This study examines Free Space Optical (FSO) communication’s performance in different fog conditions, focusing on Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) setups. In low fog, SISO handles signal degradation well. MIMO improves link robustness in moderate fog. High fog challenges traditional FSO, leading to ML integration to optimize communication parameters. For weather channel classification, a preprocessing scheme reduces features from 12 to 2 given that the 12 features are Bit Error Rate (BER), Quality Factor (Q-factor) and Received Optical Power (ROP) for different 4 users. A Gaussian Process Classifier (GPC) achieves an accuracy greater than 99%, surpassing SVM, Decision Tree, and Random Forest. GPC’s fit and predict functions execute in 0.15 s, outperforming NuSVM (0.2 s). This study highlights FSO, SISO, MIMO, and Machine Learning (ML) practicality in enhancing communication resilience in adverse weather, especially in fog-prone areas.

A novel Machine Learning (ML) framework is proposed, in this paper to predict and optimize key performance indicators in an 18-tupling optical communication system. It starts with delivering and training on a public 24,572-row OptiSystem dataset along with an open-source Python package, tupling_ml, containing rigorous preprocessing that includes correlation-based feature selection and dimensionality reduction through Principal Components Analysis (PCA). This procedure reduces the feature space from 68 to 4 dimensions while preserving over 80% of the variance. Several multi-output regression models are then evaluated, including multioutput Linear Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN), and a Regressor Chain of RF (RCRF). Validation is performed by means of 5-fold cross-validation (CV). The RCRF approach produces the best results, with a Mean Absolute Error (MAE) of 0.476, Mean Squared Error (MSE) of 0.963, and a coefficient of determination ($R^{2}$) of 0.893 on the test set. Finally, Particle Swarm Optimization (PSO) is employed to fine-tune two critical hyperparameters of the RF regressor: number of estimators and maximum depth, thereby further enhancing model performance by 3.3% better than the untuned RF chain. By enabling proactive resource allocation and lowering signal penalties, the proposed framework expedites the deployment of next-generation ($>$100 Gb/s) optical fronthaul and fixed-wireless backhaul links.

Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant Colony Optimization (ER-ACO) is introduced as a lightweight ACO variant in which an exponentially fading randomness factor is integrated into the state-transition mechanism. Strong early-stage exploration is enabled, and a smooth transition to exploitation is induced, improving convergence behavior and solution quality. Low computational overhead is maintained while exploration and exploitation are dynamically balanced. ER-ACO is positioned within real-time healthcare logistics, with a focus on Emergency Medical Services (EMS) routing and hospital resource scheduling, where rapid and adaptive decision-making is critical for patient outcomes. These systems face dynamic constraints such as fluctuating traffic conditions, urgent patient arrivals, and limited medical resources. Experimental evaluation on benchmark instances indicates that solution cost is reduced by up to 14.3% relative to the slow-fade configuration ($\gamma =1$) in the 20-city TSP sweep, and faster stabilization is indicated under the same iteration budget. Additional comparisons against Standard ACO on TSP/QAP benchmarks indicate consistent improvements, with unchanged asymptotic complexity and negligible measured overhead at the tested scales. TSP/QAP benchmarks are used as controlled proxies to isolate algorithmic behavior; EMS deployment is treated as a motivating application pending validation on EMS-specific datasets and formulations. These results highlight ER-ACO’s potential as a lightweight optimization engine for smart healthcare systems, enabling real-time deployment on edge devices for ambulance dispatch, patient transfer, and operating room scheduling.

Deception detection is considered a concern for all individuals in their everyday lives, as it greatly affects human interactions. While multiple automatic lie detection systems exist, their accuracy still needs to be improved. Additionally, the lack of adequate and realistic datasets hinders the development of reliable systems. This paper presents a new multimodal dataset with physiological data (heart rate, galvanic skin response, and body temperature), in addition to demographic data (age, weight, and height). The presented dataset was collected from 49 unique subjects. Moreover, this paper presents a polygraph-based lie detection system utilizing multimodal sensor fusion. Different machine learning algorithms are used and evaluated. Random Forest has achieved an accuracy of 97%, outperforming Logistic Regression (58%), Support Vector Machine (58% with perfect recall of 1.00), and k-Nearest Neighbor (83%). The model shows excellent precision and recall (0.97 each), making it effective for applications such as criminal investigations. With a computation time of 0.06 s, Random Forest has proven to be efficient for real-time use. Additionally, a robust k-fold cross-validation procedure was conducted, combined with Grid Search and Particle Swarm Optimization (PSO) for hyperparameter tuning, which substantially reduced the gap between training and validation accuracies from several percentage points to under 1%, underscoring the model’s enhanced generalization and reliability in real-world scenarios.

This paper addresses the handover problem in Free space optical (FSO) communication, a critical challenge in maintaining stable connections under varying atmospheric conditions. A novel multi-user dataset comprising 3000 data points is introduced, capturing key FSO handover parameters such as range, received optical power (ROP), quality factor (Q-factor), and bit error rate (BER) across fog conditions and single-input-single-output/ multiple-input-multiple-output wavelength division multiplexing configurations. The system predicts Q-factor, BER, and ROP for new or relocating users in near real time, minimizing latency during handovers and avoiding packet loss, thereby improving the quality of service. The proposed solution employs statistical analysis along with multi-regression machine learning models, including random forest (RF), gradient boosting, K-nearest neighbor, Linear Regression, and Bayesian ridge, to predict communication parameters under four scenarios (Sce1–Sce4). The system is designed to handle four handover scenarios, each with conditions related to the link range and the communication link label. In Scenario 1 (Sce1), both the link range and the communication link label are unknown. Scenario 2 (Sce2) involves a known link range, but the communication link label remains unknown. In Scenario 3 (Sce3), the link range is unknown, while the communication link label is known. Finally, Scenario 4 (Sce4) involves both the link range and the communication link label being known. In the most challenging scenario (Sce1), where both link range and channel label are unknown, the RF model achieved 98% prediction accuracy, MAE as low as 0.002428, and the lowest root mean square error of 0.02%, significantly outperforming other models. Hypothesis testing using the Kolmogorov–Smirnov test confirmed the statistical consistency of the training and testing datasets, ensuring robust predictions. Compared to neural network, the RF model demonstrated an order-of-magnitude improvement in time efficiency while maintaining similar accuracy, highlighting its practicality for real-time FSO communication optimization.