Artificial Intelligence and Robotics Commons^™

An Improved Clipped Sub-Histogram Equalization Technique Using Optimized Local Contrast Factor For Mammogram Image Analysis, Nurshafira Hazim Chan

Student Works (2020-2029)

Mammography has been known worldwide as the most common imaging modalities utilized for early detection of breast cancer. The mammographic images produced are in greyscale, however they often produced poor contrast images, non-uniform illumination, and the image often contain artefacts and noise. These limitations can be overcame during the pre-processing stage by improving the image enhancement process. Therefore, in this research an optimized enhancement framework is developed where the local contrast factor is manipulated to preserve details of the image. This technique aims to improve the overall image visibility without altering histogram of the original image, which will affect the …

Exploring The Use Of Neural Transformers For Psycholinguistics, Antonio Laverghetta Jr.

USF Tampa Graduate Theses and Dissertations

Deep learning has the potential to help solve numerous problems in cognitive science andeducation, by providing us a way to model the cognitive profiles of individual people. If this were possible, it would allow us to design targeted tests and suggest specific remediation based on each individual’s needs. On the flip side, employing techniques from psychology can give us insight into the underlying skillsets neural networks have acquired during training, addressing the interpretability concern. This thesis explores these ideas in the context of transformer language models, which have achieved state-of-the-art results on virtually every natural language processing (NLP) task. First, …

A Deep Learning Approach To Diagnostic Classification Of Prostate Cancer Using Pathology–Radiology Fusion, Pegah Khosravi, Maria Lysandrou, Mahmoud Eljalby, Qianzi Li, Ehsan Kazemi, Pantelis Zisimopoulos, Alexandros Sigaras, Matthew Brendel, Josue Barnes, Camir Ricketts, Dmitry Meleshko, Andy Yat, Timothy D. Mcclure, Brian D. Robinson, Andrea Sboner, Olivier Elemento, Bilal Chughtai, Iman Hajirasouliha

Publications and Research

Background

A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications.

Purpose

To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information.

Study Type

Retrospective.

Population

Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases).

Field Strength/Sequence

1.5 to 3.0 Tesla, T2-weighted image pulse sequences.

Assessment

MR images reviewed and selected …

Wider Vision: Enriching Convolutional Neural Networks Via Alignment To External Knowledge Bases, Xuehao Liu, Sarah Jane Delany, Susan Mckeever

Conference papers

Deep learning models suffer from opaqueness. For Convolutional Neural Networks (CNNs), current research strategies for explaining models focus on the target classes within the associated training dataset. As a result, the understanding of hidden feature map activations is limited by the discriminative knowledge gleaned during training. The aim of our work is to explain and expand CNNs models via the mirroring or alignment of the network to an external knowledge base. This will allow us to give a semantic context or label for each visual feature. Using the resultant aligned embedding space, we can match CNN feature activations to nodes …

Node Classification On Relational Graphs Using Deep-Rgcns, Nagasai Chandra

Master's Theses

Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data characteristic of realistic knowledge graphs for node property classification tasks. We propose a deep and improved variant, …

Deep Learning For Anomaly Detection: Challenges, Methods, And Opportunities, Guansong Pang, Longbing Cao, Charu Aggarwal

Research Collection School Of Computing and Information Systems

In this tutorial we aim to present a comprehensive survey of the advances in deep learning techniques specifically designed for anomaly detection (deep anomaly detection for short). Deep learning has gained tremendous success in transforming many data mining and machine learning tasks, but popular deep learning techniques are inapplicable to anomaly detection due to some unique characteristics of anomalies, e.g., rarity, heterogeneity, boundless nature, and prohibitively high cost of collecting large-scale anomaly data. Through this tutorial, audiences would gain a systematic overview of this area, learn the key intuitions, objective functions, underlying assumptions, advantages and disadvantages of different categories of …

Brain Tumor Detection And Classification From Mri Images, Anjaneya Teja Sarma Kalvakolanu

Master's Theses

A brain tumor is detected and classified by biopsy that is conducted after the brain surgery. Advancement in technology and machine learning techniques could help radiologists in the diagnosis of tumors without any invasive measures. We utilized a deep learning-based approach to detect and classify the tumor into Meningioma, Glioma, Pituitary tumors. We used registration and segmentation-based skull stripping mechanism to remove the skull from the MRI images and the grab cut method to verify whether the skull stripped MRI masks retained the features of the tumor for accurate classification. In this research, we proposed a transfer learning based approach …

Low-Cost Terrestrial Demonstration Of Autonomous Satellite Proximity Operations, Zackary R. Hewitt

Theses and Dissertations

The lack of satellite servicing capabilities significantly impacts the development and operation of current orbital assets. With autonomous solutions under consideration for servicing, the purpose of this research is to build and validate a low-cost hardware platform to expedite the development of autonomous satellite proximity operations. This research aims to bridge the gap between simulation and existing higher fidelity hardware testing with an affordable alternative. An omnidirectional variant of the commercially available TurtleBot3 mobile robot is presented as a 3-DOF testbed that demonstrates a satellite servicing inspection scenario. Reference trajectories for the scenario are generated via optimal control using the …

Cyclic Pursuit, Daniel E. Oke

Theses and Dissertations

This thesis analyzes cyclic pursuit with the intent of developing swarm attack strategies for autonomous agents. Research was focused on finding the effects of pursuers capture range, evader speed and size of formation on the probability of escape. The temporal evolution of several polygonal formations was analyzed. The polygons could be regular or arbitrary polygons. The thesis demonstrated that an increased capture range, formation size, reduced evader speed aided capture probability. Irregular n-gon formations reduced to n-1 gon repeatedly, pursuer clusters formed until two clusters remained which eventually came together, so all the n pursuers coalesced until convergence. Regular n-gon …

Learning To Assess The Quality Of Stroke Rehabilitation Exercises, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia

Research Collection School Of Computing and Information Systems

Due to the limited number of therapists, task-oriented exercises are often prescribed for post-stroke survivors as in-home rehabilitation. During in-home rehabilitation, a patient may become unmotivated or confused to comply prescriptions without the feedback of a therapist. To address this challenge, this paper proposes an automated method that can achieve not only qualitative, but also quantitative assessment of stroke rehabilitation exercises. Specifically, we explored a threshold model that utilizes the outputs of binary classifiers to quantify the correctness of a movements into a performance score. We collected movements of 11 healthy subjects and 15 post-stroke survivors using a Kinect sensor …

Wg2An: Synthetic Wound Image Generation Using Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Ozgur Guler

Engineering Technology Faculty Publications

In part due to its ability to mimic any data distribution, Generative Adversarial Network (GAN) algorithms have been successfully applied to many applications, such as data augmentation, text-to-image translation, image-to-image translation, and image inpainting. Learning from data without crafting loss functions for each application provides broader applicability of the GAN algorithm. Medical image synthesis is also another field that the GAN algorithm has great potential to assist clinician training. This paper proposes a synthetic wound image generation model based on GAN architecture to increase the quality of clinical training. The proposed model is trained on chronic wound datasets with various …

How Do Users Answer Matlab Questions On Q&A Sites? A Case Study On Stack Overflow And Mathworks, Mahshid Naghashzadeh, Amir Hagshenas, Ashkan Sami, David Lo

Research Collection School Of Computing and Information Systems

MATLAB is an engineering programming language with various toolboxes that has a dedicated Question and Answer (Q&A) platform on the MathWorks website, which is similar to Stack Overflow (SO). Moreover, some MATLAB users ask their questions on SO. This paper aims to compare these two Q&A platforms to see what kind of questions are asked and how developers answer these questions in each platform. The result of our analysis on 80,382 MATLAB questions on SO and 266,367 questions on MathWorks show that MATLAB questions on topics ranging from the MATLAB software installation to questions related to programming received high votes …

Is The Ground Truth Really Accurate? Dataset Purification For Automated Program Repair, Deheng Yang, Yan Lei, Xiaoguang Mao, David Lo, Huan Xie, Meng Yan

Research Collection School Of Computing and Information Systems

Datasets of real-world bugs shipped with human-written patches are intensively used in the evaluation of existing automated program repair (APR) techniques, wherein the human-written patches always serve as the ground truth, for manual or automated assessment approaches, to evaluate the correctness of test-suite adequate patches. An inaccurate human-written patch tangled with other code changes will pose threats to the reliability of the assessment results. Therefore, the construction of such datasets always requires much manual effort on isolating real bug fixes from bug fixing commits. However, the manual work is time-consuming and prone to mistakes, and little has been known on …

A High-Precision Machine Learning Algorithm To Classify Left And Right Outflow Tract Ventricular Tachycardia, Jianwei Zhang, Guohua Fu, Islam Abudayyeh, Magdi Yacoub, Anthony Chang, William Feaster, Louis Ehwerhemuepha, Hesham El-Askary, Xianfeng Du, Bin He, Mingjun Feng, Yibo Yu, Binhao Wang, Jing Liu, Hai Yao, Hulmin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Introduction: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods: We randomly sampled training, validation, and testing …

Operational Structural Damage Identification Using De-Noised Modal Feature In Machine Learning, Chen Shilei

Student Works (2020-2029)

Structural damage can severely affect the safety and functionality of the structure and lead to economic loss. Vibration-based structural damage detection has raised continuous interest over the decades, as a non-destructive way to provide warnings and predict certain faults at early stages. Compared with conventional modal parameters such as the natural frequency and mode shape, the upstream modal data, namely the frequency response function (FRF), can be a better alternative, because it is rich in modal information and can be easily obtained. However, the FRF is usually measured through experimental modal analysis (EMA) when the test object is in shut-down …

An Improved Firefly Algorithm And Its Application In Washout Optimization, Wang Hui, Xingshun Lü

Journal of System Simulation

Abstract: To improve the accuracy of the firefly algorithm (FA) and solve the problem of fixed iteration step of the algorithm and easy to fall into local optimum, an improved firefly algorithm is proposed, i.e., EOFA. The EOFA algorithm combines the strong local search ability of extremal optimization algorithm with the strong search ability of firefly algorithm, and adopts the iterative step size of inverted s-type function to improve the optimization ability of the firefly algorithm. The simulation results of function optimization test shows that the improved EOFA algorithm has better optimization performance than firefly algorithm and particle swarm optimization …

Spatio-Temporal Memory Models In Spatial Cognitive Behavioral Modeling, Hu Yue, Xu Kai, Qin Long, Quanjun Yin, Yabing Zha

Journal of System Simulation

Abstract: It is crucial for military simulation systems to model the layouts of spatial environments and the temporal logic of entity activities. To improve the human-likeness of Computer Generated Forces (CGFs) and the runtime efficiency of the systems, building spatio-temporal memory representations and reasoning mechanisms that accord to mankind cognition is required. The neuro cognition science background of spatio-temporal memory is presented, and the related work on modeling the memory in deep learning and computational intelligence communities is summarized. Following the OODA (Observe-Orient-Decide-Act) loop, a general framework of spatial cognitive behavioral modeling is designed and the application of spatio-temporal memory …

Research On Cgf-Oriented Virtual Human Perceptual Attention Model, Weilong Yang, Xu Kai, Xie Xu, Sun Lin

Journal of System Simulation

Abstract: Perception model is an important research field of CGF behavior modeling. As the input of the reasoning module, the information obtained from the perception model has an important influence on the fidelity of the simulation. The CGF perception model is researched, and the attention mechanism, effective time of attention model is analyzed, compared with the traditional model of perception. By modeling the knowledge base and information feedback, a virtual human perception framework based on attention mechanism is proposed, and the experiment is carried out in the simulation environment.

Modeling Of Adversarial Behavior On Road Network Based On Non-Cooperative Game, Xiangyu Wei, Zhang Qi

Journal of System Simulation

Abstract: The modeling of adversarial behavior is the key to the study of various military-like confrontation problems. Existing research mainly focus on the target domain, but in reality, many confrontation problems occur on the road network. Combined with the network flow representation of adversarial behavior, a network adversarial game modeling framework based on non-cooperative games is proposed, and a novel road network confrontation problem—network evasion interdiction game is given based on the framework. Simulation experiments show that the new double oracle algorithm performs better than the original linear solution algorithm. Data experiments based on real road networks further verify …

Preliminary Research On Verification And Validation For Cgf Behavioral Modeling, Jianbing Tang, Zhang Qi, Jiao Peng, Yabing Zha

Journal of System Simulation

Abstract: Building computer generated force model is a very important job in combat simulation. Its core content is modeling of complicated human behavior, including cognition behavioral modeling and physical behavioral modeling. The credibility is vital for behavioral model. Verification and validation (V&V) in all life circle of behavioral modeling can insure the credibility of behavioral model. On the basis of explaining V&V of behavioral modeling and other conceptions, the process model of behavioral modeling is proposed. The V&V process of CGF behavioral modeling is studied emphasizly. Many effective V&V methods for CGF behavioral modeling are explored.