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DNA microarray technology relies on a chip containing thousands of DNA probes to simultaneously quantify the activity levels of many genes. The resulting gene expression data, typically arranged in matrix form, serves as the foundation for pattern-recognition techniques that separate samples taken from healthy subjects from those obtained from cancer patients. Nonetheless, extracting genuinely informative biomarkers from such datasets remains extremely difficult due to the curse of dimensionality and the substantial presence of uninformative or noisy genes. This study presents an effective gene selection framework that combines the powerful Minimum Redundancy Maximum Relevance (MRMR) filter with a newly designed hybrid metaheuristic. The proposed optimizer merges an enhanced version of the Marine Predator Algorithm (MPA) with a crossover mechanism, yielding the so-called MPAC algorithm. MPAC is tailored to identify a small yet highly discriminative set of genes that dramatically improves cancer classification accuracy. Classification is performed using the k-nearest neighbors (k-NN) classifier. The main strength of MPAC resides in its marked improvement of the exploration and exploitation capabilities of the original MPA population. It systematically identifies optimal gene subsets suitable as cancer biomarkers while carefully balancing global search (exploration) and local refinement (exploitation). The proposed method was comprehensively benchmarked on nine widely recognized microarray benchmark datasets. Its classification performance was rigorously compared with several standard and recently published optimization-based approaches. Results indicate that MPAC achieves superior accuracy across most datasets and delivers very competitive results on the remaining datasets.

Ongoing progress in intelligent systems and state-of-the-art technology has significantly shaped the evolution of intelligent vehicles. In recent years, the integration of multiple sensors into automobiles has enhanced and expanded advanced driver-assistance systems (ADAS), bringing the goal of full autonomy closer to reality. Despite existing self-driving techniques and implementations, autonomous driving remains an unresolved research challenge that must ensure driver safety and system dependability. This study uses images from two cameras and Global Positioning System (GPS) data to introduce a 3D vision-based approach for object localization and classification, aimed at supporting vehicle navigation. The experimental setup consists of a prototype two-seater electric vehicle, specifically developed and built for campus navigation under controlled driving conditions. At the same time, color and depth images from the main camera are fused to derive 2D features, which are then projected back into 3D space. Road detection combined with depth information isolates point clouds corresponding to objects, enabling the creation of an environmental occupancy map. A convolutional neural network was trained to categorize common urban objects within the color images. Experimental evaluations confirm the accuracy of vehicle and object positioning within the occupancy map across various scenarios, strengthening the visually derived vehicle position through integration with GPS measurements. 

Modern smart grids aim to deliver exceptional reliability and operational flexibility in power delivery. Contemporary electrical networks rely on synchronized wide-area monitoring tools, notably Phasor Measurement Units (PMUs), to supply instantaneous system status updates. These devices support prompt problem identification and corrective actions while ensuring uninterrupted grid operation. The surge in available measurement data has accelerated the adoption of machine learning techniques for tasks such as power system protection and state estimation. This work presents an intelligent fault detection and classification (FD&C) framework for transmission lines that employs an auto-encoder neural network architecture. The study evaluates and contrasts the capabilities of a Multi-Layer Extreme Learning Machine (ML-ELM) with those of a Stacked Auto-Encoder (SAE) neural network. These models are further benchmarked against established classifier approaches that rely on wavelet-based feature extraction, as well as against various other deep learning strategies. The proposed auto-encoder solutions attain perfect (100%) fault detection rates and achieve fault classification accuracies of 99.92% and 99.79%, all while demanding far less computation time during the testing phase. The ML-ELM variant stands out for its efficiency, maintaining excellent classification performance with both training and testing durations below 50 milliseconds. To reflect practical field conditions, the models were rigorously tested on datasets incorporating additive noise levels corresponding to signal-to-noise ratios (SNR) between 10 dB and 40 dB. Validation was performed using simulated data from the IEEE 39-bus benchmark system.