Digital Commons Network^™

Thermodynamics Of Learning With Parametric Probabilistic Models, Shervin Sadat Parsi

Dissertations, Theses, and Capstone Projects

This study delves into the learning process within the Probabilistic Parametric Models (PPMs) framework from a unique thermodynamic perspective. By exploring the core concepts of thermodynamics and its innate connection with information theory, we showcase how this interdisciplinary approach can effectively contribute to the domain of machine learning. In the initial chapter, we establish the link between the learning problem in PPMs and a thermodynamic process by reframing various elements of the learning process within the context of thermodynamics. We introduce novel information-theoretic measurements that provide insights into the information learned in both the parameter space and the overall performance …

Reconstructing 42 Years (1979–2020) Of Great Lakes Surface Temperature Through A Deep Learning Approach, Miraj Kayastha, Tao Liu, Daniel Titze, Timothy C. Havens, Chenfu Huang, Pengfei Xue

Michigan Tech Publications

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 >years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at …

Intrusion Detection: Machine Learning Techniques For Software Defined Networks, Jacob S. Rodriguez

Masters Theses

In recent years, software defined networking (SDN) has gained popularity as a novel approach towards network management and architecture. Compared to traditional network architectures, this software-based approach offers greater flexibility, programmability, and automation. However, despite the advantages of this system, there still remains the possibility that it could be compromised. As we continue to explore new approaches to network management, we must also develop new ways of protecting those systems from threats. Throughout this paper, I will describe and test a network intrusion detection system (NIDS), and how it can be implemented within a software defined network. This system will …

2023 Annual Estimate Of The Strategic Security Environment, Usawc Ssi

Books, Monographs & Collaborative Studies

The Annual Estimate of the Strategic Security Environment serves as a guide for academics and practitioners in the defense community on the current challenges and opportunities in the strategic environment. This year’s publication outlines key strategic issues across the four broad themes of Regional Challenges and Opportunities, Domestic Challenges, Institutional Challenges, and Domains Impacting US Strategic Advantage. These themes represent a wide range of topics affecting national security and provide a global assessment of the strategic environment to help focus the defense community on research and publication. Strategic competition with the People’s Republic of China and the implications of …

Machine Learning-Based Classification Of Chronic Traumatic Brain Injury Using Hybrid Diffusion Imaging, Jennifer Muller, Ruixuan Wang, Devon Middleton, Mahdi Alizadeh, Kichang Kang, Ryan Hryczyk, George Zabrecky, Chloe Hriso, Emily Navarreto, Nancy Wintering, Anthony J. Bazzan, Chengyuan Wu, Daniel A. Monti, Xun Jiao, Qianhong Wu, Andrew B. Newberg, Feroze Mohamed

Marcus Institute of Integrative Health Faculty Papers

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging.

MATERIALS AND METHODS: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging …

In Silico Prediction Of Acute Chemical Toxicity Of Biocides In Marine Crustaceans Using Machine Learning, Rama Krishnan, Ian S. Howard, Sean Comber, Awadhesh N. Jha

School of Engineering, Computing and Mathematics

Biocides are a heterogeneous group of chemical substances intended to control the growth or kill undesired organisms. Due to their extensive use, they enter marine ecosystems via non-point sources and may pose a threat to ecologically important non-target organisms. Consequently, industries and regulatory agencies have recognized the ecotoxicological hazard potential of biocides. However, the prediction of biocide chemical toxicity on marine crustaceans has not been previously evaluated. This study aims to provide in silico models capable of classifying structurally diverse biocidal chemicals into different toxicity categories and predict acute chemical toxicity (LC50) in marine crustaceans using a set of calculated …

Multiomic Investigations Into Lung Health And Disease, Sarah E Blutt, Cristian Coarfa, Josef Neu, Mohan Pammi

Faculty, Staff and Students Publications

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning …

Improving Xrd Analysis With Machine Learning, Rachel E. Drapeau

Theses and Dissertations

X-ray diffraction analysis (XRD) is an inexpensive method to quantify the relative proportions of mineral phases in a rock or soil sample. However, the analytical software available for XRD requires extensive user input to choose phases to include in the analysis. Consequently, analysis accuracy depends greatly on the experience of the analyst, especially as the number of phases in a sample increases (Raven & Self, 2017; Omotoso, 2006). The purpose of this project is to test whether incorporating machine learning methods into XRD software can improve the accuracy of analyses by assisting in the phase-picking process. In order to provide …

Autonomous Shipwreck Detection & Mapping, William Ard

LSU Master's Theses

This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …

A Morphological Analysis Of High Redshift Galaxy Mergers Using Machine Learning, Caitlin Rose

Theses

Galaxy mergers play an important role in the formation and evolution of galaxies. However, identifying mergers can be difficult, especially at high redshift, due to effects such as: cosmological surface brightness dimming, poor resolution of images, the shifting of optical light to the infrared, and the inherently more irregular morphologies of younger galaxies. The advent of JWST and new deep, high-resolution near-infrared NIRCam images from the Cosmic Evolution Early Release Science Survey (CEERS) will help mitigate some of these problems to better detect high redshift merger features. Simultaneously, sophisticated machine learning analysis techniques have the po- tential to more accurately …

Effectiveness Of An Emergency Department-Based Machine Learning Clinical Decision Support Tool To Prevent Outpatient Falls Among Older Adults: Protocol For A Quasi-Experimental Study, Daniel J Hekman, Amy L Cochran, Apoorva P Maru, Hanna J Barton, Manish N Shah, Douglas Wiegmann, Maureen A Smith, Frank Liao, Brian W Patterson

Faculty, Staff and Student Publications

Background

Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether …

Comparison Of Condition Prediction Models To Prioritize Sewer Pipe Inspections, Madhuri Arjun

Civil Engineering Dissertations - Archive

Over time, wastewater collection systems deteriorate, necessitating ongoing adjustments and the development of asset management frameworks by utility proprietors to maintain the performance of their assets. Any asset management framework should emphasize the importance of asset inspection and condition assessment for system-efficient operation and maintenance. In the United States, closed-circuit television (CCTV) is the most common method for inspecting the interior of sewer pipelines. This procedure is expensive and time-consuming due to a city's extensive inventory of pipes. Due to the immense quantity of these pipes, every municipality can only inspect some sections of sanitary sewer pipes promptly. Therefore, the …

Estimation Of Suspended Sediment Concentration Along The Lower Brazos River Using Satellite Imagery And Machine Learning, Trevor Stull

Civil Engineering Theses - Archive

ABSTRACT: Suspended sediment transport in river basins is important for many water management planning activities to maintain safe drinking water for the community and maintenance of water quality and waterways for the ecosystem. Currently, the traditional way to measure suspended sediment effectively and reliably is by collecting field samples in the river body, which is very time consuming and only provide a point value of suspended sediment within the waterbody at the instant the sample was taken. This thesis focuses on developing models that estimate suspended sediment concentrations for the lower Brazos River using satellite imagery from publicly available data …

Evaluating Chatgpt For Recommendation: How Does The Ability To Converse Impact Recommendation?, Kyle Spurlock

Electronic Theses and Dissertations

Recommendation algorithms have become an absolute necessity in the modern world to avoid information overload. However, the interaction between the human and the system is largely superficial and without any real contact. If you are given poor recommendations, you have no choice but to sift through mountains of content on your own until the model learns to accommodate your tastes more. This is bad for business as well as the consumer. Recently, large language models like ChatGPT have seen a significant rise in popularity due to their ease of use and wide range of knowledge. It has now become nearly …

Liquid Flow And Gas Diffusion In Natural Rocks: Experimental And Simulation Studies, Xuewei Ning

Earth & Environmental Sciences Dissertations - Archive

Natural rocks, as key components in energy and environmental geosciences, play crucial roles in groundwater extraction, geological storage of high-level nuclear waste, geothermal energy mining, and petroleum exploration in both conventional and unconventional reservoirs. Unconventional reservoirs, specifically the tight shale formations, have seen a significant usage in petroleum production in recent decades, largely due to advances in fracturing stimulation techniques which counter the inherently low permeability and connectivity of shale reservoirs by expanding natural fractures and creating artificial ones for an enhanced petroleum production. However, the low mobility (liquid flow and gas diffusion) of stored fluids in naturally fractured formations …

Pitfalls In Machine Learning-Based Assessment Of Tumor-Infiltrating Lymphocytes In Breast Cancer: A Report Of The International Immuno-Oncology Biomarker Working Group On Breast Cancer, Jeppe Thagaard, Glenn Broeckx, Chowdhury Arif Jahangir, Sara Verbandt, Rajarsi Gupta, Reena Khiroya, Khalid Abduljabbar, Gabriela Acosta Haab, Balazs Acs, Shahin Sayed

Pathology, East Africa

Abstract: The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL …

Log File Anomaly Detection Using Knowledge Graphs And Graph Neural Networks, Lucas Payne

Masters Theses and Doctoral Dissertations

Log files contain valuable information for detecting abnormal behavior. To detect anomalies, researchers have proposed representing log files as knowledge graphs (KGs) and using KG completion (KGC) techniques to predict new facts. However, current research in this area is limited, and there is no end-to-end system that includes both KG generation and KGC for log-based anomaly detection. In this study, we present an end-to-end system that utilizes graph neural networks (GNNs) and KGC to detect anomalies in log files. The proposed system has two main components. The first component employs templates to generate a KG from logs that capture normal …

Cyber Attack Surface Mapping For Offensive Security Testing, Douglas Everson

All Dissertations

Security testing consists of automated processes, like Dynamic Application Security Testing (DAST) and Static Application Security Testing (SAST), as well as manual offensive security testing, like Penetration Testing and Red Teaming. This nonautomated testing is frequently time-constrained and difficult to scale. Previous literature suggests that most research is spent in support of improving fully automated processes or in finding specific vulnerabilities, with little time spent improving the interpretation of the scanned attack surface critical to nonautomated testing. In this work, agglomerative hierarchical clustering is used to compress the Internet-facing hosts of 13 representative companies as collected by the Shodan search …

Residual-Based Error Correction For Neural Operator Accelerated Infinite-Dimensional Bayesian Inverse Problems, Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas

Faculty Publications

We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs) with models governed by nonlinear parametric partial differential equations (PDEs). Neural operators have gained significant attention in recent years for their ability to approximate the parameter-to-solution maps defined by PDEs using as training data solutions of PDEs at a limited number of parameter samples. The computational cost of BIPs can be drastically reduced if the large number of PDE solves required for posterior characterization are replaced with evaluations of trained neural operators. However, reducing error in the resulting …

Seek And Classify: End-To-End Joint Multi-Signal Detection And Classification Using Deep Learning, Prashant Subedi

School of Computing: Dissertations, Theses, and Student Research

The rise in the use of wireless communication has led to the problem of spectrum scarcity in licensed bands. The popularity of Internet of Things (IoT) requires innovative solutions that maximize the use of available spectrum to support the increasing number of connected devices. This thesis tackles two significant problems in wireless communication: the need for efficient spectrum sensing techniques and the scarcity of large, diverse raw in-phase (I) and quadrature (Q) datasets.

The ability to detect and classify modulation of the signals efficiently can enable a cognitive radio to monitor the spectrum activity in real time and utilize unused …

Multi-Granularity Detector For Vulnerability Fixes, Truong Giang Nguyen, Cong, Thanh Le, Hong Jin Kang, Ratnadira Widyasari, Chengran Yang, Zhipeng Zhao, Bowen Xu, Jiayuan Zhou, Xin Xia, Ahmed E. Hassan, David Lo, David Lo

Research Collection School Of Computing and Information Systems

With the increasing reliance on Open Source Software, users are exposed to third-party library vulnerabilities. Software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerability-fixing commits. However, identifying such commits is highly challenging, as only a very small minority of commits are vulnerability fixing. Moreover, code changes can be noisy and difficult to analyze. We observe that noise can occur at different levels of detail, making it challenging to detect vulnerability fixes accurately. To address these challenges and …

Mathematics Behind Machine Learning, Rim Hammoud

Electronic Theses, Projects, and Dissertations

Artificial intelligence (AI) is a broad field of study that involves developing intelligent
machines that can perform tasks that typically require human intelligence. Machine
learning (ML) is often used as a tool to help create AI systems. The goal of ML is
to create models that can learn and improve to make predictions or decisions based on given data. The goal of this thesis is to build a clear and rigorous exposition of the mathematical underpinnings of support vector machines (SVM), a popular platform used in ML. As we will explore later on in the thesis, SVM can be implemented …

Virtual Curtain: A Communicative Fine-Grained Privacy Control Framework For Augmented Reality, Aakash Shrestha

Boise State University Theses and Dissertations

Augmented Reality (AR) technologies have advanced significantly due to continuous sensing technology and ongoing advancements in mobile technologies such as device portability, camera quality, and system performance. Continuous sensing technology is the key to enabling an AR experience. However, untrusted applications leverage continued access to these sensor data, posing significant privacy concerns for both AR users and bystanders. The rapid growth of AR devices has resulted in broad commercialization and daily use. As a newer field, many users are unaware of the potential privacy risks these AR devices pose due to unintended information leakage. As a result, a privacy control …

On The Feasibility Of Machine Learning Algorithms Towards Low-Cost Flow Cytometry, Noah Vandal

Biomedical Engineering

Utilization low cost, scalable architectures for detection of specific cells for both mass flow and minute incidence analysis is something that is attractive for the clinical researcher, in order to expand access to otherwise costly devices. We demonstrate the use of a low-cost microfluidics device that performs detection of beads and cells, both for cell counting and for discrete cell type identification. This was accomplished using polymer technology via implementation of polydimethylsiloxane microfluidics, which were created by using a 3-D printed mold, and machine learning technologies with algorithms that can inference and track analyte particles within the microfluidic of interest. …

Novel Breath Collection Techniques For Detection Of Covid-19., James Morris

Electronic Theses and Dissertations

Volatile Organic Compounds (VOC) generated endogenously in the human body can be used to detect diseases that induce oxidative stress and inflammation. Breath analysis has been used for the detection of diseases such as COPD, Depression, Lung Cancer and most recently COVID-19. Methods such as Exhaled Breath Condensate (EBC), Sorbent Tubes, Solid Phase Microextraction (SPME), and silicon microreactors have shown considerable capability in extracting and concentrating trace VOC’s present in human breath. Silicon microreactors functionalized with capture agents to derivatize carbonyl compounds are effective but the overall maximum flow rate of breath sample possible during analysis is low, making direct …

Benchmarking And Practical Evaluation Of Machine And Statistical Learning Methods In Credit Scoring: A Method Selection Perspective, Gwen Verbeck

UNLV Theses, Dissertations, Professional Papers, and Capstones

Predictive models are important tools used in all scientific fields. Machine learning (ML) algorithms and statistical models are widely used for decision-making because of their capability to tackle intricate and unique problems. In domains where data are high-dimensional and contain irrelevant and redundant features, ML algorithms are known to have superior performance over traditional (statistical) learning methods. However, researchers and analysts are often faced with a myriad of techniques to choose from, with no clear consensus on which will perform best for their specific task. Considering resource limitations, exhaustive exploration of all available methods is impractical and often fails to …

Multi-Parametric Mri For Radiotherapy Simulation, Tian Li, Jihong Wang, Yingli Yang, Carri K Glide-Hurst, Ning Wen, Jing Cai

Faculty, Staff and Student Publications

Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.

Increasing The Efficiency And Accuracy Of Collective Intelligence Methods For Image Classification, Md Mahmudulla Hassan

Open Access Theses & Dissertations

Collective intelligence has emerged as a powerful methodology for annotating and classifying challenging data that pose difficulties for automated classifiers. It works by leveraging the concept of "wisdom of the crowds" which approximates a ground truth after aggregating experts' feedback and filtering out noise. However, challenges arise when certain applications, such as medical image classification, security threat detection, and financial fraud detection, demand accurate and reliable data annotation. The unreliability of experts due to inconsistent expertise and competencies, coupled with the associated cost and time-consuming judgment extraction, presents additional challenges.

Input aggregation is the process of consolidating and combining multiple …

Pitfalls In Machine Learning-Based Assessment Of Tumor-Infiltrating Lymphocytes In Breast Cancer: A Report Of The International Immuno-Oncology Biomarker Working Group On Breast Cancer, Jeppe Thagaard, Glenn Broeckx, David B Page, Chowdhury Arif Jahangir, Sara Verbandt, Zuzana Kos, Rajarsi Gupta, Reena Khiroya, Khalid Abduljabbar, Gabriela Acosta Haab, Balazs Acs, Guray Akturk, Jonas S Almeida, Isabel Alvarado-Cabrero, Mohamed Amgad, Farid Azmoudeh-Ardalan, Sunil Badve, Nurkhairul Bariyah Baharun, Eva Balslev, Enrique R Bellolio, Vydehi Bheemaraju, Kim Rm Blenman, Luciana Botinelly Mendonça Fujimoto, Najat Bouchmaa, Octavio Burgues, Alexandros Chardas, Maggie Chon U Cheang, Francesco Ciompi, Lee Ad Cooper, An Coosemans, Germán Corredor, Anders B Dahl, Flavio Luis Dantas Portela, Frederik Deman, Sandra Demaria, Johan Doré Hansen, Sarah N Dudgeon, Thomas Ebstrup, Mahmoud Elghazawy, Claudio Fernandez-Martín, Stephen B Fox, William M Gallagher, Jennifer M Giltnane, Sacha Gnjatic, Paula I Gonzalez-Ericsson, Anita Grigoriadis, Niels Halama, Matthew G Hanna, Aparna Harbhajanka, Steven N Hart, Johan Hartman, Søren Hauberg, Stephen Hewitt, Akira I Hida, Hugo M Horlings, Zaheed Husain, Evangelos Hytopoulos, Sheeba Irshad, Emiel Am Janssen, Mohamed Kahila, Tatsuki R Kataoka, Kosuke Kawaguchi, Durga Kharidehal, Andrey I Khramtsov, Umay Kiraz, Pawan Kirtani, Liudmila L Kodach, Konstanty Korski, Anikó Kovács, Anne-Vibeke Laenkholm, Corinna Lang-Schwarz, Denis Larsimont, Jochen K Lennerz, Marvin Lerousseau, Xiaoxian Li, Amy Ly, Anant Madabhushi, Sai K Maley, Vidya Manur Narasimhamurthy, Douglas K Marks, Elizabeth S Mcdonald, Ravi Mehrotra, Stefan Michiels, Fayyaz Ul Amir Afsar Minhas, Shachi Mittal, David A Moore, Shamim Mushtaq, Hussain Nighat, Thomas Papathomas, Frederique Penault-Llorca, Rashindrie D Perera, Christopher J Pinard, Juan Carlos Pinto-Cardenas, Giancarlo Pruneri, Lajos Pusztai, Arman Rahman, Nasir Mahmood Rajpoot, Bernardo Leon Rapoport, Tilman T Rau, Jorge S Reis-Filho, Joana M Ribeiro, David Rimm, Anne Roslind, Anne Vincent-Salomon, Manuel Salto-Tellez, Joel Saltz, Shahin Sayed, Ely Scott, Kalliopi P Siziopikou, Christos Sotiriou, Albrecht Stenzinger, Maher A Sughayer, Daniel Sur, Susan Fineberg, Fraser Symmans, Sunao Tanaka, Timothy Taxter, Sabine Tejpar, Jonas Teuwen, E Aubrey Thompson, Trine Tramm, William T Tran, Jeroen Van Der Laak, Paul J Van Diest, Gregory E Verghese, Giuseppe Viale, Michael Vieth, Noorul Wahab, Thomas Walter, Yannick Waumans, Hannah Y Wen, Wentao Yang, Yinyin Yuan, Reena Md Zin, Sylvia Adams, John Bartlett, Sibylle Loibl, Carsten Denkert, Peter Savas, Sherene Loi, Roberto Salgado, Elisabeth Specht Stovgaard

Faculty, Staff and Student Publications

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. …

Predicting Narcissistic Personality Traits From Brain And Psychological Features: A Supervised Machine Learning Approach, Khanitin Jornkokgoud, Teresa Baggio, Md Faysal, Richard Bakiaj, Peera Wongupparaj, Remo Job, Alessandro Grecucci

School of Psychology

Narcissism is a multifaceted construct often linked to pathological conditions whose neural correlates are still poorly understood. Previous studies have reported inconsistent findings related to the neural underpinnings of narcissism, probably due to methodological limitations such as the low number of participants or the use of mass univariate methods. The present study aimed to overcome the previous methodological limitations and to build a predictive model of narcissistic traits based on neural and psychological features. In this respect, two machine learning-based methods (Kernel Ridge Regression and Support Vector Regression) were used to predict narcissistic traits from brain structural organization and from …