This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter o...
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An accurate prediction of breast cancer is essential to help physicians make appropriate treatment recommendations to reduce the chance of excessive treatment, avoiding unnecessary anxiety for patients. Cancer prognos...
An accurate prediction of breast cancer is essential to help physicians make appropriate treatment recommendations to reduce the chance of excessive treatment, avoiding unnecessary anxiety for patients. Cancer prognosis is highly related to patients’ genomic features, which are high-dimensional in nature. In this study, we utilize a systems biology feature selector for dimension reduction to select 20 prognostic biomarkers that are considered closely related to breast cancer prognosis from the high dimensional RNA Sequencing (RNA-Seq) data. Furthermore, we establish a graph neural network (GNN) and a multi-layer perception (MLP) graph-level readout method to better extract the underlying gene interactions from the corresponding gene interaction network (GIN). With the help of GINs, the model performs the best among all baseline models, especially in the area under the precision-recall curve (AUPRC) by as large as 23%. The results demonstrate that our approach using GNNs can successfully extract high-dimensional and complicated interactions within genomic data.
Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By di...
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In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes t...
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The degradation of high-density polyethylene (HDPE) in marine environments was investigated under various weathering conditions. HDPE debris were collected from coastal areas near Korinthos, Greece which had been expo...
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This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including R...
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ISBN:
(数字)9798331519643
ISBN:
(纸本)9798331519650
This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.
Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduce...
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Drug–target affinity (DTA) prediction is crucial to speed up drug development. The advance in deep learning allows accurate DTA prediction. However, most deep learning methods treat protein as a 1D string which is no...
Drug–target affinity (DTA) prediction is crucial to speed up drug development. The advance in deep learning allows accurate DTA prediction. However, most deep learning methods treat protein as a 1D string which is not informative to models compared to a graph representation. In this paper, we present a deep-learning-based DTA prediction method called N-gram Graph DTA (NG-DTA) that takes molecular graphs of drugs and n-gram molecular sub-graphs of proteins as inputs which are then processed by graph neural networks (GNNs). Without using any prediction tool for protein structure, NG-DTA performs better than other methods on two datasets in terms of concordance index (CI) and mean square error (MSE) (CI: 0.905, MSE: 0.196 for the Davis dataset; CI: 0.904, MSE: 0.120 for Kiba dataset). Our results showed that using n-gram molecular sub-graphs of proteins as input improves deep learning models’ performance in DTA prediction.
Complaint resolution that arise due to internal and external factors in a company can be monitored through the Service Recovery Index (SRI), and SRI is developed through a number of factors that influence it. Meanwhil...
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Global energy consumption has increased over the years due to population growth, economic expansion, and the pursuit of a higher quality of life. The building sector is a critical sector for high consumption, contribu...
Global energy consumption has increased over the years due to population growth, economic expansion, and the pursuit of a higher quality of life. The building sector is a critical sector for high consumption, contributing to about 36% of the total global energy consumption in 2020. The global interest in reducing energy consumption allows different sectors to adopt renewable resources such as solar photovoltaics (PV). However, utilizing solar PV in buildings has limitations due to rooftop space constraints in newly designed buildings. Due to this limitation and continuous innovation in solar PV technologies, Building Integrated Photovoltaics (BIPV) have been introduced as a potential alternative for solar PV in buildings. As such, BIPV energy performance and panel temperatures are affected by many factors such as orientation setup and technology type. Therefore, this research aims to analyze the impact of technology type (indium gallium selenide (CIGS) and monocrystalline (MONO) & orientation on BIPV energy production & module temperature. The data used for this study is based on a project completed in Dubai for BIPV in building applications. The results indicate that BIPV type and system orientation during each session significantly affect BIPV energy production and BIPV module temperature.
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