Building Information Modeling (BIM) is extensively used in the construction industry due to its benefits throughout the Project Life Cycle (PLC). BIM can simulate buildings throughout PLC, detect and resolve problems, and improve building visualization that contributes to the representation of actual project details in the construction stage. BIM contributes to project management promotion by detecting problems that lead to conflicts, cost overruns, and time delays. This work aims to implement an effective BIM for the Iraqi construction projects’ life cycle. The methodology used is a literature review to collect the most important factors contributing to the success of BIM implementation, interview the team of the Central Bank of Iraq (CBI) building, and strive to improve the BEP of the CBI building. However, previous studies indicate collaborative work and communications enhance effective BIM implementation, which can improve BIM use by applying a BEP and an AEC (UK) BIM protocol that leads to positive BIM impact. BEP comprises important information and goals related to the intended project, including the BIM collaborative process (process map), information exchange requirements, BIM data management, BIM model management, and quality control, which are considered essential for enhancing BIM collaboration during PLC. This paper concludes that implementing BIM effectively requires overcoming obstacles faced by Iraqi construction projects. Effective BIM implementation requires improving collaboration and communication throughout the construction process, which could be achieved by depending on the BIM Execution Planning Guide(BEP Guide) and the AEC (UK) BIM Protocol 2012 V2.0
The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines
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