Machine Learning for Networking

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 1 0   Download

This study seeks to introduce a more comprehensive assessment method than previous endeavors, particularly concerning the bond strength of FRP bars with various surface types within concrete, spanning normal, high-strength, and ultra-high-strength concrete.

16p vithomson 02-07-2024 0 0   Download

In this study, four machine learning models have been studied which are Artificial Neural Networks, Convolutional Neural Networks, Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM). They have been used to forecast the solar power of Nhi Ha solar farm in short-term. First, data from Nhi Ha solar farm were collected and underwent preprocessing before being utilized by aforementioned distinct machine learning models.

8p vialicene 02-07-2024 0 0   Download

Part 2 of ebook "Introduction to artificial intelligence (Second edition)" provides readers with contents including: Chapter 8 - Machine learning and data mining; Chapter 9 - Neural networks; Chapter 10 - Reinforcement learning; Chapter 11 - Solutions for the exercises;...

178p daonhiennhien 03-07-2024 1 1   Download

Part 2 of ebook "Machine learning in medicine - A complete overview" provides readers with contents including: Chapter 50 - Neural networks for assessing relationships that are typically nonlinear; Chapter 51 - Complex samples methodologies for unbiased sampling; Chapter 52 - Correspondence analysis for identifying the best of multiple treatments in multiple groups; Chapter 53 - Decision trees for decision analysis; Chapter 54 - Multi-dimensional scaling for visualizing experienced drug efficacies; Chapter 55 - Stochastic processes for long term predictions from short term observations;...

194p daonhiennhien 03-07-2024 1 1   Download

Part 2 of ebook "Practical machine learning and image processing: For facial recognition, object detection, and pattern recognition using Python" provides readers with contents including: Chapter 4 - Advanced image processing using OpenCV; Chapter 5 - Image processing using machine learning; Chapter 6 - Real-time use cases;...

105p daonhiennhien 03-07-2024 2 1   Download

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.

8p viwalton 02-07-2024 3 1   Download

Paclitaxel is commonly used as a second-line therapy for advanced gastric cancer (AGC). The decision to proceed with second-line chemotherapy and select an appropriate regimen is critical for vulnerable patients with AGC progressing after first-line chemotherapy.

9p vishanshan 27-06-2024 2 1   Download

In this study, the simultaneous determination of Co, Cd, Ni, Cu, and Pb was carried out as a color complex with 4-(2-pyridylazo) resorcinol in an aqueous solution under the assesting of machine learning. A partial least-squares multivariate linear regression and artificial neuron network for the analysis of mixtures of metals were developed. MATLAB is a powerful software machine learning program that was used to support matrix calculations and displays.

8p visergeyne 18-06-2024 0 0   Download

This paper addresses the challenge of fault detection in Wireless Sensor Networks (WSNs), commonly used in fields like environmental monitoring and healthcare. WSNs, prone to various faults due to their deployment in unpredictable environments, require effective solutions for fault detection. Traditional machine learning approaches show limitations such as unsuitability for streaming data and the detection of a single fault type.

10p visergeyne 18-06-2024 1 0   Download

Ebook "Optimizing decision making in the apparel supply chain using artificial intelligence (AI): From production to retail" provides detailed coverage of these techniques, outlining how they are used to assist decision makers in tackling key supply chain problems.

245p dongmelo 26-05-2024 5 2   Download

This modeling is performed on an experimental data set of complexes, where the metal ions of these complexes include transition ion metals and lanthanide ion metals. We use these models to develop a series of new thiosemicarbazone and their complexes; simultaneously, the complexes are worked out the stability constants from the novel models.

9p dianmotminh02 03-05-2024 4 1   Download

This paper investigates the effectiveness of a swarm intelligence algorithm namely the time-varying binary particle swarm algorithm in finding an optimal subset of the breast cancer dataset’s features. After the feature selection phase, an artificial neural network was used for predicting the presence of malignant lesions in female breasts.

8p viohoyo 25-04-2024 4 1   Download

The research subject is the process of economic and mathematical modelling of time series characterizing the bitcoin exchange rate volatility, based on the use of artificial neural networks. The purpose of the work is to search and scientifically substantiate the tools and mechanisms for developing prognostic estimates of the crypto currency market development. The paper considers the task of financial time series trend forecasting using the LSTM neural network for supply chain strategies.

5p longtimenosee09 08-04-2024 6 1   Download

Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes.

21p viellison 28-03-2024 8 2   Download

In this paper, a damage detection methodology for steel frame structures under fire load using time-history acceleration and machine learning (ML) is proposed. A randomly created dataset by finite element analysis (FEA) is utilized to develop deep neural networks (DNNs).

5p vicwell 06-03-2024 7 3   Download

Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging.

19p vicwell 29-02-2024 4 2   Download

This research paper presents a framework for smart factory applications in ceramic manufacturing, harnessing data collection and machine learning algorithms to seamlessly align with the demands of Industry 4.0 and facilitate digital transformation. At its core, the framework is exemplified through the creation of a dynamic web application, consisting of three pivotal modules.

7p vigojek 02-02-2024 8 0   Download

This approach can be problematic because SDN networks have different characteristics than traditional computer systems. In this paper, we propose a new method for SDN intrusion detection using machine learning. Our method addresses the problem of data imbalance, which is a common problem with machine learning datasets.

14p vigojek 02-02-2024 3 1   Download

In this paper, we introduce a novel approach to enhance the capabilities of the humanoid robot IVastBot by in- tegrating various software components. This integration enables IVastBot to effectively recognize and respond to a wide array of human gestures and behaviors.

12p vimichaelfaraday 28-12-2023 10 6   Download