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Artykuły w czasopismach na temat „LIMITED DATASET”

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Data publikacji: 11 września 2023

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1

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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Meier, Deborah, and Wolfgang Tschacher. "Beyond Dyadic Coupling: The Method of Multivariate Surrogate Synchrony (mv-SUSY)." Entropy 23, no. 11 (2021): 1385. http://dx.doi.org/10.3390/e23111385.

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Measuring interpersonal synchrony is a promising approach to assess the complexity of social interaction, which however has been mostly limited to dyads. In this study, we introduce multivariate Surrogate Synchrony (mv-SUSY) to extend the current set of computational methods. Methods: mv-SUSY was applied to eight datasets consisting of 10 time series each, all with n = 9600 observations. Datasets 1 to 5 consist of simulated time series with the following characteristics: white noise (dataset 1), non-stationarity with linear time trends (dataset 2), autocorrelation (dataset 3), oscillation (dat

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Dinh, Thi Lan Anh, and Filipe Aires. "Nested leave-two-out cross-validation for the optimal crop yield model selection." Geoscientific Model Development 15, no. 9 (2022): 3519–35. http://dx.doi.org/10.5194/gmd-15-3519-2022.

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Abstract. The use of statistical models to study the impact of weather on crop yield has not ceased to increase. Unfortunately, this type of application is characterized by datasets with a very limited number of samples (typically one sample per year). In general, statistical inference uses three datasets: the training dataset to optimize the model parameters, the validation dataset to select the best model, and the testing dataset to evaluate the model generalization ability. Splitting the overall database into three datasets is often impossible in crop yield modelling due to the limited numb

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de Rouw, Nikki, Sabine Visser, Stijn L. W. Koolen, et al. "A limited sampling schedule to estimate individual pharmacokinetics of pemetrexed in patients with varying renal functions." Cancer Chemotherapy and Pharmacology 85, no. 1 (2019): 231–35. http://dx.doi.org/10.1007/s00280-019-04006-x.

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Abstract Purpose Pemetrexed is a widely used cytostatic agent with an established exposure–response relationship. Although dosing is based on body surface area (BSA), large interindividual variability in pemetrexed plasma concentrations is observed. Therapeutic drug monitoring (TDM) can be a feasible strategy to reduce variability in specific cases leading to potentially optimized pemetrexed treatment. The aim of this study was to develop a limited sampling schedule (LSS) for the assessment of pemetrexed pharmacokinetics. Methods Based on two real-life datasets, several limited sampling design

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Abdulraheem, Abdulkabir, Jamiu T. Suleiman, and Im Y. Jung. "Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies." Electronics 12, no. 17 (2023): 3668. http://dx.doi.org/10.3390/electronics12173668.

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Accurate recognition of characters imprinted on ship bodies is essential for ensuring operational efficiency, safety, and security in the maritime industry. However, the limited availability of datasets of specialized digits and characters poses a challenge. To overcome this challenge, we propose a generative adversarial network (GAN) model for augmenting the limited dataset of special digits and characters in ship markings. We evaluated the performance of various GAN models, and the Wasserstein GAN with Gradient Penalty (WGAN-GP) and Wasserstein GAN with divergence (WGANDIV) models demonstrat

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Xu, Xinkai, Hailan Zhang, Yan Ma, Kang Liu, Hong Bao, and Xu Qian. "TranSDet: Toward Effective Transfer Learning for Small-Object Detection." Remote Sensing 15, no. 14 (2023): 3525. http://dx.doi.org/10.3390/rs15143525.

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Small-object detection is a challenging task in computer vision due to the limited training samples and low-quality images. Transfer learning, which transfers the knowledge learned from a large dataset to a small dataset, is a popular method for improving performance on limited data. However, we empirically find that due to the dataset discrepancy, directly transferring the model trained on a general object dataset to small-object datasets obtains inferior performance. In this paper, we propose TranSDet, a novel approach for effective transfer learning for small-object detection. Our method ad

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Tran, Thi-Dung, Junghee Kim, Ngoc-Huynh Ho, et al. "Stress Analysis with Dimensions of Valence and Arousal in the Wild." Applied Sciences 11, no. 11 (2021): 5194. http://dx.doi.org/10.3390/app11115194.

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In the field of stress recognition, the majority of research has conducted experiments on datasets collected from controlled environments with limited stressors. As these datasets cannot represent real-world scenarios, stress identification and analysis are difficult. There is a dire need for reliable, large datasets that are specifically acquired for stress emotion with varying degrees of expression for this task. In this paper, we introduced a dataset for Stress Analysis with Dimensions of Valence and Arousal of Korean Movie in Wild (SADVAW), which includes video clips with diversity in faci

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Kadam, Kalyani Dhananjay, Swati Ahirrao, and Ketan Kotecha. "Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing." Data 6, no. 10 (2021): 102. http://dx.doi.org/10.3390/data6100102.

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Image forgery has grown in popularity due to easy access to abundant image editing software. These forged images are so devious that it is impossible to predict with the naked eye. Such images are used to spread misleading information in society with the help of various social media platforms such as Facebook, Twitter, etc. Hence, there is an urgent need for effective forgery detection techniques. In order to validate the credibility of these techniques, publically available and more credible standard datasets are required. A few datasets are available for image splicing, such as Columbia, Car

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Shi, Zhengxiang, Qiang Zhang, and Aldo Lipani. "StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11321–29. http://dx.doi.org/10.1609/aaai.v36i10.21383.

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Inferring spatial relations in natural language is a crucial ability an intelligent system should possess. The bAbI dataset tries to capture tasks relevant to this domain (task 17 and 19). However, these tasks have several limitations. Most importantly, they are limited to fixed expressions, they are limited in the number of reasoning steps required to solve them, and they fail to test the robustness of models to input that contains irrelevant or redundant information. In this paper, we present a new Question-Answering dataset called StepGame for robust multi-step spatial reasoning in texts. O

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Wang, Hao, Suxing Lyu, and Yaxin Ren. "Paddy Rice Imagery Dataset for Panicle Segmentation." Agronomy 11, no. 8 (2021): 1542. http://dx.doi.org/10.3390/agronomy11081542.

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Accurate panicle identification is a key step in rice-field phenotyping. Deep learning methods based on high-spatial-resolution images provide a high-throughput and accurate solution of panicle segmentation. Panicle segmentation tasks require costly annotations to train an accurate and robust deep learning model. However, few public datasets are available for rice-panicle phenotyping. We present a semi-supervised deep learning model training process, which greatly assists the annotation and refinement of training datasets. The model learns the panicle features with limited annotations and loca

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Chang, Yingxiu, Yongqiang Cheng, John Murray, Shi Huang, and Guangyi Shi. "The HDIN Dataset: A Real-World Indoor UAV Dataset with Multi-Task Labels for Visual-Based Navigation." Drones 6, no. 8 (2022): 202. http://dx.doi.org/10.3390/drones6080202.

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Supervised learning for Unmanned Aerial Vehicle (UAVs) visual-based navigation raises the need for reliable datasets with multi-task labels (e.g., classification and regression labels). However, current public datasets have limitations: (a) Outdoor datasets have limited generalization capability when being used to train indoor navigation models; (b) The range of multi-task labels, especially for regression tasks, are in different units which require additional transformation. In this paper, we present a Hull Drone Indoor Navigation (HDIN) dataset to improve the generalization capability for in

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Sahiner, Berkman, Heang-Ping Chan, and Lubomir Hadjiiski. "Classifier performance prediction for computer-aided diagnosis using a limited dataset." Medical Physics 35, no. 4 (2008): 1559–70. http://dx.doi.org/10.1118/1.2868757.

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Yu, Zhou, Dejing Xu, Jun Yu, et al. "ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9127–34. http://dx.doi.org/10.1609/aaai.v33i01.33019127.

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Recent developments in modeling language and vision have been successfully applied to image question answering. It is both crucial and natural to extend this research direction to the video domain for video question answering (VideoQA). Compared to the image domain where large scale and fully annotated benchmark datasets exists, VideoQA datasets are limited to small scale and are automatically generated, etc. These limitations restrict their applicability in practice. Here we introduce ActivityNet-QA, a fully annotated and large scale VideoQA dataset. The dataset consists of 58,000 QA pairs on

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AL-Banna, Alaa Ahmed, and Abeer K. AL-Mashhadany. "Natural Language Processing For Automatic text summarization [Datasets] - Survey." Wasit Journal of Computer and Mathematics Science 1, no. 4 (2022): 156–70. http://dx.doi.org/10.31185/wjcm.72.

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Natural language processing has developed significantly recently, which has progressed the text summarization task. It is no longer limited to reducing the text size or obtaining helpful information from a long document only. It has begun to be used in getting answers from summarization, measuring the quality of sentiment analysis systems, research and mining techniques, document categorization, and natural language Inference, which increased the importance of scientific research to get a good summary. This paper reviews the most used datasets in text summarization in different languages and t

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Pasunuru, Ramakanth, Asli Celikyilmaz, Michel Galley, et al. "Data Augmentation for Abstractive Query-Focused Multi-Document Summarization." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 15 (2021): 13666–74. http://dx.doi.org/10.1609/aaai.v35i15.17611.

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The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets. We present two QMDS training datasets, which we construct using two data augmentation methods: (1) transferring the commonly used single-document CNN/Daily Mail summarization dataset to create the QMDSCNN dataset, and (2) mining search-query logs to create the QMDSIR dataset. These two datasets have complementary properties, i.e., QMDSCNN has real summaries but queries are simulated, while QMDSIR has real queries but simulated summaries. To cov

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Duong, Huu-Thanh, Tram-Anh Nguyen-Thi, and Vinh Truong Hoang. "Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks." Complexity 2022 (June 30, 2022): 1–14. http://dx.doi.org/10.1155/2022/3188449.

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The annotated dataset is an essential requirement to develop an artificial intelligence (AI) system effectively and expect the generalization of the predictive models and to avoid overfitting. Lack of the training data is a big barrier so that AI systems can broaden in several domains which have no or missing training data. Building these datasets is a tedious and expensive task and depends on the domains and languages. This is especially a big challenge for low-resource languages. In this paper, we experiment and evaluate many various approaches on sentiment analysis problems so that they can

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Sun, Cunwei, Yuxin Yang, Chang Wen, Kai Xie, and Fangqing Wen. "Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning." Sensors 18, no. 7 (2018): 2399. http://dx.doi.org/10.3390/s18072399.

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The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint

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Hussain, Altaf, and Muhammad Aleem. "GoCJ: Google Cloud Jobs Dataset for Distributed and Cloud Computing Infrastructures." Data 3, no. 4 (2018): 38. http://dx.doi.org/10.3390/data3040038.

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Developers of resource-allocation and scheduling algorithms share test datasets (i.e., benchmarks) to enable others to compare the performance of newly developed algorithms. However, mostly it is hard to acquire real cloud datasets due to the users’ data confidentiality issues and policies maintained by Cloud Service Providers (CSP). Accessibility of large-scale test datasets, depicting the realistic high-performance computing requirements of cloud users, is very limited. Therefore, the publicly available real cloud dataset will significantly encourage other researchers to compare and benchmar

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Lee, JoonHo, Joonseok Lee, Sooah Cho, et al. "Development of Decision Support Software for Deep Learning-Based Automated Retinal Disease Screening Using Relatively Limited Fundus Photograph Data." Electronics 10, no. 2 (2021): 163. http://dx.doi.org/10.3390/electronics10020163.

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Purpose—This study was conducted to develop an automated detection algorithm for screening fundus abnormalities, including age-related macular degeneration (AMD), diabetic retinopathy (DR), epiretinal membrane (ERM), retinal vascular occlusion (RVO), and suspected glaucoma among health screening program participants. Methods—The development dataset consisted of 43,221 retinal fundus photographs (from 25,564 participants, mean age 53.38 ± 10.97 years, female 39.0%) from a health screening program and patients of the Kangbuk Samsung Hospital Ophthalmology Department from 2006 to 2017. We evaluat

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Bhattacharya, Ayan. "Posteriors in Limited Time." AppliedMath 2, no. 4 (2022): 700–710. http://dx.doi.org/10.3390/appliedmath2040041.

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This paper obtains a measure-theoretic restriction that must be satisfied by a prior probability measure for posteriors to be computed in limited time. Specifically, it is shown that the prior must be factorizable. Factorizability is a set of independence conditions for events in the sample space that allows agents to calculate posteriors using only a subset of the dataset. The result has important implications for models in mathematical economics and finance that rely on a common prior. If one introduces the limited time restriction to Aumann’s famous Agreeing to Disagree setup, one sees that

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Chen, Tingkai, Ning Wang, Rongfeng Wang, Hong Zhao, and Guichen Zhang. "One-stage CNN detector-based benthonic organisms detection with limited training dataset." Neural Networks 144 (December 2021): 247–59. http://dx.doi.org/10.1016/j.neunet.2021.08.014.

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Ahmed, Nehal K., Elsayed E. Hemayed, and Magda B. Fayek. "Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources." Big Data and Cognitive Computing 4, no. 3 (2020): 19. http://dx.doi.org/10.3390/bdcc4030019.

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In this paper, we propose an unconstrained face verification approach that is dependent on Hybrid Siamese architecture under limited resources. The general face verification trend suggests that larger training datasets and/or complex architectures lead to higher accuracy. The proposed approach tends to achieve high accuracy while using a small dataset and a simple architecture by directly learn face’s similarity/dissimilarity from raw face pixels, which is critical for various applications. The proposed architecture has two branches; the first part of these branches is trained independently, w

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Zuo, Mei, and Yang Zhang. "Dataset-aware multi-task learning approaches for biomedical named entity recognition." Bioinformatics 36, no. 15 (2020): 4331–38. http://dx.doi.org/10.1093/bioinformatics/btaa515.

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Abstract Motivation Named entity recognition is a critical and fundamental task for biomedical text mining. Recently, researchers have focused on exploiting deep neural networks for biomedical named entity recognition (Bio-NER). The performance of deep neural networks on a single dataset mostly depends on data quality and quantity while high-quality data tends to be limited in size. To alleviate task-specific data limitation, some studies explored the multi-task learning (MTL) for Bio-NER and achieved state-of-the-art performance. However, these MTL methods did not make full use of information

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Kim, Minjeong, Yujung Gil, Yuyeon Kim, and Jihie Kim. "Deep-Learning-Based Scalp Image Analysis Using Limited Data." Electronics 12, no. 6 (2023): 1380. http://dx.doi.org/10.3390/electronics12061380.

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The World Health Organization and Korea National Health Insurance assert that the number of alopecia patients is increasing every year, and approximately 70 percent of adults suffer from scalp problems. Although alopecia is a genetic problem, it is difficult to diagnose at an early stage. Although deep-learning-based approaches have been effective for medical image analyses, it is challenging to generate deep learning models for alopecia detection and analysis because creating an alopecia image dataset is challenging. In this paper, we present an approach for generating a model specialized for

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Demuynck, Thomas, and Christian Seel. "Revealed Preference with Limited Consideration." American Economic Journal: Microeconomics 10, no. 1 (2018): 102–31. http://dx.doi.org/10.1257/mic.20150343.

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We derive revealed preference tests for models where individuals use consideration sets to simplify their consumption problem. Our basic test provides necessary and sufficient conditions for consistency of observed choices with the existence of consideration set restrictions. The same conditions can also be derived from a model in which the consideration set formation is endogenous and based on subjective prices. By imposing restrictions on these subjective prices, we obtain additional refined revealed preference tests. We illustrate and compare the performance of our tests by means of a datas

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Perera, Asanka G., Yee Wei Law, and Javaan Chahl. "Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition." Drones 3, no. 4 (2019): 82. http://dx.doi.org/10.3390/drones3040082.

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Aerial human action recognition is an emerging topic in drone applications. Commercial drone platforms capable of detecting basic human actions such as hand gestures have been developed. However, a limited number of aerial video datasets are available to support increased research into aerial human action analysis. Most of the datasets are confined to indoor scenes or object tracking and many outdoor datasets do not have sufficient human body details to apply state-of-the-art machine learning techniques. To fill this gap and enable research in wider application areas, we present an action reco

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Moon, Myungjin, and Kenta Nakai. "Integrative analysis of gene expression and DNA methylation using unsupervised feature extraction for detecting candidate cancer biomarkers." Journal of Bioinformatics and Computational Biology 16, no. 02 (2018): 1850006. http://dx.doi.org/10.1142/s0219720018500063.

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Currently, cancer biomarker discovery is one of the important research topics worldwide. In particular, detecting significant genes related to cancer is an important task for early diagnosis and treatment of cancer. Conventional studies mostly focus on genes that are differentially expressed in different states of cancer; however, noise in gene expression datasets and insufficient information in limited datasets impede precise analysis of novel candidate biomarkers. In this study, we propose an integrative analysis of gene expression and DNA methylation using normalization and unsupervised fea

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Batanović, Vuk, Miloš Cvetanović, and Boško Nikolić. "A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts." PLOS ONE 15, no. 11 (2020): e0242050. http://dx.doi.org/10.1371/journal.pone.0242050.

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Choosing a comprehensive and cost-effective way of articulating and annotating the sentiment of a text is not a trivial task, particularly when dealing with short texts, in which sentiment can be expressed through a wide variety of linguistic and rhetorical phenomena. This problem is especially conspicuous in resource-limited settings and languages, where design options are restricted either in terms of manpower and financial means required to produce appropriate sentiment analysis resources, or in terms of available language tools, or both. In this paper, we present a versatile approach to ad

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Wang, Xiao, Zheng Wang, Toshihiko Yamasaki, and Wenjun Zeng. "Very Important Person Localization in Unconstrained Conditions: A New Benchmark." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 2809–16. http://dx.doi.org/10.1609/aaai.v35i4.16386.

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This paper presents a new high-quality dataset for Very Important Person Localization (VIPLoc), named Unconstrained-7k. Generally, current datasets: 1) are limited in scale; 2) built under simple and constrained conditions, where the number of disturbing non-VIPs is not large, the scene is relatively simple, and the face of VIP is always in frontal view and salient. To tackle these problems, the proposed Unconstrained-7k dataset is featured in two aspects. First, it contains over 7,000 annotated images, making it the largest VIPLoc dataset under unconstrained conditions to date. Second, our da

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Mostofi, Fatemeh, Vedat Toğan, and Hasan Basri Başağa. "Real-estate price prediction with deep neural network and principal component analysis." Organization, Technology and Management in Construction: an International Journal 14, no. 1 (2022): 2741–59. http://dx.doi.org/10.2478/otmcj-2022-0016.

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Abstract Despite the wide application of deep neural networks (DNN) models, their application over small-sized real-estate price prediction is limited due to the reduced prediction accuracy and the high-dimensionality of the dataset. This study motivates small-sized real-estate agencies to take DNN-driven decisions using the available local dataset. To improve the high-dimensionality of real-estate price datasets and thus enhance the price-prediction accuracy of a DNN model, this paper adopts principal component analysis (PCA). The PCA benefits in improving the prediction accuracy of a DNN mod

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Abo Zidan, Rawan, and George Karraz. "Gaussian Pyramid for Nonlinear Support Vector Machine." Applied Computational Intelligence and Soft Computing 2022 (May 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/5255346.

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Support vector machine (SVM) is one of the most efficient machine learning tools, and it is fast, simple to use, reliable, and provides accurate classification results. Despite its generalization capability, SVM is usually posed as a quadratic programming (QP) problem to find a separation hyperplane in nonlinear cases. This needs huge quantities of computational time and memory for large datasets, even for moderately sized ones. SVM could be used for classification tasks whose number of samples is limited but does not scale well to large datasets. The idea is to solve this problem by a smoothi

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Vobecký, Antonín, David Hurych, Michal Uřičář, Patrick Pérez, and Josef Sivic. "Artificial Dummies for Urban Dataset Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (2021): 2692–700. http://dx.doi.org/10.1609/aaai.v35i3.16373.

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Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are too difficult to capture due to safety reasons, or they are very unlikely to happen. The strict safety requirements in assisted and autonomous driving applications call for an extra high detection accuracy also in these rare situations. Having the ability to generate people images in arbitrary poses, with arbitrary appearances and embedded in different background scenes with varying illumination and weather conditio

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Deng, Fei, Shengliang Pu, Xuehong Chen, Yusheng Shi, Ting Yuan, and Shengyan Pu. "Hyperspectral Image Classification with Capsule Network Using Limited Training Samples." Sensors 18, no. 9 (2018): 3153. http://dx.doi.org/10.3390/s18093153.

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Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained

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Tachibana, Rie, Janne J. Näppi, Toru Hironaka, and Hiroyuki Yoshida. "Self-Supervised Adversarial Learning with a Limited Dataset for Electronic Cleansing in Computed Tomographic Colonography: A Preliminary Feasibility Study." Cancers 14, no. 17 (2022): 4125. http://dx.doi.org/10.3390/cancers14174125.

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Existing electronic cleansing (EC) methods for computed tomographic colonography (CTC) are generally based on image segmentation, which limits their accuracy to that of the underlying voxels. Because of the limitations of the available CTC datasets for training, traditional deep learning is of limited use in EC. The purpose of this study was to evaluate the technical feasibility of using a novel self-supervised adversarial learning scheme to perform EC with a limited training dataset with subvoxel accuracy. A three-dimensional (3D) generative adversarial network (3D GAN) was pre-trained to per

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Jin, Ye-Ji, Erkinov Habibilloh, Ye-Seul Jang, et al. "A Photoplethysmogram Dataset for Emotional Analysis." Applied Sciences 12, no. 13 (2022): 6544. http://dx.doi.org/10.3390/app12136544.

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In recent years, research on emotion classification based on physiological signals has actively attracted scholars’ attention worldwide. Several studies and experiments have been conducted to analyze human emotions based on physiological signals, including the use of electrocardiograms (ECGs), electroencephalograms (EEGs), and photoplethysmograms (PPGs). Although the achievements with ECGs and EEGs are progressive, reaching higher accuracies over 90%, the number of studies utilizing PPGs are limited and their accuracies are relatively lower than other signals. One of the difficulties in studyi

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Tomaszewski, Michał, Paweł Michalski, and Jakub Osuchowski. "Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset." Applied Sciences 10, no. 6 (2020): 2104. http://dx.doi.org/10.3390/app10062104.

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This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural network

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Majid, Haneen, and Khawla Ali. "Expanding New Covid-19 Data with Conditional Generative Adversarial Networks." Iraqi Journal for Electrical and Electronic Engineering 18, no. 1 (2022): 103–10. http://dx.doi.org/10.37917/ijeee.18.1.12.

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COVID-19 is an infectious viral disease that mostly affects the lungs. That quickly spreads across the world. Early detection of the virus boosts the chances of patients recovering quickly worldwide. Many radiographic techniques are used to diagnose an infected person such as X-rays, deep learning technology based on a large amount of chest x-ray images is used to diagnose COVID-19 disease. Because of the scarcity of available COVID-19 X-rays image, the limited COVID-19 Datasets are insufficient for efficient deep learning detection models. Another problem with a limited dataset is that traini

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Pushpanathan, Kalananthni, Marsyita Hanafi, Syamsiah Masohor, and Wan Fazilah Fazlil Ilahi. "MYLPHerb-1: A Dataset of Malaysian Local Perennial Herbs for the Study of Plant Images Classification under Uncontrolled Environment." Pertanika Journal of Science and Technology 30, no. 1 (2022): 413–31. http://dx.doi.org/10.47836/pjst.30.1.23.

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Research in the medicinal plants’ recognition field has received great attention due to the need of producing a reliable and accurate system that can recognise medicinal plants under various imaging conditions. Nevertheless, the standard medicinal plant datasets publicly available for research are very limited. This paper proposes a dataset consisting of 34200 images of twelve different high medicinal value local perennial herbs in Malaysia. The images were captured under various imaging conditions, such as different scales, illuminations, and angles. It will enable larger interclass and intra

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YU, HUI, KANG TU, LU XIE, and YUAN-YUAN LI. "DIGOUT: VIEWING DIFFERENTIAL EXPRESSION GENES AS OUTLIERS." Journal of Bioinformatics and Computational Biology 08, supp01 (2010): 161–75. http://dx.doi.org/10.1142/s0219720010005208.

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With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate

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Hemedan, Ahmed Abdelmonem, Aboul Ella Hassanien, Mohamed Hamed N. Taha, and Nour Eldeen Mahmoud Khalifa. "Deep bacteria: robust deep learning data augmentation design for limited bacterial colony dataset." International Journal of Reasoning-based Intelligent Systems 11, no. 3 (2019): 256. http://dx.doi.org/10.1504/ijris.2019.10023444.

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Khalifa, Nour Eldeen Mahmoud, Mohamed Hamed N. Taha, Aboul Ella Hassanien, and Ahmed Abdelmonem Hemedan. "Deep bacteria: robust deep learning data augmentation design for limited bacterial colony dataset." International Journal of Reasoning-based Intelligent Systems 11, no. 3 (2019): 256. http://dx.doi.org/10.1504/ijris.2019.102610.

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