Relevant bibliographies by topics / Multilayer Perceptron neural network model

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

Academic literature on the topic 'Multilayer Perceptron neural network model'

Author: Grafiati
Published: 3 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Multilayer Perceptron neural network model"

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Madhiarasan, M., Mohamed Louzazni, and Partha Pratim Roy. "Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting." International Journal of Photoenergy 2021 (October 27, 2021): 1–24. http://dx.doi.org/10.1155/2021/7238293.

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To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed
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Rudenko, Oleg, Oleksandr Bezsonov, and Oleksandr Romanyk. "Neural network time series prediction based on multilayer perceptron." Development Management 17, no. 1 (2019): 23–34. http://dx.doi.org/10.21511/dm.5(1).2019.03.

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Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural
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Pukach, A. I., and V. M. Teslyuk. "SUBJECTIVE PERCEPTION MODEL OF SOFTWARE COMPLEXES SUPPORT OBJECT WITH THE ENCAPSULATION OF A MULTILAYER PERCEPTRON ARTIFICIAL NEURAL NETWORKS." Ukrainian Journal of Information Technology 6, no. 2 (2024): 1–10. https://doi.org/10.23939/ujit2024.02.001.

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The object of research in this article – is the process of subjective perception of supported software complexes or their support processes by relevant human entities directly or indirectly interacting with these supported software complexes. Subjective perception model of the software complexes support object with the possibility of encapsulation of artificial neural networks, in particular – a multilayer perceptron, has been developed. Developed model provides possibility to perform modelling of the subjective perception processes of support objects (both the supported software complex itsel
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Serhiienko, A. V., and E. A. Kolomoichenko. "Study of handwritten character recognition algorithms for different languages using the KAN Neural Network Model." Reporter of the Priazovskyi State Technical University. Section: Technical sciences 1, no. 49 (2024): 36–47. https://doi.org/10.31498/2225-6733.49.1.2024.321184.

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The paper analyzed the most effective existing methods of optical character recognition that use deep learning neural networks in their structure. The analysis revealed that modern neural network architectures with the best recognition accuracy indicators have a constant accuracy limit. It was also found that each analyzed neural network architecture contains a multilayer perceptron in its structure. To optimize the recognition performance of neural networks, it was proposed to use the Kolmogorov-Arnold network as an alternative to multilayer perceptron based networks. The architecture of the
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Al-Hroot, Yusuf Ali. "A Comparison of Jordanian Bankruptcy Models: Multilayer Perceptron Neural Network and Discriminant Analysis." International Business Research 9, no. 12 (2016): 121. http://dx.doi.org/10.5539/ibr.v9n12p121.

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<p>The main purpose of this study is to develop and compare the classification accuracy of bankruptcy prediction models using the multilayer perceptron neural network, and discriminant analysis, for the industrial sector in Jordan. The models were developed using the ten popular financial ratios found to be useful in earlier studies and expected to predict bankruptcy. The study sample was divided into two samples; the original sample (n=14) for developing the two models and a hold-out sample (n=18) for testing the prediction of models for three years prior to bankruptcy during the period
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Li, Yong, Qidan Zhu, and Ahsan Elahi. "Quadcopter Trajectory Tracking Based on Model Predictive Path Integral Control and Neural Network." Drones 9, no. 1 (2024): 9. https://doi.org/10.3390/drones9010009.

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This paper aims to address the trajectory tracking problem of quadrotors under complex dynamic environments and significant fluctuations in system states. An adaptive trajectory tracking control method is proposed based on an improved Model Predictive Path Integral (MPPI) controller and a Multilayer Perceptron (MLP) neural network. The technique enhances control accuracy and robustness by adjusting control inputs in real time. The Multilayer Perceptron neural network can learn the dynamics of a quadrotor by its state parameter and then the Multilayer Perceptron sends the model to the Model Pre
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Khan, Mohd Jawad Ur Rehman, and Anjali Awasthi. "Machine learning model development for predicting road transport GHG emissions in Canada." WSB Journal of Business and Finance 53, no. 2 (2019): 55–72. http://dx.doi.org/10.2478/wsbjbf-2019-0022.

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Abstract Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network m
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Maca, Petr, and Pavel Pech. "Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks." Computational Intelligence and Neuroscience 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/3868519.

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The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive
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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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Buevich, A. G., I. E. Subbotina, A. V. Shichkin, A. P. Sergeev, and E. M. Baglaeva. "Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics." Геоэкология. Инженерная геология. Гидрогеология. Геокриология, no. 2 (May 18, 2019): 77–86. http://dx.doi.org/10.31857/s0869-78092019277-86.

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Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screeni
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Dissertations / Theses on the topic "Multilayer Perceptron neural network model"

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Goosen, Johannes Christiaan. "Comparing generalized additive neural networks with multilayer perceptrons / Johannes Christiaan Goosen." Thesis, North-West University, 2011. http://hdl.handle.net/10394/5552.

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In this dissertation, generalized additive neural networks (GANNs) and multilayer perceptrons (MLPs) are studied and compared as prediction techniques. MLPs are the most widely used type of artificial neural network (ANN), but are considered black boxes with regard to interpretability. There is currently no simple a priori method to determine the number of hidden neurons in each of the hidden layers of ANNs. Guidelines exist that are either heuristic or based on simulations that are derived from limited experiments. A modified version of the neural network construction with cross–validation sa
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Wilgenbus, Erich Feodor. "The file fragment classification problem : a combined neural network and linear programming discriminant model approach / Erich Feodor Wilgenbus." Thesis, North-West University, 2013. http://hdl.handle.net/10394/10215.

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The increased use of digital media to store legal, as well as illegal data, has created the need for specialized tools that can monitor, control and even recover this data. An important task in computer forensics and security is to identify the true le type to which a computer le or computer le fragment belongs. File type identi cation is traditionally done by means of metadata, such as le extensions and le header and footer signatures. As a result, traditional metadata-based le object type identi cation techniques work well in cases where the required metadata is available and unaltered
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Ridhagen, Markus, and Petter Lind. "A comparative study of Neural Network Forecasting models on the M4 competition data." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445568.

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The development of machine learning research has provided statistical innovations and further developments within the field of time series analysis. This study seeks to investigate two different approaches on artificial neural network models based on different learning techniques, and answering how well the neural network approach compares with a basic autoregressive approach, as well as how the artificial neural network models compare to each other. The models were compared and analyzed in regards to the univariate forecast accuracy on 20 randomly drawn time series from two different time fre
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Gao, Zhenning. "Parallel and Distributed Implementation of A Multilayer Perceptron Neural Network on A Wireless Sensor Network." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1383764269.

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Midhall, Ruben, and Amir Parmbäck. "Utvärdering av Multilayer Perceptron modeller för underlagsdetektering." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43469.

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Antalet enheter som är uppkopplade till internet, Internet of Things (IoT), ökar hela tiden. År 2035 beräknas det finnas 1000 miljarder Internet of Things-enheter. Samtidigt som antalet enheter ökar, ökar belastningen på internet-nätverken som enheterna är uppkopplade till. Internet of Things-enheterna som finns i vår omgivning samlar in data som beskriver den fysiska tillvaron och skickas till molnet för beräkning. För att hantera belastningen på internet-nätverket flyttas beräkningarna på datan till IoT-enheten, istället för att skicka datan till molnet. Detta kallas för edge computing. IoT-
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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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Birkmire, Brian Michael. "Weapon Engagement Zone Maximum Launch Range Approximation using a Multilayer Perceptron." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1313763379.

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Ignatavičienė, Ieva. "Tiesioginio sklidimo neuroninių tinklų sistemų lyginamoji analizė." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120801_133809-03141.

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Pagrindinis darbo tikslas – atlikti kelių tiesioginio sklidimo neuroninių tinklų sistemų lyginamąją analizę siekiant įvertinti jų funkcionalumą. Šiame darbe apžvelgiama: biologinio ir dirbtinio neuronų modeliai, neuroninių tinklų klasifikacija pagal jungimo konstrukciją (tiesioginio sklidimo ir rekurentiniai neuroniniai tinklai), dirbtinių neuroninių tinklų mokymo strategijos (mokymas su mokytoju, mokymas be mokytojo, hibridinis mokymas). Analizuojami pagrindiniai tiesioginio sklidimo neuroninių tinklų metodai: vienasluoksnis perceptronas, daugiasluoksnis perceptronas realizuotas „klaidos skle
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Steinholtz, Tim. "Skip connection in a MLP network for Parkinson’s classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303130.

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In this thesis, two different architecture designs of a Multi-Layer Perceptron network have been implemented. One architecture being an ordinary MLP, and in the other adding DenseNet inspired skip connections to an MLP architecture. The models were used and evaluated on the classification task, where the goal was to classify if subjects were diagnosed with Parkinson’s disease or not based on vocal features. The models were trained on an openly available dataset for Parkinson’s classification and evaluated on a hold-out set from this dataset and on two datasets recorded in another sound recordi
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Tamas, Wani Théo. "Prévision statistique de la qualité de l’air et d’épisodes de pollution atmosphérique en Corse." Thesis, Corte, 2015. http://www.theses.fr/2015CORT0010/document.

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L’objectif de ces travaux de doctorat est de développer un modèle prédictif capable de prévoir correctement les concentrations en polluants du jour pour le lendemain en Corse. Nous nous sommes intéressés aux PM10 et à l’ozone, les deux polluants les plus problématiques sur l’île. Le modèle devait correspondre aux contraintes d’un usage opérationnel au sein d’une petite structure, comme Qualitair Corse, l’association locale de surveillance de la qualité de l’air.La prévision a été réalisée à l’aide de réseaux de neurones artificiels. Ces modèles statistiques offrent une grande précision tout en
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Books on the topic "Multilayer Perceptron neural network model"

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Anderson, James A. Brain Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.003.0012.

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What form would a brain theory take? Would it be short and punchy, like Maxwell’s Equations? Or with a clear goal but achieved by a community of mechanisms—local theories—to attain that goal, like the US Tax Code. The best developed recent brain-like model is the “neural network.” In the late 1950s Rosenblatt’s Perceptron and many variants proposed a brain-inspired associative network. Problems with the first generation of neural networks—limited capacity, opaque learning, and inaccuracy—have been largely overcome. In 2016, a program from Google, AlphaGo, based on a neural net using deep learn
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Book chapters on the topic "Multilayer Perceptron neural network model"

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Hoya, Tetsuya. "Review of the Two Existing Artificial Neural Network Models—Multilayer Perceptron and Probabilistic Neural Networks." In Syntactic Networks—Kernel Memory Approach. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57312-5_2.

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Thinh, Le Vinh, Nguyen Le Van Thanh, Tran Thien Huan, and Nguyen Thanh Nha. "Human Gait Classification Model Based on Data of IMU Sensor and Multilayer Perceptron Neural Network Model." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99666-6_121.

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El-Hassani, Fatima Zahrae, Youssef Ghanou, and Khalid Haddouch. "A Novel Model for Optimizing Multilayer Perceptron Neural Network Architecture Based on Genetic Algorithm Method." In Artificial Intelligence and Industrial Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43520-1_31.

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Sreedevi, B., Durga Karthik, J. Glory Thephoral, M. Jeya Pandian, and G. Revathy. "A Novel Neural Network Based Model for Diabetes Prediction Using Multilayer Perceptron and Jrip Classifier." In Pervasive Computing and Social Networking. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2840-6_27.

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Ghaleb, Sanaa A. A., Mumtazimah Mohamad, Engku Fadzli Hasan Syed Abdullah, and Waheed A. H. M. Ghanem. "An Integrated Model to Email Spam Classification Using an Enhanced Grasshopper Optimization Algorithm to Train a Multilayer Perceptron Neural Network." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6835-4_27.

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Sykes, Edward R., Jinhe Zhang, and Uri Sevilla. "Assisting Personal Support Worker’s e-Training with AI Prediction." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90341-0_13.

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Abstract The increasing need for effective caregiver training, particularly for Personal Support Workers, has led to the development of innovative e-training platforms. This study explores the application of advanced ML models to predict training outcomes and identify at-risk learners early in the process. The primary goal is to improve training completion rates while ensuring compliance with industry standards. We employed a range of ML models, including Decision Trees, Random Forest, Support Vector Machines, Neural Networks, to predict the likelihood of successful course completion using a dataset comprising over 27 million user interaction records. Feature engineering was used to extract key metrics such as module and lesson completion ratios. The results indicate that the Multilayer Perceptron model performed best, achieving an AUC score of 0.99, while K-NN also demonstrated strong performance with an AUC of 0.98. Key features such as module completion ratio and temporal progress were found to be significant predictors of training success. These findings suggest that integrating predictive analytics into e-training platforms can significantly enhance the effectiveness of PSW certification processes, ultimately supporting the growing demand for skilled caregivers in healthcare.
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Gaci, Said. "A NOVEL MODEL TO ESTIMATE S-WAVE VELOCITY INTEGRATING HÖLDERIAN REGULARITY, EMPIRICAL MODE DECOMPOSITION, AND MULTILAYER PERCEPTRON NEURAL NETWORKS." In Oil and Gas Exploration. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119227519.ch12.

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Weick, M. "Hopfield Model, Boltzmann Machine, Multilayer Perceptron and Selected Applications." In Neural and Synergetic Computers. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74119-7_8.

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Hao, Jianbin, Shaohua Tan, and Joos Vandewalle. "A Geometric Approach to the Structural Synthesis of Multilayer Perceptron Neural Networks." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_120.

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Woo, Dong-Min, and Dong-Chul Park. "Application of MultiLayer Perceptron Type Neural Network to Camera Calibration." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03156-4_15.

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Conference papers on the topic "Multilayer Perceptron neural network model"

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López Flores, Walter Jeremías. "Evaluation of Neural Network and Logit Models for Classification of Default in Banking Loans." In I Conferencia Internacional de Ciencia, Tecnología e Innovación. Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-dxrv7c.

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The purpose of the study was to evaluate the performance of neural networks as modern techniques to classify the risk of default against the traditional Logit statistical method, taking a Honduran bank as a case study. The data was obtained from its credit portfolio made up of 38,156 personal loans and 9 available characteristics, choosing the most representative independent variables to design a Multilayer Perceptron type base model and its Logit equivalent to which characteristics were added to analyze their impact on the classification of the dependent variable Default, leaving in the end a
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Pupezescu, Valentin. "PULSATING MULTILAYER PERCEPTRON." In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-035.

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The Knowledge Discovery in Databases represents the process of extracting useful information from data that are stored in real databases. The Knowledge Discovery in Databases process consists of multiple steps which include selection target data from raw data, preprocessing, data transformation, Data Mining and interpretation of mined data. As we see, the Data Mining is one step from the whole process and it will perform one of these Data Mining task: classification, regression, clustering, association rules, summarization, dependency modelling, change and deviation detection. In this experime
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Saromo, Daniel, Elizabeth Villota, and Edwin Villanueva. "Auto-Rotating Perceptrons." In LatinX in AI at Neural Information Processing Systems Conference 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai2019120826.

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This paper proposes an improved design of the perceptron unit to mitigate the vanishing gradient problem. This nuisance appears when training deep multilayer perceptron networks with bounded activation functions. The new neuron design, named auto-rotating perceptron (ARP), has a mechanism to ensure that the node always operates in the dynamic region of the activation function, by avoiding saturation of the perceptron. The proposed method does not change the inference structure learned at each neuron. We test the effect of using ARP units in some network architectures which use the sigmoid acti
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Ghorbanian, Kaveh, and Mohammad Gholamrezaei. "Axial Compressor Performance Map Prediction Using Artificial Neural Network." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27165.

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The application of artificial neural network to compressor performance map prediction is investigated. Different types of artificial neural network such as multilayer perceptron network, radial basis function network, general regression neural network, and a rotated general regression neural network proposed by the authors are considered. Two different models are utilized in simulating the performance map. The results indicate that while the rotated general regression neural network has the least mean error and best agreement to the experimental data, it is however limited to curve fitting app
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Xu, Dongxin, Dao Wen Chen, Qian Ma, Bo Xu, and Taiyi Huang. "Adaptation of neural network model: comparison of multilayer perceptron and LVQ." In 3rd International Conference on Spoken Language Processing (ICSLP 1994). ISCA, 1994. http://dx.doi.org/10.21437/icslp.1994-406.

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Xiao-Wei, Wang. "A Multilayer Perceptron Neural Network Model for UAV Sensor Fault Detection." In 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2021. http://dx.doi.org/10.1109/iciscae52414.2021.9590669.

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Fei Yang, Wing W. Y. Ng, Eric C. C. Tsang, Xiao-Qin Zeng, and Daniel S. Yeung. "Localized generalization error model for Multilayer Perceptron Neural Networks." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620512.

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Orhan, Umut, Mahmut Hekim, and Mahmut Ozer. "Discretization approach to EEG signal classification using Multilayer Perceptron Neural Network model." In 2010 15th National Biomedical Engineering Meeting (BIYOMUT 2010). IEEE, 2010. http://dx.doi.org/10.1109/biyomut.2010.5479842.

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Yiming Tang, Jianzhong Zhao, and Wen Wu. "Analysis of quadruple-ridged square waveguide by multilayer perceptron neural network model." In 2006 Asia-Pacific Microwave Conference. IEEE, 2006. http://dx.doi.org/10.1109/apmc.2006.4429782.

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Rodriguez, Miguel Angel, John Felipe Sotomonte, Jenny Cifuentes, and Maximiliano Bueno-Lopez. "Classification of Power Quality Disturbances using Hilbert Huang Transform and a Multilayer Perceptron Neural Network Model." In 2019 International Conference on Smart Energy Systems and Technologies (SEST). IEEE, 2019. http://dx.doi.org/10.1109/sest.2019.8849114.

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Reports on the topic "Multilayer Perceptron neural network model"

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Ramakrishnan, Aravind, Fangyu Liu, Angeli Jayme, and Imad Al-Qadi. Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-030.

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For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic–Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effecti
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Rivera-Casillas, Peter, and Ian Dettwiller. Neural Ordinary Differential Equations for rotorcraft aerodynamics. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48420.

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High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential
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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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