Background: Accurate and early detection of hematological disorders from microscopic peripheral blood smear images remains a technically challenging task due to inherent imaging limitations, including noise contamination, low contrast, staining variability, and significant cellular overlap. Conventional deep learning-based object detection frameworks often exhibit limited robustness under such conditions and demonstrate reduced sensitivity to small-scale morphological structures, particularly platelets and abnormal cell variants. Methods: To address these challenges, this study proposes a hybrid detection framework that integrates a fuzzy logic-driven image preprocessing module with the YOLOv11 object detection architecture. The proposed preprocessing pipeline employs adaptive fuzzy membership functions to normalize pixel intensity distributions, suppress high-frequency noise, and enhance edge-defined cellular boundaries. This transformation produces a structurally optimized feature representation, improving downstream feature extraction and localization performance. The proposed framework was evaluated on a curated dataset of 3000 annotated microscopic blood smear images spanning five hematological classes. Results: Experimental results show that the fuzzy logic module improves mAP@0.5 by +3.4% and mAP@0.5:0.95 by +3.6%, confirming its effectiveness in enhancing both classification and localization accuracy. Conclusions: These findings demonstrate the robustness and practical applicability of the proposed hybrid approach under challenging imaging conditions.

Wireless Sensor Networks (WSNs) are extensively used for monitoring large-scale areas with sensors having different coverage zones. Unmanned Aerial Vehicles (UAVs) are deployed to efficiently collect data from these distributed nodes. The effectiveness of this process depends on optimizing the UAV’s flight path, formulated as the Close Enough Traveling Salesman Problem (CETSP), known to be NP-hard. This study presents hybrid methods that integrate heuristic algorithms with geometric strategies to solve the CETSP efficiently. The main contribution lies in proposing efficient, fast-executing, and easily programmable approaches that follow a structured process: identifying new target points when zones intersect, determining near-optimal visiting sequences with heuristic algorithms, and applying iterative geometric refinements to shorten the route. A total of 92 hybrid algorithms based on seven distinct approaches are evaluated using four performance metrics. Experimental results demonstrate the high efficiency of the proposed methods and their strong potential for real-world UAV-assisted data collection tasks in wireless sensor networks.