Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3573 2018 2018 Dhofar University, Sultanate of Oman Aditi Aditi Department of Computer Sc. and Engg, Symbosis Institute of Technology, Pune, India; Symbiosis International (Deemed) University, Pune, India Aditi Sharma IEEE Senior Member, USA Preetish Kakkar University of Houston, Victoria, Texas, USA Daya Nand E&I Engineering Department, Institute of Technology, Nirma University, Ahmedabad, India Arvind R. Yadav Department of Computer Sc. and Engg, Parul Institute of Technology, Parul University, Vadodara, India Gaurav Kumar Ameta Fusion techniques on enhancing the efficiency of Long Short-Term Memory (LSTM) networks are dominating across a variety of domains. To handle sequential data while integrating from various sources is often challenging using LSTM techniques. Fusion methods that integrate different models enhances LSTM’ ability to handle complex correlations in the data. This paper examines early, late and hybrid fusion techniques. The study provides fusion approaches to enhance LSTM networks to efficiently handle complex multimodal data across self-navigating models. The findings reveal that the hybrid fusion techniques outperform traditional methods in terms of accuracy and generalization of various tasks. This paper proposes the Gated Recurrent Fusion (GRF) approach to demonstrate its performance to handle multimodal and temporal models in a supervised recurrence. The findings report 10% enhancement in terms of precision rate 2025 2025 50 56 10.54216/FPA.190105 https://www.americaspg.com/articleinfo/3/show/3573