This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.

The purpose of this study was to examine the current state of integrated science, technology, engineering, arts, and mathematics (STEAM) education. We conducted an extensive review of the literature, followed by document analysis to construct concepts and themes associated with prospects, priorities, processes, and problems of STEAM education. The analysis of STEAM learning concepts from the literature provided three sub-constructs under the prospects – STEAM movement, the purpose of STEAM education, and benefits of STEAM education. The sub-constructs under priorities of STEAM education were – curriculum integration in STEAM and STEAM education as a curriculum reform. The sub-constructs of STEAM education as a process were – the pedagogical process and assessment in STEAM education. The sub-constructs of the problems in STEAM education were – critiques against STEAM education and the challenges of STEAM education. Finally, we discussed findings and presented some implications of STEAM education.

Effective classroom instruction requires monitoring student participation and interaction during class, identifying cues to simulate their attention. The ability of teachers to analyze and evaluate students’ classroom behavior is becoming a crucial criterion for quality teaching. Artificial intelligence (AI)-based behavior recognition techniques can help evaluate students’ attention and engagement during classroom sessions. With rapid digitalization, the global education system is adapting and exploring emerging technological innovations, such as AI, the Internet of Things, and big data analytics, to improve education systems. In educational institutions, modern classroom systems are supplemented with the latest technologies to make them more interactive, student centered, and customized. However, it is difficult for instructors to assess students’ interest and attention levels even with these technologies. This study harnesses modern technology to introduce an intelligent real-time vision-based classroom to monitor students’ emotions, attendance, and attention levels even when they have face masks on. We used a machine learning approach to train students’ behavior recognition models, including identifying facial expressions, to identify students’ attention/non-attention in a classroom. The attention/no-attention dataset is collected based on nine categories. The dataset is given the YOLOv5 pre-trained weights for training. For validation, the performance of various versions of the YOLOv5 model (v5m, v5n, v5l, v5s, and v5x) are compared based on different evaluation measures (precision, recall, mAP, and F1 score). Our results show that all models show promising performance with 76% average accuracy. Applying the developed model can enable instructors to visualize students’ behavior and emotional states at different levels, allowing them to appropriately manage teaching sessions by considering student-centered learning scenarios. Overall, the proposed model will enhance instructors’ performance and students at an academic level.

Elderly population (over the age of 60) is predicted to be 1.2 billion by 2025. Most of the elderly people would like to stay alone in their own house due to the high eldercare cost and privacy invasion. Unobtrusive activity recognition is the most preferred solution for monitoring daily activities of the elderly people living alone rather than the camera and wearable devices based systems. Thus, we propose an unobtrusive activity recognition classifier using Deep Convolutional Neural Network (DCNN) and anonymous binary sensors (PIR sensors and door sensors). The real world long-term fully annotated Aruba open dataset collected by binary sensors has been employed for the training and evaluation of the classifier. First, ten basic daily activities, namely: Bed_to_Toilet, Eating, Meal_Preparation, Relax, Sleeping, Work, Housekeeping, Wash_Dishes, Enter_Home, and Leave_Home are segmented with different sliding window sizes, and then converted into binary activity images. Next, the activity images are employed for training the DCNN classifiers with different parameters. Finally, the trained classifiers are evaluated with the 10-fold cross validation method, and the results show that the best DCNN classifier gives 0.79 and 0.951 of F1-score for all 10 activities and eight activities (excluding Leave_Home and Wash_Dishes), respectively.

Current environmental concerns have led to a search of more environmentally friendly manufacturing methods; thus, natural fibers have gained attention in the 3D printing industry to be used as bio-filters along with thermoplastics. The utilization of natural fibers is very convenient as they are easily available, cost-effective, eco-friendly, and biodegradable. Using natural fibers rather than synthetic fibers in the production of the 3D printing filaments will reduce gas emissions associated with the production of the synthetic fibers that would add to the current pollution problem. As a matter of fact, natural fibers have a reinforcing effect on plastics. This review analyzes how the properties of the different polymers vary when natural fibers processed to produce filaments for 3D Printing are added. The results of using natural fibers for 3D Printing are presented in this study and appeared to be satisfactory, while a few studies have reported some issues.

In recent years, the number of yoga practitioners has been drastically increased and there are more men and older people practice yoga than ever before. IoT-based yoga training system is needed for those who want to practice yoga at home. Some studies have proposed RGB/Kinect camera-based or wearable device-based yoga posture recognition methods with a high accuracy; however, the former has a privacy issue and the latter is impractical in the long-term application. Thus, this study proposes an IoT-based privacy-preserving yoga posture recognition system employing a deep convolutional neural network (DCNN) and a low-resolution infrared sensor-based wireless sensor network (WSN). The WSN has three nodes (x, y, and z-axes) where each integrates 8×8 pixels’ thermal sensor module and a Wi-Fi module for connecting the deep learning server. We invited 18 volunteers to perform 26 yoga postures for two sessions each lasted for 20 seconds. First, recorded sessions are saved as.csv files, then preprocessed and converted to grayscale posture images. Totally, 93200 posture images are employed for the validation of the proposed DCNN models. The 10-fold cross-validation results revealed that F1-scores of the models trained with xyz (all 3-axes) and y (only y-axis) posture images were 0.9989 and 0.9854, respectively. An average latency for a single posture image classification on the server was 107 ms. Thus, we conclude that the proposed IoT-based yoga posture recognition system has a great potential in the privacy-preserving yoga training system.