Attn_CNN_LSTM: IoT-Based Automated Alzheimer's Disease Classification using Deep Learning Approach

Attn_CNN_LSTM: IoT-Based Automated Alzheimer's Disease Classification using Deep Learning Approach
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-7
Year of Publication : 2023
Author : Anjani Yalamanchili, D. Venkatasekhar, G. Vijay Kumar
DOI : 10.14445/22315381/IJETT-V71I7P214

How to Cite?

Anjani Yalamanchili, D. Venkatasekhar, G. Vijay Kumar, "Attn_CNN_LSTM: IoT-Based Automated Alzheimer's Disease Classification using Deep Learning Approach," International Journal of Engineering Trends and Technology, vol. 71, no. 7, pp. 132-146, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I7P214

Abstract
Accurate recognition of Alzheimer's disease (AD) is a major concern in the Internet of Things (IoT) data. As IoT data deliver valuable recognition in the case of AD, suitable techniques are preferred for the detection process. Manual examinations undertaken by clinicians are found to be time-consuming and highly complex over instantaneous situations. Diverse approaches are developed for earlier stage AD detection but are found to be less robust, difficult in handling data, less convergent, time-consuming and lead to over-exploited losses. Hence to conquer the existing complexities, the proposed research article introduces an efficient automated deep learning (DL) based AD recognition using IoT data. The processes included in AD classification are data acquisition, Pre-processing, feature extraction, feature selection and classification. The initial step of the proposed work is data acquisition which targets gathering IoT data from the Daphnet dataset. The gathered data are pre-processed using data normalization and balancing. Normalization is performed using the Z-score-based Median and Median Absolute deviation (ZS-MMAD) approach, whereas data balancing is done using the Synthetic Minority Over-sampling Technique (SMOTE) approach. The pre-processed data are fed into mean, variance, and covariance-based Principal component analysis (MVC-based PCA) to extract the relevant features. Optimal features are selected using the Tweaked Archimedes optimization algorithm (TAOA). The non-part of the experiment, Experiment (No freeze) and Freeze classes of AD, are effectively classified through an attention-based Cascaded convolutional recurrent framework (Attn_CNN_LSTM). The proposed research work is implemented using the PYTHON simulation tool, and overall accuracy of 95.68% is obtained in classifying AD.

Keywords
Deep learning, Alzheimer, IoT data, Optimization, Data balancing, Optimal features, Attention, Classification.

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