Papers by Sherif E Abdelhamid
IEEE Access
Machine learning in Education is receiving more attention from researchers as the number of stude... more Machine learning in Education is receiving more attention from researchers as the number of students at all levels globally is increasing. To ensure students' success in K-12 educational institutions and higher education institutions work needs to be done to assist students, teachers/professors, parents, and all stakeholders to provide the support that students need. The need and motivation for such systems are very well-established and thus the aim of this work is to develop a system based on modified machine learning models to automatically predict students' performance and subsequently identify students at risk. The DEEDs dataset is used in this study. Novel features were extracted and applied to well-known classifiers some of which are ensemble classifiers. These classifiers were also combined with base learners such as bagging and boosting. The problem was divided into three scenarios; binary classification of the pass and fail, three class scenarios, and four class scenarios. It was shown that ensemble methods combined with base learners of boosting and bagging significantly increase the accuracy for binary classification, slightly increase accuracy for three class problems, and have no significance in increasing the accuracy when the problem is 4 classes. The ensemble algorithm of bagging and boosting FDT achieved an accuracy of 98.25% for binary classification and 89.47% for three classes. The standard ensemble FDT achieved an accuracy of 77.19% for four classes. The results obtained for binary classification were compared with results reported in the extant literature using the same dataset proving that the proposed modified algorithms achieved better results than similarly proposed methods. The three-class and four-class results could not be compared because according to the author's knowledge, there are no research papers published for the same dataset for multi-class classification.
Plants
Rice is considered one the most important plants globally because it is a source of food for over... more Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The...
Diagnostics
Diabetic Retinopathy (DR) is a medical condition present in patients suffering from long-term dia... more Diabetic Retinopathy (DR) is a medical condition present in patients suffering from long-term diabetes. If a diagnosis is not carried out at an early stage, it can lead to vision impairment. High blood sugar in diabetic patients is the main source of DR. This affects the blood vessels within the retina. Manual detection of DR is a difficult task since it can affect the retina, causing structural changes such as Microaneurysms (MAs), Exudates (EXs), Hemorrhages (HMs), and extra blood vessel growth. In this work, a hybrid technique for the detection and classification of Diabetic Retinopathy in fundus images of the eye is proposed. Transfer learning (TL) is used on pre-trained Convolutional Neural Network (CNN) models to extract features that are combined to generate a hybrid feature vector. This feature vector is passed on to various classifiers for binary and multiclass classification of fundus images. System performance is measured using various metrics and results are compared wit...
Geotechnical Engineering Cyberinfrastructure (GTCI)
Geo-Congress 2022, 2022
Twitter, a popular social networking and microblogging platform, harvests and stores large amount... more Twitter, a popular social networking and microblogging platform, harvests and stores large amounts of data about myriad topics through millions of short messages (tweets). Among this array of topics, some tweets can contain valuable information related to engineering education and first-year engineering experiences. Unfortunately, despite the existence of such related tweets, the engineering education community writ large typically does not have adequate background and statistics on their number and content in order to glean information from this corpus of tweets. In general, data from tweets can be very useful for both qualitative and quantitative studies focusing on first-year engineering experiences. By incorporating data collected from Twitter, we can have the opportunity to discover interesting patterns and themes. In this paper, we report on the results of a study in which we collected and analyzed tweets related to engineering education and first-year engineering experiences....
2016 ASEE Annual Conference & Exposition Proceedings
and is a graduate research assistant at Network Dynamics and Simulations Science Laboratory. Sher... more and is a graduate research assistant at Network Dynamics and Simulations Science Laboratory. Sherif's research work lies at the intersection of computation, biology and education: in particular, he is interested in designing and building software systems to enable domain experts to easily access and effectively use high performance computing to perform and share the findings of simulations and large scale data analyses. Other aspects of his research focus on how to use these systems as learning tools for students and teachers.
Procedia Computer Science, 2016
Network science is moving more and more to computing dynamics on networks (so-called contagion pr... more Network science is moving more and more to computing dynamics on networks (so-called contagion processes), in addition to computing structural network features (e.g., key players) and other parameters. Generalized contagion processes impose additional data storage and processing demands that include more generic and versatile manipulations of networked data that can be highly attributed. In this work, we describe a new network services and workflow system called MARS that supports structural network analyses and generalized network dynamics analyses. It is accessible through the internet and can serve multiple simultaneous users and software applications. In addition to managing various types of digital objects, MARS provides services that enable applications (and UIs) to add, interrogate, query, analyze, and process data. We focus on several network services and workflows of MARS. We also provide a case study using a web-based application that MARS supports, and several performance evaluations of scalability and work loads. We find that MARS efficiently processes networks of hundreds of millions of edges from many hundreds of simultaneous users.
Edison
Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, 2015
SPublic health issues, from virus and disease transmission, to the spread of unhealthy behaviors ... more SPublic health issues, from virus and disease transmission, to the spread of unhealthy behaviors (such as smoking and obesity) are global priorities. They can lead not only to fatalities, but also to decreased quality of life, large expenditures for health care, and the onset of other ailments. Here we present EDISON: a web-based modeling environment that can be used to perform complex computational experiments involving very general (epidemiological) contagion processes over social networks. EDISON is publicly accessible by scientists and domain experts interested in carrying out in-silico social and epidemiological experiments. EDISON is unique in that: (i) the experiments can be carried out at scale (populations may range from a few hundred to millions of agents), (ii) it is web-based with an easy to use UI---it is specifically designed for use by epidemiologists, social scientists, and (government) practitioners who are not computing experts, and (iii) it has a digital library that contains a large number of open source social networks and many behavior models. EDISON has been used for a number of theoretical studies. We illustrate its utility through case studies of disease and behavioral contagion transmission.
PLOS ONE, 2015
Discrete dynamical systems are used to model various realistic systems in network science, from s... more Discrete dynamical systems are used to model various realistic systems in network science, from social unrest in human populations to regulation in biological networks. A common approach is to model the agents of a system as vertices of a graph, and the pairwise interactions between agents as edges. Agents are in one of a finite set of states at each discrete time step and are assigned functions that describe how their states change based on neighborhood relations. Full characterization of state transitions of one system can give insights into fundamental behaviors of other dynamical systems. In this paper, we describe a discrete graph dynamical systems (GDSs) application called GDSCalc for computing and characterizing system dynamics. It is an open access system that is used through a web interface. We provide an overview of GDS theory. This theory is the basis of the web application; i.e., an understanding of GDS provides an understanding of the software features, while abstracting away implementation details. We present a set of illustrative examples to demonstrate its use in education and research. Finally, we compare GDSCalc with other discrete dynamical system software tools. Our perspective is that no single software tool will perform all computations that may be required by all users; tools typically have particular features that are more suitable for some tasks. We situate GDSCalc within this space of software tools.
2012 IEEE 8th International Conference on E-Science, 2012
Networks are an effective abstraction for representing real systems. Consequently, network scienc... more Networks are an effective abstraction for representing real systems. Consequently, network science is increasingly used in academia and industry to solve problems in many fields. Computations that determine structure properties and dynamical behaviors of networks are useful because they give insights into the characteristics of real systems. We introduce a newly built and deployed cyberinfrastructure for network science (CINET) that performs such computations, with the following features: (i) it offers realistic networks from the literature and various random and deterministic network generators; (ii) it provides many algorithmic modules and measures to study and characterize networks; (iii) it is designed for efficient execution of complex algorithms on distributed high performance computers so that they scale to large networks; and (iv) it is hosted with web interfaces so that those without direct access to high performance computing resources and those who are not computing experts can still reap the system benefits. It is a combination of application design and cyberinfrastructure that makes these features possible. To our knowledge, these capabilities collectively make CINET novel. We describe the system and illustrative use cases, with a focus on the CINET user.
Interactive exploration and understanding of contagion dynamics in networked populations
2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), 2016
Modeling and simulation of contagion processes on networked populations are used to understand pr... more Modeling and simulation of contagion processes on networked populations are used to understand protests, social unrest, the spread of information, and virus and disease epidemics, among other phenomena. Network structure and attributes of vertices and edges are often useful in explaining contagion spreading processes. However, particularly for larger networks (e.g., those with hundreds of thousands or millions of vertices), reasoning about and making sense of contagion propagation results is difficult owing to the scale of these simulations. We present a web application called NEMO for assisting an analyst in understanding contagion processes and in establishing causality. It has several features to query and visualize networks, subnetworks, and their properties. In addition to explaining NEMO's features, we provide a real case study of the spread of Ebola on a 4-million-vertex social network of Liberia, Africa. We demonstrate how NEMO can be used to explore interactively networks to understand the reasons for the effectiveness of different interventions.
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Papers by Sherif E Abdelhamid