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检索条件"机构=Scientific Computing and Computer sciences"

共 416 条记录，以下是31-40 订阅

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Attention Detection using Electro-oculography Signals in E-learning Environment 10

Attention Detection using Electro-oculography Signals in E-l...

引用

10th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2021

作者： Abdo, Yassmina Yahya, Ehab Ismail, Hussein Saleh, Mostafa Reda, Radwa Shedeed, Howida A. Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Department Cairo Egypt

ISBN: (纸本)9781665440769

Recently, on account of the Covid-19 pandemic online learning has become a main strategy of learning in any educational environment. The main problem is the lack of focus or attention during a student watching an online lecture or video due to ways of distraction that can occur and that's what interrupts the students and make them lose their focus. In this research paper, we propose the attention detection system using an Electro-oculogram (EOG) signal generated by eye movement. The first goal of this study was detecting the un-focusing periods in the video. The proposed process of recognizing the un-focusing period depends on recognizing the eye movement direction. 6 classes of eye movements were classified in this study: left, right, up, down, blinking, and no movement. The process started by collecting our benchmark dataset from 50 subjects, which was considered the largest dataset at all for eye movements, using a special hardware device. The pre-processing step was down to filter the signals from any noise that may be caused by the surrounding environment or involuntary movements using the band-pass filter. Finally, the classification step was down. Three models of deep learning were examined for the best classifier: Convolution Neural Network (CNN), Inception Network, and VGG Network. Our experiments achieved the best results with the Inception model, reaching an average accuracy of 93.63%. The second goal of this study was to recognize the Attention-deficit hyperactivity disorder (ADHD) pattern in the student's recording. The model succeeded to recognize this disordered pattern. © 2021 IEEE.

关键词： Students

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Bladder Cancer Microarray Analysis and Biomarker Discovery Using Machine Learning

Bladder Cancer Microarray Analysis and Biomarker Discovery U...

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作者： Ghaleb, Moshira S. Ebied, Hala M. Tolba, Mohamed F. Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

One of the most prevalent types of malignant cancers is bladder cancer (BC), which also happens to be the most widespread genitourinary cancer globally. The development and progression of BC are influenced significantly by both genetic and environmental factors. The objective of this study was to identify biomarker genes using three approaches: statistical analysis, biological packages, and machine learning and ensure that the machine learning is a robust way to identify the differential Gene expression. The study utilized GSE7476 expression profiles, which were obtained by downloading data from the Gene Expression Omnibus (GEO) database. ElasticNet was used as one of the methods to identify biomarker genes. The accuracy of the results was assessed by testing on unseen samples, and a perfect accuracy of 100% was achieved. Additionally, the identified biomarker genes were further analyzed using the Go ontology pathway to understand their functional significance and potential involvement in biological processes. Lastly, we compared the pathways associated with the ElasticNet method to those of state-of-the-art methods. This comprehensive approach provides a robust and reliable method for biomarker identification in the context of bladder cancer research using machine learning. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Gene expression

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Feature extraction using PCA and Kernel-PCA for face recognition

Feature extraction using PCA and Kernel-PCA for face recogni...

引用

2012 8th International Conference on Informatics and Systems, INFOS 2012

作者： Ebied, Hala M. Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9789774035067

The face recognition system consists of a feature extraction step and a classification step. In this paper, the researcher studies the use of linear and nonlinear methods for feature extraction in the face recognition system. The linear Principal component analysis (PCA) which is widely used in the face recognition is used to construct the feature space and extract features. The Kernel-PCA is extended from PCA to represent nonlinear mappings in a higher-dimensional feature space. Several parameters of Kernel functions are investigated and expected to affect the recognition performance. The k-nearest neighbor classifier with Euclidean distance is used in the classification step. Our experiments are carried out on the ORL face database which contains variability in expression, pose, and facial details. Experimental results show that Kernel-PCA with Gaussian function can give a correct recognition rate similar to PCA and higher than Kernel-PCA with polynomial function. © 2012 Cairo University.

关键词： Principal component analysis

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Coronary artery tree segmentation in computed tomography angiography using Otsu method 8

Coronary artery tree segmentation in computed tomography ang...

引用

8th International Conference on Intelligent computing and Information Systems, ICICIS 2017

作者： Shams, Marwa Salem, Mohammed A.M. Hamad, Safwat Shedeed, Howida A. Department of Scientific Computing Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9772371723

Analysis of coronary artery in medical images has been regarded as crucial step in the assessment of cardiovascular diseases during clinical diagnosis and surgical planning. Segmentation of coronaries in medical images is the most important step in such analysis. In this paper, an automatic segmentation method is presented for segmenting coronary artery lumen in Computed Tomography Angiography (CTA) datasets. Our proposed segmentation approach consists of two main steps. The first one is an enhancement step to enhance coronary vessels. The second is the coronary segmentation step done by applying Otsu method. Otsu thresholding is applied with different number of thresholds to check the proper number of thresholds that can better identify coronary vessel regions. Experiments are carried out on 18 real CTA datasets. Segmentation results are quantitatively evaluated using three different evaluation metrics and the results at each number of thresholds are compared. © 2017 IEEE.

关键词： Image segmentation

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Controlling Directed Particle Swarm Optimization for Delivering Nano-robots to Cancer Cells 1st

Controlling Directed Particle Swarm Optimization for Deliver...

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1st International Conference on Artificial Intelligence and computer Visions, AICV 2020

作者： Ezzat, Doaa Amin, Safaa Shedeed, Howida A. Tolba, Mohamed F. Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9783030442880

Cancer is one of the most dangerous diseases in this century. There are two traditional methods for treating cancer (radio-therapy and chemo-therapy). Using these methods causes very harmful side effects on healthy organs. Because of these side effects, doctors sometimes need to decrease the drug dose or delay the therapy. Some researchers proposed to use Nano-robots to deliver anti-cancer drugs to only cancer cells without touching the healthy tissues. One of the most recently proposed algorithms for delivering Nano-robots to cancer area is Directed Particle Swarm Optimization (DPSO) [1]. This algorithm can efficiently deliver all Nano-robots to the target area in a very short time. In this paper, we deeply discuss this algorithm and we also propose a new method for controlling this algorithm by tuning its direct step. © 2020, Springer Nature Switzerland AG.

关键词： Cancer cells

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A New Nano-robots Control Strategy for Killing Cancer Cells Using Quorum Sensing Technique and Directed Particle Swarm Optimization Algorithm 4th

引用

4th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2019

作者： Ezzat, Doaa Amin, Safaa Shedeed, Howida A. Tolba, Mohamed F. Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9783030141172

Nowadays cancer is considered one of the most killing diseases. Traditional cancer therapy leads to dangerous side effects on healthy tissues. A recent direction has been proposed to overcome these side effects. This direction is to use Nano-robots to deliver drugs directly to tumor cells without harming healthy ones. In this paper, we propose a new Nano-robots control strategy that uses Directed Particle Swarm Optimization (DPSO) algorithm for delivering Nano-robots to the cancer area. A Quorum Sensing (QS) algorithm is also used in this strategy to control drug concentration in the cancer area. The results show that using the proposed control strategy increases the rate of killing cancer cells efficiently. This study also proposes to use a certain number of Nano-robots for destroying 100 cancer cells. © 2020, Springer Nature Switzerland AG.

关键词： Cancer cells

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Using MLP and RBF neural networks for face recognition: An insightful comparative case study

Using MLP and RBF neural networks for face recognition: An i...

引用

2011 International Conference on computer Engineering and Systems, ICCES'2011

作者： Ebeid, Hala M. Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9781457701283

In this paper, two architectures neural network (NN) classifier models have been compared, multilayer perceptron (MLP) neural network with back-propagation algorithm and radial basis function (RBF) neural network. Capabilities of the presented architectures have been compared. The feature projection vectors, obtained through the Principal Component Analysis or called Eigenfaces method, are used as the input vectors for the training and testing of both NN architectures. Several factors affect the recognition performance;experimental results are applied to the ORL database which contains variability in expression, pose, and facial details. The experimental result showed that the Eigenfaces/RBF system has recognition error rates that are lower than those of the Eigenfaces/MLP system by 3%. Thus the Eigenfaces/RBF system performs better than the Eigenfaces/MLP system in terms of correct recognition rates and training convergence speed of the network. © 2011 IEEE.

关键词： Face recognition

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Diagnosing heart diseases using morphological and dynamic features of electrocardiogram (ECG) 3rd

Diagnosing heart diseases using morphological and dynamic fe...

引用

3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017

作者： El-Saadawy, Hadeer Tantawi, Manal Shedeed, Howida A. Tolba, M.F. Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9783319648606

In this paper, an automatic method is proposed for the heart beat classification of 15 classes mapped into five main categories. The proposed method is applied separately to both leads 1 and 2. Dynamic segmentation is considered to reduce the effect of the heart beat rate variation. The segmented beats are subjected to discrete wavelet decomposition (DWT) to extract the morphological features besides the dynamic features represented by four RR intervals. Principle component analysis (PCA) is considered to reduce the dimension of the extracted morphological features. After that, the reduced features are concatenated with the dynamic features and fed into Support vector machine (SVM) classifier. Finally, the rejection fusion step is applied to combine the results from both leads 1 and 2 with a 93.84% average accuracy and 99.5% overall accuracy having been achieved using MIT-BIH dataset as a validation database. © 2018, Springer International Publishing AG.

关键词： Discrete wavelet transforms

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Bag of compact HKS-based feature descriptors 8

Bag of compact HKS-based feature descriptors

引用

8th Eurographics Workshop on 3D Object Retrieval, 3DOR 2015

作者： ElNaghy, H. Hamad, S. Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9783905674781

3D object retrieval has become an integral part in many today's applications attracting extensive research efforts. This paper introduces an enhanced 3D object retrieval technique using a compact and highly discriminative feature point descriptor. The key idea is based on integrating Bag of features (BoF) paradigm with Heat Kernel Signature (HKS) for feature description and detection. Initially, HKS computation phase defines HKS point signatures for each 3D model. Then, an innovative feature point detection algorithm provides a succinct set of feature points to be associated with a compact HKS-based descriptor vectors computed at local time scales. Finally, we take advantage of the BoF paradigm to encode a given 3D model with an informative feature frequency vector. The proposed approach has been evaluated on SHREC 2015 dataset of non-rigid models. The experimental results demonstrate the effective retrieval performance, invariance to different kinds of deformation and possible noise. © The Eurographics Association 2015.

关键词： Feature extraction

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Image Retrieval Based on Self-Organizing Feature Map and Multilayer Perceptron Neural Networks Classifier 9

Image Retrieval Based on Self-Organizing Feature Map and Mul...

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9th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2019

作者： Ghaleb, Moshira S. Ebied, Hala M. Shedeed, Howida A. Tolba, Mohamed F. Faculty of Computer and Information Sciences Ain Shams University Scientific Computing Department Cairo Egypt

ISBN: (纸本)9781728139951

Content-based image retrieval (CBIR), is a type of computer vision application. It searches for an image by image content not text in large database images. According to the huge existence of image databases, the searching time and high accuracy retrieved images become a great challenge. This paper aims to find a solution to retrieve images with high accuracy results. Neural network became a hot topic in the image processing field for the past few years. This paper presents two approaches to Content-based image retrieval. The first approach used the Self-Organized Feature Map (SOFM) as a clustering method to image retrieval. The second approach consists of two phases. The first one used the SOFM as a feature extraction method. The second phase used the Multilayer Perceptron (MLP) as a classifier method. The paper studied the impact of changing some parameters values on recognition accuracy. The experiments carried out using the Wang Corel 1000 database. The results show that the SOFM+MLP improved the recognition accuracy compared to SOFM. The SOFM+MLP achieved approximately 99% average recognition accuracy. © 2019 IEEE.

关键词： Feature extraction

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