Telecom Fraud Detection: SMS Spam Classifier built with Python, Scikit-learn, and Streamlit. Achieves ~98% accuracy using TF-IDF + Naive Bayes. Includes EDA, fraud trend visualization, and real-time ...
Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!
Abstract: The transmission line is the most important component of the power system. Classifying the faults occurring on the transmission line helps the system operator activate the mechanism of ...
Abstract: The key to whether artificial intelligence can play a transformative role in the field of education lies in whether it can effectively collaborate with educators and learners. For teachers, ...
Hands-on coding of a multiclass neural network from scratch, with softmax and one-hot encoding. #Softmax #MulticlassClassification #PythonAI Trump announces two new national holidays, including one on ...
Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Ave. Eugenio Garza Sada 2501, Monterrey, Nuevo León 64849, Mexico ...
This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive ...
A Naive Bayes spam/ham classifier based on Bayes' Theorem. A bunch of emails is first used to train the classifier and then a previously unseen record is fed to predict the output.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results