification tasks that combines the deep neural network with the object detection algorithm to obtain

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Wang, Miao, Xu Yang, Yunchong Qian, et al. "Adaptive Neural Network Structure Optimization Algorithm Based on Dynamic Nodes." Current Issues in Molecular Biology 44, no. 2 (2022): 817–32. http://dx.doi.org/10.3390/cimb44020056.

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Large-scale artificial neural networks have many redundant structures, making the network fall into the issue of local optimization and extended training time. Moreover, existing neural network topology optimization algorithms have the disadvantage of many calculations and complex network structure modeling. We propose a Dynamic Node-based neural network Structure optimization algorithm (DNS) to handle these issues. DNS consists of two steps: the generation step and the pruning step. In the generation step, the network generates hidden layers layer by layer until accuracy reaches the threshold

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Ali, Maria, Fatima Pervez, Muhammad Nouman Atta, Abdullah Khan, and Asfandyar Khan. "Sine Cosine Algorithm for Enhancing Convergence Rates of Artificial Neural Network: A Comparative Study." Journal of Engineering Technology and Applied Physics 6, no. 2 (2024): 32–37. http://dx.doi.org/10.33093/jetap.2024.6.2.5.

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Artificial neural networks (ANNs) is widely adopted by researchers for classification tasks due to their simplicity and superior performance. This study offerings the ANN and it variant such as Elman Neural Network (NN) model to address its strengths, although it faces with issues like local minima and slow convergence. This study presents a comprehensive evaluation of four distinct algorithms for classification tasks, focusing on their performance on both training and testing datasets. These algorithms such as Sine Cosine Algorithm is integrated with Artificial Neural Networks (SCA_ANN), Back

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Yang, Yingfei, and Lei Li. "Classification of Datasets Based on Combination Algorithm of Clustering and Neural Network." Information 26, no. 4 (2023): 201–31. http://dx.doi.org/10.47880/inf2604-04.

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How to get the needed information from the data accurately and effectively for analysis is a hot research topic nowadays. Accurate classification of data is the basis for smooth data analysis. In order to classify data effectively, scholars have proposed some classification algorithms, and the most frequently mentioned one is k-means algorithm. However, in previous studies, scholars have directly determined the number of classes to be classified for the data set to be classified. Therefore, in this paper, a combinatorial algorithm is proposed to improve the classification of data with unknown

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Manoharan J, Samuel. "Capsule Network Algorithm for Performance Optimization of Text Classification." March 2021 3, no. 1 (2021): 1–9. http://dx.doi.org/10.36548/jscp.2021.1.001.

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In regions of visual inference, optimized performance is demonstrated by capsule networks on structured data. Classification of hierarchical multi-label text is performed with a simple capsule network algorithm in this paper. It is further compared to support vector machine (SVM), Long Short Term Memory (LSTM), artificial neural network (ANN), convolutional Neural Network (CNN) and other neural and non-neural network architectures to demonstrate its superior performance. The Blurb Genre Collection (BGC) and Web of Science (WOS) datasets are used for experimental purpose. The encoded latent dat

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Wang, Yong Lin, Yan Liu, and Sheng Bing Che. "Research on Pattern Recognition Based on BP Neural Network." Advanced Materials Research 282-283 (July 2011): 161–64. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.161.

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BP neural network has strong fault-tolerant and adaptive learning capacity, so it is widely used in pattern recognition. Based on the classic BP neural network, parameters of the BP algorithm has been optimized, which achieved a classification based on the improved BP neural network algorithm. By discussing the use of BP neural network in the application of pattern classification recognition, this paper detailedly studies the recognition effect of various parameters. Experimental results show that the improved algorithms has very good practical value.

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Lin, Liqun, Weixing Wang, and Bolin Chen. "Leukocyte recognition with convolutional neural network." Journal of Algorithms & Computational Technology 13 (November 28, 2018): 174830181881332. http://dx.doi.org/10.1177/1748301818813322.

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Accurate segmentation of leukocytes is a primary and very difficult problem because of the non-uniform color, uneven illumination of blood smear image. An improved algorithm based on feature weight adaptive K-means clustering for extracting complex leukocytes is proposed. In this paper, the initial clustering center is chosen according to the histogram distribution of a cell image; this approach not only improves the clustering effect but also reduces the time complexity of the algorithm from O (n) to O (1). Prior to white blood cell extraction, the color space is decomposed. Then, color space

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Sahoo, Soumya, Sunil Kumar Mohapatra, and Bijayalaxmi Panda. "Classification Using Extreme Learning Machine." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 12 (2013): 415–21. https://doi.org/10.5281/zenodo.14613563.

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Extreme Learning Machine (ELM) has become popular for solving classification problem due to its fast speed. The performance of ELM often relies on random input hidden node parameters. Neural network also uses artificial intelligence by adjusting weights and minimizing the error. The learning speed of feedforward neural network is very slow. Due to two slow gradient-based learning algorithms and iterative tuning of various parameters. This paper presents a comparative study of back propagation algorithm and an extremely fast ELM technique for single layer feedforward neural network which takes

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Shreyash, Tambe*, Thakur Bhushan, and Vishwakarma Avnish. "EFFECTIVE DATA MINING USING NEURAL NETWORKS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 4 (2016): 8–13. https://doi.org/10.5281/zenodo.48819.

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Classification is one of the data mining problems receiving great attention recently in the database community. This paper presents an approach to discover symbolic classification rules using neural networks. Neural networks have not been thought suited for data mining because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by humans. With the proposed approach, concise symbolic rules with high accuracy can be extracted from a neural network. The network is first trained to achieve the required accuracy rate. Red

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Novizon and Zulkurnain Abdul-Malek. "Neutral Networks for Fault Classification: Comparison between Feed-Forward Back-Propagation, RBF and LVQ Neural Network." Applied Mechanics and Materials 818 (January 2016): 96–100. http://dx.doi.org/10.4028/www.scientific.net/amm.818.96.

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— Neural networks are frequently used as a classifier for tasks in many classifications. However there are disadvantages in terms of amount of training data required, and length of training time. This paper, develop an intelligent diagnosis system for zinc oxide (ZnO) surge arrester fault classification. First the features were extracted from 600 ZnO surge arrester thermal images and leakage currents. Then these features were presented to several neural network architectures to investigate the most suitable network model for classifying the ZnO surge arrester fault condition effectively. Three

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Shi, Chengxiang, and Xiaoqing Li. "Research on Clustering Algorithm Based on Improved SOM Neural Network." Computational Intelligence and Neuroscience 2022 (August 10, 2022): 1–7. http://dx.doi.org/10.1155/2022/1482250.

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Clustering algorithm is a statistical method to study sample classification. With the rapid development of science and technology, people have higher and higher requirements for data classification, so there are more and more researches on clustering in modern society. Various mathematical algorithms are introduced to further improve the accuracy of clustering. Therefore, this paper proposes an improved SOM neural network algorithm to evaluate the comprehensive quality of students. SOM neural network can automatically find the internal laws and essential attributes in the samples, self-organiz

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Dudekula, Usen, and Purnachand N. "Linear fusion approach to convolutional neural networks for facial emotion recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1489. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1489-1500.

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Facial expression recognition is a challenging problem in the scientific field of computer vision. Several face expression recognition (FER) algorithms are proposed in the field of machine learning, and deep learning to extract expression knowledge from facial representations. Even though numerous algorithms have been examined, several issues like lighting changes, rotations and occlusions. We present an efficient approach to enhance recognition accuracy in this study, advocates transfer learning to fine-tune the parameters of the pre-trained model (VGG19 model ) and non-pre-trained model conv

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Qing Yang, Guan. "Modulation Classification Based on Extensible Neural Networks." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6416019.

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A deep learning architecture based on Extensible Neural Networks is proposed for modulation classification in multipath fading channel. Expanded Neural Networks (ENN) are established based on energy natural logarithm model. The model is set up using hidden layers. Modulation classification based on ENN is implemented through the amplitude, phase, and frequency hidden network, respectively. In order to improve Probability of Correct classification (PCC), one or more communication signal features are extracted using hidden networks. Through theoretical proof, ENN learning network is demonstrated

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Setiono, Rudy. "Feedforward Neural Network Construction Using Cross Validation." Neural Computation 13, no. 12 (2001): 2865–77. http://dx.doi.org/10.1162/089976601317098565.

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This article presents an algorithm that constructs feedforward neural networks with a single hidden layer for pattern classification. The algorithm starts with a small number of hidden units in the network and adds more hidden units as needed to improve the network's predictive accuracy. To determine when to stop adding new hidden units, the algorithm makes use of a subset of the available training samples for cross validation. New hidden units are added to the network only if they improve the classification accuracy of the network on the training samples and on the cross-validation samples. E

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P, Shanmugam, Rohit Gangadhar P, and Rifhath Aslam J. "Survey On Medical Image Classification Using CAPSGNN." Recent Research Reviews Journal 2, no. 1 (2023): 81–100. http://dx.doi.org/10.36548/rrrj.2023.1.07.

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The general Convolutional Neural Networks (CNNs) have been in practice, being the most conventional algorithm for image-based detection and classification. But over the years, after extensive use of CNN algorithms with different architectures, it has been shown that CNN tends to lose details and features of the image. This led to the use of Capsule-based neural networks for image detection and classification. On the other side, CNN has evolved and integrated with another type of neural network called the Graph Neural Network (GNN). Many existing systems have drawbacks such as feature loss and

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Yan, Ruixia, Zhijie Xia, Yanxi Xie, Xiaoli Wang, and Zukang Song. "Research on Sentiment Classification Algorithms on Online Review." Complexity 2020 (September 8, 2020): 1–6. http://dx.doi.org/10.1155/2020/5093620.

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The product online review text contains a large number of opinions and emotions. In order to identify the public’s emotional and tendentious information, we present reinforcement learning models in which sentiment classification algorithms of product online review corpus are discussed in this paper. In order to explore the classification effect of different sentiment classification algorithms, we conducted a research on Naive Bayesian algorithm, support vector machine algorithm, and neural network algorithm and carried out some comparison using a concrete example. The evaluation indexes and th

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Feng, Kai, Xitian Pi, Hongying Liu, and Kai Sun. "Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network." Applied Sciences 9, no. 9 (2019): 1879. http://dx.doi.org/10.3390/app9091879.

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Myocardial infarction is one of the most threatening cardiovascular diseases for human beings. With the rapid development of wearable devices and portable electrocardiogram (ECG) medical devices, it is possible and conceivable to detect and monitor myocardial infarction ECG signals in time. This paper proposed a multi-channel automatic classification algorithm combining a 16-layer convolutional neural network (CNN) and long-short term memory network (LSTM) for I-lead myocardial infarction ECG. The algorithm preprocessed the raw data to first extract the heartbeat segments; then it was trained

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Cucu Ika Agustyaningrum, Muhammad Haris, Riska Aryanti, and Titik Misriati. "Online Shopper Intention Analysis Using Conventional Machine Learning And Deep Neural Network Classification Algorithm." Jurnal Penelitian Pos dan Informatika 11, no. 1 (2021): 89–100. http://dx.doi.org/10.17933/jppi.v11i1.341.

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The use of e-commerce throughout the world in recent years is very rapid. The continuous increase in sales shows that e-commerce has huge market potential. Store profits are derived from the process of assessing data to identify and classify online shopper intentions. The process of assessing the data uses conventional machine learning algorithms and deep neural networks. Comparison of algorithms in this study using the python programming language by knowing the value of Accuracy, F1-Score, Precision, Recall, and ROC AUC. The test results show that the accuracy of the deep neural network algor

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Sibieude, Emeric, Akash Khandelwal, Pascal Girard, Jan S. Hesthaven, and Nadia Terranova. "Population pharmacokinetic model selection assisted by machine learning." Journal of Pharmacokinetics and Pharmacodynamics 49, no. 2 (2021): 257–70. http://dx.doi.org/10.1007/s10928-021-09793-6.

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AbstractA fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained usin

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Muhammad, Hasanat, Khan Waleed, Minallah Nasru, Aziz Najam, and Rashid Durrani Awab-Ur-. "Performance evaluation of transfer learning based deep convolutional neural network with limited fused spectro-temporal data for land cover classification." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6882–90. https://doi.org/10.11591/ijece.v13i6.pp6882-6890.

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Deep learning (DL) techniques are effective in various applications, such as parameter estimation, image classification, recognition, and anomaly detection. They excel with abundant training data but struggle with limited data. To overcome this, transfer learning is commonly used, leveraging complex learning abilities, saving time, and handling limited labeled data. This study assesses a transfer learning (TL)-based pre-trained “deep convolutional neural network (DCNN)” for classifying land use land cover using a limited and imbalanced dataset of fused spectro-temporal data. I

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Leema N., Khanna H. Nehemiah, Elgin Christo V. R., and Kannan A. "Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms." International Journal of Operations Research and Information Systems 11, no. 4 (2020): 62–85. http://dx.doi.org/10.4018/ijoris.2020100104.

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Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 diffe

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Yuan, Yongjie, Yongjun Zhang, Junyuan Wang, and Ping Fang. "Classification of Electrocardiogram of Congenital Heart Disease Patients by Neural Network Algorithms." Scientific Programming 2021 (August 31, 2021): 1–8. http://dx.doi.org/10.1155/2021/3801675.

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The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted bet

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Chang, Haibin, and Ying Cui. "Image Classification Algorithm Based on Big Data and Multilabel Learning of Improved Convolutional Neural Network." Wireless Communications and Mobile Computing 2021 (September 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/3138398.

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More and more image materials are used in various industries these days. Therefore, how to collect useful images from a large set has become an urgent priority. Convolutional neural networks (CNN) have achieved good results in certain image classification tasks, but there are still problems such as poor classification ability, low accuracy, and slow convergence speed. This article mainly introduces the image classification algorithm (ICA) research based on the multilabel learning of the improved convolutional neural network and some improvement ideas for the research of the ICA based on the mu

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Gevaert, Wouter, Georgi Tsenov, and Valeri Mladenov. "Neural networks used for speech recognition." Journal of Automatic Control 20, no. 1 (2010): 1–7. http://dx.doi.org/10.2298/jac1001001g.

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In this paper is presented an investigation of the speech recognition classification performance. This investigation on the speech recognition classification performance is performed using two standard neural networks structures as the classifier. The utilized standard neural network types include Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions Neural Networks.

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Liu, Qinghua, Lu Sun, Alain Kornhauser, Jiahui Sun, and Nick Sangwa. "Road roughness acquisition and classification using improved restricted Boltzmann machine deep learning algorithm." Sensor Review 39, no. 6 (2019): 733–42. http://dx.doi.org/10.1108/sr-05-2018-0132.

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Purpose To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small. Design/methodology/approach The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power sp

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Msaad, Jenan Jader, Ruwaidah Fadil Obaied, and Alaa Majeed Shnin. "Network Classifier on Iris data Using MATLAB." مجلة العلوم الهندسية و تكنولوجيا المعلومات 3, no. 8 (2024): 46–54. http://dx.doi.org/10.26389/ajsrp.n220724.

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Classification is the task of assigning object to one of several predefined categories. Classification analysis is the organization of data in given classes. Also known as supervised classification, the classification uses given class labels to order the objects in the data collection. Classification approaches normally use a training set to train the model where all objects are already associated with known class labels. The classification algorithm learns from the training set and builds a model to classify the target,. The classification analysis would generate a model that could be used to

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Dudekula, Usen, and Purnachand N. "Linear fusion approach to convolutional neural networks for facial emotion recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1489–500. https://doi.org/10.11591/ijeecs.v25.i3.pp1489-1500.

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Facial expression recognition is a challenging problem in the scientific field of computer vision. Several face expression recognition (FER) algorithms are proposed in the field of machine learning, and deep learning to extract expression knowledge from facial representations. Even though numerous algorithms have been examined, several issues like lighting changes, rotations and occlusions. We present an efficient approach to enhance recognition accuracy in this study, advocates transfer learning to fine-tune the parameters of the pre-trained model (VGG19 model) and non-pre-trained model convo

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A., Gopi Kannanand, and Balasubramanian R. "An Efficient Hybrid Genetic-Grey Wolf Based Neural Network (G2NN) for Breast Cancer Data Classification." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 1 (2020): 268–71. https://doi.org/10.35940/ijitee.A8183.1110120.

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Machine learning is the one of the famous Artificial Intelligence (AI) technique. Data Mining or Machine Learning techniques are most popular in medical diagnosis, classification, forecasting etc. K-Nearest Neighbor, SVM (Support Vector Machine), DT (Decision Tree),RF (Random Forest),NN (Neural Network) are famous classification algorithms. Neural Network is one of the popular techniques, which is used to refine the verdict of breast cancer. A neural network is otherwise known as Artificial Neural Network(ANN), which is mimicking of biological neurons of human brain. Genetic Algorithm (GA) is

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Zhang, Chaoyan, Yan Zheng, Baolong Guo, Cheng Li, and Nannan Liao. "SCN: A Novel Shape Classification Algorithm Based on Convolutional Neural Network." Symmetry 13, no. 3 (2021): 499. http://dx.doi.org/10.3390/sym13030499.

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Shape classification and matching is an important branch of computer vision. It is widely used in image retrieval and target tracking. Shape context method, curvature scale space (CSS) operator and its improvement have been the main algorithms of shape matching and classification. The shape classification network (SCN) algorithm is proposed inspired by LeNet5 basic network structure. Then, the network structure of SCN is introduced and analyzed in detail, and the specific parameters of the network structure are explained. In the experimental part, SCN is used to perform classification tasks on

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Tiancheng, Li, Ren Qing-dao-er-ji, and Qiu Ying. "Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia." Advances in Meteorology 2019 (December 6, 2019): 1–13. http://dx.doi.org/10.1155/2019/5176576.

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Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a

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Kai-Feng Liu, Kai-Feng Liu, Yu Zhang Kai-Feng Liu, Quan-Xin Zhang Yu Zhang, Yan-Ge Wang Quan-Xin Zhang, and Kai-Long Gao Yan-Ge Wang. "Chinese News Text Classification and Its Application Based on Combined-Convolutional Neural Network." 電腦學刊 33, no. 4 (2022): 001–14. http://dx.doi.org/10.53106/199115992022083304001.

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<p>A method based on combined-convolutional neural network (Combined-CNN) for Chinese news text classification is proposed. First of all, in order to solve the problem of a lack of special term set for Chi-nese news classification, a vocabulary suitable for Chinese long text classification is made by construct-ing a data index method. The Word2Vec pre-trained model was used to embed the text features word vectors. Second, by optimizing the structure of the classical convolutional neural network (CNN) model, a new idea of Combined-CNN model is proposed, which solves the problem of incompl

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Issabayeva, D. N., and G. A. Abdulkarimova. "TO THE QUESTION OF LEARNING MODELING A NEURAL NETWORK IN SCHOOL COURSE OF INFORMATICS." BULLETIN Series of Physics & Mathematical Sciences 72, no. 4 (2020): 180–87. http://dx.doi.org/10.51889/2020-4.1728-7901.28.

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The article describes an approach to computer modeling of a neural network. To simulate a neural network, it is necessary to collect a large amount of data about the simulated object, identify its features, create an algorithm that is a set of operations performed on a computer. The function of neural networks is based on classification (sorting), prediction and recognition of objects or events. Modeling neural networks can be carried out by collecting data about an object, highlighting its distinctive features and creating an algorithm using the example of Iris-Fisher flowers. The article als

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Li, Feng, Xiaoyu Li, Fei Wang, Dengyong Zhang, Yi Xia, and Fan He. "A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network." Applied Sciences 10, no. 4 (2020): 1546. http://dx.doi.org/10.3390/app10041546.

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Aiming at enhancing the classification accuracy of P300 Electroencephalogram signals in a non-invasive brain–computer interface system, a novel P300 electroencephalogram signals classification algorithm is proposed which is based on improved convolutional neural network. In the data preprocessing part, the proposed P300 classification algorithm used the Principal Component Analysis algorithm to not only remove the noise and artifacts in the data, but also increase the data processing speed. Furthermore, the proposed P300 classification algorithm employed the parallel convolution method to impr

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Li, Jingmei, Weifei Wu, Di Xue, and Peng Gao. "Multi-Source Deep Transfer Neural Network Algorithm." Sensors 19, no. 18 (2019): 3992. http://dx.doi.org/10.3390/s19183992.

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Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In

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Suzaki, Hirofumi, and Satoru Kuhara. "An Extension Approach for Neural Networks by Introducing a Nearest Neighbor Algorithm in Relative Coordinates." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 4 (2010): 325–43. http://dx.doi.org/10.20965/jaciii.2010.p0325.

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Computational models known as neural networks discriminate among different types of nonlinear data, enabling the design of flexible calculation through machine-learning algorithms. Thanks to the simplicity of calculation, the nearest neighbor algorithm is a well-studied classification method. If the nearest neighbor algorithm inference is shown by a network model consisting of a neuron model representing data, it may become deterministic with adjustable parameters. We propose a new neuron model using the generalized mean and have designed a practical neural network framework based on the neare

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Ye, Hankun. "Research on Network Customer Classification Based on BP Neural Network Algorithm." Frontiers in Business, Economics and Management 6, no. 1 (2022): 177–80. http://dx.doi.org/10.54097/fbem.v6i1.2416.

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Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. A new customer classification model for online trading customer classification is presented based on analyzing customer characteristics and behaviors .First the paper designs 21 customer classification indicators based on consumer characteristics and behaviors analysis, including customer characteristics type variables and customer behaviors type variables; Second, Aiming at the shortages of th

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Vasudeva, Nisha, Vivek Kumar Sharma, Shashi Sharma, Ravi Shankar Sharma, Satyajeet Sharma, and Gajanand Sharma. "Kidney stone classification using deep learning neural network." Journal of Discrete Mathematical Sciences and Cryptography 26, no. 5 (2023): 1393–401. http://dx.doi.org/10.47974/jdmsc-1762.

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Kidney stones are the common problem in the healthcare system. It is rapidly increasing day by day and becomes a global health crisis in worldwide. Various deep learning algorithms are used for classificationof stone in kidney area. The computer aided design approach can be used for assist doctor for finding out the stone in kidney area. For kidney transplantation and dialysis, a proper treatment is required. It is important to have reliable techniques for predicting kidney stone size atits early stages. Different machine learning (ML) algorithmsare given excellent results in predicting stone.

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Xu, Hao, Huiying Feng, and Zhangrong Liu. "An Empirical Study on the Training Characteristics of Weekly Load of Calisthenics Teaching and Training Based on Deep Learning Algorithm." Scientific Programming 2022 (August 16, 2022): 1–8. http://dx.doi.org/10.1155/2022/5159027.

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Load training is an important part of the daily training of aerobics athletes. Therefore, the research on the characteristics of aerobics movement of athletes in load training has been paid extensive attention by teaching units. Deep learning algorithm in artificial intelligence algorithm is used to study the characteristics of calisthenics teaching and training, which is a beneficial exploration to improve the scientific of calisthenics training programs. The basic principles of the neural network algorithm and implementation process are described. After the basic structure and characteristic

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Liu, Xiaodong. "Art Painting Image Classification Based on Neural Network." Computational Intelligence and Neuroscience 2022 (July 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/3119604.

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Neural network (NN) is among the most important and vital form of artificial intelligence which are utilized for the classification of data, information, or images. Moreover, NN has been extensively utilized in various research domains throughout the world, and it is because of overwhelming properties. Painting is a form formed by China’s long history and culture, and a large number of paintings reflect the living conditions of China in different periods, which is of great value to the development of China’s culture. Image classification has become a key research content in the field of image

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