Digital Commons Network^™

Beyond Anomaly Detection: Classifying Attacker Automation Level From Ssh Honeypot Behavioral Signatures, Ashley Alt

Theses

The proliferation of AI-assisted offensive tools has introduced a new category of cyber attacker that combines the speed of automation with the adaptive reasoning previously associated only with skilled human operators. Despite the richness of behavioral data captured by SSH honeypots, existing analyses treat interaction logs primarily as evidence of malicious activity rather than as a dataset capable of distinguishing between attacker types. This thesis investigates whether human-driven, traditionally automated, and AI-assisted attackers produce distinguishable behavioral signatures within SSH honeypot interactions, and whether machine learning techniques can reliably classify attacker behavior from session-level features. A controlled experimental architecture was developed …

Prioritising Follow-Up For People With Suspected Epilepsy Using A Digital Eeg Biomarker, Rohit Shankar, John Terry

Peninsula Medical School

Lengthy waits for follow-up testing are common for people with suspected epilepsy. This delays diagnosis, prolongs uncertainty and increases seizure risk. Initial EEGs are frequently inconclusive, yet follow-ups are often dictated by referral date, and there is no established method for risk-based prioritisation. Here, we tested whether an established digital EEG biomarker could help prioritise those most likely to have epilepsy for expedited follow-up EEG testing. We analysed 196 normal non-contributory (non-diagnostic) initial EEGs collected from six National Health Service (NHS) sites in England. From these recordings, we extracted eight previously validated computational features that quantify the likelihood that the …

Secure Machine Learning In Networking Systems, Wenwei Zhao

USF Tampa Graduate Theses and Dissertations

With the rapid integration of Machine Learning (ML) into networking systems, ensuring the security and trustworthiness of these intelligent frameworks has become a paramount concern. While ML offers unprecedented capabilities in spectrum management and collaborative learning, it also introduces novel vulnerabilities that can be exploited by sophisticated adversaries. This dissertation investigates and addresses critical security challenges across three key dimensions of ML-driven networking: adversarial spectrum sensing, the security of privacy-preserving unlearning processes, and efficient post-attack model recovery.

First, we address the threat of adversarial spectrum attacks in cognitive radio networks, where malicious nodes manipulate sensing reports to disrupt spectrum access. …

Iah-Net (Informative Air Quality And Heat Index Network): Drone-Assisted System For Real-Time Air Quality And Heat Index Monitoring, Brent Breo B. Magbanua

DLSU Senior High School Research Congress Conference Proceedings

Air pollution and rising temperatures threaten public health and safety, necessitating advanced monitoring solutions. This study developed a drone-assisted system for autonomous monitoring of air quality and heat index, ensuring real-time alerts even in the absence of human presence. By integrating the K-Nearest Neighbors (K-NN) machine learning algorithm, the system provides an innovative and cost-effective approach to identifying environmental risks and raising awareness. The system consists of a Drone system, ESP32, MQ-135 gas sensor, and DHT11 temperature and humidity sensor for data acquisition. Software development includes implementing the K-NN algorithm and calibrating sensors to enhance classification accuracy. The system categorizes …

Clinical Prediction Of Posttreatment Migraine Recurrence Using Biofeedback Data: A Machine Learning Framework For Enhanced Patient Stratification And Treatment Monitoring, Shibbir Ahmed Arif, Ferdib-Al-Islam, Mehidy Hasan Sium

School of Computing Faculty Scholarship and Creative Works

Migraine is a complex neurological disorder with significant implications for individual well-being and public health. Predicting migraine occurrences after treatment is crucial for evaluating therapeutic efficacy and enabling personalized care, yet remains largely underexplored. This study proposes a robust machine learning framework to predict posttreatment migraine headache occurrences using real-world headache log data collected from 133 patients undergoing biofeedback therapy. The methodology includes rigorous data preprocessing, outlier removal via the interquartile range (IQR) method, and class imbalance correction through the synthetic minority oversampling technique (SMOTE). A total of 10 classical and a hybrid ensemble machine learning models were developed and …

The Developing Role Of Ai In Modern Engineering Research, Rianna Pais

The Cardinal Edge

No abstract provided.

Applications Of Artificial Intelligence In Nanotechnology, Shahad Eabd Alrida, Ola Obed, Elaf Taha, Thamer Abdullah, Mustafa Hathal, Viola Somogyi

Engineering and Technology Journal

Artificial intelligence (AI) is emerging as a prominent technological advancement. It is the act of replicating human intelligence for many purposes. In contrast to conventional methodologies, artificial intelligence (AI) is undergoing tremendous advancements. The present state of artificial intelligence (AI) technology enables them to effectively address numerous intricate difficulties with proficiency comparable to a human's. The significance of advancements in AI is particularly evident in machine learning, where the techniques and algorithms are effectively applied to address many problems, including those in nanotechnology. In contemporary nanotechnology, it is crucial to expedite the search for the most favorable synthesis parameters while …

Detection Of Arabic Sign Language By Machine Learning Techniques With Pca And Lda, Mosab Hassan, Atheer Sabri, Alaa Ali

Engineering and Technology Journal

People need a communication channel and a way to communicate with a person or group. Deaf people communicate with others through sign language. The remarkable and rapid development in image and video recognition systems has made researchers use this development to solve many problems, including sign language for the deaf, and reduce their suffering in communicating with ordinary people. This work aims to use and apply machine learning technology to build an automatic recognition system for Arabic Sign Language (ArSL). In this work, images of ArSL characters were recognized using four classification techniques (Naïve Bayes (NB), Decision Trees (DTs), and …

State-Of-The-Art Review Into Signal Processing And Artificial Intelligence-Based Approaches Applied In Gearbox Defect Diagnosis, Asaad Dubaish, Alaa Jaber

Engineering and Technology Journal

Various industrial applications, including rotating and reciprocating machinery, depend on gears. Therefore, a sudden breakdown of the gears could result in substantial financial losses. Due to this, extensive studies have focused on defect diagnosis. Both machinery maintenance decisions and preventive maintenance techniques have been aided by vibration analysis. An increased vibration is a warning sign that a machine is about to malfunction or break down. Observing and evaluating the machine's vibration pulses can identify the nature and extent of the issue and, as a result, predict when the machine will fail. The vibration signal may identify gearbox defects early on …

Enhancing Wind Tunnel Computational Simulations Of Finite Element Analysis Using Machine Learning-Based Algorithms, Luttfi Al-Haddad, Alaa Jaber, Latif Ibraheem, Sinan Al-Haddad, Naseem Ibrahim, Fawaz Abdulwahed

Engineering and Technology Journal

Wind tunnels are essential for examining aircraft model aerodynamics, accurately simulating real-world conditions, and enhancing design and performance evaluations. This study introduces a novel technique to improve the time and accuracy of stress distribution forecasts in wind tunnel simulations. This method combines Finite Element Analysis (FEA) with two regression models: Support Vector Machine (SVM) and k-Nearest Neighbors (kNN). The investigation begins with a thorough analysis of ANSYS fluent flow data, which reveals intricate fluid dynamics details within the wind tunnel. A comparative analysis of stress projections, supplemented by Root Mean Square Error (RMSE) metric, demonstrates the proposed methodology’s viability. High …

The Effect Of Synthetic Minority Oversampling For Enhancing Brain Tumor Image Classification, Farah Fadhil, Zainab Sultani

Engineering and Technology Journal

Brain tumors are among the most serious neurological diseases, posing significant diagnostic challenges due to their diverse nature and complexity imbalance in medical imaging datasets. To address these challenges, machine learning (ML) has demonstrated high potential in brain tumor classification; nevertheless, its overall performance can suffer when minority classes are underrepresented. This study examines the impact of the Synthetic Minority Over-sampling Technique (SMOTE) on MRI brain tumor classification using handcrafted features, including Gray Level Co-occurence Matrix (GLCM), Histogram of Oriented Gradients (HOG), and their combination (HOG + GLCM), with three classifiers, namely, Logistic Regression (LR), Support Vector Machines (SVM), and …

Using Artificial Neural Networks To Predict The Compressive Strength Of Cement And Sawdust Ash-Treated Lateritic Soil, James Ukpai, Ugochukwu Okonkwo

Engineering and Technology Journal

Sawdust industrial residue could be hazardous to the environment, but it becomes pozzolanic when incinerated. Thus, harnessing sawdust ash (SDA) and lateritic soil as road construction materials for low-cost roads is apt. However, modeling and prediction of the properties of soils treated with SDA have received very little attention. This study predicted the compressive strength of lateritic soil treated with cement and SDA using an Artificial Neural Network (ANN). The soil was found to belong to A-2-4(0) in the AASHTO rating and clayey sand (SC) in the Unified Soil Classification System. The soil minerals composition was conducted using an x-ray …

An Optimize Canny Algorithm With Traditional Machine Learning For Edge Detection Enhancement, Russel Lafta, Zainab Sultani

Engineering and Technology Journal

Edge detection still represents a major challenge in image processing and computer vision because of the complexity and variability of real-world images. The canny algorithm is powerful in detecting edges. However, it is sensitive to noise, and as a result, may produce weak edges. The use of machine learning algorithms has significantly improved the performance of edge detection techniques. In this paper, an improved Canny edge detection algorithm is proposed by replacing the Gaussian filter with a bilateral filter. Also, a new approach for estimating Canny algorithm thresholds has been developed using the Flower Pollination algorithm. Subsequently, the improved Canny …

Development Of A Framework For Identifying Asphalt Pavement Cracking Distresses Using Machine Learning, Dingxin Cheng

Mineta Transportation Institute

Asphalt pavement cracking is one of the most critical distresses affecting pavement performance and service life. When pavement deteriorates, it can lead to safety hazards, higher vehicle maintenance costs, and expensive repairs for cities and states—making early detection essential for everyone who relies on the roadway system. To address this challenge, the research team developed a prototype cracking identification system that integrates a customized machine learning model with computer vision algorithms. High-resolution images collected from drones or ground-based cameras are processed within the system to automatically detect and classify major cracking types. The core of the framework utilizes the You …

Key Predictors Of Postpartum Depression And Anxiety Symptoms Among Mothers In Kilifi, Kenya: A Machine Learning Approach, Faith Benson, Rachel Odhiambo, Willie Brink, Anthony Ngugi, Akbar Waljee, Eileen Weinheimer-Haus, Cheryl Moyer, Ji Zhu, Amina Abubakar

Institute for Human Development, East Africa

Background:

The burden of maternal postpartum depression and anxiety is disproportionately high in sub-Saharan Africa (SSA), yet the use of advanced analytical methods to capture the complex interplay of variables influencing these conditions remains underexplored.

Objective:

To apply machine learning (ML) methods to predict depressive and anxiety symptoms in postpartum mothers and to identify key and actionable predictors.

Methods:

This cross-sectional study included 1,995 biological mothers of singleton infants aged 0–6 months, using survey data collected between March 2023 and March 2024 in Kaloleni and Rabai sub-counties, Kilifi County, Kenya, within the Kaloleni–Rabai Health and Demographic Surveillance …

Deep Learning Approaches For Anti-Money Laundering On Mobile Transactions: Review, Framework, And Directions, Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies, and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech …

Machine Learning–Driven Prediction Of Dementia From Mri And Clinical Features: A Comparative Analysis Of Ensemble And Baseline Models, Sarah Raad Hameed, Zainab Muhannad Nahid, Rawan Ahmed Abdulmahdi

AUIQ Technical Engineering Science

Early detection of dementia remains a pressing challenge in clinical neuroscience, as delayed diagnosis limits therapeutic impact and healthcare planning. Leveraging the Open Access Series of Imaging Studies (OASIS) cross-sectional dataset of 436 participants, this study developed a robust machine learning pipeline integrating sociodemographic, clinical, and neuroimaging-derived features. Preprocessing included removal of highly sparse variables (Delay), median imputation of partially missing but clinically essential measures (SES, MMSE, CDR, Educ), Winsorization of extreme values, and skewness correction. The target Clinical Dementia Rating (CDR) was binarized (0 = no dementia, ≥ 0.5 = dementia) to align with clinically actionable screening. Categorical features …

Exploring Music Representation Learning For Detection Of Finer-Grained Details, Vishnu Pendyala, Samhita Konduri, Kriti Pendyala

Faculty Research, Scholarly, and Creative Activity

The foundational tonic in Indian classical music presents a crucial element for algorithmic understanding. This research investigates the efficacy of modern machine learning techniques for detecting this fine-grained musical attribute from diverse audio representations. Addressing limitations in prior work that used spe-cialized techniques, this study pioneers the integration of audio representations with non-linear dimensionality reduction and machine learning classifiers for tonic classification. The methodology employs various machine learning algorithms on music represented by audio features such as Mel-frequency cepstral coefficients and Mel spectrograms. Spectral analysis using Uniform Manifold Approximation and Projection (UMAP) offers novel insights into music representation learning. The …

Enhancing Performance Of Intrusion Prevention Systems Through Machine Learning: A Comparative Study, Rawaa Hamza Ali, Zainab Saad Karam

Baghdad Science Journal

This research introduces a Machine Learning-Powered Intrusion Prevention System (ML-IPS) as a robust solution to address the challenges posed by evolving cyber threats. The ML-IPS combines timely threat detection with enhanced accuracy for real-time attack prevention, offering a resilient defense against a broad spectrum of cyber-attack. This study delves into the comprehensive evaluation of a machine learning-powered IPS that ingeniously harnesses the power of advanced algorithms to facilitate real-time threat detection and significantly enhance the overall accuracy of the system. The efficacy of intrusion prevention systems (IPS) that employ machine learning (ML) is greatly dependent on the choice of suitable …

Structure–Activity Modeling And Hybrid Machine Learning-Based Prediction Of Bioactivity In Pyrazole Derivatives For Drug Discovery Applications, Kader Şahi̇n, Serhat Kiliçarslan, Serdar Durdaği, Emi̇n Saripinar

Turkish Journal of Biology

Background/aim: Pyrazole derivatives are of growing interest due to their diverse pharmacological activities. However, their biological activity is often highly sensitive to subtle structural modifications. Existing quantitative structure–activity relationships (QSAR) approaches frequently fail to capture the conformational flexibility and nonlinear structure–activity relationships (SAR) of such heterocyclic scaffolds, creating a gap in the accurate prediction of their biological profiles. Therefore, there is a strong need for more robust and predictive computational frameworks. This study addresses this gap by integrating four-dimensional (4D)-QSAR descriptors with hybrid machine learning (ML) techniques to improve predictive accuracy and provide a more reliable tool for structure-based drug …

Hybrid 3d Modelling Framework For Indoor Navigation Using Federated Learning And Internet Of Things-Enabled Edge Devices, Noopur Tyagi, Jaiteg Singh, Saravjeet Singh, Ahmad Ali Alzubi, Farman Ali, Sukhjit Singh Sehra, Babar Shah

All Works

Background There has been a recent trend towards using three-dimensional (3D) models to enhance spatial awareness and maximize resource utilization in complex environments. 3D building model can be used in various applications, such as real-time guidance and tracking the positions of individuals in multi storied buildings. Cities are now being modeled and studied in three dimensions as an improved method of urban planning. Method This study proposes an advanced indoor navigation framework that combines 3D modelling, federated learning (FL), and Internet of Things (IoT) integration to deliver reliable floor-level localization and real-time guidance. In Phase 1, highly accurate 3D models …

An Ai Approach To Lunar Phase Detection: Enhancing The Identification Of The New Crescent With Astronomical Data Integration, Murad Al-Rajab, Samia Loucif, Raed Abu Zitar, Mubarak Gwaza Abdu-Aguye

All Works

Introduction: The observation of the lunar crescent is significant in astronomy, cultural traditions, and religious lunar calendar determinations. However, earth-based imaging that captures all lunar phases, particularly the new crescent across multiple months, remains limited. This study explores the feasibility of using artificial intelligence (AI) techniques to detect and analyze the birth of the new lunar crescent using space-borne imagery from NASA’s Lunar Reconnaissance Orbiter (LRO), spanning over 13 years. Methods: This study evaluates both deep learning and traditional machine learning approaches for new crescent detection. Convolutional Neural Networks (CNN), Random Forests (RF), and Support Vector Machines (SVM) were applied …

Noninvasive Patient-Level Detection Of Nafld Using Mri Radiomics And Machine Learning, Vladimir Kirichenko, Rajanikanth Vadigepalli

Phase 1

Introduction— Current noninvasive assessment of nonalcoholic fatty liver disease (NAFLD) relies primarily on MRI-based fat quantification, such as proton density fat fraction (PDFF). This study explored the use of an MRI radiomics–based machine learning approach for accurate patient-level classification of NAFLD.

Methods— Liver MRI images from patients with suspected NAFLfD were retrospectively analyzed. Radiomics features were extracted using PyRadiomics from full-liver masks across 4–6 axial slices per patient. Slice-level features were aggregated to the patient level using summary statistics (mean, median, and standard deviation). To reduce redundancy and multicollinearity, features were pruned using Pearson correlation analysis with a threshold of …

Artificial Intelligence In Radiology: Hidden Fragilities And The Path To Resilience, Mustafa S. Alhasan

Saudi Medical Journal

Radiology artificial intelligence (AI) is advancing however its adoption faces fragile foundations that threaten sustainability. Despite bold promises of efficiency and accuracy, current deployment is undermined by weaknesses in economics, evidence, infrastructure, human factors, regulation, security, and environmental impact. Nearly 90% of radiology AI studies report process metrics rather than patient outcomes, while hidden costs elevate ownership to 400% to 500% of subscription fees. Technical fragilities include 25% or greater performance loss with routine protocol or scanner shifts, compounded by vendor consolidation that has eliminated 63% of companies since 2020, creating migration costs averaging 180,000 dollars per exit. Human factor …

Scper: A Rigorous Computational Approach To Determine Cellular Subtypes In Tumors Aligned With Cancer Phenotypes From Total Rna Sequencing, Bingrui Li, Xiaobo Zhou, Raghu Kalluri

Faculty, Staff and Student Publications

Single-cell RNA sequencing (scRNA-seq) is a powerful technique for understanding cellular diversity, but processing large patient cohorts to identify phenotype-associated cell populations remains challenging. Here, scPER (Estimating cell Proportions using single-cell RNA-seq Reference), a rigorous approach combining adversarial autoencoder and extreme gradient boosting to estimate tumor microenvironment cell compositions and identify phenotype-associated subclusters for bulk RNA-seq samples. Integrating scRNA-seq datasets from diverse studies, scPER constructed comprehensive reference panels and disentangled confounders from true signals. scPER achieved superior accuracy in cellular proportion estimation compared to CIBERSORTx, BayesPrism, Scaden, MuSiC, SCDC, DeSide and ReCIDE. It showed high accuracy in predicting metastatic melanoma …

Inferring High-Fat Dietary Patterns From Electronic Health Record Data Using Machine Learning, Ya-Yun Yeh, Hsin-Yueh Lin, Jingchuan Guo, Ramon C Sun, Sizun Jiang, Jiang Bian, Hao Dai

Faculty, Staff and Student Publications

Objectives: Electronic health records (EHRs) rarely capture dietary detail, limiting diet-disease research. We aimed to develop machine learning (ML) computable phenotypes to identify high-fat diet (HFD) using variables typically available in EHRs.

Materials and methods: We used National Health and Nutrition Examination Survey (NHANES) 1999-2020 data, where 24-h dietary recall served as ground truth. Dietary fat intake was summarized into a score (0-30) based on percent energy from fat, carbohydrate, and protein; lower scores indicated HFD. We defined HFD at cutoffs of 10, 15, and 20, and trained ML models (Extreme Gradient Boosting, logistic regression, random forest) using EHR-compatible variables …

Obstructive Sleep Apnea Prediction: A Comprehensive Review And Comparative Study, Thi Khanh Chi Huynh, Amonae Dabbs-Brown, Anna Jurek-Loughrey, James Mulhall, Tuan Dung Pham, Ngoc Phu Doan, Viet Hung Tran, Zichi Zhang, Xuan Hoang Nguyen, Yimeng An, Peixin Li, Phi Hung Nguyen, Thi Linh Hoang, Xinming Shi, Hans Vandierendonck, Sebastien Bailly, Jean-Louis Pépin, Thai Son Mai

Research Collection School Of Computing and Information Systems

Obstructive Sleep Apnea (OSA) is a highly prevalent sleep disorder linked to considerable public health burdens and comorbidities. However, its heterogeneous presentation and the limited accessibility of traditional diagnostic tools such as polysomnography (PSG) lead to widespread underdiagnosis. As a result, artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, have attracted attention as an alternative pathway to detection. This paper first provides a comprehensive review of AI-driven OSA diagnosis, covering different diagnosis problems, input-data types, data biases, pre-processing techniques, and model performance. We then leverage the largest clinical dataset used in OSA prediction to date, …

Prediction Of First And Multiple Antiretroviral Therapy Interruptions In People Living With Hiv: Comparative Survival Analysis Using Cox And Explainable Machine Learning Models, Donald Salami, Emily Koech, Janet Turan, Kristen Stafford, Lilly Nyagah, Stephen Ohakanu, Anthony Ngugi, Manhattan Charurat

Institute for Human Development, East Africa

Background: The Cox proportional hazards (CPH) model is a common choice for analyzing time-to-treatment interruptions in patients on antiretroviral therapy (ART), valued for its straightforward interpretability and flexibility in handling time-dependent covariates. Machine learning (ML) models have increasingly been adapted for handling temporal data, with added advantages of handling complex, nonlinear relationships and large datasets, and providing clear practical interpretations.

Objective: This study aims to compare the predictive performance of the traditional CPH model and ML models in predicting treatment interruptions among patients on ART, while also providing both global and individual-level explanations to support personalized, data-driven interventions …

Aladdin: A Machine Learning Approach To Enhance The Prediction Of Significant Fibrosis Or Higher In Metabolic Dysfunction-Associated Steatotic Liver Disease, Naim Alkhouri, Terry Cheuk-Fung Yip, Laurent Castera, Marina Takawy, Leon A Adams, Nipun Verma, Juan Pablo Arab, Syed-Mohammed Jafri, Bihui Zhong, Julie Dubourg, Vincent L Chen, Ashwani K Singal, Luis Antonio Díaz, Nicholas Dunn, Rida Nadeem, Vincent Wai-Sun Wong, Manal F Abdelmalek, Zhengyi Wang, Ajay Duseja, Yousef Almahanna, Haya A Omeish, Junzhao Ye, Stephen A Harrison, Jessica Cristiu, Marco Arrese, Sage Robert, Grace Lai-Hung Wong, Amani Bajunayd, Congxiang Shao, Matthew Kubina, Winston Dunn

Faculty, Staff and Students Publications

Introduction: The recent US Food and Drug Administration approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis in patients necessitates patient selection for significant fibrosis or higher (≥F2). No existing vibration-controlled transient elastography (VCTE) algorithm targets ≥F2.

Methods: The mAchine Learning ADvanceD fibrosis and rIsk metabolic dysfunction-associated steatohepatitis Novel predictor (ALADDIN) study addressed this gap by introducing a machine-learning-based web calculator that estimates the likelihood of significant fibrosis using routine laboratory parameters with and without VCTE. Our study included a training set of 827 patients, a testing set of 504 patients with biopsy-confirmed metabolic dysfunction-associated steatotic liver disease from 6 centers, …

Ai-Driven Stratified Modeling For Early Liver Disease Detection: A Comparative Study Of Ensemble And Conventional Machine Learning Classifiers, Ghadeer Murtadha Ali, Ali Aqeel Hadi, Mustafa Abdulkareem Abbas, Abdullah Alkarar Mohammad, Ahmed Fadhil Abdulhussein

AUIQ Technical Engineering Science

Background: Early prediction of liver disease remains challenging in routine clinical diagnostics due to the multifactorial nature of hepatic dysfunction and the limited discriminative capacity of conventional laboratory-only assessments.

Objective: This study aims to develop and rigorously evaluate a robust machine learning framework for binary liver disease classification, emphasizing predictive stability, diagnostic balance (sensitivity–specificity), and statistical reproducibility across repeated experiments.

Methodology: A structured dataset of 1,700 records with 11 features representing demographic, behavioral, genetic, and clinical determinants was used to train and compare five supervised models: CatBoost, AdaBoost, Random Forest, Support Vector Machine (SVM), and Decision Tree. Performance was assessed …