Deep learning

The process of neural stem cell (NSC) differentiation into neurons is crucial for the development of potential cell-centered treatments for central nervous system disorders. However, predicting, identifying, and anticipating this differentiation is complex. In this study, we propose the implementation of a convolutional neural network model for the predictable recognition of NSC fate, utilizing single-cell brightfield images.

7p viengfa 28-10-2024 1 1   Download

This paper investigates the impact of word embedding techniques on enhancing SMS spam detection models. Traditional statistical methods (BoW, TF-IDF) are compared with advanced techniques (Word2Vec, fastText, GloVe, PhoBERT) using a proprietary dataset.

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In this paper, we propose an effective AMC using deep learning (DL) for flexible and adaptive OFDM-based optical networks. The proposed DL-based AMC is able to classify four typical modulation schemes such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), 8-PSK, and 16- quadrature amplitude modulation (QAM) in dynamic network conditions.

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In this article, we establish a Digital Radio over Fiber (DRoF) information system with two wireless channels utilizing two advanced phase modulation techniques, namely Differential Phase Shift Keying (DPSK), for the CRAN connection and investigate parameters related to nonlinearity such as refractive index n2.

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This paper is structured as follows. The following section presents related work. Section 3 summarizes the characteristics of the two datasets utilized in the model and the system’s overall architecture for image-based disease diagnosis. Section 4 provides our experimental results that compare the performance metrics with other studies.

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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 viengfa 28-10-2024 1 1   Download

Despite certain advancements achieving high accuracy, current methods still require substantial improvements to be applicable in practical scenarios. Diverging from text detection in images/videos, this paper addresses the issue of text detection within license plates by amalgamating multiple frames of distinct perspectives.

10p viengfa 28-10-2024 4 1   Download

Bài viết trình bày về cách sử dụng nhiều GPU để huấn luyện mô hình trong học sâu (Deep Learning). Chúng tôi khảo sát các chiến lược học sâu trên mạng nơ-ron tích chập (Convolutional Neural Network – CNN).

7p viling 11-10-2024 1 0   Download

In this paper, we used Convolution neural network (CNN) that exploits the visual properties of the input data to obtain features from network traffic, thereby achieving good intrusion detection performance.

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In this study, we explore the potential of graph neural networks (GNNs), in combination with transfer learning, for the prediction of molecular solubility, a crucial property in drug discovery and materials science. Our approach begins with the development of a GNN-based model to predict the dipole moment of molecules.

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This study aims to build a classifier for credit scoring based on deep learning. We use a credit scoring dataset publicly available on the UC Irvine Machine Learning Repository, a source of machine learning datasets commonly used by researchers.

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This research proposes a new approach that leverages low-cost digital cameras and deep learning technology for counting and extracting rice grain traits. Our study introduces a preprocessing step to separate rice grain regions from the input image background using color space conversion.

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This research paper focuses on the critical role of demand forecasting in FMCG, emphasizing the need for LSTM-based deep learning models to deal with demand uncertainty and improve predictive outcomes. Through this exploration, we aim to illuminate the link between demand forecasting and advanced deep learning, enabling FMCG companies to thrive in a highly dynamic business landscape.

8p vifilm 11-10-2024 2 1   Download

In this paper, a Convolutional Neural Network (CNN) method is employed to classify the crack/noncrack aerial images captured on the surface of concrete structures. The CNN model was trained and validated using the available experimental data of 4000 previously published images.

4p vibecca 01-10-2024 1 1   Download

This paper presents a novel for Doppler frequency compensation in highspeed railway communication based on the results of estimating the train's velocity using machine learning algorithms. By leveraging advanced algorithm such as neural networks, our method dynamically predicts and compensates for Doppler shifts in real-time.

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This paper scrutinizes two lane detection methods: image processing in isolation and integrating deep learning techniques with Hough transformation. The deep learning techniques specifically discuss two models, namely YOLOv8 pose estimation and YOLOv8 object detection.

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Rolling bearing faults have been capturing substantial research attention as they are the root causes of malfunctions in mechatronics systems than any other factors. The detection of rolling bearing faults in the early stage is therefore a mandatory requirement demanded by reliable industrial plants.

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This paper is aimed at evaluating the efficiency of Vietnamese SMS spam detection methods on different variants of Vietnamese datasets by utilizing both traditional machine learning models and deep learning models.

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This paper addresses the problem of automatically extracting threat intelligence from unstructured text sources. We focus specifically on the possibility of multiple relations between two entities and propose a two-stage process that allows any binary classifier to be used for multiclass classification without interfering with the binary algorithm used.

9p viyoko 01-10-2024 2 1   Download

This paper proposes a new method of representing malicious code as an image by arranging highly correlated bytes in close pixels in the image. The current research trains deep learning models on self-built datasets and compare the performance of different image representation methods.

9p viyoko 01-10-2024 3 1   Download