Buchumschlag

Machine Learning Algorithms for Detecting Mental Stress in College Students

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Singh, Ashutosh, Singh, Khushdeep, Kumar, Amit, Shrivastava, Abhishek, Kumar, Santosh
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Machine Learning Computers and Society
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Predicting and Understanding College Student Mental Health with Interpretable Machine Learning
von: Chowdhury, Meghna Roy, et al.
Veröffentlicht: (2025)

Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring
von: Ovi, Md Sultanul Islam, et al.
Veröffentlicht: (2025)

The Biosecurity Blind Spot: Systematic Dual-use Detection in Open Science Infrastructure
von: Sharma, Vasudha, et al.
Veröffentlicht: (2026)

Unlocking Mental Health: Exploring College Students' Well-being through Smartphone Behaviors
von: Xuan, Wei, et al.
Veröffentlicht: (2025)

Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges
von: Sahili, Zahraa Al, et al.
Veröffentlicht: (2024)

Machine Learning-Driven Student Performance Prediction for Enhancing Tiered Instruction
von: Chen, Yawen, et al.
Veröffentlicht: (2025)

Advanced Gesture Recognition for Autism Spectrum Disorder Detection: Integrating YOLOv7, Video Augmentation, and VideoMAE for Naturalistic Video Analysis
von: Singh, Amit Kumar, et al.
Veröffentlicht: (2024)

The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions
von: Ebrahimi, Maryam, et al.
Veröffentlicht: (2025)

Breaking the Cycle of Incarceration With Targeted Mental Health Outreach: A Case Study in Machine Learning for Public Policy
von: Rodolfa, Kit T., et al.
Veröffentlicht: (2025)

Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning
von: Wang, Chun, et al.
Veröffentlicht: (2024)

Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models
von: Kumar, Abhishek, et al.
Veröffentlicht: (2024)

SoK: Machine Learning for Misinformation Detection
von: Xiao, Madelyne, et al.
Veröffentlicht: (2023)

Development and Validation of a Machine Learning Algorithm for Clinical Wellness Visit Classification in Cats and Dogs
von: Szlosek, Donald, et al.
Veröffentlicht: (2024)

Model Multiplicity and Predictive Arbitrariness in Recidivism Risk Assessment
von: Singh, Ashwin, et al.
Veröffentlicht: (2026)

Convolutional Neural Networks can achieve binary bail judgement classification
von: Barman, Amit, et al.
Veröffentlicht: (2024)

Comparative Evaluation of Embedding Representations for Financial News Sentiment Analysis
von: Roy, Joyjit, et al.
Veröffentlicht: (2025)

Evaluation of Machine Learning Models in Student Academic Performance Prediction
von: Sandeepa, A. G. R., et al.
Veröffentlicht: (2025)

Fairness-Aware Few-Shot Learning for Audio-Visual Stress Detection
von: Shelke, Anushka Sanjay, et al.
Veröffentlicht: (2025)

Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotation
von: Stanley, Miles, et al.
Veröffentlicht: (2026)

Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising
von: Schechtman, Kara, et al.
Veröffentlicht: (2025)

Detecting Struggling Student Programmers using Proficiency Taxonomies
von: Schwartz, Noga, et al.
Veröffentlicht: (2025)

Estimating Item Difficulty Using Large Language Models and Tree-Based Machine Learning Algorithms
von: Razavi, Pooya, et al.
Veröffentlicht: (2025)

Human-AI Co-Mentorship in Project-Based Learning: A Case Study in Financial Forecasting
von: Chawla, Freyaa, et al.
Veröffentlicht: (2026)

Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study
von: Qu, Xiaodong, et al.
Veröffentlicht: (2024)

MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept
von: Tkáč, Michal, et al.
Veröffentlicht: (2024)

Machine Learning in Epidemiology
von: Wright, Marvin N., et al.
Veröffentlicht: (2026)

Use of Boosting Algorithms in Household-Level Poverty Measurement: A Machine Learning Approach to Predict and Classify Household Wealth Quintiles in the Philippines
von: Salvador, Erika Lynet
Veröffentlicht: (2024)

Democratizing Signal Processing and Machine Learning: Math Learning Equity for Elementary and Middle School Students
von: Vaswani, Namrata, et al.
Veröffentlicht: (2024)

The Role of Learning Algorithms in Collective Action
von: Ben-Dov, Omri, et al.
Veröffentlicht: (2024)

Know, Grow, and Protect Net Worth: Using ML for Asset Protection by Preventing Overdraft Fees
von: Kumar, Avishek, et al.
Veröffentlicht: (2023)

"Which LLM should I use?": Evaluating LLMs for tasks performed by Undergraduate Computer Science Students
von: Agarwal, Vibhor, et al.
Veröffentlicht: (2024)

DECAF: Learning to be Fair in Multi-agent Resource Allocation
von: Kumar, Ashwin, et al.
Veröffentlicht: (2025)

Real-Time Stress Monitoring, Detection, and Management in College Students: A Wearable Technology and Machine-Learning Approach
von: Ta, Alan, et al.
Veröffentlicht: (2025)

Leveraging Machine Learning for Early Autism Detection via INDT-ASD Indian Database
von: Shrivastava, Trapti, et al.
Veröffentlicht: (2024)

Non-Determinism and the Lawlessness of Machine Learning Code
von: Cooper, A. Feder, et al.
Veröffentlicht: (2022)

Insights From Insurance for Fair Machine Learning
von: Fröhlich, Christian, et al.
Veröffentlicht: (2023)

Global Patterns in Student Stress and Academic Performance: A Machine Learning Study Using PISA 2022
von: Ghazanchyan, Ani, et al.
Veröffentlicht: (2026)

Detection of Problem Gambling with Less Features Using Machine Learning Methods
von: Jiao, Yang, et al.
Veröffentlicht: (2024)

Temporal and Between-Group Variability in College Dropout Prediction
von: Glandorf, Dominik, et al.
Veröffentlicht: (2024)

Quantum-Enhanced Forecasting for Deep Reinforcement Learning in Algorithmic Trading
von: Chen, Jun-Hao, et al.
Veröffentlicht: (2025)