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Chatter is an unstable and self-excited vibration that adversely affects part quality and tool life in various machining processes. To achieve high-performance machining, chatter identification has attracted considerable interest from many researchers in recent decades. Nevertheless, most existing chatter detection approaches fail to consider the p...
Article
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Laser Metal Deposition (LMD) is an additive manufacturing technology that attracts great interest from the industry, thanks to its potential to realize parts with complex geometries in one piece, and to repair damaged ones, while maintaining good mechanical properties. Nevertheless, the complexity of this process has limited its widespread adoption...
Article
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In the field of agricultural information, the identification and prediction of rice leaf disease have always been the focus of research, and deep learning (DL) technology is currently a hot research topic in the field of pattern recognition. The research and development of high-efficiency, high-quality and low-cost automatic identification methods...
Article
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The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is requ...
Article
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The ongoing COVID-19 pandemic has created an unprecedented predicament for the global supply chains (SCs). Shipments of essential and life-saving products, ranging from pharmaceuticals, agriculture, healthcare, to manufacturing, have been significantly impacted or delayed, making the global SCs vulnerable. A better understanding of the shipment ris...
Article
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Existing neural network segmentation schemes perform well in the task of segmenting images of organs with large areas and clear morphology, such as the liver and lungs. However, it is difficult to segment organs with variable morphology and small target area, such as pancreas and tumors. In order to achieve accurate segmentation of pancreas and its...
Article
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As an indispensable non-destructive testing technique, digital image correlation (DIC) has been increasingly applied to various engineering areas concerning deformation characterization. Inspired by artificial intelligence-related technologies, we here develop a new convolutional neural network-based theoretical framework for DIC analyses, hereafte...
Article
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With the development of IoT technology, more and more IoT devices are connected to the network. Due to the hardware constraints of IoT devices themselves, it is difficult for developers to embed security software into them. Therefore, it is better to protect IoT devices at the traffic level. The effect of malicious traffic detection based on neural...
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ABSTRACT Identifying and assessing the disaster risk of landslide-prone regions is very critical for disaster prevention and mitigation. Owning to their special advantages, neural network algorithms have been widely used for landslide susceptibility mapping (LSM) in recent decades. In the present study, three advanced neural network models popularl...
Conference Paper
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Due to the character of 360 • video, it is often a challenge for filmmakers to guide the attention of users to the region of interest. Visual effects as a type of user guidance is frequently applied to traditional film. Nevertheless, the influence of visual effects in 360 • video has been rarely explored. For this reason, the purpose of this paper...
Article
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The question about behavior of gaps between zeros of polynomials under differentiation is classical and goes back to Marcel Riesz. Recently, Stefan Steinerberger [42] formally derived a nonlocal nonlinear partial differential equation which models dynamics of roots of polynomials under differentiation. In this paper, we connect rigorously solutions...
Article
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While machine learning (ML) has shown increasing effectiveness in optimizing materials properties under known physics, its application in discovering new physics remains challenging due to its interpolative nature. In this work, we demonstrate a general-purpose adaptive ML-accelerated search process that can discover unexpected lattice thermal cond...
Article
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Background To train a machine-learning model to locate the transition zone (TZ) of adhesion-related small bowel obstruction (SBO) on CT scans. Materials and methods We used 562 CTs performed in 2005–2018 in 404 patients with adhesion-related SBO. Annotation of the TZs was performed by experienced radiologists and trained residents using bounding b...
Article
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Deep convolutional neural networks (CNNs) represent one of the state-of-the-art methods for image classification in a variety of fields. Because the number of training dataset images in biomedical image classification is limited, transfer learning with CNNs is frequently applied. Breast cancer is one of most common types of cancer that causes death...
Article
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In recent years, human motion prediction has become an active research topic in computer vision. However, owing to the complexity and stochastic nature of human motion, it remains a challenging problem. In previous works, human motion prediction has always been treated as a typical inter-sequence problem, and most works have aimed to capture the te...
Article
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The Local Climate Zone (LCZ) classification is already widely used in urban heat island and other climate studies. The current classification method does not incorporate crucial urban auxiliary GIS data on building height and imperviousness that could significantly improve urban-type LCZ classification utility as well as accuracy. This study utiliz...
Article
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Segmenting the tongue body is an essential step for automated tongue diagnosis, which is a challenge task due to the tongue body’s specificity and heterogeneity. The current deep-learning based tongue image segmentation networks are bloated with high computational complexity. In this study, a light-weight segmentation network for tongue images is p...
Article
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The use of machine learning in the field of reactor safety and noise diagnostics has recently seen great potential given the advancements made in computational tools, hardware and noise simulations. In this work we demonstrate how deep neural networks, specifically recurrent and convolutional neural networks can be trained in a synthetic setting an...
Article
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Limited previous studies focused on the death and progression risk stratification of colorectal cancer (CRC) lung metastasis patients. The aim of this study is to construct a nomogram model combing machine learning-pathomics, radiomics features, Immunoscore and clinical factors to predict the postoperative outcome of CRC patients with lung metastas...
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In additive manufacturing of metal parts, the ability to accurately predict the extremely variable temperature field in detail, and relate it quantitatively to structure and properties, is a key step in predicting part performance and optimizing process design. In this work, a finite element simulation of the directed energy deposition (DED) proces...
Article
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Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its ad...
Article
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Recent advances in camera-equipped drone applications increased the demand for visual object detection algorithms with deep learning for aerial images. There are several limitations in accuracy for a single deep learning model. Inspired by ensemble learning can significantly improve the generalization ability of the model in the machine learning fi...
Article
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The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train rob...
Article
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Background Accurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable assistant to help physicians have more reliable and accurate cardiac measurements using a deep neural network. E...
Article
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Deep hashing method is widely applied in the field of image retrieval because of its advantages of low storage consumption and fast retrieval speed. There is a defect of insufficiency feature extraction when existing deep hashing method uses the convolutional neural network (CNN) to extract images semantic features. Some studies propose to add chan...
Article
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Network-level metro passenger flow prediction has always been one of the most important but most challenging scientific problems in intelligent transportation systems (ITS). However, conventional methods ignore the multi-dimensional topological relationships in the subway system or directly learn from the physical topological structure and fail to...
Article
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Objective Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and detection can effectively help in increasing the chances of survival rate. For this reason, the diagnosis and classification of breast cancer using Deep learning algorithms have attracted a lot of att...
Article
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Objective We aim to develop and validate a three-dimensional convolutional neural network (3D-CNN) model for automatic liver segment segmentation on MRI images. Methods This retrospective study evaluated an automated method using a deep neural network that was trained, validated, and tested with 367, 157, and 158 portal venous phase MR images, res...
Article
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Purpose To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. Methods This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and...
Article
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Glioma as one of the most common types of brain tumor in the world has three different classes based on its cell types. They are astrocytoma, ependymoma, oligodendroglioma, each has different characteristics depending on the location and malignance level. Radiological examination by medical personnel is still carried out manually using magnetic res...
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Accelerated design of hard-coating materials requires state-of-the-art computational tools, which include data-driven techniques, building databases, and training machine learning models. We develop a heavily automated high-throughput workflow to build a database of industrially relevant hard-coating materials, such as binary and ternary nitrides....
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To minimize the effect of optical crosstalk-generated noise (crosstalk), we present a deep learning approach to precisely estimate the full-field displacements for depth-resolved wavelength-scanning interferometry (DRWSI). A deep convolution neural network, where the transformer block is introduced to effectively capture higher-order features of th...
Article
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Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of...
Article
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Photos shared by tourists are being generated at an unprecedented speed, creating new opportunities to study tourism destination images. Nevertheless, little research has focused on the tourist's perception of images from multiple perspectives and how to construct differentiated marketing strategies that link tourists with destinations. With the su...
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Malware detection is one of the most important tasks in cybersecurity. Recently, increasing interest in Convolutional Neural Networks (CNN) and Machine Learning algorithms, which are widely used in image analysis and predictive modelling, led to their use in static malware classification and to the application of these powerful tools in computer in...
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G-equivariant convolutional neural networks (GCNNs) is a geometric deep learning model for data defined on a homogeneous G-space M\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{d...
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Sea monitoring is essential for a better understanding of its dynamics and to measure the impact of human activities. In this context, remote sensing plays an important role by providing satellite imagery every day, even in critical climate conditions, for the detection of sea events with a potential risk to the environment. The present work propos...
Article
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In the CORTEX project, methods to simulate neutron flux oscillations were enhanced and machine-learning based tools to determine the causes of measured neutron flux oscillations were developed, using the results of simulations as training and validation data. For a selected combination of those methods and tools, several sensitivity analyses were p...
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Machine vision faces bottlenecks in computing power consumption and large amounts of data. Although opto-electronic hybrid neural networks can provide assistance, they usually have complex structures and are highly dependent on a coherent light source; therefore, they are not suitable for natural lighting environment applications. In this paper, we...
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The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task. A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-scale fabrication processes is essential for quality control and, ultimately, their industrial a...
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BACKGROUND : Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic se...
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Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography (MRA) is essential for medical auxiliary treatments, which can effectively prevent subarachnoid hemorrhages. This paper proposes an image segmentation model based on a dense convolutional attention U-Net, which fuses deep and rich semantic information with sh...
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Image classification refers to the classification of the input image according to some algorithms. The general steps of image classification include image preprocessing, image feature extraction and image classification judgment. Convolutional neural network (CNN) imitates the visual perception mechanism of biology, solves the complicated engineeri...
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In this work, we investigate different machine learning-based strategies for denoising raw simulation data from the ProtoDUNE experiment. The ProtoDUNE detector is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a forthcoming experiment in neutrino physics. The reconstruction workchain consists of converting digital dete...
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Due to stochastic occurrence of surface defects in a structure, size of acquired image datasets may vary for cracked and un-cracked classes. Further, in crack detection and classification, among misclassified predictions, while, false-positives can be particularly important that can provide added safety factor to the structural health monitoring sy...
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Background Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed...
Book
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Link; https://www.routledge.com/Convolutional-Neural-Networks-for-Medical-Image-Processing-Applications/Ozturk/p/book/9781032104003
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Train rolling stock examination (TRSE) is a physical procedure for inspecting the bogie parts during transit at a little over 30 kmph. Currently, this process is manually performed across many railway networks across the world. This work proposes to automate the process of TRSE using artificial intelligence techniques. The previous works have propo...
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The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical...
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Techniques for learning vectorial representations of graphs ( graph embeddings ) have recently emerged as an effective approach to facilitate machine learning on graphs. Some of the most popular methods involve sophisticated features such as graph kernels or convolutional networks. In this work, we introduce two straightforward supervised learning...
Article
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Objective Breast cancer is the most common among women, and it causes many deaths every year. Early diagnosis increases the chance of cure through treatment. The traditional manual diagnosis requires effort and time from pathological experts, as it needs a joint experience of a number of pathologists. Diagnostic mistakes can lead to catastrophic re...
Article
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Vision-based hand gesture recognition involves a visual analysis of handshape, position and/or movement. Most of the previous approaches require complex gesture representation as well as the selection of robust features for proper gesture recognition. To eliminate the problem of illumination variation and occlusion in gesture videos, a simple model...
Article
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SARS-CoV-2’s population structure might have a substantial impact on public health management and diagnostics if it can be identified. It is critical to rapidly monitor and characterize their lineages circulating globally for a more accurate diagnosis, improved care, and faster treatment. For a clearer picture of the SARS-CoV-2 population structure...
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Background Whole-body bone scan is the widely used tool for surveying bone metastases caused by various primary solid tumors including lung cancer. Scintigraphic images are characterized by low specificity, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. Convolutional neural network can be used to devel...