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Multi-Method Technics and Deep Neural Networks Tools on Board ARGO USV for the Geoarchaeological and Geomorphological Mapping of Coastal Areas: The Case of Puteoli Roman Harbour.
Sensors (Basel)
February 2024
Department of Science and Technology, Parthenope University of Naples, 80133 Napoli, Italy.
The ARGO-USV (Unmanned Surface Vehicle for ARchaeological GeO-application) is a technological project involving a marine drone aimed at devising an innovative methodology for marine geological and geomorphological investigations in shallow areas, usually considered critical areas to be investigated, with the help of traditional vessels. The methodological approach proposed in this paper has been implemented according to a multimodal mapping technique involving the simultaneous and integrated use of both optical and geoacoustic sensors. This approach has been enriched by tools based on artificial intelligence (AI), specifically intended to be installed onboard the ARGO-USV, aimed at the automatic recognition of submerged targets and the physical characterization of the seabed.
View Article and Find Full Text PDFDate fruit detection dataset for automatic harvesting.
Data Brief
February 2024
MAIS Laboratory, Department of Computer Science, Faculty of Sciences, Moulay Ismail University Meknes, Morocco.
Date fruit is one of the most beneficial and delicious fruits in the world through its nutritional value, it's a rich source of sugar, Protein, Manganese, Fibers, and many other vitamins, on the other hand, date production in the world reached up to 9.45 million tons, one palm tree can produce dates for 40-50 years which makes date production one of the main pillars of the economy. Generally, the methods used for pre and post-harvesting are done manually, making the operation heavy in terms of time and economy.
View Article and Find Full Text PDFChatGPT for phenotypes extraction: one model to rule them all?
Annu Int Conf IEEE Eng Med Biol Soc
July 2023
Information Extraction (IE) is a core task in Natural Language Processing (NLP) where the objective is to identify factual knowledge in textual documents (often unstructured), and feed downstream use cases with the resulting output. In genomic medicine for instance, being able to extract the most precise list of phenotypes associated to a patient allows to improve genetic disease diagnostic, which represents a vital step in the modern deep phenotyping approach. As most of the phenotypic information lies in clinical reports, the challenge is to build an IE pipeline to automatically recognize phenotype concepts from free-text notes.
View Article and Find Full Text PDFPrediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems.
Heliyon
November 2023
Department of Petroleum Engineering, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.
Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations.
View Article and Find Full Text PDF"OCTOPUS": An Intelligent Tool for Assisted Multidisciplinary ORL Oncology Meetings-Preliminary Study.
OTO Open
July 2023
ENT, Head and Neck Surgery Department, Anatomy Surgery and Anesthesiology Laboratory Hassan II University Hospital Fez Morocco.
Current sessions of multidisciplinary team meetings (MDMs) have several limitations, including increased costs and duration, as well as limited discussion time for each case. To address these issues, we have developed a computer application called OCTOPUS, which helps manage the patients' list for MDMs. The application allows for the generation of automatic MDM decisions based on predetermined criteria filled in by the patient's doctor and algorithms that comply with the latest oncology guidelines from the National Comprehensive Cancer Network.
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