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Identifying hand configuration is a critical feature of sign language translation. In this paper, we describe our approach to recognize hand configurations in real time with the purpose of providing accurate predictions to be used in automatic sign language translation. To capture the hand configuration we rely on data gloves with 14 sensors that measure finger joints bending. These inputs are sampled...
Parametric statistical tests (e.g., t-tests) can sometimes return highly significant results in cases that would be considered uninformative, such as when the individuals’ accuracies are just above chance. This paper demonstrates that permutation tests can produce the expected non-significant results in these datasets. The properties of null distributions underlying this difference in significance...
Diabetes is one of the most common metabolic diseases and the statistics show that one in eleven adults has diabetes, but one in two adults with diabetes is undiagnosed, and in 2040 one in 10 adults will have diabetes. In this paper is proposed a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classifying patients with diabetes based on data sets with diabetic patients (Pima Indians...
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...
Data mining techniques is rapidly increasing in the research of educational domains. Educational data mining aims to discover hidden knowledge and patterns about student performance. This paper proposes a student performance prediction model by applying two classification algorithms: KNN and Naïve Bayes on educational data set of secondary schools, collected from the ministry of education in Gaza...
Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naïve Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to...
Personality is the defining essence of an individual as it guides the way we think, act and interpret external stimuli. Classification of personality is important as it can serves as a framework in the job assignment task, particularly, in the high risk job including the Police Force. There are many attributes of individual traits but not all of them can be used to indicate individual personality...
MOOCs are Massive Open Online Courses, which are offered on web and have become a focal point for students preferring e-learning. Regardless of enormous enrollment of students in MOOCs, the amount of dropout students in these courses are too high. For the success of MOOCs, their dropout rates must decrease. As the proportion of continuing and dropout students in MOOCs varies considerably, the class...
In recent years, Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) has been successfully employed in food science as a control technique for the prevention of fraud according to food and labeling regulations. In this work, we propose the use of GC-IMS technique to assess the quality of Iberian ham with regard to the Iberian Pig's diet (either nourished with feed or with acorns). For this purpose,...
Face recognition is an active and challenging task in pattern recognition and computer vision application. Sparse representation based classification has been verified to be powerful for face recognition. This paper proposes the metaface block sparse bayesian learning (MBSBL) based on the framework of sparse representation. The MBS-BL combines the metaface learning and block sparse bayesian learning...
In this paper, we present auto-encoder (AE), stacked auto-encoder (SAE) and sparse auto-encoder (SPAE) to classify gaits of horse riding for real riding coaching. The parameters of each auto-encoder are adjusted to compare the performance. The data is collected from 16 inertial sensors attached to a motion capture suit to construct a motion database. We build the motion features as the method of gaits...
This paper presents an extension of a comparative study of classifier architectures for automatic fault diagnosis, with a special emphasis on the Extreme Learning Machine (ELM), with and without kernel mapping. Besides the explanation of the ELM model, an attempt is made to find theoretical hints of the excellent generalization capabilities of this model, based on the findings of Cover about dichotomies...
Traditional stacked autoencoders have an equal number of encoders and decoders. However, while fine-tuned as a deep neural network the decoder portion is detached and never used. This begs the question: ‘do we need equal number of decoders and encoders’? In this study we explore asymmetric autoencoders — unequal number of encoders and decoders. We specifically address two tasks — 1. Classification...
A conformai predictor outputs prediction regions, for classification label sets. The key property of all conformai predictors is that they are valid, i.e., their error rate on novel data is bounded by a preset significance level. Thus, the key performance metric for evaluating conformal predictors is the size of the output prediction regions, where smaller (more informative) prediction regions are...
AdaBoost is one of the most popular algorithm for classification and has been successfully used for text classification, face detection and tracking. However noise sensitivity is regarded as a major disadvantage and previous works show that AdaBoost will be overfitting when dealing with the data sets with noisy data. To improve the noise tolerance of conventional AdaBoost, this paper proposed a preprocessing...
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...
Multi-temporal PolSAR data is suitable for crops classification and growth monitoring. It is still difficult to establish a classifier with good robustness and high generation over a long temporal acquisition duration. This work aims to provide a solution to this task by exploring benefits from both the target scattering mechanism interpretation and the advanced deep learning. A polarimetric-feature-driven...
Recurrent Neural Networks are widely used tools for the classification of variable length data. However, their training is generally a very time-consuming task, especially for problems with high dimensions. The classification method proposed in this paper aims to provide a fast and simple alternative. Extended Sequential Fuzzy Indexing Tables are following the principle behind lookup table classifiers...
Accurate and robust risk prediction methods are of critical importance in calculating insurance costs. In the present paper, we study the case of vehicle insurance and develop a computational intelligence based method for obtaining risk estimates based on the data provided by the client to the insurance company. The method is based on analyzing the contracts, processing the input data, applying classification,...
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