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Ratings and Reviews 0 Ratings
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What is SOT?
SOT distinguishes itself as a specialized financial optimization tool designed specifically for both surface and underground mining activities, significantly boosting productivity and profitability by optimizing the Net Present Value (NPV) of mining schedules. This sophisticated scheduling solution utilizes an evolutionary search algorithm to generate enhanced schedules focused on maximizing resource NPV. Tailored for scenario analysis and strategic evaluations, SOT provides mine planners with the capability to examine different strategies and select alternative schedules that enhance NPV. By implementing an NPV-optimized schedule, mining operations can extract increased value from their resources, thereby minimizing risk and expediting the payback period. The system integrates custom heuristics with the evolutionary algorithm to further improve NPV results. It empowers users to reveal robust, high-value schedules that greatly impact strategic mine planning, while also facilitating optimization across a range of metrics, including ore feed goals and profitability indices. Furthermore, SOT not only enhances operational efficiency but also ensures that stakeholders are equipped with the critical insights necessary for making strategic decisions that foster sustained success in the mining sector. This ability to adapt and optimize positions SOT as an invaluable asset for future-oriented mining enterprises.
What is Keel?
KEEL, which stands for Knowledge Extraction based on Evolutionary Learning, is an open-source software tool developed in Java and licensed under GPLv3, aimed at supporting a wide range of knowledge data discovery tasks. It features a user-friendly graphical interface that prioritizes data flow, allowing users to create experiments that utilize different datasets and computational intelligence algorithms, particularly those based on evolutionary strategies, to assess their performance. The software offers a broad spectrum of standard knowledge extraction methodologies, as well as data preprocessing techniques—such as training set selection, feature selection, discretization, and imputation for missing values—alongside various computational intelligence learning algorithms, hybrid models, and statistical methods for comparing experimental results. This all-encompassing toolkit enables researchers to perform in-depth analyses of novel computational intelligence strategies against traditional approaches. Moreover, KEEL has been intentionally designed to fulfill two main objectives: to promote research advancement and to improve educational experiences in the domain of knowledge discovery. Its adaptability and functionality make it an essential tool for both scholarly pursuits and real-world applications in the field of knowledge extraction. Ultimately, the ongoing development of KEEL ensures that it remains relevant and effective for its users.
Integrations Supported
Additional information not provided
Integrations Supported
Additional information not provided
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided
Free Version
Free Trial Offered?
Pricing Information
Pricing not provided
Free Version
Free Trial Offered?
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
RPMGlobal
Company Location
Australia
Company Website
rpmglobal.com/product/sot/
Company Facts
Organization Name
Keel
Company Website
www.keel.es/
Categories and Features
Mining
3D Modeling
Cross Section Creation
Data Exchange
Data Storage
Exception Notification
Fence Diagrams
Log Map Creation
People Tracking
Pit Optimization Reporting
Risk Management
Volumetrics
Categories and Features
Data Mining
Data Extraction
Data Visualization
Fraud Detection
Linked Data Management
Machine Learning
Predictive Modeling
Semantic Search
Statistical Analysis
Text Mining