Relevant bibliographies by topics / Neural network classification algorithm

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neural network classification algorithm'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Neural network classification algorithm"

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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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Dissertations / Theses on the topic "Neural network classification algorithm"

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Albarakati, Noor. "FAST NEURAL NETWORK ALGORITHM FOR SOLVING CLASSIFICATION TASKS." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2740.

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Classification is one-out-of several applications in the neural network (NN) world. Multilayer perceptron (MLP) is the common neural network architecture which is used for classification tasks. It is famous for its error back propagation (EBP) algorithm, which opened the new way for solving classification problems given a set of empirical data. In the thesis, we performed experiments by using three different NN structures in order to find the best MLP neural network structure for performing the nonlinear classification of multiclass data sets. A developed learning algorithm used here is the ba
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Rimer, Michael Edwin. "Improving Neural Network Classification Training." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2094.pdf.

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Vasilic, Slavko. "Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/436.

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This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combin
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Labarge, Isaac E. "Neural Network Pruning for ECG Arrhythmia Classification." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2136.

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Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made in effectively reducing these overheads by pruning and compressing large CNNs with only a slight decline in model accuracy. In this study, two pruning methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase inter
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Rahman, Sardar Muhammad Monzurur, and mrahman99@yahoo com. "Data Mining Using Neural Networks." RMIT University. Electrical & Computer Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080813.094814.

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Data mining is about the search for relationships and global patterns in large databases that are increasing in size. Data mining is beneficial for anyone who has a huge amount of data, for example, customer and business data, transaction, marketing, financial, manufacturing and web data etc. The results of data mining are also referred to as knowledge in the form of rules, regularities and constraints. Rule mining is one of the popular data mining methods since rules provide concise statements of potentially important information that is easily understood by end users and also actionable pat
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Alaskar, Haya Mohmmad. "Dynamic self-organised neural network inspired by the immune algorithm for financial time series prediction and medical data classification." Thesis, Liverpool John Moores University, 2014. http://researchonline.ljmu.ac.uk/4562/.

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Artificial neural networks have been proposed as useful tools in time series analysis in a variety of applications. They are capable of providing good solutions for a variety of problems, including classification and prediction. However, for time series analysis, it must be taken into account that the variables of data are related to the time dimension and are highly correlated. The main aim of this research work is to investigate and develop efficient dynamic neural networks in order to deal with data analysis issues. This research work proposes a novel dynamic self-organised multilayer neura
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PACIFICI, FABIO. "Novel neural network-based algorithms for urban classification and change detection from satellite imagery." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2010. http://hdl.handle.net/2108/1239.

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L`attività umana sta cambiando radicalmente l`ecosistema ambientale, unito anche alla rapida espansione demografica dei sistemi urbani. Benche` queste aree rappresentano solo una minima frazione della Terra, il loro impatto sulla richiesta di energia, cibo, acqua e materiali primi, e` enorme. Per cui, una informazione accurata e tempestiva risulta essere essenziale per gli enti di protezione civile in caso, ad esempio, di catastrofi ambientali. Negli ultimi anni il forte sviluppo di sistemi satellitari, sia dal punto di vista della risoluzione spaziale che di quella radiometrica e temporale,
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Du, Zekun. "Algorithm Design and Optimization of Convolutional Neural Networks Implemented on FPGAs." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254575.

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Deep learning develops rapidly in recent years. It has been applied to many fields, which are the main areas of artificial intelligence. The combination of deep learning and embedded systems is a good direction in the technical field. This project is going to design a deep learning neural network algorithm that can be implemented on hardware, for example, FPGA. This project based on current researches about deep learning neural network and hardware features. The system uses PyTorch and CUDA as assistant methods. This project focuses on image classification based on a convolutional neural netwo
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Nune, Rakesh. "Path Prediction and Path Diversion Identifying Methodologies for Hazardous Materials Transported by Malicious Entities." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/36238.

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Safe and secure transportation of hazardous materials (hazmat) is a challenging issue in terms of optimizing risk to society and simultaneously making the shipment delivery economical. The most important safety concern of hazardous material transportation is accidents causing multiple causalities. The potential risk to society from hazmat transportation has led to the evolution of a new threat from terrorism. Malicious entities can turn hazmat vehicles into weapons causing explosions in high profile locations. The present research is divided into two parts. First, a neural network model is d
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Galáž, Zoltán. "Analýza ručně psaného projevu u pacientů postižených neurologickými onemocněními." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-221083.

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The master‘s thesis deals with the analysis of the hand-written text. There is a design and a realization of a system for the purpose of diagnosing a Parkinson’s desease based on the analysis of hand-written text. The system consists from several modules and it is programmed in the programming environment of MATLAB. The first module provides pre-processing of the records to adjust records to the form suitable for the segmentation. Afterwards, the records are divided into those with signals onto the surface of the tablet and those with the signals above the surface of the tablet. In the next mo
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Books on the topic "Neural network classification algorithm"

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Todd, Ian K. A new neural network algorithm for classification problems. The author], 1999.

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Amezcua, Jonathan, Patricia Melin, and Oscar Castillo. New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73773-7.

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Errington, Phillip Anthony. Application of neural network models to chromosome classification. University of Manchester, 1995.

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Wichert, Terry S. Feature based neural network acoustic transient signal classification. Naval Postgraduate School, 1993.

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Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Bennett, Richard Campbell. Classification of underwater signals using a back-propagation neural network. Naval Postgraduate School, 1997.

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Neelanarayanan, ed. Wavelet Based Decomposition and Neural Network Classification for Melanoma Diagnosis. Association of Scientists, Developers and Faculties, 2014.

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Book chapters on the topic "Neural network classification algorithm"

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Palmer-Brown, Dominic, and Chrisina Jayne. "Hypercube Neural Network Algorithm for Classification." In Engineering Applications of Neural Networks. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23957-1_5.

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Wirasati, Ilsya, Zuherman Rustam, and Jane Eva Aurellia. "Cerebral Infarction Classification Using Genetic Algorithm Neural Network and Stochastic Neural Network." In Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90633-7_42.

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Labbaf, Faezeh, Tomáš Kolárik, Martin Blicha, Grigory Fedyukovich, Michael Wand, and Natasha Sharygina. "Space Explanations of Neural Network Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-98682-6_15.

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Abstract We present a novel logic-based concept called Space Explanations for classifying neural networks that gives provable guarantees of the behavior of the network in continuous areas of the input feature space. To automatically generate space explanations, we leverage a range of flexible Craig interpolation algorithms and unsatisfiable core generation. Based on real-life case studies, ranging from small to medium to large size, we demonstrate that the generated explanations are more meaningful than those computed by state-of-the-art.
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Dong, Guang-jun, Yong-sheng Zhang, and Chao-jie Zhu. "Remote Sensing Image Classification Algorithm Based on Hopfield Neural Network." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760023_49.

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Nuntalid, Nuttapod, Kshitij Dhoble, and Nikola Kasabov. "EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24955-6_54.

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Sun, Zhan-Li, Han Wang, Wai-Shing Lau, Gerald Seet, Danwei Wang, and Kin-Man Lam. "A Novel Ensemble Algorithm for Tumor Classification." In Advances in Neural Networks – ISNN 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39068-5_36.

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do Carmo Nicoletti, Maria, João R. Bertini, David Elizondo, Leonardo Franco, and José M. Jerez. "Constructive Neural Network Algorithms for Feedforward Architectures Suitable for Classification Tasks." In Constructive Neural Networks. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04512-7_1.

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Xu, Yuemei, Zuwei Fan, and Han Cao. "A Multi-task Text Classification Model Based on Label Embedding Learning." In Communications in Computer and Information Science. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9229-1_13.

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AbstractDifferent text classification tasks have specific task features and the performance of text classification algorithm is highly affected by these task-specific features. It is crucial for text classification algorithms to extract task-specific features and thus improve the performance of text classification in different text classification tasks. The existing text classification algorithms use the attention-based neural network models to capture contextualized semantic features while ignores the task-specific features. In this paper, a text classification algorithm based on label-improv
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Nawi, Nazri Mohd, R. S. Ransing, Mohd Najib Mohd Salleh, Rozaida Ghazali, and Norhamreeza Abdul Hamid. "An Improved Back Propagation Neural Network Algorithm on Classification Problems." In Database Theory and Application, Bio-Science and Bio-Technology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17622-7_18.

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Qin, Shan, Baoju Zhang, Wei Wang, and Sijie Cheng. "Throat Polyp Detection Based on the Neural Network Classification Algorithm." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08991-1_89.

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Conference papers on the topic "Neural network classification algorithm"

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Gao, Erdi, Haowei Yang, Dan Sun, Haohao Xia, Yuhan Ma, and Yuanjing Zhu. "Text Classification Optimization Algorithm Based on Graph Neural Network." In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2024. https://doi.org/10.1109/icpics62053.2024.10796365.

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Zhang, Le, Jie Zhang, and Yanghui Tan. "A convolutional neural network-based method for marine ship waste classification." In 4th International Conference on Automation Control. Algorithm and Intelligent Bionics, edited by Jing Na and Shuping He. SPIE, 2024. http://dx.doi.org/10.1117/12.3039399.

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Hui, Qiang, and Yuxiang Zhang. "Convolutional recurrent neural network-based EEG signal classification in motor imagery." In Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), edited by Hui Yuan and Lu Leng. SPIE, 2025. https://doi.org/10.1117/12.3055974.

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Shanthi, K., and S. Manimekalai. "Classification of Cervical Cancer using Artificial Neural Network." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721706.

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Liu, Shilong, and Yongjian Gu. "Quantum spiking neural networks for image classification." In Third International Conference on Algorithms, Network and Communication Technology (ICANCT 2024), edited by Fabrizio Marozzo. SPIE, 2025. https://doi.org/10.1117/12.3059865.

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Buvaneswari, Pr, Shivani S. Bhasgi, Haider Alabdeli, M. Soundarya, and T. Saravanan. "Addax Optimization Algorithm with Convolutional Neural Network for Rheumatoid Arthritis classification." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796971.

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Cheng, Tzu-Ying, and Zhehao Li. "Research on Image Classification Algorithm Based on Improved Convolutional Neural Network." In 2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2024. https://doi.org/10.1109/iciscae62304.2024.10761305.

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Yumil, Hana Magdelina, Florence Sia, Tan Soo Fun, and Lai Po Hung. "Optimized Convolutional Neural Network Using Genetic Algorithm for Music Genre Classification." In 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). IEEE, 2024. http://dx.doi.org/10.1109/iicaiet62352.2024.10729917.

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Liu, Dan, and Shuangxin Yang. "Multi-scale image classification algorithm based on convolutional neural network (CNN)." In 2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI). IEEE, 2025. https://doi.org/10.1109/icetci64844.2025.11084102.

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Qu, Guangkai, Xianglong Yu, Zhimin Wang, and Yongjian Gu. "Three quantum neural network architectures for multiclass image classification." In Third International Conference on Algorithms, Network and Communication Technology (ICANCT 2024), edited by Fabrizio Marozzo. SPIE, 2025. https://doi.org/10.1117/12.3060077.

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Reports on the topic "Neural network classification algorithm"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45902.

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The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a da
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Miles, Gaines E., Yael Edan, F. Tom Turpin, et al. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were st
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Downard, Alicia, Stephen Semmens, and Bryant Robbins. Automated characterization of ridge-swale patterns along the Mississippi River. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40439.

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The orientation of constructed levee embankments relative to alluvial swales is a useful measure for identifying regions susceptible to backward erosion piping (BEP). This research was conducted to create an automated, efficient process to classify patterns and orientations of swales within the Lower Mississippi Valley (LMV) to support levee risk assessments. Two machine learning algorithms are used to train the classification models: a convolutional neural network and a U-net. The resulting workflow can identify linear topographic features but is unable to reliably differentiate swales from o
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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe
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Wilson, C. L., G. Candela, P. J. Grother, C. I. Watson, and R. A. Wilkinson. Massively parallel neural network fingerprint classification system. National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4880.

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Hawkins, Rupert S., K. F. Heideman, and Ira G. Smotroff. Cloud Data Set for Neural Network Classification Studies. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada256181.

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Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculatio
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Mu, Ruihui, and Xiaoqin Zeng. Improved Webpage Classification Technology Based on Feedforward Backpropagation Neural Network. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.09.11.

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