Browsing by Author "Labib S.S."

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A comparative study to classify big data using fuzzy techniques
(IEEE Computer Society, 2017) Labib S.S.; Faculty of Computer Science; October University for Modern Science and Arts; Egypt
It is very difficult to implement an efficient analysis by using the customary techniques currently available; this is due to the fact that the data size has had a huge increase. Many complications were faced because of the numerous characteristics of big data; some of them include complexity, value, variability, variety, velocity, and volume. The objective of this paper is to implement classification techniques using the map reduce framework using fuzzy and crisp methods, also to arrange for a study that can compare and contrast the outcomes of the suggested systems against the methods appraised in the documented works. For this research the applied method for the fuzzy technique is the fuzzy k-nearest neighbor, and for the non-fuzzy techniques both the support vector machine and the k-nearest neighbor are used. The use of the map reduce paradigm is applied to be able to process big data. We also implemented an integrated system using the Support Vector Machine with the fuzzy soft label and Gaussian fuzzy membership. Results show that fuzzy k-nearest neighbor classifier gives higher accuracy but it takes a lot of time in classification compared to the other techniques. But the outcomes when projected onto other data sets demonstrate that the suggested method that used fuzzy logic in the Reducer function gives higher accuracy and lower time than the new suggested methods and the methods revised in the paper. � 2016 IEEE.
A novel brain computer interface based on Principle Component Analysis and Fuzzy Logic
(Institute of Electrical and Electronics Engineers Inc., 2016) Labib S.S.; Faculty of Computer Science; October University for Modern Science and Arts; Giza; Egypt
Brain computer interface (BCI) systems measure brain signal and translate it into control commands in an attempt to mimic specific human thinking activities. In recent years, many researchers have shown their interests in BCI systems, which has resulted in many experiments and applications. The main issue to build applicable Brain-Computer Interfaces is the capability to classify the Electroencephalograms (EEG). The purpose behind this research is to improve a model for brain signals analysis. We have used high pass filter to remove artifacts, discrete wavelet transform algorithms for feature extraction and statistical features like Mean Absolute Value, Root Mean Square, and Simple Square Integral are used, also we have used principle component analysis to reduce the size of feature vector and we used fuzzy Gaussian membership function to optimize the classification phase. It has been depicted from results that the proposed integrated techniques outperform a better performance than methods mentioned in literature. � 2016 IEEE.
Prediction of potential-diabetic obese-patients using machine learning techniques
(Science and Information Organization, 2019) Ali R.E.; El-Kadi H.; Labib S.S.; Saad Y.I.; Faculty of Computer Science; MSA University; Faculty of Computers and Artificial Intelligence; Giza; Egypt; Faculty of Computers and Artificial Intelligence; Giza; Egypt; Cairo University; Cairo; Egypt; Hepatogastroenterology and Clinical Nutrition; Faculty of Medicine; Cairo University; Giza; Egypt
Diabetes is a disease that is chronic. Improper blood glucose control may cause serious complications in diabetic patients as heart and kidney disease, strokes, and blindness. Obesity is considered to be a massive risk factor of type 2 diabetes. Machine Learning has been applied to many medical health aspects. In this paper, two machine learning techniques were applied; Support Vector Machine (SVM) and Artificial Neural Network (ANN) to predict diabetes mellitus. The proposed techniques were applied on a real dataset from Al-Kasr Al-Aini Hospital in Giza, Egypt. The models were examined using four-fold cross validation. The results were conducted from two phases in which forecasting patients with fatty liver disease using Support Vector Machine in the first phase reached the highest accuracy of 95% when applied on 8 attributes. Then, Artificial Neural Network technique to predict diabetic patients were applied on the output of phase 1 and another different 8 attributes to predict non-diabetic, pre-diabetic and diabetic patients with accuracy of 86.6%. � 2018 The Science and Information (SAI) Organization Limited.
Teeth periapical lesion prediction using machine learning techniques
(Institute of Electrical and Electronics Engineers Inc., 2016) Mahmoud Y.E.; Labib S.S.; Mokhtar H.M.O.; Faculty of Computer Sciences; Modern Sciences and Arts University; Giza; Egypt; Faculty of Computers and Information; Cairo University; Giza; Egypt
Teeth Periapical lesion is used to be diagnosed by dentists according to patient's x-ray. But most of the time there were a problematic issue to reach a definitive diagnosis. It takes too much time, case and chief complaint history needed, many tests and tools are needed and sometimes taking too many radiographs is required. Even though, before starting the treatment sometimes reaching definitive diagnosis is difficult. Therefore, the objective of this research is to predict whether the patient has teeth periapical lesion or not and its type using machine learning techniques. The proposed system consists of four main steps: Data collection, image preprocessing using median and average filters for removing noise and Histogram equalization for image enhancement, feature extraction using two dimensional discrete wavelet transform algorithm, and finally machine learning (classification) using Feed Forward Neural Networks and K-Nearest Neighbor Classifier. It has been concluded from the results that the K-Nearest Neighbor Classifier performs better than Feed Forward Neural Network on our real database. � 2016 IEEE.