Energy optimization is crucial for environmental sustainability, as it reduces resource consumption, minimizes greenhouse gas emissions, and promotes the use of renewable energy. Efficient energy use helps combat climate change and preserves natural ecosystems for future generations. In this paper, a system to support the distribution of photovoltaic energy for Emilia Romagna Energy Communities is proposed. The system will manage and integrate large amounts of data and offer innovative services based on them for calculating climate and energy forecasts. To enable more reliable production estimates and efficient energy storage and distribution, the system will use a platform for managing and integrating data from Regional Climate Models. It will incorporate Machine Learning and Deep Learning models for accurate climate forecasts and optimize energy flows by considering consumption profiles, production forecasts, and storage characteristics. The application background, the proposed methodology, and the current challenges related to the domain will be discussed, with a particular focus on data sources and management operations.

Technological innovation, conservation and enhancement are key elements for promoting cultural heritage and attracting visitors worldwide. Cultural tourism represents a significant economic and social strategy, capable of stimulating regional development and revitalizing marginalized areas. In recent years, artificial intelligence (AI) has transformed the sector, offering new ways to engage with and preserve cultural assets. This study explores the application of advanced AI techniques, particularly Convolutional Neural Networks (CNNs), to improve the classification and recognition of images related to architectural heritage. A deep learning algorithm specifically designed for cultural heritage enhancement is presented, focusing on the automatic classification of images of buildings and monuments. The analysis includes a comparison between pre-trained models and custom models, highlighting the performance of different approaches. The experimental results demonstrate that our ensemble model achieved 90% accuracy in classifying architectural heritage elements across 10 categories, with the Majority Vote Ensemble approach outperforming individual models by 6-10%. This improved classification accuracy enables more reliable automated systems for cultural heritage documentation and interactive tourist experiences. The research addresses key challenges in architectural heritage classification including variations in preservation state, lighting conditions, and complex backgrounds with multiple elements. The study also investigates the impact of data augmentation and class balancing techniques on model performance, demonstrating how these methods can mitigate limitations in training data availability. The developed ensemble combines state-of-the-art CNN architectures like ResNet50 and EfficientNet with custom models, leveraging their complementary strengths to achieve robust classification across diverse architectural styles and conditions. The adopted approach is supervised, using a training dataset where images are pre-labeled according to specific categories. This allows the algorithm to learn and subsequently predict the categories of new images based on the acquired information. A practical application is proposed to improve visitor experiences and heritage management, paving the way for future technological advancements in the field.

Advances in artificial intelligence are revolutionizing agricultural diagnostics, particularly in addressing the critical challenge of cassava disease detection. Cassava, a vital food source for millions worldwide, faces significant yield losses due to various diseases that threaten food security in developing regions. This research presents a novel approach integrating transfer learning with explainable AI to create a robust disease detection system. Through extensive experimentation with multiple deep learning architectures, our ResNet-based model achieves a remarkable accuracy of 92% in distinguishing among four major cassava diseases and healthy specimens. The integration of SHAP (SHapley Additive exPlanations) technology provides unprecedented transparency in the model’s decision-making process, allowing stakeholders to understand how the neural network identifies disease-specific features. Our system demonstrates particular strength in identifying Cassava Mosaic Disease, achieving 98% accuracy, while maintaining robust performance across bacterial blight, brown spot and green mite detection. The methodology presented here not only advances the technical frontier of agricultural AI but also provides a practical tool for enhancing food security through early disease detection. This research establishes a foundation for developing accessible and interpretable AI systems that can be deployed in resource-limited agricultural settings, potentially transforming how farmers manage crop health in the digital age.