Search results for "Recurrent"
showing 10 items of 256 documents
Sign Languages Recognition Based on Neural Network Architecture
2017
In the last years, many steps forward have been made in speech and natural languages recognition and were developed many virtual assistants such as Apple’s Siri, Google Now and Microsoft Cortana. Unfortunately, not everyone can use voice to communicate to other people and digital devices. Our system is a first step for extending the possibility of using virtual assistants to speech impaired people by providing an artificial sign languages recognition based on neural network architecture.
Forecasting Aquaponic Systems Behaviour With Recurrent Neural Networks Models
2022
Aquaponic systems provide a reliable solution to grow vegetables while cultivating fish (or other aquatic organisms) in a controlled environment. The main advantage of these systems compared with traditional soil-based agriculture and aquaculture installations is the ability to produce fish and vegetables with low water consumption. Aquaponics requires a robust control system capable of optimizing fish and plant growth while ensuring a safe operation. To support the control system, this work explores the design process of Deep Learning models based on Recurrent Neural Networks to forecast one hour of pH values in small-scale industrial Aquaponics. This implementation guides us through the m…
Artificial Neural Networks to Predict the Power Output of a PV Panel
2014
The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma m…
Exploiting deep learning algorithms and satellite image time series for deforestation prediction
2022
In recent years, we have witnessed the emergence of Deep Learning (DL) methods, which have led to enormous progress in various fields such as automotive driving, computer vision, medicine, finances, and remote sensing data analysis. The success of these machine learning methods is due to the ever-increasing availability of large amounts of information and the computational power of computers. In the field of remote sensing, we now have considerable volumes of satellite images thanks to the large number of Earth Observation (EO) satellites orbiting the planet. With the revisit time of satellites over an area becoming shorter and shorter, it will probably soon be possible to obtain daily imag…
Preamble Transmission Prediction for mMTC Bursty Traffic : A Machine Learning based Approach
2020
The evolution of Internet of things (IoT) towards massive IoT in recent years has stimulated a surge of traffic volume among which a huge amount of traffic is generated in the form of massive machine type communications. Consequently, existing network infrastructure is facing challenges when handling rapidly growing traffic load, especially under bursty traffic conditions which may more often lead to congestion. By proactively predicting the occurrence of congestion, we can implement necessary means and conceivably avoid congestion. In this paper, we propose a machine learning (ML) based model for predicting successful preamble transmissions at a base station and subsequently forecasting th…
Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy
2007
Abstract Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between …
A NEW CASE OF LOUSE-BORNE RELAPSING FEVER IN SICILY: CASE REPORT AND MINI REVIEW
2017
Body lice transport B. recurrentis from man to man and humans are the only host. The presence of lice in Italy and an increasing number of cases in migrants can contribute to the onset of autochthonous cases. In this paper, we report a new case of Louse-borne Relapsing Fever (LBRF) diagnosed among migrants in Sicily exactly one year after the first case was recorded. We reviewed all cases reported in Europe from February 2016 until now. Our study identified two new cases of LBRF in migrants arrived in Europe: one who came from Somalia and one from Mali. Here we report data on a new case in Sicily. The number of migrants and refugees to transit in Sicily has increased, and this has led to th…
PSA and PSA Kinetics Thresholds for the Presence of 68Ga-PSMA-11 PET/CT-Detectable Lesions in Patients with Biochemical Recurrent Prostate Cancer
2020
68Ga-PSMA-11 positron-emission tomography/computed tomography (PET/CT) is commonly used for restaging recurrent prostate cancer (PC) in European clinical practice. The goal of this study is to determine the optimum time for performing these PET/CT scans in a large cohort of patients by identifying the prostate-specific-antigen (PSA) and PSA kinetics thresholds for detecting and localizing recurrent PC. This retrospective analysis includes 581 patients with biochemical recurrence (BC) by definition. The performance of 68Ga-PSMA-11 PET/CT in relation to the PSA value at the scan time as well as PSA kinetics was assessed by the receiver-operating-characteristic-curve (ROC) generated by plottin…
T-cell Receptor Therapy Targeting Mutant Capicua Transcriptional Repressor in Experimental Gliomas
2021
Abstract Purpose: Gliomas are intrinsic brain tumors with a high degree of constitutive and acquired resistance to standard therapeutic modalities such as radiotherapy and alkylating chemotherapy. Glioma subtypes are recognized by characteristic mutations. Some of these characteristic mutations have shown to generate immunogenic neoepitopes suitable for targeted immunotherapy. Experimental Design: Using peptide-based ELISpot assays, we screened for potential recurrent glioma neoepitopes in MHC-humanized mice. Following vaccination, droplet-based single-cell T-cell receptor (TCR) sequencing from established T-cell lines was applied for neoepitope-specific TCR discovery. Efficacy of intravent…
RNA-sequencing and bioinformatic analysis to pre-assess sensitivity to targeted therapeutics in recurrent glioblastoma.
2019
e13533 Background: This study developed molecular guided tools for individualized selection of chemotherapeutics for recurrent glioblastoma (rGB). A consortium involving clinical neurooncologists, molecular biologists and bioinformaticians identified gene expression patterns in rGB and quantitatively analyzed pathways involved in response to FDA approved oncodrugs. Methods: From2016 to 2018 biopsies from GB were collected using a multisampling approach. Biopsy material was used to isolate glioma stem-like cells and examined by RNA-sequencing. RNA-seq results were subjected to differential expression (DE) analysis and Oncobox analysis – a bioinformatic tool for quantitative pathway activati…