Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems.

The RIME optimization algorithm is a newly developed physics-based optimization algorithm used for solving optimization problems. The RIME algorithm proved high-performing in various fields and domains, providing a high-performance solution. Nevertheless, like many swarm-based optimization algorithms, RIME suffers from many limitations, including the exploration-exploitation balance not being well balanced.

A new metaheuristic optimization model for financial crisis prediction: Towards sustainable development.

Global crises such as the COVID-19 pandemic and other recent environmental, financial, and economic disasters have weakened economies around the world and marginalized efforts to build a sustainable economy and society. Financial crisis prediction (FCP) has a significant impact on the economy. The growth and strength of a country's economy can be gauged by accurately predicting how many companies will fail and how many will succeed.

Intelligent energy aware optimization protocol for vehicular adhoc networks.

Vehicular adhoc network (VANET) plays a vital role in smart transportation. VANET includes a set of vehicles that communicate with one another via wireless links. The vehicular communication in VANET necessitates an intelligent clustering protocol to maximize energy efficiency.

Deep Learning-Based Model for Financial Distress Prediction.

Predicting bankruptcies and assessing credit risk are two of the most pressing issues in finance. Therefore, financial distress prediction and credit scoring remain hot research topics in the finance sector. Earlier studies have focused on the design of statistical approaches and machine learning models to predict a company's financial distress.

Advanced Cognitive Algorithm for Biomedical Data Processing: COVID-19 Pattern Recognition as a Case Study.

Automated disease prediction has now become a key concern in medical research due to exponential population growth. The automated disease identification framework aids physicians in diagnosing disease, which delivers accurate disease prediction that provides rapid outcomes and decreases the mortality rate. The spread of Coronavirus disease 2019 (COVID-19) has a significant effect on public health and the everyday lives of individuals currently residing in more than 100 nations.

Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction.

The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in the developed world. Cardiovascular disease and its complications, including dementia, can be averted with early detection.

A hybrid feature selection model based on butterfly optimization algorithm: COVID-19 as a case study.

The need to evolve a novel feature selection (FS) approach was motivated by the persistence necessary for a robust FS system, the time-consuming exhaustive search in traditional methods, and the favourable swarming manner in various optimization techniques. Most of the datasets have a high dimension in many issues since all features are not crucial to the problem, which reduces the algorithm's accuracy and efficiency. This article presents a hybrid feature selection approach to solve the low precision and tardy convergence of the butterfly optimization algorithm (BOA).