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Predicting mortality of Middle East respiratory syndrome (MERS) patients with identified outcomes is a core goal for hospitals in deciding whether a new patient should be hospitalized or not in the presence of limited resources of the hospitals. We present an oversampling approach that we call Greedy-Based Oversampling Approach (GBOA). We evaluate our approach and compare it against the standard oversampling...
In recent years the power wall has prevented the continued scaling of single core performance. This has lead to the rise of dark silicon and motivated a move toward parallelism and specialization. As a result, energy-efficient high-throughput GPU cores are increasingly favored for accelerating data-parallel applications. However, the best way to efficiently communicate and synchronize across heterogeneous...
Cyberattacks threatening the industrial processes and the critical infrastructures have become more and more complex, sophisticated, and hard to detect. These cyberattacks may cause serious economic losses and may impact the health and safety of employees and citizens. Traditional Intrusion Detection Systems (IDS) cannot detect new types of cyberattacks not existing in their databases. Therefore,...
In this paper, we propose two novel active learning algorithms: 1) k-mode for classifying the certain and uncertain dataset in a whole dataset, 2) Priority R-Tree clustering the certain and uncertain data for each domain. They handle both supervised and unsupervised dataset. These techniques improve the robustness and accuracy of the clustering outcome to a great extent. By minimizing the expected...
Support vector machine is a classifier, based on the structured risk minimization principle. The performance of the SVM, depends on different parameters such as: penalty factor, C, and the kernel factor, o. Also choosing an appropriate kernel function can improve the Recognition Score and lower the amount of computation. Furthermore, selecting the useful features among several features in dataset...
The classification of an image scene having multiple class labels produces significant challenge to the researchers. A semantic scene may be described by multiple objects or by multiple classes. For example, a beach scene may also contain mountain or buildings in the background. This research work proposes a multi-label scene classification model by using Binary Relevance (BR) based one-versus-rest...
Face recognition (FR) has received significant attention as one of the most successful applications of image analysis and understanding, during the past several years and is an active yet challenging topic in computer vision applications. Also potentially will help in identifying ultra-rare and developmental disorders. Linear discriminant analysis (LDA) has been widely used for feature extraction...
Paying attention to different pictures is related to complex information processing in the brain. Categorizing visual objects using the electroencephalogram (EEG) signal of subject along with paying attention to pictures, is properly possible. The aim of this paper is to analyze the mental signal in order to show the differences in cognitive patterns during paying attention to sets of different pictures...
Information needs of the users have grown exponentially with the advent of advancements in information and communication technology. The traditional ways of searching information from the online resources has been evolved and the tendency is geared more towards getting quality contents. In healthcare domain, the clinical researchers and physicians are even more interested to find quality information...
An accelerometer embedded wrist-worn device is widely used for sleep assessment. However, conventional methods determine a state of user to "sleep" or "wakefulness" according to whether the accelerometer value of individual epoch exceeds a certain threshold or not. As a result, high miss-classification rate is observed due to user's small intermittent movements while sleeping and...
With the rapid development of the Internet, the demand of people on the Internet retrieval is increasing gradually. The meta-search engine is different from general search engine. It combines multiple search engine results and returns them to the user, but in order to meet the needs of different users, we need classify the results returned by the meta-search engine. Therefore, this article will discuss...
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one...
In pattern classification or machine learning, instance-based learning (IBL) has gained much attention and can yield superior performance in many domains. In IBL, however, the storage requirement is proportional to the number of training instances. Furthermore, it usually takes too much time to classify a new, unseen instance because all training instances need to be considered in determining the...
The explosive growth of webpage number on the Web has brought up some problems in the search process. One of these problems is that the general purpose search engines often return too many irrelevant results when users are searching for specific information on a given topic. Another problem is the massive increase in the number of pages to be indexed by Web search systems. In this research, two steps...
The k Nearest Neighbour (kNN) method is one of the most popular algorithm in clustering and data classification. The kNN algorithm founds to be performed very efficient in the experiments on different dataset. In this paper, we focus on the classification problem. The algorithm is experienced over Leukemia dataset. Initially three feature selection algorithm Consistency Based Feature Selection (CBFS),...
Hyperspectral imaging is the procedure to gather and handle information across the electromagnetic spectrum. The fundamental objective of hyperspectral imaging is to achieve the spectrum for every pixel in the picture. The spectrum helps in computer vision, i.e., locating items, material detection or process discovery. This approach is constantly developing in the field of remote sensing applications...
Aiming at the problem of features instability in specific emitter identification, this paper presents a based on one-class classifier feature preprocessing algorithm to detect the unstable features. The algorithm takes advantage of one-class classifier of property that can describe the distribution of given data sets. Its basic steps include: Firstly, we divide the feature time series into N segments...
Network security has become one of the well-known concerns in the last decades. Machine learning techniques are robust methods in detecting malicious activities and network threats. Most previous works learn offline supervised classifiers while they require large amounts of labeled examples and also should update models because the data change over time in real world applications. To alleviate these...
Alzheimer disease is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. The diagnosis of Alzheimer's disease is often made very late. Several years pass after the start of the first manifestations before the diagnosis is made. According to many researchers, roll back 5 years to the start of the disease would reduce the frequency of 50%. In this work, we propose...
A concept of Four Properties (SiQi) of Chinese herbs is the important part of traditional Chinese medicine theory. The Chinese clinical medicine is a process of dialectical theory of governance of Chinese medicine prescriptions based these four properties. The Chinese medicine prescription uses a "Cold" and "Hot" model to judge the properties of Chinese herbs, and also judge the...
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