Applicability of genetic algorithms for stock market prediction: A systematic survey of the last decade
A Thakkar, K Chaudhari - Computer Science Review, 2024 - Elsevier
Stock market is one of the attractive domains for researchers as well as academicians. It
represents highly complex non-linear fluctuating market behaviours where traders …
represents highly complex non-linear fluctuating market behaviours where traders …
Time series analysis and forecast of the COVID-19 pandemic in India using genetic programming
COVID-19 declared as a global pandemic by WHO, has emerged as the most aggressive
disease, impacting more than 90% countries of the world. The virus started from a single …
disease, impacting more than 90% countries of the world. The virus started from a single …
Towards data-driven discovery of governing equations in geosciences
Governing equations are foundations for modelling, predicting, and understanding the Earth
system. The Earth system is undergoing rapid change, and the conventional approaches for …
system. The Earth system is undergoing rapid change, and the conventional approaches for …
Predicting permeability of tight carbonates using a hybrid machine learning approach of modified equilibrium optimizer and extreme learning machine
It is a problematic task to perform petro-physical property prediction of carbonate reservoir
rocks in most cases, specifically for permeability prediction since a carbonate rock most …
rocks in most cases, specifically for permeability prediction since a carbonate rock most …
Identifying patterns in multiple biomarkers to diagnose diabetic foot using an explainable genetic programming-based approach
G D'Angelo, D Della-Morte, D Pastore… - Future Generation …, 2023 - Elsevier
Diabetes mellitus is a global health problem, recognized as the seventh cause of death in
the world. One of the most debilitating complications of diabetes mellitus is the diabetic foot …
the world. One of the most debilitating complications of diabetes mellitus is the diabetic foot …
Estimating the density of hybrid nanofluids for thermal energy application: Application of non-parametric and evolutionary polynomial regression data-intelligent …
There is no doubt that density is one of the most crucial thermophysical properties of hybrid
nanofluids in thermal energy applications. Various research papers have been devoted to …
nanofluids in thermal energy applications. Various research papers have been devoted to …
On the specific heat capacity estimation of metal oxide-based nanofluid for energy perspective–A comprehensive assessment of data analysis techniques
The main aim of the present study is to investigate the capabilities of four robust machine
learning method-the Kernel Extreme Learning Machine (KELM), Adaptive Regression …
learning method-the Kernel Extreme Learning Machine (KELM), Adaptive Regression …
Prediction of maximum scour depth near spur dikes in uniform bed sediment using stacked generalization ensemble tree-based frameworks
The scouring process near spur dikes could jeopardize the stability of riverbanks. Thus,
accurate estimation of the maximum scour depth near spur dikes is crucial in river …
accurate estimation of the maximum scour depth near spur dikes is crucial in river …
Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms
Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of
concrete structures, either through external bonding or internal reinforcement. However, the …
concrete structures, either through external bonding or internal reinforcement. However, the …
Predicting the ultimate axial capacity of uniaxially loaded cfst columns using multiphysics artificial intelligence
The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop
a prediction Multiphysics model for the circular CFST column by using the Artificial Neural …
a prediction Multiphysics model for the circular CFST column by using the Artificial Neural …