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We present a new approach for remote sensing image classification. The methodology combines many related tasks namely non linear source separation, feature extraction, feature fusion and learning classification. Nonlinear source separation is a pre-processing stage that aims to compensate the nonlinear mixing natural phenomenon. Latent signals, called sources are transformed to the feature presentation...
Domain adaptation methods show better ability to learn when the training data is not identically and independently distributed. The key task of domain adaptation is to find a suitable measure to scale the distributed difference between source domain and target domain. So a projected maximum divergence discrepancy distance measure is proposed. Based on the structural risk minimization theory and the...
This paper introduces a novel approach to predict human motion for the Non-binding Lower Extremity Exoskeleton (NBLEX). Most of the exoskeletons must be attached to the pilot, which exists potential security problems. In order to solve these problems, the NBLEX is studied and designed to free pilots from the exoskeletons. Rather than applying Electromyography (EMG) and Ground Reaction Force (GFR)...
With the emerging increase of diabetes, that recently affects around 346 million people, of which more than one-third go undetected in early stage, a strong need for supporting the medical decision-making process is generated. A number of researches have focused either in using one of the algorithms or in the comparisons of the performances of algorithms on a given, usually predefined and static datasets...
Changes in the network topology such as large-scale power outages or Internet worm attacks are events that may induce routing information updates. Border Gateway Protocol (BGP) is by Autonomous Systems (ASes) to address these changes. Network reachability information, contained in BGP update messages, is stored in the Routing Information Base (RIB). Recent BGP anomaly detection systems employ machine...
The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being...
Recent developments in radio technology and processing systems, Wireless Sensor Networks (WSNs) are tremendously being used to perform an assortment of tasks from their atmosphere. Localization plays the most important task in WSNs. Accuracy is the one of the major problems facing localization. In this paper, we propose an improved localization algorithm based on the learning concept of support vector...
While systematic reviews (SRs) are positioned as an essential element of modern evidence-based medical practice, the creation and update of these reviews is resource intensive. In this research, we propose to leverage advanced analytics techniques for automatically classifying articles for inclusion and exclusion for systematic review update. Specifically, we used the soft-margin Support Vector Machine...
Aiming at the problem of low accuracy in intrusion detection system, this paper established a genetic support vector machine (SVM) model according to the features of genetic algorithm and support vector machine algorithm. The model firstly optimizes the support vector parameters according to genetic algorithm, then we build the intrusion detection model with support vector machine optimized and use...
Indonesia have a massive number of SMEs, but with a very low revenue. An alternative to increase revenue is by using internet. Some SMEs already develop their website, but they don't have same navigation. The websites confuse the potential buyers. So, a website's aggregator is essential. This aggregator is made without the owner of the SMEs to register their website, which means it can automatically...
Microarray studies and gene expression analysis have received tremendous attention over the last few years and provide many promising paths toward the understanding of fundamental questions in biology and medicine. High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Feature selection (FS) and clustering are among the methods used in...
Todays, feature selection is an active research in machine learning. The main idea of feature selection is to select a subset of available features, by eliminating features with little or no predictive information. This paper presents a hybrid model with a new local search technique based on reinforcement learning for feature selection. We combined the particle swarm optimization (PSO) with support...
Credit scoring is becoming a competitive issue with rapid growth and significant advance. Building a satisfactory credit model has attracted lots of researchers in the past decades and it is still one of the hottest research topics in the field of credit industry. This paper emphasizes on surveying the development of the artificial intelligence technologies for solving credit scoring. It covers algorithms...
Mammography is a standard method for early diagnosis of breast cancer. In this paper, a method has been provided for improving quality of mammographic images to help radiologists so that probability of benign or malign breast lesions can be detected faster and more accurate and false positive rate (FPR) can be reduced. The presented algorithm includes 3 main parts of preprocessing, feature extraction...
The research reported in biomedical articles often involves large numbers of investigators at different institutions. To properly credit these investigators, an article's authors frequently name them together in some part of the article. These Investigator Names (IN) now constitute a required field in the MEDLINE® citation for the article. The automated extraction of these names is implemented in...
This paper designs SVM radar emitter classification and identification methods based on the AP clustering. Using AP clustering algorithm to optimize the data set obtains a high-quality, small-sample training set of SVM classifier. Experimental results show that compared with the traditional SVM classifiers, the hybrid classifier has higher classification accuracy and furthermore Radar emitter classification...
Malware family identification is a complex process involving extraction of distinctive characteristics from a set of malware samples. Malware authors employ various techniques to prevent the identification of unique characteristics of their programs, such as, encryption and obfuscation. In this paper, we present n-gram based sequential features extracted from content of the files. N-grams are extracted...
To improve automotive active safety and guarantee the safety of pedestrians at night time, a fast pedestrian detection method based on monocular far-infrared camera for driver assistance systems is proposed. According to the distribution of gray-level intensity of pedestrian samples, an adaptive local dual threshold segmentation algorithm is executed first to extract candidate regions. The presented...
Motor imagery electroencephalogram signals are the only bio-signals that enable locked-in patients, who have lost control over every motor output, to communicate with and control their surroundings. Brain Machine Interface is collaboration between a human and machines, which translates brain waves to desired, understandable commands for a machine. Classification of motor imagery tasks for BMIs is...
Musical murmur, a typical occurrence of heart sound, frequently found in the pediatric population reflects no harm compared to murmur. 8 out of 10 children are then by nature prone to this physiological phenomenon. Correctly distinguishing musical murmur from pathological one is a critical assessment to avoid unnecessary treatment at vain cost disbursement and ease parents' concern. This research...
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