Articles

Predictive Modeling in Remote Sensing Using Machine Learning Algorithms

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Predictive modeling in remote sensing using machine learning (ML) algorithms has emerged as a powerful approach for addressing various environmental and climatic challenges. This paper explores the integration of advanced ML techniques with remote sensing data to enhance predictive capabilities for applications such as land cover classification, crop yield prediction, climate change monitoring, and disaster management. We review related works and existing systems, highlighting platforms like Google Earth Engine (GEE), NASA Earth Exchange (NEX), and Sentinel Hub, which leverage cloud computing to handle large-scale data processing and model deployment. The proposed system incorporates data acquisition, preprocessing, feature extraction, model selection and training, and prediction and visualization to provide accurate and timely predictions. Future enhancements, including deep learning integration, real-time data processing, enhanced user interfaces, and collaboration with Internet of Things (IoT) devices, are discussed to further strengthen the system’s capabilities. The paper concludes by emphasizing the potential of ML algorithms in transforming remote sensing applications, supporting informed decision-making, and improving the management of Earth’s resources.

Data Security and Privacy in Cloud Computing Platforms: A Comprehensive Review

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Cloud computing is the on-demand usage of computer resources through the internet, allowing us to relocate application software and databases. Its purpose is to deliver IT services that enable clients to benefit from the pay-per-use model’s substantial cost advantages and the ability to flexibly scale up or down without having to make significant investments in new hardware. On the other hand, the provider manages the data and services under this cloud architecture. Consequently, cloud clients have less control over their outsourced data and depend on cloud service providers to safeguard their data and infrastructure from both external and internal threats. In this study, we look at information security in cloud computing. It is the consideration of information on the cloud, considering security concerns. This article will go through data protection strategies utilized all around the globe to provide optimum data security by lowering risks and threats. Information accessibility is beneficial for various cloud applications, but it poses risks by exposing data to apps that already have security escape clauses. In addition, the presentation will outline information security highlights for Data in Transit and Data at Rest.