Md. Raihan Tapader | Searcharxiv

arXiv2026

Hybrid Approach for Driver Behavior Analysis with Machine Learning, Feature Optimization, and Explainable AI

Progressive driver behavior analytics is crucial for improving road safety and mitigating the issues caused by aggressive or inattentive driving. Previous studies have employed machine learning and deep learning techniques, which often result in low feature optimization, thereby compromising both high performance and interpretability. To fill these voids, this paper proposes a hybrid approach to driver behavior analysis that uses a 12,857-row and 18-column data set taken from Kaggle. After applying preprocessing techniques such as label encoding, random oversampling, and standard scaling, 13 machine learning algorithms were tested. The Random Forest Classifier achieved an accuracy of 95%. After deploying the LIME technique in XAI, the top 10 features with the most significant positive and negative influence on accuracy were identified, and the same algorithms were retrained. The accuracy of the Random Forest Classifier decreased slightly to 94.2%, confirming that the efficiency of the model can be improved without sacrificing performance. This hybrid model can provide a return on investment in terms of the predictive power and explainability of the driver behavior process.

cs.LG↗

arXiv2025

Code Refactoring with LLM: A Comprehensive Evaluation With Few-Shot Settings

In today's world, the focus of programmers has shifted from writing complex, error-prone code to prioritizing simple, clear, efficient, and sustainable code that makes programs easier to understand. Code refactoring plays a critical role in this transition by improving structural organization and optimizing performance. However, existing refactoring methods are limited in their ability to generalize across multiple programming languages and coding styles, as they often rely on manually crafted transformation rules. The objectives of this study are to (i) develop an Large Language Models (LLMs)-based framework capable of performing accurate and efficient code refactoring across multiple languages (C, C++, C#, Python, Java), (ii) investigate the impact of prompt engineering (Temperature, Different shot algorithm) and instruction fine-tuning on refactoring effectiveness, and (iii) evaluate the quality improvements (Compilability, Correctness, Distance, Similarity, Number of Lines, Token, Character, Cyclomatic Complexity) in refactored code through empirical metrics and human assessment. To accomplish these goals, we propose a fine-tuned prompt-engineering-based model combined with few-shot learning for multilingual code refactoring. Experimental results indicate that Java achieves the highest overall correctness up to 99.99% the 10-shot setting, records the highest average compilability of 94.78% compared to the original source code and maintains high similarity (Approx. 53-54%) and thus demonstrates a strong balance between structural modifications and semantic preservation. Python exhibits the lowest structural distance across all shots (Approx. 277-294) while achieving moderate similarity ( Approx. 44-48%) that indicates consistent and minimally disruptive refactoring.

cs.SE↗