Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Computer Sciences (25)
- Physical Sciences and Mathematics (25)
- Electrical and Computer Engineering (18)
- Other Computer Engineering (10)
- Artificial Intelligence and Robotics (7)
-
- Life Sciences (3)
- Numerical Analysis and Scientific Computing (3)
- Operations Research, Systems Engineering and Industrial Engineering (3)
- Robotics (3)
- Systems Science (3)
- Biomedical Engineering and Bioengineering (2)
- Civil and Environmental Engineering (2)
- Digital Communications and Networking (2)
- Library and Information Science (2)
- Medicine and Health Sciences (2)
- Other Computer Sciences (2)
- Plant Sciences (2)
- Social and Behavioral Sciences (2)
- Agricultural Science (1)
- Analytical, Diagnostic and Therapeutic Techniques and Equipment (1)
- Anatomy (1)
- Biomedical (1)
- Biomedical Devices and Instrumentation (1)
- Business (1)
- Cardiovascular Diseases (1)
- Cataloging and Metadata (1)
- Civil Engineering (1)
- Computer and Systems Architecture (1)
- Institution
-
- TÜBİTAK (11)
- China Simulation Federation (3)
- New Jersey Institute of Technology (3)
- University of Nevada, Las Vegas (3)
- Florida International University (2)
-
- Technological University Dublin (2)
- University of Denver (2)
- Wright State University (2)
- Air Force Institute of Technology (1)
- Chapman University (1)
- Edith Cowan University (1)
- Georgia Southern University (1)
- Kennesaw State University (1)
- Marquette University (1)
- Old Dominion University (1)
- Portland State University (1)
- Union College (1)
- University of Georgia School of Law (1)
- University of Kentucky (1)
- University of Louisville (1)
- University of Massachusetts Amherst (1)
- University of Nebraska - Lincoln (1)
- University of New Mexico (1)
- University of South Carolina (1)
- University of the Pacific (1)
- Washington University in St. Louis (1)
- Western University (1)
- Publication
-
- Turkish Journal of Electrical Engineering and Computer Sciences (11)
- Electronic Theses and Dissertations (4)
- Dissertations (3)
- Journal of System Simulation (3)
- Browse all Theses and Dissertations (2)
-
- Conference papers (2)
- UNLV Theses, Dissertations, Professional Papers, and Capstones (2)
- Articles, Chapters and Online Publications (1)
- Computer Science ETDs (1)
- Electrical & Computer Engineering Faculty Publications (1)
- Electrical and Computer Engineering Faculty Research and Publications (1)
- Electronic Thesis and Dissertation Repository (1)
- Engineering Faculty Articles and Research (1)
- FIU Electronic Theses and Dissertations (1)
- Honors Theses (1)
- Library Philosophy and Practice (e-journal) (1)
- Master of Science in Computer Science Theses (1)
- Masters Theses (1)
- McKelvey School of Engineering Theses & Dissertations (1)
- Public Health Faculty Publications (1)
- Publications (1)
- Theses and Dissertations (1)
- Theses and Dissertations--Computer Science (1)
- Theses: Doctorates and Masters (1)
- Undergraduate Research & Mentoring Program (1)
- University of the Pacific Theses and Dissertations (1)
- Works of the FIU Libraries (1)
- Publication Type
Articles 1 - 30 of 47
Full-Text Articles in Computer Engineering
Intelligent Networks For High Performance Computing, William Whitney Schonbein
Intelligent Networks For High Performance Computing, William Whitney Schonbein
Computer Science ETDs
There exists a resurgence of interest in `smart' network interfaces that can operate on data as it flows through a network. However, while smart capabilities have been expanding, what they can do for high-performance computing (HPC) is not well-understood. In this work, we advance our understanding of the capabilities and contributions of smart network interfaces to HPC. First, we show current offloaded message demultiplexing can mitigate (but not eliminate) overheads incurred by multithreaded communication. Second, we demonstrate current offloaded capabilities can be leveraged to provide Turing complete program execution on the interface. We elaborate with a framework for offloading arbitrary …
Machine Learning Prediction Of Shear Capacity Of Steel Fiber Reinforced Concrete, Wassim Ben Chaabene
Machine Learning Prediction Of Shear Capacity Of Steel Fiber Reinforced Concrete, Wassim Ben Chaabene
Electronic Thesis and Dissertation Repository
The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to …
Geographic Data Mining And Knowledge Discovery, Liangdong Deng
Geographic Data Mining And Knowledge Discovery, Liangdong Deng
FIU Electronic Theses and Dissertations
Geographic data are information associated with a location on the surface of the Earth. They comprise spatial attributes (latitude, longitude, and altitude) and non-spatial attributes (facts related to a location). Traditionally, Physical Geography datasets were considered to be more valuable, thus attracted most research interest. But with the advancements in remote sensing technologies and widespread use of GPS enabled cellphones and IoT (Internet of Things) devices, recent years witnessed explosive growth in the amount of available Human Geography datasets. However, methods and tools that are capable of analyzing and modeling these datasets are very limited. This is because Human Geography …
Flight Simulator Modeling Using Recurrent Neural Networks, Nickolas Sabatini, Andreas Natsis
Flight Simulator Modeling Using Recurrent Neural Networks, Nickolas Sabatini, Andreas Natsis
Undergraduate Research & Mentoring Program
Recurrent neural networks (RNNs) are a form of machine learning used to predict future values. This project uses RNNs tor predict future values for a flight simulator. Coded in Python using the Keras library, the model demonstrates training loss and validation loss, referring to the error when training the model.
A Bibliometric Survey On The Reliable Software Delivery Using Predictive Analysis, Jalaj Pachouly, Swati Ahirrao, Ketan Kotecha
A Bibliometric Survey On The Reliable Software Delivery Using Predictive Analysis, Jalaj Pachouly, Swati Ahirrao, Ketan Kotecha
Library Philosophy and Practice (e-journal)
Delivering a reliable software product is a fairly complex process, which involves proper coordination from the various teams in planning, execution, and testing for delivering software. Most of the development time and the software budget's cost is getting spent finding and fixing bugs. Rework and side effect costs are mostly not visible in the planned estimates, caused by inherent bugs in the modified code, which impact the software delivery timeline and increase the cost. Artificial intelligence advancements can predict the probable defects with classification based on the software code changes, helping the software development team make rational decisions. Optimizing the …
Embedded Power Optimization Method Based On User Behavior, Wang Hai, Gao Ling, Dongqi Chen, Ren Jie
Embedded Power Optimization Method Based On User Behavior, Wang Hai, Gao Ling, Dongqi Chen, Ren Jie
Journal of System Simulation
Abstract: In recent years, with the rapid development of embedded device represented by mobile phone and tablet computer, low power technology has been one of the hotspots in the embedded research field. Because the battery capacity of embedded device is limited due to its restricted volume and weight, there are often users suffering the problem that their phone battery being dead. There are many research directions in embedded low power field at present. The relationship between low power and user behavior recognition was aimed, which started with recognizing user behavior using machine learning and then obtains the user’s daily usage …
Joint 1d And 2d Neural Networks For Automatic Modulation Recognition, Luis M. Rosario Morel
Joint 1d And 2d Neural Networks For Automatic Modulation Recognition, Luis M. Rosario Morel
Theses and Dissertations
The digital communication and radar community has recently manifested more interest in using data-driven approaches for tasks such as modulation recognition, channel estimation and distortion correction. In this research we seek to apply an object detector for parameter estimation to perform waveform separation in the time and frequency domain prior to classification. This enables the full automation of detecting and classifying simultaneously occurring waveforms. We leverage a lD ResNet implemented by O'Shea et al. in [1] and the YOLO v3 object detector designed by Redmon et al. in [2]. We conducted an in depth study of the performance of these …
Hybrid Deep Neural Networks For Mining Heterogeneous Data, Xiurui Hou
Hybrid Deep Neural Networks For Mining Heterogeneous Data, Xiurui Hou
Dissertations
In the era of big data, the rapidly growing flood of data represents an immense opportunity. New computational methods are desired to fully leverage the potential that exists within massive structured and unstructured data. However, decision-makers are often confronted with multiple diverse heterogeneous data sources. The heterogeneity includes different data types, different granularities, and different dimensions, posing a fundamental challenge in many applications. This dissertation focuses on designing hybrid deep neural networks for modeling various kinds of data heterogeneity.
The first part of this dissertation concerns modeling diverse data types, the first kind of data heterogeneity. Specifically, image data and …
Live Media Production: Multicast Optimization And Visibility For Clos Fabric In Media Data Centers, Ammar Latif
Live Media Production: Multicast Optimization And Visibility For Clos Fabric In Media Data Centers, Ammar Latif
Dissertations
Media production data centers are undergoing a major architectural shift to introduce digitization concepts to media creation and media processing workflows. Content companies such as NBC Universal, CBS/Viacom and Disney are modernizing their workflows to take advantage of the flexibility of IP and virtualization.
In these new environments, multicast is utilized to provide point-to-multi-point communications. In order to build point-to-multi-point trees, Multicast has an established set of control protocols such as IGMP and PIM. The existing multicast protocols do not optimize multicast tree formation for maximizing network throughput which lead to decreased fabric utilization and decreased total number of admitted …
Changing The Focus: Worker-Centric Optimization In Human-In-The-Loop Computations, Mohammadreza Esfandiari
Changing The Focus: Worker-Centric Optimization In Human-In-The-Loop Computations, Mohammadreza Esfandiari
Dissertations
A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back …
Water Quality Prediction Based On Machine Learning Techniques, Zhao Fu
Water Quality Prediction Based On Machine Learning Techniques, Zhao Fu
UNLV Theses, Dissertations, Professional Papers, and Capstones
Water is one of the most important natural resources for all living organisms on earth. The monitoring of treated wastewater discharge quality is vitally important for the stability and protection of the ecosystem. Collecting and analyzing water samples in the laboratory consumes much time and resources. In the last decade, many machine learning techniques, like multivariate linear regression (MLR) and artificial neural network (ANN) model, have been proposed to address the problem. However, simple linear regression analysis cannot accurately forecast water quality because of complicated linear and nonlinear relationships in the water quality dataset. The ANN model also has shortcomings …
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Public Health Faculty Publications
The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …
Sundown: Model-Driven Per-Panel Solar Anomaly Detection For Residential Arrays, Menghong Feng
Sundown: Model-Driven Per-Panel Solar Anomaly Detection For Residential Arrays, Menghong Feng
Masters Theses
There has been significant growth in both utility-scale and residential-scale solar installa- tions in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential- scale installations often lack sensing and instrumentation for performance monitoring and fault detection. As a result, faults may go undetected for long periods of time, resulting in generation and revenue losses for the homeowner. In this thesis, we present SunDown, a sensorless approach designed to detect per-panel faults in residential solar arrays. SunDown does not require any new sensors for its fault detection and …
Gep Automatic Clustering Algorithm With Dynamic Penalty Factors, Chen Yan, Kangshun Li, Yang Lei
Gep Automatic Clustering Algorithm With Dynamic Penalty Factors, Chen Yan, Kangshun Li, Yang Lei
Journal of System Simulation
Abstract: Various problems such as sensitive selection of initial clustering center, easily falling into local optimal solution, and determining numbers of clusters, still exist in the traditional clustering algorithm. A GEP automatic clustering algorithm with dynamic penalty factors was proposed. This algorithm combines penalty factors and GEP clustering algorithm, and doesn't rely on any priori knowledge of the data set. And a dynamic algorithm was proposed to generate the penalty factors according to the distribution characteristics of different data sets, which is a better solution for the impact of isolated points and noise points. According to four dataset, penalty factors' …
Rethinking The Weakness Of Stream Ciphers And Its Application To Encrypted Malware Detection, William T. Stone, Junggab Son
Rethinking The Weakness Of Stream Ciphers And Its Application To Encrypted Malware Detection, William T. Stone, Junggab Son
Master of Science in Computer Science Theses
Encryption key use is a critical component to the security of a stream cipher: because many implementations simply consist of a key scheduling algorithm and logical exclusive or (XOR), an attacker can completely break the cipher by XORing two ciphertexts encrypted under the same key, revealing the original plaintexts and the key itself. The research presented in this paper reinterprets this phenomenon, using repeated-key cryptanalysis for stream cipher identification. It has been found that a stream cipher executed under a fixed key generates patterns in each character of the ciphertexts it produces and that these patterns can be used to …
Moving Targets: Addressing Concept Drift In Supervised Models For Hacker Communication Detection, Susan Mckeever, Brian Keegan, Andrei Quieroz
Moving Targets: Addressing Concept Drift In Supervised Models For Hacker Communication Detection, Susan Mckeever, Brian Keegan, Andrei Quieroz
Conference papers
Abstract—In this paper, we are investigating the presence of concept drift in machine learning models for detection of hacker communications posted in social media and hacker forums. The supervised models in this experiment are analysed in terms of performance over time by different sources of data (Surface web and Deep web). Additionally, to simulate real-world situations, these models are evaluated using time-stamped messages from our datasets, posted over time on social media platforms. We have found that models applied to hacker forums (deep web) presents an accuracy deterioration in less than a 1-year period, whereas models applied to Twitter (surface …
Conference Roundup: Smart Cataloging - Beginning The Move From Batch Processing To Automated Classification, Rachel S. Evans
Conference Roundup: Smart Cataloging - Beginning The Move From Batch Processing To Automated Classification, Rachel S. Evans
Articles, Chapters and Online Publications
This article reviewed the Amigos Online Conference titled “Work Smarter, Not Harder: Innovating Technical Services Workflows” keynote session delivered by Dr. Terry Reese on February 13, 2020. Excerpt:
"As the developer of MarcEdit, a popular metadata suite used widely across the library community, Reese’s current work is focused on the ways in which libraries might leverage semantic web techniques in order to transform legacy library metadata into something new. So many sessions related to using new technologies in libraries or academia, although exciting, are not practical enough to put into everyday use by most librarians. Reese’s keynote, titled Smart Cataloging: …
Human Facial Emotion Recognition System In A Real-Time, Mobile Setting, Claire Williamson
Human Facial Emotion Recognition System In A Real-Time, Mobile Setting, Claire Williamson
Honors Theses
The purpose of this project was to implement a human facial emotion recognition system in a real-time, mobile setting. There are many aspects of daily life that can be improved with a system like this, like security, technology and safety.
There were three main design requirements for this project. The first was to get an accuracy rate of 70%, which must remain consistent for people with various distinguishing facial features. The second goal was to have one execution of the system take no longer than half of a second to keep it as close to real time as possible. Lastly, …
Alexa, Ask My Library: How Do I Build A Custom Skill To Extend Reference Services?, Christopher M. Jimenez
Alexa, Ask My Library: How Do I Build A Custom Skill To Extend Reference Services?, Christopher M. Jimenez
Works of the FIU Libraries
The Reference Technology team at Florida International University recently published an Alexa Skill that incorporates the LibAnswers API into the device’s answer bank. We have several Echo Show devices at our public service desks to meet the demands of extended hours while also enhancing public service presence beyond the reference desk.
The Green Library at FIU’s Modesto Maidique Campus now operates on a 24/5 schedule, allowing students to access library facilities at any time during the week. In addition, both the Hubert Library and the Engineering Library Service Center stay open past times when personal reference assistance is available. This …
Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead
Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead
Engineering Faculty Articles and Research
Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …
Computational Techniques In Medical Image Analysis Application For White Blood Cells Classification., Omar Dekhil
Computational Techniques In Medical Image Analysis Application For White Blood Cells Classification., Omar Dekhil
Electronic Theses and Dissertations
White blood cells play important rule in the human body immunity and any change in their count may cause serious diseases. In this study, a system is introduced for white blood cells localization and classification. The dataset used in this study is formed by two components, the first is the annotation dataset that will be used in the localization (364 images), and the second is labeled classes that will be used in the classification (12,444 images). For the localization, two approaches will be discussed, a classical approach and a deep learning based approach. For the classification, 5 different deep learning …
A Framework For Vector-Weighted Deep Neural Networks, Carter Chiu
A Framework For Vector-Weighted Deep Neural Networks, Carter Chiu
UNLV Theses, Dissertations, Professional Papers, and Capstones
The vast majority of advances in deep neural network research operate on the basis of a real-valued weight space. Recent work in alternative spaces have challenged and complemented this idea; for instance, the use of complex- or binary-valued weights have yielded promising and fascinating results. We propose a framework for a novel weight space consisting of vector values which we christen VectorNet. We first develop the theoretical foundations of our proposed approach, including formalizing the requisite theory for forward and backpropagating values in a vector-weighted layer. We also introduce the concept of expansion and aggregation functions for conversion between real …
Knowledge Infused Learning (K-Il): Towards Deep Incorporation Of Knowledge In Deep Learning, Ugur Kursuncu, Manas Gaur, Amit Sheth
Knowledge Infused Learning (K-Il): Towards Deep Incorporation Of Knowledge In Deep Learning, Ugur Kursuncu, Manas Gaur, Amit Sheth
Publications
Learning the underlying patterns in data goes beyondinstance-based generalization to external knowledge repre-sented in structured graphs or networks. Deep learning thatprimarily constitutes neural computing stream in AI hasshown significant advances in probabilistically learning la-tent patterns using a multi-layered network of computationalnodes (i.e., neurons/hidden units). Structured knowledge thatunderlies symbolic computing approaches and often supportsreasoning, has also seen significant growth in recent years,in the form of broad-based (e.g., DBPedia, Yago) and do-main, industry or application specific knowledge graphs. Acommon substrate with careful integration of the two willraise opportunities to develop neuro-symbolic learning ap-proaches for AI, where conceptual and probabilistic repre-sentations are combined. …
Monocular Depth Image Mark-Less Pose Estimation Based On Feature Regression, Chen Ying, Shen Li
Monocular Depth Image Mark-Less Pose Estimation Based On Feature Regression, Chen Ying, Shen Li
Journal of System Simulation
Abstract: Monocular camera mark-less pose estimation system suffers low accuracy, robustness and efficiency due to variety of action, self-occlusion of human body. A method of feature exaction from point clouds was proposed, in which a single-to-multiple (S2M) feature regressor and a joint position regressor were designed to quickly and accurately predict the 3D positions of body joints from a single depth image without any temporal information. Experiment result shows that the estimation accuracy is superior to that of state-of-the-arts and multi-camera based methods.
Investigating Patterns In Convolution Neural Network Parameters Using Probabilistic Support Vector Machines, Yuqiu Zhang
Investigating Patterns In Convolution Neural Network Parameters Using Probabilistic Support Vector Machines, Yuqiu Zhang
McKelvey School of Engineering Theses & Dissertations
Artificial neural networks(ANNs) are recognized as high-performance models for classification problems. They have proved to be efficient tools for many of today's applications like automatic driving, image and video recognition and restoration, big-data analysis. However, high performance deep neural networks have millions of parameters, and the iterative training procedure thus involves a very high computational cost. This research attempts to study the relationships between parameters in convolutional neural networks(CNNs). I assume there exists a certain relation between adjacent convolutional layers and proposed a machine learning model(MLM) that can be trained to represent this relation. The MLM's generalization ability is evaluated …
Ai-Assisted Network-Slicing Based Next-Generation Wireless Networks, Xuemin Shen, Jie Gao, Wen Wu, Kangjia Lyu, Mushu Li, Weihua Zhuang, Xu Li, Jaya Rao
Ai-Assisted Network-Slicing Based Next-Generation Wireless Networks, Xuemin Shen, Jie Gao, Wen Wu, Kangjia Lyu, Mushu Li, Weihua Zhuang, Xu Li, Jaya Rao
Electrical and Computer Engineering Faculty Research and Publications
The integration of communications with different scales, diverse radio access technologies, and various network resources renders next-generation wireless networks (NGWNs) highly heterogeneous and dynamic. Emerging use cases and applications, such as machine to machine communications, autonomous driving, and factory automation, have stringent requirements in terms of reliability, latency, throughput, and so on. Such requirements pose new challenges to architecture design, network management, and resource orchestration in NGWNs. Starting from illustrating these challenges, this paper aims at providing a good understanding of the overall architecture of NGWNs and three specific research problems under this architecture. First, we introduce a network-slicing based …
Improving Pain Management In Patients With Sickle Cell Disease Using Machine Learning Techniques, Fan Yang
Improving Pain Management In Patients With Sickle Cell Disease Using Machine Learning Techniques, Fan Yang
Browse all Theses and Dissertations
Sickle cell disease (SCD) is an inherited red blood cell disorder that can cause a multitude of complications throughout a patient's life. Pain is the most common complication and a significant cause of morbidity. Since pain is a highly subjective experience, both medical providers and patients express difficulty in determining ideal treatment and management strategies for pain. Therefore, the development of objective pain assessment and pain forecasting methods is critical to pain management in SCD. On the other hand, the rapidly increasing use of mobile health (mHealth) technology and wearable devices gives the ability to build a remote health intervention …
Automated Testing And Bug Reproduction Of Android Apps, Yu Zhao
Automated Testing And Bug Reproduction Of Android Apps, Yu Zhao
Theses and Dissertations--Computer Science
The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). The corresponding increase in app complexity has made app testing and maintenance activities more challenging. During app development phase, developers need to test the app in order to guarantee its quality before releasing it to the market. During the deployment phase, developers heavily rely on bug reports to reproduce failures reported by users. Because of the rapid releasing cycle of apps and limited human resources, it is difficult for developers to manually construct test cases for testing the apps or diagnose failures from a …
Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani
Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani
Electronic Theses and Dissertations
Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.
Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …
Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal
Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal
Electronic Theses and Dissertations
The facial features are the most important tool to understand an individual's state of mind. Automated recognition of facial expressions and particularly Facial Action Units defined by Facial Action Coding System (FACS) is challenging research problem in the field of computer vision and machine learning. Researchers are working on deep learning algorithms to improve state of the art in the area. Automated recognition of facial action units has man applications ranging from developmental psychology to human robot interface design where companies are using this technology to improve their consumer devices (like unlocking phone) and for entertainment like FaceApp. Recent studies …