Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being. Traditional forest mapping and monitoring methods are often costly and limited in scope, necessitating the adoption of advanced, automated approaches for improved forest conservation and management. This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery. A dataset of 3157 images was collected and divided into training (2528), validation (495), and testing (134) sets. To enhance model robustness and generalization, data augmentation was applied to the training part of the dataset. Various YOLO-based models, including YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12, were evaluated using different hyperparameters and optimization techniques, such as stochastic gradient descent (SGD) and auto-optimization. These models were assessed in terms of detection accuracy and the number of detected trees. The highest-performing model, YOLOv12m, achieved a mean average precision (mAP@50) of 0.908, mAP@50:95 of 0.581, recall of 0.851, precision of 0.852, and an F₁-score of 0.847. The results demonstrate that YOLO-based object detection offers a highly efficient, scalable, and accurate solution for individual tree detection in satellite imagery, facilitating improved forest inventory, monitoring, and ecosystem management. This study underscores the potential of AI-driven tree detection to enhance environmental sustainability and support data-driven decision-making in forestry.

Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering, economics, and computer science. These algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising solutions. While heuristic optimization algorithms vary in their specific details, they often exhibit common patterns that are essential to their effectiveness. This paper aims to analyze and explore common patterns in heuristic optimization algorithms. Through a comprehensive review of the literature, we identify the patterns that are commonly observed in these algorithms, including initialization, local search, diversity maintenance, adaptation, and stochasticity. For each pattern, we describe the motivation behind it, its implementation, and its impact on the search process. To demonstrate the utility of our analysis, we identify these patterns in multiple heuristic optimization algorithms. For each case study, we analyze how the patterns are implemented in the algorithm and how they contribute to its performance. Through these case studies, we show how our analysis can be used to understand the behavior of heuristic optimization algorithms and guide the design of new algorithms. Our analysis reveals that patterns in heuristic optimization algorithms are essential to their effectiveness. By understanding and incorporating these patterns into the design of new algorithms, researchers can develop more efficient and effective optimization algorithms.

Brain imaging is important in detecting Mild Cognitive Impairment (MCI) and related dementias. Magnetic Resonance Imaging (MRI) provides structural insights, while Positron Emission Tomography (PET) evaluates metabolic activity, aiding in the identification of dementia-related pathologies. This study integrates multiple data modalities—T1-weighted MRI, Pittsburgh Compound B (PiB) PET scans, cognitive assessments such as Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR) and Functional Activities Questionnaire (FAQ), blood pressure parameters, and demographic data—to improve MCI detection. The proposed improved Convolutional Mixer architecture, incorporating B-cos modules, multi-head self-attention, and a custom classifier, achieves a classification accuracy of 96.3% on the Mayo Clinic Study of Aging (MCSA) dataset (sagittal plane), outperforming state-of-the-art models by 5%–20%. On the full dataset, the model maintains a high accuracy of 94.9%, with sensitivity and specificity reaching 89.1% and 98.3%, respectively. Extensive evaluations across different imaging planes confirm that the sagittal plane offers the highest diagnostic performance, followed by axial and coronal planes. Feature visualization highlights contributions from central brain structures and lateral ventricles in differentiating MCI from cognitively normal subjects. These results demonstrate that the proposed multimodal deep learning approach improves accuracy and interpretability in MCI detection.