Modified Bald Eagle Search Algorithm With Deep Learning-Driven Sleep Quality Prediction for Healthcare Monitoring Systems

Sleep habits are strongly related to health behaviors, with sleep quality serving as a major health indicator. Current approaches for evaluating sleep quality, namely polysomnography and questionnaires, are often time-consuming, costly, or invasive. Thus, there is a pressing need for a more convenient, nonintrusive, and cost-effective method. The applications of deep learning (DL) in sleep quality prediction represent a groundbreaking technique for addressing sleep-related disorders. In this aspect, the article offers the design of a Modified Bald Eagle Search Algorithm with Deep Learning-Driven Sleep Quality Prediction (MBES-DLSQP) for Healthcare Monitoring Systems. The MBES-DLSQP technique combines the strengths of a DL model with a hyperparameter tuning strategy to provide precise sleep quality predictions. At the primary stage, the MBES-DLSQP technique undergoes data pre-processing. Besides, the MBES-DLSQP technique uses a stacked sparse autoencoder (SSAE)-based prediction model, which can extract and encode high-dimensional sleep data. The MBES-DLSQP incorporates MBESA-based hyperparameter tuning which assures its optimal configurations to further boost the efficiency of the SSAE model. The experimental outcome of the MBES-DLSQP algorithm is tested on the sleep dataset from the Kaggle repository. The experimental value infers that the MBES-DLSQP technique shows promising performance in sleep quality prediction with a maximum accuracy of 98.33%

Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging

Intracranial haemorrhage (ICH) has become a critical healthcare emergency that needs accurate assessment and earlier diagnosis. Due to the high rates of mortality (about 40%), the early classification and detection of diseases through computed tomography (CT) images were needed to guarantee a better prognosis and control the occurrence of neurologic deficiencies. Generally, in the earlier diagnoses test for severe ICH, CT imaging of the brain was implemented in the emergency department. Meanwhile, manual diagnoses are labour-intensive, and automatic ICH recognition and classification techniques utilizing artificial intelligence (AI) models are needed. Therefore, the study presents an Intracranial Haemorrhage Diagnosis using Willow Catkin Optimization with Voting Ensemble (ICHD-WCOVE) Model on CT images. The presented ICHD-WCOVE technique exploits computer vision and ensemble learning techniques for automated ICH classification. The presented ICHD-WCOVE technique involves the design of a multi-head attention-based CNN (MAFNet) model for feature vector generation with optimal hyperparameter tuning using the WCO algorithm. For automated ICH detection and classification, the majority voting ensemble deep learning (MVEDL) technique is used, which comprises recurrent neural network (RNN), Bi-directional long short-term memory (BiLSTM), and extreme learning machine-stacked autoencoder (ELM-SAE). The experimental analysis of the ICHD-WCOVE approach can be tested by a medical dataset and the outcomes signified the betterment of the ICHD-WCOVE technique over other existing approaches

Binary Chimp Optimization Algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor Networks

An Internet of Things (IoT)-assisted Wireless Sensor Network (WSNs) is a system where WSN nodes and IoT devices together work to share, collect, and process data. This incorporation aims to enhance the effectiveness and efficiency of data analysis and collection, resulting in automation and improved decision-making. Security in WSN-assisted IoT can be referred to as the measures initiated for protecting WSN linked to the IoT. This article presents a Binary Chimp Optimization Algorithm with Machine Learning based Intrusion Detection (BCOA-MLID) technique for secure IoT-WSN. The presented BCOA-MLID technique intends to effectively discriminate different types of attacks to secure the IoT-WSN. In the presented BCOA-MLID technique, data normalization is initially carried out. The BCOA is designed for the optimal selection of features to improve intrusion detection efficacy. To detect intrusions in the IoT-WSN, the BCOA-MLID technique employs a class-specific cost regulation extreme learning machine classification model with a sine cosine algorithm as a parameter optimization approach. The experimental result of the BCOA-MLID technique is tested on the Kaggle intrusion dataset, and the results showcase the significant outcomes of the BCOA-MLID technique with a maximum accuracy of 99.36%, whereas the XGBoost and KNN-AOA models obtained a reduced accuracy of 96.83% and 97.20%, respectively