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Supervised machine learning
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In this paper, the author proposed a supervised machine learning network to identify and classify different crack types in asphalt-surface pavements. A laser camera captured surface images from pavement surface then classified them into 3 classes following the manual of pavement distress identification by the Federal Highways Administration (FHWA).
8p
viohoyo
25-04-2024
3
1
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The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one.
13p
viarnault
25-04-2024
1
1
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Radiotherapy has been widely used to treat various cancers, but its efcacy depends on the individual involved. Traditional gene-based machine-learning models have been widely used to predict radiosensitivity.
15p
vileonardodavinci
23-12-2023
6
3
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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
7
5
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Ebook "Machine learning in computer vision" includes content: Introduction, probabilistic classifiers, generalization bounds, semi supervised learning, maximum likelihood minimum entropy hmm, margin distribution optimization, learning the structure of bayesian network classifiers, office activity recognition, multimodal event detection, facial expression recognition, bayesian network classifiers for face detection.
249p
haojiubujain07
20-09-2023
4
3
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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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This paper proposes a Convolutional Neural Network (CNN)- based detection model for website defacements. The model is an extension of previous models based on traditional supervised machine learning techniques and its aims are to improve the detection rate and reduce the false alarm rate.
12p
viannee
02-08-2023
5
4
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Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode. EMR data from 7256 survivor PICU episodes (5632 patients) collected between 2009 and 2017 at Children’s Hospital Los Angeles was analyzed. Each episode contained 375 variables representing physiology, labs, interventions, and drugs.
8p
visteverogers
24-06-2023
4
2
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As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal relationships to the suspect medications. We combined text mining with machine learning to construct and evaluate such a system to identify medication-related adverse event reports.
8p
visteverogers
24-06-2023
4
3
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Amid electronic health records, laboratory tests, and other technology, office-based patient and provider communication is still the heart of primary medical care. Patients typically present multiple complaints, requiring physicians to decide how to balance competing demands.
12p
visteverogers
24-06-2023
3
2
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Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective of this study is to train and validate an NLP classifier for identifying patients with alcohol misuse.
8p
visteverogers
24-06-2023
4
2
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The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring.
12p
visteverogers
24-06-2023
6
2
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Existing screening tools for early detection of autism are expensive, cumbersome, time- intensive, and sometimes fall short in predictive value. In this work, we sought to apply Machine Learning (ML) to gold standard clinical data obtained across thousands of children at-risk for autism spectrum disorder to create a low-cost, quick, and easy to apply autism screening tool.
8p
visteverogers
24-06-2023
4
2
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Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data.
7p
vighostrider
25-05-2023
4
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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To improve patient safety and clinical outcomes by reducing the risk of prescribing errors, we tested the accuracy of a hybrid clinical decision support system in prioritizing prescription checks.
7p
vighostrider
25-05-2023
5
2
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When studying any specific rare disease, heterogeneity and scarcity of affected individuals has historically hindered investigators from discerning on what to focus to understand and diagnose a disease. New nongenomic methodologies must be developed that identify similarities in seemingly dissimilar conditions.
9p
vighostrider
25-05-2023
2
2
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Supervised machine learning is considered as one of the methods to find out variable relationships more informative compared to traditional statistical methods. In this article, both traditional statistical analysis and supervised machine learning approaches are used to study consumer behavior through their willingness to pay for irrigation services.
9p
vimalfoy
08-02-2023
3
2
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Lecture Introduction to Machine learning and Data mining: Lesson 3. This lesson provides students with content about: unsupervised learning; clustering; basic learning problems; partition-based clustering; evaluation of clustering quality;... Please refer to the detailed content of the lecture!
30p
hanlamcoman
26-11-2022
16
5
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