Events

Here is a useful link for the MGA operations manual: https://mga.ieee.org/images/files/Current_MGA_Operations_Manual_2024__13_December.pdf Agenda: Agenda: Slide Deck: pending IEEE Eastern North Carolina Section Executive Committee Meeting Agenda for Wednesday, 4/9/2025 6:30 pm Setup Food and room 7:00 - 7:15 Check-in and Introductions - All 7:15 - 7:20 Confirm Quorum – Secretary (Quorum = more than 1/2 of voting positions) Review/Adjust/Approve Agenda – Chair/Vice Chair 7:20 - 7:25 Welcome Remarks - Chair/Vice Chair 7:25 – 7:30 Confirmation and Approval of the Previous Meeting Minutes – Secretary 7:30 – 7:35 Action Items from the Previous Meeting – Secretary 7:35 – 7:45 Optional Treasurer Report - Treasurer 7:45 – 7:50 Volunteers Changes and Approval - Chair 7:50 – 8:10 Chapters/Affinity Groups/Committee Chairs Reports – Chairs Awards and Recognitions Chair: Abhinav Aggarwal Computational Intelligence Society (CIS) Chair: Sweetha Chinta Computer Society Chair/Vice: H. Moradi, L. Kittusamy – New Leaders Consultant's Network Affinity Group Chair: Guru Prasad Selvarajan Industrial Electronics Society Chair: Skieler Capezza Life Member Affinity Group Chair: Clay Cranford NCSU Chapter Chair: Mario Nava Nominations & Appointments Chair Abhinav Aggarwal Power Electronics Society (PELS) Chair/Vice: M. Madhu, L. Cheng Robotics & Automation Soc (RAS) Chair/Vice: A. Matthew, F. Livingston Section Student Representative (SSR): Aniketh Shenoy Student Activities Chair (SAC): Sneha Narasimhan Technology & Engineering Management Society (TEMS): Megha Ben Major achievements in 2024 Women in Engineering Affinity Group Chair/Vice: E. Klopf, P. Ramos Young Professionals Affinity Group: Prakhar Lohiya 8:15 – 8:25 Other Business 8:25 – 8:30 Concluding Remarks and Closing – Chair/Vice Chair Room: 021, Bldg: Wilkinson Engineering Building, 534 Research Drive, Durham, North Carolina, United States, 27705, Virtual: https://events.vtools.ieee.org/m/477490

Title: Detecting Cyberbullying in Images Using Deep Learning: A VGG19-Based Approach Abstract: Cyberbullying has become a widespread social issue that impacts internet users, notably through the exploitation of visual images. In past studies, the detection of cyberbullying in images was explored but faced difficulties such as limited accuracy and reliance on multimodal techniques. We address this gap by proposing an improved method for identifying cyberbullying in images using a deep learning model. Specifically, we used the VGG19 architecture to evaluate a real-world dataset of 19,300 images related to cyberbullying, achieving superior performance compared to existing methods. Our analysis identifies critical contextual characteristics in cyberbullying images that distinguish them from standard offensive image material, such as violence or nudity. We show that VGG19 outperforms the multimodal classification model proposed in previous research, with a mean detection accuracy of 95%. These findings demonstrate the utility of convolutional neural networks (CNNs) in solving the particular issues given by contextual images of cyberbullying. Our research contributes to the development of improved techniques for combating cyberbullying in visual media. Virtual: https://events.vtools.ieee.org/m/480464