Articles

Development of an Optimization Software for Bioremediation of Hydrocarbon-Contaminated Soils mechanisms

/in /by

A sophisticated software solution designed to enhance bioremediation processes in hydrocarbon-contaminated environments. This Advanced Bioremediation Optimization Software combines complex algorithms, real-time sensor data integration, and a user-friendly interface to deliver customized solutions for environmental restoration projects. The software utilizes predictive modeling to forecast remediation outcomes, optimizes treatment strategies based on ongoing data analysis, evaluates microbial communities through metagenomic sequencing data, and generates evidence-based recommendations to improve bioremediation efficiency. This tool represents a significant advancement in environmental restoration technology, offering practitioners a means to enhance the efficacy and cost-effectiveness of bioremediation projects. It also provides detailed economic projections to support informed decision-making by stakeholders, making it a valuable asset in the field of environmental remediation.

Machine Learning Approaches for Customer Churn Prediction in the Aquaculture Technology Sector

/in /by

This study investigates the application of advanced machine learning techniques for customer churn prediction in the rapidly evolving aquaculture technology sector. We employ and compare three distinct models—Logistic Regression, Random Forest, and XGBoost—to analyze a synthesized dataset representative of the industry. The research encompasses comprehensive data preprocessing, feature engineering, and model evaluation using standard performance metrics. Our findings demonstrate the superior performance of XGBoost, achieving 88% accuracy in predicting customer churn. Through feature importance analysis, we identify key churn predictors, with the difference between a customer’s last order amount and their mean order amount emerging as the most significant factor. Additionally, we utilize SHAP (SHapley Additive exPlanations) analysis to interpret model outcomes, revealing nuanced relationships between features and churn probability. The study highlights the critical role of consistent engagement, proactive customer support, and personalized retention strategies in reducing churn. Our research contributes to the growing body of knowledge on churn prediction in specialized technology sectors and provides actionable insights for improving customer retention strategies in the aquaculture industry. The paper concludes with recommendations for future research, including the integration of external data sources and exploration of deep learning approaches for temporal dependency analysis in customer behaviour.

Predictive Modeling in Remote Sensing Using Machine Learning Algorithms

/in /by

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.