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, ...
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: Robotics research encompasses a wide range of technical challenges and interdisciplinary approaches. This study introduces a dual-paradigm classification framework for organizing the stated ...
Eye Retinal DEtachment UltraSound (ERDES) is a comprehensive, open-access video dataset designed to advance computer vision research in ocular ultrasonography. Ocular ultrasound is a fast, ...
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 ...
Abstract: In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results