Predictive Modelling of Student Academic Performance – the Case of Higher Education in Middle East

One of the main issues in higher education is student retention. Predicting students' performance is an important task for higher education institutions in reducing students' dropout rate and increasing students' success. Educational Data mining is an emerging field that focuses on dealing with data related to educational settings. It includes reading the data, extracting the information and acquiring hidden knowledge. This research used data from one of the Gulf Cooperation Council (GCC) universities, as a case study of Higher Education in the Middle East. The concerned University has an enrolment of about 20,000 students of many different nationalities. The primary goal of this research is to investigate the ability of building predictive models to predict students' academic performance and identify the main factors that influence their performance and grade point average. The development of a generalized model (a model that could be applied on any institution that adopt the same grading system either on the Foundation level (that use binary response variable (Pass/ Fail) or count response variable which is the Grade Average Point for students enrol in the undergraduate academic programs) to identify students in jeopardy of dismissal will help to reduce the dropout rate by early identification of needed academic advising, and ultimately improve students' success. This research showed that data science algorithms could play a significant role in predicting students' Grade Point Average by adopting different regression algorithms. Different algorithms were carried out to investigate the ability of building predictive models to predict students' Grade Point Average after either 2, 4 or 6 terms. These methods are Linear/ Logistic Regression, Regression Trees and Random Forest. These predictive models are used to predict specific students' Grade Point Average based on other values in the dataset. In this type of model, explicit instruction is given about what the model needs to learn. An optimization function (the model) is formed to find the target output based on specific input values. This research opens the door for future comprehensive studies that apply a data science approach to higher-education systems and identifying the main factors that influence student performance

Banana KBS Diagnosis and Treatment

Abstract: This research involved the design of an initial expert system which helps farmers and specialists diagnose and provide appropriate advice on banana diseases. The management of knowledge used in the expert system was also discussed. One of the key elements of this research was to find the appropriate language to diagnose the disease and the current situation in the knowledge base. Expert systems enable effective consultation. Production rules were used to capture knowledge. The expert system was developed using CLIPS with the Delphi 10.2 as user interface. The expert system produced good results in analyzing cases of tested banana disease and enabling the system to determine the correct diagnosis in all cases

KBS for Banana Diagnosis and Treatment

Abstract: This research involved the design of an initial expert system which helps farmers and specialists diagnose and provide appropriate advice on banana diseases. The management of knowledge used in the expert system was also discussed. One of the key elements of this research was to find the appropriate language to diagnose the disease and the current situation in the knowledge base. Expert systems enable effective consultation. Production rules were used to capture knowledge. The expert system was developed using CLIPS with the Delphi 10.2 as user interface. The expert system produced good results in analyzing cases of tested banana disease and enabling the system to determine the correct diagnosis in all cases

MEASURING GEOMETRICAL TORTUOSITY OF POROUS MEDIA FROM 3D COMPUTED TOMOGRAPHY IMAGES

Tortuosity is an important parameter that has a significant impact on many environmental processes and applications. Flow in porous media, diffusion of gases in complex pore structures, and transmembrane flux in water desalination are examples of the application of the micro-scale parameter. The main objectives of this thesis are to develop functional relationships that relate tortuosity to geometrical and topological parameters of porous media using three-dimensional (3D) computed tomography images, and select the best model that has the best capability to predict geometrical tortuosity. The objectives were achieved by implementing Random Paths MATLAB code that was developed in this work and compared with available Tort3D MATLAB code using high resolution 3D synchrotron computed tomography images of representative porous media. Tortuosity factors were computed from random tortuous paths of connected voxels (Random Paths Code) and tortuous paths derived from 3D medial surface of void space (Tort3D Code). Tortuosity factors were related to geometrical and topological parameters including porosity (∅), median grain diameter (d50), uniformity coefficient (Cu), coefficient of gradation (Cc), sphericity index (Si), roundness index (Ri), and specific surface area (SSA). Tort3D code was validated by comparing measured with predicted tortuosity factors from models reported in the literature. The two codes measured geometrical tortuosity of different sand systems effectively. However, they provided different tortuosity values, since they were developed using different concepts. Models were developed and predicted tortuosity values were compared with measured tortuosity values. Good agreement was found between predicted and measured tortuosity values with low error (less than 20%). Model 3 that considers ∅, d50, Cu, and Cc has best capability to predict tortuosity compared with other developed models

Theoretical framework for real time sub-micron depth monitoring using quantum inline coherent imaging

Inline Coherent Imaging (ICI) is a reliable method for real-time monitoring of various laser processes, including keyhole welding, additive manufacturing, and micromachining. However, the axial resolution is limited to greater than 2 {\mu}m making ICI unsuitable for monitoring submicron processes. Advancements in Quantum Optical Coherence Tomography (QOCT), which uses a Hong-Ou-Mandel (HOM) interferometer, has the potential to address this issue by achieving better than 1 {\mu}m depth resolution. While time-resolved QOCT is slow, Fourier domain QOCT (FD-QOCT) overcomes this limitation, enabling submicron scale real-time process monitoring. Here we review the fundamentals of FD-QOCT and QOCT and propose a Quantum Inline Coherent Imaging system based on FD-QOCT. Using frequency entangled sources available today the system has a theoretical resolution of 0.17 microns, making it suitable for submicron real-time process monitoring.Comment: 12 pages, 8 figure

Monitoring and modelling of non methane hydrocarbons (NMHCs) in various areas in Pulau Pinang, Malaysia.

Hidrokarbon bukan metana (NMHC) memainkan peranan penting dalam proses pembentukan ozon dalam persekitaran bandar, di mana pembebasan dari asap kenderaan adalah dominan. Non Methane Hydrocarbons (NMHC) plays a vital role in the formation process of ozone in urban environment, where vehicle emissions are dominant

New Security Protocols for Offline Point-of-Sale Machines

EMV (Europay MasterCard Visa) is the protocol implement-ed to secure the communication between a client’s payment device and a Point-of-Sale machine during a contact or an NFC (Near Field Communication) purchase transaction. In several studies, researchers have analyzed the operation of this protocol in order to verify its safety: unfortunately, they have identified two security vulnerabilities that lead to multiple attacks and dangerous risks threatening both clients and merchants. In this paper, we are interested in proposing new security solutions that aim to overcome the two dangerous EMV vulnerabilities. Our solutions address the case of Point-of-Sale machines that do not have access to the banking network and are therefore in the “offline” connectivity mode. We verify the accuracy of our proposals by using the Scyther security verification tool

TORT3D: A MATLAB code to compute geometric tortuosity from 3D images of unconsolidated porous media

Tortuosity is a parameter that plays a significant role in the characterization of complex porous media systems and it has a significant impact on many engineering and environmental processes and applications. Flow in porous media, diffusion of gases in complex pore structures and membrane flux in water desalination are examples of the application of this important micro-scale parameter. In this paper, an algorithm was developed and implemented as a MATLAB code to compute tortuosity from three-dimensional images. The code reads a segmented image and finds all possible tortuous paths required to compute tortuosity. The code is user-friendly, easy to use and computationally efficient, as it requires a relatively short time to identify all possible connected paths between two boundaries of large images. The main idea of the developed algorithm is that it conducts a guided search for connected paths in the void space of the image utilizing the medial surface of the void space. Once all connected paths are identified in a specific direction, the average of all connected paths in that direction is used to compute tortuosity. Three-dimensional images of sand systems acquired using X-ray computed tomography were used to validate the algorithm. Tortuosity values were computed from three-dimensional images of nine different natural sand systems using the developed algorithm and compared with predicted values by models available in the literature. Findings indicate that the code can successfully compute tortuosity for any unconsolidated porous system irrespective of the shape (i.e., geometry) of particles. 1 2017 Elsevier B.V.Scopu

ITS for Teaching Introduction to CS

Abstract: The paper describes the design of an intelligent tutoring system for teaching Introduction to Computer Science-a compulsory curriculum in Al-Azhar University of Gaza to students who attend the university. The basic idea of this system is a systematic introduction into computer science. The system presents topics with examples. The system is dynamically checks student's individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students enrolled in computer science curriculum at Al-Azhar University, Gaza. The results showed a positive impact on the evaluators

An ITS for Teaching Introduction to Computer Science

Abstract: The paper describes the design of an intelligent tutoring system for teaching Introduction to Computer Science-a compulsory curriculum in Al-Azhar University of Gaza to students who attend the university. The basic idea of this system is a systematic introduction into computer science. The system presents topics with examples. The system is dynamically checks student's individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students enrolled in computer science curriculum at Al-Azhar University, Gaza. The results showed a positive impact on the evaluators