Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

Abstract

In the current world of the Internet of Things, cyberspace, mobile devices, businesses, social media platforms, healthcare systems, etc., there is a lot of data online today. Machine learning (ML) is something we need to understand to do smart analyses of these data and make smart, automated applications that use them. There are many different kinds of machine learning algorithms. The most well-known ones are supervised, unsupervised, semi-supervised, and reinforcement learning. This article goes over all the different kinds of machine-learning problems and the machine-learning algorithms that are used to solve them. The main thing this study adds is a better understanding of the theory behind many machine learning methods and how they can be used in the real world, such as in energy, healthcare, finance, autonomous driving, e-commerce, and many more fields. This article is meant to be a go-to resource for academic researchers, data scientists, and machine learning engineers when it comes to making decisions about a wide range of data and methods to start extracting information from the data and figuring out what kind of machine learning algorithm will work best for their problem and what results they can expect. Additionally, this article presents the major challenges in building machine learning models and explores the research gaps in this area. In this article, we also provided a brief overview of data protection laws and their provisions in different countries.

Artificial Intelligence Approach in Aerospace for Error Mitigation

“…We have used the same input data for both traditional and AutoML models. The AutoML frameworks include tools such as H 2 O-AutoML, DataRobot, Cloud AutoML, the Tree-based Pipeline Optimization Tool (TPOT), Auto-Keras, Auto-Weka, ML BOX, AutoSklearn, and Auto-Pytorch [38][39][40][41]. In this study, we have used the TPOT, which uses a Genetic Algorithm (GA) to automatically find which AI methods present the best performance for the dataset [42].…”

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

“…Regression tasks, however, map input variables to continuous outputs, representing a data-fitting problem (Figure 2b). Logistic regression and linear regression are the simplest algorithms for classification and regression, respectively [31].…”

“…Regression tasks, however, map input variables to continuous outputs, representing a data-fitting problem (Figure 2b). Logistic regression and linear regression are the simplest algorithms for classification and regression, respectively [31]. The main challenge faced in ML is overfitting, in which the model tends to memorize patterns, including noise, from the data it is trained on, consequentially performing poorly when deployed on unseen data.…”

AI Advancements: Comparison of Innovative Techniques

“…The development of data-driven techniques became apparent as AI advanced and changed the game. This paradigm change made it possible for AI systems to use data for learning and adaptation [39], and it was fueled by developments in machine learning, particularly supervised learning. These systems may generalize patterns from enormous datasets, enabling adaptability in the face of varied and dynamic settings, as opposed to being restricted by strict rules [40].…”