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 = ...

This study presents data on sex differences in gene expression across organs of four mice taxa. The authors have generated a unique and convincing dataset that fills a gap left by previous studies.

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 ...

ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...

This repository compares the performance of Adaline, Logistic Regression, and Perceptron models on binary classification tasks using linearly, non-linearly, and marginally separable datasets from the ...

In this study, we propose a novel modularized Quantum Neural Network (mQNN) model tailored to address the binary classification problem on the MNIST dataset. The mQNN organizes input information using ...

The advancement of large language models (LLMs) has significantly influenced interactive technologies, presenting both benefits and challenges. One prominent issue arising from these models is their ...

1 Department of Computer Studies, Arab Open University, Riyadh, Saudi Arabia 2 Department of Computer Sciences, ISSAT, University of Gafsa, Gafsa, Tunisia Cybersecurity has become a significant ...

ABSTRACT: Delirium is a common yet critical condition among Intensive Care Unit (ICU) patients, characterized by acute cognitive disturbances that can lead to severe complications, prolonged hospital ...

Abstract: In this paper, a model was built to compare the performance of the following machine learning (ML) models: DT, RF, SVM, and MLP, using two types of classification: binary classification and ...