I.Ghali, N., R. Wahid, and A. E. Hassanien,
"Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models",
International Journal of Systems Biology and Biomedical Technologies, , vol. 1, issue 4, pp. 75-88, 2012.
AbstractIn this paper the possibility of using the human heart sounds as a human print is investigated. To evaluate the performance and the uniqueness of the proposed approach, tests using a high resolution auscultation digital stethoscope are done for nearly 80 heart sound samples. The verification approach consists of a robust feature extraction with a specified configuration in conjunction with Gaussian mixture modeling. The similarity of two samples is estimated by measuring the difference between their log-likelihood similarities of the features. The experimental results obtained show that the overall accuracy offered by the employed Gaussian mixture modeling reach up to 85%. The identification approach consists of a robust feature extraction with a specified configuration in conjunction with LBG-VQ. The experimental results obtained show that the overall accuracy offered by the employed LBG-VQ reach up to 88.7%
Inbarani, H., U. S. Kum, A. T. Azar, and A. E. Hassanien,
"Hybrid Rough-Bijective Soft Set Classification system,",
Neural Computing and Applications (NCAA) , pp. , pp, 1-21, 2017 , 2017.
AbstractIn today’s medical world, the patient’s data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely not possible by medical experts. Hence, the essential for an intelligent system to reflect the different issues and recognize an appropriate model between the different parameters is evident. In recent decades, rough set theory (RST) has been broadly applied in various fields such as medicine, business, education, engineering and multimedia. In this study, a hybrid intelligent system that combines rough set (RST) and bijective soft set theory (BISO) to build a robust classifier model is proposed. The aim of the hybrid system is to exploit the advantages of the constituent components while eliminating their limitations. The resulting approach is thus able to handle data inconsistency in datasets through rough sets, while obtaining high classification accuracy based on prediction using bijective soft sets. Toward estimating the performance of the hybrid rough-bijective soft set (RBISO)-based classification approach, six benchmark medical datasets (Wisconsin breast cancer, liver disorder, hepatitis, Pima Indian diabetes, echocardiogram data and thyroid gland) from the UCI repository of machine learning databases are utilized. Experimental results, based on evaluation in terms of sensitivity, specificity and accuracy, are compared with other well-known classification methods, and the proposed algorithm provides an effective method for medical data classification.
Inbarani, H. H., S. Udhaya Kumar, A. T. Azar, and A. E. Hassanien,
"Hybrid rough-bijective soft set classification system",
Neural Computing and Applications, , vol. 29, issue 8, pp. 67–78., 2018.
AbstractIn today’s medical world, the patient’s data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely not possible by medical experts. Hence, the essential for an intelligent system to reflect the different issues and recognize an appropriate model between the different parameters is evident. In recent decades, rough set theory (RST) has been broadly applied in various fields such as medicine, business, education, engineering and multimedia. In this study, a hybrid intelligent system that combines rough set (RST) and bijective soft set theory (BISO) to build a robust classifier model is proposed. The aim of the hybrid system is to exploit the advantages of the constituent components while eliminating their limitations. The resulting approach is thus able to handle data inconsistency in datasets through rough sets, while obtaining high classification accuracy based on prediction using bijective soft sets. Toward estimating the performance of the hybrid rough-bijective soft set (RBISO)-based classification approach, six benchmark medical datasets (Wisconsin breast cancer, liver disorder, hepatitis, Pima Indian diabetes, echocardiogram data and thyroid gland) from the UCI repository of machine learning databases are utilized. Experimental results, based on evaluation in terms of sensitivity, specificity and accuracy, are compared with other well-known classification methods, and the proposed algorithm provides an effective method for medical data classification.
Ismail, F. H., M. A. Aziz;, and A. E. Hassanien,
"Optimizing the parameters of Sugeno based adaptive neuro fuzzy using artificial bee colony: A case study on predicting the wind speed",
Federated Conference on Computer Science and Information Systems (FedCSIS),, Poland, , 11-14 Sept. , 2016.
AbstractThis paper presents an approach based on Artificial Bee Colony (ABC) to optimize the parameters of membership functions of Sugeno based Adaptive Neuro-Fuzzy Inference System (ANFIS). The optimization is achieved by Artificial Bee Colony (ABC) for the sake of achieving minimum Root Mean Square Error of ANFIS structure. The proposed ANFIS-ABC model is used to build a system for predicting the wind speed. To ensure the accuracy of the model, a different number of membership functions has been used. The experimental results indicates that the best accuracy achieved is 98% with ten membership functions and least value of RMSE which is 0.39.
Issaa, M., A. E. Hassanien, D. Oliva, A. Helmi, and I. Z. A. and Alzohairy,
"ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment",
Expert Systems with Applications, vol. 99, issue 1, pp. 56-70, 2018.
AbstractThe sine cosine algorithm (SCA), a recently proposed population-based optimization algorithm, is based on the use of sine and cosine trigonometric functions as operators to update the movements of the search agents. To optimize performance, different parameters on the SCA must be appropriately tuned. Setting such parameters is challenging because they permit the algorithm to escape from local optima and avoid premature convergence. The main drawback of the SCA is that the parameter setting only affects the exploitation of the prominent regions. However, the SCA has good exploration capabilities. This article presents an enhanced version of the SCA by merging it with particle swarm optimization (PSO). PSO exploits the search space better than the operators of the standard SCA. The proposed algorithm, called ASCA-PSO, has been tested over several unimodal and multimodal benchmark functions, which show its superiority over the SCA and other recent and standard meta-heuristic algorithms. Moreover, to verify the capabilities of the SCA, the SCA has been used to solve the real-world problem of a pairwise local alignment algorithm that tends to find the longest consecutive substrings between two biological sequences. Experimental results provide evidence of the good performance of the ASCA-PSO solutions in terms of accuracy and computational time.