Extreme learning machine (ELM) has gained increasing interest from various research fields
recently. In this review, we aim to report the current state of the theoretical research and …

Feedforward neural networks (FFNN) have been utilised for various research in machine
learning and they have gained a significantly wide acceptance. However, it was recently …

Moth-flame optimization algorithm (MFO) is a new nature-inspired meta-heuristic based on
the navigation routine of moths in the environment known as transverse orientation. For …

The classification problems with imbalanced datasets widely exist in real word. An Extreme
Learning Machine is found unsuitable for imbalanced classification problems. This work …

As the business ecosystem is very becoming volatile with uncertainty accompanied with
poor process centric practices leading to complexity and contributing to ambiguous decision …

This paper empirically evaluates the several machine learning algorithms adaptable for lung
cancer detection linked with IoT devices. In this work, a review of nearly 65 papers for …

Globally, breast cancer is one of the most significant causes of death among women. Early
detection accompanied by prompt treatment can reduce the risk of death due to breast …

There are many machine learning approaches available and commonly used today,
however, the extreme learning machine is appraised as one of the fastest and, additionally …

Background and objectives It is important to be able to accurately distinguish between
benign and malignant thyroid nodules in order to make appropriate clinical decisions. The …

Soil moisture (SM) is an essential component of the environmental and the agricultural
system. Continuous monitoring and forecasting of soil moisture is a desirable strategy to …