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Practical Cataloging And Classification Training By Library And Information Science Users Education At Kwara State University Library, Nigeria, Abdrahman Atanda Moustapha, Odushina Babatunde Julius

Library Philosophy and Practice (e-journal)

Purpose

The study examined the cataloging and classification training of users in library and information science and their use of library catalogs at the Kwara State University Library, Nigeria. Training is a vital necessity for performing any professional responsibility. Therefore, the trainee's actions towards the training program cannot be affected.

Design/methodology/approach

The study adopted a descriptive research approach. It surveyed a sample of 250 university library users selected from a population of 515 through random sampling.

The findings

Library and Information Science users at Kwara State University Library, Nigeria, have a negative perception of the practical study of cataloging and …

Radiomic Biomarkers Of Locoregional Recurrence: Prognostic Insights From Oral Cavity Squamous Cell Carcinoma Preoperative Ct Scans, Xiao Ling, Gregory S. Alexander, Jason Molitoris, Jinhyuk Choi, Lisa Schumaker, Phuoc Tran, Ranee Mehra, Daria Gaykalova, Lei Ren

Department of Radiation Oncology Faculty Papers

INTRODUCTION: This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients.

METHODS: Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The …

Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang

Mathematics, Physics, and Computer Science Faculty Articles and Research

Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai …

Functional Data Learning Using Convolutional Neural Networks, Jose Galarza, Tamer Oraby

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this paper, we show how convolutional neural networks (CNNs) can be used in regression and classification learning problems for noisy and non-noisy functional data (FD). The main idea is to transform the FD into a 28 by 28 image. We use a specific but typical architecture of a CNN to perform all the regression exercises of parameter estimation and functional form classification. First, we use some functional case studies of FD with and without random noise to showcase the strength of the new method. In particular, we use it to estimate exponential growth and decay rates, the bandwidths of …

Correlation Enhanced Distribution Adaptation For Prediction Of Fall Risk, Ziqi Guo, Teresa Wu, Thurmon Lockhart, Rahul Soangra, Hyunsoo Yoon

Physical Therapy Faculty Articles and Research

With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different …

Blood Cell Image Segmentation And Classification: A Systematic Review, Muhammad Shahzad, Farman Ali, Syed Hamad Shirazi, Assad Rasheed, Awais Ahmad, Babar Shah, Daehan Kwak

All Works

Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking …

Taxonomic Advances Driven By The Genomic Analysis Of Butterflies, Jing Zhang, Qian Cong, Jinhui Shen, Leina Song, Nick V. Grishin

The Taxonomic Report of the International Lepidoptera Survey

This study presents new findings based on a large-scale analysis of butterfly genomic sequences. Focusing on species identification through comparative genomics, we define subspecies as populations differentiated to a lesser extent than distinct species ("species in the making"). Additionally, we propose further adjustments to the current butterfly classification. As a result, 3 subgenera, 12 species, and 4 subspecies are described as new. New subgenera are (type species in parenthesis): Hyalaus Grishin, subgen. n. (Papilio epidaus E. Doubleday, 1846) of Eurytides Hübner, [1821] (Papilionidae Latreille, [1802]) and Astria Grishin, subgen. n. (Lycaena astraea Freyer, 1851) of Glaucopsyche Scudder, 1872 …

Dataset Of Arabic Spam And Ham Tweets, Sanaa Kaddoura, Safaa Henno

All Works

This data article provides a dataset of 132421 posts and their corresponding information collected from Twitter social media. The data has two classes, ham or spam, where ham indicates non-spam clean tweets. The main target of this dataset is to study a way to classify whether a post is a spam or not automatically. The data is in Arabic language only, which makes the data essential to the researchers in Arabic natural language processing (NLP) due to the lack of resources in this language. The data is made publicly available to allow researchers to use it as a benchmark for …

Perianth Evolution And Implications For Generic Delimitation In The Eucalypts (Myrtaceae), Including The Description Of The New Genus, Blakella, Michael D. Crisp, Bui Q. Minh, Bokyung Choi, Robert D. Edwards, James Hereward, Carsten Kulheim, Yen Po Lin, Karen Meusemann, Andrew H. Thornhill, Alicia Toon, Lyn G. Cook

Michigan Tech Publications, Part 2

Eucalypts (Myrtaceae tribe Eucalypteae) are currently placed in seven genera. Traditionally, Eucalyptus was defined by its operculum, but when phylogenies placed Angophora, with free sepals and petals, as sister to the operculate bloodwood eucalypts, the latter were segregated into a new genus, Corymbia. Yet, generic delimitation in the tribe Eucalypteae remains uncertain. Here, we address these problems using phylogenetic analysis with the largest molecular data set to date. We captured 101 low-copy nuclear exons from 392 samples representing 266 species. Our phylogenetic analysis used maximum likelihood (IQtree) and multispecies coalescent (Astral). At two nodes critical to generic delimitation, we tested …

Deep Learning-Based Multimodality Classification Of Chronic Mild Traumatic Brain Injury Using Resting-State Functional Mri And Pet Imaging, Faezeh Vedaei, Najmeh Mashhadi, Mahdi Alizadeh, George Zabrecky, Daniel A. Monti, Md, Nancy Wintering, Emily Navarreto, Chloe Hriso, Andrew B. Newberg, Feroze B. Mohamed

Department of Radiology Faculty Papers

Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and …

Rigid Rideshares And Driver Monitoring, Seth D. Goldstein

Student Scholarship

(Excerpt)

Since 2018, Uber has submitted applications for numerous patents that use algorithms to “define” safety. These patents “calculate” safety through multiple factors, including crime reports and statistics, news databases, academic databases of reports of violent conflicts in a location, the car’s condition, how often the driver swerves, and “social media.” These machine-learning models attempt to predict “the likelihood that a driver will be involved in dangerous driving or interpersonal conflict.” Drivers are generally outraged by these patents and have commented that these recorded metrics will be “used to manipulate and influence” driver behavior. There is merit to this fear. …

Dilf: Differentiable Rendering-Based Multi-View Image-Language Fusion For Zero-Shot 3d Shape Understanding, Xin Ning, Zaiyang Yu, Lusi Li, Weijun Li, Prayag Tiwari

Computer Science Faculty Publications

Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model’s …

Ice-Marginal Lava Delta In Iceland Found On A Nondescript Shallow Slope: An Unexpected Record Of Ice Thickness Late In Deglacian, Audrey Putnam, Kirsten Siebach, Candice Bedford, Sarah Simpson, Elizabeth Rampe, Joseph Tamborski, Michael Thorpe

OES Faculty Publications

Volcanism increases when glaciers melt because isostatic rebound during deglaciation decreases the pressure on the mantle, which enhances decompression melting. Anthropogenic climate change is now causing ice sheets and valley glaciers to melt around the world and this deglaciation could stimulate volcanic activity and associated hazards in Iceland, Antarctica, Alaska, and Patagonia. However, current model predictions for volcanic activity associated with anthropogenic deglaciation in Iceland are poorly constrained, in part due to uncertainties in past volcanic output over time compared to ice sheet arrangements. Further work specifically characterizing glaciovolcanic and ice-marginal volcanoes in Iceland is needed to reconstruct volcanic output …

Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …