Aerodynamic parameter estimation entails modelling force and moment coefficients as well as computing stability and control derivatives from flight data. This topic has been thoroughly researched utilizing traditional...
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structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images con...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. This, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed information for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. This is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://***/3jN1pJJ.
In the recent era, everybody is dealing with the digital data. In such scenario individual one heavily depends on credit card. Therefore, the demand of online transactions and usage of e-commerce sites are rising at t...
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The proceedings contain 127 papers. The topics discussed include: automated Bengali abusive text classification: using deep learning techniques;bio algorithms for resource optimization and analysis of data;prediction ...
ISBN:
(纸本)9798350348057
The proceedings contain 127 papers. The topics discussed include: automated Bengali abusive text classification: using deep learning techniques;bio algorithms for resource optimization and analysis of data;prediction of rheumatoid arthritis susceptibility using gene mutation rate;a survey paper on emerging techniques used to translate audio or text to sign language;a novel development of blockchain based messaging application;deep machinelearning based usage pattern and application classifier in network traffic for anomaly detection;analyzing paralinguistic information from human speech and its applications in medicine;strategic placement of electric vehicle charging stations using grading algorithm;malware detection in android applications using machinelearning;the impact of online reviews on product perception and purchase intention;design and implementation of smart classroom using cisco packet tracer;intrusion detection in networks using gradient boosting;real-time lane detection and departure caution gadget for automobiles based on Raspberry pi;development of a Bengali speech-based emotion analysis system;large vocabulary continuous speech recognition system for Marathi;and low-cost smart glasses for people with visual impairments.
Covid-19 has been posing a serious challenge to scientists and health organizations around the world in terms of detection and its treatment. Common methods are CT-Scans and X-rays to analyze the images of lungs for C...
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Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pretrained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. The Dockerised guidelines, inference codes and saved trained models are publicly available at https://***/masih4/Foot Ulcer Segmentation.
The highly individualized production processes for long products in the steel industry is subject to a variety of influencing variables with mutual interactions in a complex manner. To handle this complexity, modern d...
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The highly individualized production processes for long products in the steel industry is subject to a variety of influencing variables with mutual interactions in a complex manner. To handle this complexity, modern datamining methods can be used for a highly efficient analysis of process data, to detect process anomalies in the process data, e.g. from rolling mills by statistical patternrecognition. This paper proposes a data-based strategy for detecting process anomalies within a hot rolling mill for long products. Suitable data are identified and selected from existing sensors and processed within a new database. This central database is used to train classification algorithms. The reliability of two prominent classifiers based on Principal Component Analysis (PCA) and One-Class Support Vector machines (OC-SVM) has been evaluated. From the comparison in this respective use case, it has been concluded that satisfying results can be obtained, but PCA is highly dependent on the data distribution. The OC-SVM has also been implemented and tested and offers advantages when the data sets have a more complex distribution. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
In the field of cybersecurity, the complexity and diversity of data present significant challenges for effective analysis. This paper explores the use of knowledge graphs as a tool to enhance the analysis of honeypot ...
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Robust object recognition is thought to rely on neural mechanisms that are selective to complex stimulus features while being invariant to others (e.g., spatial location or orientation). To better understand biologica...
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Robust object recognition is thought to rely on neural mechanisms that are selective to complex stimulus features while being invariant to others (e.g., spatial location or orientation). To better understand biological vision, it is thus crucial to characterize which features neurons in different visual areas are selective or invariant to. In the past, invariances have commonly been identified by presenting carefully selected hypothesis-driven stimuli which rely on the intuition of the researcher. One example is the discovery of phase invariance in V1 complex cells. However, to identify novel invariances, a data-driven approach is more desirable. Here, we present a method that, combined with a predictive model of neural responses, learns a manifold in the stimulus space along which a target neuron's response is invariant. Our approach is fully data-driven, allowing the discovery of novel neural invariances, and enables scientists to generate and experiment with novel stimuli along the invariance manifold. We test our method on Gabor-based neuron models as well as on a neural network fitted on macaque V1 responses and show that 1) it successfully identifies neural invariances, and 2) disentangles invariant directions in the stimulus space
Outstanding success of CNN image classification affected using it as an instrument for time series classification. Powerful graph clustering methods have capabilities to come across entity relationships. In this study...
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