Rapid urbanization in the twenty-first century has significantly accelerated the adoption of artificial intelligence (AI) technologies to address growing challenges in governance, mobility, energy, and urban security. This paper explores how AI is transforming smart city infrastructure, analyzing more than 92 academic publications published between 2012 and 2024. Key AI applications ranging from predictive analytics in e-governance to machine learning models in renewable energy management and autonomous mobility systems are synthesized domain-wise throughout this study. This paper highlights the benefits of AI-enabled decision making, finds current implementation barriers, and discusses the associated ethical implications. Furthermore, it presents a research agenda that stresses data interoperability, transparency, and human–AI collaboration to steer upcoming advancements in smart urban ecosystems.

Unmanned Aerial Vehicles (UAVs) have rapidly grown into different essential applications, including surveillance, disaster response, agriculture, and urban monitoring. However, for UAVS to steer safely and autonomously, the ability to detect obstacles and nearby aircraft remains crucial, especially under hard environmental conditions. This study comprehensively analyzes the recent landscape of obstacle and aircraft detection techniques tailored for UAVs acting in difficult scenarios such as fog, rain, smoke, low light, motion blur, and disorderly environments. It starts with a detailed discussion of key detection challenges and continues with an evaluation of different sensor types, from RGB and infrared cameras to LiDAR, radar, sonar, and event-based vision sensors. Both classical computer vision methods and deep learning-based detection techniques are examined in particular, highlighting their performance strengths and limitations under degraded sensing conditions. The paper additionally offers an overview of suitable UAV-specific datasets and the evaluation metrics generally used to evaluate detection systems. Finally, the paper examines open problems and coming research directions, emphasising the demand for lightweight, adaptive, and weather-resilient detection systems appropriate for real-time onboard processing. This study aims to guide students and engineers towards developing stronger and intelligent detection systems for next-generation UAV operations.

Industrial control systems (ICS) are increasingly vulnerable to evolving cyber threats due to the convergence of operational and information technologies. This research presents a robust cybersecurity framework that integrates machine learning-based anomaly detection with advanced cryptographic techniques to protect ICS communication networks. Using the ICS-Flow dataset, we evaluate several ensemble models, with XGBoost achieving 99.92% accuracy in binary classification and Decision Tree attaining 99.81% accuracy in multi-class classification. Additionally, we implement an LSTM autoencoder for temporal anomaly detection and employ the ADWIN technique for real-time drift detection. To ensure data security, we apply AES-CBC with HMAC and AES-GCM with RSA encryption, which demonstrates resilience against brute-force, tampering, and cryptanalytic attacks. Security assessments, including entropy analysis and adversarial evaluations (IND-CPA and IND-CCA), confirm the robustness of the encryption schemes against passive and active threats. A hardware implementation on a PYNQ Zynq board shows the feasibility of real-time deployment, with a runtime of 0.11 s. The results demonstrate that the proposed framework enhances ICS security by combining AI-driven anomaly detection with RSA-based cryptography, offering a viable solution for protecting ICS networks from emerging cyber threats.

This study presents YOLOv11n-GhostLite, an innovative lightweight deep learning architecture optimized for real-time localization of photovoltaic (PV) faults in electroluminescence (EL) images, specifically designed for edge deployment. A Deep Convolutional Generative Adversarial Network (DCGAN)-based synthetic augmentation pipeline is presented to address the issues of class imbalance and limited resource availability, generating high-fidelity, class-conditional EL images that include realistic banding artifacts. This method enhances the representation of minority defect categories by more than 150%, elevating the mean Average Precision (mAP@50) by 4% and decreasing false negatives by 5%. The proposed model incorporates GhostConv for efficient early feature extraction, C3k2 residual blocks for deep representation learning, GhostSPPF for multi-scale context aggregation, C2PSA attention for adaptive feature refinement, and an anchor-free detection head, achieving high performance with only 2.34 million parameters and 6.2 GFLOPs. Detailed experiments on two benchmark datasets PVEL-AD and PV Multi-Defect exhibit the model’s efficacy, attaining 97.2% mAP@50 on PVEL-AD, and 96.4% mAP@50 on PV Multi-Defect, outperforming larger models in both accuracy and speed. The model is further deployed on a Google Coral Edge TPU, demonstrating its real-time functionality with minimal power consumption (~2W) and suitable latency for drone-based solar inspections. YOLOv11n-GhostLite’s integration of efficient architecture and data-driven augmentation renders it an effective solution for scalable, real-time photovoltaic fault detection in resource-limited settings.

The increase in Supervisory Control and Data Acquisition (SCADA) solutions in industrial systems has made these systems more vulnerable to cyberattacks due to insecure communication protocols. This paper proposes a privacy-aware intrusion detection framework that reduces these risks. The proposed framework implements, through machine learning, anomaly detection methods, classification techniques and feature selection methodologies, integrating these approaches to optimize the identification of potential cyber threats, improve model accuracy, and enhance computational efficiency in SCADA systems. Anomaly detection is strengthened through Isolation Forests, time-series analysis, and Fourier-based burst detection while dynamically adapting to concept drift adaptation with Kullback-Leibler divergence. It also utilizes important feature optimization methods, implemented in multiple ways, such as SHAP, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA). Furthermore, the proposed framework includes a federated learning-based scheme that utilizes differential privacy and homomorphic encryption to ensure the privacy and integrity of the data to enhance model interpretability and efficiency with feature ranking to provide insights into attack patterns and anomaly characteristics. Defences against adversarial attacks use FGSM-based training, feature smoothing, and ensemble-based defences, to reduce susceptibility to evasion tactics. Honeypots and automated IP firewalls enhance attack profiling and proactive mitigation of incidents. The experimental results demonstrate the framework’s operational capability and effectiveness for SCADA network security, achieving 99.29% accuracy, 94.8% recall and 4.1% false positive rates.