Detection of COVID-19 Enhanced by a Deep Extreme Learning Machine

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for the diagnosis of disease are needed to help prevent the rapid spread of COVID-19. Various detection methods are being used to diagnose coronavirus. Deep extreme learning is the most successful artificial intelligence (AI) technique that efficiently supports medical experts in making smart decisions for the detection of COVID-19. In this study, a novel detection model to diagnose COVID-19 has been introduced to achieve a better accuracy rate. The study focuses on quantitative analysis and disease detection of COVID-19 empowered by a statistical real-time sequential deep extreme learning machine (D2C-RTS-DELM). The experimental results show 98.18% accuracy and 98.87% selectivity, and the probability of detection is 98.84%. The results demonstrate that the quantitative analysis and statistical real-time sequential deep extreme learning machine used in this study perform well in forecasting COVID-19 as well as in making timely decisions for treatment.

A Fused Machine Learning Approach for Intrusion Detection System

“…The IDS-FMLT model uses fused machine learning approaches to protect the networks from malicious attacks. Various dataset such as KDD dataset [25], CUP-99 dataset [26], and NetML-2020 dataset [27] and NSL-KDD dataset [28] were used in RNN [29], adaptive voting algorithm [30], ANN [31], DELM [32], SVM [33], SOMNN [34], ANN [35], NAÏVE BAYES + RP [36] and GANS [37].…”

“…The training and validation phases of the proposed model are divided into two parts. First, the data repository layer has been used to gather data from various datasets, such as KDD [25], CUP-99 [26], and NetML-2020 [27]. After storing the data, the pre-processing layer activates and handles repeated and missing values using different techniques, such as the mean, mode, and moving average.…”

“…And z = Layer of Output Value Eq. ( 13) demonstrates the weight update and biases, where σ represents the learning rate [24][25][26][27][28].…”

Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine

“…Deep extreme learning machine (DELM) is a derivative algorithm of ELM, which builds a multi-layer network structure by stacking extreme learning machine-automatic encoder (ELM-AE), which improves the characterization ability of the network. When ELM is faced with input and output variables with a too large amount and too high dimensionality of input data, the problem that the extreme learning machine with a single hidden layer cannot capture the effective features of the data is solved [38]. The DELM is a combination of extreme learning machine and automatic encoder to form an extreme learning machine-automatic encoder, whose structure is shown in Figure 1.…”

An IoMT‐Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique

“…The SH idea is a viable and cost-effective approach to improve non-intrusive home treatment for seniors, enabling greater independence, ensuring good health, and avoiding social isolation. According to [ 7 ], SH solutions are regarded as information-based technology that gathers and disseminates user data with the resident, family members, and primary care physicians in a passive manner. SH solution also refers to using essential and assistive gadgets to create an environment where many house features are automated and devices may connect.…”