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Vehicular Ad-hoc Networks (VANETs) are a crucial component of Cooperative Intelligent Transportation Systems (C-ITS), enabling vehicles to communicate and share vital information to enhance road safety and efficiency. Basic Safety Messages (BSMs), periodically broadcast by vehicles to provide real-time kinematic data, form the foundation of numerous safety applications within VANETs. Ensuring the security of BSMs is paramount, as malicious entities can exploit vulnerabilities to launch attacks that could have catastrophic consequences. In this study, we provide a comprehensive analysis of BSM attacks and detection mechanisms in VANETs. We begin by outlining the system model, security requirements, and attacker models relevant to BSMs. Then, we categorize and describe a range of attacks, from simple position falsification to more sophisticated and evasive techniques, such as the SixPack attack. We also classify existing attack detection methods into machine learning-based, deep learning-based, plausibility and consistency-based, and software-defined networking (SDN)-based mechanisms, analyzing their effectiveness and limitations. Additionally, we highlight the challenges in securing BSMs, such as the trade-off between model accuracy and real-time performance. Future research directions are also discussed. This survey paper serves as a foundational step towards building safe, secure, and reliable cooperative intelligent transportation systems and their associated applications. 

The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.