Artificial Intelligence (AI) has emerged in popularity recently, recording great progress in various industries. However, the environmental impact of AI is a growing concern, in terms of the energy consumption and carbon footprint of Machine Learning (ML) and Deep Learning (DL) models, making essential investigate Green AI, an attempt to reduce the climate impact of AI systems. This paper presents an assessment of different programming languages and Feature Selection (FS) methods to improve computation performance of AI focusing on Network Intrusion Detection (NID) and cyber-attack classification tasks. Experiments were conducted using five ML models - Random Forest, XGBoost, LightGBM, Multi-Layer Perceptron, and Long Short-Term Memory - implemented in four programming languages - Python, Java, R, and Rust - along with three FS methods - Information Gain, Recursive Feature Elimination, and Chi-Square. The obtained results demonstrated that FS plays an important role enhancing the computational efficiency of AI models without compromising detection accuracy, highlighting languages like Python and R, that benefit from a rich AI libraries environment. These conclusions can be useful to design efficient and sustainable AI systems that still provide a good generalization and a reliable detection.

翻译：近年来，人工智能（AI）日益普及，在各行业取得显著进展。然而，人工智能的环境影响日益受到关注，特别是在机器学习（ML）和深度学习（DL）模型的能耗与碳足迹方面，这使得研究绿色AI——一种旨在降低AI系统气候影响的尝试——变得至关重要。本文评估了不同编程语言与特征选择（FS）方法，以提升AI在面向网络入侵检测（NID）和网络攻击分类任务中的计算性能。实验采用五种ML模型——随机森林、XGBoost、LightGBM、多层感知机和长短期记忆网络，分别在四种编程语言——Python、Java、R和Rust中实现，并结合三种FS方法——信息增益、递归特征消除和卡方检验。所得结果表明，特征选择在提升AI模型计算效率的同时不损害检测精度方面发挥重要作用，并突显了Python和R等受益于丰富AI库环境的编程语言。这些结论有助于设计高效、可持续且仍能提供良好泛化能力与可靠检测性能的AI系统。