Often in real world applications only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data. Co-training is a popular semi-supervised learning technique that uses a small set of labeled data and enough unlabeled data to create more accurate classification models. A key feature for successful co-training is to split the features among more than one view. In this paper we propose new splitting criteria based on the confidence of the views, the diversity of the views, and compare them to random and natural splits. We

In this study, we propose a novel data acquisition and image reconstruction method for parallel magnetic resonance imaging (MRI). The proposed method improves the GRAPPA algorithm by simultaneously collecting data using the body coil in addition to localized surface coils. The body coil data is included in the GRAPPA reconstruction as an additional coil. The reconstructed body coil image shows greater uniformity over the field of view than the conventional sum-of-squares (SoS) reconstruction that is conventionally used with GRAPPA. The body coil image can also be used to correct for spatial

Detecting trending topics or events from Twitter is an active research area. The first step in detecting such topics focuses on efficiently capturing textual features that exhibit an unusual high rate of appearance during a specific timeframe. Previous work in this area has resulted in coining the term "detecting bursty features" to refer to this step. In this paper, TFIDF, entropy, and stream chunking are adapted to investigate a new technique for detecting bursty features from an Arabic Twitter stream. Experimental results comparing bursty features extracted from Twitter streams, to Twitter

Spatial domain facial recognition Modular IMage Principal Component Analysis (MIMPCA) has an improved recognition rate compared to the conventional PCA. In the MPCA, face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. In this work, the Transform Domain implementation of MPCA is presented. The facial image has two representations. The Two Dimensional MPCA (TD-2D-MPCA) and the Diagonal matrix MPCA (TD-Dia-MPCA). The sub-images are processed using both non-overlapping and overlapping windows. All the test results, for noise free and noisy

Building a system to extract Arabic named entities is a complex task due to the ambiguity and structure of Arabic text. Previous approaches that have tackled the problem of Arabic named entity recognition relied heavily on Arabic parsers and taggers combined with a huge set of gazetteers and sometimes large training sets to solve the ambiguity problem. But while these approaches are applicable to modern standard Arabic (MSA) text, they cannot handle colloquial Arabic. With the rapid increase in online social media usage by Arabic speakers, it is important to build an Arabic named entity

Meta-heuristics and nature inspired algorithms have been prominent solvers for highly complex, nonlinear and hard optimization problems. The Gaining-Sharing Knowledge algorithm (GSK) is a recently proposed nature-inspired algorithm, inspired by human and their tendency towards growth and gaining and sharing knowledge with others. The GSK algorithm have been applied to different optimization problems and proved robustness compared to other nature-inspired algorithms. The GSK algorithm has two main control parameters kfand kr which controls how much individuals gain and share knowledge with

For the past decade, Internet of Things (IoT) had an important role in our lives. It connects a large number of embedded devices. These devices fulfill very difficult and complicated tasks, which facilitate our work. Till now the security of IoT faces many challenges such as privacy, authentication, confidentiality, trust, middleware security, mobile security and policy enforcement. In order to provide a secure environment for IoT, this paper proposes a framework for IoT devices. © 2017 IEEE.

Ad hoc networks are peer mobile nodes that self configure to form a network. In these types of networks there is no routing infrastructure, and usually nodes have limited resources. This imposes a serious problem due to some nodes' selfishness and willingness to preserve their resources. Many approaches have been proposed to deal with this problem and mitigate the selfishness; amongst these approaches are reputation systems. This paper proposes a reputation system scheme that helps isolating misbehaving nodes and decreasing their ability to launch an attack on the network. The idea of this

In the field of pattern recognition multiple classifier systems based on the combination of outputs from different classifiers have been proposed as a method of high performance classification systems. The objective of this work is to develop a fuzzy Gaussian classifier for combining multiple learners, we use a fuzzy Gaussian model to combine the outputs obtained from K-nearest neighbor classifier (KNN), Fuzzy K-nearest neighbor classifier and Multi-layer Perceptron (MLP) and then compare the results with Fuzzy Integral, Decision Templates, Weighted Majority, Majority Naïve Bayes, Maximum

This paper proposes an equivalent lumped element model for various multi-TSV arrangements and introduces closed form expressions for the capacitive, resistive, and inductive coupling between those arrangements. The closed form expressions are in terms of physical dimensions and material properties and are driven based on the dimensional analysis method. The model's compactness and compatibility with SPICE simulators allows the electrical modeling of various TSV arrangements without the need for computationally expensive field-solvers and the fast investigation of a TSV impact on a 3-D circuit