A5010294470- Digital Imaging for Blood Diseases
- AI in cancer detection
- Cell Image Analysis Techniques
- Smart Agriculture and AI
- Image Processing Techniques and Applications
- Spectroscopy and Chemometric Analyses
- Leaf Properties and Growth Measurement
- Radiomics and Machine Learning in Medical Imaging
- Medical Image Segmentation Techniques
- Advanced Chemical Sensor Technologies
- Identification and Quantification in Food
- Mosquito-borne diseases and control
- Infrared Target Detection Methodologies
- COVID-19 diagnosis using AI
- Machine Learning in Healthcare
- Malaria Research and Control
- Maritime Navigation and Safety
- Image Retrieval and Classification Techniques
- Human Pose and Action Recognition
- Brain Tumor Detection and Classification
- Anomaly Detection Techniques and Applications
- Video Surveillance and Tracking Methods
- Blockchain Technology Applications and Security
- Nasal Surgery and Airway Studies
- Plant tissue culture and regeneration
University of Cagliari
2016-2025
Since the inception of Bitcoin in 2009, market cryptocurrencies has grown beyond initial expectations, as witnessed by thousands tokenised assets available on market, whose daily trades amount to dozens USD billions. The pseudonymity features these have attracted attention cybercriminals, who exploit them carry out potentially untraceable scams. wide range cryptocurrency-based scams observed over last ten years fostered research analysis their effects, and development techniques counter...
In microscopy, laboratory tests make use of cell counters or flow cytometers to perform on blood cells, like the complete count, rapidly. However, a manual smear examination is still needed verify counter results and monitor patients under therapy. Moreover, inspection permits description cells’ appearance, as well any abnormalities. Unfortunately, analysis long tedious, its result can be subjective error-prone. Nevertheless, using image processing techniques, it possible automate entire...
Malaria is a severe infectious disease caused by the Plasmodium parasite. The early and accurate detection of this crucial to reducing number deaths it causes. However, current method detecting malaria parasites involves manual examination blood smears, which time-consuming labor-intensive process, mainly performed skilled hematologists, especially in underdeveloped countries. To address problem, we have developed two deep learning-based systems, YOLO-SPAM YOLO-SPAM++, can detect responsible...
Malaria is a globally widespread disease caused by parasitic protozoa transmitted to humans infected female mosquitoes of Anopheles. It in only the parasite Plasmodium, further classified into four different species. Identifying malaria parasites possible analysing digital microscopic blood smears, which tedious, time-consuming and error prone. So, automation process has assumed great importance as it helps laborious manual review diagnosis. This work focuses on deep learning-based models,...
The 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> Workshop on Maritime Computer Vision (MaCVi) 2023 focused maritime computer vision for Unmanned Aerial Vehicles (UAV) and Surface Vehicle (USV), organized several subchallenges in this domain: (i) UAV-based Object Detection, (ii) Mar-itime Tracking, (iii) USV-based Obstacle Segmentation (iv) Detection. were based the SeaDronesSee MODS benchmarks. This report summarizes main findings...
Radiomics is an innovative discipline in medical imaging that uses advanced quantitative feature extraction from radiological images to provide a non-invasive method of interpreting the intricate biological panorama diseases. This takes advantage unique characteristics imaging, where radiation or ultrasound combines with tissues, reveal disease features and important biomarkers are invisible human eye. plays crucial role healthcare, spanning diagnosis, prognosis, recurrences, treatment...
COVID-19, an infectious coronavirus disease, caused a pandemic with countless deaths. From the outset, clinical institutes have explored computed tomography as effective and complementary screening tool alongside reverse transcriptase-polymerase chain reaction. Deep learning techniques shown promising results in similar medical tasks and, hence, may provide solutions to COVID-19 based on images of patients. We aim contribute research this field by: (i) Comparing different architectures...
This work explores using Large Language Models (LLMs) to translate user preferences into energy optimization constraints for home appliances. We describe a task where natural language utterances are converted formal smart appliances, within the broader context of renewable community (REC) and in Italian scenario. evaluate effectiveness various LLMs currently available translating these resorting classical zero-shot, one-shot, few-shot learning settings, pilot dataset requests paired with...
Ripening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes occur during this period. Therefore, monitoring ripening time fundamental task market quality product in timely manner. However, it difficult accurately determine degree of ripeness. Although some scientific methods have also been proposed literature, conventional adopted dairy industries typically based on visual and weight control. This study proposes...
Accurate segmentation of the nasal cavity and paranasal sinuses in CT scans is crucial for disease assessment, treatment planning, surgical navigation. It also facilitates advanced computational modeling airflow dynamics enhances endoscopic surgery preparation. This work presents a novel ensemble framework 3D that synergistically combines CNN-based transformer-based architectures, 3D-NASE. By integrating U-Net, UNETR, Swin UNETR with majority soft voting strategies, our approach leverages...
Early detection of Trypanosoma parasites is critical for prompt treatment trypanosomiasis, a neglected tropical disease that poses severe health and socioeconomic challenges in affected regions. To address the limitations traditional manual microscopy prior automated methods, we propose YOLO-Tryppa, novel YOLO-based framework specifically engineered rapid accurate small images. YOLO-Tryppa incorporates ghost convolutions to reduce computational complexity while maintaining robust feature...
Early detection of Trypanosoma parasites is critical for the prompt treatment trypanosomiasis, a neglected tropical disease that poses severe health and socioeconomic challenges in affected regions. To address limitations traditional manual microscopy prior automated methods, we propose YOLO-Tryppa, novel YOLO-based framework specifically engineered rapid accurate small images. YOLO-Tryppa incorporates ghost convolutions to reduce computational complexity while maintaining robust feature...
Accurate segmentation of the nasal cavity and paranasal sinuses in CT scans is crucial for disease assessment, treatment planning, surgical navigation. It also facilitates advanced computational modeling airflow dynamics enhances endoscopic surgery preparation. This work presents a novel ensemble framework 3D that synergistically combines CNN-based transformer-based architectures, 3D-NASE. By integrating U-Net, UNETR, Swin SegResNet, DAF3D, V-Net with majority soft voting strategies, our...