Chasing Bubbles And Riding Value In Another Year Leading The S&P 500

Discover top portfolio strategies blending market timing & sector insights. See how 10 proprietary models consistently beat ...
Frontiers

A dynamic multitask evolutionary algorithm for high-dimensional feature selection based on multi-indicator task construction and elite competition learning

High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, ...
Scientific Research Publishing

Comparative Analysis of Machine Learning Algorithms for Optimal Land Use and Land Cover Classification: Guiding Method Selection for Resource-Limited Settings in Tiaty, Baringo ...

Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion, especially in Tiaty, Baringo, Kenya. These issues create mixed opportunities for pastoral and ...
IEEE

Two-Stage Hybrid Feature Selection: Integrating ACO Algorithms With a Statistical Ensemble Technique for EV Demand Prediction

Abstract: This paper presents a novel hybrid algorithm combining Ant Colony Optimization (ACO) and multi-model ensemble feature selection techniques to enhance the accuracy of Support Vector ...
Frontiers

Comparative analysis and application of rockburst prediction model based on secretary bird optimization algorithm

Rockburst is a dynamic instability disaster occurring in deep underground engineering under high ground stress, caused by the release of elastic strain energy within the surrounding rock during ...
IEEE

Adaptive Particle Swarm Optimization Algorithm and Application Model Based on Diversity-Driven Optimization

Abstract: The rapid development of Internet of Things technologies has led to the continuous increase and complexity of data feature dimensions. Therefore, a diversity-driven optimization-based ...