Digital Commons Network^™

An Interpretable Remote Sensing–Machine Learning Framework For Wildfire Soil Burn Severity Prediction In The Northwestern United States, Subhankar Das, Mariana Dobre, Roger Lew, Sarah A. Lewis, Erin S. Brooks, Peter R. Robichaud, Anurag Srivastava, Mary E. Miller, Alex M. Watanabe, Marta Basso

Michigan Tech Publications

Background: Although soil burn severity (SBS) is traditionally mapped post-fire, pre-fire predictive approaches remain limited despite their potential to identify areas at risk of soil degradation, runoff and erosion. Aim: To develop a large-scale model for predicting post-fire SBS from pre-fire conditions at ~30 m spatial resolution. Methods: A harmonized dataset of 62 wildfires (2018–2024) was used to build machine learning (ML) frameworks based on Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), using pre-fire vegetation, climate, soil–terrain and anthropogenic predictors. Performance was evaluated using five-fold cross-validation (CV), leave-one-fire-out (LOFO) validation and independent testing on 2025 …

Ensemble Learning Framework For Predicting Close Proximity Tire–Pavement Noise On Expressways, Woo Young Cho, Jin Hwan Kim, Guk Gon Song, Kyungnam Kim, Youngguk Seo

Faculty Articles

Traffic noise is a critical public health concern affecting millions of highway users and adjacent residents worldwide. In response, many transportation agencies have adopted functional surface materials to reduce noise at the source on pavement, but assessing their effectiveness remains expensive and logistically challenging. Close Proximity (CPX) testing quantifies tire-pavement noise but requires specialized equipment costing $50,000-$126,000 and is limited to existing pavement, preventing proactive noise assessment during pavement design. This study develops machine learning models to predict CPX noise levels from readily available pavement characteristics, eliminating the need for costly tests during design and planning phases. To train and …

Machine Learning In Flow Cytometry For Acute Myeloid Leukemia Diagnosis: A Scoping Review, Katrina Jezzela M. Dela Pena Md, Mls (Ascp)

Other Specialties

Manuscript version:

Introduction: Flow cytometry is essential for the diagnosis of acute myeloid leukemia (AML), but conventional analysis is labor-intensive, operator-dependent, and increasingly complex. Machine learning (ML) may support automated analysis; however, its readiness for clinical implementation remains unclear. This scoping review mapped ML approaches applied to flow cytometry for AML-related diagnosis.

Methods: PubMed was searched for English-language primary studies applying artificial intelligence, ML, or deep learning to human flow cytometry data for AML diagnosis or classification. Eligible studies reported diagnostic performance metrics. Study characteristics, preprocessing, data representation, model architecture, validation strategy, performance metrics, interpretability, and implementation considerations were descriptively …

Predicting Rental Prices For Dubai Properties, Abdulrahman Mohammad Arab

Theses

This study explores the application of machine learning models to predict rental prices in the dynamic real estate market of Dubai. With increasing demand for data-driven valuation tools, accurate rental price estimation has become essential for tenants, landlords, investors, and property consultants. The research is grounded in the theory of supervised machine learning, focusing on the use of structured property features—such as location, size, furnishing status, and number of rooms—to forecast monthly rent values. The study employed the “Dubai Real Estate Goldmine, UAE Rental Market Data” from Kaggle as its primary dataset. This data, which reflects thousands of residential listings …

Bridging Models And Real-World Performance: Capturing Represented, Hidden, And Unrepresented Dynamics In Complex Systems, Dixizi Liu

All Dissertations

Complex systems often depend on adaptive performance that is difficult to capture using structured data or formal models alone. This dissertation asks how predictive modeling, coordination-cost measurement, and simulation can be integrated to better represent the visible, hidden, and unrepresented dynamics that shape real-world system performance.

Sterile Processing Departments (SPDs) provide the empirical context for this work because they support safe and timely surgical care while operating under high workload variability, time pressure, and tight coordination with operating rooms. Hospitals rely on SPDs to clean, assemble, sterilize, and deliver surgical instrument trays so surgeries can start on time and proceed …

From Geometry To Recognition: A Quantitative Framework For Tcr-Pmhc Structural Analysis In The Age Of Predicted Structures, Kun Hee Kim

Dissertations and Theses (Open Access)

Recognition of antigen by a T cell begins with a single physical event, the engagement of the T cell receptor (TCR) with a peptide-major histocompatibility complex (pMHC), and this interaction is encoded in the geometry with which the receptor docks onto the composite pMHC surface. Whether that docking geometry is biologically organized, and whether the structures now produced at scale by prediction preserve recognition or only reproduce that organization, are the two questions this thesis addresses. Two gaps stand in the way. The first is a gap of measurement, because docking geometry has never been made quantitatively comparable across many …

A Hybrid Machine Learning-Based Feasibility Prediction Of 3d Mechanical Designs Using Scalar And Geometric Features, Md Mohsin Uddin Fahim

Open Access Theses & Dissertations

The computational bottleneck of structural feasibility screening frequently hinders the transition from a digital 3D model to a physically manufactured component. Traditionally, engineers have relied on either overly rigid heuristic constraint checks or computationally exhaustive finite element simulations. To address this inefficiency, this thesis proposes and validates a hybrid machine-learning framework to predict the structural manufacturability of 3D mechanical designs. Moving beyond the conventional reliance on isolated scalar parameters, the proposed methodology extracts and integrates both scalar manufacturing constraints (e.g., tolerance, minimum feature thickness) and spatial geometric descriptors (e.g., bounding volume, aspect ratio) directly from STL mesh data. Utilizing a …

Unsupervised Learning For Minimum Error Adaptive Sampling Of Atmospheric Vertical Temperature Profiles, Alejandro Medina

Open Access Theses & Dissertations

Uncrewed aerial systems (UAS) collect atmospheric data on fixed schedules, and endurance limits make blind searching costly. This thesis develops an unsupervised representation that summarizes a multi-decade radiosonde archive into a compact library of atmospheric states, giving a UAS an expectation of the column before it flies. The method standardizes both axes of a profile against the sounding's own surface conditions, which makes the representation independent of season and of station elevation. Applied to 27,270 soundings from Norman, Oklahoma, over the lowest 1.5 km of the atmosphere, 12 representative profiles reconstruct the record to within 1.0 °C of mean absolute …

Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems, Niharika Deshpande

Engineering Management & Systems Engineering Theses & Dissertations

Many physical and networked systems evolve under continuously changing spatial and temporal conditions. Transportation networks respond to fluctuating demand, atmospheric fields reorganize as storms intensify, and coastal response depends on localized forcing pathways. Modeling such systems requires learning formulations that adapt to evolving structure, operate on irregular geometries, and provide interpretable measures of predictive uncertainty. This dissertation develops a physics-guided spatiotemporal learning framework designed for structured dynamical systems whose governing interactions are neither static nor Euclidean. The central premise is that spatial relationships in these systems are dynamic and geometry-dependent. To represent this behavior, system states are modeled on time-varying …

Integrating Ai-Based Electricity Demand Forecasting With Solar Grid Planning To Enhance Sustainability And Reliability, Anas Thamer Mustafa, Omar Sharaf Al-Deen Al-Yozbaky

AUIQ Technical Engineering Science

Proper electricity-demand forecasting is essential for reliable power-system planning, particularly in urban networks facing rapid demand growth and transformer overloading. However, many previous studies have treated load forecasting and renewable-energy integration as separate tasks, which limits their usefulness for practical planning. This study develops an integrated forecasting–planning framework that links AI-based electricity-demand forecasting with photovoltaic (PV) system design and transformer-loading assessment. The framework is applied to real daily data from the Al-Intisar 132/33 kV substation in Mosul, Iraq, covering electrical load, temperature, population, and date-related variables for the period 2022–2024. Fourteen forecasting models from four methodological categories were evaluated: machine-learning …

Developing A Fitted Cutoff Function For Mtp, Michael Wlochal, Doyl Dickel

Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science

Machine learning interatomic potentials (MLIPs) bridge the gap between high-accuracy quantum mechanics and high-speed classical force fields, enabling quantum-accurate, large-scale molecular dynamics. Cutoff functions, which smoothly truncate atomic interactions beyond a specific radial neighborhood are crucial due to their local scaling and computational efficiency. However, geometric constraints and boundary discontinuities—particularly at the short- range inner cutoff can cause severe force spikes and simulation instabilities. This work investigates Moment Tensor Potentials (MTP) with different cutoff functions using a diverse benchmark suite encompassing three distinct structural phases: bec metals (Li, Mo), fcc metals (Cu, Ni), and diamond-structure semiconductors (Si, Ge) to span …

Evaluation Of Gridded Precipitation Products And Machine-Learning Models For Rainfall Prediction In Arid Regions - Case Study In Qassim Region, Ksa, Ragab Rabeiy

Yanbu Journal of Engineering and Science

Receiving rainfall predictions in dry regions such as Saudi Arabia is challenging due to limited observational stations and the temporal and spatial variability of rainfall events. This study explores the performance of nine remote sensing rainfall datasets over the Qassim region of central Saudi Arabia. High-resolution rainfall data from ground-based sources were used to evaluate the accuracy of CHIRPS, TRMM, GPM, PERSIANN, CMORPH, CFSR, TerraClimate, TerraClimate_Monthly, and ERA5 datasets. Performance metrics such as R2, RMSE, MAE, and bias were calculated to quantify the accuracy of each dataset. Analysis revealed that ERA5 generally demonstrated better accuracy than the other datasets with …

Application Of Machine Learning For Correcting Defect-Induced Neuromorphic Circuit Inference Errors, Vedant V. Sawal, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

This paper presents a machine learning-based approach to correct inference errors caused by stuck-at faults in fully analog ReRAM-based neuromorphic circuits. Using a Design-Technology Co-Optimization (DTCO) simulation framework, the study models and analyzes six spatial defect types - circular, circular-complement, ring, row, column, and checkerboard - across multiple layers of a multi-array neuromorphic architecture for handwritten digit recognition tasks. It is demonstrated that the proposed correction method, which employs a lightweight neural network trained on the circuit's 10 output voltages, can improve inference accuracy from 55% to 90% (+ 35 percentage points) in highly degraded defective scenarios. Results show that …

A Machine Learning Approach For Water Quality Assessment In The Lower Rio Grande Valley Watershed, Saika Nowshin Nowrin, Chu-Lin Cheng, Jungseok Ho, Jinwoo An, Fatemeh Nazari

Civil Engineering Faculty Publications

Water quality analysis plays an essential role in maintaining the health and sustainability of river ecosystems, especially in semi-arid regions like the Arroyo Colorado Watershed in South Texas. Since the river is a vital source of water supply for local communities, agriculture, and wildlife, it faces significant challenges and pollution from land use changes, climate variation, and agricultural runoff. Continuous monitoring and assessment of water quality parameters and their temporal variability are essential to ensure the drinking water supply and aquatic ecosystem health. However, comprehensive laboratory-based water quality investigations are often constrained by higher costs, logistical complexity, and limited manpower. …

Privacy-Preserving Intrusion Detection For The Internet Of Medical Things Using Ensemble And Federated Learning, Theyab Alsolami

Electronic Theses and Dissertations 2020 - Present

The rapid proliferation of the Internet of Medical Things (IoMT) has transformed healthcare by enabling continuous monitoring, intelligent diagnostics, and data-driven clinical decision-making. However, this increased connectivity has significantly expanded the attack surface of healthcare systems, exposing sensitive patient data and critical medical devices to cyber threats such as intrusion and data exfiltration attacks. Ensuring both strong security and strict privacy preservation in IoMT environments remains a fundamental and unresolved challenge.

This dissertation investigates the design and evaluation of robust and privacy-preserving intrusion detection systems (IDS) for IoMT networks using advanced machine learning techniques. The research first examines the effectiveness …

A Holistic Approach For Workforce Scheduling And Routing, Kerem Can Manalp, Ansel Kaplan Erol, Kutluhan Erol, Cem Evrendi̇lek

Turkish Journal of Electrical Engineering and Computer Sciences

The workforce scheduling and routing problem (WSRP) involves assigning tasks across multiple locations while accounting for varying travel times, service durations, time windows, and skill requirements in a wide range of industries, from healthcare to telecommunications. This paper presents a mixed-integer programming model for the WSRP that balances the trade-off between cost and customer satisfaction using a score-generation function and subsequently evaluates the trade-off between solution quality and computation time for several algorithms on well-known datasets. We demonstrate that our model effectively balances cost, service-level agreement satisfaction, and task priorities while providing high-quality solutions in a timely manner. Observing that …

Quantitative Analysis Of Sorghum Starch Using Machine Learning And Near-Infrared Spectroscopy, Li Ke, Ouyang Runkun, Dai Yijie, Li Jie, Hou Aixiang, Yang Yu, Li Zongjun

Food and Machinery

[Objective] To establish a model for the rapid quantification of sorghum starch based on near-infrared spectroscopy and machine learning algorithms. [Methods] Brewing sorghum was used as the raw material. Near-infrared diffuse reflectance spectra were collected using a near-infrared spectrometer. The Isolation Forest algorithm was employed to remove outlier samples and optimize the dataset. Subsequently, multiple preprocessing methods and regression models were compared using various modeling software to analyze and establish a quantitative model for sorghum starch. [Results] After preprocessing with the Isolation Forest algorithm, the partial least squares regression (PLSR) model exhibited the best performance, with a coefficient of determination …

Advances In The Application Of Electroencephalography In Food Sensory Science, Li Wenbin, Hou Xiaoyi, Deng Na, Leng Tuo, Fu Haohua, Wang Jianhui

Food and Machinery

Traditional sensory evaluation of food is affected by subjectivity and other factors, resulting in significant limitations. Electroencephalography (EEG), owing to its advantages of high temporal resolution, non-invasiveness, and real-time dynamic monitoring, offers an objective neurophysiological perspective and analytical approach to address this issue, effectively advancing food science research from subjective descriptions to objective neural decoding. This paper systematically reviews the applications of EEG in food flavor recognition and prediction, objective evaluation of food quality, and analysis of food nutrition and consumer behavior. In light of the current state of technological development, it is proposed that multimodal technology integration and machine …

To Study The Reward And Recognition Practices On Employee Motivation And Performance With Referenceto It Industry In Pune Region, Harsha Sammangi, Aditya Jagatha, Ruthwik Gullipalli

Research & Publications

Machine learning has substantially improved consumer credit-risk prediction, yet its deployment in lending decisionsraises persistent concerns regarding demographic fairness, financial exclusion, explainability, and regulatory defensibility. Thisstudy develops and empirically evaluates a Fairness-Aware Credit Intelligence (FACI) framework — a four-layer architectureintegrating predictive modeling, in- and post-processing fairness intervention, explainability and human policy override, andportfolio-level simulation and governance — using loan-level data from 412,683 consumer lending applications spanning 2021–2026. The study compares a traditional credit scorecard, gradient boosting and deep neural network models, two single-constraint fairness-aware models (demographic parity and equal opportunity), and the integrated FACI framework acrosspredictive accuracy (AUC-ROC), approval rates, …

Artificial Intelligence In Pharmaceutical Care: Transforming Drug Discovery, Development, And Patient-Care Delivery, Abdulrahman G. Alharbi, Abdulrahman A. Aljabri

Saudi Medical Journal

The integration of artificial intelligence (AI) into pharmaceutical care represents a paradigm shift in healthcare delivery, offering unprecedented opportunities to revolutionize medication management, accelerate drug development, and enhance patient outcomes. This comprehensive review synthesized evidence from global literature (2021–2025) examining AI applications across drug discovery and development, clinical trials optimization, medication therapy management, and pharmacy operations. The AI demonstrated prediction accuracies of 86–98% in drug discovery, 80% improvement in clinical trial recruitment efficiency, and 32.7% increase in medication adherence over standard care. The global AI drug discovery market, valued at \$1.5 billion (2023), is projected to reach \$11.8 billion by …

Coupled-Pendulum Modeling In An Ode Class: An Assignment On Fourier-Initialized Gradient Descent In Machine Learning, Huy Truong, Andrew Bennett

CODEE Journal

As data-driven methods are increasingly used in science and engineering, students benefit from learning to integrate machine learning techniques with traditional mathematical modeling. We present a hands-on extra-credit assignment for an undergraduate ordinary differential equations (ODE) course that enables students to compare classical analytical methods with data-driven approaches on the same physical system. Using a coupled-pendulum system---two pendulums connected by a spring---with real experimental data acquired via video tracking of a real physical setup, students work through three models in a guided Jupyter notebook with all code provided. First, they fit a neural network with Fourier features as a purely …

Ai-Driven Detection Of Neurodevelopmental Disorder From Emotional Speech Using A Hybrid Cnn–Bilstm–Attention Framework, Nayarah Shabir, Parveen Lehana, Sheema Khan

School of Medicine Publications

Neurodevelopmental disorders (NDDs) are associated with impairments in communication, behavior, and social interaction, making accurate diagnosis clinically challenging. Autism Spectrum Disorder (ASD), a major NDD, often exhibits atypical speech patterns characterized by altered prosody and reduced emotional expressiveness. The study proposes a hybrid dual-path framework for ASD detection from emotional speech using two strategies: PCA–GMM-based acoustic modeling and a CNN–BiLSTM–Attention architecture for spectral–temporal feature learning. The proposed framework captures probabilistic, spectral, and temporal speech characteristics for robust ASD classification. Acoustic analysis demonstrated clear separability between ASD and non-ASD speech, while the deep learning framework achieved stable and reliable performance across …

Machine Learning-Based Forecasting Of Energy Consumption In A Nigerian Tertiary Institution, O. A. Adebimpe, S. S. Showunmi, F. T. Adedeji

Engineering and Technology Journal

An accurate electricity consumption forecast is key to effective energy planning and management in Nigerian tertiary institutions. While applications of machine learning (ML) in forecasting have increased, their deployment in predicting energy consumption in Nigerian tertiary institutions has been limited. Also, the significance of weather-related variables, academic and non-academic activities, and the staff and students' population in predicting energy consumption at a tertiary institution has been underexplored. This study addresses these by developing and evaluating six forecasting models, including Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX), Support Vector Regression (SVR), Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), …

Smart-Twin: Systematic Modeling And Real-Time Transportation With Digital Twins, Hovannes Kulhandjian

Mineta Transportation Institute

Urban transportation networks are increasingly affected by everyday congestion, non-recurring disruptions, and limited real-time visibility into what is happening on the road. Transportation agencies require advanced tools that support faster, proactive management and data-driven infrastructure decisions rather than passive monitoring. This report presents SMART-TWIN, an Artificial Intelligence (AI)–driven Digital Twin framework designed for real-time traffic operations, disruption detection, and performance-based decision-making support. The SMART-TWIN architecture integrates microscopic traffic simulation (detailed traffic modeling), long-horizon synthetic demand generation simulating real travel patterns, supervised machine learning, and performance analysis within a unified and reproducible workflow. A corridor-scale Digital Twin was developed for the …

Interpretable Machine Learning Of Plasma Proteomics Reveals Stage-Specific Signatures Across The Alzheimer's Disease Continuum, Hemshankar Laugi

2026 Spring Honors Capstones Projects

Alzheimer’s pathology begins years before clinical symptoms, starting early with amyloid-β accumulation, followed by tau deposition and neurodegeneration. Although existing tools, such as cerebrospinal fluid (CSF) screening and PET/MRI imaging, can accurately track disease progression, they are invasive, expensive, and not scalable for population-level screening. So, there is a growing interest in using blood-based plasma biomarkers as a scalable alternative. While recent studies demonstrate strong predictive performance with plasma biomarkers, most rely on a small set of canonical blood-based protein biomarkers such as amyloid-β, p-tau, and neurofilament light. These biomarkers primarily reflect downstream brain pathology and may fail to capture …

Ai-Powered Synthetic Biology: Current Situation, Challenges, And Future Perspectives, Izem Olcay Sahin, Nuriye Gokce, Duygu T. Yildirim, A. Baki Yildirim, Hilal Akalın, Donald Martin, Tommaso Beccari, Oscar Vicente, Iza Radecka, Fideline Tchuenbou-Magaia, Robert S. Marks, Ratnesh Lal, Satya Prakash, Adam Mechler, Mario Petrov Milkov, Svetlana Fotkova Georgieva, Ilia Iliev, Kosi Gramatikoff, Ed Judge, Milica Markovic, Radka Kaneva, Galina Aleksieva Yaneva, Nadya Vasileva Agova, Nikoleta Dobromirova Ivanova, Mariya Kiryakova Tsvetkova, Ivelin Rosenov Iliev, Michel Salzet, Kisung Ko, Michele Maffia, Chiara Coppola, Matteo Bertelli

Research Outputs: 2025-Present

Synthetic biology has evolved from a set of engineering aspirations to an operationally sophisticated discipline, and artificial intelligence (AI) is its fastest-growing accelerant. This review traces that convergence across six interlocking domains: systems-level biological modeling, de novo protein engineering, metabolic and microbial programming, multi-omics data integration, regulatory element design, and clinical translation. For each domain, we survey established results, integrate findings from 2010–2026 literature, and articulate the trajectories that will define the next decade. Emerging themes include physics-informed neural networks for mechanistically constrained biological modeling, drug design, and federated learning architectures that allow global omics collaboration without centralizing sensitive data, …

Hsv-1 Us3 Hijacks Conserved Actin Regulatory Complexes To Drive F-Actin Remodeling, Md Imran Hossain, Md Arifuzzaman, Md Mehedi Hasan, Seung Jong Park, Leila Rahimian, Ojasvi Dutta, Vladimir Chouljenko, Harikrishnan Mohan, Reza Ghavimi, Konstantin G. Kousoulas

Computer Science Faculty Research & Creative Works

The herpes simplex virus 1 (HSV-1) US3 is a multifunctional serine/threonine kinase that promotes HSV-1 replication and spread. But its role and the mechanisms by which US3 regulates actin cytoskeletal remodeling remain poorly defined. We combined flow cytometry, confocal microscopy, immunoprecipitation-mass spectrometry (IP-MS), protein complex mapping, and machine learning to characterize US3-mediated F-actin dynamics. Flow cytometry and confocal microscopy showed that wild-type HSV-1 induces significant F-actin remodeling, while the ΔUS3 mutant displays F-actin levels comparable to uninfected cells, identifying US3 as a key regulator. IP-MS identified 47 high-confidence US3 interactors enriched in conserved actin regulatory complexes, including Arp2/3 nucleation machinery, …

Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models, Chunyan Liu, Yan Lei, Huan Xie, Jinping Wang, Yue Yu, David Lo

Research Collection School Of Computing and Information Systems

Learning-based dynamic fault localization techniques play a crucial role in the field of software engineering. These techniques dynamically execute test cases to meticulously extract useful knowledge from the execution information in the program, with the aim of identifying fault locations by leveraging machine learning, deep learning, and large language models. Currently, there is already a flourishing body of research that is intensely focused on learning-based dynamic fault localization. Research literature can be categorized into two main aspects for learning-based dynamic fault localization: data-based enhancements (i.e., the datasets) and model-based enhancements (i.e., the suspiciousness algorithms). Thus, we conduct an extensive literature …

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …

Confidenceai: A Wearable And Predictive Ai Ecosystem, Denzil Paul, Xuejun Kong, Raymond Wang, Flora Fleith Lopes, Zhengjia Yang, Brain Chu, Weijian Wang, Leo Chen

Paul English Applied Artificial Intelligence (AI) Institute Publications

This group project presents ConfidenceAI, a conceptual wearable and predictive artificial intelligence ecosystem designed to help teenagers build confidence and manage stress in high-pressure situations such as classroom participation and public speaking. The system combines wrist-based biosensing with a personalized machine-learning model that uses physiological signals, including heart rate and heart rate variability, electrodermal activity, skin temperature, and movement, to learn an individual's baseline patterns and anticipate heightened stress responses. Rather than relying on universal thresholds, ConfidenceAI is designed to personalize the timing, method, and delivery of interventions based on each user's physiological patterns and prior responses. The proposed ecosystem …