Relevant bibliographies by topics / 5-fold cross validation

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Academic literature on the topic '5-fold cross validation'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "5-fold cross validation"

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OSANYINLOKUN, Oluwatoyin E. "MACHINE LEARNING MODEL FOR PREDICTING THE BENDING MOMENTS AND SHEAR FORCES IN REINFORCED CONCRETE BEAMS CONTAINING SAWDUST ASH AS A PARTIAL CEMENT SUBSTITUTE." OAUSTECH Journal of Engineering and Intelligent Technology 1, no. 1 (2025): 246–58. https://doi.org/10.36108/ojeit/5202.10.0152.

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An artificial neural network (ANN) was used to predict sawdust ash (SDA)-reinforced concrete beams’ (RCBs) bending moment (BM) and shear force (SF). With compressive strength-test results data using an ANN, a numerical maximum SF and BM-predicting concrete model for various mixtures of RC containing different percentages of SDA as a partial replacement for cement was developed and validated. The ANN used in this study was a multi-layer perceptron (MLP) with three hidden layers. The MLP was trained on the data using the back-propagation algorithm. A cross-validation technique that repeatedly sp
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Cai, Zongyou, Lun M. Wong, Ye Heng Wong, Hok-lam Lee, Kam-yau Li, and Tiffany Y. So. "Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading." Cancers 15, no. 22 (2023): 5459. http://dx.doi.org/10.3390/cancers15225459.

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Background: Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI). Methods: This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative
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Dankwa, Stephen, and Wenfeng Zheng. "Special Issue on Using Machine Learning Algorithms in the Prediction of Kyphosis Disease: A Comparative Study." Applied Sciences 9, no. 16 (2019): 3322. http://dx.doi.org/10.3390/app9163322.

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Machine learning (ML) is the technology that allows a computer system to learn from the environment, through re-iterative processes, and improve itself from experience. Recently, machine learning has gained massive attention across numerous fields, and is making it easy to model data extremely well, without the importance of using strong assumptions about the modeled system. The rise of machine learning has proven to better describe data as a result of providing both engineering solutions and an important benchmark. Therefore, in this current research work, we applied three different machine l
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Kann, Bonpagna, Thodsaporn Chay-intr, Hour Kaing, and Thanaruk Theeramunkong. "Khmer Treebank Construction via Interactive Tree Visualization." IJITEE (International Journal of Information Technology and Electrical Engineering) 3, no. 3 (2019): 67. http://dx.doi.org/10.22146/ijitee.48545.

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Despite the fact that there are a number of researches working on Khmer Language in the field of Natural Language Processing along with some resources regarding words segmentation and POS Tagging, we still lack of high-level resources regarding syntax, Treebanks and grammars, for example. This paper illustrates the semi-automatic framework of constructing Khmer Treebank and the extraction of the Khmer grammar rules from a set of sentences taken from the Khmer grammar books. Initially, these sentences will be manually annotated and processed to generate a number of grammar rules with their prob
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Jasmir, Jasmir, Xaverius Sika, Mulyadi Mulyadi, and Rischa Amelia. "Klasifikasi Kelayakan Pemberian Kredit Pada Calon Debitur Menggunakan Naïve Bayes." JURIKOM (Jurnal Riset Komputer) 9, no. 6 (2022): 1833. http://dx.doi.org/10.30865/jurikom.v9i6.5131.

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As a lending company, PT. PRIMA KONSUMEN FINANCE certainly has the possibility of bad credit in extending credit to its debtors which can reduce the company's income. Therefore the author performs data mining analysis on debtor data that has borrowed at PT. PRIMA KONSUMEN FINANCE to become valuable information for the company. The author uses debtor data in 2019 as many as 265 data. In conducting the analysis the author uses the WEKA Tools tool. The method used is the Naïve Bayes classification method with 9 attributes. The contribution of this research is to build a creditworthiness classific
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Asim Shahid, Muhammad, Muhammad Mansoor Alam, and Mazliham Mohd Su’ud. "Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm." PLOS ONE 18, no. 4 (2023): e0284209. http://dx.doi.org/10.1371/journal.pone.0284209.

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The benefits and opportunities offered by cloud computing are among the fastest-growing technologies in the computer industry. Additionally, it addresses the difficulties and issues that make more users more likely to accept and use the technology. The proposed research comprised of machine learning (ML) algorithms is Naïve Bayes (NB), Library Support Vector Machine (LibSVM), Multinomial Logistic Regression (MLR), Sequential Minimal Optimization (SMO), K Nearest Neighbor (KNN), and Random Forest (RF) to compare the classifier gives better results in accuracy and less fault prediction. In this
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Attanayake, A. M. C. H., D. D. M. Jayasundara, and T. S. G. Peiris. "AN APPLICATION OF 5-FOLD CROSS VALIDATION ON A BINARY LOGISTIC REGRESSION MODEL." Advances and Applications in Statistics 49, no. 6 (2016): 443–51. http://dx.doi.org/10.17654/as049060443.

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Sooai, Adri Gabriel, Sisilia Daeng Bakka Mau, Yovinia Carmeneja Hoar Siki, Donatus Joseph Manehat, Shine Crossifixio Sianturi, and Alicia Herlin Mondolang. "OPTIMIZING LANTANA CLASSIFICATION: HIGH-ACCURACY MODEL UTILIZING FEATURE EXTRACTION." Jurnal Ilmiah Kursor 12, no. 2 (2023): 49–58. http://dx.doi.org/10.21107/kursor.v12i2.347.

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As an invasive and poisonous plant, Lantana has become a pest in the agricultural world. Still, on the other hand, it becomes an ornamental plant with different positive potentials. Lantana flower datasets are not yet widely available for open image classification research, given that the research needs are still broad in remote sensing. This study aims to provide a model with classifier accuracy that outperforms similar studies and Lantana datasets for classification needs using several algorithms that can be run on small source computers. This study used five types of lantana colors, red, wh
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Dake, Delali Kwasi, and Charles Buabeng-Andoh. "Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions." Mobile Information Systems 2022 (November 2, 2022): 1–9. http://dx.doi.org/10.1155/2022/2670562.

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Recently, students dropping out of school at the tertiary level without prior notice or permission has intrigued deep concern among academic authorities, instructors, and counsellors. It has therefore become necessary to understand factors that lead to high attrition rates among learners and identify at-risk students for urgent academic counselling. In providing a proactive response to learner attrition, the study deployed a machine learning algorithm with high model accuracy to predict students’ drop-out rates and identify dominant attributes that affect learner attrition and retention. An at
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Zer, P. P. P. A. N. W. Fikrul Ilmi R. H., Fazli Nugraha Tambunan, Rika Rosnelly, and Wanayumini Wanayumini. "Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods." SinkrOn 8, no. 2 (2023): 939–47. http://dx.doi.org/10.33395/sinkron.v8i2.12195.

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Tomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are one of the popular crops that can grow in low and high areas but are susceptible to disease. For this reason, farmers take precautions by looking at the characteristics and texture of tomato leaves. However, this requires more time and money and a long process. One of the efforts that can be made is to classify tomato leaf diseases. This research aims to classify using the Support Vector Machine and K-Nearest Neighbor methods. The dataset used is tomato leaf image data with 4 classes of leaves affecte
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Book chapters on the topic "5-fold cross validation"

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Mol, Frank N., Luuk van der Hoek, Baoqiang Ma, et al. "MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83274-1_13.

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Abstract The superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment planning. The Head and Neck Tumor Segmentation for MR-Guided Applications challenge (HNTSMRG-24) comprises two tasks: segmentation of primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) on T2-weighted MRI volumes obtained at (1) pre-radiotherapy (pre-RT) and (2) mid-radiotherapy (mid-RT). The training dataset consists of data from 150 patients, including MRI volumes of pre-RT, mid-RT, and pre-
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Saukkoriipi, Mikko, Jaakko Sahlsten, Joel Jaskari, Ahmed Al-Tahmeesschi, Laura Ruotsalainen, and Kimmo Kaski. "Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83274-1_14.

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Abstract Accurate segmentation of the primary gross tumor volumes and metastatic lymph nodes in head and neck cancer is crucial for radiotherapy but remains challenging due to high interobserver variability, highlighting a need for an effective auto-segmentation tool. Tumor delineation is used throughout radiotherapy for treatment planning, initially for pre-radiotherapy (pre-RT) MRI scans followed-up by mid-radiotherapy (mid-RT) during the treatment. For the pre-RT task, we propose a dual-stage 3D UNet approach using cascaded neural networks for progressive accuracy refinement. The first-stag
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Haberbusch, Max, Lisa A. Bernardo, Laura Galassi, Calogero M. Oddo, and Francesco Moscato. "Electrocardiogram Delineation Using Deep Neural Networks." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220356.

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Background: In recent years, there has been a rising interest in the application of deep neural networks (DNN) for the delineation of the electrocardiogram (ECG). Objectives: A variety of DNN architectures has been investigated in a 5-fold cross-validation approach. Results: The best performing network achieved 100% sensitivity and >97% positive predictive value for all ECG waves. Conclusion: Our DNN could achieve similar classification performance as other DNN approaches described in the literature at a reduced computational cost.
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Sy, Michael Aaron, Mattia Prosperi, Mohammadali Serajian, Christina Boucher, Panayotis Takis Benos, and Simone Marini. "Prediction of A. Baumannii Amikacin Resistance in Clinical Metagenomics." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240381.

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Respiratory tract infections are a serious threat to health, especially in the presence of antimicrobial resistance (AMR). Existing AMR detection methods are limited by slow turnaround times and low accuracy due to the presence of false positives and negatives. In this study, we simulate 1,116 clinical metagenomics samples on both Illumina and Nanopore sequencing from curated, real-world sequencing of A. baumannii respiratory infections and build AI models to predict resistance to amikacin. The best performance is achieved by XGBoost on Illumina sequencing (area under the ROC curve = 0.7993 on
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Varsha, M., Poornima Basavaraju, and Pavan Kumar M. P. "Advanced Machine Learning Techniques for Disease Management in Paddy Crop for Sustainable Rice Production." In Practice, Progress, and Proficiency in Sustainability. IGI Global, 2024. http://dx.doi.org/10.4018/978-1-6684-7051-0.ch004.

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Rice blast disease is strongly dependent on environmental and climate factors. Integration of rice blast disease severity prediction model based on climate factors provides decision support framework for farmers to overcome from the problems of climate change scenarios. Major contribution of the proposed study is to predict the severity of rice blast disease using Linear SVM model. Prediction of severity of rice blast disease is Severity of rice blast disease is divided into four classes 0,1,2 and 3. Data imbalance is the most difficult problem in multi-class classification. Proposed study has
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Muralidharan, Neha, Shaurya Gupta, Anurag Gade, Manas Ranjan Prusty, Rajesh Kumar Tripathy, and Ram Bilas Pachori. "Two-Dimensional Automatic SSA Domain Multi-Modal Deep Neural Network for Detection of COVID-19 From Lung Ultrasound Images." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2703-6.ch012.

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This chapter proposes an image decomposition-based multi-modal deep convolutional neural network (CNN) for the automated detection of COVID-19 using ultrasound images. The two-dimensional (2D) automatic-singular spectral analysis (Auto-SSA) is introduced to decompose ultrasound images into four modes or sub-images. The obtained modes are then used as input to the proposed multi-modal CNN model for COVID-19 detection. The performance of the proposed model is assessed on a dataset consisting of 3710 ultrasound images. The classification schemes such as COVID-19 versus pneumonia versus other ailm
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Knoll, Benjamin C., Melissa Gunderson, Geetanjali Rajamani, et al. "Advanced Care Planning Content Encoding with Natural Language Processing." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti231037.

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While advanced care planning (ACP) is an essential practice for ensuring patient-centered care, its adoption remains poor and the completeness of its documentation variable. Natural language processing (NLP) approaches hold promise for supporting ACP, including its use for decision support to improve ACP gaps at the point of care. ACP themes were annotated on palliative care notes across four annotators (Fleiss kappa = 0.753) and supervised models trained (Huggingface models bert-base-uncased and Bio_ClinicalBERT) using 5-fold cross validation (F1=0.8, precision=0.75, recall=0.86, any theme).
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Ajčević, Miloš, Aleksandar Miladinović, Giovanni Furlanis, et al. "Wake-up Stroke Outcome Prediction by Interpretable Decision Tree Model." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220527.

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Outcome prediction in wake-up ischemic stroke (WUS) is important for guiding treatment strategies, in order to improve recovery and minimize disability. We aimed at producing an interpretable model to predict a good outcome (NIHSS 7-day<5) in thrombolysis treated WUS patients by using Classification and Regression Tree (CART) method. The study encompassed 104 WUS patients and we used a dataset consisting of demographic, clinical and neuroimaging features. The model was produced by CART with Gini split criterion and evaluated by using 5-fold cross-validation. The produced decision tree model
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Xie, Hanshuang, Jiayi Yan, Huaiyu Zhu, et al. "Unreadable Segment Recognition of Single-Lead ECG Signals Based on XGBoost: Fuision of Shannon Energy Envelope and Empirical Mode Decomposition." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210301.

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The quality of ECG signals is commonly affected by severe noise, especially for the single-lead ECG signals acquired from long-term wearable devices. Recognizing and ignoring these interfered signals can reduce the error rate of automatic ECG analysis system, and in addition, improve the efficiency of cardiologists. Based on XGBoost classifier, we propose an unreadable ECG segment recognition method using features extracted through Shannon Energy Envelope (SEE) and Empirical Mode Decomposition (EMD). An unreadable CarePatchTM ECG patch database is established, containing 8169 readable segments
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Coskun, Huseyin. "AI-Based Mobile Application for Personalized Monitoring of Healthy Sitting Posture." In Advances in Healthcare Information Systems and Administration. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7858-8.ch001.

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In modern workplaces, many individuals spend extended periods sitting, often in positions not recommended by health professionals, which can lead to skeletal, back, muscle, and heart issues. This study developed a mobile application that monitors sitting postures based on health guidelines. Using a smart seat cover embedded with electro-textile sensors from previous research, sitting experiments were conducted to assess posture. Participants were first instructed in healthy posture, then asked to sit as they preferred, including the healthy posture, for 5 minutes. The recorded data were catego
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Conference papers on the topic "5-fold cross validation"

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Dey, Lipika, B. Naik, Oppangi Poojita, and Kovidh Pothireddi. "SMM4H 2024: 5 Fold Cross Validation for Classification of tweets reporting children’s disorders." In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.smm4h-1.12.

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Woollam, Richard C., Ethan Proudlove, Michael Jones, Harvey M. Thompson, and Richard Barker. "Symbolic Regression Based Surrogate Modelling of a High-Fidelity Multiphysics CO2 Corrosion Model." In CONFERENCE 2025. AMPP, 2025. https://doi.org/10.5006/c2025-00433.

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Abstract A high-fidelity mechanistic carbon steel/carbon dioxide (CO2) corrosion prediction model developed at the University of Leeds has been implemented in the multi-physics (MP) software package COMSOL. The model integrates the bulk solution equilibria, interfacial mass transport and solution reactions, and surface anodic and cathodic electrochemical processes. In this work, the high-fidelity model is randomly sampled in the 5-input dimensions (bulk pH, temperature, CO2 partial pressure, piping velocity and diameter) using a Beta distribution, B(α, β), with the parameters α and β set to 12
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Sari, Indah Purnama, Al-Khowarizmi, Fanny Ramadhani, Andy Satria, and Oris Krianto Sulaiman. "Leukocoria Identification: A 5-Fold Cross Validation CNN and Adaboost Hybrid Approach." In 2023 6th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2023. http://dx.doi.org/10.1109/isriti60336.2023.10467242.

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Satria, Andy, Opim Salim Sitompul, and Herman Mawengkang. "5-Fold Cross Validation on Supporting K-Nearest Neighbour Accuration of Making Consimilar Symptoms Disease Classification." In 2021 International Conference on Computer Science and Engineering (IC2SE). IEEE, 2021. http://dx.doi.org/10.1109/ic2se52832.2021.9792094.

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Pal, Kaushika, and Biraj V. Patel. "Data Classification with k-fold Cross Validation and Holdout Accuracy Estimation Methods with 5 Different Machine Learning Techniques." In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2020. http://dx.doi.org/10.1109/iccmc48092.2020.iccmc-00016.

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Vogado, Luis H. S., Rodrigo M. S. Veras, and Kelson R. T. Aires. ""LeukNet" - A Model of Convolutional Neural Network for the Diagnosis of Leukemia." In Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.12993.

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Leukemia is a disorder that affects the bone marrow, causing uncontrolled production of leukocytes, impairing the transport of oxygen and causing blood coagulation problems. In this article, we propose a new computational tool, named LeukNet, a Convolutional Neural Network (CNN) architecture based on the VGG-16 convolutional blocks, to facilitate the leukemia diagnosis from blood smear images. We evaluated different architectures and fine-tuning methods using 18 datasets containing 3536 images with distinct characteristics of color, texture, contrast, and resolution. Additionally, data augment
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Grimaldi, Nicolas, David Kaber, Ryan McKendrick, and Yunmei Liu. "Deep Learning Forecast of Perceptual Load Using fNIRS Data." In 2024 AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2024 Hawaii Edition). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005563.

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In this paper, we present a novel approach to forecasting perceptual load in demanding piloting tasks, based on neurophysiological response data. We introduce a forecasting framework using a multinomial classification model paired with deep learning sequence-to-sequence models. The study compared the performance of seven different deep learning models, including GRU, Transformer, and linear models with a 10s outlook against a statical model benchmark. For analysis and validation purposes, the dataset was first split into training and testing sets, and the training set was further used to perfo
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Lima, Lucas, and Marcelo Oliveira. "Using Deep Learning for Classification of Early Lung Nodules on Computed Tomography Images." In XVIII Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbcas.2018.3681.

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Lung cancer is the leading cause of cancer mortality, accounting for approximately 20% of all cancer-related deaths. Nevertheless, despite the development of new therapeutic agents and technologies, only 16% of lung cancer patients are diagnosed at early stages. Therefore, to diagnose in early stages, when the nodules are very small, is a complex task for specialists and presents some challenges. To assist the specialists, the main purpose of this work is to propose the use of Deep Learning to classify 25,200 small pulmonary nodules balanced with diameter 5-10mm. The result was of 0.992 (+/- 0
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Wang, Xiaowei, and YeongAe Heo. "Artificial Neural Network Surrogate Modeling for Offshore Wind Turbines Under Multi-Hazards." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-81048.

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Abstract Surrogate modeling of offshore wind turbines (OWTs) under multiple hazards such as wind, wave, and earthquakes provides an expedient path toward rapid risk assessment and management of these structures. This paper explores the applicability of artificial neural network (NN) for the surrogate modeling using a synthetic database based on finite element analyses of a typical OWT under combined loading conditions due to winds, waves, and earthquakes. A multi-layer NN with multiple output variables that can effectively capture the OWT’s multi-hazard behavior is developed. Considered input
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Fitipaldi, Camila, Rodrigo Monteiro, Diego Pinheiro, Waldemar Ferreira, Liliane Fonseca, and Andrea Maria Ribeiro. "Forecasting of Wind Power Generation Cristalândia wind farm using Tree-Based Machine Learning Approaches." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2023. http://dx.doi.org/10.21528/cbic2023-028.

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Wind power has gained increasing attention as a rapidly growing source of sustainable electricity generation. As variable renewable energy, however, its reliability, stability, and efficiency depend on factors such as wind speeds, air density, and turbine characteristics. As a result, an effective energy management strategy requires the accurate forecasting of wind power generation. Machine learning approaches have been applied to forecasting wind power generation, but their proper fine-tuning is still not fully understood. In this work, we trained using 5-fold cross-validation and fine-tuned
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