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By analyzing the disadvantages of the traditional KNN using lazy learning that directly classify the data based on the K neighboring classes using the majority voting method, a new Sigmoid weighted classification algorithm WKS (Weighted KNN Based On Sigmoid) was proposed. WKS provides a new method for learning and training, since each training data di ∊ D contributes to the correct classification...
Research on activity recognition provides a wide range of ubiquitous computing applications. Once activities are recognized, computers can use this information to provide people with suitable services. In the past decade, many classification algorithms have been applied to activity recognition. However, most of them were based on the use of inertial measurement sensors, such as tri-axial accelerometers...
This paper shows a simple approach for fake news detection using naive Bayes classifier. This approach was implemented as a software system and tested against a data set of Facebook news posts. We achieved classification accuracy of approximately 74% on the test set which is a decent result considering the relative simplicity of the model. This results may be improved in several ways, that are described...
In the arena of biomedical engineering, the classification and analysis of epilepsy from Electroencephalography (EEG) signals forms an important area of research. When the neurons get hyper excited, seizures occur causing a lot of inconvenience and trouble to the patient. For the study of the predominant abnormalities in the cerebral cortex of the brain, EEG is used widely. Due to the long nature...
This research presents framework for real time face recognition and face emotion detection system based on facial features and their actions. The key elements of Face are considered for prediction of face emotions and the user. The variations in each facial feature are used to determine the different emotions of face. Machine learning algorithms are used for recognition and classification of different...
The main objective of the spatial image classification is to extract information classes from a multiband raster spatial image. The network structure and number of inputs are the key factors in deciding the performance and accuracy of the traditional pixel based image classification techniques like Support Vector Machines (SVM), Artificial Neural Networks (ANN), Fuzzy logic, Decision Trees (DT) and...
Detection of string and column delimiters is a critical first step in the automated ingestion of files containing tabular data. In this paper we present an algorithm that uses a logistic-regression classifier to evaluate whether a particular choice of delimiters is correct. The delimiter choice that is given the highest score by the classifier is chosen as the one most likely to be correct. The algorithm...
In this paper, we try to make an author identification of two ancient Arabic religious books dating from the 6th century: The holy Quran and the Hadith. The authorship identification process is achieved through four phases which are: documents collection, text preprocessing, features extraction and classification model building. Thus, two series of experiments are undergone and commented. The first...
Nowadays Opinion mining is given more important, since it provides decision makers to estimate the success of a newly proposed techniques, novel ad campaign or novel product launch. In general, supervised methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify the opinions. In some cases SVM performs better classification and some cases ANN performs better...
In this paper, an implementation of gesture recognition using Hidden Markov Model to classify particular gestures on Sigeh Penguten traditional Dance is presented. The preliminary research is focused on recognition of dancers' hand gestures, i.e. ‘Sembah Depan’, ‘Sembah Kiri’, and ‘Sembah Kanan’ gestures based on their collected hands marker positions. The experimental results show that the proposed...
In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed. New output neurons corresponding to new labels are added and the neural network connections and parameters are automatically restructured as if the label has been introduced from the beginning. This work is the first of the kind in multi-label...
When traditional sample selection methods are used to compress large data sets, the computational complexity turns out to be very high and it is really time consuming. To avoid these shortcomings, we propose a new method to select samples based on non-stable cut points. With the basic characteristic of convex function that its extreme values occur at the endpoints of intervals, the method measures...
Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-label classification. Traditional binary and multi-class classifications are sub-categories of single-label classification. Several classifiers are developed for binary, multi-class...
A novel method called boundary distance is proposed for pre-extracting support vectors. It first calculates the distance between the sample and the other class sample. According to sort distance, the less distance samples, and nearest neighbor samples in the other class, are used as boundary samples. As the boundary samples include most support vectors, it greatly declines the training time without...
Nearest Neighbor Classifiers demand high computational resources i.e, time and memory. Two distinct methods are followed by researchers in Pattern Recognition to reduce this computational burden. The first method is reducing the reference set or training set and the second method is dimensionality reduction which are referred as Prototype Selection and Feature Reduction(a.k.a Feature Extraction or...
The increasing number of training algorithms along with their convincing results will make this question that which algorithm will be more efficient. This study aims to perform some widespread tests on some well-known training algorithms (Levenberg - Marquardt, Resilient back propagation and Scaled conjugate gradient) to evaluate their performance for scene illumination classification. The results...
Nowadays, there are a lot of online repositories containing thousands of very useful educational resources for the educational community. To take full advantage of these resources requires a simple, direct and effective access to those resources that are of interest, therefore, it is necessary that those resources are ordered or ranked based on some criteria? -- that is to say, they have to be classified...
Random subspace method (RSM), which randomly selects low dimensional feature subspace from the original high dimensional feature space to form new training subsets, is an effective ensemble learning method for high dimensional samples. However, RSM also has the drawbacks: Random selection of features does not guarantee that the selected inputs have the necessary discriminant information. If such is...
This study presents a comparative algorithms for oil spill automatic detection from different RADARSAT-1 SAR different mode data and ENVISAT ASAR data. Three algorithms are involved: Entropy, Mahalanobis, and Artificial Neural Network (ANN) algorithms. The study shows that ANN provide automatically oil spill detection with error of standard deviation of 0.12 which is lower than Entropy and the Mahalanobis...
In this work, a pedestrian detection method based on adaptive boosting is proposed. The proposed method works on still images. The features utilized in the work are derived from Haar-like templates. An Adaboost classifier is utilized for both feature selection and classification. To show the effectiveness of the proposed algorithm, the system is trained by using Nicta Pedestrian Dataset and tested...
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