Farkhod Akhmedov

Proper management of pedestrian flow at urban crosswalks is crucial for alleviating congestion, reducing waiting times, and improving general traffic safety. This paper presents AH-YOLOFlow, a computer vision framework for pedestrian detection, multi-object tracking, and zone-based flow analytics at urban crosswalks. The proposed framework integrates an attention-enhanced YOLO26n detector, Hungarian assignment-based multi-object tracking, polygon-based spatial zone modeling, and key performance indicator aggregation to estimate pedestrian flow rate, waiting behavior, crossing activity, and localized pedestrian density. The framework was evaluated using a 228.74-s CCTV crosswalk video with a fixed split and an augmented training dataset. Experimental results demonstrate that the proposed detector achieved a mAP@50-95 of 0.5794, mAP@50 of 0.8000, precision of 0.9003, and recall of 0.6847, outperforming the evaluated YOLO-family baselines in terms of mAP@50-95 and precision while remaining competitive with RT-DETR. In the end-to-end pedestrian analytics evaluation, AH-YOLOFlow identified 638 zone-entry events and 495 directional movement events, and successfully associated 54.01% of tracked pedestrian identities with predefined spatial regions. Furthermore, a pseudo-MOT evaluation based on spatially propagated identity annotations demonstrated enhanced tracking consistency following data augmentation, achieving a pseudo-MOTA of 0.7671, pseudo-MOTP of 0.9205, pseudo-HOTA of 0.4527, and pseudo-IDF1 of 0.3844. These findings demonstrate that AH-YOLOFlow provides an effective and computationally efficient framework for real-time crosswalk monitoring by integrating pedestrian detection, tracking, and spatial analytics. In future work, we will focus on validating the system across diverse urban environments using larger multi-scene datasets with manually verified identity annotations to further improve tracking robustness and generalization.

Pathogenic fungi and their synergistic interactions with bark beetles, leading to vascular dysfunction, physiological stress, and eventual tree mortality, increasingly threaten forest ecosystems. Because fungal colonization often precedes visible macroscopic symptoms, early detection remains a critical yet challenging task in forest health monitoring. This study proposes a real-time deep learning-based object detection framework for identifying harmful fungi in proximity to host trees to support early intervention strategies. A custom dataset comprising 8900 images was constructed to represent two classes: Healthy and Unhealthy trees, where fungal presence is detected either directly on the tree or within its immediate ecological vicinity (e.g., near root systems). A fine-tuned YOLOv11 detection architecture is developed and augmented with a squeeze-and-excitation (SE)-like attention mechanism to enhance texture-sensitive feature representation. The model is trained and evaluated using precision, recall, F1-score and mean Average Precision (mAP). Experimental results demonstrate an overall mAP@0.5 of 0.825, with class-wise average precision values of 0.926 (Healthy) and 0.724 (Unhealthy). The Healthy class achieved classification accuracy of 0.92, while 0.71 of Unhealthy instances were correctly detected. F1-Confidence and recall-Confidence metrics indicate that optimal operational performance occurs within a confidence threshold range of 0.30–0.35, balancing false positives and false negatives. Despite the approximately balanced class distribution (50.6% Healthy and 49.4% Unhealthy), detection performance for the Unhealthy class was comparatively lower because of its greater intra-class variability, heterogeneous fungal appearance, and subtle visual manifestations. Findings demonstrate the feasibility of deploying real-time object detection models for early-stage fungal surveillance and highlight the importance of confidence calibration for operational disease monitoring systems.

The rapid growth of cloud and distributed computing systems has increased the complexity of real-time request distribution under dynamic and multi-factor conditions. In such environments, load-balancing decisions must simultaneously consider uncertain and interdependent parameters, including server load, response time, and resource capacity. Fuzzy logic is an effective tool for modeling such uncertainty; however, the expansion of linguistic variables often leads to a rule-explosion problem, which increases computational complexity and reduces the real-time applicability of fuzzy load-balancing systems. This study proposes an explainable multi-objective quantum-inspired fuzzy optimization approach for scalable load balancing in complex computing environments. The proposed model integrates fuzzy inference with a Grover-inspired classical search strategy to optimize the selection of fuzzy rule subsets. The Grover-inspired component is implemented as a classical simulation rather than a gate-based quantum circuit. A multi-objective evaluation function is formulated to jointly assess rule accuracy, coverage, interpretability, and compactness. This formulation enables the model to reduce redundant fuzzy rules while preserving decision transparency and maintaining reliable load distribution performance. The proposed approach is evaluated in a simulated cloud computing environment with heterogeneous servers and dynamic request arrival patterns. Comparative experiments are conducted against classical load-balancing strategies, conventional fuzzy load balancing, and evolutionary fuzzy optimization methods, including GA-FLB and PSO-FLB. The experimental results show that the proposed model reduces the size of the fuzzy rule base while maintaining competitive response time, load distribution quality, SLA compliance, and decision interpretability. These findings indicate that the integration of Grover-inspired classical search mechanisms with fuzzy reasoning provides a promising direction for developing scalable, compact, and explainable load-balancing models for next-generation intelligent computing systems.

Oil spills represent a severe environmental hazard, threatening marine and coastal ecosystems, biodiversity, and socio-economic stability. Timely and accurate detection of such incidents is critical for mitigating their ecological and economic consequences. Conventional detection techniques, including manual inspection and satellite-based observation, remain limited by high operational costs, temporal delays, and restricted spatial coverage. To overcome these limitations, this study introduces a comprehensive computer vision framework that addresses two core challenges: (i) the construction of a large-scale, high-quality synthetic oil spill dataset through mask extraction and seamless blending of oil spill regions with diverse oceanic backgrounds, and (ii) the development of a fine-tuned YOLOv11m-seg detection model trained on this enriched dataset. To further enhance the realism and spatial distinctiveness of oil spill textures, the Line Integral Convolution (LIC) is applied to estimate and visualize ocean surface flow patterns, generating coherent streamline textures that simulate the natural diffusion and transport of oil in water. The model exhibited strong generalization and precision, achieving a training accuracy exceeding IoU@0.50-0.95 to 85% over 50 epochs. Evaluation metrics confirmed its reliability, with an F1 score of 94%, precision of 94%, and recall (mAP@0.50) of 94%. These results demonstrate that the developed approach not only enhances dataset diversity but also substantially improves the accuracy and representativeness of real-time oil spill detection in marine environments.

The increasing application of digitalization has created unprecedented opportunities to enhance access while simultaneously introducing complex challenges to student well-being. This study presents a comprehensive empirical analysis of the Student Engagement Biometrics Dataset (SEBD-2024), comprising multimodal learning sessions recorded from 300 students across three content types (Text, Video, Interactive) and three difficulty levels (Easy, Medium, Hard). Using electroencephalography (EEG) frequency-band power spectral density measurements across five neural bands (Delta, Theta, Alpha, Beta, Gamma) alongside eye-tracking metrics (pupil dilation, blink rate, fixation duration, saccade velocity), we extract, analyse, and contextualize biometric correlates of student engagement. Our Random Forest classifier achieved 35.3% cross-validation accuracy across three engagement classes, only marginally above random baseline (33.3%), revealing a critical finding: raw biometric signals from these modalities, analysed in isolation, exhibit insufficient discriminative power to reliably stratify engagement states. This null-hypothesis insight is itself a major contribution, highlighting the multi-modal complexity of educational well-being and the need for longitudinal, contextualized, and subjective-report-integrated frameworks. We draw direct connections between these findings and the broader discourse on digital well-being, cognitive fatigue, and the neurological substrate of engaged learning, proposing a five-pillar Well-Being Estimation Framework (WEF) for digital education systems.

Driver drowsiness remains a critical factor in road safety, necessitating the development of robust detection methodologies. This study presents a dual-framework approach that integrates a convolutional neural network (CNN) and a facial landmark analysis model to enhance drowsiness detection. The CNN model classifies driver states into “Awake” and “Drowsy”, achieving a classification accuracy of 92.5%. In parallel, a deep learning-based facial landmark analysis model analyzes a driver’s physiological state by extracting and analyzing facial features. The model’s accuracy was significantly enhanced through advanced image preprocessing techniques, including image normalization, illumination correction, and face hallucination, reaching a 97.33% classification accuracy. The proposed dual-model architecture leverages imagery analysis to detect key drowsiness indicators, such as eye closure dynamics, yawning patterns, and head movement trajectories. By integrating CNN-based classification with precise facial landmark analysis, this study not only improves detection robustness but also ensures greater resilience under challenging conditions, such as low-light environments. The findings underscore the efficacy of multi-model approaches in drowsiness detection and their potential for real-world implementation to enhance road safety and mitigate drowsiness-related vehicular accidents.

Electric vehicle (EV) charging systems are now integral to smart grids, increasing the need for robust and scalable cyberattack detection. This study presents an online intrusion detection system that leverages an Adaptive Random Forest classifier with Adaptive Windowing drift detection to identify real-time and evolving threats in EV charging infrastructures. The system is evaluated using real-world network traffic from the CICEVSE2024 dataset, ensuring practical applicability. For binary intrusion detection, the model achieves 0.9913 accuracy, 0.9999 precision, 0.9914 recall, and an F1-score of 0.9956, demonstrating highly accurate threat detection. It effectively manages concept drift, maintaining an average accuracy of 0.99 during drift events. In multiclass detection, the system attains 0.9840 accuracy, precision, and recall, with an F1-score of 0.9831 and an average drift event accuracy of 0.96. The system is computationally efficient, processing each instance in just 0.0037 s, making it well-suited for real-time deployment. These results confirm that online machine learning methods can effectively secure EV charging infrastructures. The source code is publicly available on GitHub, ensuring reproducibility and fostering further research. This study provides a scalable and efficient cybersecurity solution for protecting EV charging networks from evolving threats.

Oil spills pose significant threats to marine and coastal ecosystems, biodiversity and local economies, necessitating efficient and accurate detection systems. Traditional detection methods, such as manual inspection and satellite imaging, are often resource-intensive and time consuming. This study addresses these challenges by developing a novel approach to enhance the quality and diversity of oil spill datasets. Several studies have mentioned that the quality and size of a dataset is crucial for training robust vision-based deep learning models. The proposed methodology combines advanced object extraction techniques with traditional data augmentation strategies to generate high quality and realistic oil spill images under various oceanic conditions. A key innovation in this work is the application of image blending techniques, which ensure seamless integration of target oil spill features into diverse environmental ocean contexts. To facilitate accessibility and usability, a Gradio-based web application was developed, featuring a user-friendly interface that allows users to input target and source images, customize augmentation parameters, and execute the augmentation process effectively. By enriching oil spill datasets with realistic and varied scenarios, this research aimed to improve the generalizability and accuracy of deep learning models for oil spill detection. For this, we proposed three key approaches, including oil spill dataset creation from an internet source, labeled oil spill regions extracted for blending with a background image, and the creation of a Gradio web application for simplifying the oil spill dataset generation process.

The detection of ship fires is a critical aspect of maritime safety and surveillance, demanding high accuracy in both identification and response mechanisms. However, the scarcity of ship fire images poses a significant challenge to the development and training of effective machine learning models. This research paper addresses this challenge by exploring advanced data augmentation techniques aimed at enhancing the training datasets for ship and ship fire detection. We have curated a dataset comprising ship images (both fire and non-fire) and various oceanic images, which serve as target and source images. By employing diverse image blending methods, we randomly integrate target images of ships with source images of oceanic environments under various conditions, such as windy, rainy, hazy, cloudy, or open-sky scenarios. This approach not only increases the quantity but also the diversity of the training data, thus improving the robustness and performance of machine learning models in detecting ship fires across different contexts. Furthermore, we developed a Gradio web interface application that facilitates selective augmentation of images. The key contribution of this work is related to object extraction-based blending. We propose basic and advanced data augmentation techniques while applying blending and selective randomness. Overall, we cover eight critical steps for dataset creation. We collected 9200 ship fire and 4100 ship non-fire images. From the images, we augmented 90 ship fire images with 13 background images and achieved 11,440 augmented images. To test the augmented dataset performance, we trained Yolo-v8 and Yolo-v10 models with “Fire” and “No-fire” augmented ship images. In the Yolo-v8 case, the precision-recall curve achieved 96.6% (Fire), 98.2% (No-fire), and 97.4% mAP score achievement in all classes at a 0.5 rate. In Yolo-v10 model training achievement, we got 90.3% (Fire), 93.7 (No-fire), and 92% mAP score achievement in all classes at 0.5 rate. In comparison, both trained models’ performance is outperforming other Yolo-based SOTA ship fire detection models in overall and mAP scores.