Virtual reality in experiential pharmacy education: A quasi-experimental study.

Introduction: Extended reality (XR) technologies are an umbrella term for simulated-based learning tools that cover 3-dimensional technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR). At King Saud University, first-year pharmacy students are required to experience hospital observational training during the Introductory Pharmacy Practice Experience (IPPE). We aimed to measure the effectiveness and satisfaction of the VR learning experience among IPPE students. Methods: A Quasi-Experimental study was conducted. The experimental arm included first-year PharmD students. VR headset was used to watch three narrated videos capturing 360° views of the outpatient, inpatient pharmacy, and counseling clinic. A test measuring students' general knowledge was required prior to and post the experience, followed by a satisfaction survey. The control arm included second-year PharmD students who had traditional hospital visits and were administered a knowledge test and satisfaction survey. Results: A total of 336 students were enrolled, 174 in the experimental arm and 162 in the control arm. The results showed improvement in the knowledge scores average among the experimental arm, 1.9 vs 3.5 in the pre-test and post-test. The control arm had a comparable score with an average of 3.7. Regarding self-assessment using four 5-likert scales assessing pharmacist role, skills, and responsibilities, 31.8 % and 42 % in the experimental arm compared to 28.9 % and 28.9 % in the control group answered strongly agree and agree, respectively. Regarding satisfaction, using five 5-Likert scales assessing the experience time, quality, and content, 53 % and 25 % in the experimental group compared to 34 % and 23 % in the control group answered strongly agree and agree, respectively. Conclusion: VR provides pharmacy students with a standardized and effective learning and training experience. The experimental arm reported higher satisfaction rates and self-reported outcomes. Thus, implementing VR experiences within the pharmacy curriculum will provide students with an advanced educational advantage.

Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis.

Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization's network security. This is because IDSs serve as the organization's first line of defense against several cyberattacks and are accountable for accurately detecting any possible network intrusions. Several implementations of IDSs accomplish the detection of potential threats throughout flow-based network traffic analysis. Traditional IDSs frequently struggle to provide accurate real-time intrusion detection while keeping up with the changing landscape of threat. Innovative methods used to improve IDSs' performance in network traffic analysis are urgently needed to overcome these drawbacks. In this study, we introduced a model called a deep neural decision forest (DNDF), which allows the enhancement of classification trees with the power of deep networks to learn data representations. We essentially utilized the CICIDS 2017 dataset for network traffic analysis and extended our experiments to evaluate the DNDF model's performance on two additional datasets: CICIDS 2018 and a custom network traffic dataset. Our findings showed that DNDF, a combination of deep neural networks and decision forests, outperformed reference approaches with a remarkable precision of 99.96% by using the CICIDS 2017 dataset while creating latent representations in deep layers. This success can be attributed to improved feature representation, model optimization, and resilience to noisy and unbalanced input data, emphasizing DNDF's capabilities in intrusion detection and network security solutions.

Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker's Chest X-ray Radiography.

Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis. There is no automated system for detecting and monitoring diseases in coal miners, except for specialist radiologists. This paper proposes ensemble learning techniques for detecting pneumoconiosis disease in chest X-ray radiographs (CXRs) using multiple deep learning models. Three ensemble learning techniques (simple averaging, multi-weighted averaging, and majority voting (MVOT)) were proposed to investigate performances using randomised cross-folds and leave-one-out cross-validations datasets. Five statistical measurements were used to compare the outcomes of the three investigations on the proposed integrated approach with state-of-the-art approaches from the literature for the same dataset. In the second investigation, the statistical combination was marginally enhanced in the ensemble of multi-weighted averaging on a robust model, CheXNet. However, in the third investigation, the same model elevated accuracies from 87.80 to 90.2%. The investigated results helped us identify a robust deep learning model and ensemble framework that outperformed others, achieving an accuracy of 91.50% in the automated detection of pneumoconiosis.