"The Power of Machine Learning: Evaluating its Effectiveness in Cyber Security, Healthcare, and Agricultural Enhancements"

Abstract

The rapid advancement of machine learning technologies has brought transformative changes across multiple sectors, addressing complex challenges with innovative solutions. In cybersecurity, machine learning models are increasingly utilized to detect and mitigate threats such as Distributed Denial of Service (DDoS) attacks. The ISCX dataset provided by the Canadian Institute of Cybersecurity has emerged as a crucial resource for the development and evaluation of these models. In healthcare, machine learning techniques have demonstrated significant potential in predicting and diagnosing diseases, including COVID-19, through the analysis of extensive datasets. This capability has proven particularly valuable during the COVID-19 pandemic, facilitating improvements in early detection and intervention strategies. Furthermore, machine learning has been leveraged to enhance agricultural practices, leading to the advent of smart farming techniques that forecast crop yields and promote sustainability.This study utilizes a range of datasets and sources, including the ISCX dataset, WHO data, and Google Trends, to explore the application of machine learning across various domains. Through an in-depth analysis of recent literature and methodologies, the study presents a proposed method with impressive performance metrics: an accuracy of 97.6%, a Mean Absolute Error (MAE) of 0.403, and a Root Mean Square Error (RMSE) of 0.203. These findings underscore the effectiveness of the proposed machine learning models and highlight their potential for further research and development in enhancing predictive capabilities and decision-making processes.