Unveiling schizophrenia's neural and mental signatures with machine learning

Schizophrenia is a severe and often highly debilitating psychiatric disorder characterized by distorted emotions, thinking patterns and altered perceptions of reality, as well as mental impairments.
AZoOptics

Deep Learning in Optical Microscopy: Advancements and Applications

Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving classification and segmentation tasks.
Proceedings of the National Academy of Sciences (PNAS)

Identifying variation in dinosaur footprints and classifying problematic specimens via unbiased unsupervised machine learning

Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...
blockchain

XPENG P7+ AI-Driven Light Signals: Decoding Binary Messages and Autonomous Vehicle Trends

According to XPeng Motors (@XPengMotors), the XPENG P7+ electric vehicle showcased synchronized tail light flickers and a binary code '0101' display, hinting at advanced AI integration and ...
GitHub

binary-classification

This project is a full machine learning pipeline for Star/Galaxy classification using the SDSS dataset. It also contains a detailed report on the development and a DockerFile to easily replicate the ...
InfoWorld

AI and machine learning outside of Python

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.
Scientific Research Publishing

Performance of Different Machine Learning Algorithms for Credit Risk Classification ()

1 Department of Information Technology and Computer Science, School of Computing and Mathematics, The Cooperative University of Kenya, Nairobi, Kenya. 2 Department of Computing and Informatics, School ...
Visual Studio Magazine

Tsetlin Machine Binary Classification Using C#

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
Frontiers

Comparative analysis of machine learning techniques on the BraTS dataset for brain tumor classification

1 Information Statistics Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 2 School of Computer Science and Technology, Hubei Business ...
IEEE

Calibrating F1 Scores for Fair Performance Comparison of Binary Classification Models with Application to Student Dropout Prediction

Abstract: The F1 score has been widely used to measure the performance of machine learning models. However, it is variant to the ratio of the positive class in the training data, π. Depending on how ...
Business Wire

Dubai Launches World’s First Icon Classification for Human–Machine Collaboration in Research and Publications

DUBAI, United Arab Emirates--(BUSINESS WIRE)--Dubai has launched the world’s first icon-based classification system to represent the level of human–machine collaboration in the process of producing ...