The lack of precise, autonomous tools for monitoring and classifying cattle behavior limits farmers’ ability to make proactive and informed decisions regarding grazing and herd management. Currently, ...

Abstract: Robust road crack classification is essential for road maintenance. Recently, Vision Transformers (ViT) have demonstrated superior performance in object detection under the complex ...

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 = ...

ABSTRACT: This article explores the use of Support Vector Machines (SVM) for diagnosing diabetes based on fourteen medical and behavioral variables. Following a theoretical overview of diabetes and ...

i am running binary classification report. my "target" column is binary 0,1 values, "pred_lablel" is binary 01, values and "prediction" is probabilities between 0-1 i ...

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 ...

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 ...

Abstract: Network intrusion detection systems (NIDS) are a critical part of ensuring the integrity and reliability of a network, which is essential in secure communication. To achieve this, an ...

Cluster analysis can be used on symptom and behavior data to identify groups of similar individuals who may share underlying disease etiology or health risks. However, there are few clustering methods ...