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This paper discusses how the software development team at the German Jordanian University (GJU) adopted the project management and software development processes in the ISO/IEC 29110 series to implement a complex Student Information System (SIS). Specifically, it identifies the key points to be taken into consideration in the analysis, design, implementation, testing, and deployment phases during the iterative and incremental SIS development process. The SIS is a distributed three‐tier web‐based application that enables registrars to perform various tasks such as system setup, admission, registration, grades processing, graduation, and reporting. It was launched in the first 2015/2016 semester and enabled administration to maintain a comfortable learning environment, assess instructor performance, enhance teaching practices, and improve course content. The results of the system measurements and user survey assert that the SIS is feature rich, easy to use, fast, reliable, stable, highly available, and scalable. © 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:242–263, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21794

A web‐based examination management system that is implemented in‐house is discussed in this paper. It is designed based on the Java enterprise edition three‐tier architecture. It also allows defining and setting up exams according to a flexible tree‐based exams structure. Moreover, it integrates a rich text editor for composing exams suitable for different engineering and language disciplines. In addition, it automates the scheduling, grading, and reporting processes to relieve instructors from such cumbersome tasks. Furthermore, its capabilities and integration with different databases enable it to offer several security schemes that support strong multifactor authentication and authorization, detect impersonation, and prevent cheating. Besides, it provides an easy to use and informative wizard that enables students to take exams. Not to mention, the deployment results illustrate that the system has been successfully used to organize online exams in several semesters over the past 3 academic years. Finally, the conducted user surveys responses assert that the system is also user‐friendly, cutting edge, capable, reliable, fast, and highly available.

The need for effective approaches to handle big data that is characterized by its large volume, different types, and high velocity is vital and hence has recently attracted the attention of several research groups. This is especially the case when traditional data processing techniques and capabilities proved to be insufficient in that regard. Another aspect that is equally important while processing big data is its security, as emphasized in this paper. Accordingly, we propose to process big data in two different tiers. The first tier classifies the data based on its structure and on whether security is required or not. In contrast, the second tier analyzes and processes the data based on volume, variety, and velocity factors. Simulation results demonstrated that using classification feedback from a MPLS/GMPLS core network proved to be key in reducing the data evaluation and processing time.

Abstract—This paper discusses an Accounting Information System (AIS) that is developed in-house by the German Jordanian University (GJU). The AIS is a web-based distributed application that is comprised of three separate tiers: the client, web (application), and business (data) tiers. The three tiers are designed and developed such that the system is easy to use, effective, fast, accurate, reliable, and scalable. The AIS mainly enables accountants to manage (i.e., record, view, edit, and delete) the daily financial transactions such as payments, refunds, and adjustments. Furthermore, it allows managing fees (e.g., services and tuition fees) and student data (e.g., academic information, schedules, transcripts, holds, registration invoices, and statement of account). Moreover, it allows specifying sponsors and supports the definition of complex scholarship tuition fees coverage combinations related to the GJU as well as most of the Jordanian universities. Besides that, it enables the quick generation of financial statements and accounting reports (e.g., daily summary, daily journal, and student balances) at the click of a button. The generated reports can be used by the staff and management of the financial department for auditing, financial accounting, and decision making purposes. The system was successfully deployed in the first 2015/2016 academic semester and since then it accurately recorded and processed thousands of accounting transactions.   Index Terms: software engineering, student information system, web applications, accounting,scholarships, tuition fees.

This paper investigates the possibility of using visual imagery tasks, which are mental imagery tasks that involve imagining the images of objects perceptually without seeing them, as a control paradigm that can increase the control's dimensionality of electroencephalography (EEG)-based braincomputer interfaces. Specifically, we propose an EEG-based approach for decoding visually imagined objects by using the Choi-Williams time-frequency distribution to analyze the EEG signals in the joint timefrequency domain and extract a set of twelve time-frequency features (TFFs). The extracted TFFs are used to construct a multi-class support vector machine classifier to decode various visually imagined objects. To validate the performance of our proposed approach, we have recorded an EEG dataset for 16 healthy subjects while imagining objects that belong to four different categories, namely nature (fruits and animals), decimal digits, English alphabet (capital letters), and arrow shapes (arrows with different colors and orientations). Moreover, we have designed two performance evaluation analyses, namely the channel-based analysis and feature-based analysis, to quantify the impact of utilizing different groups of EEG electrodes that cover various regions on the scalp and the effect of reducing the dimensionality of the extracted TFFs on the performance of our proposed approach in decoding the imagined objects within each of the four categories. The experimental results demonstrate the efficacy of our proposed approach in decoding visually imagined objects. Particularly, the average decoding accuracies obtained for each of the four categories were as high as 96.67%, 93.64%, 88.95%, and 92.68%.

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This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by CONV features that include convolution features extracted from all convolution blocks of the VGG19 model. In particular, the CONV features achieved mean accuracy, sensitivity, and specificity values of 94.2%, 93.3%, and 94.9%, respectively. The analysis also shows that the performance of the CONV features degrades substantially when the features selection algorithm is not applied. The classification performance of the CONV features is improved by combining these features with handcrafted morphological features to achieve mean accuracy, sensitivity, and specificity values of 96.1%, 95.7%, and 96.3%, respectively. Furthermore, the cross-validation analysis demonstrates that the CONV features and the combined CONV and morphological features outperform the handcrafted texture and morphological features as well as the fine-tuned VGG19 model. The generalization performance of the CONV features and the combined CONV and morphological features is demonstrated by performing the training using the 380 breast ultrasound images and the testing using another dataset that includes 163 images. The results suggest that the combined CONV and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.