Abstract Effective intervention and detection of developmental disabilities (DDs) should be carried out early and accurately. This paper discusses a novel AI-based approach for detecting DDs precisely, by incorporating both speech and behavioral data in a unique manner. The new architecture comprises several key stages, with innovations attributed to the system: Enhanced Gaussian-Based Noise Filtering (EGNF) is used to enhance speech; Conv-BiLSTM and Transformer Embeddings for performing strong feature extraction from speech and behavior; Harmony-ReliefF Optimization (HRO) for feature selection, and BioNeuroFusionNet (BNFN) as a novel classification network integrating IndRNN and MnasNet, with GNA selected as the class superiors. The Harmony-ReliefF Optimization (HRO) strategy combines the exploration capabilities of the Harmony Search Algorithm (HSA) with the feature ranking ability of ReliefF for optimizing the feature space. The features selected and optimally extracted are then fed into the novel BioNeuroFusionNet (BNFN) intended for final classification. The BNFN is another significant contribution of this work, where Independently Recurrent Neural Networks (IndRNN) are incorporated for fast processing of sequential data set and MnasNet (MN) for extracting high-level abstract features. The experimental result indicates that this integrated architecture works effectively with high accuracy (0.98942 for voice; 0.99632 for behavior) and very high scores in precision, recall, specificity, and other relevant metrics. Indeed, these results indicate that the system proposed in this work represents a considerable improvement in the area of early diagnosis and intervention of developmental disorders.
Al-Shqeerat et al. (Sat,) studied this question.