On constructing a fused image by employing only individual pixels or a set of pixels within a small neighbourhood of the images (SIs) acquired from the same scene, pixel‐based fusion techniques suffer from some drawbacks, such as blurring effects, high sensitivity to noise and misregistration. To overcome these drawbacks, this study proposes a new region‐based image fusion method for thermal and visible images. Since different regions with certain properties need to be emphasised differently in the fused image, the corresponding regions of the SIs are optimally merged to obtain the fused image by employing multiple weighting factors (WFs). To improve the quality of the fused images, WFs were optimised by employing the differential evolution algorithm. Furthermore, a new quality metric was also developed to measure the quality of the fused images during the optimisation process. Experimental results show the feasibility of the proposed method.

One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization algorithms. These algorithms are artificial bee colony, butterfly optimization, cuckoo search, chicken swarm optimization, dragonfly algorithm, firefly algorithm, grasshopper optimization algorithm, krill herd algorithm, particle swarm optimization, salp swarm algorithm, selfish herd optimizer, tunicate swarm algorithm, and tuna swarm optimization. Mean squared error is used as the error metric, and the performances of the algorithms in different network structures are evaluated. Considering the results, a success ranking score is obtained for each algorithm. The three most successful algorithms in both training and testing processes are the firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm, respectively. The training error values obtained with these algorithms are 4.5 × 10⁻⁴, 1.6 × 10⁻³, and 2.3 × 10⁻³, respectively. The test error values are 4.6 × 10⁻⁴, 1.6 × 10⁻³, and 2.4 × 10⁻³, respectively. With these algorithms, effective results have been achieved in a low number of evaluations. In addition to these three algorithms, other algorithms have also achieved mostly acceptable results. This shows that the related algorithms are generally successful ANFIS training algorithms for maximum power point tracking.

Improving diagnostic accuracy in EBUS image analysis using machine learning is a current challenge. This study aimed to identify the most effective transfer learning model for predicting lymph node malignancy. EBUS images collected between 2020-2023 were retrospectively analyzed. Demographic data, sampled lymph nodes, and pathology results were retrospectively collected from the files. Eight pre-trained CNN models (VGG, ResNet, InceptionNet, Xception, MobileNet, DenseNet, NasNet, EfficientNet) were evaluated. The study shows that the VGG19, EfficientNetV2L and DenseNet201 models have the highest performance in malignancy prediction, achieving areas under the curve of 0.96, 0.96 and 0.95 respectively, with consistent training and testing accuracy, indicating successful models without overfitting. In contrast, the ResNet152V2, Xception, and NasNet models show lower performance with areas under the curve of 0.88, 0.85, and 0.84 respectively, indicating overfitting due to discrepancies between training and test data. The MobileNetV2 model, with an area under the curve of 0.50, fails to discriminate between benign and malignant cases, resulting in an accuracy of only 0.51. The application of transfer learning to the analysis of EBUS images offers significant potential for improving diagnostic accuracy in thoracic medicine, particularly in lung cancer.