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This paper aimed at introducing a completely automated Arabic phone recognition system based on Enhanced Wavelet Packets Best Tree Encoding (EWPBTE) 15-point speech feature. The process of enhancing of WPBTE is provided by adding energy component to WPBTE, which is implemented in Matlab software and makes an enhancement of 65 % to recognizer accuracy which is the most contribution in this paper. EWPBTE...
Sequential learning-based pattern classification aims at providing more accurate labeled maps by adding an extra step of classification using an augmented feature vector. In this paper, we evaluated the robustness of Optimum-Path Forest (OPF) classifier in the context of land-cover classification using both satellite and radar images, showing OPF can benefit from sequential learning theoretical basis.
Recurrent drift, as a specific type of concept drift, is characterised by the appearance of previously seen concepts. Therefore, in those cases the learning process could be saved or at least minimized by applying an already trained classification model. In this paper we propose Fuzzy-Rec, a framework that is able to deal with recurrent concept drifts by means of a repository of classification models...
An idea of contextual classifier ensembles extends the application possibility of additional measures of quality of base and ensemble classifiers in the process of contextual ensembles design. These measures besides the obvious classifier accuracy and diversity/similarity take under consideration the complexity, interpretability and significance. The complexity (the number of used measures and multi...
The paper describes a method of supervised context classification for an industrial machinery. The main objective of this study is to compare single and ensemble classifiers in order to classify groups of contexts which are based on an operating state of the device. The applied research was conducted with the assumption that only classic and well-practised classification methods would be adopted....
The Spatial Pyramid Matching approach has become very popular to model images as sets of local bag-of-words. The image comparison is then done region-by-region with an intersection kernel. Despite its success, this model presents some limitations: the grid partitioning is predefined and identical for all images and the matching is sensitive to intra- and inter-class variations. In this paper, we propose...
Joint target tracking and classification is a challenging problem where the class of a target must be estimated in addition to its kinematic states, such as position and velocity. This problem is of special importance both in civilian and in military domain, where target classification plays an important role in the decisions that an operator makes. Moreover, when several sensing options are available...
In this paper, we discuss the evaluation of the probabilistic extraction as introduced in [1], by considering three different datasets introduced in [1] -- [3]. the results show the potential of the approach, as well as its reliability and efficiency when analyzing datasets with different properties and structures. This is part of ongoing research aiming to provide a tool to extract, assess and visualize...
The collective communication operations, which are widely used in parallel applications for global communication and synchronization are critical for application's performance and scalability. However, how faulty collective communications impact the application and how errors propagate between the application processes is largely unexplored. One of the critical reasons for this situation is the lack...
Text classification is one of the key methods used in text mining. Generally, traditional classification algorithms from machine learning field are used in text classification. These algorithms are primarily designed for structured data. In this paper, we propose a new classifier for textual data, called Supervised Meaning Classifier (SMC). The new SMC classifier uses meaning measure, which is based...
An online recognition system must analyze the changes in the sensing data and at any significant detection; it has to decide if there is a change in the activity performed by the person. Such a system can use the previous sensor readings for decision-making (decide which activity is performed), without the need to wait for future ones. This paper proposes an approach of human activity recognition...
In order to help people obtain useful information from patent documents in different languages. This paper proposes a cross-language retrieval system to search Chinese and English patent documents simultaneously. This system consists of query translation module, document retrieval module and user interaction module. Query translation module is used to translate query based on bilingual dictionaries...
Confirmation bias is the human tendency to search for, collect, interpret, analyse, or recall information in a way that confirms one's prior beliefs or preferences. In this paper, we review previous research and demonstrate confirmation bias and its effect in two software engineering contexts. The first study documents that managers bias their interpretation of randomly generated project data towards...
In this paper, we propose a novel approach for reader-emotion categorization using word embedding learned from neural networks and an SVM classifier. The primary objective of such word embedding methods involves learning continuous distributed vector representations of words through neural networks. It can capture semantic context and syntactic cues, and subsequently be used to infer similarity measures...
For the functioning of American democracy, the Lobbying Disclosure Act (LDA), for the very first time, provides data to empirically research interest groups behaviors and their influence on congressional policymaking. One of the main research challenges is to automatically find the topic(s), by short & sparse text classification, in a large corpus of unorganized, semi-structured, and poorly...
Software evolution has been extensively studied in the past decade for various properties and interesting patterns. In this work, we study the effect of evolution on branch prediction techniques. Typically for any program, at the hardware level, all dynamic branch prediction strategies learn the branch behaviors at run time and later re-use them to predict the direction of future branches. The duration...
The unprecedented growth of data in web, social media and the attempt to make the cognitive process using computers make Sentiment Analysis a challenging and interesting research problem. Sentiment Analysis mainly deals with the process of analyzing the sentiments or feelings from someone's expression or piece of information, and also in discovering the cognitive behavior of humans. The usage of computers...
The opinion conveyed by the user towards a movie can be understood by doing Sentiment Analysis on the movie review. In the current work we focus on Genre Specific Aspect Based Sentiment Analysis of Movie Reviews. Using the aforementioned dataset and considering movie genres like action, comedy, crime, drama and horror, we develop a fine grained unsupervised analysis model using lexicons that are context...
While empowering a wide range of software engineering tasks, the traditional fine-grained software dependence (TSD) model can face great scalability challenges that hinder its applications. Many dependence abstraction approaches have been proposed, yet most of them either target very specific clients or model partial dependencies only, while others have not been fully evaluated for their accuracy...
Due to the rapid increase in the use of personal smart devices, more sensitive data is stored and viewed on these smart devices. This trend makes it easier for attackers to access confidential data by physically compromising (including stealing) these smart devices. Currently, most personal smart devices employ one of the one-time user authentication schemes, such as four-to-six digits, fingerprint...
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