Dr. Sidike Paheding, assistant professor of Applied Computing, is the co-author of a paper published June 3, 2021, the journal “IEEE Access.” The paper is titled, “U-net and its variants for medical image segmentation: A review of theory and applications.”
The paper discusses U-net, an image segmentation technique developed primarily for image segmentation tasks.
U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.
IEEE Access is a multidisciplinary, applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE’s fields of interest. Supported by article processing charges, its hallmarks are a rapid peer review and publication process with open access to all readers.
Sidike Paheding (AC/ICC) is the principal investigator on a project that has received a $19,037 research and development grant from Purdue University. The two-year project is titled, “Cybersecurity Modules Aligned with Undergraduate Computer Science and Engineering Curricula.”
The project aims to serve the national interest by improving how cybersecurity concepts are taught in undergraduate computing curricula.
The grant is a sub-award of a $159,417 Purdue University NSF project . View that project here.
This project aims to serve the national interest by improving how cybersecurity concepts are taught in undergraduate computing curricula. The need to design and maintain cyber-secure computing systems is increasingly important. As a result, the future technology workforce must be trained to have a security mindset, so that they consider cybersecurity during rather than after system design. This project aims to achieve this goal by building plug-and-play, hands-on cybersecurity modules for core courses in Computer Engineering, and Computer Science and Engineering. The modules will align with the curricula recommended by the Association for Computing Machinery and will be designed for easy adoption into computing programs nationwide. Modules will be designed for integration into both introductory and advanced courses, thus helping students develop in-depth understanding of cybersecurity as they progress through their computing curriculum. It is expected that the project will encourage more students to pursue careers or higher degrees in the field of cybersecurity.
The project will examine how the modules may be best integrated into existing curricula and the effects of the modules on student learning and interest in cybersecurity. Assessment will leverage several methods including (a) a task load index to quantify rigor, (b) surveys to gain insight into the development of students’ security mindset and perceptions of cybersecurity, and (c) analysis of learning using analytical course rubrics. Deliverables of this project will include a suite of plug-and-play cybersecurity modules for Computer Engineering and Computer Science and Engineering courses that span from introductory to advanced levels and that meet standards for content breadth and depth. The results will be disseminated through publications, presentations, press releases, and social media to ensure that project outcomes are shared widely. The NSF Improving Undergraduate STEM Education: Education and Human Resources Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.
- Course Number: 84859, EET 4996-01
- Class Times: T/R, 9:30-10:45 am
- Location: EERC 0723
- Instructor: Dr. Sidike Paheding
- Course Levels: Graduate, Undergraduate
- Prerequisite: Python Programming and basic knowledge of statistics.
- Preferred knowledge: Artificial Intelligence (CS 4811) or Data Mining (CS4821) or Intro to Data Sciences (UN 5550)
Rapid growth and remarkable success of machine learning can be witnessed by tremendous advances in technology, contributing to the fields of healthcare, finance, agriculture, energy, education, transportation and more. This course will emphasize on intuition and real-world applications of Machine Learning (ML) rather than statistics behind it. Key concepts of some popular ML techniques, including deep learning, along with hands-on exercises will be provided to students. By the end of this course, students will be able to apply a variety of ML algorithms to practical
- Instructor: Sidike Paheding, Ph.D., Asst. Prof., Applied Computing
- Office: EERC 417
- E-mail: firstname.lastname@example.org
- Faculty Website: mtu.edu/computing/about/faculty/paheding
- Object Detection
- Digital Recognition
- Face Recognition
- Self-Driving Cars
- Medical Image Segmentation
- Covid-19 Prediction
- Spam Email Detection
- Spectral Signal Categorization
- scikit learn
- Open CV
Download the course description flyer: