Engineering Commons^™

Enhancing Electrical Network Vulnerability Assessment With Machine Learning And Deep Learning Techniques, M Mishkatur Rahman, Ayman Sajjad Akash, Harun Pirim, Chau Le, Trung Le, Om Prakash Yadav

Northeast Journal of Complex Systems (NEJCS)

This research utilizes advanced machine learning techniques to evaluate node vul-
nerability in power grid networks. Utilizing the SciGRID and GridKit datasets, con-
sisting of 479, 16,167 nodes and 765, 20,539 edges respectively, the study employs
K-nearest neighbor and median imputation methods to address missing data. Cen-
trality metrics are integrated into a single comprehensive score for assessing node
criticality, categorizing nodes into four centrality levels informative of vulnerability.
This categorization informs the use of traditional machine learning (including XG-
Boost, SVM, Multilayer Perceptron) and Graph Neural Networks in the analysis.
The study not only benchmarks the capabilities of these …

Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu

Military Cyber Affairs

Deep learning finds rich applications in the tactical domain by learning from diverse data sources and performing difficult tasks to support mission-critical applications. However, deep learning models are susceptible to various attacks and exploits. In this paper, we first discuss application areas of deep learning in the tactical domain. Next, we present adversarial machine learning as an emerging attack vector and discuss the impact of adversarial attacks on the deep learning performance. Finally, we discuss potential defense methods that can be applied against these attacks.

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

Anti-Phishing Approach For Iot System In Fog Networks Based On Machine Learning Algorithms, Mahmoud Gad Awwad, Mohamed M. Ashour, El Said A. Marzouk, Eman Abdelhalim

Mansoura Engineering Journal

As the Internet of Things (IoT) continues to expand, ensuring the security and privacyِ of IoT systems becomes increasingly critical. Phishing attacks pose a significant threat to IoT devices and can lead to unauthorized access, data breaches, and compromised functionality. In this paper, we propose an anti-phishing approach for IoT systems in fog networks that leverages machine learning algorithms, including a .fusion with deep learning techniques We explore the effectiveness of eleven traditional machine learning algorithms combined with deep learning in detecting and preventing phishing attacks in IoT systems. By utilizing a diverse range of algorithms, we aim to enhance …

Motion Magnification-Inspired Feature Manipulation For Deepfake Detection, Aydamir Mirzayev, Hamdi Di̇bekli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in deep learning, increased availability of large-scale datasets, and improvement of accelerated graphics processing units facilitated creation of an unprecedented amount of synthetically generated media content with impressive visual quality. Although such technology is used predominantly for entertainment, there is widespread practice of using deepfake technology for malevolent ends. This potential for malicious use necessitates the creation of detection methods capable of reliably distinguishing manipulated video content. In this work we aim to create a learning-based detection method for synthetically generated videos. To this end, we attempt to detect spatiotemporal inconsistencies by leveraging a learning-based magnification-inspired feature manipulation …

Automated Identification Of Vehicles In Very High-Resolution Uav Orthomosaics Using Yolov7 Deep Learning Model, Esra Yildirim, Umut Güneş Seferci̇k, Taşkın Kavzoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

The utilization of remote sensing products for vehicle detection through deep learning has gained immense popularity, especially due to the advancement of unmanned aerial vehicles (UAVs). UAVs offer millimeter-level spatial resolution at low flight altitudes, which surpasses traditional airborne platforms. Detecting vehicles from very high-resolution UAV data is crucial in numerous applications, including parking lot and highway management, traffic monitoring, search and rescue missions, and military operations. Obtaining UAV data at desired periods allows the detection and tracking of target objects even several times during a day. Despite challenges such as diverse vehicle characteristics, traffic congestion, and hardware limitations, the …

Action Recognition Model Of Directed Attention Based On Cosine Similarity, Chen Li, Ming He, Chen Dong, Wei Li

Journal of System Simulation

Abstract: Aiming at the lack of directionality of traditional dot product attention, this paper proposes a directed attention model (DAM) based on cosine similarity. To effectively represent the direction relationship between the spatial and temporal features of video frames, the paper defines the relationship function in the attention mechanism using the cosine similarity theory, which can remove the absolute value of the relationship between features. To reduce the computational burden of the attention mechanism, the operation is decomposed from two dimensions of time and space. The computational complexity is further optimized by combining linear attention operation. The experiment is divided …

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso

Theses and Dissertations--Electrical and Computer Engineering

The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …

A Wavegan Approach For Mmwave-Based Fanet Topology Optimization, Enas Odat, Hakim Ghazzai, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

The integration of dynamic Flying Ad hoc Networks (FANETs) and millimeter Wave (mmWave) technology can offer a promising solution for numerous data-intensive applications, as it enables the establishment of a robust flying infrastructure with significant data transmission capabilities. However, to enable effective mmWave communication within this dynamic network, it is essential to precisely align the steerable antennas mounted on Unmanned Aerial Vehicles (UAVs) with their corresponding peer units. Therefore, it is important to design a novel approach that can quickly determine an optimized alignment and network topology. In this paper, we propose a Generative Adversarial Network (GAN)-based approach, called WaveGAN, …

Sub-Band Backdoor Attack In Remote Sensing Imagery, Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in …

Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall

Civil & Environmental Engineering Faculty Publications

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. …

Deep Learning Based Classification Of Focal Liver Lesions With 3 And 4 Phase Contrast-Enhanced Ct Protocols, Ahmed El-Emam, Hossam El-Din Moustafa, Mohamed Moawad, Mohamed Aouf

Mansoura Engineering Journal

It had been noticed that 3-phase and 4-phase computed tomography protocols with contrast serve as standard examinations for diagnosing liver tumors. Additionally, many patients require periodic follow-up, which entails significant radiation exposure for them. Advancements in image processing facilitate automated liver lesion segmentation. However, the challenge remains in classifying these small lesions by doctors, especially when the liver has different types of lesions with very little intensity difference. Therefore, deep learning can be utilized for the classification of liver lesions. The present work introduces a CNN-based module for the classification of liver lesions. The module consists of four stages: data …

Integration Of Infrared Thermography And Deep Learning For Real-Time In-Situ Defect Detection And Rapid Elimination Of Defect Propagation In Material Extrusion, Asef Ishraq Sadaf

Electronic Theses and Dissertations

This study presents a novel approach to overcoming process reliability challenges in Material Extrusion (ME), a prominent additive manufacturing (AM) technique. Despite ME's advantages in cost, versatility, and rapid prototyping, it faces significant barriers to commercial-scale production, primarily due to quality issues such as overextrusion and underextrusion, which compromise final part performance. Traditional manual monitoring methods severely lack the capability to efficiently detect these defects and highlight the necessity for an efficient and real-time monitoring solution. Considering these challenges, an innovative and field-deployable infrared thermography-based in-situ real-time defect detection and feedback control system is proposed in this thesis. A novel …

Investigation Of Software Defined Radio And Deep Learning For Ground Penetrating Radar, Yan Zhang

Graduate College Dissertations and Theses

Ground Penetrating Radar (GPR) is a non-invasive geophysical method that uses radar pulses to image the subsurface. This technology is widely used to detect and map subsurface structures, utilities, and features without the need for physical excavation. Traditional GPR systems, which rely on fixed radio frequency electronics like Application-Specific Integrated Circuits (ASICs), have significant limitations in their flexibility and adaptability. Adjusting operational parameters such as waveform, frequency, and modulation schemes is challenging, which is crucial for tailoring performance to specific tasks or conditions. The considerable size and weight of these systems restrict their applicability in harsh or confined spaces where …

Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

Sc-Fuse: A Feature Fusion Approach For Unpaved Road Detection From Remotely Sensed Images, Aniruddh Saxena

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Road network extraction from remote sensing imagery is crucial for numerous applications, ranging from autonomous navigation to urban and rural planning. A particularly challenging aspect is the detection of unpaved roads, often underrepresented in research and data. These roads display variability in texture, width, shape, and surroundings, making their detection quite complex. This thesis addresses these challenges by creating a specialized dataset and introducing the SC-Fuse model.

Our custom dataset comprises high resolution remote sensing imagery which primarily targets unpaved roads of the American Midwest. To capture the diverse seasonal variation and their impact, the dataset includes images from different …

Melanoma Detection Based On Deep Learning Networks, Sanjay Devaraneni

Electronic Theses, Projects, and Dissertations

Our main objective is to develop a method for identifying melanoma enabling accurate assessments of patient’s health. Skin cancer, such as melanoma can be extremely dangerous if not detected and treated early. Detecting skin cancer accurately and promptly can greatly increase the chances of survival. To achieve this, it is important to develop a computer-aided diagnostic support system. In this study a research team introduces a sophisticated transfer learning model that utilizes Resnet50 to classify melanoma. Transfer learning is a machine learning technique that takes advantage of trained models, for similar tasks resulting in time saving and enhanced accuracy by …

Statistical And Deep Learning Models For Reference Evapotranspiration Time Series Forecasting: A Comparison Of Accuracy, Complexity, And Data Efficiency, Arman Ahmadi, Andre Daccache, Mojtaba Sadegh, Richard L. Snyder

Civil Engineering Faculty Publications and Presentations

Reference evapotranspiration (ETo) is an essential variable in agricultural water resources management and irrigation scheduling. An accurate and reliable forecast of ETo facilitates effective decision-making in agriculture. Although numerous studies assessed various methodologies for ETo forecasting, an in-depth multi-dimensional analysis evaluating different aspects of these methodologies is missing. This study systematically evaluates the complexity, computational cost, data efficiency, and accuracy of ten models that have been used or could potentially be used for ETo forecasting. These models range from well-known statistical forecasting models like seasonal autoregressive integrated moving average (SARIMA) to state-of-the-art deep learning (DL) algorithms like temporal fusion transformer …

A Comparative Study Of Yolo Models And A Transformer-Based Yolov5 Model For Mass Detection In Mammograms, Damla Coşkun, Dervi̇ş Karaboğa, Alper Baştürk, Bahri̇ye Akay, Özkan Ufuk Nalbantoğlu, Serap Doğan, İshak Paçal, Meryem Altin Karagöz

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer is a prevalent form of cancer across the globe, and if it is not diagnosed at an early stage it can be life-threatening. In order to aid in its diagnosis, detection, and classification, computer-aided detection (CAD) systems are employed. You Only Look Once (YOLO)-based CAD algorithms have become very popular owing to their highly accurate results for object detection tasks in recent years. Therefore, the most popular YOLO models are implemented to compare the performance in mass detection with various experiments on the INbreast dataset. In addition, a YOLO model with an integrated Swin Transformer in its backbone …

An In-Depth Analysis Of Domain Adaptation In Computer And Robotic Vision, Muhammad Hassan Tanveer, Zainab Fatima, Shehnila Zardari, David A. Guerra-Zubiaga

Faculty and Research Publications

This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of …

Prediction Of Rockburst Intensity Grade Based On Convolutional Neural Network, Li Kangnan, Wu Yaqin, Du Feng, Zhang Xiang, Wang Yiqiao

Coal Geology & Exploration

Rockburst is one of the urgent problems to be addressed in the process of deep resource extraction. In order to predict the rockburst disasters safely and efficiently, a rockburst intensity grade prediction model (MICE-CNN) based on the Multiple Imputation by Chained Equations (MICE) and Convolutional Neural Network (CNN) was proposed. Specifically, a predictive indicator system was established based on the main influencing factors and the acquisition conditions of rockburst. A total of 120 sets of raw data from rockburst cases were collected, with the outliers processed by pauta criterion. Then, the missing data were interpolated with the four interpolation models …

Revolutionary Artificial Intelligence Architectural Design Solutions; Is It An Opportunity Or A Threat?, Mahmoud Desouki, T. A. El-Haddad, Bahaa El-Boshey

Mansoura Engineering Journal

Architectural design is uniquely complex, time-consuming work, so AI promises major benefits but also risks. Architectural design software programs have recently made an impressive development, especially those that use artificial intelligence. AI could transform design and construction with tools enabling generative design, parametric modeling, simulation, and optimization. However, AI’s impact remains unclear. Adoption depends on architects leveraging AI to augment judgment rather than replace it. Factors limiting AI include limited education/experience, threats to creative skills architects value, and unreliable AI design tools. While many see AI as an opportunity, not an endangerment, optimism and uncertainty prevail. AI may ultimately enhance …

Ehf-Fndm: An Efficient Hybrid Features Fake News Detection Methodology On Social Media, Haidy Samir Fahim, Asmaa Mohamed Al-Saied, Ahmed Shaban Samra, Abeer Twakol Khalil

Mansoura Engineering Journal

People are increasingly using social media to consume and share news. The inherent benefits of social media over traditional news media include its low cost and ease of access. In addition, publishing a news article requires less content censorship on social media. The rapid spread of "fake news" on social media, that is, news that contains intentionally false information, has a significant negative impact on society. For instance, false information about the coronavirus disease "2019" has spread around the world like a virus. Therefore, developing effective methods to detect fake news early has great importance. In this paper, the (Efficient …

Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni

Turkish Journal of Electrical Engineering and Computer Sciences

Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …

Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi

Turkish Journal of Electrical Engineering and Computer Sciences

Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice …

Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook

Doctoral Dissertations and Master's Theses

With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …

Faster, Cheaper, And Better Cfd: A Case For Machine Learning To Augment Reynolds-Averaged Navier-Stokes, John Peter Romano Ii

Mechanical & Aerospace Engineering Theses & Dissertations

In recent years, the field of machine learning (ML) has made significant advances, particularly through applying deep learning (DL) algorithms and artificial intelligence (AI). The literature shows several ways that ML may enhance the power of computational fluid dynamics (CFD) to improve its solution accuracy, reduce the needed computational resources and reduce overall simulation cost. ML techniques have also expanded the understanding of underlying flow physics and improved data capture from experimental fluid dynamics.

This dissertation presents an in-depth literature review and discusses ways the field of fluid dynamics has leveraged ML modeling to date. The author selects and describes …

An Intelligent Prediction Method And Interpretability For Drag And Torque Of Drill String, Liu Muchen, Song Xianzhi, Li Dayu, Zhu Shuo, Fu Li, Zhu Zhaopeng, Zhang Chengkai, Pan Tao

Coal Geology & Exploration

The accurate characterization and dynamic analysis of drilling string mechanics are essential to ensure the safe and efficient drilling. In the classical soft/rigid string model for drag & torque of drilling string, the friction coefficient of the drilling string is determined by empirical estimation or post-drilling inversion, of which the accuracy and timeliness needs to be improved. Based on the effectiveness of artificial intelligence technology applied in complex nonlinear mapping, a drag and torque prediction method of drill string with mechanism-data fusion was proposed by predicting the friction coefficient. Firstly, the friction coefficient was inversed using the drilled and logged …