Computer Sciences Commons^™

Artificial Intelligence For Para Rubber Identification Combining Five Machine Learning Methods, Chairote Yaiprasert Ph.D.

Karbala International Journal of Modern Science

This study aims to identify Para rubber species using a combination of five machine learning techniques to classify leaf images. The learning process is defined using a dataset for each classification method. Approximately 1,472 leaf images are prepared consisting of various sizes, shapes, quality provided for the model. The classification indicators are defined with the help of an algorithm to identify at least three of the top five potential classification outcomes. The algorithm accurately predicts 100% of the five classification methods. Methods can provide precise and rapid classification of large quantities, without the need for image preprocessing prior to classification.

Predicting Occurrence Of The Term Sarcopenia With Semi-Supervised Machine Learning, Kevin Flasch

Theses and Dissertations

Sarcopenia is a medical condition that involves loss of muscle mass. It has been difficult todefine and only recently assigned an official medical code, leading to many medical records lacking a coded diagnosis although the clinical note text may discuss it or symptoms of it. This thesis investigates the application of machine learning and natural language processing to analyze clinical note text to see how well the term ’sarcopenia’ can be predicted in clinical note text from records concerning the condition.

A variety of machine learning models combined with different features and text processingare tested against training data that mentions …

Modelling Customers Credit Card Behaviour Using Bidirectional Lstm Neural Networks, Maher Ala’Raj, Maysam F. Abbod, Munir Majdalawieh

All Works

With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive …

Fingerlings Mass Estimation: A Comparison Between Deep And Shallow Learning Algorithms, Adair Da Silva Oliveira Junior, Diego André Sant’Ana, Marcio Carneiro Brito Pache, Vanir Garcia, Vanessa Aparecida De Moares Weber, Gilberto Astolfi, Fabricio De Lima Weber, Geazy Vilharva Menezes, Gabriel Kirsten Menezes, Pedro Lucas França Albuquerque, Celso Soares Costa, Eduardo Quirino Arguelho De Queiroz, João Victor Araújo Rozales, Milena Wolff Ferreira, Marco Hiroshi Naka, Hemerson Pistori

School of Computing: Faculty Publications

The paper presents some results regarding the automatic mass estimation of Pintado Real fingerlings, using machine learning techniques to support the fish production process. For this purpose, an image dataset called FISHCV1206FSEG, was created which is composed of 1206 images of fingerlings with their respective annotated masses. Through the fish contours, the area and perimeter were extracted, and submitted to the J48, SVM, and KNN classification algorithms and a linear regression algorithm. The images were also submitted to ResNet50, In- ceptionV3, Exception, VGG16, and VGG19 convolutional neural networks. As a result, the classification algorithm J48 reached an accuracy of 58.2% …

Shape-Based Classification Of Partially Observed Curves, With Applications To Anthropology, Gregory J. Matthews, Karthik Bharath, Sebastian Kurtek, Juliet K. Brophy, George K. Thiruvathukal, Ofer Harel

Computer Science: Faculty Publications and Other Works

We consider the problem of classifying curves when they are observed only partially on their parameter domains. We propose computational methods for (i) completion of partially observed curves; (ii) assessment of completion variability through a nonparametric multiple imputation procedure; (iii) development of nearest neighbor classifiers compatible with the completion techniques. Our contributions are founded on exploiting the geometric notion of shape of a curve, defined as those aspects of a curve that remain unchanged under translations, rotations and reparameterizations. Explicit incorporation of shape information into the computational methods plays the dual role of limiting the set of all possible completions …

Deep Learning Applications In Medical Bioinformatics, Ziad Omar

Electronic Theses and Dissertations

After a patient’s breast cancer diagnosis, identifying breast cancer lymph node metastases is one of the most important and critical factor that is directly related to the patient’s survival. The traditional way to examine the existence of cancer cells in the breast lymph nodes is through a lymph node procedure, biopsy. The procedure process is time-consuming for the patient and the provider, costly, and lacks accuracy as not every lymph node is examined. The intent of this study is to develop an artificial neural network (ANNs) that would map genetic biomarkers to breast lymph node classes using ANNs. The neural …

Analysis Of Music Genre Clustering Algorithms, Samuel Walter Stern

Theses and Dissertations

Classification and clustering of music genres has become an increasingly prevalent focusin recent years, prompting a push for research into relevant algorithms. The most successful algorithms have typically applied the Naive Bayes or k-Nearest Neighbors algorithms, or used Neural Networks to perform classification. This thesis seeks to investigate the use of unsupervised clustering algorithms such as K-Means or Hierarchical clustering, and establish their usefulness in comparison to or conjunction with established methods.

Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals

Faculty Publications

Random forest and neural network algorithms are applied to identify cloud cover using 10 of the wavelength bands available in Landsat 8 imagery. The methods classify each pixel into 4 different classes: clear, cloud shadow, light cloud, or cloud. The first method is based on a fully connected neural network with ten input neurons, two hidden layers of 8 and 10 neurons respectively, and a single-neuron output for each class. This type of model is considered with and without L2 regularization applied to the kernel weighting. The final model type is a random forest classifier created from an ensemble of …

Development Of Deep Learning Neural Network For Ecological And Medical Images, Shaobo Liu

Dissertations

Deep learning in computer vision and image processing has attracted attentions from various fields including ecology and medical image. Ecologists are interested in finding an effective model structure to classify different species. Tradition deep learning model use a convolutional neural network, such as LeNet, AlexNet, VGG models, residual neural network, and inception models, are first used on classifying bee wing and butterfly datasets. However, insufficient data sample and unbalanced samples in each class have caused a poor accuracy. To make improvement the test accuracy, data augmentation and transfer learning are applied. Recently developed deep learning framework based on mathematical morphology …

Machine Learning With Topological Data Analysis, Ephraim Robert Love

Doctoral Dissertations

Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine learning. Methods of exploiting the geometry of data, such as clustering, have proven theoretically and empirically invaluable. TDA provides a general framework within which to study topological invariants (shapes) of data, which are more robust to noise and can recover information on higher dimensional features than immediately apparent in the data. A common tool for conducting TDA is persistence homology, which measures the significance of these invariants. Persistence homology has prominent realizations in methods of data visualization, statistics and machine learning. Extending ML with …

How Does Land Cover Classification In Google Earth Engine Compare With Traditional Methods Of Land Cover Classification? What Are The Tradeoffs?, Carlos Sebastian Reyes

Open Access Theses & Dissertations

The project focuses on comparing land cover classification of traditional methods such as ArcGIS with newer ones such as Google Earth Engine (GEE) as well as discussing any potential tradeoffs. Two studies were performed in both platforms, the first involved analyzing land cover change in the Middle Rio Grande (MRG) region of southern New Mexico, far west Texas, and northern Chihuahua, Mexico. The MRG study focused on urban and agricultural change in the region using two different classification methods. The second study focused on creating a post-hurricane damage assessment (PDA) with the goal of developing an automated method of estimating …

Data-Driven Recommendation Of Academic Options Based On Personality Traits, Aashish Ghimire

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The choice of academic major and, subsequently, an academic institution has a massive effect on a person’s career. It not only determines their career path but their earning potential, professional happiness, etc. [1] About 40% of people who are admitted to a college do not graduate within six years. Yet, very limited resources are available for students to help make those decisions, and each guidance counselor is responsible for roughly 400 to 900 students across the United States. A tool to help these decisions would benefit students, parents, and guidance counselors.

Various research studies have shown that personality traits affect …

Fingerprint Classification Using Transfer Learning Technique, Aseel H. Aloweiwi

Theses, Dissertations and Culminating Projects

Fingerprints play a significant role in many sectors. Nowadays, fingerprints are used for identification purposes in criminal investigations. They are also used as an authentication method since they are considered more secure than passwords. Fingerprint sensors are already widely deployed in many devices, including mobile phones and smart locks. Criminals try to compromise biometric fingerprint systems by purposely altering their fingerprints or entering fake ones. Therefore, it is critical to design and develop a highly accurate fingerprint classification. However, some fingerprint datasets are small and not sufficient to train a neural network. Thus, transfer learning is utilized. A large Sokoto …

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 …

Can We Classify Cashless Payment Solution Implementations At The Country Level?, Dennis Ng, Robert J. Kauffman, Paul Robert Griffin

Research Collection School Of Computing and Information Systems

This research commentary proposes a 3-D implementation classification framework to assist service providers and business leaders in understanding the kinds of contexts in which more or less successful cashless payment solutions are observed at point-of-sale (PoS) settings. Three constructs characterize the framework: the digitalization of the local implementation environment; the relative novelty of a given payment technology solution in a country at a specific point in time; and the development status of the country’s national infrastructure. The framework is motivated by a need to support cross-country research in this domain. We analyze eight country mini-cases based on an eight-facet (2 …

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 …

Human Age And Gender Classification Using Convolutional Neural Networks, Eamon Kelliher

Dissertations

In a world relying ever more on human classification, this papers aims to improve on age and gender image classification through the use of Convolutional Neural Networks (CNN). Age and gender classification has become a popular area of study in the past number of years however there are still improvements to be made, particularly in the area of age classification. This research paper aims to test the currently accepted fact that CNN models are the superior model type for image classification by comparing CNN performance against Support Vector Machine performance on the same dataset. Using the Adience image classification dataset, …

Impact Of Image Segmentation Techniques On Celiac Disease Classification Usingscale Invariant Texture Descriptors For Standard Flexible Endoscopic Systems, Manarbek Saken, Munkhtsetseg Banzragch Yağci, Nejat Yumuşak

Turkish Journal of Electrical Engineering and Computer Sciences

Celiac disease (CD) is quite common and is a proximal small bowel disease that develops as a permanentintolerance to gluten and other cereal proteins in cereals. It is considered as one of the most di?icult diseases to diagnose.Histopathological evidence of small bowel biopsies taken during endoscopy remains the gold standard for diagnosis.Therefore, computer-aided detection (CAD) systems in endoscopy are a newly emerging technology to enhance thediagnostic accuracy of the disease and to save time and manpower. For this reason, a hybrid machine learning methodshave been applied for the CAD of celiac disease. Firstly, a context-based optimal multilevel thresholding technique wasemployed …

An Improved Version Of Multi-View K-Nearest Neighbors (Mvknn) For Multipleview Learning, Eli̇fe Öztürk Kiyak, Derya Bi̇rant, Kökten Ulaş Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

Multi-view learning (MVL) is a special type of machine learning that utilizes more than one views, where views include various descriptions of a given sample. Traditionally, classification algorithms such as k-nearest neighbors (KNN) are designed for learning from single-view data. However, many real-world applications involve datasets with multiple views and each view may contain different and partly independent information, which makes the traditional single-view classification approaches ineffective. Therefore, this article proposes an improved MVL algorithm, called multi-view k-nearest neighbors (MVKNN), based on the existing KNN algorithm. The experimental results conducted in this research show that a significant improvement is achieved …

A New Approach: Semisupervised Ordinal Classification, Ferda Ünal, Derya Bi̇rant, Özlem Şeker

Turkish Journal of Electrical Engineering and Computer Sciences

Semisupervised learning is a type of machine learning technique that constructs a classifier by learning from a small collection of labeled samples and a large collection of unlabeled ones. Although some progress has been made in this research area, the existing semisupervised methods provide a nominal classification task. However, semisupervised learning for ordinal classification is yet to be explored. To bridge the gap, this study combines two concepts ?semisupervised learning? and "ordinal classification" for the categorical class labels for the first time and introduces a new concept of "semisupervised ordinal classification". This paper proposes a new algorithm for semisupervised learning …

A Comparison Of Instructional Efficiency Models In Third Level Education, Murali Rajendran

Dissertations

This study investigates the validity and sensitivity of a novel model of instructional efficiency: the parabolic model. The novel model is compared against state-of-the-art models present in instructional design today; Likelihood model, Deviational model and Multidimensional model. This models is based on the assumption that optimal mental workload and high performance leads to high efficiency, while other models assume that low mental workload and high performance leads to high efficiency. The investigation makes use of two instructional design conditions: a direct instructions approach to learning and its extension with a collaborative activity. A control group received the former instructional design …

A Linear Programming Approach To Multiple Instance Learning, Emel Şeyma Küçükaşci, Mustafa Gökçe Baydoğan, Zeki̇ Caner Taşkin

Turkish Journal of Electrical Engineering and Computer Sciences

Multiple instance learning (MIL) aims to classify objects with complex structures and covers a wide range of real-world data mining applications. In MIL, objects are represented by a bag of instances instead of a single instance, and class labels are provided only for the bags. Some of the earlier MIL methods focus on solving MIL problem under the standard MIL assumption, which requires at least one positive instance in positive bags and all remaining instances are negative. This study proposes a linear programming framework to learn instance level contributions to bag label without emposing the standart assumption. Each instance of …

Diagnosis Of Paroxysmal Atrial Fibrillation From Thirty-Minute Heart Ratevariability Data Using Convolutional Neural Networks, Murat Sürücü, Yalçin İşler, Resul Kara

Turkish Journal of Electrical Engineering and Computer Sciences

Paroxysmal atrial fibrillation (PAF) is the initial stage of atrial fibrillation, one of the most common arrhythmia types. PAF worsens with time and affects the patient?s life quality negatively. In this study, we aimed to diagnose PAF early, so patients can start taking precautions before this disease gets worse. We used the atrial fibrillation prediction database, an open data from Physionet and constructed our approach using convolutional neural networks. Heart rate variability (HRV) features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations (fast Fourier transform, Lomb-Scargle, and Welch periodogram), time-frequencydomain measures using wavelet transform, and nonlinear …

Visual-Saliency-Based Abnormality Detection For Mri Brain Images—Alzheimer’S Disease Analysis, A. Diana Andrushia, K. Martin Sagayam, Helen Dang, Marc Pomplun, Lien Quach

Faculty Works: MCS (1984-2023)

In recent years, medical image analysis has played a vital role in detecting diseases in their early stages. Medical images are rapidly becoming available for various applications to solve human problems. Therefore, complex medical features are needed to develop a diagnostic system for physicians to provide better treatment. Traditional methods of abnormality detection suffer from misidentification of abnormal regions in the given data. Visual-saliency detection methods are used to locate abnormalities to improve the accuracy of the proposed work. This study explores the role of a visual saliency map in the classification of Alzheimer’s disease (AD). Bottom-up saliency corresponds to …

The Nearest Polyhedral Convex Conic Regions For High-Dimensional Classification, Hakan Çevi̇kalp, Emre Çi̇men, Gürkan Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

In the nearest-convex-model type classifiers, each class in the training set is approximated with a convexclass model, and a test sample is assigned to a class based on the shortest distance from the test sample to these classmodels. In this paper, we propose new methods for approximating the distances from test samples to the convex regionsspanned by training samples of classes. To this end, we approximate each class region with a polyhedral convex conicregion by utilizing polyhedral conic functions (PCFs) and its extension, extended PCFs. Then, we derive the necessary formulations for computing the distances from test samples to these …

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 …

Plant Species Identification In The Wild Based On Images Of Organs, Meghana Kovur

Graduate Theses, Dissertations, and Problem Reports

Image-based plant species identification in the wild is a difficult problem for several reasons. First, the input data is subject to a very high degree of variability because it is captured under fully unconstrained conditions. The same plant species may look very different in different images, while different species can often appear very similar, challenging even the recognition skills of human experts in the field. The large intra-class and small inter-class image variability makes this a fine-grained visual classification problem. One way to cope with this variability and to reduce image background noise is to predict species based on the …

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

Graduate Theses, Dissertations, and Problem Reports

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- …