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Akhtar, Zahid
- Arabic Handwritten Characters Recognition Via Multi-Scale Hog Features and Multi-Layer Deep Rule-Based Classification
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Authors
Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Computer Science, University of Memphis, US
3 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj, DZ
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Computer Science, University of Memphis, US
3 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj, DZ
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 4 (2020), Pagination: 2195-2200Abstract
Optical character recognition systems for handwritten Arabic language still face challenges, owing to high level of ambiguity, complexity and tremendous variations in human writing styles. In this paper, we propose a new and effective Arabic handwritten characters recognition framework using multi-scale histogram oriented gradient (HOG) features and the deep rule-based classifier (DRB). In the feature extraction stage, the proposed framework combines multi-scale HOG features, and then the DRB is applied on comprehensive HOG features to obtain the final classification label/class. This study involves experimental analyses that were conducted on the publicly available cursive Arabic Handwritten Characters Database (AHCD) containing 16800 characters. Experimental results demonstrate the efficacy of the proposed recognition system compared to the existing state-of-the-art-systems.Keywords
Arabic Character Recognition, Writing, DRB Classifier, HOG, AHCD.References
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- A Survey on Machine And Deep Learning for Detection of Diabetic Retinopathy
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Authors
Affiliations
1 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Network and Computer Security, State University of New York Polytechnic Institute, US
3 Department of Computer Science, Mohamed Boudiaf University, DZ
4 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
1 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Network and Computer Security, State University of New York Polytechnic Institute, US
3 Department of Computer Science, Mohamed Boudiaf University, DZ
4 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 2 (2020), Pagination: 2337-2344Abstract
Diabetic Retinopathy (DR) is one of the mainly causes of visual loss worldwide. In fact, DR is leading source of impaired vision in people between 25 and 74 years old. DR exists in wide ranged and its detection is a challenging problem. The gradual deterioration of retina leads to DR with several types of lesions, including hemorrhages, exudates, micro aneurysms, etc. Early detection and diagnosis can prevent and save the vision of diabetic patients or at least the progression of DR can be slowed down. The manual diagnosis and analysis of fundus images to substantiate morphological changes in micro aneurysms, exudates, blood vessels, hemorrhages, and macula are usually time-consuming and monotonous task. It can be made easy and fast with the help of computer-aided system based on advanced machine learning techniques that can greatly help doctors and medical practitioners. Thus, the main focus of this paper is to provide a summary of the numerous methods designed for discovering hemorrhages, microaneurysms and exudates are discussed for eventual recognition of non-proliferative diabetic retinopathy. This survey will help the budding researchers, scientists, and practitioners in the field.Keywords
Diabetic Retinopathy, Deep Learning, Machine Learning, Computer-Aided Diagnosis.- User Activities Analysis in Location Based Social Network Via Association Rules
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Authors
Affiliations
1 Department of Computer Science, Ferhat Abbas University, DZ
2 Department of Computer Science, Mohamed El-Bachir Ibrahimi University of Bordj Bou Arreridj, DZ
3 State University of New York Polytechnic Institute, US
1 Department of Computer Science, Ferhat Abbas University, DZ
2 Department of Computer Science, Mohamed El-Bachir Ibrahimi University of Bordj Bou Arreridj, DZ
3 State University of New York Polytechnic Institute, US
Source
ICTACT Journal on Soft Computing, Vol 11, No 3 (2021), Pagination: 2328-2336Abstract
In recent years, the field of the Internet of Things (IoT), including smart and wearable devices, has witnessed a tremendous advancement leading to the collection of a wide variety of information not only about users but also their activities via various systems such as social networks, apps and so on. Thus, the collection of this large amount of data allows social systems to reach a wide variety of targets and gives more visibility about users and their profiles. It can also help to improve the services and functionalities of the users. Besides, the analysis and prediction of user’s activities in location-based social networks (LBSNs) have received much attention both from industries and research communities, especially in smart city developments, which give much importance to the automation of the LBSNs. In this paper, we present a new method based on association rules for user activity analysis in LBSNs. In particular, the Apriori algorithm has been applied to extract the consequential and advantageous rules to categorize users’ profiles. Empirical evaluations on a publicly available large-scale real-world dataset, named Gowalla, demonstrate the effectiveness of the presented association rules-based system in analyzing users’ activities via LBSNs.Keywords
Complex System, Social Networks, Association Rules, Apriori Algorithm, Gowalla Dataset.References
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