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.

The conventional PV array configurations fail to eliminate the ill effects of partial shading (PS) thereby resulting in mismatch losses. PV array reconfiguration is considered as the most competent solution for shading-related issues. However, many reconfiguration techniques reported in the literature fail to disperse the shade effectively, not compatible with large-scale arrays, and are not generalized for all array sizes. To overcome these limitations, a highly efficient generalized Henon Map technique which is extensively used in the image encryption process is proposed in this paper to reconfigure the PV array. The proposed technique is extensively analyzed for 9 × 9, 8 × 8, 4 × 4, 3 × 5 and 4 × 3 PV arrays under distinct shading patterns with seven parameters. The obtained results are compared with conventional Series-Parallel, Total-Cross-Tied, and very recently reported Odd-Even, Odd-Even-Prime, Chaotic Baker’s Map, Magic square, and Diagonal-TCT reconfiguration techniques. Besides, an experimental prototype model is developed and tested under artificially shading conditions to validate the competence of the proposed configuration. From the comprehensive analysis, it is noted that the proposed technique offers consistently superior performance with the least percentage mismatch and maximum power enhancement of 24.17%, 25.65%, and 47.81% for various shading cases of 9 × 9, 8 × 8, and 4 × 4 PV arrays, respectively. Further, a qualitative comparative assessment of the proposed technique with other state-of-art techniques is presented.

The partial shading phenomenon significantly limits the PV array output. To maximize the output power during shading, various reconfiguration techniques have been reported in the literature. However, many of these techniques are not scalable, ineffective, and inconsistent in uniform shade dispersion. Therefore, two highly efficient Arnold’s Cat Map and Henon Map-based chaotic approaches which are widely used in image encryption are employed to effectively disperse the shade by reconfiguring the modules without disturbing the electrical circuitry. The proposed approaches are evaluated in a MATLAB environment for symmetrical 8 × 8 PV array and unsymmetrical 5 × 7 PV array under different groups of progressive shading like left-to-right, triangular, top-to-bottom, and diagonal shading conditions. The efficacy of the proposed approaches is compared with conventional Series-Parallel, Total-Cross-Tied, existing Chaotic Baker-Map, Odd-Even, and Odd-Even-Prime-based reconfiguration techniques and extensively analyzed using different performance indices. A laboratory experimental setup of a 4 × 4 PV array reconfiguration system is developed and examined under distinct progressive shading cases. From the quantitative results obtained, it is noted that the proposed approaches offer consistently superior and reliable performance compared to existing state-of-art configurations under shading reinforcing their effectiveness.

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.