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Attia, M., K. Shaalan, L. Tounsi, and J. van Genabith, "Automatic Extraction and Evaluation of Arabic LFG Resources", The eighth international conference on Language Resources and Evaluation (LREC'12), Istanbul, Turkey, 22 May , 2012. Abstract609_paper.pdf

This paper presents the results of an approach to automatically acquire large-scale, probabilistic Lexical-Functional Grammar (LFG) resources for Arabic from the Penn Arabic Treebank (ATB). Our starting point is the earlier, work of (Tounsi et al., 2009) on automatic LFG f(eature)-structure annotation for Arabic using the ATB. They exploit tree configuration, POS categories, functional tags, local heads and trace information to annotate nodes with LFG feature-structure equations. We utilize this annotation to automatically acquire grammatical function (dependency) based subcategorization frames and paths linking long-distance dependencies (LDDs). Many state-of-the-art treebank-based probabilistic parsing approaches are scalable and robust but often also shallow: they do not capture LDDs and represent only local information. Subcategorization frames and LDD paths can be used to recover LDDs from such parser output to capture deep linguistic information. Automatic acquisition of language resources from existing treebanks saves time and effort involved in creating such resources by hand. Moreover, data-driven automatic acquisition naturally associates probabilistic information with subcategorization frames and LDD paths. Finally, based on the statistical distribution of LDD path types, we propose empirical bounds on traditional regular expression based functional uncertainty equations used to handle LDDs in LFG.

Yan, W. Y., S. Morsy, A. Shaker, and M. Tulloch, "Automatic extraction of highway light poles and towers from mobile LiDAR data", Optics & Laser Technology, vol. 77: Elsevier, pp. 162–168, 2016. Abstract
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Zawbaa, H. M., M. Abbass, S. Basha, M. Hazman, and A. E. Hassanien, "An Automatic Flower Classification Approach Using Machine Learning Algorithms", Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on, New Delhi, 24-27 Sept. 2014.
Zawbaa, H. M., M. Abbass, S. H.Basha, M. Hazman, and A. E. Hassenian, "An automatic flower classification approach using machine learning algorithms", 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014., 2014.
Zawbaa, H. M., M. Hazman, M. Abbass, and A. E. Hassanien, "Automatic fruit classification using random forest algorithm", Hybrid Intelligent Systems (HIS), 2014 14th International Conference on: IEEE, pp. 164–168, 2014. Abstract
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Zawbaa, H. M., M. Abbass, M. Hazman, and A. E. Hassenian, "Automatic Fruit Image Recognition System based on Shape and Color Features ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Khadega Khaled, Mohamed A. Wahby Shalaby, K. M. E. S., "Automatic Fuzzy-based Hybrid Approach for Segmentation and Centerline Extraction of Main Coronary Arteries", International Journal of Advanced Computer Science and Applications(IJACSA), vol. 8, no. 6: The Science and Information (SAI) Organization, pp. 258–264, 2017. Abstract
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Shalash, A. F., Automatic gain control circuit, : Google Patents, 2009. Abstract
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Shalash, A. F., Automatic gain control circuit, : Google Patents, 2009. Abstract
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Shalash, A. F., "An automatic gain control topology for CMOS digital radio receiver", Circuits and Systems, 2009. ISCAS 2009. IEEE International Symposium on: IEEE, pp. 2033–2036, 2009. Abstract
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Al-Akwaa, F. M., and Y. M. Kadah, "An automatic gene ontology software tool for bicluster and cluster comparisons", Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 163–167, 2009. Abstract
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I, A., M. S. Saad, A. A. El-Amari, and M. A. M. Hassan, "Automatic generation control of an interconnected power system", International Journal of Ambient Energy, 2021. AbstractWebsite
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Cabanillas, C., M. Resinas, A. R. Cortés, and A. Awad, "Automatic Generation of a Data-Centered View of Business Processes", Advanced Information Systems Engineering - 23rd International Conference, CAiSE 2011, London, UK, June 20-24, 2011. Proceedings, vol. 6741: Springer, pp. 352–366, 2011. Abstract
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Fouad, A., A. Rafea, and H. A. Hassan, "Automatic generation of explanation for expert systems implemented with different knowledge representations", WSEAS TRANSACTIONS on SYSTEMS, vol. Volume 8, issue Issue 1, 2009.
Said, A. F., A. Rafea, S. R. El-Beltagy, and H. Hassan, "Automatic generation of explanation for expert systems implemented with different knowledge representations", WSEAS Transactions on Systems, vol. 8, issue 1, pp. 55-64, 2009. Abstract
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Said, A. F., A. Rafea, S. R. El-Beltagy, and H. Hesham, "Automatic generation of Explanation for Expert Systems Implemented with Different Representations", WSEAS Trans. on Systems, issue 1, pp. 55-64, 2009. Abstract
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Mandouh, E. E., and A. Wassal, "Automatic Generation of Functional Coverage Models", in Proceedings of the 2016 IEEE International Symposium on Circuits and Systems (ISCAS), Montreal, QC, Canada, May 23-25, 2016.
Hamid, S., and M. Rashwan, "Automatic generation of hypotheses for automatic diagnosis of pronunciation errors", Proc. NEMLAR Conf. on Arabic Language Resources and Tools, Cairo, Egypt, 2004. Abstract
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Refaey, M. A. A., "Automatic Generation of Membership Functions and Rules in a Fuzzy Logic System", Fifth International Conference on Informatics and Applications, Japan, pp. 117-122, 2016. Abstract

Fuzzy logic is playing a significant role in many control and classification systems. This arises from its simplicity, natural language based construction, dealing with ambiguity, and its ability to model linear and non-linear complex systems. But, with larger number of input and output variables, the building process of fuzzy system manually becomes daunting and error-prone process. In this work we suggest new methods for automatically creation and generation of fuzzy membership functions and fuzzy rule base respectively. The membership functions are created adaptively from the training data set using histogram of each feature individually. And in the beginning, a rule is generated for each membership function, then according to the weight assigned to each rule and membership function, the membership functions are merged according to successiveness of their domain or support. Also, the rules are started to be co-operated and merged to enhance the classification process. The resulted system is flexible, and is able to receive more rules and/or membership functions if needed.

Sabry, F., A. Erradi, M. Nassar, and Q. Malluhi, "Automatic Generation of Optimized Workflow for Distributed Computations on Large-Scale Matrices", International Conference on Service Oriented Computing, ICSOC 2014, Paris, Springer, 2014.
Abdelbarr, M. H. M. F., Automatic generation of strut-and-tie models using topology optimization, , Cairo, Cairo University, 2012.
Abou-El-Ezz, A., A. Asaad, A. H. Kandil, E. - B. AM, and S. A. Ahmed, "AUTOMATIC IDENTIFICATION OF CEPHALOMETRIC LANDMARKS USING ACTIVE APPEARANCE MODEL ALGORITHM", Official Journal of the Egyptian Dental Association, vol. 53, pp. 2-3, 2007.
El-Bendary, N., T. - H. Kim, A. E. Hassanien, and M. Sami, "Automatic image annotation approach based on optimization of classes scores", Computing -Spriner , vol. 96, issue 5, pp. 381-402 , 2014. Website
El-Bendary, N., T. - H. Kim, A. E. Hassanien, and M. Sami, "Automatic image annotation approach based on optimization of classes scores", Computing, vol. 96, no. 5: Springer Vienna, pp. 381–402, 2014. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization ", World Congress on Information and Communication Technologies (WICT), pp. 790 - 794, India, Oct. 30 2012-Nov. Abstract

This paper presents an automatic image annotation approach that integrates the Naive Bayes classifier with particle swarm optimization algorithm for classes' probabilities weighting. The proposed hybrid approach refines the output of multi-class classification that is based on the usage of Naive Bayes classifier for automatically labeling images with a number of words. Each input image is segmented using the normalized cuts segmentation algorithm in order to create a descriptor for each segment. One Naive Bayes classifier is trained for all the classes. Particle swarm optimization algorithm is employed as a search strategy in order to identify an optimal weighting for classes probabilities from Naive Bayes classifier. The proposed approach has been applied on Corel5K benchmark dataset. Experimental results and comparative performance evaluation, for results obtained from the proposed approach and other related researches, demonstrate that the proposed approach outperforms the performance of the other approaches, considering annotation accuracy, for the experimented dataset.