AI and ML-powered manufacturing systems revolutionize industries with smart monitoring, fault detection, and smart controls.

The study highlights the primary Industrial AI trends in research and implementation of modern technologies and application domains for AI based on real-time data analytics. It recognizes, identifies, and explains the fundamental designing principles that play a role in the integration of AI into various applications.

The accurate segmentation of medical images is essential for reliable Computer-Aided Diagnosis (CAD) and treatment planning. Although deep learning has advanced the segmentation accuracy, challenges, such as modality variability, low contrast, and imaging artifacts, still exist. This study introduces a novel integration of a spike-based attention module into a DenseNet-169 backbone, combining biological spiking dynamics with deep convolutional feature representations to improve the focus on salient image features. The architecture utilizes ResNet blocks in the encoder for hierarchical feature extraction and applies spike-based attention at both the bottleneck and decoder stages to enhance the performance and computational efficiency. Experiments on two benchmark datasets demonstrate a 4.2% relative gain in Dice coefficient and a 3.9% increase in Intersection-over-Union compared to state-of-the-art baselines, while preserving the inference latency under 50 ms on standard GPU hardware. An extensive ablation study confirms that the dual placement of the attention module and the choice of DenseNet-169 maximize both the accuracy and efficiency. These results highlight the potential of spike-based attention for advancing real-time, high-fidelity medical image segmentation, with implications for improved clinical workflows and edge device deployment.

Economic diversification across the Gulf region, including Kuwait Vision 2035, positions smart manufacturing as a key enabler of sustainable growth. Yet, industrial datasets in the region are typically small, heterogeneous, and incomplete, limiting the performance and trustworthiness of conventional AI models. This paper introduces a Scalable Random Forest (SRF) framework enhanced with a Decision Path Search (DPS) mechanism to address these challenges through both technical robustness and practical interpretability. The SRF pipeline incorporates leakage-safe preprocessing, mixed-type imputation, and small-data augmentation to improve prediction stability under real industrial constraints, while DPS transforms model internals into actionable operational causal knowledge identifying optimal and avoidance parameter ranges. Case studies, including investment casting, demonstrate that SRF + DPS not only outperforms established baselines such as Random Forest (RF), XGBoost, LightGBM, and CatBoost but also deliver transparent insights that engineers can directly apply to reduce defects and enhance process control. The findings highlight how interpretable AI frameworks can accelerate industrial modernization, strengthen regional manufacturing competitiveness, and support national economic diversification strategies.