Titlebook: Data Science and Artificial Intelligence; First International Chutiporn Anutariya,Marcello M. Bonsangue Conference proceedings 2023 The Ed

拱形大桥

吸气

Data Augmentation for EEG Motor Imagery Classification Using Diffusion Modelthod outperformed other methods in terms of classification accuracy by 17.49%. The Kullback-Leibler (KL) divergence is used for assessing the similarity between the training set (with and without augmentation) and validation set, thus showing the effectiveness of the diffusion approach compared to other techniques.

Largess

Thai Conversational Chatbot Classification Using BiLSTM and Data Augmentation2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep Learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance.

Gene408

Deep Learning Implementation for Pineapple Sweetness ClassificationtNet-50, both with a learning rate of 0.000001, to differentiate between sweet and not-so-sweet pineapple. Both algorithms detected pineapple sweetness with the same accuracy of 84.09%. However, RestNet50 had a greater loss than EffisintNetB4.

enchant

Small Footprint JavaScript Engineet of 150 images and nearly 6000 instances, and the results are evaluated on many different epochs, the results show that the highest accuracy belongs to YOLOv7, which is 89.93% (Precision), 87.96% (Recall), and 91.33% (mAP). The study also opens up further studies in detecting diseases on rice, such as grain blight, cotton neck blast, etc.

Prosaic

BRACE

Improving Low Light Object Detection Using Image Enhancement Modelslight. We also propose a new method for robust low-light object detection that shows substantial improvements over state-of-the-art baselines. The proposed approach increases detection robustness to different lighting conditions and establishes a state-of-the-art mAP. of 79.5% on the ExDark dataset.

CURT

Feedback

Ricardo A. Natalin MD,Jaime Landmanrtinent features. Our experimental results showcase robust model performance, achieving an F1-score of 90% on our experimental dataset, surpassing other approaches. Further results and discussions are provided in this paper, .

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