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- Just Physio kidding: NUI and Gamification based Therapeutic Intervention for Children with Special NeedsPublication . Madeira, Rui Neves; Antunes, André; Postolache, OctavianThis paper presents the “just Physio kidding” approach, which intends to improve the engaging qualities of therapy programmes towards children with special needs, mainly with cerebral palsy, spinal muscular atrophy, or developmental delay. Therefore, “just Physio kidding” intends to address both physiotherapy and cognitive stimulation therapy. The system is functioning as a complement to the work of therapists, with and without their live supervision. It is part of a project with the aim of developing software based on the concept of personalized serious games for rehabilitation. The paper presents the concept and the prototype behind “just Physio kidding”.
- AI-powered solution for plant disease detection in viticulturePublication . Madeira, Miguel; Porfírio, Rui; Santos, Pedro Albuquerque; Madeira, Rui NevesIn an era dominated by the intersection of advanced technology and traditional industries, the domain of agriculture is on the verge of a revolutionary transformation. This article introduces a solution for vineyard producers, harnessing satellite imagery, weather data, and deep learning (DL) to identify vineyard diseases robustly. This solution, designed for proactive plant health management, stands as a transformative tool towards digital viticulture. Such tools transition from luxuries to essentials as vineyards confront evolving challenges like climate change and new pathogens. Our research builds on the hypothesis that customising deep learning architectures for specific tasks is crucial in enhancing their effectiveness. We contribute by introducing a tailored convolutional neural network (CNN) architecture, developed specifically for the classification of plant diseases using vineyard imagery. The experimental results demonstrate that our custom CNN architecture exhibits performance on par with established state-of-the-art models like ResNet50 and MobileNetV2, underscoring the value of specialized solutions in addressing the unique challenges of viticulture. This paper introduces an overview of the solution’s architecture, presents the implementation of DL modules with their corresponding results, and describes use case scenarios.