GeoSimMR: A MapReduce Algorithm for Detecting Communities based on Distance and Interest in Social Networks

Analyzing social networks has received a lot of reviews in the recent literature. Many papers have been proposed to provide new techniques for mining social networks to help further study this huge amount of data. However, to the best of our knowledge, none of them considered the 'semantic meaning' of the nodes interests while clustering the network. In this work, we propose a new algorithm, namely GeoSim, for clustering users in any social network site into communities based on the 'semantic meaning' of the nodes interests as well as their relationships with each other. Moreover, this paper proposes a parallel version of the GeoSim algorithm that utilizes the MapReduce model to run on multiple machines simultaneously and get faster results. The two versions of the algorithm (centralized and parallel) are examined thoroughly to test their performance. The experiments show that both versions of the GeoSim algorithm achieve high community detection accuracy and scale linearly with the size of the cluster

Interestingness filtering engine: Mining Bayesian networks for interesting patterns

In this paper, we present a new measure of interestingness to discover interesting patterns based on the user’s background knowledge, represented by a Bayesian network. The new measure (sensitivity measure) captures the sensitivity of the Bayesian network to the patterns discovered by assessing the uncertainty-increasing potential of a pattern on the beliefs of the Bayesian network. Patterns that attain the highest sensitivity scores are deemed interesting. In our approach, mutual information (from information theory) came in handy as a measure of uncertainty. The Sensitivity of a pattern is computed by summing up the mutual information increases incurred by a pattern when entered as evidence/findings to the Bayesian network. We demonstrate the strength of our approach experimentally using the KSL dataset of Danish 70 year olds as a case study. The results were verified by consulting two doctors (internists)

Off-line Handwritten Arabic Words Segmentation Based on Structural Features and Connected Components Analysis

A precise and efficient segmentation for handwritten Arabic text is a vital prerequisite for the accuracy of the subsequent recognition phase. In this paper, we present a dualphase segmentation approach. The proposed approach starts first by detecting and resolving sub-words overlapping, then a topological features based segmentation is applied by means of a set of heuristic rules. Because of its crucial importance, the segmentation phase is preceded by a handwritten specific preprocessing phase, that considers issues like word’s skew- and slant- correction. The proposed approach has been successfully tested on a database of handwritten Arabic words, that contains more than 3000 words images. The results were very promising and indicating the efficiency of our approach

A systematic survey on multimodal emotion recognition using learning algorithms

Emotion recognition is the process to detect, evaluate, interpret, and respond to people's emotional states and emotions, ranging from happiness to fear to humiliation. The COVID- 19 epidemic has provided new and essential impetus for emotion recognition research. The numerous feelings and thoughts shared and posted on social networking sites throughout the COVID-19 outbreak mirrored the general public's mental health. To better comprehend the existing ecology of applied emotion recognition, this work presents an overview of different emotion acquisition tools that are readily available and provide high recognition accuracy. It also compares the most widely used emotion recognition datasets. Finally, it discusses various machine and deep learning classifiers that can be employed to acquire high level features for classification. Different data fusion methods are also explained in detail highlighting their benefits and limitations