Facial Image Detection for Severity Level Prediction of Autism Spectrum Disorder Using Machine Learning Algorithm

Darmiyati, Iin; Nugroho, Hanung Adi; Soesanti, Indah

doi:10.1109/aims61812.2024.10512928

Cited by 1 publication

(1 citation statement)

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“…Using Euclidean and Geodesic distance features, a novel machine learning method for determining the severity of autism via facial expression recognition was suggested (Darmiyati et al, 2024). This approach, which uses landmark detection to incorporate gaze probabilities, achieves an 83% accuracy rate that has been veri ed by experts [17]. When it comes to predicting the severity of autism from facial images, Decision Tree outperforms all other classi ers with an accuracy of 83.42%, outperforming KNN, Random Forest, Gradient Boosting, XGBoost, Adaboost, and Logistic Regression.…”

Section: Tanwar Et Al Published In a Bookmentioning

confidence: 86%

Utilizing Pretrained Models to Enhance Hybrid Neural Networks in Low Data Situations

Borah,

Nandi

2024

Preprint

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Marine ecosystems are vital to the survival of life on Earth, but they are difficult to monitor and comprehend due to their complexity. Evaluating biodiversity and ecological health is hampered by the labor-intensive and scope-restricted nature of traditional observation techniques. We use deep learning and computer vision technologies to analyze underwater imagery taken by remote vehicles in order to overcome these limitations. To accurately identify marine organisms, we systematically evaluate five state-of-the-art neural network architectures: ResNet50V2, ResNet152V2, InceptionV3, Xception, and MobileNetV2. In addition, we suggest a novel hybrid ensemble method that improves detection robustness and accuracy by combining predictions from several models. Our research offers promising new directions for marine management and conservation efforts as it signifies a major breakthrough in automated sea animal detection.

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Section: Tanwar Et Al Published In a Bookmentioning

confidence: 86%