Faculty Of Computer Science Research Paper

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4-BIT PIPELINE ADC FOR MONOLITHIC ACTIVE PIXEL SENSORS
(AMER SOC MECHANICAL ENGINEERS, 2009) Agieb, Ramy Said; El Ghitany, Hassan Ahmed; Shehata, Khaled Ali
Low voltage low power 4-bits 90Ms/s, 40 mu w, with DNL (+0.19/-0.4)LSB and INL (+0.47/-0.46)LSB is designed using 0.13um UMC CMOS technology operated with 1.2V voltage supply. The converter is composed of three stages the first, second stages produce 1.5bit/stage and last stage produce 2 bit/stage. Using Bottom-Plate Switching and fully digital error correction which corrects errors due to capacitor mismatch, charge injection, and comparator offsets. The calibration is performed without any additional analog circuitry, and the conversion does not need extra clock cycle
Adaptive guided differential evolution algorithm with novel mutation for numerical optimization
(Springer Berlin Heidelberg, 2019) Wagdy Mohamed, Ali; Khater Mohamed, Ali
This paper presents adaptive guided differential evolution algorithm (AGDE) for solving global numerical optimization problems over continuous space. In order to utilize the information of good and bad vectors in the DE population, the proposed algorithm introduces a new mutation rule. It uses two random chosen vectors of the top and the bottom 100p% individuals in the current population of size NP while the third vector is selected randomly from the middle [NP-2(100p %)] individuals. This new mutation scheme helps maintain effectively the balance between the global exploration and local exploitation abilities for searching process of the DE. Besides, a novel and effective adaptation scheme is used to update the values of the crossover rate to appropriate values without either extra parameters or prior knowledge of the characteristics of the optimization problem. In order to verify and analyze the performance of AGDE, Numerical experiments on a set of 28 test problems from the CEC2013 benchmark for 10, 30, and 50 dimensions, including a comparison with classical DE schemes and some recent evolutionary algorithms are executed. Experimental results indicate that in terms of robustness, stability and quality of the solution obtained, AGDE is significantly better than, or at least comparable to state-of-the-art approaches.
ADVANCING DIABETIC FOOT ULCER DETECTION BASED ON RESNET AND GAN INTEGRATION
(Little Lion Scientific, 2024-03) El-Kady, Ahmed Mostafa; Abbassy, Mohamed M; Ali, Heba Hamdy; Ali, Moussa Farid
Diabetes, characterized by the body's inability to effectively regulate sugar levels due to insulin complications, leads to various serious health issues. Among these, Diabetic Foot Ulcer stands out as a critical yet often ignored consequence. This condition, if not addressed in time, can result in severe outcomes including amputations, posing a substantial burden on both individuals and healthcare systems, particularly in areas where medical care is costly. Addressing this pressing issue, our research focused intensively on the analysis of medical images, with the goal of enhancing the accuracy of Diabetic Foot Ulcer diagnosis. We assessed two different models: the renowned ResNet50 model and hybrid model that fuses ResNet50 with Generative Adversarial Networks. The findings were noteworthy; the ResNet50 demonstrated commendable performance, achieving an average accuracy and precision of 0.76, and an F1-Score of 0.75. However, the hybrid model surpassed these metrics, registering an average accuracy of 0.84, precision of 0.85, and an F1-Score of 0.84. This research contributes to the evolving landscape of medical image analysis, offering a promising avenue for more precise and effective DFU diagnosis in clinical settings. The marked advancement in diagnostic precision afforded by the hybrid model suggests a significant stride forward in effectively managing and treating DFU.
AEDA: Arabic edit distance algorithm Towards a new approach for Arabic name matching
(IEEE, 2011) H Abdel Ghafour, Hesham; El-Bastawissy, Ali; A Heggazy, Abdel Fattah
String matching algorithms play a vital & crucial role in many applications such as search engines, object identification, hand written recognition, name searching in large databases, data cleansing, and automatic spell checking. Many algorithms have been developed to measure string similarity but most of them designed mainly to handle Latin-based languages. In this paper, we propose a new algorithm for Arabic string matching which takes into consideration the unique features of the Arabic language and the different similarity levels of the Arabic letters such as phonetic similarity and character form similarity in addition to keyboard distance.
Aero engines remaining useful life prediction based on enhanced adaptive guided diferential evolution
(Springer Verlag, 2022-12) Abdelghafar, Sara ; Khater, Ali ; Wagdy, Ali ; Darwish, Ashraf ; Hassanien, Aboul Ella
Remaining Useful Life (RUL) prediction is a key process for prognostic health management in almost all engineering real- world applications, especially which are in hazardous and challenging environments where the failures and disastrous faults cannot be avoided such as space vehicles and aircraft. This paper proposes a predictive approach based on our proposed algorithm Enhanced Adaptive Guided Diferential Evolution (EAGDE) is used to optimize the parameter selection of Support Vector Machine (SVM) to give high RUL prediction accuracy. The advantages of the proposed approach (EAGDE–SVM) are verifed using the popular benchmark C-MAPSS which describes the degradation of the aircraft turbofan engine datasets. The experimental study compares EAGDE–SVM with the basic SVM with randomized parameter selection and with an optimized SVM using three diferent optimization algorithms. Also, the EAGDE–SVM is evaluated against three popular classifer models that have been used in the comparisons of recent research. Diferent evaluation criteria of classifcation, prediction, and optimization aspects have been used, the obtained results show that the EAGDE is capable to achieve the lowest classifcation error rates and RUL high prediction accuracy through fnding the optimum values of the SVM param- eters with high stability and fast convergence rate.
Agile tailoring tool (ATT):A project specific agile method
(2009) El-Said S.M.; Hana M.; Eldin A.S.; Faculty of Computer Science; October University for Modem Sciences and Arts; Egypt; Faculty of Computers and Information; Helwan University; Egypt
After decades from introducing and using agile methodologies, project mangers realized that no methodology is sufficient by itself. Thus, merging their principles is the solution yet no formal solution has been proposed. Relying on previous work, ATT provides a mathematical model to act as a tailoring tool to formulate a new agile method based on experienced agile methods and the project specifications. It requires project managers to understand well the project requirements in terms of SDLC phases, and accordingly the new agile methodology is tailored. � 2009 IEEE.
AI-based Flexible Online Laboratory Learning System
(Institute of Electrical and Electronics Engineers Inc., 5/27/2021) Elmesalawy, M.M; Atia, A; Yousef, A.M.F; El-Haleem, A.M.A; Anany, M.G; Elmosilhy, N.A; Salama, A.I; Hamdy, A; Zoghby, H MK; Din, E.S.E
The worldwide outbreak due to COVID-19 pandemic has led to a great interest in e-learning. However, the lack of suitable online laboratory management systems has posed a particular challenge for sectors that need laboratory activities such as engineering, science and technology. In this paper, the requirements and design for a flexible AI-based laboratory learning system (LLS) that can support online laboratory experimentations are presented. The elicitation of the LLS design requirements is decided based on a conducted survey for a set of LLS features. The LLS is designed with the flexibility to support various types of online experimentations such as virtual or remote controlled experiments using either desktop or web applications. The virtualization technique is used to manage the laboratory resources and allow multiple users to access the LLS. Moreover, the proposed LLS introduces the use of AI techniques to provide efficient virtual lab assistant and adaptive assessment process. © 2021 IEEE.
Amelio-rater: Detection and Classification of Driving Abnormal Behaviours for Automated Ratings and Real-Time Monitoring
(IEEE, 2018) El Masry, Noha; El-Dorry, Passant; El Ashram, Mariam; Atia, Ayman; Tanaka, Jiro
Real-time monitoring of the drivers may be a factor that would force them to drive safely. In this paper, we introduce a system named 'Amelio-Rater", that focuses on detection and classification of abnormal driving behaviours for automatically generating driver ratings and real-time monitoring. To reduce malicious ratings, the Amelio-rater introduces an automatic rating system which is calculated purely based on the driver's driving behaviours only. Each driver will be given his own Amelio-rater rate and a manual user rate. There are multiple types of driving abnormal behaviours monitored by the proposed system such as meandering, single weaves, sudden changing of lanes and speeding. The classification results achieved showed that the Amelio-rater reached an accuracy of 95%. Our experiments showed that the manual user rates given for the driving behaviour are not far from the rates given by Amelio-rater. Amelio-rater rates were very close to the actual rates given by the users
Anti-Bump: A Bump/Pothole Monitoring and Broadcasting System for Driver Awareness
(SPRINGER, 2013) Fekry, Mohamed; Hamdy, Aya; Atia, Ayman
This paper presents a system for bump detection and alarming system for drivers. We have presented an architecture that adopts context awareness and Bump location broadcasting to detect and save bumps locations. This system uses motion sensor to get the readings of the bump then we classify it using Dynamic Time Wrapping, Hidden Markov Model and Neural Network. We keep records for the bump location through tracking its geographic position. We developed a system that alarms the driver within appropriate profiled distance for bump occurrence. We conducted two experiments for testing the system in a street modeled architect with different kinds of bumps and potholes. The other experiment was on real street bumps. The results show that the system can detect bumps and potholes with reasonably accepted accuracy.
Applying Deep Learning to Track Food Consumption and Human Activity for Non-intrusive Blood Glucose Monitoring
(Institute of Electrical and Electronics Engineers, 04/12/2021) Samir, Mohamed Amr; Mohamed, Zeinab A; Hussein, Mona Abdelmotaleb A; Atia, Ayman
Blood glucose monitoring is a wide area of research as it plays a huge part in controlling diabetes and many of its symptoms. A common human disease 'Diabetes Mellitus' (DM), which is characterized by hyperglycemia, has a number of harmful complications. In addition, the low glucose level in blood caused by hypoglycemia is correlated to fatal brain failure and death. In this paper, we explore a variety of related research to have a grasp on some of the systems and concepts that can assist in forming an autonomous system for glucose monitoring, including deep learning techniques. The proposed system in this paper utilizes non-intrusive Continuous Glucose Monitoring (CGM) devices for tracking glucose levels, combined with food classification and Human Activity Recognition (HAR) using deep learning. We relate the preprandial and peak postprandial glucose levels extracted from CGM with the Glycimc Load (GL) present in food, which makes it possible to form an estimation of blood sugar increase as well as predict hyperglycemia. The system also relates human activity with decrease in blood glucose to warn against possible signs of hypoglycemia before it occurs. We have conducted 3 different experiments; two of which are comparison between deep learning models for food classification and HAR with good results achieved, as well as an experimental result that we obtained by testing hyperglycemia prediction on real data of diabetic patients. The system was able to predict hyperglycemia with an accuracy percentage of 93.2%. © 2021 IEEE.
Applying machine learning techniques for classifying cyclin-dependent kinase inhibitors
(Science and Information Organization, 2018) Abdelbaky I.Z.; Al-Sadek A.F.; Badr A.A.; Agricultural Research Center; Cairo; Egypt; Computer Science Department; October University for Modern Sciences and Arts; MSA; Egypt; Computer Science department; Faculty of Computers and Information; Cairo University Cairo; Egypt
The importance of protein kinases made them a target for many drug design studies. They play an essential role in cell cycle development and many other biological processes. Kinases are divided into different subfamilies according to the type and mode of their enzymatic activity. Computational studies targeting kinase inhibitors identification is widely considered for modelling kinase-inhibitor. This modelling is expected to help in solving the selectivity problem arising from the high similarity between kinases and their binding profiles. In this study, we explore the ability of two machine-learning techniques in classifying compounds as inhibitors or non-inhibitors for two members of the cyclin-dependent kinases as a subfamily of protein kinases. Random forest and genetic programming were used to classify CDK5 and CDK2 kinases inhibitors. This classification is based on calculated values of chemical descriptors. In addition, the response of the classifiers to adding prior information about compounds promiscuity was investigated. The results from each classifier for the datasets were analyzed by calculating different accuracy measures and metrics. Confusion matrices, accuracy, ROC curves, AUC values, F1 scores, and Matthews correlation, were obtained for the outputs. The analysis of these accuracy measures showed a better performance for the RF classifier in most of the cases. In addition, the results show that promiscuity information improves the classification accuracy, but its significant effect was notably clear with GP classifiers. � 2018 International Journal of Advanced Computer Science and Applications.
Automatic Identification of Student’s Cognitive Style from Online Laboratory Experimentation using Machine Learning Techniques
(Institute of Electrical and Electronics Engineers, 04/12/2021) Yousef, Ahmed Mohamed Fahmy; Atia, Ayman; Youssef, Amira; Saad Eldien, Noha A; Hamdy, Alaa; Abd El-Haleem, Ahmed M; Elmesalawy, Mahmoud M
Online learning has emerged as powerful learning methods for the transformation from traditional education to open learning through smart learning platforms due to Covid-19 pandemic. Despite its effectiveness, many studies have indicated the necessity of linking online learning methods with the cognitive learning styles of students. The level of students always improves if the teaching methods and educational interventions are appropriate to the cognitive style of each student individually. Currently, psychological measures are used to assess students' cognitive styles, but about the application in virtual environment, the matter becomes complicated. The main goal of this study is to provide an efficient solution based on machine learning techniques to automatically identify the students' cognitive styles by analyzing their mouse interaction behaviors while carrying out online laboratory experiments. This will help in the design of an effective online laboratory experimentation system that is able to individualize the experiment instructions and feedback according to the identified cognitive style of each student. The results reveal that the KNN and SVM classifiers have a good accuracy in predicting most cognitive learning styles. In comparison to KNN, the enlarged studies ensemble the KNN, linear regression, neural network, and SVM reveal a 13% increase in overall total RMS error. We believe that this finding will enable educators and policy makers to predict distinct cognitive types in the assessment of students when they interact with online experiments. We believe that integrating deep learning algorithms with a greater emphasis on mouse location traces will improve the accuracy of our classifiers' predictions. © 2021 IEEE.
Automatic scoring for answers to Arabic test questions
(ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 2014) Gomaa, Wael Hassan; Fahmy, Aly Aly
Most research in the automatic assessment of free text answers written by students address English language. This paper handles the assessment task in Arabic language. This research focuses on applying multiple similarity measures separately and in combination. Many aspects are introduced that depend on translation to overcome the lack of text processing resources in Arabic, such as extracting model answers automatically from an already built database and applying K-means clustering to scale the obtained similarity values. Additionally, this research presents the first benchmark Arabic data set that contains 610 students' short answers together with their English translations. (C) 2013 Elsevier Ltd. All rights reserved.
Autonomous Checkers Robot Using Enhanced Massive Parallel Game Tree Search
(IEEE, 2014) Elnaggar, Ahmed A.; Gadallah, Mahmoud; Aziem, Mostafa Abdel; Aldeeb, Hisham
The dream of building intelligent robotic systems to interact and communicate with people and help them in their lives is a very old and ongoing study. In this research, the massive parallel autonomous checkers agent "MPACA" can autonomously play checkers with a human upto Grandmaster level without requiring a special checkers board for detecting human movements. The main aim and contribution of this research is proposing enhanced algorithms for a game tree search using two different approaches. The first was a task-based approach on CPU with a parallel database, while the second was a threads-based approach on the GPU with no divergence and dynamic parallelism. The two approaches were compared with previous studies using various approaches, including threads on CPU for up to 6x speedup for an 8-core processor and threads on GPU using iterative dependence and fixed grid and block size of up to 40x speedup at 14 depth. Furthermore, the approaches were tested with different depths on the CPU and the GPU. The result shows speed up for parallel CPU tasks up to 7x for an 8-core processor and parallel GPU of up to 80x at 14 depth.
Benchmarking the Higher Education Institutions in Egypt using Composite Index Model
(citseer, 2014) Rashad M El-Hefnawy, Mohamed; Hamed El-Bastawissy, Ali; Ahmed Kadry, Mona
Egypt has the largest and most significant higher educationsystem in the Middle East and North Africa but ithad been continuously facing serious and accumulated challenges. The Higher Education Institutions in Egypt are undergoing important changes involving the development of performance, they are implementing strategies to enhance the overall performance of their universities using ICT, but still the gap between what is existing and what is supposed to be for theself-regulation and improvement processesis not entirely clearto face these challenges. The using ofstrategiccomparativeanalysis model and tools toevaluate thecurrent and future stateswill affect the overall performance of universities and shape new paradigms in development of Higher EducationSystem (HES), severalstudies have investigated the evaluation of universities through the development and use of ranking and benchmarksystems
Blockchain for tracking serial numbers in money exchanges
(WILEY, 2019) Mohamed, Kareem; Aziz, Amr; Mohamed, Belal; Abdel‐Hakeem, Khaled; Mostafa, Mostafa; Attia, Ayman
Money exchange is one of the most common day‐to‐day activities performed by humans in the daily market. This paper presents an approach to money tracking through a blockchain. The proposed approach consists of three main components: serial number localization, serial number recognition, and a blockchain to store all transactions and ownership transfers. The approach was tested with a total of 110 banknotes of different currency types and achieved an average accuracy of 91.17%. We conducted a user study in real‐time with 21 users, and the mean accuracy across all users was 86.42%. Each user gave us feedback on the proposed approach, and most of them welcomed the idea
BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment
(Institute of Statistical Studies and Research, Department of Computer Sciences, Faculty of Computers and Information, Cairo University, 2017) Soliman, Ashraf; Salah, Akram; Hefny, Hesham
Knowledge gives a strong support to autonomous agents in multi-agent systems and thus the evolution of agent’s knowledge needs a great attention since it has a control on agents’ behaviors and has effect on their decisions making. The problem is to allow agents to detect and decide whether they need more domain knowledge and allow their knowledge to evolve consistently and automatically. This paper utilizes ontologies to represent the internal knowledge of agents instead of utilizing them only as a shared conceptualization. Consequently, the paper proposes a model of bottom-up instance-driven ontology evolution that allows the internal ontologies of agents to evolve automatically and consistently in run time based on agents’ interactions. Experiments are designed and implemented to evaluate our model in different situations. One of its results shows that an empty internal ontology of one agent could evolve automatically in runtime by 88.3% through its interactions with other agents. Moreover, a comparison between the proposed approach and literature review approaches is presented to compare between their different features and techniques. This paper is considered a step forward to automate ontology evolution for agents in multiagent environment.
Bovines Muzzle Classification Based on Machine Learning Techniques
(Elsevier, 2015-11) Mousa, Farid Ali; Mahmoud, Hamdi A; El Hadad, Hagar M
Bovines muzzle classification is considered as a biometric classifier to maintain the safety of bovines and guarantee the livestock products. This paper presents two different bovines classifications models using Artificial Neural Network (ANN) and K-Nearest Neighbor Classifier (KNN). The proposed ANN model consists of three phases; pre-processing, feature extraction and classifications. Pre-processing techniques; histogram equalization and mathematical morphology filtering has been used. The ANN model use Segmentation-based Fractal Texture Analysis (SFTA) for extract muzzle features. The proposed KNN model consists of two phases; Expectation Maximization image segmentation and classification. Expectation Maximization image segmentation (EM) depends on extracts bovine image color and texture feature extraction. The experimental result evaluation proves the advancement of KNN model than ANN as it achieves 100% classification accuracy in case of increase number of classification groups to twenty-five compared to 92.76% classification accuracy achieved from ANN classification model
Brain computer interfacing: Applications and
(Elsevier, 2015-07) Mostafa, Mostafa-Sami M.; Atia, Ayman; Abdulkader, Sarah N.
Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabil- itation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding sys- tems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain sig- nals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed. - 2015 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information,
BRCA1-IRIS overexpression promotes and maintains the tumor initiating phenotype: implications for triple negative breast cancer early lesions
(Impact Journals, LLC, 2017) Sinha, Abhilasha; Bibbin, T Paul; Lisa, M Sullivan; Hillary, Sims; El Bastawisy, Ahmed; F Yousef, Hend; N Zekri, Abdel-Rahman; A Bahnassy, Abeer; M ElShamy, Wael
Tumor-initiating cells (TICs) are cancer cells endowed with self-renewal, multi-lineage differentiation, increased chemo-resistance, and in breast cancers the CD44+/CD24-/ALDH1+ phenotype. Triple negative breast cancers show lack of BRCA1 expression in addition to enhanced basal, epithelial-to-mesenchymal transition (EMT), and TIC phenotypes. BRCA1-IRIS (hereafter IRIS) is an oncogene produced by the alternative usage of the BRCA1 locus. IRIS is involved in induction of replication, transcription of selected oncogenes, and promoting breast cancer cells aggressiveness. Here, we demonstrate that IRIS overexpression (IRISOE) promotes TNBCs through suppressing BRCA1 expression, enhancing basal-biomarkers, EMT-inducers, and stemness-enforcers expression. IRISOE also activates the TIC phenotype in TNBC cells through elevating CD44 and ALDH1 expression/activity and preventing CD24 surface presentation by activating the internalization pathway EGFR→c-Src→cortactin. We show that the intrinsic sensitivity to an anti-CD24 cross-linking antibody-induced cell death in membranous CD24 expressing/luminal A cells could be acquired in cytoplasmic CD24 expressing IRISOE TNBC/TIC cells through IRIS silencing or inactivation. We show that fewer IRISOE TNBC/TICs cells form large tumors composed of TICs, resembling TNBCs early lesions in patients that contain metastatic precursors capable of disseminating and metastasizing at an early stage of the disease. IRIS-inhibitory peptide killed these IRISOE TNBC/TICs, in vivo and prevented their dissemination and metastasis. We propose IRIS inactivation could be pursued to prevent dissemination and metastasis from early TNBC tumor lesions in patient

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