The book explores the use of artificial intelligence (AI) in power monitoring systems for SMEs to enhance energy efficiency, reduce operational costs, and ensure sustainability. It discusses current energy challenges faced by SMEs, emphasizes real-time monitoring, and highlights the benefits of AI integration. The chapter details the components of an AI-integrated power monitoring system, including data acquisition, analysis, and control strategies. It examines AI techniques like machine learning, deep learning, and predictive analytics for identifying energy usage patterns. The chapter also discusses successful cases of SMEs using AI-based systems, highlighting their optimization of energy consumption and reduced costs.

This article covers a second-order sliding mode control-based scheme for the closed loop control of DC-DC buck converters. The proposed control technique adjusts online the sliding surface of the second-order sliding mode controller to enhance the dynamic performance of the closed loop system. The sliding surface adjustment is achieved by a fuzzy logic inference system based on sliding variables. While maintaining its simplicity concerning design and implementation, the controller makes sure the system achieves the design requirements in terms of improved dynamic performance, stability, and robustness. Theoretical analysis confirms the global finite-time error convergence of the buck converter system. The performance of this novel control approach is compared with that of the conventional second-order sliding mode controller with a pre-defined sliding surface using SIMULINK based simulations for a buck converter model. From the analysis of the simulation results, it is concluded that the proposed controller provides a remarkable improvement in the dynamic performance of the buck converter control system compared with the conventional second-order sliding mode controller with a pre-defined sliding surface.

Varuna Kumara and Ezhilarasan Ganesan. International Journal of Fuzzy Logic and Intelligent Systems 2024;24:440-50. https://doi.org/10.5391/IJFIS.2024.24.4.440

The utilization of mammography images plays a vital role in the prompt detection and treatment of breast cancer. Breast imaging techniques aid medical professionals in assessing the dimensions, morphology, and spatial orientation of breast lesions, facilitating the differentiation between benign and malignant conditions. Breast tissue can vary widely in terms of density, composition, and structure, leading to complexities in distinguishing between benign and malignant conditions. The primary contribution of this paper is the proposal of a spatial attention-based neural architecture search network (SANAS-Net) technique that incorporates a spatial attention mechanism, enabling the model to learn and prioritize key regions within mammograms (MMs). Multi-head attention is employed within the transformer blocks to effectively capture a wide range of spatial relations and feature interactions. Global contextual information was integrated into the transformer blocks by means of introducing positional embeddings. Several practical studies have been undertaken to verify the effectiveness of our methodology in identifying fully attentive networks that exhibit good performance in distinguishing between malignant and benign breast cancer cases. The experimental study reached a test accuracy of 89.95%, which is way higher than previously proposed algorithms for mammography imagebased breast cancer detection.

Abdul Kareem and Varuna Kumara. International Journal of Fuzzy Logic and Intelligent Systems 2024;24:30-42. https://doi.org/10.5391/IJFIS.2024.24.1.30