The learning process of artificial neural networks is an important and complex task in the supervised learning field. The main difficulty of training a neural network is the process of fine-tuning the best set of control parameters in terms of weight and bias. This paper presents a new training method based on hybrid particle swarm optimization with Multi-Verse Optimization (PMVO) to train the feedforward neural networks. The hybrid algorithm is utilized to search better in solution space which proves its efficiency in reducing the problems of trapping in local minima. The performance of the proposed approach was compared with five evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using six bio-medical datasets. The results of the comparative study show that PMVO outperformed other training methods in most datasets and can be an alternative to other training methods.

“Support vector machine (SVM)” is one of the well-known machine learning algorithms used for classification and regression tasks. The “SVM” parameters and features selection have a significant impact on the “SVM” model's complexity and classification accuracy. Finding the best set of features and selecting the appropriate parameters of “SVM” is considered an optimization problem. Different metaheuristic algorithms are employed in the literature to choose the best set of features and to optimize “SVM” parameters simultaneously. This paper presents a new algorithm hybrid based on “Aquila Optimizer” and “Whale Optimization Algorithm” with Adaptive inertial weight, called AOWOA. The AOWOA is used for global optimization and feature selection and optimizing “SVM” parameters to achieve a higher classification accuracy. We utilized two experiment series to test the presented (AOWOA) algorithm. In the first experiment, 13 standard “benchmark functions” are used and the AOWOA algorithm is compared to AO, MVO, WOA, MFO, PSO, and SSA. In the second experiment, the results of AOWOA-SVM are compared with four metaheuristic algorithms: MVO, GWO, WOA, and BAT using ten labeled datasets. The experiment results proved that AOWOA can reduce the number of features, find the optimal parameters of “SVM”, and avoids local optima with high classification accuracy in most datasets compared to other algorithms.

Attention Deficit Hyperactivity Disorder (ADHD) is a heterogeneous neurodevelopmental disorder whose diagnosis is mainly based on behavioral assessment and is often delayed due to clinical complexity and limited availability of specialists. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable source of information for supporting automated and objective diagnosis. However, existing studies often do not fully capture the complex interactions of functional connectivity between different brain regions. To address this limitation, this work proposes a graph-based deep learning framework for ADHD classification from rs-fMRI that combines functional connectivity modeling with graph representation learning. The approach used Phase-Locking Value (PLV)-based connectivity estimation and Graph Sample and Aggregate (GraphSAGE) to jointly capture regional brain activity and inter-regional interactions in a scalable and efficient manner. GraphSAGE improves robustness to noise and inter-subject variability by aggregating information from stable local graph neighborhoods. This integration allows the model to learn discriminative connectivity-aware representations while remaining robust to signal variability and adaptable to multi-site data. The proposed framework was evaluated on the publicly available ADHD-200 dataset across multiple acquisition sites as well as on a combined multi-site dataset. The results indicate consistent performance across individual sites and on the combined dataset. The model achieved an Accuracy of 0.89, an AUC of 0.96, and a Specificity of 0.96 on the combined dataset, outperforming several existing methods in this setting. By integrating PLV-based connectivity with GraphSAGE learning, the approach provides an effective and scalable solution for automated ADHD classification from rs-fMRI data, contributing to data-driven approaches for the analysis of neurodevelopmental disorders.