Dr. Amani Saad | Arab Academy for Science and Technology (original) (raw)

Papers by Dr. Amani Saad

Zenodo (CERN European Organization for Nuclear Research), Sep 30, 2022

In every software project, a software effort estimation process is not only vital but also extrem... more In every software project, a software effort estimation process is not only vital but also extremely critical. Project success or failure depends massively on, the concise knowledge of effort and schedule estimates. The development of agile techniques in the field of software development has presented researchers and practitioners with many opportunities and challenges. An estimated effort for agile software development is one of the main challenges. Although traditional estimates of effort are used to estimate effort for agile software projects, most of them lead to inaccurate estimates. This paper focuses on the development of the agile effort estimation model. A machine learning classifier that uses information contained within an issue report is proposed to classify the difficulty or the weight of a given task according to a range of story point scales. The model has two levels of attention mechanisms implemented at the word and sentence levels, allowing it to pay distinguished attention to more and less relevant semantic features when constructing the document representation. The proposed model has achieved 87% classification accuracy. An empirical evaluation demonstrates that our approach has a greater or at least equivalent F score, Precision, and Recall when compared to classical classifiers.

Advances in Intelligent Systems and Computing, 2019

Breast cancer is the most common type of cancer in Egyptian women. According to the International... more Breast cancer is the most common type of cancer in Egyptian women. According to the International Agency for Research on Cancer, the causes of breast cancer are not yet fully known but some of the main risk factors have been identified and taken into consideration in researches performed on patients with high risk of breast cancer. In this paper various classification techniques are used to classify whether breast cancer is recurrent or non-recurrent for a number of patients. Classification techniques used are K-Nearest Neighbor (KNN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machines (SVM) and ensemble techniques Bagging, Voting and Random Forest (RF). The dataset is taken from the University of California Irvine (UCI) machine learning repository and experiments are conducted with Waikato Environment for Knowledge Analysis (WEKA) data mining tool. The research conducted goes through two phases, in the first phase the Random Forest classifier produced the best results with (84.3%) accuracy and the second phase, voting ensemble classifier produced the best results of 89.9% accuracy. The system model show an improvement in the overall accuracy compared to other researches done on the same dataset.

e-Informatica Software Engineering Journal, 2023

Indian journal of computer science and engineering, Dec 20, 2022

Prostate cancer diagnosis and staging is of paramount importance to effective treatment planning ... more Prostate cancer diagnosis and staging is of paramount importance to effective treatment planning and better prognosis. Computer aided diagnosis and staging can contribute to improving and speeding up these stages, especially with the advances of deep learning. The International Society of Urological Pathology (ISUP) grading of stained Whole Slide histopathological Images (WSIs) can be considered the gold standard for grading. However, WSIs suffer from large size and wide background areas which hinder the learning process. Hence, a segmentation-based fractal analysis approach is applied to address this issue and elect relevant patches to be input to the learning algorithm. EfficientNet Convolution Neural Network (CNN) achieves a promising accuracy of 80.7% and Quadratic Weighted Kappa (QWK) of 95.4%. The proposed approach remedies the size problem of WSIs and improves the grading accuracy using light weight learning models.

2022 6th International Conference on Computer, Software and Modeling (ICCSM)

World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering, Jan 23, 2015

Heart Rate Variability (HRV) is a measure of variation in the time interval between consecutive h... more Heart Rate Variability (HRV) is a measure of variation in the time interval between consecutive heart beats. HRV analysis is highly sensitive for risks linked with cardiovascular diseases are main cause of death in Egypt and all over the Middle East. Early detection of cardiac arrhythmia diseases achieves effective treatment by makes it easy to choose appropriate anti-arrhythmic drugs, also very important for improving arrhythmia therapy and preventing number of death in individuals. In this paper, an efficient cardiac arrhythmia detection algorithm was introduced. Different classifiers are deployed and examined on ECG signal. SVM and RF as ensemble classifier show accuracy of 98.18 % in 0.145 sec which is best accuracy among all other classifiers. In addition this paper also proposes a mobile based system architecture integrated with the algorithm for diagnosis and classification of cardiac arrhythmia diseases. The proposed system can be easily used by patients to check their heart...

Department of Computer Science [CS], Oct 31, 1996

2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), 2022

Eyes are considered to be the most sensitive and important organ for human being. Thus,... more Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine S...

Human disease data is a cornerstone of biomedical research for diseases ’ classification and reco... more Human disease data is a cornerstone of biomedical research for diseases ’ classification and recommended treatments so; there is a significant need for a standardized representation of human diseases and an efficient algorithm for retrieving information from it. The Semantic Doctor Assistant (SDA) has been designed to help doctors to find proper information about a specific disease using semantic web technology rather than other simple keyword-based search. A preliminary usability study has been done to evaluate the system by measuring user’s satisfaction through a statistical analysis of surveys. This study would measure the relevance of the information retrieved for each search query and how the system is important in the field of medicine and how it will help academic doctors in their research and non-academic doctors in their work.

World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering, 2015

International Journal of Computer Applications, 2015

Human disease data is a cornerstone of biomedical research for diseases' classification and recom... more Human disease data is a cornerstone of biomedical research for diseases' classification and recommended treatments so; there is a significant need for a standardized representation of human diseases and an efficient algorithm for retrieving information from it. The Semantic Doctor Assistant (SDA) has been designed to help doctors to find proper information about a specific disease using semantic web technology rather than other simple keyword-based search. A preliminary usability study has been done to evaluate the system by measuring user's satisfaction through a statistical analysis of surveys. This study would measure the relevance of the information retrieved for each search query and how the system is important in the field of medicine and how it will help academic doctors in their research and non-academic doctors in their work.

2010 10th International Conference on Intelligent Systems Design and Applications, 2010

Inspite of the huge amounts of image data on the web, mining image data from the web is paid less... more Inspite of the huge amounts of image data on the web, mining image data from the web is paid less attention than mining text data, since treating the semantics of images is much more difficult. This paper introduces a new system to mine visual knowledge on the web that aims to build a Domain Oriented Image Directory by using the Earth Mover's Distance and Color signatures. Instead of using a flat classifier to combine text and image classification, the system suggests dividing the classification task into smaller classification problems corresponding to the branches in the classification hierarchy. Thus a multiple classifier system is presented. This paper illustrates the suggested system and discusses each of its components. Extensive experiments were conducted to test the system and also to compare it with commercial search engines. By the experiments we show that the proposed system accuracy outperforms the mostly used commercial search engines .

... Addison-Wesley Publishing Company, 1986. [4] Amani A. Saad, and Ghada M. Badr. The ALEX Objec... more ... Addison-Wesley Publishing Company, 1986. [4] Amani A. Saad, and Ghada M. Badr. The ALEX Object Manager. ... [7] Peter Kueng. Comparison of ten OODBMS's. The Swiss Computer Magazine OUTPUT, pages 60-63, June 1994. [8] M. Kifer, W. Kim, and Y. Sagiv. ...

Breast tumour detection in ultrasound images has been a challenge due to the presence of differen... more Breast tumour detection in ultrasound images has been a challenge due to the presence of different kinds of noise caused by various factors. The focus of this research is the design, implementation andperformance evaluation ofseveral tumour detection systems based on different classifiers and using ultrasound breast images. First, Gaussian and anisotropic diffusion filters are applied to remove additive and speckle noise, respectively, and histogram equalization is used for image enhancement. Second, textural features are extracted from the input image followed by principal component analysis to reduce the dimensionality of the data set. Finally, the classification process is performed using two different classifiers includingsupport vector machine (SVM) andBootstrap aggregating (bagging) on REP tree. A comparison of the performance of these classifiers is presented.

Biomedical research for diseases' classification and recommended treatments need a standardiz... more Biomedical research for diseases' classification and recommended treatments need a standardized representation of human disease and an efficient algorithm for retrieve information from it. This paper pro- posed a Semantic Doctor Assistant (SDA) to help doctors to find proper information about a specific disease using semantic web technology rather than other simple keyword-based search. A preliminary usability study has been done to evaluate overall system by measuring user's satisfaction and synonym expansion through a statistical analysis of surveys. This study would measure the relevance of the information retrieved for each search query and how the system is important in the field of medicine and how it will help academic doctors in their research and non-academic doctors in their work.