Artificial Intelligence and Robotics Commons^™

Eeg Signals Classification Using Lstm-Based Models And Majority Logic, James A. Orgeron

College of Graduate Studies: Theses & Dissertations

The study of elecroencephalograms (EEGs) has gained enormous interest in the last decade with the increase of computational power and availability of EEG signals collected from various human activities or produced during medical tests. The applicability of analyzing EEG signals ranges from helping impaired people communicate or move (using appropriate medical equipment) to understanding people's feelings and detecting diseases.

We proposed new methodology and models for analyzing and classifying EEG signals collected from individuals observing visual stimuli. Our models rely on powerful Long-Short Term Memory (LSTM) Neural Network models, which are currently the state of the art models for performing …

Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos

Electronic Theses and Dissertations

Recently, strategies of National Basketball Association teams have evolved with the skillsets of players and the emergence of advanced analytics. One of the most effective actions in dynamic offensive strategies in basketball is the dribble hand-off (DHO). This thesis proposes an architecture for a classification pipeline for detecting DHOs in an accurate and automated manner. This pipeline consists of a combination of player tracking data and event labels, a rule set to identify candidate actions, manually reviewing game recordings to label the candidates, and embedding player trajectories into hexbin cell paths before passing the completed training set to the classification …

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 …

Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii

Masters Theses

“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …

Automatic Subtyping Of Individuals With Primary Progressive Aphasia, Charalambos Themistocleous, Bronte Ficek, Kimberly Webster, Dirk B. Den Ouden, Argye Hillis, Kyrana Tsapkini

Communication Sciences and Disorders Faculty Articles and Research

Background:

Objective:

Methods:

Understanding And Predicting Retractions Of Published Work, Sai Ajay Modukuri, Sarah Rajtmajer, Anna Cinzia Squicciarini, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

Recent increases in the number of retractions of published papers reflect heightened attention and increased scrutiny in the scientific process motivated, in part, by the replication crisis. These trends motivate computational tools for understanding and assessment of the scholarly record. Here, we sketch the landscape of retracted papers in the Retraction Watch database, a collection of 19k records of published scholarly articles that have been retracted for various reasons (e.g., plagiarism, data error). Using metadata as well as features derived from full-text for a subset of retracted papers in the social and behavioral sciences, we develop a random forest classifier …

Identification And Classification Of Radio Pulsar Signals Using Machine Learning, Di Pang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Automated single-pulse search approaches are necessary as ever-increasing amount of observed data makes the manual inspection impractical. Detecting radio pulsars using single-pulse searches, however, is a challenging problem for machine learning because pul- sar signals often vary significantly in brightness, width, and shape and are only detected in a small fraction of observed data.

The research work presented in this dissertation is focused on development of ma- chine learning algorithms and approaches for single-pulse searches in the time domain. Specifically, (1) We developed a two-stage single-pulse search approach, named Single- Pulse Event Group IDentification (SPEGID), which automatically identifies and clas- …

Signature Identification And Verification Systems: A Comparative Study On The Online And Offline Techniques, Nehal Hamdy Al-Banhawy, Heba Mohsen, Neveen I. Ghali Prof.

Future Computing and Informatics Journal

Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for preprocessing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and …

Data Mining For Structural Damage Identification Using Hybrid Artificial Neural Network Based Algorithm For Beam And Slab Girder, Gordan Meisam

Student Works (2020-2029)

One of the approaches for structural health monitoring (SHM) consists of two major components, i.e. a network of sensors to collect the response data and an extraction method to obtain information on the structural health condition. Data mining (DM) is a novel data extraction technology which can employ for development of inverse analysis. Implementation of DM techniques in different areas of civil engineering has recently given very good results. However, application of DM in SHM is not used as much as expected, thus, many challenges are still ahead. Therefore, it is necessary to develop the applicability of DM in SHM. …

Computational Astronomy: Classification Of Celestial Spectra Using Machine Learning Techniques, Gayatri Milind Hungund

Master's Projects

Lightyears beyond the Planet Earth there exist plenty of unknown and unexplored stars and Galaxies that need to be studied in order to support the Big Bang Theory and also make important astronomical discoveries in quest of knowing the unknown. Sophisticated devices and high-power computational resources are now deployed to make a positive effort towards data gathering and analysis. These devices produce massive amount of data from the astronomical surveys and the data is usually in terabytes or petabytes. It is exhaustive to process this data and determine the findings in short period of time. Many details can be missed …

A Survey Of Feature Extraction And Fusion Of Deep Learning For Detection Of Abnormalities In Video Endoscopy Of Gastrointestinal-Tract, Hussam Ali, Muhammad Sharif, Mussarat Yasmin, Mubashir Husain Rehmani, Farhan Riaz

Publications

A standard screening procedure involves video endoscopy of the Gastrointestinal tract. It is a less invasive method which is practiced for early diagnosis of gastric diseases. Manual inspection of a large number of gastric frames is an exhaustive, time-consuming task, and requires expertise. Conversely, several computer-aided diagnosis systems have been proposed by researchers to cope with the dilemma of manual inspection of the massive volume of frames. This article gives an overview of different available alternatives for automated inspection, detection, and classification of various GI abnormalities. Also, this work elaborates techniques associated with content-based image retrieval and automated systems for …

A Description Of A Humans Knowledge Using Artificial Intelligence, Dj Price

Mahurin Honors College Capstone Experience/Thesis Projects

There currently does not exist a way to easily view the relationships between a collection of written items (e.g. sports articles, diary entries, research papers). In recent years, novel machine learning methods have been developed which are very good at extracting semantic relationships from large numbers of documents. One of them is the (unsupervised) machine learning model Doc2Vec which constructs vectors for documents. The research project detailed in this paper uses this and other already existing algorithms to analyze the relationship between pieces of text. We set forth a broader ambition for this project before discussing the use and need …

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …

Virtual Wrap-Up Presentation: Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

School of Computing: Conference and Workshop Papers

Includes framing, overview, and discussion of the explorations pursued as part of the Digital Libraries, Intelligent Data Analytics, and Augmented Description demonstration project, pursued by members of the Aida digital libraries research team at the University of Nebraska-Lincoln through a research services contract with the Library of Congress. This presentation covered: Aida research team and background for the demonstration project; broad outlines of “Digital Libraries, Intelligent Data Analytics, and Augmented Description”; what changed for us as a research team over the collaboration and why; deliverables of our work; thoughts toward “What next”; and deep-dives into the explorations. The machine learning …

Classifying Fiction And Non-Fiction Works Using Machine Learning, Rachna Gupta '21

Student Publications & Research

The objective of this project was to create a program that can determine whether an unknown text is a work of fiction or non-fiction using machine learning. Various datasets of speeches, ebooks, poems, scientific papers, and texts from Project Gutenberg and the Wolfram Example Data were utilized to train and test a Markov Chain machine learning model. A microsite was deployed with the final product that returns a probability of fictionality based on input from the user with 95% accuracy.

Human Activity Recognition Based On Multimodal Body Sensing, Anish Hemant Narkhede

Master's Projects

In the recent years, human activity recognition has been widely popularized by a lot of smartphone manufacturers and fitness tracking companies. It has allowed us to gain a deeper insight into our physical health on a daily basis. However, with the evolution of fitness tracking devices and smartphones, the amount of data that is being captured by these devices is growing exponentially. This paper aims at understanding the process of dimensionality reduction such as PCA so that the data can be used to make meaningful predictions along with novel techniques using autoencoders with different activation functions. The paper also looks …

Toward On-Demand Profile Hidden Markov Models For Genetic Barcode Identification, Jessica Sheu

Master's Projects

Genetic identification aims to solve the shortcomings of morphological identification. By using the cytochrome c oxidase subunit 1 (COI) gene as the Eukaryotic “barcode,” scientists hope to research species that may be morphologically ambiguous, elusive, or similarly difficult to visually identify. Current COI databases allow users to search only for existing database records. However, as the number of sequenced, potential COI genes increases, COI identification tools should ideally also be informative of novel, previously unreported sequences that may represent new species. If an unknown COI sequence does not represent a reported organism, an ideal identification tool would report taxonomic ranks …

Species Classification Using Dna Barcoding And Profile Hidden Markov Models, Sphoorti Poojary

Master's Projects

Traditional classification systems for living organisms like the Linnaean taxonomy involved classification based on morphological features of species. This traditional system is being replaced by molecular approaches which involve using gene sequences. The COI gene, also known as the ”DNA barcode” since it is unique in every species, can be used to uniquely identify organisms and thus, classify them. Classifying using gene sequences has many advantages, including correct identification of cryptic species(individuals which appear similar but belong to different species) and species which are extremely small in size. In this project, I worked on classifying COI sequences of unknown species …

Machine Learning Pipeline For Exoplanet Classification, George Clayton Sturrock, Brychan Manry, Sohail Rafiqi

SMU Data Science Review

Planet identification has typically been a tasked performed exclusively by teams of astronomers and astrophysicists using methods and tools accessible only to those with years of academic education and training. NASA’s Exoplanet Exploration program has introduced modern satellites capable of capturing a vast array of data regarding celestial objects of interest to assist with researching these objects. The availability of satellite data has opened up the task of planet identification to individuals capable of writing and interpreting machine learning models. In this study, several classification models and datasets are utilized to assign a probability of an observation being an exoplanet. …

Watersheds For Semi-Supervised Classification, Aditya Challa, Sravan Danda, B. S.Daya Sagar, Laurent Najman

Journal Articles

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered - How do the boundaries of the watershed operator behave? Which loss function does the watershed operator optimize? How does watershed operator relate with existing ideas from machine learning. In this letter, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, morphMedian, resulting …

Classification Of Vegetation In Aerial Imagery Via Neural Network, Gevand Balayan

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis focuses on the task of trying to find a Neural Network that is best suited for identifying vegetation from aerial imagery. The goal is to find a way to quickly classify items in an image as highly likely to be vegetation(trees, grass, bushes and shrubs) and then interpolate that data and use it to mark sections of an image as vegetation. This has practical applications as well. The main motivation of this work came from the effort that our town takes in conserving water. By creating an AI that can easily recognize plants, we can better monitor the …

Multiple-Attribute Entity Recommendation Based On Classification, Meina Song, Xuejun Zhao, Haihong E

Journal of System Simulation

Abstract: In the process of exploring entity recommendation, the entity containing diverse attributes has gained more and more attention. Most of the current researchers mainly select one attribute, and embody it in the related algorithms and their extensions even though the entity is combined with multiple attributes in entity recommendation. In this paper, on the basis of the classification method, we delve into physical properties of the recommended entities, divide entity’s attribute information network into multiple sub ones. In sub information network, bounded by the amount of attributes, the single attribute and even multiple attributes can be diverted into diverse …

Work-In-Progress Reports Submitted To The Library Of Congress As Part Of Digital Libraries, Intelligent Data Analytics, And Augmented Description, Chulwoo Pack, Yi Liu, Leen-Kiat Soh, Elizabeth Lorang

School of Computing: Technical Reports

This document includes work-in-progress reports submitted to the Library of Congress as part of the Aida digital libraries research team's work on Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project. These work-in-progress reports provide a snapshot glimpse, as well as underlying rationale and decision-making, at various points in the development of the project and its machine learning explorations. Reports cover explorations on historic newspapers, minimally-processed manuscript collections, materials digitized from physical originals and those digitized from microform surrogates, and investigate challenges related to image segmentation and document zoning, classification, document image quality analysis, metadata generation, and more.

Transfer Learning For Detecting Unknown Network Attacks, Juan Zhao, Sachin Shetty, Jan Wei Pan, Charles Kamhoua, Kevin Kwiat

VMASC Publications

Network attacks are serious concerns in today’s increasingly interconnected society. Recent studies have applied conventional machine learning to network attack detection by learning the patterns of the network behaviors and training a classification model. These models usually require large labeled datasets; however, the rapid pace and unpredictability of cyber attacks make this labeling impossible in real time. To address these problems, we proposed utilizing transfer learning for detecting new and unseen attacks by transferring the knowledge of the known attacks. In our previous work, we have proposed a transfer learning-enabled framework and approach, called HeTL, which can find the common …

Effective Plant Discrimination Based On The Combination Of Local Binary Pattern Operators And Multiclass Support Vector Machine Methods, Vi N T Le, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the …

The Algorithmic Composition Of Classical Music Through Data Mining, Tom Donald Richmond, Imad Rahal

All College Thesis Program, 2016-2019

The desire to teach a computer how to algorithmically compose music has been a topic in the world of computer science since the 1950’s, with roots of computer-less algorithmic composition dating back to Mozart himself. One limitation of algorithmically composing music has been the difficulty of eliminating the human intervention required to achieve a musically homogeneous composition. We attempt to remedy this issue by teaching a computer how the rules of composition differ between the six distinct eras of classical music by having it examine a dataset of musical scores, rather than explicitly telling the computer the formal rules of …

Deep Convolutional Neural Networks Enable Discrimination Of Heterogeneous Digital Pathology Images, Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, Iman Hajirasouliha

Publications and Research

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error.

In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer.

In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry …

Deep Learning-Based Framework For Autism Functional Mri Image Classification, Xin Yang, Saman Sarraf, Ning Zhang

Journal of the Arkansas Academy of Science

The purpose of this paper is to introduce deep learning-based framework LeNet-5 architecture and implement the experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.

The Impact Of Cost On Feature Selection For Classifiers, Richard Clyde Mccrae

CCAC Theses and Dissertations

Supervised machine learning models are increasingly being used for medical diagnosis. The diagnostic problem is formulated as a binary classification task in which trained classifiers make predictions based on a set of input features. In diagnosis, these features are typically procedures or tests with associated costs. The cost of applying a trained classifier for diagnosis may be estimated as the total cost of obtaining values for the features that serve as inputs for the classifier. Obtaining classifiers based on a low cost set of input features with acceptable classification accuracy is of interest to practitioners and researchers. What makes this …

Multiclass Classification Using Support Vector Machines, Duleep Prasanna W. Rathgamage Don

College of Graduate Studies: Theses & Dissertations

In this thesis, we discuss different SVM methods for multiclass classification and introduce the Divide and Conquer Support Vector Machine (DCSVM) algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates one or more classes in a single partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step until a final binary decision is made between the last two classes …