Facial Expression Recognition is an essential component in the development of Human-computer Interaction (HCI) because it enables the system to comprehend and appropriately react to human feelings. This study presents...
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ISBN:
(纸本)9798331504403
Facial Expression Recognition is an essential component in the development of Human-computer Interaction (HCI) because it enables the system to comprehend and appropriately react to human feelings. This study presents deep learning approaches for FER that are based on the combination of a Convolutional Neural Network and a Bidirectional Long Short-Term Memory Network. These approaches are intended to be utilized for the purpose of utilizing spatial and temporal variables in order to develop robust emotion categorization processes. The JAFFE and CK+ datasets are taken into consideration in the suggested technique. Both sets contain images that pertain to seven fundamental emotion classes, which are as follows: happy, sorrow, surprise, anger, disgust, fear, and neutral. The process of preprocessing begins with the detection of faces through the utilization of Multi-task Cascaded Convolutional Networks (MTCNN), followed by the normalization and scaling of the faces in order to maintain uniformity in the input dimensions. On the other hand, the BiLSTM component is responsible for capturing temporal dynamics that describe face expressions over time, while the CNN component is responsible for extracting spatial characteristics from the images. Considering that both sorts of features are incorporated into a single structure, it is evident that such a combination architecture will result in an improvement in recognition accuracy. Following that, it was trained on the preprocessed datasets for a total of 25 epochs, which resulted in a significant improvement in its accuracy. The fact that the model generalizes effectively to data that it has not before encountered is demonstrated by the fact that the accuracy it achieved during training and validation was 95.2% and 93.1%, respectively. The combination of CNN and BiLSTM is able to effectively address the challenges that are associated with FER, such as issues pertaining to illumination, position, and the variable intensity of
The Traffic Analysis System utilizes machine learning for real-time traffic insights at a junction. It provides instant stats like vehicle count, density, and FPS, while summarizing average vehicle crossings, densitie...
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Single-carrier frequency domain contention (S-FDC) is an efficient wireless contention mechanism based on orthogonal frequency-division multiplexing (OFDM). In each round of S-FDC, each node randomly selects and signa...
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The amount of exploration done for the available medical literature is quite less and at the same time, there is less awareness of information mining in this specific field. The accessibility of immense quantity of bi...
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Monkeypox detection is essential for effective public health management and controlling its spread. Timely detection enables early intervention in outbreaks, reducing transmission risk. This project presents an innova...
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This research paper explores the application of pre-trained ResNet-18 with transfer learning for the classification of yoga postures. The study utilizes a dataset comprising images of various yoga poses taken from Kag...
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Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different m...
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Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different methods and *** Learning(ML)techniques are used for analyz-ing the images andfinding out the location of the *** restriction of the ML is a dataset size,which is used for model *** problem has been overcome by using an augmentation method by generating larger datasets with multidimensional *** models are using only one augmentation tech-nique,which produces limited features of dataset and also lacks in the association of those data during DR detection,so multilevel augmentation is proposed for *** proposed method performs in two phases namely integrated aug-mentation model and dataset correlation(***).It eliminates overfit-ting problem by considering relevant *** method is used for solving the Diabetic Retinopathy problem with a thin vessel identification using the UNET *** based image segmentation achieves 98.3%accuracy when com-pared to RV-GAN and different UNET models with high detection rate.
This research introduces a cutting-edge framework leveraging advanced AI and IoT technologies to revolutionize lion conservation. The primary focus is on real-time lion detection, individual identification using uniqu...
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A novel method for detecting deepfakes by presenting an AI architecture that is dynamic and adaptable and is designed and exclusively for picture authentication. Conventional deepfake detection models frequently find ...
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University 5.0 thrives on the transformative power of ICT. Digital learning platforms, virtual labs, and collaborative software elevate the educational experience by fostering creativity and inclusivity. These tools r...
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