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Hegab, H., and H. Kishawy, "Machining of Inconel 718 using Nano-Fluid Minimum Quantity Lubrication", 7th International Conference on Virtual Machining Process Technology (VMPT), 2018. Abstract
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Hegab, H., and H. Kishawy, "Machining of Inconel 718 using Nano-Fluid Minimum Quantity Lubrication", 7th International Conference on Virtual Machining Process Technology (VMPT), 2018. Abstract
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Hassan, H. E., A. A. A. El-Rahman, and F. M. Shehata, "Machine vision method for quality evaluation of cow meat", Misr J. of Agric. Eng., vol. 28, issue 2, pp. 416 - 439, 2011.
Zohdy, B. S. M., M. A. Mahmood, N. R. Darwish, and H. A. Hefny, "Machine Vision Application on Science and Industry: Machine Vision Trends", Optoelectronics in Machine Vision-Based Theories and Applications, ISBN-13: 9781522557517, DOI: 10.4018/978-1-5225-5751-7: IGI Global, pp. 233-254, 2019. Abstract
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Zohdy, B. S. M., M. A. Mahmood, N. R. Darwish, and H. A. Hefny, "Machine Vision Application on Science and Industry: Machine Vision Trends", Optoelectronics in Machine Vision-Based Theories and Applications: IGI Global, pp. 233–254, 2019. Abstract
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Shaalan, K., A. Rafea, A. Abdel-Moneim, and H. Baraka, "Machine Translation of English Noun Phrases into Arabic", The International Journal of Computer Processing of Oriental Languages, vol. 17, no. 2, pp. 121–134, 2004. Abstractmt_nlp.pdfWebsite

The present work reports our attempt in automating the translation of English noun phrase (NP) into Arabic. Translating NP is a very important task toward sentence translation since NPs form the majority of textual content of the scientific and technical documents. The system is implemented in Prolog and the parser is written in DCG formalism. The paper also describes our experience with the developed MT system and reports results of its application on real titles of theses from the computer science domain.

Shaalan, K., "Machine Translation of Arabic Interrogative Sentence into English", the 8th International conference on Artificial Intelligence Applications, Cairo, Egypt, American University in Cairo, pp. 473–483, 2000. Abstractmt_interrogative.pdf

The present work reports our attempt in developing a bi-lingual Machine Translation (MT) tool in the agriculture domain. The work described here is part of an ongoing research to automate the translation of user interfaces of knowledge-based systems. In particular, we describe the translation of Arabic interrogative sentence into English. In Central Laboratory for Agricultural Expert Systems (CLAES), this tool is found to be essential in developing bilingual (Arabic-to-English) expert systems because both the Arabic and the English versions are needed for development and usage purpose. The tool follows the transfer-based MT approach. A major design goal of this tool is that it can be used as a stand-alone tool and can be very well integrated with a general MT system for Arabic sentence. The paper also describes our experience with the developed MT system and reports results of its application on interrogatives from real agricultural expert systems.

A, H., S. K, and F. A, "Machine Translation Model Using Inductive Logic Programming", In the Proceedings of The IEEE NLP-KE '09, Dalian, China, September , 2009.
Hossny, A., K. Shaalan, and A. Fahmy, "Machine translation model using inductive logic programming", the 2009 IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE’09), Dalian, China, pp. 1–8, sep, 2009. Abstract101.pdf

Rule based machine translation systems face different challenges in building the translation model in a form of transfer rules. Some of these problems require enormous human effort to state rules and their consistency. This is where different human linguists make different rules for the same sentence. A human linguist states rules to be understood by human rather than machines. The proposed translation model (from Arabic to English) tackles the mentioned problem of building translation model. This model employs Inductive Logic Programming (ILP) to learn the language model from a set of example pairs acquired from parallel corpora and represent the language model in a rule-based format that maps Arabic sentence pattern to English sentence pattern. By testing the model on a small set of data, it generated translation rules with logarithmic growing rate and with word error rate 11%.

Hassanien, A. E., "Machine Learning-Based Soccer Video Summarization System.", Multimedia, Computer Graphics and Broadcasting - International Conference, MulGraB 2011,, Jeju Island, Korea, December 8-10, 2011. Abstract

This paper presents a machine learning (ML) based event detection and summarization system for soccer matches. The proposed system is composed of six phases. Firstly, in the pre-processing phase, the system segments the whole video stream into small video shots. Then, in the shot processing phase, it applies two types of classification to the video shots resulted from the pre-processing phase. Afterwards, in the replay detection phase, the system applies two machine learning algorithms, namely; support vector machine (SVM) and neural network (NN), for emphasizing important segments with logo appearance. Also, in the score board detection phase, the system uses both ML algorithms for detecting the caption region providing information about the score of the game. Subsequently, in the excitement event detection phase, the system uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor filter for detecting goal net. Finally, in the logo-based event detection and summarization phase, the system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. Compared to the performance results obtained using SVM classifier, the proposed system attained good NN-based performance results concerning recall ratio, however it attained poor NN-based performance results concerning precision ratio.

Hassanien, A. E., "Machine Learning-Based Soccer Video Summarization System.", Multimedia, Computer Graphics and Broadcasting-International Conference, MulGraB 2011,, 2017. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Machine learning-based soccer video summarization system", Multimedia, Computer Graphics and Broadcasting: Springer Berlin Heidelberg, pp. 19–28, 2011. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Machine learning-based soccer video summarization system", Multimedia, Computer Graphics and Broadcasting: Springer Berlin Heidelberg, pp. 19–28, 2011. Abstract
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El-Zahab, S., A. Al-Sakkaf, E. M. Abdelkader, and T. Zayed, "A machine learning-based model for real-time leak pinpointing in buildings using accelerometers", Journal of Vibration and Control, pp. 1-15, 2022.
Rehab Mahmoud, Nashwa El-Bendary, H. M. A. E. H. H. S. M. O. A., "Machine Learning-Based Measurement System for Spinal Cord Injuries Rehabilitation Length of Stay", Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, , Ostrava, Czech Republic, , June 29 - July , 2015. Abstract

Disabilities, specially Spinal Cord Injuries (SCI), affect people behaviors, their response, and the participation in daily activities. People with SCI need long care, cost, and time to improve their heath status. So, the rehabilitation of people with SCI on different period of times is required. In this paper, we proposed an automated system to estimate the rehabilitation length of stay of patients with SCI. The proposed system is divided into three phases; (1) pre-processing phase, (2) classification phase, and (3) rehabilitation length of stay measurement phase. The proposed system is automating International Classification of Functioning, Disability and Health classification (ICF) coding process, monitoring progress in patient status, and measuring the rehabilitation time based on support vector machines algorithm. The proposed system used linear and radial basis (RBF) kernel functions of support vector machines (SVMs) classification algorithm to classify data. The accuracy obtained was full match on training and testing data for linear kernel function and 93.3 % match for RBF kernel function.

Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, A. E. Hassanien, and H. A. Shaheen, "Machine Learning-Based Measurement System for Spinal Cord Injuries Rehabilitation Length of Stay", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 523–534, 2015. Abstract
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Kheir, A. M. S., K. A. Ammar, A. Amer, M. G. M. Ali, Z. Ding, and A. Elnashar, "Machine learning-based cloud computing improved wheat yield simulation in arid regions", Computers and Electronics in Agriculture, vol. 203, pp. 107457, 2022. AbstractWebsite

Combining machine learning (ML) with dynamic models is recommended by recent research for creating a hybrid approach for robust simulations but has received less attention thus far. Herein, we combined multi- ML algorithms with multi-crop models (CMs) of the DSSAT platform to develop a hybrid approach for wheat yield simulation over 40 years in different locations. The simulation analysis included temperatures (minimum and maximum), solar radiation, and precipitation as important key ecological factors in wheat production that varied across sites and years. Detailed observed datasets of wheat yield from 1981 to 2020 were used for training and testing Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest Regressor (RFR), and Support Vector Regressor (SVR) using Google Colaboratory (Colab). Such models were built to create four main approaches, including two approaches as hybrid (CMs-ML) and benchmark (pure ML), as well as two testing methods for each approach such as default (75 % training and 25 % testing) and warmest years (2001, 2006, 2009, 2010, and 2018). In addition to wheat yield simulations, ML approaches were used to identify the important features, improve accuracy, and reduce overfitting. We developed ML approaches by novel cells on the built models (i.e., pure ML and hybrid) to eliminate less important features from permutation. Our results revealed that ANN and RFR outperformed other ML algorithms (SVR and KNN) in wheat yield simulation accuracy. Application of ML algorithms reduced yield change from 31.7 % under DSSAT simulations to 8.1 % and uncertainty from 12.8 % to 7.2 % relative to observed wheat yield over the last four decades (1981–2020). Our novel approach, which includes a hybrid CMs-ML model, cloud computing, and a new permutation tool, could be effectively used for robust crop yield simulation on a regional and global scale, contributing to better aid decision-making strategies.

Hassanien, A. E., Machine Learning Techniques for Prostate Ultrasound Image Diagnosis, , German, Studies in Computational Intelligence - Springer, 2010. Abstract

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this chapter, we present a machine learning scheme, employing a combination of fuzzy sets, wavelets and rough sets, for analyzing prostrate ultrasound images in order diagnose prostate cancer. To address the image noise problem we first utilize an algorithm based on type-II fuzzy sets to enhance the contrast of the ultrasound image. This is followed by performing a modified fuzzy c-mean clustering algorithm in order to detect the boundary of the prostate pattern. Then, a wavelet features are extracted and normalized, followed by application of a rough set analysis for discrimination of different regions of interest to determine whether they represent cancer or not. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including decision trees, discriminant analysis, rough neural networks, and neural networks.

Hassanien, A. E., H. Al-Qaheri, Václav Snášel, and J. F. Peters, "Machine learning techniques for prostate ultrasound image diagnosis", Advances in Machine Learning I: Springer Berlin Heidelberg, pp. 385–403, 2010. Abstract
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Hassanien, A. E., H. Al-Qaheri, Václav Snášel, and J. F. Peters, "Machine learning techniques for prostate ultrasound image diagnosis", Advances in Machine Learning I: Springer Berlin Heidelberg, pp. 385–403, 2010. Abstract
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Abdel-Aziz, A. S., A. E. Hassanien, A. T. Azar, and S. E. - O. Hanafi, "Machine learning techniques for anomalies detection and classification", Advances in security of information and communication networks: Springer Berlin Heidelberg, pp. 219–229, 2013. Abstract
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Hashem, S., M. ElHefnawi, S. Habashy, M. El-Adawy, G. Esmat, W. elakel, A. O. Abdelazziz, M. M. Nabeel, A. H. Abdelmaksoud, T. M. Elbaz, et al., "Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease.", Computer methods and programs in biomedicine, vol. 196, pp. 105551, 2020. Abstract

BACKGROUND AND OBJECTIVE: Considered as one of the most recurrent types of liver malignancy, Hepatocellular Carcinoma (HCC) needs to be assessed in a non-invasive way. The objective of the current study is to develop prediction models for Chronic Hepatitis C (CHC)-related HCC using machine learning techniques.

METHODS: A dataset, for 4423 CHC patients, was investigated to identify the significant parameters for predicting HCC presence. In this study, several machine learning techniques (Classification and regression tree, alternating decision tree, reduce pruning error tree and linear regression algorithm) were used to build HCC classification models for prediction of HCC presence.

RESULTS: Age, alpha-fetoprotein (AFP), alkaline phosphate (ALP), albumin, and total bilirubin attributes were statistically found to be associated with HCC presence. Several HCC classification models were constructed using several machine learning algorithms. The proposed HCC classification models provide adequate area under the receiver operating characteristic curve (AUROC) and high accuracy of HCC diagnosis. AUROC ranges between 95.5% and 99%, plus overall accuracy between 93.2% and 95.6%.

CONCLUSION: Models with simplistic factors have the power to predict the existence of HCC with outstanding performance.

S, H., E. H. M, H. S, E. TM, and et al, "Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease", Computer Methods and Programs in Biomedicine, vol. 196, pp. 105551, 2020.
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