CNN models

This study proposes to test a combination model between CNN network and XGBoost algorithm for weather image classification problem. The proposed model uses deep learning network, namely CNN for feature extraction, then feeds the features into the XGBoost classifier to recognize the images.

6p vithomson 02-07-2024 0 0   Download

The risk category of gastric gastrointestinal stromal tumors (GISTs) are closely related to the surgical method, the scope of resection, and the need for preoperative chemotherapy. We aimed to develop and validate convolutional neural network (CNN) models based on preoperative venous-phase CT images to predict the risk category of gastric GISTs.

10p vikoch 27-06-2024 2 1   Download

This study introduces an approach to detect deepfake images using transfer learning methods, including XceptionNet, RestNet101, InceptionResV2, MobileNetv2, VGG19 and DenseNet121, along with comparing it with a traditional CNN model.

8p vigrab 02-02-2024 6 3   Download

This study focus on the image captioning problem in Vietnamese. In detail, an empirical study of grid-based and region-based feature extraction approaches using current state-of-the-art object detection methods is investigated to explore the suitable way to represent the images in the model space.

20p vimulcahy 18-09-2023 7 4   Download

This paper analyses the combination of IMU sensors and electromyography sensors (EMG) to improve the identification accuracy of human movements. We propose the hybrid convolutional neural network (CNN) and long short-term memory neuron network (LSTM) for the human gait analysis problem to achieve an accuracy of 0.9418, better than other models including pure CNN models.

18p vimulcahy 18-09-2023 8 4   Download

In this article, we present a solution to apply the convolutional neural network (CNN) to classify landcovers from remote sensing image data. This result shows great potential for the application of deep learning models in remote sensing image analysis.

12p viengels 25-08-2023 6 3   Download

This paper proposes a Convolutional Neural Network (CNN)- based detection model for website defacements. The model is an extension of previous models based on traditional supervised machine learning techniques and its aims are to improve the detection rate and reduce the false alarm rate.

12p viannee 02-08-2023 5 4   Download

In this paper, the CNN-based model was developed to identify crack/non-crack images collected on the surface of a concrete structure. The CNN model was adapted from the pre-trained, open-sourced model developed by Google and distributed through TensorFlow.

4p vifalcon 16-05-2023 10 4   Download

This paper investigates convolutional neural networks (CNN) to classify 26 types of signal modulation under the influence of five different fading channels and Gausian noise with SNR from -20 dB to +18 dB. Specifically, five CNN models, including ResNet18, SqueezeNet, GoogleNet, MobileNet, and RepVGG, are taken into account for a accuracy competition to discover the best one.

8p vigeneralmotors 13-07-2022 1084 9   Download

In recent years with the explosion of research in artificial intelligence, deep learning models based on convolutional neural networks (CNNs) are one of the promising architectures for practical applications thanks to their reasonably good achievable accuracy. However, CNNs characterized by convolutional layers often have a large number of parameters and computational workload, leading to large energy consumption for training and network inference.

10p vikissinger 03-03-2022 11 2   Download

In this paper, we intend to insight the CNN model regarding its capability on text analysis. Vanilla CNNs do have weaknesses when it comes to the representation and feature extraction. Duplicate filters are inevitable with vanilla CNNs, which reduces the discriminative power of the representations.

15p guernsey 28-12-2021 10 0   Download

In a simple but effective manner, convolutional neural networks (CNNs) are proposed to exploit their well-known advantages on images for temporal educational data. As a result, the task is resolved by our enhanced CNN models with more effectiveness and practicability on real datasets. Our CNN models outperform other traditional models and their various variants on a consistent basis for program-level student classi¯cation.

25p redemption 20-12-2021 24 0   Download

This model experimented on collector leaves data set Flavia leaf data set and the Swedish leaf data set. The classication results indicate that the proposed CNN model is effective for leaf recognition with the best accuracy greater than 98.22%.

12p redemption 20-12-2021 11 0   Download

This paper focuses on SVM, CNN, DenseNet, VGG, and ResNet models to detect traffic congestion from camera images collected at Nga 5 Dai Hoc, Da Lat. These images are labeled with the words traffic congestion or no traffic congestion. The experimental results have an accuracy of over 93%. The study is an initial contribution to a future system for predicting traffic congestion in Da Lat when the camera system is fully installed.

13p spiritedaway36 28-11-2021 12 4   Download

This paper proposes a method for improving the feature set of Android malware classification based on co-concurrence matrix (co-matrix). The co-matrix is established based on a list of raw features extracted from .apk files. The proposed feature can take the advantage of CNN while remaining important features of the Android malwares. Experimental results of CNN model conducted on a very popular Android malware dataset, Drebin, prove the feasibility of our proposed co-matrix feature.

8p spiritedaway36 28-11-2021 12 1   Download

Bài báo nghiên cứu về đề tài phân tích cảm xúc trong câu nói, chủ yếu tập trung những bình luận, đánh giá của người dùng trên các mạng xã hội. Trong bài nghiên cứu khoa học này, chúng tôi sẽ sử dụng mạng thần kinh nhân tạo (mạng hồi quy LSTM) để giải quyết bài toán tìm được liệu trong câu bình luận mang ý nghĩa tích cực hay tiêu cực. Bài làm của chúng tôi đạt được kết quả tốt sau khi so sánh với các giải pháp hiện có trong vấn đề này. Cụ thể là chúng tôi đã giải quyết vấn đề overfitting của những bài làm trước đó.

8p huyetthienthan 23-11-2021 36 2   Download

The pretrained BERT multilingual model is used to generate embedding vectors from the input text. These vectors are combined with TF-IDF values to produce the input of the text summarization system. Redundant sentences from the output summary are eliminated by the Maximal Marginal Relevance method. Our system is evaluated with both English and Vietnamese languages using CNN and Baomoi datasets, respectively. Experimental results show that our system achieves better results compared to existing works using the same dataset.

21p spiritedaway36 25-11-2021 7 1   Download

Bài viết đề xuất một giải pháp có thể hỗ trợ bác sỹ hình ảnh phát hiện chính xác bệnh ung thư vú và phân loại ung thư khi chụp X-quang tuyến vú bằng cách sử dụng phương pháp huấn luyện end-to-end kết hợp với mô hình CNN state-of-the-art EfficientNetB3.

9p vijihyo2711 25-09-2021 30 1   Download

This paper experiments with the deep learning model convolution neural network (CNN), long short-term memory (LSTM), and the combined model of CNN and LSTM. The training data set comprise reviews of cars in Vietnamese that are pre-processed according to the method of aspect analysis based on an ontology of semantic and sentimental approaches.

7p viaespa2711 31-07-2021 17 1   Download

This paper proposes and develops a web attack detection model that combines a clustering algorithm and a multi-branch convolutional neural network (CNN). The original feature set was clustered into clusters of similar features. Each cluster of similar features was generalized in a convolutional structure of a branch of the CNN. The component feature vectors are assembled into a synthetic feature vector and included in a fully connected layer for classification.

7p chauchaungayxua12 11-05-2021 18 4   Download