Abstract

Abstract: Ensemble multi featured deep learning methodologies have gained significant traction as a solution to overcome the limitations of single deep learning models in terms of generalization, robustness, and overall performance. This survey offers a comprehensive review of ensemble multi featured models, highlighting their applications across critical domains, including computer vision, medical imaging, natural language processing, and speech recognition. By integrating multiple models and diverse feature sets, these ensemble techniques have demonstrated superior adaptability and performance in solving complex, real-world problems. In addition to covering practical applications, this paper discusses the challenges associated with ensemble models, such as interpretability, computational complexity, and adversarial robustness. We delve into cutting-edge solutions to these challenges, particularly focusing on advancements in personalized and federated learning, as well as improved ensemble selection techniques. The need for novel algorithms, frameworks, and hardware architectures that can manage the intensive computational demands of ensemble models is also emphasized. Looking ahead, the survey highlights future research directions aimed at optimizing trade-offs between model complexity, accuracy, and computational resource usage. This is crucial for achieving scalable, efficient, and practical deployment of ensemble multi featured deep learning systems across various industries and domains. Keywords: Ensemble Learning, Multi Featured Deep Learning, Model Generalization, , Personalized Learning, Medical Imaging, Natural Language Processing, Speech Recognition, Model Interpretability, Ensemble Model Selection, Deep Learning Architectures