Developing a green curriculum for introduction to information technology course

© Springer Nature Switzerland AG 2020. No one can deny that information technology courses can be an excellent initiative for increasing the awareness of environmental and health problems. Hence, information technology instructors play a critical role in spreading awareness of green technology and reinforcing sustainability. The Introduction to Information Technology (IIT) course contains many topics that can be directed in an intelligent way to spread awareness about sustainability. In this paper, we suggest a detailed curriculum for the IT course that converts a standard curriculum into a green one. The goal of this curriculum is to produce a new generation that is aware enough about the role of technology in our life and its impact on the environment. The course topics will be the answers to the following questions. First, how to avoid the negative impact of technology on health and the environment. Second, how to decrease the negative impact that cannot be avoided or find a solution for it. Finally, what are the alternative green computing techniques that can be used to increase sustainability? The proposed curriculum should involve the instructor and the students. Both should collaborate in a set of activities that will give society a generation with high awareness of green computing and sustainability

Evaluation of Machine Learning Algorithm on Drinking Water Quality for Better Sustainability

Water has become intricately linked to the United Nations\u27 sixteen sustainable development goals. Access to clean drinking water is crucial for health, a fundamental human right, and a component of successful health protection policies. Clean water is a significant health and development issue on a national, regional, and local level. Investments in water supply and sanitation have been shown to produce a net economic advantage in some areas because they reduce adverse health effects and medical expenses more than they cost to implement. However, numerous pollutants are affecting the quality of drinking water. This study evaluates the efficiency of using machine learning (ML) techniques in order to predict the quality of water. Thus, in this paper, a machine learning classifier model is built to predict the quality of water using a real dataset. First, significant features are selected. In the case of the used dataset, all measured characteristics are chosen. Data are split into training and testing subsets. A set of existing ML algorithms is applied, and the results are compared in terms of precision, recall, F1 score, and ROC curve. The results show that support vector machine and k-nearest neighbor are better according to F1-score and ROC AUC values. However, The LASSO LARS and stochastic gradient descent are better based on recall values

Towards effective and efficient online exam systems using deep learning-based cheating detection approach

With the high growth of digitization and globalization, online exam systems continue to gain popularity and stretch, especially in the case of spreading infections like a pandemic. Cheating detection in online exam systems is a significant and necessary task to maintain the integrity of the exam and give unbiased, fair results. Currently, online exam systems use vision-based traditional machine learning (ML) methods and provide examiners with tools to detect cheating throughout the exam. However, conventional ML methods depend on handcrafted features and cannot learn the hierarchical representations of objects from data itself, affecting the efficiency and effectiveness of such systems. The proposed research aims to develop an effective and efficient approach for online exam systems that uses deep learning models for real-time cheating detection from recorded video frames and speech. The developed approach includes three essential modules, which constantly estimate the critical behavior of the candidate student. These modules are the front camera-based cheating detection module, the back camera-based cheating detection module, and the speech-based detection module. It can classify and detect whether the candidate is cheating during the exam by automatically extracting useful features from visual images and speech through deep convolutional neural networks (CNNs) and the Gaussian-based discrete Fourier transform (DFT) statistical method. We evaluate our system using a public dataset containing recorded audio and video data samples collected from different subjects carrying out several types of cheating in online exams. These collected data samples are used to obtain the experimental results and demonstrate the proposed work\u27s efficiency and effectiveness

Promoting Teaching Practices in IT Higher Education

© 2020 Owner/Author. Lecture-based classes became an old strategy of teaching even in higher education. Education now focuses on student-centered strategies that actively engage students in their learning and how teacher can design classes to facilitate learning process. This paper presents some practices for teaching in higher education

Dual Learning Model for Multiclass Brain Tumor Classification

A brain tumor occurs in the human body when the brain develops abnormal cells. Tumors are called either benign (noncancerous) or malignant (cancerous). The function of the nervous system is affected by the growth rate and the location of the tumor. The tumor treatment depends on tumor type, size, and location. Artificial intelligence has been widely used to automatically predict various brain tumors using multiple imaging technologies such as magnetic resonance imaging (MRI) and computerized tomography (CT) scan during the last few years. This paper applies a hybrid learning based classifier on an MRI dataset containing benign and malignant images. Moreover, deep learning is also applied to the same dataset. The proposed learning approach’s performance is compared to other existing supervised machine learning approaches. The experimental results show that our proposed approach outperforms the existing approaches available in the literature

The rising trend of Metaverse in education: challenges, opportunities, and ethical considerations

Metaverse is invading the educational sector and will change human-computer interaction techniques. Prominent technology executives are developing novel ways to turn the Metaverse into a learning environment, considering the rapid growth of technology. Since the COVID-19 outbreak, people have grown accustomed to teleworking, telemedicine, and numerous other forms of distance interaction. Recently, the Metaverse has been the focus of many educators. With Facebook’s statement that it was rebranding and promoting itself as Meta, this field saw a surge in interest in the areas of computer science and education. There is a literature gap in studying the Metaverse’s role in education. This article is a systematic review following the PRISMA framework that reviews the role of the Metaverse in education to shrink the literature gap. It presents various educational uses to aid future research in this field. Additionally, it demonstrates how enabling technologies like extended reality (XR) and the internet of everything (IoE) will significantly impact educational services in the Metaverses of the future of teaching and learning. The article also outlines key challenges, ethical issues, and potential threats to using the Metaverse for education to offer a road map for future research that will investigate how the Metaverse will improve learning and teaching experiences

On-Line Learning on Information Security Based on Critical Thinking Andragogy

There is a demand in higher educational institutions for an on-line learning model that positively stimulates learners’ critical thinking. Higher education learners are considered adults. Thus, standard pedagogical approaches do not always work. Adults need an andragogical approach that takes into consideration that teaching is relevant to them. Andragogy refers to adult teaching theories. In this article, the authors propose a new model that intends to provide an effective on-line teaching environment that applies critical thinking andragogy in higher education. The model was applied to 140 learners of the Basics of Information Security course at Zayed University in Abu Dhabi, the United Arab Emirates. This course was specifically chosen because the skills taught in this course are used in real life, while dealing with e-mails, Web sites, social networks and other on-line platforms. Thus, an andragogical approach seemed appropriate. Learners’ response to the applied model was measured through a quantitative research method. There was noticeable student satisfaction regarding the applied framework. The authors describe the model in detail and how it was utilised

A Spam Email Detection Mechanism for English Language Text Emails Using Deep Learning Approach

© 2020 IEEE. Phishing emails are emails that pretend to be from a trusted company that target users to provide personal or financial information. Sometimes, they include links that may download malicious software on user\u27s computers, when clicked. Such emails are easily detected by spam filters that classify any email with a link as a phishing email. However, emails that have no links, link-less emails, requires more effort from the spam filters. Although many researches have been done on this topic, spam filters are still classifying some benign emails as phishing and vice-versa. This paper is focused on classifying link-less emails using machine learning approach, deep neural networks. Deep neural networks differs from simple neural network by having multiple hidden layers where data must be processed before reaching the output layer. The data used in this research is publicly available online. Hyper parameter optimization, was performed, using different settings on the data. In order to demonstrate the effectiveness of the approach, precision, recall and accuracy were computed. The results show that the deep neural network performed well in many of its settings

An Effective Hash-Based Assessment and Recovery Algorithm for Healthcare Systems

The immense improvements in the latest internet inventions encouraged the adaptation of technology within the healthcare sector. The healthcare systems storing highly sensitive information can be targeted by attackers aiming to insert, delete, or modify the data stored. These malicious activities may cause severe damage to the database accessibility and lead to catastrophic long-term harm to the patients’ health. The adaptation of the most advanced security paradigm does not guarantee full protection. It is possible that the attack is not directly detected. This highlights the need to assess the widespread damage scale before starting the repair of the inconsistent medical database. Within the scope of the damage assessment and recovery, several matrices-based, cluster-based, and graph-based models were introduced. The objective of this work is to correctly assess the damage and recover the database within a suitable time frame and efficient utilization of memory. We use a lightweight structure based on hash tables to gauge the incurred damage and recuperate quickly following an attack. The presented approach is contrasted with other existing ones and demonstrated superior performance

Introduction to Artificial Intelligence

Artificial intelligence (AI) has been a topic of high interest in this day and age. AI has emerged through the early nineties and continues to grow at an unprecedented rate. The idea of having machines that are able to process certain cognition to come to a decision without the intervention of humans is the ultimate idea that is being pursued. Though the stage in which AI is able to completely outperform humans in its cognitive skills is yet to be achieved, there has been remarkable progress towards that area. This chapter aims to provide a brief introduction about AI and the area covered under the topic. Various algorithms are used in programming AI on machines such as evolutionary algorithms, genetic algorithms, and swarm intelligence. AI encompasses machine learning, which will be further discussed in this chapter. Furthermore, the impact of AI on society and futuristic predictions the chapter reviews