Advances in Machine Learning for Field Monitoring: Examining Unsupervised Data Methods

• Author(s): Loveth Johnson
• Paper ID: 1701108
• Page: 158-163
• Published Date: 31-01-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 2 Issue 7 January-2019

Abstract

The development in machine learning has significantly improved the field monitoring process, especially through unsupervised learning. This is because unsupervised learning methods such as clustering, anomaly detection, and dimensionality reduction have become very essential in field-based environments characterized by unstructured and big data to unearth hidden patterns and insights. These methods allow analyzing complex data without the need for labeled datasets and are hence of special value in a wide range of applications, such as predictive maintenance, environmental monitoring, and resource management. Unsupervised learning allows the identification of trends and outliers to detect equipment failures, environmental hazards, and operational inefficiencies that improve decision-making and minimize downtime. While noisy and variable field data present challenges, advances in machine learning-including deep learning and hybrid models-are surmounting these challenges. Techniques such as autoencoders and GANs enhance the robustness and accuracy of unsupervised learning for dynamic fields. Applications from various industries, including agriculture, industrial monitoring, and energy management, demonstrate how these techniques can bring transformation to the industry by making operations more efficient and enabling data-driven decisions. With the increasing integration of machine learning with IoT and sensor networks, the ability of unsupervised learning to revolutionize field monitoring is only further increased, paving the way for smarter, proactive management of field operations.

Keywords

Machine Learning (ML), Unsupervised Learning, Field Monitoring, Predictive MaintenanceAnomaly Detection, Clustering Algorithms, Dimensionality Reduction, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE Autoencoders, Generative Adversarial Networks (GANs), Environmental Monitoring, Resource Management, Data Preprocessing, Feature Engineering, Real-Time Data Analysis, Internet of Things (IoT), Sensor Networks, Operational Efficiency, Data-Driven Decision Making, Industrial Monitoring, Agriculture Monitoring, Energy Management

Citations

IRE Journals:
Loveth Johnson "Advances in Machine Learning for Field Monitoring: Examining Unsupervised Data Methods" Iconic Research And Engineering Journals Volume 2 Issue 7 2019 Page 158-163

IEEE:
Loveth Johnson "Advances in Machine Learning for Field Monitoring: Examining Unsupervised Data Methods" Iconic Research And Engineering Journals, 2(7)