Category: Paheding

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.