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Relevant bibliographies by topics / LIMITED DATASET

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Journal articles

Academic literature on the topic 'LIMITED DATASET'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "LIMITED DATASET"

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Gusarova, Nataliya, Artem Lobantsev, Aleksandra Vatian, et al. "Generative augmentation to improve lung nodules detection in resource-limited settings." Information and Control Systems, no. 6 (December 15, 2020): 60–69. http://dx.doi.org/10.31799/1684-8853-2020-6-60-69.

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Introduction: Lung cancer is one of the most formidable cancers. The use of neural networks technologies in its diagnostics is promising, but the datasets collected from real clinical practice cannot cover a variety of lung cancer manifestations. Purpose: Assessment of the possibility of improving the classification of pulmonary nodules by means of generative augmentation of available datasets under resource constraints. Methods: We used part of LIDC-IDRI dataset, the StyleGAN architecture for generating artificial lung nodules and the VGG11 model as a classifier. We generated pulmonary nodule

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Sarwati Rahayu, Sulis Sandiwarno, Erwin Dwika Putra, Marissa Utami, and Hadiguna Setiawan. "Model Sequential Resnet50 Untuk Pengenalan Tulisan Tangan Aksara Arab." JSAI (Journal Scientific and Applied Informatics) 6, no. 2 (2023): 234–41. http://dx.doi.org/10.36085/jsai.v6i2.5379.

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Research for Arabic handwriting recognition is still limited. The number of public datasets regarding Arabic script is still limited for this type of public dataset. Therefore, each study usually uses its dataset to conduct research. However, recently public datasets have become available and become research opportunities to compare methods with the same dataset. This study aimed to determine the implementation of the transfer learning model with the best accuracy for handwriting recognition in Arabic script. The results of the experiment using ResNet50 are as follows: training accuracy is 91.

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Mohammad Alfadli, Khadijah, and Alaa Omran Almagrabi. "Feature-Limited Prediction on the UCI Heart Disease Dataset." Computers, Materials & Continua 74, no. 3 (2023): 5871–83. http://dx.doi.org/10.32604/cmc.2023.033603.

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Ko, Yu-Chieh, Wei-Shiang Chen, Hung-Hsun Chen, et al. "Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets." Biomedicines 10, no. 6 (2022): 1314. http://dx.doi.org/10.3390/biomedicines10061314.

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Automated glaucoma detection using deep learning may increase the diagnostic rate of glaucoma to prevent blindness, but generalizable models are currently unavailable despite the use of huge training datasets. This study aims to evaluate the performance of a convolutional neural network (CNN) classifier trained with a limited number of high-quality fundus images in detecting glaucoma and methods to improve its performance across different datasets. A CNN classifier was constructed using EfficientNet B3 and 944 images collected from one medical center (core model) and externally validated using

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Guo, Runze, Bei Sun, Xiaotian Qiu, Shaojing Su, Zhen Zuo, and Peng Wu. "Fine-Grained Recognition of Surface Targets with Limited Data." Electronics 9, no. 12 (2020): 2044. http://dx.doi.org/10.3390/electronics9122044.

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Recognition of surface targets has a vital influence on the development of military and civilian applications such as maritime rescue patrols, illegal-vessel screening, and maritime operation monitoring. However, owing to the interference of visual similarity and environmental variations and the lack of high-quality datasets, accurate recognition of surface targets has always been a challenging task. In this paper, we introduce a multi-attention residual model based on deep learning methods, in which channel and spatial attention modules are applied for feature fusion. In addition, we use tran

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Gaikwad, Mayur, Swati Ahirrao, Shraddha Phansalkar, Ketan Kotecha, and Shalli Rani. "Multi-Ideology, Multiclass Online Extremism Dataset, and Its Evaluation Using Machine Learning." Computational Intelligence and Neuroscience 2023 (March 1, 2023): 1–33. http://dx.doi.org/10.1155/2023/4563145.

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Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check

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Huč, Aleks, Jakob Šalej, and Mira Trebar. "Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices." Sensors 21, no. 14 (2021): 4946. http://dx.doi.org/10.3390/s21144946.

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The Internet of Things (IoT) consists of small devices or a network of sensors, which permanently generate huge amounts of data. Usually, they have limited resources, either computing power or memory, which means that raw data are transferred to central systems or the cloud for analysis. Lately, the idea of moving intelligence to the IoT is becoming feasible, with machine learning (ML) moved to edge devices. The aim of this study is to provide an experimental analysis of processing a large imbalanced dataset (DS2OS), split into a training dataset (80%) and a test dataset (20%). The training da

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Muniraj, Inbarasan, Changliang Guo, Ra'ed Malallah, Harsha Vardhan R. Maraka, James P. Ryle, and John T. Sheridan. "Subpixel based defocused points removal in photon-limited volumetric dataset." Optics Communications 387 (March 2017): 196–201. http://dx.doi.org/10.1016/j.optcom.2016.11.047.

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Shin, Changho, Seungeun Rho, Hyoseop Lee, and Wonjong Rhee. "Data Requirements for Applying Machine Learning to Energy Disaggregation." Energies 12, no. 9 (2019): 1696. http://dx.doi.org/10.3390/en12091696.

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Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not adequate for NILM research. In this study, we report the findings from a newly collected dataset that contains 10 Hz sampling data for 58 houses. The dataset not only contains the aggregate measurements, but also individual appliance measurements for three types of appliances. B

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Althnian, Alhanoof, Duaa AlSaeed, Heyam Al-Baity, et al. "Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain." Applied Sciences 11, no. 2 (2021): 796. http://dx.doi.org/10.3390/app11020796.

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Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performanc

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