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Titlebook: Applied Machine Learning and Data Analytics; 5th International Co M. A. Jabbar,Fernando Ortiz-Rodríguez,Patrick Siar Conference proceedings

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樓主: Abeyance
11#
發(fā)表于 2025-3-23 11:34:35 | 只看該作者
,RU-Net: A Novel Approach for?Gastro-Intestinal Tract Image Segmentation Using Convolutional Neural are present in the food are absorbed by the walls of the GI tract. The GI tract consists of the mouth, esophagus, stomach, intestine, and anus in the human digestive system. Many people get affected by GI tract cancer leading to the development of tumors in the tract. To treat these tumors, gastroen
12#
發(fā)表于 2025-3-23 17:15:27 | 只看該作者
A Credit Card Fraud Detection Model Using Machine Learning Methods with a Hybrid of Undersampling aas well as offline purchases. As a consequence, there is substantially more fraud involved in the transaction processes. Data mining has an issue for prediction, and data classification, therefore finding occurrences is essential. Unusual events are challenging to be identified due to their irregula
13#
發(fā)表于 2025-3-23 19:45:36 | 只看該作者
Online Grocery Shopping: - Key Factors to Understand Shopping Behavior from Data Analytics Perspectfter the deadly COVID-19 pandemic, people are forced to stay back at home, which made grocery shopping online. Not only traditional shopping but general online shopping differs from this because of its variability and perishability nature as compared to other products. With the leaping momentum of o
14#
發(fā)表于 2025-3-23 22:30:49 | 只看該作者
15#
發(fā)表于 2025-3-24 04:52:12 | 只看該作者
16#
發(fā)表于 2025-3-24 08:26:38 | 只看該作者
17#
發(fā)表于 2025-3-24 13:58:39 | 只看該作者
Handwriting Recognition for Predicting Gender and Handedness Using Deep Learning,yzed various Deep learning approaches using different CNN models. Our proposed approach uses two data sets IAM English dataset and Real-Time dataset that was collected by us for recognizing the handwriting pattern to perform the defined task. Using different Deep learning approaches, experiments wer
18#
發(fā)表于 2025-3-24 16:53:19 | 只看該作者
19#
發(fā)表于 2025-3-24 20:57:09 | 只看該作者
Origin of entropy in the work of Clausius, method was utilized for data augmentation to overcome the problem of overfitting. After compiling the Unet model on the dataset and evaluating the model metrics, the results and the output that was generated show that the model achieved good results. The model achieved an Accuracy of approximately
20#
發(fā)表于 2025-3-25 00:16:47 | 只看該作者
Dissipative entropy source of the earth, its classification using Transformers ensure that many more auxiliary knowledge substrates are selectively added to the framework. Its classification by a Deep Learning Transformer classifier ensures its ease of handling. Subsequently, the classification of the dataset using the AdaBoost, the encom
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