Intuitive Classification meets Natural Language.
Lumina AI has developed a prototype ChatBot that demonstrates the capabilities of our Random Contrast Learning (RCL) technology.
By leveraging OpenAI’s GPT-4o API, we’ve created an accessible window into the capabilities of RCL, inviting you to explore its potential in an intuitive, conversational format.
RCL in Action: An Interactive Showcase
This prototype offers a unique glimpse into the world of RCL:
Intuitive User Interaction
Experience how RCL works through a user-friendly chat interface. Simply type your queries or select from the available classification options to see RCL in action.
Versatile Classification Tasks
Explore a range of classification capabilities, from medical diagnostics to e-commerce applications, demonstrating RCL’s adaptability across diverse data types and domains.
Cross-Industry Potential
Witness how RCL can be applied across diverse sectors, illustrating its broad applicability and future possibilities in healthcare, retail, telecommunications, and beyond.
How It Works
Select a model.
Choose from our range of classification models, including cancer-related text analysis, breast cancer prediction, e-commerce product categorization, or ionosphere radar signal classification.
Upload Data.
Upload your data in the file format accepted by your chosen model. Click ‘Send’ to process your input using our algorithm, Random Contrast Learning (RCL).
Review Results.
Examine the classification results, explore insights, and use the information to inform your decision-making process.
Learn More about the Example Models.
- Breast Cancer Prediction
- Cancer Text Data Classification
- E-Commerce Product Descriptions
- Ionosphere Radar Signals
This model analyzes biopsy images to classify them as “Malignant” or “Benign”. Malignant tumors are cancerous and may spread, requiring timely treatment. Benign tumors are non-cancerous, reducing unnecessary procedures and patient anxiety.
This model can be used for early detection and classification of breast cancer, aiding in accurate diagnosis and personalized patient care.
Input Type: PNG image files.
This model classifies cancer-related text data into thyroid, lung, or colon cancer categories. It identifies specific terminologies and patterns in patient reports, aiding targeted diagnostics and supporting oncology research.
This model can enhance diagnostic and research processes in oncology by accurately categorizing text data, improving cancer care and study.
Input Type: TXT text files.
This model classifies e-commerce product descriptions into four categories: Electronics, Household, Books, and Clothing & Accessories. It leverages the Random Contrast Learning (RCL) algorithm to automate the categorization process.
This model can improve efficiency in product management, enhances user experience, and ensures accurate and consistent product listings on e-commerce platforms.
Input Type: TXT text files
This model classifies radar signals into “Good” or “Bad” categories using the ionosphere dataset. Good signals pass through the ionosphere, indicating clear transmission paths, while bad signals are reflected, suggesting disruptions.
This model can enhance accuracy in telecommunications, customer support, and data analysis by classifying radar signals for better transmission path assessment.
Input Type: TXT text files.
Dislaimer: Access to these models is intended solely for illustrative and educational purposes to showcase how RCL technology works. These models are not production-ready and should not be used for any commercial, medical, or mission-critical purposes. The results produced by these models should not be considered as definitive or actionable insights. Lumina makes no warranties, express or implied, regarding the accuracy, reliability, or completeness of any outputs generated by these prototype models. Users are advised to treat all results as experimental and not rely on them for any decision-making processes.
Experience RCL in Action.
See Random Contrast Learning at work. Our prototype lets you interact with hosted models, bringing abstract AI concepts into the realm of practical application.