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    A machine learning-based analytical intelligence system for forecasting demand of new products based on chlorophyll : a hybrid approach
    (Springer, 2024)
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    Marmolejo Saucedo, José Antonio
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    Garcia-Llamas, Eduardo
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    Rodríguez-Aguilar, Miriam
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    Marmolejo-Saucedo, Liliana
    This manuscript addresses the problem of forecasting the demand for innovative products with limited and inhomogeneous sales data over time. The main objective of the study is to use the information available from a group of innovative chlorophyll-based food products to build a coherent demand forecasting system. From a transactional database, time series were constructed for each group of products, analyzing the stationarity and seasonality of the time series through the Dickey–Fuller and Canova–Hansen tests. Likewise, an ARIMA model, a long short-term memory (LSTM) recurrent deep neural network, and a support vector machine (SVM) were trained to select the best model for each product based on a forecast performance metric. A comparison between classical forecasting techniques and machine learning models is shown. The LSTM neural network was the best model for most products because the internal architecture of the network allows not only to capture non-linear relationships between variables but is also capable of controlling the flow of information to preserve characteristics over time that are relevant for forecasts. The second-best model was the SVM, which allows capturing non-linear behaviors through kernel functions and uses a smaller amount of data for its estimation. Finally, the ARIMA model presented the lowest performance for all products. The objective of having various methodologies is that the system allows the best forecast to be selected according to the type of product, availability of information and methodology used, which will allow the company to integrate new products into the system over time. ©Springer
      23  1
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    Automatic classification of coronary stenosis using convolutional neural networks and simulated annealing
    (CRC Press, 2022)
    Gutiérrez, Sebastián
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    Cruz-Aceves, Ivan
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    Fernandez-Jaramillo, Arturo Alfonso
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    Automatic detection of coronary stenosis plays an essential role in systems that perform computer-aided diagnosis in cardiology. Coronary stenosis is a narrowing of the coronary arteries caused by plaque that reduces the blood flow to the heart. Automatic classification of coronary stenosis images has been re-cently addressed using deep and machine learning techniques. Generally, the machine learning methods form a bank of empirical and automatic features from the angiographic images. In the present work, a novel method for the automatic classification of coronary stenosis X-ray images is presented. The method is based on convolutional neural networks, where the neural architecture search is performed by using the path-based metaheuristics of simulated annealing. To perform the neural architecture search, the maximization of the F1-score metric is used as the fitness function. The automatically generated convolutional neural network was compared with three deep learning methods in terms of the accuracy and F1-score metrics using a testing set of images obtaining 0.88 and 0.89, respectively. In addition, the proposed method was evaluated with different sets of coronary stenosis images obtained via data augmentation. The results involving a number of different instances have shown that the proposed architecture is robust preserving the efficiency with different datasets © 2023 Şaban öztürk. All rights reserved.
      53  1
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    A novel artificial organic control system for mobile robot navigation in assisted living using vision- and neural-based strategies
    (2018)
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    Robots in assisted living (RAL) are an alternative to support families and professional caregivers with a wide range of possibilities to take care of elderly people. Navigation of mobile robots is a challenging problem due to the uncertainty and dynamics of environments found in the context of places for elderly. To accomplish this goal, the navigation system tries to replicate such a complicated process inspired on the perception and judgment of human beings. In this work, we propose a novel nature-inspired control system for mobile RAL navigation using an artificial organic controller enhanced with vision-based strategies such as Hermite optical flow (OF) and convolutional neural networks (CNNs). Particularly, the Hermite OF is employed for obstacle motion detection while CNNs are occupied for obstacle distance estimation. We train the CNN using OF visual features guided by ultrasonic sensor-based measures in a 3D scenario. Our application is oriented to avoid mobile and fixed obstacles using a monocular camera in a simulated environment. For the experiments, we use the robot simulator V-REP, which is an integrated development environment into a distributed control architecture. Security and smoothness metrics as well as quantitative evaluation are computed and analyzed. Results showed that the proposed method works successfully in simulation conditions.
    Scopus© Citations 7  14  1
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    Credit Risk Models in the Mexican Context Using Machine Learning
    (2022)
    López, Ana Lilia
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    López, Estefanía
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    The Default Rate is related to the period of the economic cycle in which they are observed, during expansion periods of the economy the default rate tends to be lower. But in contraction periods, the default rate tends to increase and this could be a risk for the stability of a country’s economy. Therefore, it is important to monitor the perspective of the economy in case it is expected to decrease or have abrupt movements. This work aims to identify the economic variables that determine the default rate of the Mexican Financial System and to find a machine learning model that forecasts the default rate. For this, we aggregate a dataset based on three official Mexican sources that compile data from 2013 to 2022, including the COVID-19 pandemic time frame. Then, we propose the analysis using two machine learning models. After the analysis, the results confirm that the artificial neural networks model shows better predictive power for the default rate values. We also implement an easy to use web application to estimate the default rate based on three simple variables. We anticipate this work might help on estimating the default rate and might impact on the strategic policies in the Mexican economy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
      19  2
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    A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset
    (2019)
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    The automatic recognition of human falls is currently an important topic of research for the computer vision and artificial intelligence communities. In image analysis, it is common to use a vision-based approach for fall detection and classification systems due to the recent exponential increase in the use of cameras. Moreover, deep learning techniques have revolutionized vision-based approaches. These techniques are considered robust and reliable solutions for detection and classification problems, mostly using convolutional neural networks (CNNs). Recently, our research group released a public multimodal dataset for fall detection called the UP-Fall Detection dataset, and studies on modality approaches for fall detection and classification are required. Focusing only on a vision-based approach, in this paper, we present a fall detection system based on a 2D CNN inference method and multiple cameras. This approach analyzes images in fixed time windows and extracts features using an optical flow method that obtains information on the relative motion between two consecutive images. We tested this approach on our public dataset, and the results showed that our proposed multi-vision-based approach detects human falls and achieves an accuracy of 95.64% compared to state-of-the-art methods with a simple CNN network architecture. © 2019 Elsevier Ltd
    Scopus© Citations 96  29  1