Now showing items 1-14 of 14

Aplicación de técnicas de machine Learning para la detección de fugas en una tubería horizontal que transporta una mezcla de agua y glicerol

Gámez De León, Adalberto (Corporación Universidad de la CostaIngeniería Electrónica, 2021)

During the last decades, the problem of detecting leaks in pipes with the help of software has been much discussed. Timely leak detection prevents water loss and helps prevent economic and environmental catastrophes. It ...
Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness

Alanazi, Abdullah; Alizadeh, Seyed Mehdi; Nurgalieva, Karina; Nesic, Slavko; Grimaldo Guerrero, John William; Abo-Dief, Hala M.; Eftekhari-Zadeh, Ehsan; nazemi, ehsan; Igor, Narozhnyy (MDPI Multidisciplinary Digital Publishing InstituteSwitzerland, 2022-01-27)

One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and ...
Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer

Mayet, Abdulilah; Salama, Ahmed S.; Alizadeh, Mehdi; Nesic, Slavko; Grimaldo Guerrero, John William; Eftekhari-Zadeh, Ehsan; nazemi, ehsan; Iliyasu, Abdullah (MDPI Multidisciplinary Digital Publishing InstituteSwitzerland, 2022)

Scale formation inside oil and gas pipelines is always one of the main threats to the efficiency of equipment and their depreciation. In this study, an artificial intelligence method method is presented to provide the flow ...
Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images

Mansour, Romany F.; Escorcia-Gutierrez, Jose; Gamarra, Margarita; Garcia-Diaz, Vicente; Gupta, Deepak; kumar, sachin (Corporación Universidad de la Costa, 2021-06-21)

Due to the fast development of medical imaging technologies, medical image analysis has entered the period of big data for proper disease diagnosis. At the same time, intracerebral hemorrhage (ICH) becomes a serious disease ...
Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition

Diaz Martinez, Jorge Luis; Aziz Butt, Shariq; Michael Onyema, Edeh; Chakraborty, Dr. Chinmay; Shaheen, Qaisar; De-La-Hoz-Franco, Emiro; Ariza Colpas, Paola Patricia (International Journal of Systems Assurance Engineering and Management, 2021-07-17)

Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. This study presents Kubernetes Container Scheduling ...
Green building's heat loss reduction analysis through two novel hybrid approaches

Moayedi, Hossein; YILDIZHAN, Hasan; Aungkulanon, Pasura; Cardenas Escorcia, Yulineth; Al-Bahrani, Mohammed; Binh, Le Nguyen (Elsevier Ltd.United Kingdom, 2023)

One of the key reasons for the performance discrepancy between a building's intended usage and the actual operation is Heat Loss, which describes a building's envelope efficiency during in-use circumstances. In this setting, ...
Implementación web de redes neuronales artificiales aplicadas a la predicción de series de tiempo

Martínez Gómez, Edinson Jabid (Ingeniería de Sistemas, 2013)

In this Project we show the development and deployment of a web platform with two kinds of artificial Neural Networks applied to forecast time series. It has been developed with the language of programming Python and use ...
Intelligent deep learning-enabled autonomous small ship detection and classification model

Escorcia-Gutierrez, Jose; Gamarra, Margarita; BELEÑO SAENZ, KELVIN; Soto, Carlos; Mansour, Romany (Elsevier Ltd.United Kingdom, 2022)

Autonomous ship technologies have gained considerable interest due to the minimization of the challenging issues faced by the unpredictable errors of manual navigation, and therefore reduces human labor, increasing navigation ...
Modelado y simulación de un panel fotovoltaico empleando técnicas de inteligencia artificial

Ospino Castro, Adalberto Jose; Robles Algarín, Carlos; Duran Pabón, Alejandro (Universidad de la Costa CUC, 2014)

El trabajo presenta la modelación del comportamiento energético y la determinación de los parámetros del circuito equivalente de un panel fotovoltaico con el empleo de técnicas de inteligencia artificial. Para tal efecto, ...
Multi-agent system for steel manufacturing process

Ricardo Rodríguez, Angel Raúl; Benítez, Israel F; González Yero, Guillermo; Núñez Alvarez, José Ricardo (Institute of Advanced Engineering and Science (IAES)Indonesia, 2022)

This work was carried out in the company ACINOX Las Tunas, Cuba, to design an integrated automation architecture based on intelligent agents for control, monitoring, and decision-making in the production process that ...
Optimization of X-ray tube voltage to improve the precision of two phase flow meters used in petroleum industry

Alanazi, Abdullah; Alizadeh, Mehdi; Nurgalieva, Karina; Grimaldo Guerrero, John William; Abo-Dief, Hala M.; Eftekhari-Zadeh, Ehsan; nazemi, ehsan; Igor, Narozhnyy (Corporación Universidad de la Costa, 2021-12-09)

To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations ...
Prediction of the yield of grains through artificial intelligence

Silva, Jose; Varela Izquierdo, Noel; Cabrera, Danelys; Lezama, Omar (Corporación Universidad de la Costa, 2021)

Grass turns out to be an appropriate food for cattle, mainly in tropical climate countries such as Latin American countries. This is due to the high number of species that can be used, the possibility of growing them ...
Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems

Escorcia-Gutierrez, José; Mansour, Romany F.; Leal, Esmeide; Villanueva, Jair; Jiménez-Cabas, Javier; Soto, Carlos; Soto-Diaz, Roosvel (Springer NetherlandsNetherlands, 2023)

Due to advancements in information technology, the healthcare sector becomes beneficial and provides distinct methods of managing medical data and enhancing the quality of medical services. The advanced e-healthcare ...
Sistema de control de acceso basado en hardware y software libre para la detección de potenciales infectados de Covid-19

Solano-Escorcia, Andrés Felipe; Valle tapia , Diego Andres; Anguila, Jose; Salcedo, Dixon; Henriquez Miranda, Carlos; Ternera Muñoz, Yesith; RAMOS BARRERA, LUIS FERNANDO; Martínez, Nicolás (Corporación Universidad de la CostaBarranquilla, 2021)

La pandemia generada por el virus SARS-COVD-19 ha afectado a la humanidad. Así, a nivel global, regional, y local, se han adoptado diferentes medidas para su detección, contención, y eliminación. En consecuencia, se han ...

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Aplicación de técnicas de machine Learning para la detección de fugas en una tubería horizontal que transporta una mezcla de agua y glicerol
...
Gámez De León, Adalberto | 2021

During the last decades, the problem of detecting leaks in pipes with the help of software has been much discussed. Timely leak detection prevents water loss and helps prevent economic and environmental catastrophes. It is evident that companies choose to have good leak control policies, detection systems in Control Centers and teams of operators who locate leaks directly on the ground. However, these strategies do not allow for real-time leak detection. This project implements techniques based on machine learning that, through the introduction of process data from the studied pipes, systematically determine the presence of leaks. With this project, it is hoped to improve the speed and accuracy of leak detection, using only process data, system knowledge and intelligent software.

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Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness
...
Alanazi, Abdullah | 2022-01-27

One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.

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Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer
...
Mayet, Abdulilah | 2022

Scale formation inside oil and gas pipelines is always one of the main threats to the efficiency of equipment and their depreciation. In this study, an artificial intelligence method method is presented to provide the flow regime and volume percentage of a two-phase flow while considering the presence of scale inside the test pipe. In this non-invasive method, a dual-energy source of barium-133 and cesium-137 isotopes is irradiated, and the photons are absorbed by a detector as they pass through the test pipe on the other side of the pipe. The Monte Carlo N Particle Code (MCNP) simulates the structure and frequency features, such as the amplitudes of the first, second, third, and fourth dominant frequencies, which are extracted from the data recorded by the detector. These features use radial basis function neural network (RBFNN) inputs, where two neural networks are also trained to accurately determine the volume percentage and correctly classify all flow patterns, independent of scale thickness in the pipe. The advantage of the proposed system in this study compared to the conventional systems is that it has a better measuring precision as well as a simpler structure (using one detector instead of two).

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Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images
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Mansour, Romany F. | 2021-06-21

Due to the fast development of medical imaging technologies, medical image analysis has entered the period of big data for proper disease diagnosis. At the same time, intracerebral hemorrhage (ICH) becomes a serious disease which affects the injury of blood vessels in the brain regions. This paper presents an artificial intelligence and big data analytics-based ICH e-diagnosis (AIBDA-ICH) model using CT images. The presented model utilizes IoMT devices for data acquisition process. The presented AIBDA-ICH model involves graph cut-based segmentation model for identifying the affected regions in the CT images. To manage big data, Hadoop Ecosystem and its elements are mainly used. In addition, capsule network (CapsNet) model is applied as a feature extractor to derive a useful set of feature vectors. Finally, the presented AIBDA-ICH model makes use of the fuzzy deep neural network (FDNN) model to carry out classification process. For validating the superior performance of the AIBDA-ICH method, an extensive set of simulations were performed and the outcomes are examined under diverse aspects. The experimental values pointed out the improved e-diagnostic performance of the AIBDA-ICH model over the other compared methods with the precision and accuracy of 94.96% and 98.59%, respectively.

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Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition
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Diaz Martinez, Jorge Luis | 2021-07-17

Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. This study presents Kubernetes Container Scheduling Strategy (KCSS) based on Artificial Intelligence (AI) that can assist in decision making to control the scheduling and shifting of load to nodes. The aim is to improve the container’s schedule requested digitally from users to enhance the efficiency in scheduling and reduce cost. The constraints associated with the existing container scheduling techniques which often assign a node to every new container based on a personal criterion by relying on individual terms has been greatly improved by the new system presented in this study. The KCSS presented in this study provides multicriteria node selection based on artificial intelligence in terms of decision making systems thereby giving the scheduler a broad picture of the cloud's condition and the user's requirements. AI Scheduler allows users to easily make use of fractional Graphics Processing Units (GPUs), integer GPUs, and multiple-nodes of GPUs, for distributed training on Kubernetes. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.

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Green building's heat loss reduction analysis through two novel hybrid approaches
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Moayedi, Hossein | 2023

One of the key reasons for the performance discrepancy between a building's intended usage and the actual operation is Heat Loss, which describes a building's envelope efficiency during in-use circumstances. In this setting, the ANN models’ use for energy analysis of green buildings has become more established. This research aims to anticipate the heat loss of green buildings utilizing two artificial neural network-based methodologies (ANN). In particular, TLBO and BBO are used and contrasted. Additionally, RMSE, MAE, and R2 are used to calculate an absolute error for predicting heat loss to gauge the accuracy of the findings. The suggested TLBO-MLP standard is a reliable method with a positive outcome (RMSE = 0.01012 and 0.05216, and R2 = 0.99536 and 0.9651). Also, according to the training error ranges of [−0.0006078, 0.01133] and [−0.00040708, 0.010181] and testing error ranges of [0.0004724, 0.068666] and [0.0021984, 0.057688] for BBO-MLP and TLBO-MLP, respectively, shows that the TLBO-MLP reaches the lower range of error and can predict the heat loss with higher accuracy and it could properly forecast the heat loss of building technologies. Even so, the BBO-MLP standard provides this research with satisfactory performance (R2 = 0.9943 and 0.95175, and RMSE = 0.01122 and 0.06112). To increase the precision of calculating the heat loss of buildings, specifically integrating them with optimization algorithms, further study is required.

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Implementación web de redes neuronales artificiales aplicadas a la predicción de series de tiempo
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Martínez Gómez, Edinson Jabid | 2013

In this Project we show the development and deployment of a web platform with two kinds of artificial Neural Networks applied to forecast time series. It has been developed with the language of programming Python and use Extjs 4 for the client side. Allows the simulation of multilayer perceptron and neural networks based on radial basis functions. For the first algorithm it is used as the resilient backpropagation learning, which attempts to minimize the mean square error function to adjust the network weights. The training process of the RBF network is performed in two phases, using initially unsupervised learning, through the algorithm of k-means, for the centers of the radial basis function, subsequently standard deviations are found by the LMS algorithm and adjustment of the weights is obtained with the rule of the pseudo-inverse. The inputs to the simulation platform have to be imported through files (.csv) format, once we have obtained the results are graphically represented each of them. Simulations performed on a time series basis allow to obtain a good approximation in that is known if the variable value will increase or decrease. But because the algorithms implemented require expensive hardware, we need to look for alternatives such as parallel computing and optimization algorithms implemented.

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Intelligent deep learning-enabled autonomous small ship detection and classification model
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Escorcia-Gutierrez, Jose | 2022

Autonomous ship technologies have gained considerable interest due to the minimization of the challenging issues faced by the unpredictable errors of manual navigation, and therefore reduces human labor, increasing navigation security and profit margin. On autonomous shipping technologies, small ship detection is vital in ensuring shipping safety. With this motivation, this paper presents an efficient optimal mask regional convolutional neural network (Mask-CNN) technique for small ship detection (OMRCNN-SHD) on autonomous shipping technologies. Primarily, the data augmentation process is performed to resolve the issue of the limited number of real-world samples of small ships and helps to detect small ships in most cases accurately. Besides, the Mask RCNN with SqueezeNet model is used to detect ships and the hyperparameter tuning of the SqueezeNet model takes place by the use of the Adagrad optimizer. Furthermore, the Colliding Body's Optimization (CBO) algorithm with the weighted regularized extreme learning machine (WRELM) technique is employed to classify detected ships effectively. The comparative results analysis demonstrates the betterment of the OMRCNN-SHD technique over the current methods with the maximum accuracy of 98.63%.

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Modelado y simulación de un panel fotovoltaico empleando técnicas de inteligencia artificial
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Ospino Castro, Adalberto Jose | 2014

El trabajo presenta la modelación del comportamiento energético y la determinación de los parámetros del circuito equivalente de un panel fotovoltaico con el empleo de técnicas de inteligencia artificial. Para tal efecto, se desarrolló en Matlab un algoritmo genético que permite obtener los parámetros físicos no brindados por los fabricantes para la implementación de funciones matemáticas que se homologuen al comportamiento dinámico de un panel solar. El algoritmo desarrollado en Matlab permite determinar, además, la eficiencia del algoritmo seguidor del punto de máxima potencia del inversor, el cual es un parámetro de no simple obtención o no determinado en condiciones de campo. Finalmente, el algoritmo se probó satisfactoriamente en un panel solar monocristalino de 50W obteniendo adecuados resultados en comparación con el modelo experimental para diferentes niveles de irradiación solar. Los resultados demuestran que el proceso desarrolladoconstituye una herramienta que puede ser aplicada a paneles fotovoltaicos en condiciones de campo

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Multi-agent system for steel manufacturing process
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Ricardo Rodríguez, Angel Raúl | 2022

This work was carried out in the company ACINOX Las Tunas, Cuba, to design an integrated automation architecture based on intelligent agents for control, monitoring, and decision-making in the production process that guarantees an improvement in planning and management of the process in the steelwork plant. The great differences of technologies and systems of each steel mill and the multiple restrictions, methods, and techniques, within a wide dynamic strongly concatenated, do not generalize automation systems feasibly. In our research, we use international research results and the experience of the plant technologists to create three levels of distributed intelligent architecture: business, production planning-control, and steel manufacturing. Each level manages to integrate and balance the particular and general interests for efficient decision-making combined between hierarchy and heterarchy in this steelwork plant, which will be reflected in a reduction of at least 99% of the time used for decision-making concerning the current system, which can lead to a decrease in refractory costs, energy consumption, and production cost. The effectiveness of the solution is demonstrated with scenario validation and expert evaluation.

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Optimization of X-ray tube voltage to improve the precision of two phase flow meters used in petroleum industry
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Alanazi, Abdullah | 2021-12-09

To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations that depends on the tube voltage can significantly affect the measurement precision of the system. The purpose of present study is to find the optimum tube voltage to increase the accuracy and efficiency of an intelligent X-ray radiation-based two-phase flow meter. The detection system consists of an industrial X-ray tube and one detector located on either side of a steel pipe. Tube voltages in the range of 125–300 kV with a step of 25 kV were investigated. For each tube voltage, different gas volume percentages (GVPs) in the range of 10–90% with a step of 5% were modeled. A feature extraction method was performed on the output signals of the detector in every case, and the obtained matrixes were applied to the designed radial basis function neural networks (RBFNNs). The desired output of the networks was GVP. The precision of the networks in every voltage and every number of neurons in the hidden layer were obtained. The results showed that 225 kV tube voltage is the optimum voltage for this purpose. The obtained mean absolute error (MAE) for this case is less than 0.05, which demonstrates the very high precision of the metering system with an optimum X-ray tube voltage.

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Prediction of the yield of grains through artificial intelligence
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Silva, Jose | 2021

Grass turns out to be an appropriate food for cattle, mainly in tropical climate countries such as Latin American countries. This is due to the high number of species that can be used, the possibility of growing them year-round, the ability of the ruminant to use fibrous supplies and be an economic source (Sánchez et al., Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham, 2018, [1]). In this work, an application of neural networks was carried out in the forecasting of more accurate values of production and quality of grasslands.

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Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems
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Escorcia-Gutierrez, José | 2023

Due to advancements in information technology, the healthcare sector becomes beneficial and provides distinct methods of managing medical data and enhancing the quality of medical services. The advanced e-healthcare applications are mainly based on the Internet of Things (IoT) and cloud computing platforms. In IoT enabled healthcare sector, the IoT devices usually record the patient data and transfer it to the cloud for further processing. Energy efficiency and security are treated as critical problems in designing IoT networks in the healthcare environment. As IoT devices are limited to energy, designing an effective technique to reduce energy utilization is needed. At the same time, secure transmission of medical data also poses a major challenging design issue. This paper presents a novel artificial intelligence with a blockchain scheme for IoT healthcare systems named AIBS-IoTHS. The AIBS-IoTH model aims to achieve secure and energy-efficient data transmission in IoT networks. The IoT devices are primarily used to collect patients’ medical data. The AIBS- IoTH model involves a metaheuristic-based modified sunflower optimization-based clustering (MSFOC) technique to achieve energy efficiency. Then, the blockchain empowered secure medical data transmission process is carried out for both inter-cluster and intra-cluster communication. At last, the Classification Enhancement Generative Adversarial Networks (CEGAN) model performs the diagnostic process on the secured medical data to determine the existence of the diseases. The design of MSFOC and CEGAN techniques shows the novelty of the work. An extensive experimental analysis of the benchmark dataset pointed out the superior performance of the proposed AIBS-IoTH model over the other compared methods.

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Sistema de control de acceso basado en hardware y software libre para la detección de potenciales infectados de Covid-19
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Solano-Escorcia, Andrés Felipe | 2021

La pandemia generada por el virus SARS-COVD-19 ha afectado a la humanidad. Así, a nivel global, regional, y local, se han adoptado diferentes medidas para su detección, contención, y eliminación. En consecuencia, se han generado diferentes campañas, estrategias y soluciones, basadas en diferentes desarrollos tecnológicos, que van desde el uso de la inteligencia artificial hasta componentes electrónicos. Así mismo, muchas soluciones tecnológicas se han basado en la detección temprana de síntomas generados por posible contagio por COVID-19; por lo tanto, han dado respuestas creando herramientas y plataformas que permitan a los usuarios tener información en tiempo real de su temperatura corporal. Por lo anterior, este trabajo presenta un prototipo funcional a escala real, que basado en plataformas de hardware y software libre permite a los usuarios conocer su temperatura corporal en tiempo real, y permitió determinar que, aunque existan soluciones privativas en el mercado, es posible proponer nuevas tecnologías que con aspectos de innovación permitan a organizaciones de bajos recursos acceder a tecnologías emergentes a bajo costo que potencien sus procesos y servicios. En segundo lugar, se encontró que uno de los efectos más comunes para detectar posibles contagios de COVID-19, es la fiebre.

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Browsing by Subject "Artificial intelligence"

Aplicación de técnicas de machine Learning para la detección de fugas en una tubería horizontal que transporta una mezcla de agua y glicerol ﻿

Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness ﻿

Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer ﻿

Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images ﻿

Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition ﻿

Green building's heat loss reduction analysis through two novel hybrid approaches ﻿

Implementación web de redes neuronales artificiales aplicadas a la predicción de series de tiempo ﻿

Intelligent deep learning-enabled autonomous small ship detection and classification model ﻿

Modelado y simulación de un panel fotovoltaico empleando técnicas de inteligencia artificial ﻿

Multi-agent system for steel manufacturing process ﻿

Optimization of X-ray tube voltage to improve the precision of two phase flow meters used in petroleum industry ﻿

Prediction of the yield of grains through artificial intelligence ﻿

Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems ﻿

Sistema de control de acceso basado en hardware y software libre para la detección de potenciales infectados de Covid-19 ﻿

Envíos recientes

Aplicación de técnicas de machine Learning para la detección de fugas en una tubería horizontal que transporta una mezcla de agua y glicerol

Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness

Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer

Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images

Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition

Green building's heat loss reduction analysis through two novel hybrid approaches

Implementación web de redes neuronales artificiales aplicadas a la predicción de series de tiempo

Intelligent deep learning-enabled autonomous small ship detection and classification model

Modelado y simulación de un panel fotovoltaico empleando técnicas de inteligencia artificial

Multi-agent system for steel manufacturing process

Optimization of X-ray tube voltage to improve the precision of two phase flow meters used in petroleum industry

Prediction of the yield of grains through artificial intelligence

Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems

Sistema de control de acceso basado en hardware y software libre para la detección de potenciales infectados de Covid-19

Aplicación de técnicas de machine Learning para la detección de fugas en una tubería horizontal que transporta una mezcla de agua y glicerol

Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness

Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer

Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images

Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition

Green building's heat loss reduction analysis through two novel hybrid approaches

Implementación web de redes neuronales artificiales aplicadas a la predicción de series de tiempo

Intelligent deep learning-enabled autonomous small ship detection and classification model

Modelado y simulación de un panel fotovoltaico empleando técnicas de inteligencia artificial

Multi-agent system for steel manufacturing process

Optimization of X-ray tube voltage to improve the precision of two phase flow meters used in petroleum industry

Prediction of the yield of grains through artificial intelligence

Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems

Sistema de control de acceso basado en hardware y software libre para la detección de potenciales infectados de Covid-19