ASEMMS: The Adaptive Smart Environment Multi-Modal System
Journal of System and Management Sciences · 2022
The primary objective of this research is to make use of diverse information such as textual, audio, and visual to learn a model.Effective interaction between these approaches often leads to a better-performing system.In addition, a new fusion taxonomy based on a data perspective introduces a fusion-based classification model that can neglect the context domain and focus on several features of the input data.The proposed fusion taxonomy is constructed based on the data perspective not on context perspective.This interpretation relies on data modality types, the importance of extracted features, the data noise, and the streaming time.It improves an interpretation of the hybrid fusion technique.The fundamental challenge of the smart environment is illustrated by infusing big sensory data extracted from IoT sensors and devices to support the main objective.Also, an adaptive smart environment multi-modal system is proposed as a solution for the modality fusion challenge to improve classification and prediction in various contexts, whether one or more data modality types.We have proposed a new adaptive smart environment multi-modal system (ASEMMS) for improving the classifications accuracy results.It relies on the common characteristics of smart applications such as smart health for monitoring patients remotely and smart military for improving the hyper spectral in night mode.The adaptive smart environment multi-modal system (ASEMMS) is designed based on constructing five layers, a software-defined fusion layer, pre-processing layer, dynamic classification layer, hybrid fusion layer, and evaluation layer.A software-defined fusion layer is considered a controller for managing data types, model types, and noisy data.A hybrid fusion layer is designed based on a tailored neural network for making a combination between Dempster-shafer and Concatenation fusion techniques for getting bigger number of features.It measures the accuracy and optimization for the classification results.For validation, we make two comparisons between the proposed adaptive system and the baseline of Dempsyershafer fusion technique and the baseline of concatenation fusion technique.