Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER hav...Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images.展开更多
Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely c...Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely challenging among medical scientists and researchers due to unknown reasons.In India,the ratio of women being identified with breast cancer in urban areas is 22:1.Symptoms for this disease are micro calcification,lumps,and masses in mammogram images.These sources are mostly used for early detection.Digital mammography is used for breast cancer detection.In this study,we introduce a new hybrid wavelet filter for accurate image enhancement.The main objective of enhancement is to produce quality images for detecting cancer sections in images.Image enhancement is the main step where the quality of the input image is improved to detect cancer masses.In this study,we use a combination of two filters,namely,Gabor and Legendre.The edges are detected using the Canny detector to smoothen the images.High-quality enhanced image is obtained through the Gabor-Legendre filter(GLFIL)process.Further image is used by classification algorithm.Animal migration optimization with neural network is implemented for classifying the image.The output is compared to existing filter techniques.Ultimately,the accuracy achieved by the proposed technique is 98%,which is higher than existing algorithms.展开更多
文摘Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images.
文摘Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely challenging among medical scientists and researchers due to unknown reasons.In India,the ratio of women being identified with breast cancer in urban areas is 22:1.Symptoms for this disease are micro calcification,lumps,and masses in mammogram images.These sources are mostly used for early detection.Digital mammography is used for breast cancer detection.In this study,we introduce a new hybrid wavelet filter for accurate image enhancement.The main objective of enhancement is to produce quality images for detecting cancer sections in images.Image enhancement is the main step where the quality of the input image is improved to detect cancer masses.In this study,we use a combination of two filters,namely,Gabor and Legendre.The edges are detected using the Canny detector to smoothen the images.High-quality enhanced image is obtained through the Gabor-Legendre filter(GLFIL)process.Further image is used by classification algorithm.Animal migration optimization with neural network is implemented for classifying the image.The output is compared to existing filter techniques.Ultimately,the accuracy achieved by the proposed technique is 98%,which is higher than existing algorithms.
