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Down syndrome (DS) is a genetic disorder with genome dosage imbalances and micro-duplications of human chromosome 21. It is usually associated with a group of serious diseases, including intellectual disabilities, cardiac diseases, physical abnormalities, and other abnormalities. Currently, since there is no cure for human DS, screening and early detection have become the most efficient way for DS...
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...
Recently, the combination of classification systems with semi-supervised learning has attracted researchers in several fields. Usually, for tasks with high complexity such as handwriting based age prediction, individual systems, using one classifier associated with specific data features, cannot provide satisfactory performance. In this paper, we investigate the contribution of the Co-training approach,...
With the development of the aviation industry and the improvement of people's living standard, more and more people choose aircraft as their way of travel, but the airline adjusts the price according to the revenue management in real time. The purpose of this paper is to design different decision-making tools from the customer's perspective, and to provide customers with the relevant information needed...
This paper evaluates the performance of four artificial intelligence algorithms for building energy consumption prediction. The backward propagation neural network (BPNN), support vector regression (SVR), adaptive network-based fuzzy inference system (ANFIS) and extreme learning machine (ELM) methods are reviewed and their performances for predicting building energy consumption are compared. A selection...
One of the most discussed schedule in Brazil is Education, and with it comes a lot of problems to solve. This project use of artificial intelligence, but precisely in the area of Machine Learning, to enable a solution, in relation to student monitoring. The research has done with students of technical high school education at the Federal Institute of Gois — Brazil, to find a method which would be...
For better resolving the safety risk early warning of the apron effectively, the attribute reduction algorithm based on Rough Set is used to simplify the set as the warning index set of the apron is too large. The improved Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of Support Vector Machine. Combined with the Rough Set and SVM which is optimized by the improved...
Individualized blood transfusion management would benefit from the ability to prospectively identify patients at risk of complications of blood transfusion, and target them for closer monitoring or intervention. This study presents a simple and efficient multi-task learning method for predicting multiple surgical outcomes based on the weighted least squares support vector machine. To accelerate the...
Machine Learning (ML) approaches are widelyused classification/regression methods for data mining applications. However, the time-consuming training process greatly limits the efficiency of ML approaches. We use the example of SVM (traditional ML algorithm) and DNN (state-of-the-art ML algorithm) to illustrate the idea in this paper. For SVM, a major performance bottleneck of current tools is that...
Twin support vector regression and its extensions have been widely applied in machine learning and data mining. However, most of them can not achieve the satisfactory performances when the noise is involved. To this end, this paper presents a weighted least squares twin support vector regression (WLSTSVR) which can reduce the influence of the noise on prediction accuracy by using the information of...
The basic idea behind the classifier ensembles is to use more than one classifier by expecting to improve the overall accuracy. It is known that the classifier ensembles boost the overall classification performance by depending on two factors namely, individual success of the base learners and diversity. One way of providing diversity is to use the same or different type of base learners. When the...
Tuberculosis is one of the top ten causes of death worldwide. Although this disease is curable and preventable, yet many new tuberculosis cases still occur especially in developing countries. Many low-income families cannot afford the medical diagnosis for tuberculosis. Therefore, this paper proposes an initial screening for tuberculosis infection using a data mining approach. In this paper, the initial...
Multi-label text classification plays a significant role in information retrieval area. The effectiveness of the techniques is especially important in the case of medical documents. In the paper, application of feature selection methods for improving multi-label medical text classification is discussed. We examine combining problem transformation methods with different approaches to feature selection...
Gas Insulted Switchgear (GIS) plays an important role in switch, control and protection and its safe and reliable operation is vital to the power system. However, its partial discharge failure usually cause serious consequences. It is necessary to monitor SF6 insulated power equipment operation state by detecting and analyzing the gas decomposition components with the aid of pattern recognition algorithm...
Wind power prediction is very important to guarantee security and stability of the wind farm and power system operation, and wind speed forecasting is the key to wind power prediction. Due to dramatic changes and shorter collection intervals in wind speed, it generates a larger numbers of samples, which affects modeling time and accuracy. Therefore, a short-term wind speed prediction method based...
This paper proposes a new algorithm for the multiple instance learning problem (MIL) and investigates its application for detecting Attention Deficit Hyperactive Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging data. The core component of many kernel-based MIL algorithms is usually an SVM-like batch optimization framework, hence scaling to large datasets like fMRI is often...
Gene Regulatory Network (GRN) represents the regulatory interactions between genes. Experimental methods are capable of determining the nature of gene regulation in a given system, but are time-consuming and expensive. High-throughput technologies produce large number of gene expression data. These data along with additional information from heterogeneous data sources enable computational biologists...
The intent of the image classification process is to objectively categorize an image visual contents into semantic meanings. The classification process is a challenging task due to the difficulty associated with extracting and identifying relevant shape information. In this paper, we introduce a new fusion algorithm that combines the strengths of deep learning and mid-level image descriptors. Our...
In clinical practice, the magnetic resonance imaging (MRI) is a prevalent neuroimaging technique for Alzheimer's disease (AD) diagnosis. As a learning using privileged information (LUPI) algorithm, SVM+ has shown its effectiveness on the classification of brain disorders, with single-modal neuroimaging samples for testing but multimodal neuroimaging samples for training. In this work, we propose to...
Real-time crash prediction models are playing a key role in transportation information system. Support vector machine (SVM), a classification learning algorithm, was introduced to evaluate real-time crash risk. The size of traffic dataset is always large with a high accumulating speed. By applying a warm start strategy, an incremental learning algorithm is introduced to update the original model....
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