Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.

This research delves into the performance analysis of Long Short-Term Memory (LSTM) models for time series data prediction, emphasizing system resource utilization. Through extensive experiments conducted on multi-GPU and CPU-configured cloud-based environments, our objective is to discern the more effective platform for deploying LSTM algorithms. The study provides valuable insights into optimizing computational resources for enhanced LSTM performance in time series prediction tasks.

To promote knowledge and interest in computer science (CS) among historically marginalized racial and gender groups, a three-and-a-half-week CS summer camp was developed using a culturally relevant pedagogical framework. This program encompassed hands-on instruction utilizing MIT App Inventor, collaborative projects in mobile app design, and various mentoring sessions. A cohort of 42 minority students who are predominantly Black received tailored guidance based on dynamic assessment informed scaffolding. The analysis of surveys, individual interviews, classroom observations, and focus group interviews revealed a positive impact of the program. Participants not only exhibited increased confidence in block-based coding but also displayed increased interest in STEM careers and a broader understanding of the CS field. Despite these positive outcomes, the influence on CS career aspirations appeared to be limited. Although the program helped participants explore various CS career options, their initial career aspirations remained unchanged, indicating a need for an extended period to observe lasting effects.

The pivotal role of white-box testing with respect to software quality assurance, necessitates dissemination of education materials related to white-box testing in the course curriculum. In this poster, we describe our experiences in conducting an authentic learning-based exercise related to white-box testing. From a conducted survey with 124 students, we observe the authentic learning-based exercise to be helpful for students to learn about white-box testing.

The primary goal of the authentic learning approach is to engage and motivate students in learning real world problem solving. We report our experience in developing k-nearest neighbor (KNN) classification for anomaly user behavior detection, one of the authentic machine learning for cybersecurity (ML4Cybr) learning modules based on 10 cybersecurity (CybrS) cases with machine learning (ML) solutions. All portable labs are made available on Google CoLab. So students can access and practice these hands-on labs anywhere and anytime without software installation and configuration which will engage students in learning concepts immediately and getting more experience for hands-on problem solving skills.