TEACHING THE BASICS OF DEEP LEARNING IN COMPUTER SCIENCE AND ENGINEERING: A HANDS-ON WEB-BASED APPROACH

Author(s):  
Carlos Reaño
Author(s):  
Gordana Collier ◽  
Andy Augousti ◽  
Andrzej Ordys

The continual development of technology represents a challenge when preparing engineering students for future employment. At the same time, the way students interact in everyday life is evolving: their extra-curricular life is filled with an enormous amount of stimulus, from online data to rich Web-based social interaction. This chapter provides an assessment of various learning technology-driven methods for enhancing both teaching and learning in the science and engineering disciplines. It describes the past, present, and future drivers for the implementation of hands-on teaching methods, incorporating industry standard software and hardware and the evolution of learning experiments into all-encompassing online environments that include socializing, learning, entertainment, and any other aspect of student life when studying science and engineering.


2019 ◽  
pp. 801-823
Author(s):  
Gordana Collier ◽  
Andy Augousti ◽  
Andrzej Ordys

The continual development of technology represents a challenge when preparing engineering students for future employment. At the same time, the way students interact in everyday life is evolving: their extra-curricular life is filled with an enormous amount of stimulus, from online data to rich Web-based social interaction. This chapter provides an assessment of various learning technology-driven methods for enhancing both teaching and learning in the science and engineering disciplines. It describes the past, present, and future drivers for the implementation of hands-on teaching methods, incorporating industry standard software and hardware and the evolution of learning experiments into all-encompassing online environments that include socializing, learning, entertainment, and any other aspect of student life when studying science and engineering.


2011 ◽  
Vol 30 (3) ◽  
pp. 265-280 ◽  
Author(s):  
Fred G. Martin ◽  
Michelle Scribner-MacLean ◽  
Sam Christy ◽  
Ivan Rudnicki ◽  
Rucha Londhe ◽  
...  

Author(s):  
Harris Wang ◽  
Dusty Philips

In Web-based distance education, the authors’ experience has shown that some courses are more challenging than others for both students and instructors when offered at a distance. Among those challenges, providing students with access to laboratories is a big one for some science and engineering courses. Over the years, researchers and practitioners have devised and tried different ideas to solve this problem, and virtual labs are the newest and most promising solution. In computer science, such virtual labs are often called virtual computing labs or virtual programming labs. In this chapter, the authors discuss cloud computing based solutions to the development of virtual computing and programming labs. In particular, they present the design and implementation of A-VPL, a virtual programming lab designed and implemented at Athabasca University. The chapter discusses the technologies used in detail, and the unique features they have.


Author(s):  
Thangakumar Jeyaprakash ◽  
Padmaveni K

Data science plays a vital role in the research field of computer science and engineering which involves collection of data, transformation, processing, describing, and modelling. In this article, fundamental theory of Data Science, Machine learning and Deep Learning with the scope and opportunities has been discussed. This helps the researchers to get a clarity on data science and its importance.


2018 ◽  
Author(s):  
Ram P. Rustagi ◽  
Viraj Kumar

With the rapid increase in the volume of e-commerce, the security of web-based transactions is of increasing concern. A widespread but dangerously incorrect belief among web users is that all security issues are taken care of when a website uses HTTPS (secure HTTP). While HTTPS does provide security, websites are often developed and deployed in ways that make them and their users vulnerable to hackers. In this article we explore some of these vulnerabilities. We first introduce the key ideas and then provide several experiential learning exercises so that readers can understand the challenges and possible solutions to them in a hands-on manner.


2020 ◽  
Author(s):  
Catherine Arnott Smith ◽  
Deahan Yu ◽  
Juan Fernando Maestre ◽  
Uba Backonja ◽  
Andrew Boyd ◽  
...  

BACKGROUND Informatics tools for consumers and patients are important vehicles for facilitating engagement, and the field of consumer health informatics is an key space for exploring the potential of these tools. To understand research findings in this complex and heterogeneous field, a scoping review can help not only to identify, but to bridge, the array of diverse disciplines and publication venues involved. OBJECTIVE The goal of this systematic scoping review was to characterize the extent; range; and nature of research activity in consumer health informatics, focusing on the contributing disciplines of informatics; information science; and engineering. METHODS Four electronic databases (Compendex, LISTA, Library Literature, and INSPEC) were searched for published studies dating from January 1, 2008, to June 1, 2015. Our inclusion criteria specified that they be English-language articles describing empirical studies focusing on consumers; relate to human health; and feature technologies designed to interact directly with consumers. Clinical applications and technologies regulated by the FDA, as well as digital tools that do not provide individualized information, were excluded. RESULTS We identified 271 studies in 63 unique journals and 22 unique conference proceedings. Sixty-five percent of these studies were found in health informatics journals; 23% in information science and library science; 15% in computer science; 4% in medicine; and 5% in other fields, ranging from engineering to education. A single journal, the Journal of Medical Internet Research, was home to 36% of the studies. Sixty-two percent of these studies relied on quantitative methods, 55% on qualitative methods, and 17% were mixed-method studies. Seventy percent of studies used no specific theoretical framework; of those that did, Social Cognitive Theory appeared the most frequently, in 16 studies. Fifty-two studies identified problems with technology adoption, acceptance, or use, 38% of these barriers being machine-centered (for example, content or computer-based), and 62% user-centered, the most frequently mentioned being attitude and motivation toward technology. One hundred and twenty-six interventional studies investigated disparities or heterogeneity in treatment effects in specific populations. The most frequent disparity investigated was gender (13 studies), followed closely by race/ethnicity (11). Half the studies focused on a specific diagnosis, most commonly diabetes and cancer; 30% focused on a health behavior, usually information-seeking. Gaps were found in reporting of study design, with only 46% of studies reporting on specific methodological details. Missing details were response rates, since 59% of survey studies did not provide them; and participant retention rates, since 53% of interventional studies did not provide this information. Participant demographics were usually not reported beyond gender and age. Only 17% studies informed the reader of their theoretical basis, and only 4 studies focused on theory at the group, network, organizational or ecological levels—the majority being either health behavior or interpersonal theories. Finally, of the 131 studies describing the design of a new technology, 81% did not involve either patients or consumers in their design. In fact, while consumer and patient were necessarily core concepts in this literature, these terms were often used interchangeably. The research literature of consumer health informatics at present is scattered across research fields; only 49% of studies from these disciplines is indexed by MEDLINE and studies in computer science are siloed in a user interface that makes exploration of that literature difficult. CONCLUSIONS Few studies analyzed in this scoping review were based in theory, and very little was presented in this literature about the life context, motives for technology use, and personal characteristics of study participants.


Author(s):  
Hanaa Torkey ◽  
Elhossiny Ibrahim ◽  
EZZ El-Din Hemdan ◽  
Ayman El-Sayed ◽  
Marwa A. Shouman

AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.


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