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In this paper, we address interesting questions about how feng shui influences house price from a data perspective. First, is feng shui likely to influence house price? Second, how do different feng shui features, e.g., house shape, master bedroom location, and other interior room arrangements, influence the price? Third, can we automatically diagnose the feng shui problems of a house? From a dataset...
While recent advances in deep learning pushed the state-of-the-art in object detection and semantic segmentation, it often comes at the cost of a considerable annotation effort. Thus, weakly supervised learning became of increasing interest. In this paper a novel approach to the challenging task of weakly supervised segmentation and object localization will be presented. The problem is tackled from...
Convolutional Neural Network (CNN) has received remarkable achievements in hyperspectral image (HSI) classification. However, how to effectively implement spatial context that has been demonstrated to be crucial for classification of HSI is still an open issue. Current CNNs for hyperspectral classification are restricted into a small scale due to small-scale input and limited training samples. Therefore,...
Classification of Alzheimer 's disease (AD) from normal control (NC) is important for disease abnormality identification and intervention. The current study focused on distinguishing AD from NC based on the multi-feature kernel supervised within- class-similarity discriminative dictionary learning algorithm (MKSCDDL) we introduced previously, which has been derived outperformance in face recognition...
In this study, we propose a two-stage method for material segmentation in hyperspectral images. The first stage employs a Convolutional Neural Network (CNN) to predict the material label at individual pixels. The second stage further refines the segmentation by a fully-connected Conditional Random Field (CRF) framework. For the first stage, we experimented with two different network architectures...
In this paper, we address the lack of interpretability of Support Vector Machine (SVM) via rules based on support vectors. The lack of intuitive explanation of the rules in the domain of medical whole slide image classification by determining a reduced subset of Scale-Invariant Feature Transform (SIFT) features and linear dimensionality reduction. This reduced subset of SIFT features that participate...
Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable...
Fingerprinting Localization Solutions (FPSs) enjoy huge popularity due to their good performance and minimal environment information requirement. Considered as a data-driven approach, many modern data analytics can be used to improve its performance. In this paper, we propose tow learning algorithms, namely a deep learning architecture for regression and Support Vector Machine (SVM) for classification,...
Tree structures are commonly used to model relationships between body parts for articulated Human Pose Estimation (HPE). Tree structures can be used to model relationships among feature maps of joints in a structured learning framework using Convolutional Neural Networks (CNNs). This paper proposes new data-driven tree models for HPE. The data-driven tree structures were obtained using the Chow-Liu...
Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of points away from the training data. The computation of the SVDD classifier requires a kernel function, and the Gaussian kernel is a common choice for...
Predicting early signs of illness in older adults by utilizing a continuous, unobtrusive nursing home monitoring system has been shown to increase the quality of life and decrease the cost of care. Illness prediction is based on sensor data such as motion and bed and uses algorithms such as support vector machine (SVM) or k-nearest neighbor (kNN). One of the greatest challenges in developing prediction...
In the past few years, wireless sensor networks (WSNs) have been increasingly gaining impact in the real world with with various applications such as healthcare, condition monitoring, control networks, etc. Anomaly detection in WSNs is an important aspect of data analysis in order to identify data items which does not conform to an expected pattern or other items in a dataset. This paper describes...
Brain tumors, especially high-grade gliomas, are one of the most lethal cancers for humankind today. Early and accurate diagnosis of tumor grading is the key for subsequent therapy and treatment. In the past, conventional computer-aided diagnosis relies on handcrafted features from magnetic resonance images (MRI), which are usually inaccurate and laborious. Recently, deep neural networks have been...
Post-database searching is a key procedure for peptide spectrum matches (PSMs) in protein identification with mass spectrometry-based strategies. Although many machine learning-based approaches have been developed to improve the accuracy of peptide identification, the challenge remains for improvement due to the poor quality of data samples. CRanker has shown its effectiveness and efficiency in terms...
Generative models are used in an increasing number of applications that rely on large amounts of contextually rich information about individuals. Owing to possible privacy violations, however, publishing or sharing generative models is not always viable. In this paper, we introduce a novel solution for privately releasing generative models and entire high-dimensional datasets produced by these models...
Support Vector Machines (SVMs) are supervised learning models of the machine learning field whose performance strongly depended on its hyperparameters. The Bio-inspired Optimization Tool for SVM (BIOTS) tool is based on a Multi-Objective Particle Swarm Algorithm (MOPSO) to tune hyperparameters of SVMs. In this work, BIOTS is proposed along with a custom hardware design generator (VHDL) that implements...
This paper aims to develop a framework for vehicle type classification using convolutional neural network based on vehicle rear view images. Compared with the extraction of the appearance features from vehicle side view and frontal view images, there has been relatively little research on vehicle type classification by using vehicle rear view images' information. The vehicle rear view images are detected...
In order to improve the accuracy and stability of industrial fault detection and diagnosis, this paper introduces the deep learning theory and proposes an improved Deep Belief Networks (DBNs). In the first, this paper introduces the “centering trick” in the pre-training process of network. This method is done by subtracting offset values from visible and hidden variables. Then, in the process of network...
Manual wafer-level die inking is a common procedure for excluding die locations that are likely to be defective. Although this is a more cost-effective process, as compared to the expensive burn-in tests, it remains a labor-intensive step during IC testing. For each manufactured wafer, test engineers have to visually inspect every failure map in order to identify any regions where additional die need...
Convolutional neural networks (CNNs) provide the current state of the art in visual object classification, but they are far less accurate when classifying partially occluded objects. A straightforward way to improve classification under occlusion conditions is to train the classifier using partially occluded object examples. However, training the network on many combinations of object instances and...
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