Performance Evaluation of Intelligent Algorithms in Pattern Analysis within Big Data Environments

Abstract

This study aims to analyze and classify intelligent algorithms used to extract patterns from big data, focusing on both supervised and unsupervised deep learning algorithms. The importance of these algorithms is highlighted by the rapid growth of big data, characterized by its large volume, diversity, and speed, making its management and analysis using traditional tools inefficient. This data originates from multiple sources, such as sensors, smart applications, and the internet, necessitating the use of advanced techniques to extract meaningful patterns. The paper examines several intelligent algorithms, including convolutional and iterative neural networks, which are among the most prominent models of deep learning. These networks mimic the workings of the human brain, processing large amounts of data and representing a branch of artificial intelligence. These algorithms are characterized by their high accuracy in analyzing images and text. The study also includes logistic regression algorithms, support vector networks for supervised learning, and clustering algorithms for unsupervised learning. The study seeks to evaluate the performance of these algorithms in terms of efficiency, accuracy, scalability, and ability to handle unstructured data, taking into account the variation in performance depending on the type and size of the data. The study revealed that some algorithms require high computing resources and long training times, while others are negatively impacted by increasing the sample size. It also suggests that deep learning algorithms can be improved using cloud processing techniques. The paper recommends against relying on a single algorithm as a comprehensive solution, advocating instead for selecting the most suitable algorithm based on the nature of the data and available resources, while emphasizing the integration of features from different algorithms to optimize performance in big data environments.