Digital Commons Network^™

Resting State Electroencephalographic Brain Activity In Neonates Can Predict Age And Is Indicative Of Neurodevelopmental Outcome, Amir Ansari, Kirubin Pillay, Emad Arasteh, Anneleen Dereymaeker, Gabriela Schmidt Mellado, Katrien Jansen, Anderson M. Winkler, Gunnar Naulaers, Aomesh Bhatt, Sabine Van Huffel

School of Medicine Publications and Presentations

Objective: Electroencephalography (EEG) can be used to estimate neonates' biological brain age. Discrepancies between postmenstrual age and brain age, termed the brain age gap, can potentially quantify maturational deviation. Existing brain age EEG models are not well suited to clinical cot-side use for estimating neonates' brain age gap due to their dependency on relatively large data and pre-processing requirements.

Methods: We trained a deep learning model on resting state EEG data from preterm neonates with normal neurodevelopmental Bayley Scale of Infant and Toddler Development (BSID) outcomes, using substantially reduced data requirements. We subsequently tested this model in two independent datasets …

Therapeutic Potential Of Snake Venom: Toxin Distribution And Opportunities In Deep Learning For Novel Drug Discovery, Anas Bedraoui, Montamas Suntravat, Salim El Mejjad, Salwa Enezari, Naoual Oukkache, Elda E. Sanchez, Jacob Galan, Rachid El Fatimy, Tariq Daouda

School of Medicine Publications and Presentations

Snake venom is a rich source of bioactive molecules that hold great promise for therapeutic applications. These molecules can be broadly classified into enzymes and non-enzymes, each showcasing unique medicinal properties. Noteworthy compounds such as Bradykinin Potentiating Peptides (BPP) and Three-Finger Toxins (3FTx) are showing therapeutic potential in areas like cardiovascular diseases (CVDs) and pain-relief. Meanwhile, components like snake venom metalloproteinases (SVMP), L-amino acid oxidases (LAAO), and Phospholipase A2s (PLA2) are paving new ways in oncology treatments. The full medicinal scope of these toxins is still emerging. In this review, we discuss drugs derived from snake venoms that address …

A Comprehensive Survey Of Complex Brain Network Representation, Haoteng Tang, Guixiang Ma, Yanfu Zhang, Kai Ye, Lei Guo, Guodong Liu, Qi Huang, Yalin Wang, Olusola Ajilore, Alex D. Leow

Computer Science Faculty Publications and Presentations

Highlights

• Major traditional and deep learning methods on brain network representation are overviewed.

• Brain network datasets and algorithm implementation tools are summarized.

• Promising research directions in brain network analysis are discussed.

Abstract

Recent years have shown great merits in utilizing neuroimaging data to understand brain structural and functional changes, as well as its relationship to different neurodegenerative diseases and other clinical phenotypes. Brain networks, derived from different neuroimaging modalities, have attracted increasing attention due to their potential to gain system-level insights to characterize brain dynamics and abnormalities in neurological conditions. Traditional methods aim to pre-define multiple topological features of brain …

Empowering Foot Health: Harnessing The Adaptive Weighted Sub-Gradient Convolutional Neural Network For Diabetic Foot Ulcer Classification, Abdullah Alqahtani, Shtwai Alsubai, Mohamudha Parveen Rahamathulla, Abdu Gumaei, Mohemmed Sha, Yu-Dong Zhang, Muhammad Attique Khan

School of Podiatric Medicine Publications and Presentations

In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, …

P-Adic Cellular Neural Networks, B. A. Zambrano-Luna, Wilson A. Zuniga-Galindo

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this article we introduce the p-adic cellular neural networks which are mathematical generalizations of the classical cellular neural networks (CNNs) introduced by Chua and Yang. The new networks have infinitely many cells which are organized hierarchically in rooted trees, and also they have infinitely many hidden layers. Intuitively, the p-adic CNNs occur as limits of large hierarchical discrete CNNs. More precisely, the new networks can be very well approximated by hierarchical discrete CNNs. Mathematically speaking, each of the new networks is modeled by one integro-differential equation depending on several p-adic spatial variables and the time. We …

Robust Explainability: A Tutorial On Gradient-Based Attribution Methods For Deep Neural Networks, Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Nidhal Bouaynaya, Ravi P. Ramachandran

Electrical and Computer Engineering Faculty Publications and Presentations

With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods. These techniques use gradient signals to assign the burden of the decision on the input features. Later, we discuss how gradient-based methods can …

Deep Learning Model With Adaptive Regularization For Eeg-Based Emotion Recognition Using Temporal And Frequency Features, Alireza Samavat, Ebrahim Khalili, Bentolhoda Ayati, Marzieh Ayati

Computer Science Faculty Publications and Presentations

Since EEG signal acquisition is non-invasive and portable, it is convenient to be used for different applications. Recognizing emotions based on Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing the inner state of persons. There are extensive studies about emotion recognition, most of which heavily rely on staged complex handcrafted EEG feature extraction and classifier design. In this paper, we propose a hybrid multi-input deep model with convolution neural networks (CNNs) and bidirectional Long Short-term Memory (Bi-LSTM). CNNs extract time-invariant features from raw EEG data, and Bi-LSTM allows long-range lateral interactions between features. First, we propose a …

Differential Privacy In Privacy-Preserving Big Data And Learning: Challenge And Opportunity, Honglu Jiang, Yifeng Gao, S. M. Sarwar, Luis Garza Perez, Mahmudul Robin

Computer Science Faculty Publications and Presentations

Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer …

Automatic Camera Trap Classification Using Wildlife-Specific Deep Learning In Nilgai Management, Matthew Kutugata, Jeremy Baumgardt, John A. Goolsby, Alexis Racelis

Biology Faculty Publications and Presentations

Camera traps provide a low-cost approach to collect data and monitor wildlife across large scales but hand-labeling images at a rate that outpaces accumulation is difficult. Deep learning, a subdiscipline of machine learning and computer science, has been shown to address the issue of automatically classifying camera trap images with a high degree of accuracy. This technique, however, may be less accessible to ecologists, to small scale conservation projects, and has serious limitations. In this study, a simple deep learning model was trained using a dataset of 120,000 images to identify the presence of nilgai Boselaphus tragocamelus, a regionally specific …

Towards Accurate Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, Yet Effective Time Series Cnn-Based Approach, Hossein Sayadi, Yifeng Gao, Hosein Mohammadi Makrani, Jessica Lin, Paulo Cesar Costa, Setareh Rafatirad, Houman Homayoun

Computer Science Faculty Publications and Presentations

According to recent security analysis reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers, complexity, and harmful purposes to compromise the security of modern computer systems. Recently, malware detection based on low-level hardware features (e.g., Hardware Performance Counters (HPCs) information) has emerged as an effective alternative solution to address the complexity and performance overheads of traditional software-based detection methods. Hardware-assisted Malware Detection (HMD) techniques depend on standard Machine Learning (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers during execution at run-time. Prior HMD methods though effective have limited their study on …

Domain Adaptation For Vehicle Detection In Traffic Surveillance Images From Daytime To Nighttime, Jinlong Ji, Zhigang Xu, Hongkai Yu, Lan Fu, Xuesong Zhou

Computer Science Faculty Publications and Presentations

Vehicle detection in traffic surveillance images is an important approach to obtain vehicle data and rich traffic flow parameters. Recently, deep learning based methods have been widely used in vehicle detection with high accuracy and efficiency. However, deep learning based methods require a large number of manually labeled ground truths (bounding box of each vehicle in each image) to train the Convolutional Neural Networks (CNN). In the modern urban surveillance cameras, there are already many manually labeled ground truths in daytime images for training CNN, while there are little or much less manually labeled ground truths in nighttime images. In …