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    Sidike Paheding Publishes Paper in IEEE Access

    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.

    The co-authors of the paper are Nahian Siddique, Colin P. Elkin, and Vijay Devabhaktuni, all with the Department of Electrical and Computer Engineering, Purdue University Northwest, Hammond, Indiana.

    Abstract

    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.

    The paper can be accessed on the IEEE Access website.

    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, Awarded R-D Grant by Purdue University


    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.


    Abstract

    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.


    New Course: Applied Machine Learning


    Summary

    • 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)

    Course Description/Overview

    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

    Applications Covered

    • Object Detection
    • Digital Recognition
    • Face Recognition
    • Self-Driving Cars
    • Medical Image Segmentation
    • Covid-19 Prediction
    • Spam Email Detection
    • Spectral Signal Categorization

    Tools Covered

    • Python
    • scikit learn
    • TensorFlow
    • Keras
    • Open CV
    • pandas
    • matplotlib
    • NumPy
    • seaborn
    • ANACONDA
    • jupyter
    • SPYDER

    Download the course description flyer:

    Download

    Sidike Paheding, Applied Computing, Publishes Paper in IEEE Access

    A paper co-authored by Sidike Paheding, Applied Computing, has been published in the journal, IEEE Access. “Trends in Deep Learning for Medical Hyperspectral Image Analysis,” was available for early access on March 24, 2021.

    The paper discusses the implementation of deep learning for medical hyperspectral imaging.

    Co-authors of the paper are Uzair Khan, Colin Elkin, and Vijay Devabhaktuni, all with the Department of Electrical and Computer Engineering, Purdue University Northwest.

    Abstract

    Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this work aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery.

    This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.

    DOI: 10.1109/ACCESS.2021.3068392

    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 Publishes Paper in Top Journal

    A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, has been published in the April 2021 issue of ISPRS Journal of Photogrammetry and Remote Sensing, published by Science Direct.

    The title of the paper is, “Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning.”

    View the article abstract here.

    Paheding is a member of the Institute of Computer and Cybersystems’s (ICC) Center for Data Sciences.


    Sidike Paheding Awarded MSGC Seed Grant

    Michigan Space Grant Consortium

    Assistant Professor Sidike Paheding, Applied Computing, has been awarded a one-year MSGC Research Seed Grant for his project, “Monitoring Martian landslides using deep learning and data fusion.”

    Professor Thomas Oommen, Geological and Mining Engineering and Sciences, is Co-PI of the project. The grant will support part-time employment of two students during the award period.

    This grant is supported in part by funding provided by the National Aeronautics and Space Administration (NASA), under award number 80NSSC20M0124, Michigan Space Grant Consortium (MSGC).

    The MSGC Research Seed Grant Program supports junior faculty and research scientists at MSGC affiliate institutions. The program also helps mid-career and senior faculty develop new research programs. The objective of this program is to allow award recipients to develop the research expertise necessary to propose research activities in new areas to other federal or nonfederal sources.

    Sidike Paheding is an assistant professor in the Applied Computing department of the Michigan Tech College of Computing.

    His research interests cover a variety of topics in machine learning, deep learning, computer vision, and remote sensing. He has authored/coauthored close to 100 research articles, including several top peer-review journal papers. He is an invited member of Tau Beta Pi (Engineering Honor Society).


    Sidike Paheding Lecture is Dec. 11, 3 pm

    Assistant Professor Sidike Paheding, Applied Computing, will present his lecture, “Deep Neural Networks for UAV and Satellite Remote Sensing Image Analysis,” on Dec. 11, 2020, at 3:00 p.m. via online meeting.

    Paheding’s research focuses on the areas of computer vision, machine learning, deep learning, image/video processing, and remote sensing.

    The lecture is presented by the Department of Computer Science.

    Lecture Abstract

    Remote sensing data can provide non-destructive and instantaneous estimates of the earth’s surface over a large area, and has been accepted as a valuable tool for agriculture, weather, forestry, defense, biodiversity, etc. In recent years, deep neural networks (DNN), as a subset of machine learning. for remote sensing has gained significant interest due to advances in algorithm development, computing power, and sensor systems.

    This talk will start with remote sensing image enhancement framework, and then primarily focuses on DNN architectures for crop yield prediction and heterogeneous agricultural landscape mapping using UAV and satellite imagery.

    Speaker Biography

    Paheding is an associate editor of the Springer journal Signal, Image, and Video Processing, ASPRS Journal Photogrammetric Engineering & Remote Sensing, and serves as a guest editor/reviewer for a number of reputed journals. He has advised students at undergraduate, M.S., and Ph.D. levels, and authored/coauthored close to 100 research articles.


    Sidike Paheding Publishes Paper in Expert Systems and Applications Journal

    A research paper by Assistant Professor Sidike Paheding, Applied Computing, is to be published in the November 2020 issue of the journal, Expert Systems and Applications.

    An in-press version of the paper, “Binary Chemical Reaction Optimization based Feature Selection Techniques for Machine Learning Classification Problems,” is available online.

    Highlights

    • A chemical reaction optimization (CRO) based feature selection (FS) technique is proposed.
    • The proposed CRO based FS technique is improvised using particle swarm optimization.
    • Performance evaluation of proposed techniques on benchmark datasets gives promising results.

    Paper Abstract

    Feature selection is an important pre-processing technique for dimensionality reduction of high-dimensional data in machine learning (ML) field. In this paper, we propose a binary chemical reaction optimization (BCRO) and a hybrid binary chemical reaction optimization-binary particle swarm optimization (HBCRO-BPSO) based feature selection techniques to optimize the number of selected features and improve the classification accuracy.

    Three objective functions have been used for the proposed feature selection techniques to compare their performances with a BPSO and advanced binary ant colony optimization (ABACO) along with an implemented GA based feature selection approach called as binary genetic algorithm (BGA). Five ML algorithms including K-nearest neighbor (KNN), logistic regression, Naïve Bayes, decision tree, and random forest are considered for classification tasks.

    Experimental results tested on eleven benchmark datasets from UCI ML repository show that the proposed HBCRO-BPSO algorithm improves the average percentage of reduction in features (APRF) and average percentage of improvement in accuracy (APIA) by 5.01% and 3.83%, respectively over the existing BPSO based feature selection method; 4.58% and 3.12% over BGA; and 4.15% and 2.27% over ABACO when used with a KNN classifier.

    Expert Systems With Applications, published by Science Direct/Elsevier, is a refereed international journal whose focus is on exchanging information relating to expert and intelligent systems applied in industry, government, and universities worldwide. The journal’s Impact factor is 5.4.


    College of Computing Welcomes Six New Faculty Members

    The Michigan Tech College of Computing welcomed six new faculty members this fall to the Departments of Applied Computing and Computer Science.

    College off Computing Dean Adrienne Minerick says the new hires reflect the fast growth of the new College, which was launched July 1, 2019.

    “We are thrilled to welcome these six talented new faculty members,” Minerick says. “Even amid the challenges we are all facing, our proactive recruitment and retention activities are making a difference.”

    Assistant Professor Briana Bettin, Computer Science, has a Ph.D. in computer science from Michigan Tech. She is also an affiliated assistant professor for the Cognitive and Learning Sciences department. Bettin’s research interests include user experience; human factors; human-computer interactions; mental models; information representation; rural digital literacy; education, engagement, and retention; and digital anthropology. Bettin is a member of the ICC’s Computing Education Center.

    Assistant Professor Sidike Paheding, Applied Computing, has a Ph.D. in eelectrical engineering from University of Dayton, Ohio. Prior to joining Michigan Tech Paheding was a visiting assistant professor at Purdue University Northwest. His research interests include image/video processing, machine learning, deep learning, computer vision, and remote sensing. Paheding is a member of the ICC’s Center for Data Sciences.

    Assistant Professor Junqiao Qiu, Computer Science, has a
    Ph.D. in computer science and engineering from University of California Riverside. His research focuses on parallel computing, programming systems, and compiler optimization. Qiu is a member of the ICC’s Center for Scalable Architectures and Systems.

    Assistant Professor Ashraf Saleem, Applied Computing, has a Ph.D. in mechatronics engineering from DeMontfort University, UK. He comes to Michigan Tech from the electrical and computer engineering department at Sultan Qaboos University, where he served the mechatronics engineering program. Ashraf will be on campus starting in the spring 2021 semester.

    Saleem’s research interests are in autonomous systems, vision-based unmanned vehicles, Artificial Intelligence, control of Piezoelectric actuator, and servo-pneumatic systems.

    Assistant Professor Leo Ureel, Computer Science, has a Ph.D. in computer science from Michigan Tech. He has been teaching at the college level for 10 years, and has over 20 years of industry experience. Ureel is also coordinator of the College of Computing Learning Center. Ureel is a member of the ICC’s Computing Education Center.

    Ureel’s research focuses on a constructionist approach to introductory computer science that leverages code critiquers to motivate students to learn computer programming. His
    areas of expertise include software engineering, computer science education, and intelligent tutoring systems.

    Assistant Professor Brian Yuan, Applied Computing and Computer Science, has a Ph.D. in computer science from University of Florida. His areas of expertise include machine learning, security and privacy, and cloud computing. Yuan is a member of the ICC’s Center for Cybersecurity and Center for Data Sciences.