Computer Vision for Poultry Farms
Monitoring chickens on large poultry farms is labor-intensive, requiring constant attention to environmental conditions and animal well-being, which can hinder staff productivity. Using AI and ML with HPC, a solution was developed to create edge AI devices and computer vision sensors that efficiently monitor parameters like chicken behavior, body temperature, and growth. With IoT-enabled cameras and ML models trained using HPC, the approach achieved over 90% accuracy in chicken detection and segmentation, with a 10-fold reduction in model development time. This innovation supports precision agriculture, providing farmers with advanced tools to enhance productivity and ensure humane food production.
Read more at: [link].
Forecasting Electricity Market Metrics
Forecasting day-ahead electricity prices and loads is essential for decision-making in the energy market, where participants aim to avoid price volatility. By leveraging artificial neural networks (ANN) and time-series prediction models, this research explores efficient methods for predicting electricity metrics using datasets from markets like HUPX and Montenegro. The study finds that ANN architectures combining fully connected layers with recurrent or temporal convolutional layers deliver the most accurate short-term predictions, highlighting the potential of temporal convolutional networks for further exploration. Standardized comparison methods and collaboration with industry experts ensure a robust evaluation of forecasting approaches and their relevance to the energy sector.
Read more at: [link].
Personalized Banking Software Solutions
The project is developing SaaS solutions to enhance personalized banking and payment services through machine learning and data collection. Building on the SKEN expense-tracking app, the new system will integrate with mBanking and eBanking applications, providing users with automatic, detailed transaction categorization and insights. Using NLP-based ML algorithms, the system classifies transactions into predefined categories like food or services, leveraging data from SKEN and research expertise. This innovation aims to offer financial institutions advanced tools for better customer engagement and improved financial insights.
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PAID-T: Advanced Trading Simulations powered by HPC
PAID MNE specializes in crafting scalable software solutions for investment firms, leveraging advanced algorithms, machine learning, and artificial intelligence to optimize trading strategies and risk management. Their PAID-T trading solution dynamically adapts to market fluctuations, offering optimized trading experience.
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Uhura: Generative AI Intelligent Process Automation Platform
Uhura Solutions is developing AI platform for document-driven process automation in financial services. The “Generative AI Intelligent Process Automation Platform – GAIPAP” project is a transformative initiative aimed at revolutionizing the financial industry through the integration of advanced AI-driven automation solutions that combine capabilities of Large Language Model (LLM), low-code development, and process automation workflows.
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Wasco AI – Custom AI Assistant Optimization via HPC
The development of advanced AI models for video processing and visual analytics requires enormous computing power and the ability to handle complex data in real time. Standard server infrastructure was not sufficient to enable scaling the models to the level needed for testing, optimization, and the development of new functionalities. The key challenge was how to secure resources that would allow training models on thousands of GPU cores while maintaining stability, speed, and accuracy of results. In addition to technical barriers, there was also the challenge of positioning a local project within the European context, where some of the most advanced research and industrial teams compete for resources.
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Using AI and HPC to Strengthen Honey Authenticity and Trust
Honey authenticity verification remains a complex and resource-intensive process, particularly when determining botanical and geographical origin through traditional melissopalynological methods. Manual microscopic analysis of pollen grains requires highly specialized expertise, is time-consuming, and introduces variability between laboratories, making it difficult to scale verification while preserving consistency and reliability. For honey producers and farmers, especially small and medium-sized operations, these constraints limit access to premium and export markets, where verified origin and traceability are increasingly mandatory.
Read more at [link].