Artificial Intelligence-Based Smart Tele-Assisting Technology for First-Year Engineering Students

Engineering programs emphasize students career advancement by ensuring that engineering students gain technical and professional capabilities during their four-year study. In a traditional engineering laboratory, students "learn by doing", and laboratory equipment facilitates their discipline-specific knowledge acquisition. Unfortunately, there were significant educational uncertainties, such as COVID-19, which halted laboratory activities for an extended period, causing challenges for students to perform and obtain practical experiments on campus. To overcome these challenges, this research proposes and develops an Artificial Intelligence-based smart tele-assisting technology application to digitalize first-year engineering students practical experience by incorporating Augmented Reality (AR) and Machine Learning (ML) algorithms using the HoloLens 2. This application improves virtual procedural demonstrations and assists first-year engineering students in conducting practical activities remotely. This research also applies various machine learning algorithms to identify and classify different images of electronic components and detect the positions of each component on the breadboard (using the HoloLens 2). Based on a comparative analysis of machine learning algorithms, a hybrid CNN-SVM (Convolutional Neural Network - Support Vector Machine) model is developed and is observed that a hybrid model provides the highest average prediction accuracy compared to other machine learning algorithms. With the help of AR (HoloLens 2) and the hybrid CNN-SVM model, this research allows students to reduce component placement errors on a breadboard and increases students competencies, decision-making abilities, and technical skills to conduct simple laboratory practices remotely. © 2023 IEEE.

Collective resilience among scholars and businesses: A Brazilian university in the COVID-19 crisis

This is a story of collective resilience. In a two-week task force, our group of Associate Professors created the SOS-MSME project, an advisory network to support Micro, Small and Medium Enterprises suffering the impacts of the COVID-19 pandemic. Almost 1,000 people, including staff, students from different university, faculties, alumni, and professionals from the community engaged in this project supporting more than 200 entrepreneurs. It has helped our community, but also ourselves generating a new challenging academic path integrating service, research and teaching. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

An Improved Machine Learning Approach to Detect Real Time Face Mask

Several nations have implemented health protocols like maintaining a particular measure of distance from each other and use of face masks when going out in public, in an effort to stop or at least reduce the spread of Covid-19. However, manually checking whether each person have put on a mask or not is a tiring job, and is possible only if there is a particular person assigned specially for that. This paves way for the need of an electronic device or a machine that would identify whether a person has worn mask or not. Thus, this research proposes a face mask detection system using a machine learning algorithm known as Support Vector Machine (SVM). After creating and preprocessing the dataset, training the model, and evaluating the final model, an accuracy of 98% has been obtained. The model can further be developed and used in real time scenarios to detect faces without a mask and pass those faces separately into a neural network with the help of CNN to easily find out his/her identity, and punish accordingly. © 2023 IEEE.

COVID-19 prediction using machine learning based on the patient's vital signs: A case for Saudi Arabia

The world faces a rapidly spreading of COVID19 globally, for several countries around the world mitigating the consequences and spread of the pandemic remains a top priority. Researchers work to find a smart and rational solution to limit the spread of this epidemic and its repercussions. The goal of this research is to produce an early and accurate COVID-19 prediction, as well as a comparative analysis of the performance of several machine learning (ML) models based on patient vital signs, dataset balancing, and feature selection. The cases dataset was provided by King Fahad Hospital University in Al-Khobar, Saudi Arabia. The current study used the WEKA 3.8.5 and Python programming language (SKLEARN) to decide which method generated the highest level of accuracy while using fewer features. Random forest with grid search (RF with grid search), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), J48, XGB Classifier, and XGB Classifier with grid search were the techniques that were compared. The highest level of accuracy obtained with seven features was 84% achieved with the RF using grid search technique, while ANN, SVM, RF, J48, XGB Classifier, and XGB Classifier with grid search obtained 82.85%, 79%, 82.93%, 82.5%,82.21%, and 83.4% accuracy, respectively. © 2022 IEEE.

Predicting how much a consumer is willing to pay for a bottle of wine: A preliminary study

The wine industry is an important business sector, generating billions in annual revenue. In the last year, there were several lockdowns due to the COVID-19 pandemic and wine consumption at home has increased. This paper considers the problem of predicting how much a consumer is willing to pay for a bottle of wine to drink at home, in a regular occasion. As far as we know, this is the first study on the subject. The problem is treated as a classification task and several prediction models, based on artificial neural networks, support vector machines and decisions trees, are proposed and compared. © 2022 The Author(s).

Collective resilience among scholars and businesses: A Brazilian university in the COVID-19 crisis

This is a story of collective resilience. In a two-week task force, our group of Associate Professors created the SOS-MSME project, an advisory network to support Micro, Small and Medium Enterprises suffering the impacts of the COVID-19 pandemic. Almost 1,000 people, including staff, students from different university, faculties, alumni, and professionals from the community engaged in this project supporting more than 200 entrepreneurs. It has helped our community, but also ourselves generating a new challenging academic path integrating service, research and teaching. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Teaching from Home: Computer and Communication Network Perspectives

CoViD-19 has fundamentally changed how teaching and learning are done from K-12 schools to colleges and universities around the world, where many instructors have to deliver their lectures, tutorials, and even labs from their home, through the Internet. Due to the sudden and massive move to online teaching, a lot of challenges are encountered by education institutions, educators and support staff, and students and their parents. In this book chapter, we examine the challenges of “teaching from home” with the viewpoint of information technology (IT) education in general and computer network support in particular and offer some suggestions through our experience in 2020 and 2021 with input from IT support professionals, to create the much needed discussion among educators on this timely topic, which can be useful for 2021 and beyond. Online teaching may become a considerable mode of course delivery in the post-pandemic era, even without another similar event. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.