Now showing items 1-16 of 16

A deep learning-based hybrid model for recommendation generation and ranking

Sivaramakrishnan, N.; Subramaniyaswamy, V.; amelec, viloria; Vijayakumar, V.; Senthilselvan, N. (2020)

A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect ...
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 ...
Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images

Escorcia-Gutierrez, Jose; Mansour, Romany F.; Beleño, Kelvin; Jiménez-Cabas, Javier; Pérez, Meglys; Madera, Natasha; Velasquez, Kevin (Tech Science PressUnited States, 2022)

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of ...
An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems

Escorcia-Gutierrez, Jose; BELEÑO SAENZ, KELVIN; Jiménez-Cabas, Javier; Elhoseny, Mohamed; Alshehri, Dr. Mohammad Dahman; Selim, Mahmoud M. (ElsevierNetherlands, 2022)

Recent advancements in machine learning and deep learning models find them helpful in designing effective complex measurement systems. At the same time, examining the brain’s activities using Electroencephalography (EEG) ...
Classification of chest diseases using deep learning

Silva, Jesús; Zilberman, Jack; Pinillos-Patiño, Yisel; Varela Izquierdo, Noel; Pineda, Omar (Corporación Universidad de la Costa, 2020)

The field of computer vision has had exponential progress in a wide range of applications due to the use of deep learning and especially the existence of large annotated image data sets [1]. Significant improvements have ...
Data sciences and teaching methods—learning

Silva, Jesus; Portillo, Rafael; Hernandez, Ana Emilia; Varela, Noel; Martinez Caraballo, Hugo; Hernández Palma, Hugo Gaspar; Redondo Bilbao, Osman; Leon Castro, Nadia (Universidad de la Costa, 2020-03-05)

Data Science (DS) is an interdisciplinary field responsible for extracting knowledge from the data. This discipline is particularly complex in the face of Big Data: large volumes of data make it difficult to store, process ...
A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses

Cama-Pinto, Dora; Damas, Miguel; Holgado-Terriza, Juan Antonio; Arrabal Campos, Francisco Manuel; Martínez Lao, Juan Antonio; Cama-Pinto, Alejandro; Manzano-Agugliaro, Francisco (MDPI AGSwitzerland, 2023-01-13)

The production of crops in greenhouses will ensure the demand for food for the world’s population in the coming decades. Precision agriculture is an important tool for this purpose, supported among other things, by the ...
Deep learning of robust representations for multi-instance and multi-label image classification

Silva, Jesús; Varela Izquierdo, Noel; Mendoza Palechor, Fabio; Pineda, Omar (Corporación Universidad de la Costa, 2020)

In multi-instance problems (MIL), an arbitrary number of instances is associated with a class label. Therefore, the labeling of training data becomes simpler (since it is done together, instead of individually) with the ...
Detección de cáncer de seno usando imágenes de histopatología y modelos de aprendizaje profundo pre-entrenados

Agudelo Gaviria, Harold; Sarria-Paja, Milton (Corporación Universidad de la CostaBarranquilla, 2021)

El cáncer es una enfermedad que se puede originar en cualquier parte del cuerpo. Comienza cuando las células infectadas crecen de forma descontrolada sobrepasando a las células sanas. El cáncer de seno, en su mayoría ...
Human activity recognition data analysis: history, evolutions, and new trends

Ariza Colpas, Paola Patricia; sVicario, Enrico; Oviedo Carrascal, Ana Isabel; aziz, shariq ; Piñeres Melo, Marlon Alberto; Quintero linero, Alejandra paola; PATARA, FULVIO (Multidisciplinary Digital Publishing Institute (MDPI)Switzerland, 2022-04-29)

The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the ...
Improved metaheuristics with machine learning enabled medical decision support system

Althubiti , Sara ; Escorcia-Gutierrez, Jose; Gamarra, Margarita; Soto-Diaz, Roosvel; Mansour, Romany F.; Alenezi, Fayadh (Tech Science Press, 2022)

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence ...
Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques

Escorcia-Gutierrez, Jose; Gamarra, Margarita; Soto-Diaz, Roosvel; Pérez, Meglys; Madera, Natasha; Mansour, Romany F. (Multidisciplinary Digital Publishing Institute (MDPI)Switzerland, 2022-07-07)

Soil nutrients are a vital part of soil fertility and other environmental factors. Soil testing is an efficient tool used to evaluate the existing nutrient levels of soil and aid to compute the appropriate quantity of soil ...
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 ...
Method for the recovery of images in databases of skin cancer

Viloria, Amelec; Varela, Noel; Nuñez-Bravo, Narledys; Pineda Lezama, Omar Bonerge (Corporación Universidad de la Costa, 2021)

Deep learning is widely used for the classification of images since the ImageNet competition in 2012 (Zaharia et al. in Common ACM 59(11):56–65, 2016, [1]; Tajbakhsh et al. in IEEE Trans Med Imaging 35(5):1299–1312, 2016, ...
Relationships among instructor autonomy support, and university students’ learning approaches, perceived professional competence, and life satisfaction

Huéscar Hernández, Elisa; LOZANO-JIMÉNEZ, JOSÉ EDUARDO; de Roba Noguera, José Miguel; Moreno-Murcia, Juan Antonio (Public Library of ScienceUnited States, 2022-04-14)

The purpose of this study was to examine relationships among instructor autonomy support for student learning, and students’ motivational characteristics, learning approaches, perceptions of career competence and life ...
Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification

Mansour, Romany F.; Escorcia-Gutierrez, Jose; Gamarra, Margarita; Gupta, Deepak; Castillo, Oscar; kumar, sachin (Corporación Universidad de la Costa, 2021)

At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence ...

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A deep learning-based hybrid model for recommendation generation and ranking
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Sivaramakrishnan, N. | 2020

A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user–item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user’s point of interest, recommending products/services based on the user’s latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user’s point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets.

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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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Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images
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Escorcia-Gutierrez, Jose | 2022

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

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An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems
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Escorcia-Gutierrez, Jose | 2022

Recent advancements in machine learning and deep learning models find them helpful in designing effective complex measurement systems. At the same time, examining the brain’s activities using Electroencephalography (EEG) is essential to determine the mental state or thought of a person. It is essential in several application areas, such as Brain-Computer Interface (BCI), emotion recognition, and mental disease diagnosis. The proper brain signal classification using EEG finds helpful diagnose epileptic seizures. Since the traditional seizure detection process is a lengthy and challenging task, the automated identification of epilepsy is a significant problem. In order to resolve the issues that exist in the traditional brain signal classification models, this study designs Automated Deep Learning-Enabled Brain Signal Classification for Epileptic Seizure Detection (ADLBSC-ESD). The proposed ADLBSC-ESD technique aims to classify the brain signals to determine the existence of seizures or not. In addition, the presented model involves the design of the Improved Teaching and Learning-Enabled Optimization (ITLBO) technique for selecting features from EEG signals. Moreover, the Deep Belief Network (DBN) model is used for an effectual classification of EEG signals, and the hyperparameters of the DBN model are optimally tuned using the Swallow Swarm Optimization Algorithm (SSA). In order to ensure the improved brain signal classification performance of the ADLBSC-ESD technique, a series of simulations take place, and the outcomes are investigated concerning different measures. The experimental values highlighted the better performance of the ADLBSC-ESD technique over the current state of art techniques with maximum accuracy of 0.8316 and 0.8609 under binary and multiple classes, respectively.

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Classification of chest diseases using deep learning
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Silva, Jesús | 2020

The field of computer vision has had exponential progress in a wide range of applications due to the use of deep learning and especially the existence of large annotated image data sets [1]. Significant improvements have been shown in the performance of problems previously considered difficult, such as object recognition, detection and segmentation over approaches based on obtaining the characteristics of the image by hand [2]. This article presents a novel method for the classification of chest diseases in the standard and widely used data set ChestX-ray8, which contains more than 100,000 front view images with 8 diseases.

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Data sciences and teaching methods—learning
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Silva, Jesus | 2020-03-05

Data Science (DS) is an interdisciplinary field responsible for extracting knowledge from the data. This discipline is particularly complex in the face of Big Data: large volumes of data make it difficult to store, process and analyze with standard computer science technologies. The new revolution in Data Science is already changing the way we do business, healthcare, politics, education and innovation. This article describes three different teaching and learning models for Data Science, inspired by the experiential learning paradigm.

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A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses
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Cama-Pinto, Dora | 2023-01-13

The production of crops in greenhouses will ensure the demand for food for the world’s population in the coming decades. Precision agriculture is an important tool for this purpose, supported among other things, by the technology of wireless sensor networks (WSN) in the monitoring of agronomic parameters. Therefore, prior planning of the deployment of WSN nodes is relevant because their coverage decreases when the radio waves are attenuated by the foliage of the plantation. In that sense, the method proposed in this study applies Deep Learning to develop an empirical model of radio wave attenuation when it crosses vegetation that includes height and distance between the transceivers of the WSN nodes. The model quality is expressed via the parameters cross-validation, R2 of 0.966, while its generalized error is 0.920 verifying the reliability of the empirical model.

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Deep learning of robust representations for multi-instance and multi-label image classification
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Silva, Jesús | 2020

In multi-instance problems (MIL), an arbitrary number of instances is associated with a class label. Therefore, the labeling of training data becomes simpler (since it is done together, instead of individually) with the disadvantage that a weakly supervised database is produced [9]. In the PCRY, each restaurant is represented by a set of images that share the attribute label(s) of that establishment. This paper explores the use of previously learned attribute extractors, trained in 3 different databases that are similar and complementary to the PCRY database

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Detección de cáncer de seno usando imágenes de histopatología y modelos de aprendizaje profundo pre-entrenados
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Agudelo Gaviria, Harold | 2021

El cáncer es una enfermedad que se puede originar en cualquier parte del cuerpo. Comienza cuando las células infectadas crecen de forma descontrolada sobrepasando a las células sanas. El cáncer de seno, en su mayoría carcinomas, es el tipo más común entre las mujeres de todo el mundo. Los procedimientos utilizados para la detección de la enfermedad son aproximaciones diagnósticas, algunos de estos son invasivos. Usando herramientas digitales, es posible desarrollar o implementar sistemas de diagnóstico asistido para agilizar el proceso y permitir mayor confiabilidad de los análisis. El presente estudio se realiza con imágenes digitales de histopatología a partir de la base de datos de acceso abierto. Se evalúan tres escenarios, partiendo desde un esquema clásico, luego el se incluye el uso de modelos profundos pre-entrenados y finalmente se evaluá un modelo profundo con una red neuronal convolucional. El rendimiento de cada uno de los métodos sometidos a estudio se evaluaron calculando las medidas diagnósticas de precisión, sensibilidad y especificidad, logrando así encontrar el modelo que mejor se adecua a la tarea abordada. Se observa que los modelos pre-entrenados aportan información altamente discriminante a pesar de haber sido entrenados para una tarea completamente diferente. En general los modelos profundos permiten mejorar significativamente la especificidad del sistema al comparar con el enfoque clásico.

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Human activity recognition data analysis: history, evolutions, and new trends
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Ariza Colpas, Paola Patricia | 2022-04-29

The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.

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Improved metaheuristics with machine learning enabled medical decision support system
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Althubiti , Sara | 2022

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnose several diseases. Ovarian Cancer (OC) is a kind of cancer that affects women’s ovaries, and it is tedious to identify OC at the primary stages with a high mortality rate. The OC data produced by the Internet of Medical Things (IoMT) devices can be utilized to differentiate OC. In this aspect, this paper introduces a new quantum black widow optimization with a machine learning-enabled decision support system (QBWO-MLDSS) for smart healthcare. The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately. Besides, the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data. In addition, the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets. Moreover, symbiotic organisms search (SOS) with extreme learning machine (ELM) model is applied as a classifier for the detection and classification of ELM model, thereby improving the overall classification performance. The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s novelty. The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset, and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

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Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques
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Escorcia-Gutierrez, Jose | 2022-07-07

Soil nutrients are a vital part of soil fertility and other environmental factors. Soil testing is an efficient tool used to evaluate the existing nutrient levels of soil and aid to compute the appropriate quantity of soil nutrients depending upon the fertility level and crop requirements. Since the conventional soil nutrient testing models are not feasible in real time applications, an efficient soil nutrient, and potential of hydrogen (pH) prediction models are essential to improve overall crop productivity. In this aspect, this paper aims to design an intelligent soil nutrient and pH classification using weighted voting ensemble deep learning (ISNpHC-WVE) technique. The proposed ISNpHC-WVE technique aims to classify the existence of nutrients and pH levels exist in the soil. In addition, three deep learning (DL) models namely gated recurrent unit (GRU), deep belief network (DBN), and bidirectional long short term memory (BiLSTM) were used for the predictive analysis. Moreover, a weighted voting ensemble model was employed which allows a weight vector on every DL model of the ensemble depending upon the attained accuracy on every class. Furthermore, the hyperparameter optimization of the three DL models was performed using manta ray foraging optimization (MRFO) algorithm. For investigating the enhanced predictive performance of the ISNpHC-WVE technique, a comprehensive simulation analysis takes place to examine the pH and soil nutrient classification performance. The experimental results showcased the better performance of the ISNpHC-WVE technique over the recent techniques with accuracy of 0.9281 and 0.9497 on soil nutrient and soil pH classification. The proposed model can be utilized as an effective tool to improve productivity in agriculture by proper soil nutrient and pH classification.

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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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Method for the recovery of images in databases of skin cancer
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Viloria, Amelec | 2021

Deep learning is widely used for the classification of images since the ImageNet competition in 2012 (Zaharia et al. in Common ACM 59(11):56–65, 2016, [1]; Tajbakhsh et al. in IEEE Trans Med Imaging 35(5):1299–1312, 2016, [2]). This image classification is very useful in the field of medicine, in which there is a growing interest in the use of data mining techniques in recent years. In this paper, a deep learning network was selected and trained for the analysis of a set of skin cancer data, obtaining very satisfactory results, as the model surpassed the classification results of trained dermatologists using a dermatoscope, other automatic learning techniques, and other deep learning techniques.

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Relationships among instructor autonomy support, and university students’ learning approaches, perceived professional competence, and life satisfaction
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Huéscar Hernández, Elisa | 2022-04-14

The purpose of this study was to examine relationships among instructor autonomy support for student learning, and students’ motivational characteristics, learning approaches, perceptions of career competence and life satisfaction. Participated 1048 students from Spanish universities with ages between 18, and 57 years. A Structural equation modeling revealed a relationship between instructor autonomy support for student learning with students’ basic psychological need satisfaction. As a result, students’ basic need satisfaction was related to their intrinsic motivation, and to a deeper learning approach. These educational outcomes contributed to explain students’perceived professional competence, and life satisfaction. These findings highlight the importance of student choice, and decision-making in the learning process as a means to facilitating deeper learning, stronger feelings of professional competence, and enhanced well-being.

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Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification
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Mansour, Romany F. | 2021

At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively.

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Browsing by Subject "Deep learning"

A deep learning-based hybrid model for recommendation generation and ranking ﻿

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

Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images ﻿

An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems ﻿

Classification of chest diseases using deep learning ﻿

Data sciences and teaching methods—learning ﻿

A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses ﻿

Deep learning of robust representations for multi-instance and multi-label image classification ﻿

Detección de cáncer de seno usando imágenes de histopatología y modelos de aprendizaje profundo pre-entrenados ﻿

Human activity recognition data analysis: history, evolutions, and new trends ﻿

Improved metaheuristics with machine learning enabled medical decision support system ﻿

Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques ﻿

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

Method for the recovery of images in databases of skin cancer ﻿

Relationships among instructor autonomy support, and university students’ learning approaches, perceived professional competence, and life satisfaction ﻿

Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification ﻿

Envíos recientes

A deep learning-based hybrid model for recommendation generation and ranking

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

Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images

An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems

Classification of chest diseases using deep learning

Data sciences and teaching methods—learning

A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses

Deep learning of robust representations for multi-instance and multi-label image classification

Detección de cáncer de seno usando imágenes de histopatología y modelos de aprendizaje profundo pre-entrenados

Human activity recognition data analysis: history, evolutions, and new trends

Improved metaheuristics with machine learning enabled medical decision support system

Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques

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

Method for the recovery of images in databases of skin cancer

Relationships among instructor autonomy support, and university students’ learning approaches, perceived professional competence, and life satisfaction

Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification

A deep learning-based hybrid model for recommendation generation and ranking

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

Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images

An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems

Classification of chest diseases using deep learning

Data sciences and teaching methods—learning

A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses

Deep learning of robust representations for multi-instance and multi-label image classification

Detección de cáncer de seno usando imágenes de histopatología y modelos de aprendizaje profundo pre-entrenados

Human activity recognition data analysis: history, evolutions, and new trends

Improved metaheuristics with machine learning enabled medical decision support system

Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques

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

Method for the recovery of images in databases of skin cancer

Relationships among instructor autonomy support, and university students’ learning approaches, perceived professional competence, and life satisfaction

Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification