Early and accurate diagnosis of Autism Spectrum Disorder (ASD) relies heavily on identifying critical behavioral and clinical markers within high-dimensional datasets. However, standard feature selection methods frequently struggle with premature convergence and suboptimal feature identification. To address these limitations, this paper proposes an approach based on an Improved White Shark Optimizer combined with Machine Learning classifiers (IWSO-ML). The proposed approach engineers a dynamic balance between global exploration and local refinement by integrating an adaptive PSO-WSO velocity update, differential evolution, and covariance-based exploitation. To ensure robust search space navigation and prevent local optima entrapment, IWSO-ML incorporates Sobol/Halton chaotic initialization, a shrinking search radius, and stagnation-aware population control. The algorithmic superiority of the approach is first validated against the complex CEC’2022 continuous benchmark suite. The proposed IWSO-ML evaluated clinically across four diverse ASD datasets including adult, children in UCI, and prediction, screening in Kaggle using five distinct classification models (SVM, K-NN, RF, DT, and GNB). Extensive statistical analysis confirm that IWSO-ML consistently outperforms state-of-the-art metaheuristics, including RUN, HHO, IMODE, PSO, SSA, WOA and MFO. Notably, the IWSO-SVM configuration achieved a peak classification accuracy of 99.5%, demonstrating superior convergence speed, narrowed error distributions, and exceptional solution stability. Ultimately, this hybrid architecture provides a highly generalized and computationally efficient feature selection mechanism, establishing a robust foundation for reliable clinical predictive modeling and advanced ASD diagnostics.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder marked by persistent challenges in social interaction, communication, and behavioral patterns. The growing evidence shows that individuals with ASD have atypical brain connectivity, which makes electroencephalography (EEG) a valuable tool to study related neural activity. The non-invasive nature of EEG, its low cost, and its high temporal resolution have made it a focus for the early detection of ASD. However, most studies use an offline analysis, which limits their use in clinical practice. This paper introduces a real-time diagnostic platform that combines Internet of Things (IoT) technology and EEG to support the early identification of ASD using deep learning techniques.The framework integrates real-time EEG acquisition, signal preprocessing, deep learning-based classification models, and a Flask-based clinical interface. The clinical interface allows healthcare professionals to record EEG signals, perform real-time predictions, and generate diagnostic reports, whereas a secure guardian portal enables controlled access for patient monitoring. We evaluated time-series-based deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), Bidirectional Gated Recurrent Units (Bi-GRU), and hybrid architectures, using various data-splitting strategies and optimization algorithms. The CNN–BiGRU model trained with the Adam optimizer achieved the highest performance, with an F1-score of 96.40% and a Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) of 99.37%. These results show that deep learning models can effectively identify EEG patterns associated with ASD and enable reliable classification. The web-based platform also supports real-time monitoring and decision support, increasing its value in clinical settings.
As Information Technology (IT) and Operational Technology (OT) networks converge within critical infrastructure, cybersecurity risk must be quantified under uncertainty and incomplete observability, conditions under which conventional, static scoring methods perform poorly. This paper presents the Multi-Stage Composite Fuzzy and Neuro-Adaptive (MCFNA) framework for quantifying operational cyber-risk in Industrial Control System (ICS) environments. MCFNA composes three interpretable Mamdani fuzzy inference stages that resolve, in turn, threat capability, attack likelihood, and overall operational risk, and augments them with an Adaptive Neuro-Fuzzy Inference System (ANFIS) layer that calibrates the fuzzy model to observed telemetry. The coupled learning procedure is proven to converge to a first-order stationary point. The framework is evaluated on the Secure Water Treatment (SWaT) benchmark under a leakage-controlled feature pipeline and an attack-scenario-wise train/test split that prevents dependent time windows from crossing the split boundary. Across regression and classification measures, the adaptive framework improves risk-estimation accuracy over linear-regression, neural-network, and ensemble baselines, and a sensitivity analysis identifies asset vulnerability as the dominant risk driver. These results indicate that MCFNA can provide interpretable, numerically stable, and actionable risk estimates to help prioritise Security Operations Center remediation in converged IT/OT infrastructure.
Early and reliable detection of strawberry foliar diseases is essential for reducing yield loss, limiting unnecessary pesticide use, and supporting timely site-specific crop management. This paper presents FRAISE-Net, a one-stage Strawberry Disease Inspection Network for automated localization and recognition of strawberry disease symptoms. The proposed architecture consists of an enhanced disease-aware backbone, a PAN-FPN-style neck, and a three-scale detection head. It integrates three complementary modules: Scale-Aware Channel Attention (SACA) to emphasize lesion-relevant channel responses, Context Aggregation Module (CAM) to capture broader disease context, and Adaptive Cross-Scale Fusion (ACSF) to combine fine spatial details with high-level semantic features across detection scales. Experiments were conducted on a 7-class strawberry disease detection dataset constructed from KUStraw and SDDD after removing the healthy class. The final experimental split contained 4766 annotated images, including 3810 images for training, 474 images for validation, and 482 images for testing. The FRAISE-Net algorithm was implemented on a real robotic arm for real-time disease detection in a controlled greenhouse laboratory setup. Under a unified 100-epoch training protocol with an input size of 320 × 320, FRAISE-Net was compared with YOLO-v9, YOLO-v10, YOLO-v11, YOLO-v12, YOLO-v26, and RT-DETR. On the held-out test set, FRAISE-Net achieved 95.61% precision, 87.88% recall, 91.58% F1-score, 94.48% mAP50, 77.58% mAP50:95, and 35.98 FPS on an RTX 2070 GPU. Five-fold cross-validation showed low fold-to-fold variability across partitions of the current combined dataset, with mean F1-score of 92.15±0.49%, mean mAP50 of 95.91±0.32%, and mean mAP50:95 of 80.09±0.73%. Ablation analysis confirmed the complementary contributions of SACA, CAM, and ACSF, with the complete FRAISE-Net achieving the strongest overall performance. The robotic experiments support the feasibility of the approach under the controlled, greenhouse-like laboratory conditions evaluated here; however, its generalizability to different sites, camera systems, seasons, disease distributions, and open-field settings remains to be established. These results indicate strong performance on the evaluated dataset, strong false-positive control, and practical inference speed. The code will be available at GitHub: FRAISE-Net.
Automated cleaning of floating garbage is a critical component of AI-empowered environmental monitoring for sustainable Smart Cities. Intelligent mobile sensing platforms, such as Unmanned Surface Vehicles (USVs), serve as key IoT nodes for aquatic ecosystem maintenance. However, existing detectors often misidentify natural debris as garbage under complex water conditions, a challenge known as visual mimicry. This study proposes ThinkRiver, a Coarse-to-Fine Semantic Verification Framework designed to enhance the sensing reliability of IoT devices by integrating a high-recall YOLOv8s proposer with a lightweight multimodal system, Qwen3-VL. By conducting semantic verification through Explicit Attribute Reasoning, the system enables more intelligent decision-making within the sensing network. Experiments on an Out-of-Distribution Challenge Set show that ThinkRiver reduces false positives from 261 to 82 (a 68.6% decrease) and improves Precision by 23.76%. These results indicate that semantic-reasoning-enhanced sensors can significantly strengthen the reliability of detection systems in dynamic and visually ambiguous environments, supporting the deployment of scalable AI-IoT solutions.
