Emotion Detection Using Machine Learning: An Analytical Review

Low bone mass and structural degradation are the hallmarks of osteoporosis, a disorder that increases the risk of fractures, especially in the elderly. For prompt intervention and fracture prevention, early identification is essential. However, osteoporosis is frequently not detected until advanced stages by existing diagnostic techniques. In order to overcome this difficulty, scientists suggest using machine learning to automatically identify osteoporosis early in X-ray pictures. Utilizing two cutting- edge convolutional neural network architectures, ResNet50 and VGG16, their system was pretrained on extensive datasets and refined on a carefully selected dataset of X-ray pictures. When identifying images as suggestive of osteoporosis or normal bone density, the ResNet50 model showed an accuracy of 98%, whereas the VGG16 model achieved 78% accuracy. By combining these models and using sophisticated image segmentation methods, the system detects early osteoporosis indications with an overall accuracy of 96%. This automated method has the potential to decrease the incidence of fractures linked to osteoporosis, enable early treatment initiation, and increase the rate of early diagnosis.

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Potato and Maize Plant Disease Detection Using Leaf Images

Jaman Shawon,

Singh,

Sarkar

et al. 2024

International Journal of Innovative Science and Research Techno

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Plant diseases represent a serious threat to national productivity and global food security. Effective therapy for multiple diseases requires a precise and useful differentiation of them. In this work, a computerized system for the identification and categorization of diseases in potato and maize crops is developed using convolutional neural networks. The demonstration was created with the ResNet50V2 model and tested on a combined collection of images of leaves. The system achieved an astounding accuracy of 85.19. Enhancing model execution through exchange learning, fine-tuning, and information augmentation were all part of the process. With the use of another dataset, the trained model was verified and produced positive results, almost exactly differentiating between the disease-causing leaf type (potato or maize). This technology helps ranchers adopt sustainable and knowledgeable disease management methods by promoting timely mediations, which in turn advances disease discovery.

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Emotion Detection Using Machine Learning: An Analytical Review

Cited by 4 publications

References 49 publications

Listening to Feelings of Emotion: Exploring ML and DL for Speech-Based Detection and Classification

Listening to Feelings of Emotion: Exploring ML and DL for Speech-Based Detection and Classification

Osteoporosis Prediction Using VGG16 and ResNet50

Potato and Maize Plant Disease Detection Using Leaf Images

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