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Soliman, O. O., N. H. Sweilam, and D. M. Shawky, "Automatic breast cancer detection using digital thermal images", 2018 9th Cairo International Biomedical Engineering Conference (CIBEC): IEEE, pp. 110–113, 2018. Abstract
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Soliman, O. O., N. H. Sweilam, and D. M. Shawky, "Automatic breast cancer detection using digital thermal images", 2018 9th Cairo International Biomedical Engineering Conference (CIBEC): IEEE, pp. 110–113, 2018. Abstract
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Hamed, I., and M. I. Owis, "Automatic Arrhythmia Detection Using Support Vector Machine Based on Discrete Wavelet Transform", Journal of Medical Imaging and Health Informatics: American Scientific Publishers, 2015. AbstractWebsite

Arrhythmia is abnormal electrical activity in the heart bringing about less effective pumping. An abnormally fast electrical signal initiates two problems: (1) the heart pumps too quick; and (2) ventricles are filled with an inadequate amount of blood. On the other hand, an abnormally slow electrical signal pumps a sufficient amount of blood out of the heart but too slow. Arrhythmia is classified by both its location of origin and rate. Some arrhythmias are life-threatening and eventually result in cardiac arrest. Hence, the purpose of this study is to present a robust implementation algorithm to discriminate between normal sinus rhythm and three types of arrhythmia: atrial fibrillation (AF), ventricular fibrillation (VF), and supra ventricular tachycardia (SVT) that were collected from physionet database. This is attained by capturing the main features that contain both frequency and location information of the signal through discrete wavelet transform, followed by principal component analysis on each decomposed level. Features were reduced through statistical analysis as an input to support vector machine with optimized parameters that resulted in overall accuracy of 96.89%.

Megahed, S. M., "Automatic Algebraic Computation of Basic Kinematic equations of tree Structure Robot Arms: Application to Human Arms ", Int. J. of Engineering in Medicine, Part H, Imeche MEP Limited, vol. 205, pp. 135-143, 1991.
Megahed, S. M., "Automatic algebraic computation of basic kinematic equations of tree structure robot arms: application to human arms", Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 205, no. 3: SAGE Publications Sage UK: London, England, pp. 135–143, 1991. Abstract
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Alaa Tharwat, H. M. Zawbaa, T. Gaber, A. E. Hassanien, and V. Snasel, "Automated zebrafish-based toxicity test using bat optimization and adaboost classifier", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 169–174, 2015. Abstract
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Anis, Y. H., M. R. Holl, and D. R. Meldrum, "Automated vision-based selection and placement of single cells in microwell array formats", Automation Science and Engineering, 2008. CASE 2008. IEEE International Conference on: IEEE, pp. 315–320, 2008. Abstract2008_case_-_automated_vision.pdf

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Adly, A. A., and S. K. Abd-El-Hafiz, "Automated two-dimensional field computation in nonlinear magnetic media using Hopfield neural networks", IEEE transactions on magnetics, vol. 38, issue 5: IEEE, pp. 2364-2366, 2002. Abstract
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Jan-Erik Scholtz, Julian L. Wichmann, K. H. €users M. B. N. - E. A. N. - E., and T. L. Claudia Frellesen, Thomas J. Vogl, "Automated Tube Voltage Adaptation in Combination with Advanced Modeled Iterative Reconstruction in Thoracoabdominal Third-Generation 192-Slice Dual-Source Computed Tomography: Effects on Image Quality and Radiation Dose", Academic Radiology, vol. 22, pp. 1081–1087, 2015. automated_tube_voltage_adaptation_2015.pdf
Boris Bodelle, Martin Beeres, S. S. J. W. N. - E. A. N. - E. T. V. L. J., and B. S., "Automated Tube Potential Selection as a Method of Dose Reduction for CT of the Neck: First Clinical Results", American Journal of Roentgenology, vol. 204, issue April, pp. 1049–1054, 2015. automated_tube_potential.pdf
Mihany, F. A., H. Moussa, A. Kamel, E. Ezzat, and M. Ilyas, "An Automated System for Measuring Similarity between Software Requirements", Proceedings of the 2nd Africa and Middle East Conference on Software Engineering: ACM, pp. 46-51, 2016. Abstract
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Osman, H., M. Georgy, and M. E. Ibrahim, "An automated system for dynamic construction site layout planning", 10th International Colloquium on Structural and Geotechnical Engineering (ICSGE), Cairo, Egypt, pp. 22–24, 2003. Abstract
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Osman, H., M. Georgy, and M. E. Ibrahim, "An automated system for dynamic construction site layout planning", 10th International Colloquium on Structural and Geotechnical Engineering (ICSGE), Cairo, Egypt, pp. 22–24, 2003. Abstract
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Mahfouz, M. M., E. A. E. Fatah, A. M. Badawi, and R. L. Jantz, "Automated Skull 3D Geodesic and Volumetric Measurements for Cranial Morphology Tracking and Facial Reconstruction", CMBBE, 7th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering, Cote De Azure, France: University of Cardiff, 2006. Abstract
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Anis, Y. H., M. R. Holl, and D. R. Meldrum, "Automated selection and placement of single cells using vision-based feedback control", Automation Science and Engineering, IEEE Transactions on, vol. 7, no. 3: IEEE, pp. 598–606, 2010. Abstract2010j-automated_selection_tase.pdf

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Anis, Y. H., M. R. Holl, and D. R. Meldrum, "Automated selection and placement of single cells using vision-based feedback control", IEEE Transactions on Automation Science and Engineering, vol. 7, no. 3: IEEE, pp. 598–606, 2010. Abstract
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Anis, Y. H., M. R. Holl, and D. R. Meldrum, "Automated selection and placement of single cells using vision-based feedback control", IEEE Transactions on Automation Science and Engineering, vol. 7, no. 3: IEEE, pp. 598–606, 2010. Abstract
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Ghanem, S. M., M. A. Wahed, and N. Saleh, "Automated Risk Control in Medical Imaging Equipment Management Using Cloud Application", Journal of Healthcare Engineering, vol. 2018, 2018. 7125258.pdf
E.Elhariri, N.Elbendary, A.Hassanien, and A.Badr, "Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, pp. 101-131, 2014. Abstract

One of the prime factors in ensuring a consistent marketing of crops is product quality, and the process of
determining ripeness stages is a very important issue in the industry of (fruits and vegetables) production,
since ripeness is the main quality indicator from the customers’ perspective. To ensure optimum yield of
high quality products, an objective and accurate ripeness assessment of agricultural crops is important. This
chapter discusses the problem of determining different ripeness stages of tomato and presents a content-based
image classification approach to automate the ripeness assessment process of tomato via examining and
classifying the different ripeness stages as a solution for this problem. It introduces a survey about resent
research work related to monitoring and classification of maturity stages for fruits/vegetables and provides
the core concepts of color features, SVM, and PCA algorithms. Then it describes the proposed approach for
solving the problem of determining different ripeness stages of tomatoes. The proposed approach consists of
three phases, namely pre-processing, feature extraction, and classification phase. The classification process
depends totally on color features (colored histogram and color moments), since the surface color of a tomato
is the most important characteristic to observe ripeness. This approach uses Principal Components Analysis
(PCA) and Support Vector Machine (SVM) algorithms for feature extraction and classification, respectively.

Esraa Elhariri, N. El-Bendary, A. E. Hassanien, and A. Badr, "Automated ripeness assessment system of tomatoes using PCA and SVM techniques", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, IGI global, pp. 101–130, 2014. Abstract
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Esraa Elhariri, N. Elbendary, A. E. Hassanien, and A. Badr, "Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, USA, IGI, pp. 101-131, 2014. Abstract

One.of.the.prime.factors.in.ensuring.a.consistent.marketing.of.crops.is.product.quality,.and.the.process.of.
determining.ripeness.stages.is.a.very.important.issue.in.the.industry.of.(fruits.and.vegetables).production,.
since.ripeness.is.the.main.quality.indicator.from.the.customers’.perspective..To.ensure.optimum.yield.of.
high.quality.products,.an.objective.and.accurate.ripeness.assessment.of.agricultural.crops.is.important..
This.chapter.discusses.the.problem.of.determining.different.ripeness.stages.of.tomato.and.presents.a.
content-based.image.classification.approach.to.automate.the.ripeness.assessment.process.of.tomato.via.
examining.and.classifying.the.different.ripeness.stages.as.a.solution.for.this.problem..It.introduces.a.
survey.about.resent.research.work.related.to.monitoring.and.classification.of.maturity.stages.for.fruits/
vegetables.and.provides.the.core.concepts.of.color.features,.SVM,.and.PCA.algorithms..Then.it.describes.
the.proposed.approach.for.solving.the.problem.of.determining.different.ripeness.stages.of.tomatoes..The.
proposed.approach.consists.of.three.phases,.namely.pre-processing,.feature.extraction,.and.classification.
phase..The.classification.process.depends.totally.on.color.features.(colored.histogram.and.color.moments),.
since.the.surface.color.of.a.tomato.is.the.most.important.characteristic.to.observe.ripeness..This.approach.
uses.Principal.Components.Analysis.(PCA).and.Support.Vector.Machine.(SVM).algorithms.for.feature.
extraction.and.classification,.respectively

Samir, H., and A. Kamel, "Automated reverse engineering of Java graphical user interfaces for web migration", ITI 5th International Conference on Information and Communications Technology, (ICICT0), Cairo Egypt, December 16-18,, 2007.
Alfarghaly, O., R. Khaled, A. Elkorany, M. Helal, and A. Fahmy, "Automated radiology report generation using conditioned transformers", Informatics in Medicine Unlocked, vol. 24: Elsevier, pp. 100557, 2021. Abstract
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Mostafa, E., M. Roesmann, C. Maack, O. Schmittmann, and W. Buescher, "Automated pressure regulation for a silage bagging machine", Computers and Electronics in Agriculture, vol. 173, 2020. automated_pressure_regulation_for_a_silage_bagging_machine_.pdf
Ashili, S. P., L. Kelbauskas, J. Houkal, D. Smith, Y. Tian, C. Youngbull, H. Zhu, Y. H. Anis, M. Hupp, K. B. Lee, et al., "Automated platform for multiparameter stimulus response studies of metabolic activity at the single-cell level", Microfluidics, BioMEMS, and Medical Microsystems IX, vol. 7929: International Society for Optics and Photonics, pp. 79290S, 2011. Abstract2011_spie-_automated_platform.pdf

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