As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabete...As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world.Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it.Among the diabetics,it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2.To avoid this situation,we propose a new fuzzy logic based neural classifier for early detection of diabetes.A set of new neuro-fuzzy rules is introduced with time constraints that are applied for thefirst level classification.These levels are further refined by using the Fuzzy Cognitive Maps(FCM)with time intervals for making thefinal decision over the classification process.The main objective of this proposed model is to detect the diabetes level based on the time.Also,the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction.The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.展开更多
In recent times,Internet of Things(IoT)and Deep Learning(DL)mod-els have revolutionized the diagnostic procedures of Diabetic Retinopathy(DR)in its early stages that can save the patient from vision loss.At the same t...In recent times,Internet of Things(IoT)and Deep Learning(DL)mod-els have revolutionized the diagnostic procedures of Diabetic Retinopathy(DR)in its early stages that can save the patient from vision loss.At the same time,the recent advancements made in Machine Learning(ML)and DL models help in developing Computer Aided Diagnosis(CAD)models for DR recognition and grading.In this background,the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network(ODBN)model i.e.,NS-ODBN model for diagnosis of DR.The presented model involves Interval Neutrosophic Set(INS)technique to dis-tinguish the diseased areas in fundus image.In addition,three feature extraction techniques such as histogram features,texture features,and wavelet features are used in this study.Besides,Optimal Deep Belief Network(ODBN)model is utilized as a classification model for DR.ODBN model involves Shuffled Shepherd Optimization(SSO)algorithm to regulate the hyperparameters of DBN technique in an optimal manner.The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly.The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics.The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.展开更多
文摘As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world.Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it.Among the diabetics,it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2.To avoid this situation,we propose a new fuzzy logic based neural classifier for early detection of diabetes.A set of new neuro-fuzzy rules is introduced with time constraints that are applied for thefirst level classification.These levels are further refined by using the Fuzzy Cognitive Maps(FCM)with time intervals for making thefinal decision over the classification process.The main objective of this proposed model is to detect the diabetes level based on the time.Also,the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction.The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.
文摘In recent times,Internet of Things(IoT)and Deep Learning(DL)mod-els have revolutionized the diagnostic procedures of Diabetic Retinopathy(DR)in its early stages that can save the patient from vision loss.At the same time,the recent advancements made in Machine Learning(ML)and DL models help in developing Computer Aided Diagnosis(CAD)models for DR recognition and grading.In this background,the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network(ODBN)model i.e.,NS-ODBN model for diagnosis of DR.The presented model involves Interval Neutrosophic Set(INS)technique to dis-tinguish the diseased areas in fundus image.In addition,three feature extraction techniques such as histogram features,texture features,and wavelet features are used in this study.Besides,Optimal Deep Belief Network(ODBN)model is utilized as a classification model for DR.ODBN model involves Shuffled Shepherd Optimization(SSO)algorithm to regulate the hyperparameters of DBN technique in an optimal manner.The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly.The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics.The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.
