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Supervised classification
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Rolling bearing faults have been capturing substantial research attention as they are the root causes of malfunctions in mechatronics systems than any other factors. The detection of rolling bearing faults in the early stage is therefore a mandatory requirement demanded by reliable industrial plants.
15p
vithomson
02-07-2024
1
0
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Part 1 of ebook "The elements of statistical learning: Data mining, inference, and prediction (Second edition)" provides readers with contents including: Chapter 1 - Introduction; Chapter 2 - Overview of supervised learning; Chapter 3 - Linear methods for regression; Chapter 4 - Linear methods for classification; Chapter 5 - Basis expansions and regularization; Chapter 6 - Kernel smoothing methods; Chapter 7 - Model assessment and selection; Chapter 8 - Model inference and averaging; Chapter 9 - Additive models, trees, and related methods;...
355p
daonhiennhien
03-07-2024
2
1
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A deep learning approach combining autoencoder with supervised classifiers for IoT anomaly detection
Anomaly detection for Internet of things (IoT) networks is a challenging issue due to the huge number of devices that connect to each other and generate huge amounts of data. In this study, we propose a model combining Autoencoder (AE) with classification algorithms to build an endto-end architecture for processing, feature extraction and data classification.
13p
viambani
18-06-2024
7
1
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Automatic classification of software requirements in Vietnamese based on machine learning techniques
Requirements engineering is often the first stage in the software process to understand the problem statement. Finding mistakes earlier in requirements helps reduce the development cost. This paper presents a classification approach of functional and non-functional requirements in Vietnamese using different supervised machine learning techniques.
6p
visystrom
22-11-2023
11
4
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This scholarly research paper addresses the crucial and complex challenge of detecting and categorizing Internet of Things (IoT) botnets through the utilization of machine learning algorithms. The study is focused on conducting meticulous analysis and manipulation of IoT botnet data, with a specific emphasis placed on the widely acknowledged IoT23 dataset.
12p
visystrom
22-11-2023
8
5
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The Dempster-Shafer (DS) theory of evidence is frequently used to combine multiple supervised machine learning models into a robust fusion-based model. However, using the DS theory to create a fusion model from multiple one-class classifications (OCCs) for network anomaly detection is a challenging task.
16p
vimulcahy
18-09-2023
5
4
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Data clustering is applied in various fields such as document classification, dental Xray image segmentation, medical image segmentation, etc. Especially, clustering algorithms are used in satellite image processing in many important application areas, including classification of vehicles participating in traffic, logistics, classification of satellite images to forecast droughts, floods, forest fire, etc.
15p
vimulcahy
18-09-2023
2
2
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This study proposes a distributed classification framework, which adapts supervised SelfOrganizing Maps (SOM) as base learners. The supervised SOM is the integration of the SOM algorithm with the Learning Vector Quantization (LVQ) algorithm, so called SOM-LVQ model. Multiple SOM-LVQ models are created using different feature subsets, each of which represents one different local information source.
6p
viannee
02-08-2023
6
4
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Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learning capabilities and lead to a better understanding of the underlying interactions among molecular levels.
10p
visteverogers
24-06-2023
8
2
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The study sought to evaluate the feasibility of using Unified Medical Language System (UMLS) semantic features for automated identification of reports about patient safety incidents by type and severity.
8p
vighostrider
25-05-2023
5
2
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This project examines how the moving image can be used as a vehicle of memory, to express what is lost, what is gained and what changes occur in relation to personal family migratory history—a messy and ambiguous process. This practice-led research project investigates video work, using filmed materials and objects, to focus attention on the non-linear aspect of time as it is subjected to individual and collective memories. The outcomes highlight the sometimes disconnecting and unpredictable experiences of migration, storytelling, and memory.
62p
runthenight04
02-02-2023
4
1
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Lecture Introduction to Machine learning and Data mining: Lesson 4. This lesson provides students with content about: supervised learning; K-nearest neighbors; neighbor-based learnin; multiclass classification/categorization; distance/similarity measure;... Please refer to the detailed content of the lecture!
23p
hanlamcoman
26-11-2022
17
5
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In this paper, multiple classification algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), K-nearest neighbours (KNN), logistic regression, Gaussian, Bernoulli, multinomial Naïve Bayes, and linear discriminant analysis were executed on the seismic attributes for lithofacies prediction.
9p
vibentley
08-09-2022
14
4
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The study aims to apply the supervised machine learning method to the classification of product review content in online customer comment mining. The entire study was conducted automatic data collection with 2,241 customer reviews on products on Lazada.vn, then trained with Supervised Machine Learning models to find the most suitable model with the training dataset and apply this model to predict the reviews content for the dataset.
10p
visherylsandberg
18-05-2022
14
1
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Colonoscopy image classification is an image classification task that predicts whether colonoscopy images contain polyps or not. It is an important task input for an automatic polyp detection system. Recently, deep neural networks have been widely used for colonoscopy image classification due to the automatic feature extraction with high accuracy.
11p
vikissinger
03-03-2022
11
1
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Lecture Biomedical Signal Processing and Modeling: Pattern Classification and Diagnostic Decision - Nguyen Cong Phuong provide students with knowledge about introduction; pattern classification; supervised pattern classification; unsupervised pattern classification; probabilistic models and statistical decision; logistic regression analysis; neural networks;...
66p
tanmocphong
19-01-2022
11
0
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Our proposed scheme would lead to more informative GCNs. Using the revisited model, we will conduct several semi-supervised classification experiments on public image datasets containing objects
12p
guernsey
28-12-2021
5
0
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Proteins are a kind of macromolecules and the main component of a cell, and thus it is the most essential and versatile material of life. The research of protein functions is of great significance in decoding the secret of life. In recent years, researchers have introduced multi-label supervised topic model such as Labeled Latent Dirichlet Allocation (Labeled-LDA) into protein function prediction, which can obtain more accurate and explanatory prediction.
14p
viseulgi2711
31-08-2021
8
1
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Deep learning has made tremendous successes in numerous artificial intelligence applications and is unsurprisingly penetrating into various biomedical domains. High-throughput omics data in the form of molecular profile matrices, such as transcriptomes and metabolomes, have long existed as a valuable resource for facilitating diagnosis of patient statuses/stages.
12p
visilicon2711
20-08-2021
15
1
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The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods.
17p
vijeeni2711
30-06-2021
12
1
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