Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost of construction projects. By accounting for the complexity of activity networks and uncertainties within project environments, the model aims to address key challenges in project forecasting. Unlike traditional methods such as Earned Value Management (EVM) and Earned Schedule Method (ESM), which rely on static metrics, the XGBoost-SA model adapts dynamically to project data, achieving 92% prediction accuracy. This advanced model offers a more precise forecasting approach by incorporating and optimizing features from historical data. Results reveal that XGBoost-SA reduces cost prediction error by nearly 50% and time prediction error by approximately 80% compared to EVM and ESM, underscoring its effectiveness in complex scenarios. Furthermore, the model’s ability to manage limited and evolving data offers a practical solution for real-time adjustments in project planning. With these capabilities, XGBoost-SA provides project managers with a powerful tool for informed decision-making, efficient resource allocation, and proactive risk management, making it highly applicable to complex construction projects where precision and adaptability are essential. The main limitation of the developed model in this study is the reliance on data from similar projects, which necessitates additional data for application to other industries.

Connected and autonomous vehicles, along with the expanding Internet of Vehicles (IoV), are increasingly exposed to complex and evolving cyberattacks. Consequently, Intrusion Detection Systems (IDS) have become a vital component of modern vehicular cybersecurity. Federated Learning (FL) enables multiple vehicles to collaboratively train detection models while keeping their local data private, providing a decentralized alternative to traditional centralized learning. Despite these advantages, FL-based IDS frameworks remain vulnerable to attacks. To address this vulnerability, we propose an explainable federated intrusion detection framework that enhances both the security and interpretability of IDS in connected vehicles. The framework employs a Deep Neural Network (DNN) within a federated setting and integrates explainability through the Shapley Additive Explanations (SHAP) method. This Explainable Artificial Intelligence (XAI) component identifies the most influential network features contributing to detection decisions and assists in recognizing anomalies arising from malicious or corrupted clients. Experimental validation on the CICEVSE2024 and CICIoV2024 vehicular datasets demonstrates that the proposed system achieves high detection accuracy. Moreover, the XAI module improves transparency and enables analysts to verify and understand the model’s decision-making process. Compared with both centralized IDS models and conventional federated approaches without explainability, the proposed system delivers comparable performance, stronger resilience to attacks, and significantly enhanced interpretability. Overall, this work demonstrates that integrating FL with XAI provides a privacy-preserving and trustworthy approach for intrusion detection in connected vehicular networks.

One of the fundamental challenges in managing software and information technology projects is monitoring and predicting project status at the end of each sprint, release or project. Agile project management has emerged over the past two decades, significantly impacting project success. However, no comprehensive approach based on the features of this approach has been found in studies to monitor and predict the status of a sprint, release or project. This study aims to develop a data-driven approach for predicting the status of software projects based on agility features. For this purpose, 22 agility features were first identified to evaluate and predict the status of projects in four aspects: Endurance, Effectiveness, Efficiency, and Complexity. The findings indicate that the aspects of Effectiveness and Efficiency have the greatest impact on project success. Additionally, the results show that features related to team work, team capacity, experience and project objectives have the most significant impact on project success. An artificial neural network algorithm was then used, and a model was developed to predict project status, which was optimized using the Neural Architecture Search algorithm with a 93 percent accuracy rate. The neural network model was interpreted using the SHapley Additive exPlanations (SHAP) algorithm, and sensitivity analysis was performed on the important components. Finally, the behavior of the projects in each category was analyzed and evaluated using the Apriori algorithm.