Fuzzy Adaptive Bobcat Optimization Algorithm (FABOA) Feature Selection Model and Double Deep Q Network (DDQN) Learning for Big Data Analytics

2026-02-02T09:21:30+00:00

Big data analytics is challenged by high dimensionality, redundant attributes, heterogeneous data distributions, and the computational burden of processing large-scale datasets. The current filter, wrapper and matrix factorization approaches typically ignore the relationships among features or converge too early, are not very adaptive, and are not very efficient in searching. To fill this space, this study offers an Intelligent Feature Selection and Large-Scale Data Analytics Framework based on Fuzzy Adaptive Bobcat Optimization Algorithm–Double Deep Q-Network (FABOA–DDQN). In addition to the Bobcat Optimization Algorithm, FABOA introduces triangular fuzzy membership functions to adjust exploration and exploitation based on the diversity of the population, improvement in fitness and the number of iterations. Its wrapper objective takes into account predictive performance, feature-subset compactness and feature redundancy. DDQN is not just a sequential combination, but rather a policy-learning controller: Candidate feature subsets are states, adding or removing features or changing their values is an action, and the reward value is a measure of fitness improvement and feature reduction. The online and target networks alleviate Q-value overestimation and stabilize search decisions. The experimental results were obtained by applying a 70:15:15 training/validation/testing split of a widely used SVM evaluator, 30 independent trials and tested against NNMF, S4NMF, RLNMFAG, DBMD variants, BOA, GA, PSO, WOA, DQN, and DDQN. On Covertype, FABOA–DDQN achieved 98.34% precision, 96.77% recall, 97.55% F1-score, and 97.10% RI. It surpassed DBMD-DLM-BFGS in all four of these measures by as much as 1.95%, 3.44%, 2.69%, and 3.29%, respectively, across all four datasets while bringing the ratio of the selected features down to 31%.

An Adaptive Multimodal Meta-Fusion Framework for Alzheimer's Disease and Frontotemporal Dementia Classification using EEG, MRI, and Cognitive Biomarkers

2026-06-18T06:25:10+00:00

A single-modality approach to neurodegenerative disease classification is often limited by heterogeneous clinical manifestations, modality-specific feature distributions, and incomplete data availability. This study proposes an Adaptive Multimodal Meta-Fusion (AMMF) framework integrating cognitive-demographic, structural MRI, and EEG biomarkers for classification of the Alzheimer's disease (AD), Frontotemporal Dementia (FTD), and Cognitively Normal (CN) participants. Three modality-specific cohorts from two public repositories were considered: cognitive-demographic data (n = 679), structural MRI data (n = 595), and EEG data (n = 194; BrainLat n = 106 and OpenNeuro n = 88). Cognitive-demographic features are prioritized using explainable XGBoost with SHAP-based feature analysis, MRI data are processed using ConvNeXt-Small for spatial representation learning, and EEG signals are analyzed using a Transformer-based architecture for temporal and functional representation learning. For the matched BrainLat cohort, the three modality branches generated an 18-dimensional cognitive-demographic representation, a 128-dimensional EEG representation, and a 768-dimensional MRI representation. The proposed framework then applied adaptive late fusion at the modality-probability level: each modality generated an independent three-class probability vector, and a modality-availability mask was used to renormalize the predefined modality weights of 0.20, 0.30, and 0.50 assigned to the cognitive-demographic, EEG, and MRI modalities, respectively. The resulting weighted class-probability vector was supplied to a Random Forest meta-classifier for final three-class classification. The complete multimodal evaluation was performed on 106 subject-level matched participants with all three modalities. The proposed framework achieved an accuracy of 99.06%, demonstrating the utility of combining complementary cognitive, functional, and structural biomarkers through adaptive probability-level meta-fusion for AD, FTD, and CN classification. A continuity-corrected McNemar test yielded χ² = 4.000 (p = 0.0455) for C4 versus C7 and χ² = 4.167 (p = 0.0412) for C5 versus C7, indicating statistically significant paired differences, while C6 versus C7 yielded χ² = 3.200 (p = 0.0736). Integration of cognitive-demographic, MRI, and EEG modalities for multimodal disease classification. The proposed framework integrates cognitive-demographic, MRI, and EEG modalities for multimodal disease classification by combining heterogeneous features in a unified representation space and applying Random Forest-based meta-classification to the fused modality probabilities.

International Research Journal of Multidisciplinary Technovation

Fuzzy Adaptive Bobcat Optimization Algorithm (FABOA) Feature Selection Model and Double Deep Q Network (DDQN) Learning for Big Data Analytics

An Adaptive Multimodal Meta-Fusion Framework for Alzheimer's Disease and Frontotemporal Dementia Classification using EEG, MRI, and Cognitive Biomarkers