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The use of technology has grown extensively in many fields, including that of the medical sciences. However, there has not been a lot of research done in the field regarding the use of basic stance parameters to classify the presence or lack thereof of locomotive disorders. In this paper, we shall be presenting the most optimum classification algorithm for the binary classification of a variety of...
Residual classifiers are common in dictionary-based multiclass classification. This paper proposes the concept of performance functions for residual classifiers. A performance function for multiclass classifications is a conceptual measurement function that combines local and global measurements. In general, the performance function is nonlinear. To explore the properties of the performance function,...
In this paper, the single-channel EEG based classification systems using simple extracted features are investigated. Each classification system contains the following stages: data acquisition, signal decomposition, feature extraction, and classification. In addition to using the filter bank and empirical mode decomposition (EMD) methods for signal decomposition, a sparse discrete wavelet packet transform...
Chinese traditional visual culture symbols (CT-VCSs) is formed in the tradition and has the characteristic of Chinese unique ideological and cultural connotation. It is a visual cultural heritage of Chinese culture. So the research on CT-VCSs has important practical significance. In this paper, it is mainly about the recognition and classification of CT-VCSs based on machine learning. We make use...
Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity...
Nowadays, people are using stealth sensors to detect intruders due to their low power consumption and wide coverage. It is very important to use lightweight sensors for detecting real time events and taking actions accordingly. In this paper, we focus on the design and implementation of wireless surveillance sensor network with acoustic and seismic vibration sensors to detect objects and/or events...
Traditional data processing methods for electronic noses (e-noses) need to use the whole response curves (including rise, steady and recovery phases) of sensor array, which leads to a long sampling time. The traditional methods also perform many steps such as signal pre-processing, feature generation/reduction, and classification, which increase the difficulty of selecting a suitable method for each...
With the appearance and development of the technology of malicious codes and other unknown threats, information security has drawn people's attention. In this paper, we investigate on behavior-based detection which is different from traditional static detection technology. Firstly, we discuss the procedure in detail, especially feature extraction and classification. Several machine learning methods...
System availability is one of the major requirements expected from systems in the trading domain. In order to prevent system outages that can deteriorate system availability, anomaly detection must be able to assess the status of the system and detect anomalies that can lead to failures on a real-time basis. This paper presents a framework for anomaly detection for complex trading systems based on...
The development in information technology and wearable technologies has had a significant impact on clinical decision support systems. In this process a lot of data needs to be transferred and processed. Therefore, it is necessary to compress the data with minimum loss. Compression techniques in electrocardiographic signals is a topic of growing importance. In this study, we analyzed the effect of...
Nowadays, the ability to convert call records from voice to text makes it possible to apply text mining methods to extract information from calls. In this study, it is aimed not only to evaluate the sentiment (positive/negative) of the calls in general, but also to measure the customer satisfaction and representative's performance by using call record texts. New features have been extracted from texts...
This paper proposes methods to classify the plants using images taken from agricultural lands. Wheat, maize and lentil images are used. Texture features of agricultural land images are obtained using Gray Level Co-occurrence Matrix (GLCM) and Laws' Texture Energy Measures which are two of texture analysis methods. The texture features vectors which are generated with these two methods are classified...
Microarray data is measured with numerous features, selection of the most important genes from such data sets is an essential step towards maximizing classification accuracy. Machine learning algorithms have proved to be the most effective choice in gene selection and cancer classification research. In the past, human cancer classification was basically morphological, introduction of microarray technology...
Recently, polyhedral conic classifiers have become popular since they perform better compared to the Support Vector Machines (SVMs). Cone vertex of polyhedral conic classifiers is an important parameter and it is generally taken as the mean of positive data in literature. In this paper, we studied optimally estimating the cone vertex to improve the accuracy of the polyhedral conic classifiers. The...
In this study, a low-cost system which classifies different road conditions (asphalt, gravel, snowy and stony road) using acoustic signal processing is proposed. Thus it is aimed to estimate road/tire friction forces in the active safety systems. Classical acoustic signal processing methods which are linear predictive coding (LPC), power spectrum (PSC) and mel-frequency cepstrum coefficients (MFCC)...
AdaBoost is a classic ensemble learning algorithm with good classifier performance. In the past, it mainly used weak classifier as base classifier, such as KNN. They are simple and easy to train, but the essence of the weak classifier, it is impossible to get very high classification accuracy. In order to improve the correct rate, this paper introduces the AdaBoost ensemble classifier based on convolutional...
Pistachio is widely consumed as food which requires the nut to be cracked before usage. Automating the cracking process requires quality control which can be done visually. A system developed to perform this task provides over 98% accuracy. First, the pistachio is segmented from the background using support vector regression (SVR) or deep convolutional networks. Then a classification method based...
Assessing a mental workload level using electroencephalography (EEG) signals represents an active research area. The development of low-cost wireless EEG headsets drew the attention of researchers in the field of critical human-machine collaboration systems. In this paper, some classification methods are used to discriminate the working memory load levels using EEG raw data records. The brain waves...
In this paper, we propose a system that is capable of automatically differentiating between normal and abnormal heartbeats of patients using signals acquired from electrocardiography (ECG). The components of the ECG signals, that are PQRST intervals, were studied to acquire features for classification. Different time intervals of p-wave, QRS complex and t-wave were used as features. These features...
Video Analytics on low/high resolution security camera images has received a considerable interest in recent years. Traffic density estimation from traffic camera images can be considered as one of these subjects. Traditionally GPS data from commercial vehicle fleets have been utilized to estimate traffic density on roads. Traffic density estimation has been implemented using image processing and...
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