the proceedings contain 9 papers. the special focus in this conference is on machinelearning, Deep learning, and Blockchain. the topics include: Development of an Intrusion Detection Model Using a Support Vector Mach...
ISBN:
(纸本)9783031882364
the proceedings contain 9 papers. the special focus in this conference is on machinelearning, Deep learning, and Blockchain. the topics include: Development of an Intrusion Detection Model Using a Support Vector machine to Identify Cyber Attacks;vector Quest: A Faster and Better Open-Domain Question-Answering System;deep learning Approach to Indian Sign Language recognition;false data Injection Attack Prediction Using Federated Deep learning Approach;block Chain Ballot: A Secure and Transparent E-Voting System;combination of IoT and Block Chain for Safe data Management;A Prototype for Secure and Efficient Real Estate Transactions Using IPFS and Block Chain Technology.
this work focuses on is emotion recognition. Emotion shows crucial data about human communication. It's general to utilize face expressions to convey feelings throughout a discussion, and personal communication is...
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A lot of recent literature on outcome-oriented predictive process monitoring focuses on using models from machine and deep learning. In this literature, it is assumed the outcome labels of the historical cases are all...
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ISBN:
(纸本)9783031278143;9783031278150
A lot of recent literature on outcome-oriented predictive process monitoring focuses on using models from machine and deep learning. In this literature, it is assumed the outcome labels of the historical cases are all known. However, in some cases, the labelling of cases is incomplete or inaccurate. For instance, you might only observe negative customer feedback, fraudulent cases might remain unnoticed. these cases are typically present in the so-called positive and unlabelled (PU) setting, where your data set consists of a couple of positively labelled examples and examples which do not have a positive label, but might still be examples of a positive outcome. In this work, we show, using a selection of event logs from the literature, the negative impact of mislabelling cases as negative, more specifically when using XGBoost and LSTM neural networks. Furthermore, we show promising results on real-life datasets mitigating this effect, by changing the loss function used by a set of models during training to those of unbiased Positive-Unlabelled (uPU) or non-negative Positive-Unlabelled (nnPU) learning.
In this paper, we are trying to prove that environment perform main role in student life. To increase overall performance of student, peaceful environment is must, and withthe help of orange tool, analyze student per...
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Glycosuria level monitoring is a fundamental aspect of diabetes management, enabling proactive and personalized care to prevent complications, improve overall health, and enhance the quality of life for individuals wi...
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In this work, we present the auto-clustering method which can be used for patternrecognition tasks and applied to the training of a metric convolutional neural network. the main idea is that the algorithm creates clu...
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ISBN:
(数字)9781510650459
ISBN:
(纸本)9781510650459;9781510650442
In this work, we present the auto-clustering method which can be used for patternrecognition tasks and applied to the training of a metric convolutional neural network. the main idea is that the algorithm creates clusters consisting of classes similar from a network's point of view. the usage of clusters allows the network to pay more attention to classes that are hard to differ. this method improves the generation of pairs during the training process, which is a current problem because the optimal generation of data significantly affects the quality of training. the algorithm works in parallel withthe training process and is fully automatic. To evaluate this method we chose the Korean alphabet withthe corresponding PHD08 dataset and compared our auto-clustering with random-mining, hard-mining, distance-based mining. Open-source framework Tesseract OCR 4.0.0 was also considered to evaluate the baseline.
Sleep apnea, a prevalent sleep disorder affecting individuals of all demographics, poses a threat of significant disruption to daily life. the analysis of Electrocardiogram (ECG) data facilitates the accurate diagnosi...
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this work proposes QNet, a novel sequence encoder model that entirely inferences on the quantum computer using a minimum number of qubits. Let n and d represent the length of the sequence and the embedding size, respe...
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In the recent years, the coronary artery disease (CAD) becomes leading cause of mortality worldwide both in high-income nations and in poor countries to a rising extent. Studies of human genetics can provide useful in...
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Recent exponential rise of social media impact has resulted in significant changes in how people conduct their lives. Sharing one's ideas, thoughts, and feelings on a social platform like Facebook, Twitter and Red...
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