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Search results for: ATTRIBUTE PREDICTION
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Combination of instrumental and qualitative descriptive analysis for evaluation of selected tonic waters quality features
PublicationThe combination of sensory and instrumental analysis was applied for quality assurance of selected tonic waters. The Quantitative Descriptive Analysis (QDA) in terms of fourteen sensory attributes (aroma, astringency, bite, burn, numbing, tongue heaviness, carbonation, mouth coating, sweet taste, sour taste, bitter taste, sweet aftertaste, sour aftertaste, bitter aftertaste) of selected tonic waters was performed by sensory experts....
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Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
PublicationPlain weave composite is a long-lasting type of fabric composite that is stable enough when being handled. Open-hole composites have been widely used in industry, though they have weak structural performance and complex design processes. An extensive number of material/geometry parameters have been utilized for designing these composites, thereby an efficient computational tool is essential for that purpose. Different Machine Learning...
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Clothes Detection and Classification Using Convolutional Neural Networks
PublicationIn this paper we describe development of a computer vision system for accurate detection and classification of clothes for e-commerce images. We present a set of experiments on well established architectures of convolutional neural networks, including Residual networks, SqueezeNet and Single Shot MultiBox Detector (SSD). The clothes detection network was trained and tested on DeepFashion dataset, which contains box annotations...