cacophony
发表于 2025-3-30 09:48:19
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SLAG
发表于 2025-3-30 16:26:43
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无可争辩
发表于 2025-3-30 19:52:52
Conference proceedings 2023e, finance, agriculture and manufacturing, high-performance computing, computer networking, sensor and wireless networks, Internet of Things (IoT), software-defined networks, cryptography, mobile computing, digital forensics and blockchain technology.
TIA742
发表于 2025-3-31 00:15:31
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过度
发表于 2025-3-31 04:47:00
Euphonia: Music Recommendation System Based on Facial Recognition and Emotion Detection,eneral playlist pertaining to the user’s likes and dislikes which they can access whenever they wish to. Machine learning concepts and the available datasets have been utilized to classify a vast set of music that is stored using automatic music content analyses. It was implemented using Python, Pandas, OpenCV, and NumPy.
疲劳
发表于 2025-3-31 06:50:07
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不在灌木丛中
发表于 2025-3-31 09:57:19
Prediction of Anemia Disease Using Machine Learning Algorithms,sification-based ML model in which we provide the essential CBC test values for our model to predict whether a patient is anemic. With the help of machine learning techniques, we are automating the process for detecting anemia in this study work. We compared the statistical analysis of all algorithms we‘ve utilized to predict anemia in this paper.
integrated
发表于 2025-3-31 14:25:47
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Certainty
发表于 2025-3-31 17:46:56
Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification,ity reduction algorithm for classification that can be boosted in terms of performance using deep learning with Deep LDA, a transformed version of the traditional LDA. In this result oriented paper we present the Deep LDA implementation with a variation for prognostication of PCOS.
抵消
发表于 2025-3-31 23:50:58
Binary Classification for High Dimensional Data Using Supervised Non-parametric Ensemble Method,or high dimensional data using random forest for polycystic ovary syndrome dataset. We have performed the implementation and provided a detailed visualization of the data for general inference. The training accuracy that we have achieved is 95.6% and validation accuracy over 91.74% respectively.