Azra Molamasoumi
Research Subjects
Azra Molamasoumi has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Akib Mohi Ud Din Khanday
Department of Information Technology, Cluster University of Srinagar, Gogji Bagh, India
1 shared publication - Hajar Sadeghizadeh
Department of Computer Science Department of Computer Science Islamic Department of Information Science Islamic Department of Electrical Engineering Islamic Yazd University Azad University of Yazd Azad University of Meybod Azad University of Meybod
1 shared publication - Mina Rajabi1 shared publication
- Mohd Asif Hajam
Department of Information Technology, Baba Ghulam Shah Badshah University, Rajouri 185234, India
1 shared publication
Latest Publications
- Open Access
- 26 September 2023
- Published by MDPI AG in Preprints.org
Neuro Fuzzy GreyWolf Optimisation Method for Diagnosing the Liver Disorders
The hybrid method proposed in the study, ANFIS-GWO, combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO) for the diagnosis of liver disorders. ANFIS is a powerful tool that combines the advantages of neural networks and fuzzy logic to create a hybrid model capable of handling complex and uncertain data. GWO is a metaheuristic optimization algorithm inspired by the social behaviour of grey wolves. In the ANFIS-GWO method, the hyperparameters of ANFIS are optimized using GWO. This optimization process aims to fine-tune the ANFIS model based on the available dataset, which consists of 7 characteristic attributes and 354 samples related to liver diseases. By adopting the hyper-parameters, the ANFIS-GWO method enhances the overall performance and accuracy of the diagnostic system. To evaluate the effectiveness of the ANFIS-GWO intelligent medical system, the study employs classification accuracy, sensitivity, and specificity analysis. Classification accuracy measures the overall correctness of the system in predicting liver disease cases. Sensitivity refers to the system’s ability to correctly identify individuals with liver disorders, while specificity measures its ability to correctly identify those without liver disorders. Experimental results demonstrate that the performance of the ANFIS-GWO method surpasses that of traditional Fuzzy Inference Systems (FIS) and ANFIS models that do not undergo an optimization phase. This suggests that the integration of GWO optimization significantly improves the diagnostic accuracy of the ANFIS model for liver disease diagnosis.
- 26 September 2023
- Published by MDPI AG in Preprints.org
Open AccessNeuro Fuzzy GreyWolf Optimisation Method for Diagnosing the Liver Disorders
The hybrid method proposed in the study, ANFIS-GWO, combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO) for the diagnosis of liver disorders. ANFIS is a powerful tool that combines the advantages of neural networks and fuzzy logic to create a hybrid model capable of handling complex and uncertain data. GWO is a metaheuristic optimization algorithm inspired by the social behaviour of grey wolves. In the ANFIS-GWO method, the hyperparameters of ANFIS are optimized using GWO. This optimization process aims to fine-tune the ANFIS model based on the available dataset, which consists of 7 characteristic attributes and 354 samples related to liver diseases. By adopting the hyper-parameters, the ANFIS-GWO method enhances the overall performance and accuracy of the diagnostic system. To evaluate the effectiveness of the ANFIS-GWO intelligent medical system, the study employs classification accuracy, sensitivity, and specificity analysis. Classification accuracy measures the overall correctness of the system in predicting liver disease cases. Sensitivity refers to the system’s ability to correctly identify individuals with liver disorders, while specificity measures its ability to correctly identify those without liver disorders. Experimental results demonstrate that the performance of the ANFIS-GWO method surpasses that of traditional Fuzzy Inference Systems (FIS) and ANFIS models that do not undergo an optimization phase. This suggests that the integration of GWO optimization significantly improves the diagnostic accuracy of the ANFIS model for liver disease diagnosis.