Computer Engineering Commons^™

Iot In Smart Communities, Technologies And Applications., Muhammad Zaigham Abbas Shah Syed

Electronic Theses and Dissertations

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing …

Effective Short Text Classification Via The Fusion Of Hybrid Features For Iot Social Data, Xiong Luo, Zhijian Yu, Zhigang Zhao, Wenbing Zhao, Jenq-Haur Wang

Electrical and Computer Engineering Faculty Publications

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive …

A Wind Turbine Fault Diagnosis Method Based On Siamese Deep Neural Network, Jiarui Liu, Guotian Yang, Xiaowei Wang

Journal of System Simulation

Abstract: In order to effectively extract the fault features of time series data in supervisory control and data acquisition (SCADA), considering the advantages of one-dimensional convolutional neural network (1-D CNN) for extracting local time series features and the advantages of long-term memory (LSTM) which can extract long-term dependent features, a method for fault diagnosis of wind turbines based on 1-D CNN-LSTM is proposed. To solve the problem of the scarcity of fault samples of wind turbines based on the siamese network architecture, a wind fault diagnosis method based on siamese 1-D CNN-LSTM is proposed. The proposed siamese 1-D CNN-LSTM …

Load2load: Day-Ahead Load Forecasting At Aggregated Level, Mustafa Berkay Yilmaz

Turkish Journal of Electrical Engineering and Computer Sciences

A reliable and accurate short-term load forecasting (STLF) helps utilities and energy providers deal with the challenges posed by supply and demand balance, higher penetration of renewable energies and the development of electricity markets with increasingly complex pricing strategies in future smart grids. Recent advances in deep learning have been successively utilized to STLF. However, there is no certain study that evaluates the performances of different STLF methods at an aggregated level on different datasets with different numbers of daily measurements.In this study, a deep learning STLF architecture called Load2Load is proposed for day-ahead forecasting. Different forecasting methods have been …

Fatigue Detection Method Based On Facial Features And Head Posture, Rongxiu Lu, Bihao Zhang, Zhenlong Mo

Journal of System Simulation

Abstract: Aiming at the of the single fatigue characteristics, low robustness and inability to customize fatigue thresholds for different drivers of fatigue detection methods, a method based on facial features and head posture is proposed. In face detection and face key point positioning HOG feature operator and regression tree algorithm are used. In head posture estimation, head posture Euler angle is estimated by combining the face key points with the coordinate system transformation. In fatigue feature extraction, a deep residual neural network model is established to extract the eye fatigue features, which the eye, mouth aspect ratio and head posture …

Electrical Resistance Tomography And Flow Pattern Identification Method Based On Deep Residual Neural Network, Weiguo Tong, Shichao Zeng, Lifeng Zhang, Zhe Hou, Jiayue Guo

Journal of System Simulation

Abstract: Aiming at the low accuracy of inverse problem imaging and flow pattern recognition in electrical resistance tomography (ERT), a two-phase flow electrical resistance tomography and flow pattern recognition method based on the deep residual neural network is proposed. The finite element method is used to model the ERT forward problem to construct the "boundary voltage-conductivity distribution-flow pattern category" dataset of various gas-liquid two-phase flow distributions. The residual neural network for ERT image reconstruction and flow pattern identification of gas-liquid two-phase flow is built and trained. The two outputs of the residual neural network are processed respectively to obtain …

Segmentation Of Diatoms Using Edge Detection And Deep Learning, Hüseyi̇n Gündüz, Cüneyd Nadi̇r Solak, Serkan Günal

Turkish Journal of Electrical Engineering and Computer Sciences

Diatoms are photosynthesizing algae found in almost every aquatic environment. Detecting the number and diversity of diatoms is very important to analyze water quality appropriately. Accurate segmentation of diatoms is therefore crucial for this detection process. In this study, a new and effective model for the automatic segmentation of diatoms based on image processing and deep learning algorithms is proposed. In the proposed model, edge segments of a given image containing diatoms and nondiatom particles are first obtained. These edge segments are then combined, resulting in closed contours representing diatom candidates. In the final step, the diatom candidates are classified …

Comparison Of Deep Learning And Regression-Based Mppt Algorithms In Pv Systems, Murat Sali̇m Karabi̇naoğlu, Beki̇r Çakir, Mustafa Engi̇n Başoğlu, Abdülvehhab Kazdaloğlu, Azi̇z Güneroğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Solar energy systems (SES) and photovoltaic (PV) modules should be operated at the maximum power point (MPP) to achieve the highest efficiency in the energy generation processes. Maximum power point tracking (MPPT) applications using conventional methods may not be able to follow the global MPP (GMPP) of the PV system under changing atmospheric conditions and they could oscillate around the local MPP. In this study, a machine learning and deep learning (DL) based long short-term memory (LSTM) model is proposed as an innovative solution for MPPT. Contrary to the traditional MPPT applications using current and voltage sensors, the output resistance …

Deception Detection Across Domains, Languages And Modalities, Subhadarshi Panda

Dissertations, Theses, and Capstone Projects

With the increase of deception and misinformation especially in social media, it has become crucial to develop machine learning methods to automatically identify deception. In this dissertation, we identify key challenges underlying text-based deception detection in a cross-domain setting, where we do not have training data in the target domain. We analyze the differences between domains and as a result develop methods to improve cross-domain deception detection. We additionally develop approaches that take advantage of cross-lingual properties to support deception detection across languages. This involves the usage of either multilingual NLP models or translation models. Finally, to better understand multi-modal …

Analysis Of Patch And Sample Size Effects For 2d-3d Cnn Models Using Multiplatform Dataset: Hyperspectral Image Classification Of Rosis And Jilin-1 Gp01 Imagery, Taşkin Kavzoğlu, Eli̇f Özlem Yilmaz

Turkish Journal of Electrical Engineering and Computer Sciences

Modern hyperspectral sensors provide a huge volume of data at spectral and spatial domains with high redundancy, which requires robust methods for analysis. In this study, 2D and 3D CNN models were applied to hyperspectral image datasets (ROSIS and Jilin-1 GP01) using varying patch and sample sizes to determine their combined impacts on the performance of deep learning models. Differences in classification performances in relation to particle and sample sizes were statistically analysed using McNemar?s test. According to the findings, raising the patch and sample size enhances the performance of the 2D/3D CNN model and produces more accurate results in …

Artificial Neural Networks And Their Applications To Intelligent Fault Diagnosis Of Power Transmission Lines, Fatemeh Mohammadi Shakiba

Dissertations

Over the past thirty years, the idea of computing based on models inspired by human brains and biological neural networks emerged. Artificial neural networks play an important role in the field of machine learning and hold the key to the success of performing many intelligent tasks by machines. They are used in various applications such as pattern recognition, data classification, stock market prediction, aerospace, weather forecasting, control systems, intelligent automation, robotics, and healthcare. Their architectures generally consist of an input layer, multiple hidden layers, and one output layer. They can be implemented on software or hardware. Nowadays, various structures with …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Optimized Three Deep Learning Models Based-Pso Hyperparameters For Beijing Pm2.5 Prediction, Andri Pranolo, Yingchi Mao, Aji Prasetya Wibawa, Agung Bella Putra Utama, Felix Andika Dwiyanto

Knowledge Engineering and Data Science

Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, …

Aerial Target Threat Assessment Method Based On Deep Learning, Huimin Chai, Yong Zhang, Xinyue Li, Yanan Song

Journal of System Simulation

Abstract: Due to many factors of aerial target threat assessment and the lack of self-learning ability of current assessment methods, a deep neural network model for aerial target threat assessment is established using deep learning theory. In order to improve the fitting effect of the model training, a symmetric pre-training method is given. The hidden layers of the model are pre-trained layer by layer, and finally the whole model is trained. Sample data and air to air simulation scene experiments are carried out respectively. The experiments results show that the accuracy of the model using the symmetric pre-training method is …

Social Distancing Monitoring System Using Deep Learning, Amelia Ritahani Ismail, Nur Shairah Muhd Affendy, Asmarani Ahmad Puzi

Knowledge Engineering and Data Science

COVID-19 has been declared a pandemic in the world by 2020. One way to prevent COVID-19 disease, as the World Health Organization (WHO) suggests, is to keep a distance from other people. It is advised to stay at least 1 meter away from others, even if they do not appear to be sick. The reason is that people can also be the virus carrier without having any symptoms. Thus, many countries have enforced the rules of social distancing in their Standard Operating Procedure (SOP) to prevent the virus spread. Monitoring the social distance is challenging as this requires authorities to …

Generating Ad Creatives Using Deep Learning For Search Advertising, Kevser Nur Çoğalmiş, Ahmet Bulut

Turkish Journal of Electrical Engineering and Computer Sciences

We generated advertisement creatives programmatically using deep neural networks. A landing page contains relevant text data, which can be used for generating advertisement creatives, i.e. ads. We treated the ad generation task as a text summarization problem and built a sequence to sequence model. In order to assess the validity of our approach, we conducted experiments on four datasets. Our empirical results showed that our model generated relevant ads on a template-based dataset with moderate hyperparameters. Training the model with more content increased the performance of the model, which we attributed to rigorous hyperparameter tune-up. The choice of word embedding …

Generating Reality-Analogous Datasets For Autonomous Uav Navigation Using Digital Twin Areas, Thomas Lee, Susan Mckeever, Jane Courtney

Conference papers

In order for autonomously navigating Unmanned Air Vehicles(UAVs) to be implemented in day-to-day life, proof of safe operation will be necessary for all realistic navigation scenarios. For Deep Learning powered navigation protocols, this requirement is challenging to fulfil as the performance of a network is impacted by how much the test case deviates from data that the network was trained on. Though networks can generalise to manage multiple scenarios in the same task, they require additional data representing those cases which can be costly to gather. In this work, a solution to this data acquisition problem is suggested by way …

Meta-Algorithms In Machine Learning, Vishnu Pendyala

Open Educational Resources

This presentation explores how to make the best of models impacted by bias and variance. Meta-learning minimizes loss. Ensemble methods, including Bagging, Adaboost, Random Forest, Gradient Boosting, and Stacking, are discussed. These methods perturb data (X or Y) using techniques like bootstrap sampling, k-fold sampling, weighted sampling, and random subspaces. Models are generated in parallel or sequentially, with aggregation strategies such as mean, mode, weighted response, and metamodel. The presentation also touches upon deep learning and one-shot learning, and explains how distances become less meaningful in high dimensions.

More details: https://events.vtools.ieee.org/m/315184

Video Recording: https://ieeetv.ieee.org/video/meta-algorithms-in-machine-learning

Anomaly Detection In Rotating Machinery Using Autoencoders Based On Bidirectional Lstm And Gru Neural Networks, Krishna Patra, Rabi Narayan Sethi, Dhiren Kkumar Behera

Turkish Journal of Electrical Engineering and Computer Sciences

A time series anomaly is a form of anomalous subsequence that indicates future faults will occur. The development of novel techniques for detecting this type of anomaly is significant for real-time system monitoring. Several algorithms have been used to classify anomalies successfully. However, the time series anomaly detection algorithm was not studied well. We use a new bidirectional LSTM and GRU neural networks-based hybrid autoencoder to detect if a machine is operating normally in this research. An autoencoder is trained on a set of 12 features taken from healthy operating data gathered promptly after a planned maintenance period using vibration …

Modeling And Analysis Of Subcellular Protein Localization In Hyper-Dimensional Fluorescent Microscopy Images Using Deep Learning Methods, Yang Jiao

UNLV Theses, Dissertations, Professional Papers, and Capstones

Hyper-dimensional images are informative and become increasingly common in biomedical research. However, the machine learning methods of studying and processing the hyper-dimensional images are underdeveloped. Most of the methods only model the mapping functions between input and output by focusing on the spatial relationship, whereas neglect the temporal and causal relationships. In many cases, the spatial, temporal, and causal relationships are correlated and become a relationship complex. Therefore, only modeling the spatial relationship may result in inaccurate mapping function modeling and lead to undesired output. Despite the importance, there are multiple challenges on modeling the relationship complex, including the model …

Digitalization Of Construction Project Requirements Using Natural Language Processing (Nlp) Techniques, Fahad Ul Hassan

All Dissertations

Contract documents are a critical legal component of a construction project that specify all wishes and expectations of the owner toward the design, construction, and handover of a project. A single contract package, especially of a design-build (DB) project, comprises hundreds of documents including thousands of requirements. Precise comprehension and management of the requirements are critical to ensure that all important explicit and implicit requirements of the project scope are captured, managed, and completed. Since requirements are mainly written in a natural human language, the current manual methods impose a significant burden on practitioners to process and restructure them into …

Estimating Animal Pose Using Deep Learning A Trained Deep Learning Model Outperforms Morphological Analysis, Sanghoon Lee, Jarod Banzon, Kevin Le, Dal Hyung Kim

Computer Sciences and Electrical Engineering Faculty Research

INTRODUCTION: Analyzing animal behavior helps researchers understand their decision-making process and helper tools are rapidly becoming an indispensable part of many interdisciplinary studies. However, researchers are often challenged to estimate animal pose because of the limitation of the tools and its vulnerability to a specific environment. Over the years, deep learning has been introduced as an alternative solution to overcome these challenges.

OBJECTIVES: This study investigates how deep learning models can be applied for the accurate prediction of animal behavior, comparing with traditional morphological analysis based on image pixels.

METHODS: Transparent Omnidirectional Locomotion Compensator (TOLC), a tracking device, is used …

Radar Remote Sensing Data Augmentation Method Based On Generative Adversarial Network, Xu Kang, Xiaofeng Zhang

Journal of System Simulation

Abstract: In the research field of radar remote sensing, both the completeness and diversity of radar data samples cannot meet the requirement of effective training of deep learning models, and the models are prone to over-fitting, which significantly limits the wide application of deep learning techniques in this field. Targeting on the needs of intelligent application in radar remote sensing, a microwave imaging radar suited data augmentation method is proposed to solve the issue of insufficient radar data samples by leveraging the general framework of generative adversarial network. Aiming at the features of radar samples being not obvious, the label …

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …

A Deep Learning-Based Approach To Extraction Of Filler Morphology In Sem Images With The Application Of Automated Quality Inspection, Md. Fashiar Rahman, Tzu-Liang Bill Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin

Engineering Faculty Articles and Research

Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system …

Performance Analysis And Feature Selection For Network-Based Intrusion Detectionwith Deep Learning, Serhat Caner, Nesli̇ Erdoğmuş, Yusuf Murat Erten

Turkish Journal of Electrical Engineering and Computer Sciences

An intrusion detection system is an automated monitoring tool that analyzes network traffic and detects malicious activities by looking out either for known patterns of attacks or for an anomaly. In this study, intrusion detection and classification performances of different deep learning based systems are examined. For this purpose, 24 deep neural networks with four different architectures are trained and evaluated on CICIDS2017 dataset. Furthermore, the best performing model is utilized to inspect raw network traffic features and rank them with respect to their contributions to success rates. By selecting features with respect to their ranks, sets of varying size …

Biometric Identification Using Panoramic Dental Radiographic Images Withfew-Shot Learning, Musa Ataş, Cüneyt Özdemi̇r, İsa Ataş, Burak Ak, Esma Özeroğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Determining identity is a crucial task especially in the cases of mass disasters such as tsunamis, earthquakes, fires, epidemics, and in forensics. Although there are various studies in the literature on biometric identification from radiographic dental images, more research is still required. In this study, a panoramic dental radiographic (PDR) imagebased human identification system was developed using a customized deep convolutional neural network model in a few-shot learning scheme. The proposed model (PDR-net) was trained on 600 PDR images obtained from a total of 300 patients. As the PDR images of the patients were very different in terms of pose …

Extractive Text Summarization On Single Documents Using Deep Learning, Shehab Mostafa Abdel-Salam Mohamed

Theses and Dissertations

The task of summarization can be categorized into two methods, extractive and abstractive summarization. Extractive approach selects highly meaningful sentences to form a summary while the abstractive approach interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches such as statistical-based, graph-based, and deep-learning based approaches. Deep learning has achieved promising performance in comparison with the classical approaches and with the evolution of neural networks such as the attention network or commonly known as the Transformer architecture, there are potential areas for …

Brief Review On Applying Reinforcement Learning To Job Shop Scheduling Problems, Xiaohan Wang, Zhang Lin, Ren Lei, Kunyu Xie, Kunyu Wang, Ye Fei, Chen Zhen

Journal of System Simulation

Abstract: Reinforcement Learning (RL) achieves lower time response and better model generalization in Job Shop Scheduling Problem (JSSP). To explain the current overall research status of JSSP based on RL, summarize the current scheduling framework based on RL, and lay the foundation for follow-up research, the backgrounds of JSSP and RL are introduced. Two simulation techniques commonly used in JSSP are analyzed and two commonly used frameworks for RL to solve JSSP are given. In addition, some existing challenges are pointed out, and related research progress is introduced from three aspects: direct scheduling, feature representation-based scheduling, and parameter search-based scheduling.

Study On Prediction Of Crystal Properties Based On Deep Learning, Buwei Wang, Wang Min, Fan Qian, Ya'nan Wang, Hanwen Zhang, Yunliang Yue

Journal of System Simulation

Abstract: Predicting crystal properties using traditional machine learning methods requires complex feature engineering. In order to bypass time-consuming feature engineering, element network (ElemNet), representation learning from stoichiometry (Roost), compositionally-restricted attention-based network (CrabNet) and crystal graph convolution neural network (CGCNN) based on deep learning technology are used to simulate the formation energy, total energy per atom, band gap, and Fermi energy of crystal. The residual learning is introduced into CGCNN, and a crystal graph convolution residual neural network (CGCRN) is proposed. In the CGCRN, the number of hidden layers and the number of nodes in the hidden layers are increased, …