Improving Mutual Fund Performance Analysis through the Fusion of CNN-LSTM and Explainable AI Techniques Raju B P G, Navya Francis, N.Venkatarathnam, Kavita Mahar, Manyam Kethan, et al. 2024 3rd International Conference on Electrical Electronics Information and Communication Technologies Iceeict 2024, 2024 Analysis of mutual fund performance is crucial for fund managers and investors to make wise choices. Predictions made using traditional approaches are frequently not as accurate since they are unable to identify intricate patterns in financial data. Convolutional neural networks (CNN) and long short-term memory (LSTM) networks are two examples of deep learning approaches that provide promising ways to improve the predictive accuracy of financial forecasting jobs. Incorporating explainable AI techniques can also help with risk management and decision-making by offering insights into the fundamental causes influencing mutual fund performance. By utilizing the combined strength of explainable AI techniques and CNN-LSTM architecture, this work seeks to improve mutual fund performance analysis. The aim is to create a strong framework that can forecast mutual fund performance with accuracy and offer comprehensible explanations of the underlying elements. This work is interesting because it combines explainable AI methods which are particularly useful for analyzing mutual fund performance with CNN-LSTM architecture. In this work, the dual challenges of prediction accuracy and model transparency in financial forecasting are addressed by integrating deep learning with interpretability. The proposed framework for mutual fund performance analysis uses historical data, CNN-LSTM architecture, and explainable AI methods. The model outperforms traditional methods, achieving higher predictive accuracy and providing actionable insights into fund performance drivers. The model's interpretability enhances trustworthiness and utility for investors and fund managers, empowering stakeholders with better decision-making in dynamic financial markets.
RECENT SCHOLAR PUBLICATIONS
Page-Hinkley Test: Approaches, Obstacles, and Resolutions N Francis, A Ali Proceedings of International Conference on Recent Trends in Computing: ICRTC … , 2025 2025.0 Citations: 1
Improving Mutual Fund Performance Analysis through the Fusion of CNN-LSTM and Explainable AI Techniques BPG Raju, N Francis, N Venkatarathnam, K Mahar, M Kethan, II Raj 2024 Third International Conference on Electrical, Electronics, Information … , 2024 2024.0 Citations: 2
Identifying and Managing Concept Drift in Machine Learning Through Page-Hinkley Test: Approaches, Obstacles, and Resolutions N Francis, A Ali International Conference on Recent Trends in Computing, 33-46 , 2024 2024.0 Citations: 5
Enhancing Freight Auditing Efficiency: Leveraging Hadoop MapReduce for Logistic Audit Assist N Francis, A Ali International Conference on Recent Trends in Computing, 69-90 , 2024 2024.0
Data Processing for Big Data Applications using Hadoop Framework N Francis, K Sheena Kurian International Journal of Advanced Research in Computer and Communication … , 2015 2015.0 Citations: 4
Remediating Bigdata processing problems using Hadoop and Spark 3rd International Virtual Conference on Advances in Computing & Information … , 0
Freight Audit using Mapreduce Framework for Big-data Application N Francis, S Kurian
LFA-LOGISTIC FREIGHT ASSISTANCE USING HADOOP MAP REDUCE FRAMEWORK N Francis
MOST CITED SCHOLAR PUBLICATIONS
Identifying and Managing Concept Drift in Machine Learning Through Page-Hinkley Test: Approaches, Obstacles, and Resolutions N Francis, A Ali International Conference on Recent Trends in Computing, 33-46 , 2024 2024.0 Citations: 5
Data Processing for Big Data Applications using Hadoop Framework N Francis, K Sheena Kurian International Journal of Advanced Research in Computer and Communication … , 2015 2015.0 Citations: 4
Improving Mutual Fund Performance Analysis through the Fusion of CNN-LSTM and Explainable AI Techniques BPG Raju, N Francis, N Venkatarathnam, K Mahar, M Kethan, II Raj 2024 Third International Conference on Electrical, Electronics, Information … , 2024 2024.0 Citations: 2
Page-Hinkley Test: Approaches, Obstacles, and Resolutions N Francis, A Ali Proceedings of International Conference on Recent Trends in Computing: ICRTC … , 2025 2025.0 Citations: 1
Enhancing Freight Auditing Efficiency: Leveraging Hadoop MapReduce for Logistic Audit Assist N Francis, A Ali International Conference on Recent Trends in Computing, 69-90 , 2024 2024.0
Remediating Bigdata processing problems using Hadoop and Spark 3rd International Virtual Conference on Advances in Computing & Information … , 0
Freight Audit using Mapreduce Framework for Big-data Application N Francis, S Kurian
LFA-LOGISTIC FREIGHT ASSISTANCE USING HADOOP MAP REDUCE FRAMEWORK N Francis
