Bio: Mohamed Elarbi-Boudihir is an academic researcher from Islamic University. The author has contributed to research in topics: Anomaly (physics) & Artificial intelligence. The author has an hindex of 1, co-authored 1 publications receiving 68 citations.
TL;DR: This paper is the first survey that focuses on touched character segmentation and provides segmentation rates, descriptions of the test data for the approaches discussed, and the main trends in the field of touched character segmentsation.
Abstract: Character segmentation is a challenging problem in the field of optical character recognition. Presence of touched characters make this dilemma more crucial. The goal of this paper is to provide major concepts and progress in domain of off-line cursive touched character segmentation. Accordingly, two broad classes of technique are identified. These include methods that perform explicit or implicit character segmentation. The basic methods used by each class of technique are presented and the contributions of individual algorithms within each class are discussed. It is the first survey that focuses on touched character segmentation and provides segmentation rates, descriptions of the test data for the approaches discussed. Finally, the main trends in the field of touched character segmentation are examined, important contributions are presented and future directions are also suggested.
TL;DR: In this paper , an approach for anomaly detection based on deep reinforcement learning is introduced, where the authors focus on adapting the prioritized Dueling deep Q-networks to the anomaly detection problem.
Abstract: The anomaly detection in automated video surveillance is considered as one of the most critical tasks to be solved, in which we aim to detect a variety of real-world abnormalities. This paper introduces a novel approach for anomaly detection based on deep reinforcement learning. In recent years, deep reinforcement learning has been achieving a significant success in various applications with data of a high degree of complexity such as robotics and games, by mimicking the way humans learn from experiences. Generally, the state-of-the-art methods classify a video as normal or abnormal without pinpointing the exact location of the anomaly in the input video due to the unlabeled clip-level data in training videos. We focus on adapting the prioritized Dueling deep Q-networks to the anomaly detection problem. This model learns to evaluate the anomaly in video clips by exploiting the video-level label to obtain a better detection accuracy. Extensive experiments on 13 cases class of real-word anomaly show that our DRL agent achieved a near optimal performance with a high accuracy in the real world video surveillance system compared to the state-of-the-art approaches.
TL;DR: Experimental results on BRATS 2015 benchmark data show the usability of the proposed approach and its superiority over the other approaches in this area of research.
Abstract: A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research.
TL;DR: Improvements made to a lexicon directed algorithm for recognition of unconstrained handwritten words (cursive, discrete, or mixed) such as those encountered in mail pieces, in order to achieve higher recognition accuracy and speed.
Abstract: Discusses improvements made to a lexicon directed algorithm for recognition of unconstrained handwritten words (cursive, discrete, or mixed) such as those encountered in mail pieces. The procedure consists of binarization, pre-segmentation, intermediate feature extraction, segmentation recognition, and post-processing. The segmentation recognition and the post-processing are repeated for all lexicon words while the binarization to the intermediate feature extraction are applied once for an input word. The result of performance evaluation using large handwritten address block database is described, and algorithm improvements are described and discussed, in order to achieve higher recognition accuracy and speed. As a result the performance for lexicons of size 10, 100, and 1000 are improved to 98.01%, 95.46%, and 91.49% respectively. The processing speed for each lexicon is improved to 2.0, 2.5, and 3.5 sec/word on a SUN SPARC station 2.<
TL;DR: A new hybrid method has been proposed for image clustering based on combining the particle swarm optimization (PSO) with k-means clustering algorithms that uses the color and texture images as visual features to represent the images.
Abstract: In various application domains such as website, education, crime prevention, commerce, and biomedicine, the volume of digital data is increasing rapidly. The trouble appears when retrieving the data from the storage media because some of the existing methods compare the query image with all images in the database; as a result, the search space and computational complexity will increase, respectively. The content-based image retrieval (CBIR) methods aim to retrieve images accurately from large image databases similar to the query image based on the similarity between image features. In this study, a new hybrid method has been proposed for image clustering based on combining the particle swarm optimization (PSO) with k-means clustering algorithms. It is presented as a proposed CBIR method that uses the color and texture images as visual features to represent the images. The proposed method is based on four feature extractions for measuring the similarity, which are color histogram, color moment, co-occurrence matrices, and wavelet moment. The experimental results have indicated that the proposed system has a superior performance compared to the other system in terms of accuracy.
TL;DR: Cl clusters, summarize, interpret and evaluate neural networks in document Image preprocessing, and the importance of the learning algorithms in neural networks training and testing for preprocessing is highlighted.
Abstract: Neural network are most popular in the research community due to its generalization abilities. Additionally, it has been successfully implemented in biometrics, features selection, object tracking, document image preprocessing and classification. This paper specifically, clusters, summarize, interpret and evaluate neural networks in document Image preprocessing. The importance of the learning algorithms in neural networks training and testing for preprocessing is also highlighted. Finally, a critical analysis on the reviewed approaches and the future research guidelines in the field are suggested.
TL;DR: The results indicated that system quality, information quality, and computer self-efficacy all affected system use, user satisfaction, and self-managed learning behavior of students.
Abstract: This paper presents a model approach to examine the relationships among e-learning systems, self-efficacy, and students' apparent learning results for university online courses. Independent variables included in this study are e-learning system quality, information quality, computer self-efficacy, system-use, self-regulated learning behavior and user satisfaction as prospective determinants of online learning results. An aggregate of 674 responses of students completing at least one online course from Wawasan Open University (WOU) Malaysia were used to fit the path analysis model. The results indicated that system quality, information quality, and computer self-efficacy all affected system use, user satisfaction, and self-managed learning behavior of students. Proposed path analytical model suggests that hypothesized variables are useful to forecast e-learning results