Computer Engineering Commons^™

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 …

Deep-Learning-Based Classification Of Digitally Modulated Signals, John A. Snoap

Electrical & Computer Engineering Theses & Dissertations

This dissertation presents several novel deep-learning (DL)-based approaches for classifying digitally modulated signals, one method of which involves the use of capsule networks (CAPs) together with 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 …

Style Transfer Network For Generating Opera Makeup Details, Fengquan Zhang, Duo Cao, Xiaohan Ma, Baijun Chen, Jiangxiao Zhang

Journal of System Simulation

Abstract: To address the problem of the loss of local style details in cross-domain image simulation, a ChinOperaGAN network framework suitable for opera makeup is designed from the perspective of protecting the excellent traditional culture. In order to solve the style translation of differences in two image domains, multiple overlapping local adversarial discriminators are proposed in the generative adversarial network. Since paired opera makeup data are difficult to obtain, a synthetic image is generated by combining the source image makeup mapping to effectively guide the transfer of local makeup details between images. In view of the characteristics of opera makeup …

Seek And Classify: End-To-End Joint Multi-Signal Detection And Classification Using Deep Learning, Prashant Subedi

School of Computing: Dissertations, Theses, and Student Research

The rise in the use of wireless communication has led to the problem of spectrum scarcity in licensed bands. The popularity of Internet of Things (IoT) requires innovative solutions that maximize the use of available spectrum to support the increasing number of connected devices. This thesis tackles two significant problems in wireless communication: the need for efficient spectrum sensing techniques and the scarcity of large, diverse raw in-phase (I) and quadrature (Q) datasets.

The ability to detect and classify modulation of the signals efficiently can enable a cognitive radio to monitor the spectrum activity in real time and utilize unused …

Faults Segmentation In Levee Systems Using Deep Learning Approaches, Manisha Panta

LSU New Orleans Theses and Dissertations

Levees are earthen structures constructed to mitigate flooding in low-lying areas. Although levee systems can reduce flood risks, they cannot completely eliminate them. Failures within flood control systems due to inadequate maintenance or strong water currents can lead to significant property damage and catastrophic loss of life, as was seen during Hurricane Katrina. Consequently, regular inspections are essential to identify and address any issues with the levees promptly. However, current inspection methods rely on manual techniques that are time-consuming, labor-intensive, and prone to human error. Therefore, this study proposes using deep learning models for more efficient and frequent assessment of …

Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they …

Improving Unet Segmentation Performance Using An Ensemble Model In Images Containing Railway Lines, Mehmet Sevi̇, İlhan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study aims to make sense of the autonomous system and the railway environment for railway vehicles. For this purpose, by determining the railway line, information about the general condition of the line can be obtained along the way. In addition, objects such as pedestrian crossings, people, cars, and traffic signs on the line will be extracted. The rails and the rail environment in the images will be segmented with a semantic segmentation network. In order to ensure the safety of rail transport, computer vision, and deep learning-based methods are increasingly used to inspect railway tracks and surrounding objects. In …

Classification Of Arabic Social Media Texts Based On A Deep Learning Multi-Tasks Model, Ali A. Jalil, Ahmed H. Aliwy

Al-Bahir

The proliferation of social networking sites and their user base has led to an exponential increase in the amount of data generated on a daily basis. Textual content is one type of data that is commonly found on these platforms, and it has been shown to have a significant impact on decision-making processes at the individual, group, and national levels. One of the most important and largest part of this data are the texts that express human intentions, feelings and condition. Understanding these texts is one of the biggest challenges that facing data analysis. It is the backbone for understanding …

Detection Of Crypto-Ransomware Attack Using Deep Learning, Muna Jemal

Master of Science in Computer Science Theses

The number one threat to the digital world is the exponential increase in ransomware attacks. Ransomware is malware that prevents victims from accessing their resources by locking or encrypting the data until a ransom is paid. With individuals and businesses growing dependencies on technology and the Internet, researchers in the cyber security field are looking for different measures to prevent malicious attackers from having a successful campaign. A new ransomware variant is being introduced daily, thus behavior-based analysis of detecting ransomware attacks is more effective than the traditional static analysis. This paper proposes a multi-variant classification to detect ransomware I/O …

Modeling And Control Strategies For A Two-Wheel Balancing Mobile Robot, John Alan Moritz

Graduate Theses and Dissertations

The problem of balancing and autonomously navigating a two-wheel mobile robot is an increasingly active area of research, due to its potential applications in last-mile delivery, pedestrian transportation, warehouse automation, parts supply, agriculture, surveillance, and monitoring. This thesis investigates the design and control of a two-wheel balancing mobile robot using three different control strategies: Proportional Integral Derivative (PID) controllers, Sliding Mode Control, and Deep Q-Learning methodology. The mobile robot is modeled using a dynamic and kinematic model, and its motion is simulated in a custom MATLAB/Simulink environment. The first part of the thesis focuses on developing a dynamic and kinematic …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Task Offloading And Resource Allocation Based On Dl-Ga In Mobile Edge Computing, Hang Gu, Minjuan Zhang, Wenzao Li, Yuwen Pan

Turkish Journal of Electrical Engineering and Computer Sciences

With the rapid development of 5G and the Internet of Things (IoT), the traditional cloud computing architecture struggle to support the booming computation-intensive and latency-sensitive applications. Mobile edge computing (MEC) has emerged as a solution which enables abundant IoT tasks to be offloaded to edge services. However, task offloading and resource allocation remain challenges in MEC framework. In this paper, we add the total number of offloaded tasks to the optimization objective and apply algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA) to maximize the value function, which is defined as a weighted sum of energy consumption, latency, and …

A Long-Term Funds Predictor Based On Deep Learning, Shuiyi Kuang

Electronic Theses, Projects, and Dissertations

Numerous neural network models have been created to predict the rise or fall of stocks since deep learning has gained popularity, and many of them have performed quite well. However, since the share market is hugely influenced by various policy changes or unexpected news, it is challenging for investors to use such short-term predictions as a guide. In this paper, we try to find a suitable long-term predictor for the funds market by testing different kinds of neural network models, including the Long Short-Term Memory(LSTM) model with different layers, the Gated Recurrent Units(GRU) model with different layers, and the combination …

Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …

An Efficient Deep Learning Architecture For Turkish Lira Recognition And Counterfeit Detection, Burak İyi̇kesi̇ci̇, Ergun Erçelebi̇

Turkish Journal of Electrical Engineering and Computer Sciences

Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images …

3d Point Cloud Classification With Acgan-3d And Vacwgan-Gp, Onur Ergün, Yusuf Sahi̇lli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Machine learning and deep learning techniques are widely used to make sense of 3D point cloud data which became ubiquitous and important due to the recent advances in 3D scanning technologies and other sensors. In this work, we propose two networks to predict the class of the input 3D point cloud: 3D Auxiliary Classifier Generative Adversarial Network (ACGAN-3D) and Versatile Auxiliary Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (VACWGAN-GP). Unlike other classifiers, we are able to enlarge the limited data set with the data produced by generative models. We consequently aim to increase the success of the model by …

Modulation Recognition Method Of Mixed Signal Based On Intelligent Analysis Of Cyclic Spectrum Section, Yu Du, Xinquan Yang, Jianhua Zhang, Suchun Yuan, Huachao Xiao, Jingjing Yuan

Journal of System Simulation

Abstract: Aiming at the problems of low intelligence and poor adaptability for the existing mixed signal recognition methods, an intelligent recognition method based on cyclic spectral cross section and deep learning is proposed. For common mixed communication signals, the characteristics of zero frequency cross section of cyclic spectrum are theoretically deduced and analyzed. Two new pre-processing methods, nonlinear segmental mapping and directional pseudo-clustering are proposed, which can effectively improve the adaptability and consistency of cross section features. The pre-processed feature graph is combined with the residual network (ResNet), and the deep learning network is used to mine and analyze the …

Research On Intelligent Prediction Method Of Wargaming Air Mission, Dayong Zhang, Jingyu Yang, Xi Wu

Journal of System Simulation

Abstract: The efficient, accurate and automatic judgment of the combat mission or intention of the enemy's air targets in the battlefield is the basis of situation awareness and the key to the allocation of auxiliary combat resources. Combined with the calculation characteristics of feed forward deep neural network and long-term and short-term memory network model, two targeted basic index learners are designed, and then the weighted combination is carried out according to the cross entropy of the basic index, which can be used to further train the evaluation index of the learner. It can not only effectively prevent the model …

Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh

Browse all Theses and Dissertations

The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. Increased reliance on IoT raised serious information security concerns. This dissertation presents three systems for analyzing and classifying IoT traffic using Deep Learning (DL) models, and a large dataset is built for systems training and evaluation. The first system studies the effect of combining raw data and engineered features to optimize the classification of encrypted and compressed IoT traffic using Engineered Features Classification (EFC), Raw Data Classification (RDC), and combined Raw Data and Engineered Features Classification (RDEFC) approaches. Our results demonstrate …

Adversarial Training Of Deep Neural Networks, Anabetsy Termini

CCAC Theses and Dissertations

Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. In this dissertation, we performed a rigorous study to understand whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting across a wide …

Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat

Graduate Theses/Dissertations

Social media has become a domain that involves a lot of hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims. In this study, I explore the effectiveness and performance of various machine learning methods employing text processing techniques to create a robust system for hate speech identification. I assess the performance of Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors using three distinct datasets sourced from social …

Machine Learning Models To Automate Radiotherapy Structure Name Standardization, Priyankar Bose

Theses and Dissertations

Structure name standardization is a critical problem in Radiotherapy planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes and `Other' organs for monitoring present and future medications. Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and `Other' organs is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. We compare both traditional methods and deep neural network-based approaches on the multimodal vision-language prostate cancer …

Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

The brain is one of the most important and complex organs in the body, consisting of billions of individual cells. Uncontrolled growth and expansion of aberrant cell populations within or around the brain are the main causes of brain tumors. These cells have the potential to harm healthy cells and impair brain function [1]. Tumors can be detected using medical imaging techniques, which are considered the most popular and accurate way to classify different types of cancer, and this procedure is even more crucial as it is noninvasive [2]. Magnetic resonance imaging (MRI) is one such medical imaging technique that …

Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo

Articles

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …

How Visual Stimuli Evoked P300 Is Transforming The Brain–Computer Interface Landscape: A Prisma Compliant Systematic Review, Jai Kalra, Prashasti Mittal, Nirmiti Mittal, Abhishek Arora, Utkarsh Tewari, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo

Articles

Non-invasive Visual Stimuli evoked-EEGbased P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers …

Comparative Adjudication Of Noisy And Subjective Data Annotation Disagreements For Deep Learning, Scott David Williams

Browse all Theses and Dissertations

Obtaining accurate inferences from deep neural networks is difficult when models are trained on instances with conflicting labels. Algorithmic recognition of online hate speech illustrates this. No human annotator is perfectly reliable, so multiple annotators evaluate and label online posts in a corpus. Labeling scheme limitations, differences in annotators' beliefs, and limits to annotators' honesty and carefulness cause some labels to disagree. Consequently, decisive and accurate inferences become less likely. Some practical applications such as social research can tolerate some indecisiveness. However, an online platform using an indecisive classifier for automated content moderation could create more problems than it solves. …

An Empirical Study Of Pre-Trained Model Reuse In The Hugging Face Deep Learning Model Registry, Wenxin Jiang, Nicholas Synovic, Matt Hyatt, Taylor R. Schorlemmer, Rohan Sethi, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems.

In this work, we present the first empirical investigation of PTM reuse. …

Ensemble Transformer Architecture For Efficient And Real Time Sign Language Translation, Sumeet Ghegade

Master's Projects

Sign language is a form of visual language that uses face expression and hand gestures to communicate thoughts and concepts. The term refers to multiple visual languages that share some common visual cues but differ in their grammar and syntax. Sign language translation (SLT) is a crucial step in closing the communication gap between hearing and hearingimpaired people. The study of SLT using machine learning has gotten a lot of interest during the last three years but despite progress, SLT research is still in its early phases. Most of the previous approaches first convert the signs to glosses and then …

Security Of Internet Of Things (Iot) Using Federated Learning And Deep Learning — Recent Advancements, Issues And Prospects, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty

Electrical & Computer Engineering Faculty Publications

There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. …

Deep Learning-Based Classification Of Chaotic Systems Over Phase Portraits, Sezgi̇n Kaçar, Süleyman Uzun, Burak Aricioğlu

Turkish Journal of Electrical Engineering and Computer Sciences

This study performed a deep learning-based classification of chaotic systems over their phase portraits. To the best of the authors' knowledge, such classification studies over phase portraits have not been conducted in the literature. To that end, a dataset consisting of the phase portraits of the most known two chaotic systems, namely Lorenz and Chen, is generated for different values of the parameters, initial conditions, step size, and time length. Then, a classification with high accuracy is carried out employing transfer learning methods. The transfer learning methods used in the study are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet, and …