This study examines the performance trade-offs between traditional machine learning methods and large language models (LLMs) in classification tasks across two representative datasets. We evaluate five traditional ML algorithms (Logistic Regression, SVM, Random Forest, XGBoost, and Gradient Boosting) against three LLMs (Llama 3.1 8B, Mistral 7B, and Gemma 3 4B) in both zero-shot and LoRA fine-tuned configurations. Our evaluation focuses on the Activity Recognition using Embedded Mobile Sensors (AReM) dataset for structured sensor data classification and the Twenty Newsgroups dataset for text classification. Traditional ML methods maintain substantial advantages in both accuracy and computational efficiency. On structured data (AReM), traditional ML outperforms the best fine-tuned LLM by 41.4
Accurate classification of knee X-rays across diverse hardware remains a significant challenge due to domain shifts induced by varying scanner specifications, acquisition protocols, and post-processing software. This study investigates out-of-distribution (OOD) generalization techniques to improve the classification of knee calcium deposition across seven distinct X-ray scanners. We evaluated six OOD algorithms, including Empirical Risk Minimization (ERM), SagNet, GroupDRO, Mixup, Adaptive Risk Minimization (ARM), and Invariant Risk Minimization (IRM) to assess the robustness of Vision Transformer-based (DINOv2) versus convolutional (ResNet50) feature extractors within a supervised learning framework. Our experimental results demonstrate that DINOv2-extracted features generalized significantly better to unseen scanners than ResNet50. Among the tested OOD algorithms, SagNet emerged as the most robust approach, achieving 92.6 ± 0.20 and a p-value of 2.9× 10^-10 across the seven scanners by effectively decoupling anatomical content from scanner-specific style. In contrast, IRM exhibited instability across training and testing domains, yielding 85.1
Traditional transportation models inadequately account for how crime risk influences urban mobility decisions, treating safety as a secondary factor despite its significant impact on travel behavior. We develop a multi-task pipeline framework that simultaneously predicts five interconnected outcomes: crime counts, area-level risk classification, route safety assessment, transportation mode choice, and individual vulnerability. Our approach leverages shared feature engineering while maintaining task-specific models to capture complex crime-mobility interdependencies. Using the NetMob Challenge 2025 dataset containing 80,697 trips across 600 French geographic areas with integrated national crime statistics, we employ Random Forest models enhanced with interpretability analysis for actionable insights. The framework demonstrates strong predictive performance across all tasks: crime count regression (R ^2 = 0.847), crime risk classification (accuracy = 90.79
We present an integrated AI framework that predicts song popularity and analyzes sentiment directly from raw audio, using advanced feature extraction and machine learning. The system, implemented as a web application, enables industry professionals to make data-driven decisions based on objective audio indicators. Experiments on real-world data confirm the effectiveness of our approach. We detail the system’s design and discuss future directions for improvement.
Large Language Models (LLMs) have shown significant promise in automating repetitive tasks, particularly in code generation. However, most existing approaches focus on generating isolated code blocks, often lacking global context and coherence across an entire application. Key limitations include the absence of interactive previews, limited support for custom modifications, and insufficient attention to code quality and compliance with specifications. To address these challenges and accelerate application prototyping, we propose a multi-agent approach for generating complete application skeletons from textual specifications. Our framework enables real-time interactive previews and allows users to iteratively refine the generated applications through a conversational agent. To ensure quality and specification compliance, we employ an LLM-based evaluator that assesses the generated code. Once validated, the application skeleton can be downloaded for further development or deployment. This approach aims to bridge the gap between intent and implementation, streamlining early-stage software design and development. We illustrate our approach through a case study, demonstrating the applicability and effectiveness of each step in the proposed method.
