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Tipologia Anno Titolo Titolo inglese Autore File
Lauree magistrali 2022 A software framework to develop Web and Native Mobile applications with one codebase A software framework to develop Web and Native Mobile applications with one codebase COGATO, MATTEO
Lauree magistrali 2021 A Study of Efficiency Improvement in Test Automation for Electronic Invoicing Software Solutions A Study of Efficiency Improvement in Test Automation for Electronic Invoicing Software Solutions OZCANBAZ, BILGE
Lauree magistrali 2021 A virtualization-based solution for protecting Android Bluetooth Low-Energy communications A virtualization-based solution for protecting Android Bluetooth Low-Energy communications VARISCHIO, ANDREA
Lauree magistrali 2020 A wearable physiological monitoring system based on Bluetooth low energy wireless sensors A wearable physiological monitoring system based on Bluetooth low energy wireless sensors BERNARDINI, CARLO ALBERTO
Lauree magistrali 2023 Active and Semi-Supervised Learning for Semantic Segmentation in Parapharmaceutical Inspection: A Study on Reducing Labeling Workload. Active and Semi-Supervised Learning for Semantic Segmentation in Parapharmaceutical Inspection: A Study on Reducing Labeling Workload. ZANIN, DARIA
Lauree magistrali 2020 Active Learning for regression on multiphase flowmeter data. Active Learning for regression on multiphase flowmeter data. BEN SOLTANE, AHMED
Lauree magistrali 2020 Active Learning for ticket prediction Active Learning for ticket prediction LANZA, ENRICO
Lauree magistrali 2020 Advanced Optimization Techniques for Learned Point Cloud Coding Advanced Optimization Techniques for Learned Point Cloud Coding MARI, DANIELE
Lauree magistrali 2021 Advanced Pipelines For Artifact Removal From EEG Data Advanced Pipelines For Artifact Removal From EEG Data NASIRINEJADDAFCHAHI, MILAD
Lauree magistrali 2020 Advanced systems for fast control of turbulence in free space quantum channel Advanced systems for fast control of turbulence in free space quantum channel LORENZETTO, ALESSANDRO
Lauree magistrali 2023 Advancing hvEEGNet as a general-purpose deep learning model for noisy time-series Advancing hvEEGNet as a general-purpose deep learning model for noisy time-series ERTANHAN, ARDA
Lauree magistrali 2023 Advancing VR Interaction Paradigms: A User Experience Evaluation of Haptic Feedback Advancing VR Interaction Paradigms: A User Experience Evaluation of Haptic Feedback BAGHERI, MOHAMMAD HOSSEIN
Lauree magistrali 2022 Advantage distillation strategies for underwater acoustic channels Advantage distillation strategies for underwater acoustic channels GIURISATO, FRANCESCO
Lauree magistrali 2024 Age of Incorrect Information in Uncertain Adversarial Environments via Bayesian Game Theory Age of Incorrect Information in Uncertain Adversarial Environments via Bayesian Game Theory SULKU, ERJOL
Lauree magistrali 2022 Age of Information for Channels under Attack by an Adversary ​ Age of Information for Channels under Attack by an Adversary ​ CICEK, KADER
Lauree magistrali 2023 Age of information in Satellite Networks Age of Information in Satellite Networks KASTRATI, FABIO
Lauree magistrali 2024 AI Development on Rectal Cancer by using Medical Imaging. The growing incidence of rectal cancer has emphasized the need for advanced diagnostic and prognostic methodologies to enhance patient outcomes. This thesis shows the development of an artificial intelligence (AI) system that is designed to analyze rectal cancer through medical imaging. The process begins with using of 3D Slicer software for the precise identification and segmentation of tumor regions in medical images, including MRI and CT scans. This segmentation provides detailed anatomical information necessary for subsequent analytical stages. Following segmentation, a deep learning model is provided to predict various tumor characteristics such as stage, aggressiveness, and potential therapeutic response. The deep learning framework is based on convolutional neural networks (CNNs), selected for their superior performance in image analysis tasks. The model is trained and validated using a comprehensive dataset of annotated medical images, ensuring its robustness and generalizability across different patient populations. The synergy between 3D Slicer for accurate tumor identification and deep learning for predictive analytics aims to improve diagnostic precision and provide insightful prognostic information. This AI-driven approach aspires to assist clinicians in devising informed treatment strategies, thereby contributing to the advancement of personalized medicine in rectal cancer care. This research aims to demonstrate the potential of integrating advanced medical imaging techniques with AI to achieve more precise and efficient cancer management. The findings of this thesis offer a significant contribution to the field of oncology, particularly in rectal cancer, and highlight the importance of interdisciplinary collaboration in fostering medical innovation. FARMAN, AMIR MOHAMMAD
Lauree magistrali 2023 AI driven generation and classification of short sound messages for Internet of Audio Things AI driven generation and classification of short sound messages for Internet of Audio Things FAVERO, MANUELE
Lauree magistrali 2021 Algorithms for 3D data estimation from single-pixel ToF sensors and stereo vision systems Algorithms for 3D data estimation from single-pixel ToF sensors and stereo vision systems KARAKAYA, UFUK BARAN
Lauree magistrali 2024 Algoritmi di rilevamento anomalie per sistemi mobili wireless Anomaly detection algorithms for wireless mobile systems SULTANOV, RAUF
Mostrati risultati da 25 a 44 di 430
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