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This research focuses on developing a method to optimize the DCNN (Deep Convolutional Neural Network) classification model for plant diseases. We enriched the data by incorporating data from two public datasets, PlantVillage Dataset (PVD) and CroppedPlant Dataset (CPD), and we trained the model using two-step transfer learning.
6p
vithomson
02-07-2024
0
0
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In this paper, we study the efficiency of Graph Transformer Network for noisy label propagation in the task of classifying video anomaly actions. Given a weak supervised dataset, our methods focus on improving the quality of generated labels and use the labels for training a video classifier with deep network.
11p
viambani
18-06-2024
1
1
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The research "Transforming tourism experiences: Unleashing the power of ICTA - An intelligent chatbot tourist's assistant" integrates Maslow's Hierarchy of Needs into the development of an Intelligent Chatbot Tourist's Assistant (ICTA) to enhance tourism services. The study reviews existing literature on tourism, chatbot technology, and Maslow's Hierarchy, and curates a dataset aligned with different levels of the hierarchy. Using Convolutional Neural Networks (CNN) within TensorFlow, the ICTA is trained and evaluated for accuracy, response time, and user satisfaction.
9p
tonhiemm
07-06-2024
4
1
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Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model
n this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model.
10p
dianmotminh02
03-05-2024
9
2
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This paper presents our approach that addresses the problem of transplanting a source speaker’s emotional expression to a target speaker, one of the Vietnamese Language and Speech Processsing (VLSP) 2022 TTS tasks. Our approach includes a complete data preprocessing pipeline and two training algorithms.
11p
dianmotminh02
03-05-2024
6
2
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This paper evaluates the level of suitability of applying GIS, EuroSAT dataset, and Convolutional Neural Networks to recognize residential land in Thua Thien Hue, Vietnam. We employed EuroSAT dataset to train and validate the ResNet152 model. Then, the assessed model was used to detect residential land in the location.
17p
viritesh
02-04-2024
8
1
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To evaluate the discriminatory capability of spectral CT-based radiomics to distinguish benign from malignant solitary pulmonary solid nodules (SPSNs). A retrospective study was performed including 242 patients with SPSNs who underwent contrast-enhanced dual-layer Spectral Detector CT (SDCT) examination within one month before surgery in our hospital, which were randomly divided into training and testing datasets with a ratio of 7:3.
10p
vischultz
20-10-2023
4
1
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Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Active health screening for CRC yielded detection of an increasingly younger adults. However, current machine learning algorithms that are trained using older adults and smaller datasets, may not perform well in practice for large populations.
13p
vischultz
20-10-2023
1
1
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In this study, a machine learning model, namely LightGBM, is developed to predict rubberized concrete's compressive strength (CS) using 11 input parameters. The model's performance is measured using several different statistical criteria after being trained on a dataset containing 275 samples.
18p
visharma
20-10-2023
9
4
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Recently, Natural Language Inference has attracted the attention of research communities due to its application in the Natural Language Processing fields. In this paper, we describe an empirical study of data augmentation techniques with various pre-trained language models on the bilingual dataset which is presented at the VLSP 2021 - Vietnamese and English-Vietnamese Textual Entailment.
8p
viberkshire
09-08-2023
8
5
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This paper presents the Smartcall - ITS’s systems submitted to the Vietnamese Language and Speech Processing, Speaker Verification (SV) task. The challenge consists of two tasks focusing on the development of SV models with limited data and testing the robustness of SV systems.
5p
viberkshire
09-08-2023
8
5
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Named entities (NE) are phrases that contain the names of persons, organizations, locations, times, quantities, email, phone number, etc., in a document. Named Entity Recognition (NER) is a fundamental task that is useful in many applications, especially in information extraction and question answering.
11p
viberkshire
09-08-2023
7
5
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In this article, we report on evaluating the NLI task of the competition. We first implement the 5-fold cross-validation evaluation method. We following leverage model architectures pre-trained on cross-lingual language datasets such as XLM-RoBERTa and RemBERT to create contextual word embeddings for classification. Our final result reaches 90.00% on the test dataset of the organizers.
8p
viberkshire
09-08-2023
13
6
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Recently, Xvectors and ECAPA-TDNN have been considered state-of-the-art models in designing speaker verification systems. This paper proposes a novel approach that combines Attentive statistic pooling-based Xvector and pre-trained ECAPA-TDNN for Vietnamese speaker verification. Experiments are conducted on various recent Vietnamese speech datasets.
6p
viberkshire
09-08-2023
8
4
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This paper presents the algorithms for training an artificial neural network (ANN) for regression analysis; the algorithm is based on the generalized delta rule. The training method of a simple neuron model and an ANN model are presented and generalized. The models are then programed in Visual C# .NET and applied to predict the compressive strength of concrete mixes. Three datasets, collected from the literature, are used to demonstrate the applications of the models.
7p
nhanchienthien
25-07-2023
5
4
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This paper uses XGBoost to predict bearing capacity of concrete piles. The proposed model is trained and tested against a dataset of 472 samples collected from static load tests in Vietnam. The results indicate that the default XGBoost model consistently outperforms the Deep Neural Network (DNN) regression. XGBoost is a suitable tool for engineers to predict pile bearing capacity.
9p
nhanchienthien
25-07-2023
9
3
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Estimating punching shear capacity (PSC) of steel fibre reinforced concrete slabs (SFRCS) is a crucial task in structural design. This study investigates the performances of artificial neural networks trained by the adaptive moment estimation (Adam) method in dealing with the task of interest. To alleviate overfitting problem, decoupled weight decay (AdamW) and L2 regularization (AdamL2) are used. A dataset including 140 samples has been used to train and verify the machine learning approaches.
5p
nhanchienthien
25-07-2023
6
3
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Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might solve this issue by reusing input representation schemes trained on a larger dataset.
8p
visteverogers
24-06-2023
6
2
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Weighted genetic risk scores (GRS), defined as weighted sums of risk alleles of single nucleotide polymorphisms (SNPs), are statistically powerful for detection gene-environment (GxE) interactions. To assign weights, the gold standard is to use external weights from an independent study.
12p
vinarcissa
21-03-2023
5
1
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Support vector regression models are created and used to predict the retention times of oligonucleotides separated using gradient ion-pair chromatography with high accuracy. The experimental dataset consisted of fully phosphorothioated oligonucleotides. Two models were trained and validated using two pseudoorthogonal gradient modes and three gradient slopes.
10p
viginny
23-12-2022
8
3
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