Existing Machine Learning Approaches to Shadow Detection
Shadow Detection in Aerial Images using Machine Learning
... candidate shadow pixels or regions, and 2) classifying them into either foreground or ...each shadow candidate region is classified as an object or shadow by checking the ...
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Machine Learning Approaches on External Plagiarism Detection
... the machine learning approach, the performance is strongly influenced by at least 3 ...the learning process machine, then the performance will not be ...In machine learning like ...
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Machine Learning Approaches for Diabetes Risk Factor Detection
... participated in this study. Based on 38 anthropometric measures, we compared predictions of FPG status using individual against joint measures using two machine learning algorithms. The standards of the ...
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Revisiting Depression Detection Paradigms Using Machine Learning Approaches
... receiving adequate treatment (Wang et al.). This analysis of social media (Scott and Carrington) allows for development of methods for the early detection of depression in users. If the developed method could ...
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Machine learning approaches to improving mispronunciation detection on an imbalanced corpus
... error detection task then consists of identifying which realization of a given word occurred in a speaker’s ...the detection results by simply comparing the acoustic likelihoods among pronunciation variants ...
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A comparative evaluation of machine learning approaches in SMS spam detection
... Spam detection is a significant problem which is considered by many researchers by various developed ...supervised learning algorithm (Hybrid of Simple Artificial Immune System (SAIS) and Particle Swarm ...
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A Brief Bibliometric Survey of Leukemia Detection by Machine Learning and Deep Learning Approaches
... leukemia detection is designed based on image processing and machine learning techniques, which ends up being proficient when contrasted with manual detection ...
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Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection
... In machine learning problems, the high-dimensional features lead to prolonged classification ...intrusion detection systems have not dealt with classification of network traffic data with imbalanced ...
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In-depth comparative evaluation of supervised machine learning approaches for detection of cybersecurity threats
... intrusion detection with deep learning for fea- ture extraction followed by an ensemble of SVMs for classification, built on top of Spark, the distributed in- memory computation ...supervised ...
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Evaluating the Performance of Network Intrusion Detection System using Machine Learning Approaches
... Subtleties of every arrangement approach are given. 1. Support Vector Machines Support Vector Machines are maybe a standout amongst the most prominent and discussed machine learning calculations. They were ...
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Detection of Driver Drowsiness and Distraction Using Computer Vision and Machine Learning Approaches
... weak learning algorithm, which is a popular learning algorithm used for image classification and face ...promising detection accuracy, the proposed method was not validated for different illumination ...
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Fault Detection and Diagnosis of Electric Drives Using Intelligent Machine Learning Approaches
... 3.3 Experimental Results In this study, a three-phase, 1.2 KW, 380 volt, 50 Hz, 1400 rpm, four-pole induction motor is used to collect experimental data. The stator current signal is analyzed with the proposed fault ...
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Machine Learning Approaches to Image Deconvolution
... In Chapter 3, it turned out that an image affected by optical aberrations reveals the spatially- varying blur so well that additional calibration is not necessary. The physical properties of photographic lenses could be ...
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Machine Learning Approaches to Cyber Security
... of machine learning is it works fast, detection rate is also high and can detect attack vectors that are previously ...some machine learning ...anomaly detection methods does the ...
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Machine learning approaches for epitope prediction
... of existing prediction tools, users are recommended to submit their query sequence to more than one prediction ...major approaches for predicting MHC-I peptides, in one web server that does not rely on ...
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Machine Learning Approaches for Cancer Analysis
... Outlier detection from time-series cancer RNA-Seq data Studying the abundance of selected mRNA species throughout prostate cancer progression may provide insight into the molecular mechanisms that advance the ...
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Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data
... lesion detection employing state of the art microwave systems provide a safe, non-ionizing technique that can differentiate healthy and non-healthy tissues by exploiting their dielectric ...lesion detection ...
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Fake News Detection on Social Media: Variant Approaches And Comparison of Machine Learning Algorithms
... News Detection, Comparison, Support Vector Machine, Naïve Bayes classifier, Logistic ...develop approaches capable of analyzing, detecting and intervening in the performance of misleading ...
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Comparison of machine learning approaches for classification of invoices
... Multi-task learning has been used widely in different domains giving substantially better ...image detection for joining the sparse sub-hyperspectral models to detect a target in high spectral ...Multi-task ...
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Machine Learning Approaches for Music Information Retrieval
... 4.3 Experiments The accuracy of the performance is presented in Table 1. Here the accuracy measure is the Hamming accuracy, that is, the ratio of the number of True Positives and TrueNegative against the total number of ...
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