Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches
COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute
respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a …
respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a …
Multi-objective optimization techniques: a survey of the state-of-the-art and applications: Multi-objective optimization techniques
In recent years, multi-objective optimization (MOO) techniques have become popular due to
their potentiality in solving a wide variety of real-world problems, including bioinformatics …
their potentiality in solving a wide variety of real-world problems, including bioinformatics …
Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization
H Su, D Zhao, H Elmannai, AA Heidari… - Computers in Biology …, 2022 - Elsevier
COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It
usually is diagnosed by examining pathological photographs of the patient's lungs. There is …
usually is diagnosed by examining pathological photographs of the patient's lungs. There is …
[HTML][HTML] A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide …
Abstract Machine learning (ML) has been extensively applied to model geohazards, yielding
tremendous success. However, researchers and practitioners still face challenges in …
tremendous success. However, researchers and practitioners still face challenges in …
GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems
In this article, an improved variant of the grey wolf optimizer (GWO) named gaze cues
learning-based grey wolf optimizer (GGWO) is proposed. The main intentions are to reduce …
learning-based grey wolf optimizer (GGWO) is proposed. The main intentions are to reduce …
[HTML][HTML] Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach
Spam emails are unsolicited, annoying and sometimes harmful messages which may
contain malware, phishing or hoaxes. Unlike most studies that address the design of efficient …
contain malware, phishing or hoaxes. Unlike most studies that address the design of efficient …
[HTML][HTML] Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models
Predicting crop yield is an important issue for farmers. Food security is important for decision-
makers. The agriculture industry can more accurately supply human demand for food if the …
makers. The agriculture industry can more accurately supply human demand for food if the …
[HTML][HTML] Lemurs optimizer: A new metaheuristic algorithm for global optimization
The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper.
This algorithm's primary inspirations are based on two pillars of lemur behavior: leap up and …
This algorithm's primary inspirations are based on two pillars of lemur behavior: leap up and …
A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home
In this paper, the min-conflict local search algorithm (MCA) is hybridized with the grey wolf
optimizer (GWO) for the power scheduling problem in smart home (PSPSH), and the …
optimizer (GWO) for the power scheduling problem in smart home (PSPSH), and the …
Fault diagnosis of train rotating parts based on multi-objective VMD optimization and ensemble learning
Z **, D He, R Ma, X Zou, Y Chen, S Shan - Digital Signal Processing, 2022 - Elsevier
Rotating machinery is widely used in various systems of trains, and its health status is
directly related to the reliability of train operation. Therefore, the fault diagnosis of train …
directly related to the reliability of train operation. Therefore, the fault diagnosis of train …