Institute of Computing and Cybersystems (ICC) Blog

Category: Publications

Sangyoon Han Publishes Paper in eLife

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eLife, a prestigious journal in cell biology, has published a paper co-written by Sangyoon Han, “Pre-complexation of talin and vinculin without tension is required for efficient nascent adhesion maturation.”

Dr. Han is an assistant professor in the Biomedical Engineering department, and a member of the Data Sciences research group of the Institute of Computing and Cybersystems (ICC).

View the paper here.

eLife is a non-profit organization created by funders and led by researchers. Their mission is to accelerate discovery by operating a platform for research communication that encourages and recognizes the most responsible behaviors.

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Sidike Paheding, Applied Computing, Publishes Paper in IEEE Access

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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.

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Sidike Paheding Publishes Paper in Top Journal

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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.

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Yakov Nekrich Paper Accepted for Top Computing Conference

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A publication by Associate Professor Yakov Nekrich, Computer Science, has been accepted to the 53rd Annual ACM Symposium on Theory of Computing (STOC).

The paper, “Optimal-Time Dynamic Planar Point Location in Connected Subdivisions,” describes an optimal-time solution for the dynamic point location problem and answers an open problem in computational geometry. 

The data structure described in the paper supports queries and updates in logarithmic time. This result is optimal in some models of computation.  Nekrich is the sole author of the publication.

The annual ACM Symposium on Theory of Computing (STOC), is the flagship
conference of SIGACT, the Special Interest Group on Algorithms and
Computation Theory, a special interest group of the Association for
Computing Machinery (ACM).

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Sidike Paheding Wins MDPI Electronics Best Paper Award

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A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, is one of two papers to receive the 2020 Best Paper Award from the open-access journal Electronics, published by MDPI.

The paper presents a brief survey on the advances that have occurred in the area of Deep Learning.

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

Co-authors of the article, “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” are Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, and Vijayan K. Asari. The paper was published March 5, 2019, appearing in volume 8, issue 3, page 292, of the journal.

View and download the paper here.

Papers were evaluated for originality and significance, citations, and downloads. The authors receive a monetary award , a certificate, and an opportunity to publish one paper free of charge before December 31, 2021, after the normal peer review procedure.

Electronics is an international peer-reviewed open access journal on the science of electronics and its applications. It is published online semimonthly by MDPI.

MDPI, a scholarly open access publishing venue founded in 1996, publishes 310 diverse, peer-reviewed, open access journals.

Paper Abstract

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others.

This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began.

Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

Sidike Paheding
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Susanta Ghosh Publishes Paper in APS Physical Review B Journal

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Assistant Professor Susanta Ghosh, ME-EM, has published the article, “Interpretable machine learning model for the deformation of multiwalled carbon nanotubes,” in the APS publication, Physical Review B.

Co-authors of the paper are Upendra Yadav and Shashank Pathrudkar. The article was published January 11, 2021.

Ghosh is a member of the Institute of Computing and Cybersystems’ Center for Data Sciences.

Article Abstract

In the paper, researchers present an interpretable machine learning model to predict accurately the complex rippling deformations of multiwalled carbon nanotubes made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model. The proposed model accurately matches an atomistic-physics-based model whereas being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and explain how the model predicts yielding interpretability. The proposed model can form a basis for an exploration of machine learning toward the mechanics of one- and two-dimensional materials.

APS Physics advances and diffuses the knowledge of physics for the benefit of humanity, promote physics, and serve the broader physics community.

Physical Review B (PRB) is the world’s largest dedicated physics journal, publishing approximately 100 new, high-quality papers each week. The most highly cited journal in condensed matter physics, PRB provides outstanding depth and breadth of coverage, combined with unrivaled context and background for ongoing research by scientists worldwide.

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We Want Your Books and Major Scholarly Works

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by University Marketing and Communications

If you would like to be highlighted in Michigan Tech’s next Research magazine, send University Marketing and Communications more info via this Google form about your book (or album, performance, or other longform scholarly work in mixed media).

Include a link, a cover image, and a way for us to contact you with questions. All submissions will be reviewed by University Marketing and Communications. Published projects must be research focused, published in 2020, and authored by members of the Michigan Tech community. Incomplete submissions will not be included.

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Sidike Paheding Publishes Paper in Expert Systems and Applications Journal

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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.

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Hongyu An: Curious About the World and Exploring the Unknown

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by Karen S. Johnson, Communications Director, ICC

“A scientist should be a person who is always curious about nature and the world, and who tries to explore the unknown.” –Hongyu An, Assistant Professor, Electrical and Computer Engineering

Hongyu An, Assistant Professor, ECE

Exploring science and technology is always exciting for new Assistant Professor Hongyu An, Electrical and Computer Engineering. He says he is “very pleased to have the chance to mentor the next generation and share my knowledge and experience with undergraduate and graduate students.”

Several things drew Hongyu An to Michigan Tech, including his observation that as an institution Michigan Tech cares about its employees. “The excellent professors, smart students, and the supportive environment are the main reasons I joined Michigan Tech,” he says. “As a new faculty member, I am facing a lot of new challenges. There is great support in my department (ECE) and through the ICC.”

Hongyu is a member of two Institute of Computing and Cybersystems (ICC) research centers: Human-Centered Computing and Scalable Architectures and Systems. He also sees synergies with the Center for Cyber-Physical Systems.

“It is my great pleasure and honor to be a member of the ICC,” Hongyu says. “ I can collaborate with the experts in HCC for exploring the brain and artificial intelligence, and the professors in SAS for hardware and architecture designs. Moreover, the neuromorphic chips I am working on can potentially be applied to Cyber-Physical Systems.”

Hongyu’s primary research area is hardware design for AI and neuromorphic systems. He believes that Artificial Intelligence is probably one of the most challenging research topics in science, noting that recent work in deep learning and artificial neural networks is demonstrating great progress in approaching artificial intelligence. 

“But the traditional computers under von Neumann architecture cannot keep up with the development of neural networks and deep learning,” he cautions. “My research is addressing this challenge by using a new hardware design, from device to architecture levels.”

Hongyu’s teaching interests include VLSI, Circuits, and Electromagnetics. Desribing his teaching philosophy, he notes that making complicated things simple is more challenging than making simple things complicated, and that he strives for the former. This academic year, An is teaching EE 4271 VLSI Design and mentoring ECE master’s student, Sarvani Marthi Sarvani, whose project aims to design a silicon retina through CMOS and Memristors.

Hongyu and his research team are also investigating associative memory learning, a new learning method that aims to create a neuromorphic system that can learn from its surroundings directly. 

“Associative memory is a widespread self-learning method in biological livings, which enables the nervoussystem to remember the relationship between two concurrent events,” Hongyu explains. “Through this learning method, dogs can learn the sound of bells as a sign of food; people can remember a word representing an object.”

“The significance of rebuilding associative memory at a behavioral level not only reveals a way of designing a brain-like, self-learning neuromorphic system, it is also to explore a method of comprehending the learning mechanism of a nervous system,” he adds.

And finally, beyond his work as a professor and scientist Hongyu hopes that he is “a good husband to my wife, a good father to my sons, and a good son to my parents.”

Hongyu completed his Ph.D. in electrical engineering at Virginia Tech, his M.S. in electrical engineering at Missouri University of Science and Technology, and his B.S. in electrical engineering at Shenyang University of Technology.

Recent Publications

An, Hongyu, Mohammad Shah Al-Mamun, Marius K. Orlowski, Lingjia Liu, and Yang Yi. “Robust Deep Reservoir Computing through Reliable Memristor with Improved Heat Dissipation Capability. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2020).

An, Hongyu, Qiyuan An, and Yang Yi. “Realizing Behavior Level Associative Memory Learning Through Three-Dimensional Memristor-Based Neuromorphic Circuits. IEEE Transactions on Emerging Topics in Computational Intelligence (2019).

Founded in 2015, the Institute of Computing and Cybersystems (ICC) promotes collaborative, cross-disciplinary research and learning experiences in the areas of computing education, cyber-physical systems, cybersecurity, data sciences, human-centered computing, and scalable architectures and systems, for the benefit of Michigan Technological University and society at large.

The ICC creates and supports an arena in which faculty and students work collaboratively across organizational boundaries in an environment that mirrors contemporary technological innovation. The ICC’s 55 members represent more than 20 academic disciplines at Michigan Tech.

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