Computer Sciences Commons^™

Deep Transfer Learning-Based Bird Species Classification Using Mel Spectrogram Images, Mrinal Kanti Baowaly, Bisnu Chandra Sarkar, Md.Abul Ala Walid, Md. Martuza Ahamad, Bikash Chandra Singh, Eduardo Silva Alvarado, Imran Ashraf, Md. Abdus Samad

School of Cybersecurity Faculty Publications

The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet …

Bayesian Neural Netwok Variational Autoencoder Inverse Mapper (Bnn-Vaim) And Its Application In Compton Form Factors Extraction, Md Fayaz Bin Hossen, Tareq Alghamdi, Manal Almaeen, Yaohang Li

Computer Science Faculty Publications

We extend the Variational Autoencoder Inverse Mapper (VAIM) framework for the inverse problem of extracting Compton Form Factors (CFFs) from deeply virtual exclusive reactions, such as the unpolarized Deeply virtual exclusive scattering (DVCS) cross section. VAIM is an end-to-end deep learning framework to address the solution ambiguity issue in ill-posed inverse problems, which comprises of a forward mapper and a backward mapper to simulate the forward and inverse processes, respectively. In particular, we incorporate Bayesian Neural Network (BNN) into the VAIM architecture (BNN-VAIM) for uncertainty quantification. By sampling the weights and biases distributions of the BNN in the backward mapper …

Identifying New Cancer Genes Based On The Integration Of Annotated Gene Sets Via Hypergraph Neural Networks, Chao Deng, Hong-Dong Li, Li-Shen Zhang, Yiwei Liu, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation

Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.

Results

Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of …

Automatic Classification Of Activities In Classroom Videos, Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton

VMASC Publications

Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed …

Advancing Household Robotics: Deep Interactive Reinforcement Learning For Efficient Training And Enhanced Performance, Arpita Soni, Sujatha Alla, Suresh Dodda, Hemanth Volikatla

Engineering Management & Systems Engineering Faculty Publications

The market for domestic robots—made to perform household chore, is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labour, in contrast to industrial robots, which are frequently criticised for displacing human workers. But before these robots can carry out domestic chores, they need to become proficient in a number of minor activities, such as recognizing their surroundings, making decisions, and picking up on human behaviours. Reinforcement learning, or RL, has emerged as a key robotics technology that enables robots to interact with their environment and learn how to …

Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework For Civilian Urban Air Mobility, Md. Shirajum Munir, Sumit Howlader Dipro, Kamrul Hasan, Tariqul Islam, Sachin Shetty

VMASC Publications

Urban air mobility (UAM) has become a potential candidate for civilization for serving smart citizens, such as through delivery, surveillance, and air taxis. However, safety concerns have grown since commercial UAM uses a publicly available communication infrastructure that enhances the risk of jamming and spoofing attacks to steal or crash crafts in UAM. To protect commercial UAM from cyberattacks and theft, this work proposes an artificial intelligence (AI)-enabled exploratory cyber-physical safety analyzer framework. The proposed framework devises supervised learning-based AI schemes such as decision tree, random forests, logistic regression, K-nearest neighbors (KNN), and long short-term memory (LSTM) for predicting and …

A Hybrid Deep Learning Approach For Crude Oil Price Prediction, Hind Aldabagh, Xianrong Zheng, Ravi Mukkamala

Computer Science Faculty Publications

Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model …

Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides

Computer Science Faculty Publications

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run …

Dfhic: A Dilated Full Convolution Model To Enhance The Resolution Of Hi-C Data, Bin Wang, Kun Liu, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Hi-C technology has been the most widely used chromosome conformation capture(3C) experiment that measures the frequency of all paired interactions in the entire genome, which is a powerful tool for studying the 3D structure of the genome. The fineness of the constructed genome structure depends on the resolution of Hi-C data. However, due to the fact that high-resolution Hi-C data require deep sequencing and thus high experimental cost, most available Hi-C data are in low-resolution. Hence, it is essential to enhance the quality of Hi-C data by developing the effective computational methods.

Results: In this work, we propose …

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …

Adaptive Critic Network For Person Tracking Using 3d Skeleton Data, Joseph G. Zalameda, Alex Glandon, Khan M. Iftekharuddin, Mohammad S. Alam (Ed.), Vijayan K. Asari (Ed.)

Electrical & Computer Engineering Faculty Publications

Analysis of human gait using 3-dimensional co-occurrence skeleton joints extracted from Lidar sensor data has been shown a viable method for predicting person identity. The co-occurrence based networks rely on the spatial changes between frames of each joint in the skeleton data sequence. Normally, this data is obtained using a Lidar skeleton extraction method to estimate these co-occurrence features from raw Lidar frames, which can be prone to incorrect joint estimations when part of the body is occluded. These datasets can also be time consuming and expensive to collect and typically offer a small number of samples for training and …

Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner

Electrical & Computer Engineering Faculty Publications

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper …

Deapsecure Computational Training For Cybersecurity: Third-Year Improvements And Impacts, Bahador Dodge, Jacob Strother, Rosby Asiamah, Karina Arcaute, Wirawan Purwanto, Masha Sosonkina, Hongyi Wu

Modeling, Simulation and Visualization Student Capstone Conference

The Data-Enabled Advanced Training Program for Cybersecurity Research and Education (DeapSECURE) was introduced in 2018 as a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. By its third year, DeapSECURE, like many other educational endeavors, experienced abrupt changes brought by the COVID-19 pandemic. The training had to be retooled to adapt to fully online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, …

Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu

Mathematics & Statistics Faculty Publications

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables Q² and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection …

Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li

Electrical & Computer Engineering Faculty Publications

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t₁ together with low-resolution images at a …

Security Hardening Of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks, Ferhat Ozgur Catak, Murat Kuzlu, Haolin Tang, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the …

Stock Trend Prediction Using Candlestick Charting And Ensemble Machine Learning Techniques With A Novelty Feature Engineering Scheme, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu

Information Technology & Decision Sciences Faculty Publications

Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment …

Large Scale Subject Category Classification Of Scholarly Papers With Deep Attentive Neural Networks, Bharath Kandimalla, Shaurya Rohatgi, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few …

A Tool For Segmentation Of Secondary Structures In 3d Cryo-Em Density Map Components Using Deep Convolutional Neural Networks, Yongcheng Mu, Salim Sazzed, Maytha Alshammari, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Although cryo-electron microscopy (cryo-EM) has been successfully used to derive atomic structures for many proteins, it is still challenging to derive atomic structures when the resolution of cryo-EM density maps is in the medium resolution range, such as 5–10 Å. Detection of protein secondary structures, such as helices and β-sheets, from cryo-EM density maps provides constraints for deriving atomic structures from such maps. As more deep learning methodologies are being developed for solving various molecular problems, effective tools are needed for users to access them. We have developed an effective software bundle, DeepSSETracer, for the detection of protein secondary structure …

Shipbuilding Supply Chain Framework And Digital Transformation: A Project Portfolios Risk Evaluation, Rafael Diaz, Katherine Smith, Rafael Landaeta, Antonio Padovano

VMASC Publications

Program portfolio managers in digital transformation programs have a need for knowledge that can guide decisions related to the alignment of program investments with the sustainability and strategic objectives of the organization. The purpose of this research is to illustrate the utility of a framework capable of clarifying the cost-benefit tradeoffs stemming from assessing digitalization program investment risks in the military shipbuilding sector. Our approach uses Artificial Neural Network to quantify benefits and risks per project while employing scenario analysis to quantify the effects of operational constraints. A Monte Carlo model is used to generate data samples that support the …

Novel Use Of Neural Networks To Identify And Detect Electrical Infrastructure Performance, Evan Pierre Savaria

Computer Science Theses & Dissertations

Electrical grid maintenance and repairs are crucial services that keep America’s lights on. Electrical service providers make it their priority to uphold minimal interruptions to this service. Electricity is essential for modern technology within the home, such as cooking, refrigeration, and hot water. Organizations, such as schools, hospitals, and military bases, cannot properly function or operate without power. When analyzing the current electrical infrastructure, it is evident that considerable components of the power grid are aging and in need of replacement. Additionally, threats and damage continue to occur. These damages occur not only due to simple, single power line failure …

Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles

Computer Science Faculty Publications

We describe our approach for automatically generating presentation slides for scientific papers using deep neural networks. Such slides can help authors have a starting point for their slide generation process. Extractive summarization techniques are applied to rank and select important sentences from the original document. Previous work identified important sentences based only on a limited number of features that were extracted from the position and structure of sentences in the paper. Our method extends previous work by (1) extracting a more comprehensive list of surface features, (2) considering semantic or meaning of the sentence, and (3) using context around the …

Flexc: Protein Flexibility Prediction Using Context-Based Statistics, Predicted Structural Features, And Sequence Information, Ashraf Yaseen, Mais Nijim, Brandon Williams, Lei Qian, Min Li, Jianxin Wang, Yaohang Li

Computer Science Faculty Publications

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.

Deep Models For Brain Em Image Segmentation: Novel Insights And Improved Performance, Ahmed Fakhry, Hanchuan Peng, Shuiwang Ji

Computer Science Faculty Publications

Motivation: Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation.

Results: In this work, we proposed a novel design of DNNs for …

Template-Based C8-Scorpion: A Protein 8 State Secondary Structure Prediction Method Using Structural Information And Context-Based Features, Ashraf Yaseen, Yaohang Li

Computer Science Faculty Publications

Background: Secondary structures prediction of proteins is important to many protein structure modeling applications. Correct prediction of secondary structures can significantly reduce the degrees of freedom in protein tertiary structure modeling and therefore reduces the difficulty of obtaining high resolution 3D models.

Methods: In this work, we investigate a template-based approach to enhance 8-state secondary structure prediction accuracy. We construct structural templates from known protein structures with certain sequence similarity. The structural templates are then incorporated as features with sequence and evolutionary information to train two-stage neural networks. In case of structural templates absence, heuristic structural information is incorporated instead. …

Dinosolve: A Protein Disulfide Bonding Prediction Server Using Context-Based Features To Enhance Prediction Accuracy, Ashraf Yaseen, Yaohang Li

Computer Science Faculty Publications

Background: Disulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins.

Methods: In this work, we introduce an approach of enhancing disulfide bonding prediction accuracy by taking advantage of context-based features. We firstly derive the first-order and second-order mean-force potentials according to the amino acid environment around the cysteine residues from large number of cysteine samples. The mean-force potentials are integrated as context-based scores to estimate the favorability of a cysteine residue in disulfide bonding state as well as …

Solving The Vehicle Re-Identification Problem By Using Neural Networks, Tanweer Rashid

Computational Modeling & Simulation Engineering Theses & Dissertations

Vehicle re-identification is the process by which vehicle attributes measured at one point on a road network are compared to vehicle attributes measured at another point in an effort to match vehicles without using any unique identifiers such as license plate numbers. A match is made if the two measurements are estimated to belong to the same vehicle. Vehicle attributes can be sensor readings such as loop induction signatures, or they can also be actual vehicle characteristics such as length, weight, number of axles, etc. This research makes use of vehicle length, travel time, axle spacing and axle weights for …

Electroencephalogram Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen

Electrical & Computer Engineering Theses & Dissertations

A wavelet neural network (WNN) technique rs developed for electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural networks and the time/frequency property of wavelet, where the neural network was trained on a simulated dataset with known ground truths. The contribution of this thesis is two-fold. First, many EEG artifact removal algorithms, including regression based methods, require reference EOG signals, which are not always available. To remove EEG ai1ifacts, a WNN tries to learn the characteristics of the artifacts first and does not need reference EOG signals once trained. Second, WNNs are …

Analysis Of Electroencephalogram Signals For The Identification Of Mental Tasks, My Thy Thi Tran

Electrical & Computer Engineering Theses & Dissertations

Electroencephalogram (EEG) signals can be used for implicit communication such as to control robots or medical equipment by brain activity or to detect an individual's intentions of committing premeditated crimes. An EEG based brain-computer interface allows paralyzed patients to express their thoughts. However, biological and technical artifacts heavily interfered with EEG signals due to blinking of the eyes, muscle activities and line noise. Sometimes the noise interference due to signal artifacts becomes more prominent than the information content. This thesis investigates novel feature extraction methodologies in EEG signals to represent different thought processes and employs neural network-based pattern classification techniques …

Exploration Of Computational Methods For Classification Of Movement Intention During Human Voluntary Movement From Single Trial Eeg, Ou Bai, Peter Lin, Sherry Vorbach, Jiang Li, Steve Furlani, Mark Hallett

Electrical & Computer Engineering Faculty Publications

Objective: To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG).

Methods: Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis …