Martinez-Villaseñor, Lourdes
Thumbnail Image
Preferred name
Martinez-Villaseñor, Lourdes
Official Name
Martínez Villaseñor, María de Lourdes
Alternative Name
lmartine
Main Affiliation
ORCID
Scopus Author ID
55521085100
Researcher ID
N-7607-2018

Research Output
Now showing 1 - 10 of 11
No Thumbnail Available
Publication

Artificial Hydrocarbon Networks for Online Sales Prediction

2015 , Ponce, Hiram , Miralles-Pechuán, Luis , Martinez-Villaseñor, Lourdes

Online retail sales have been growing worldwide in the last decade. In order to cope with this high dynamicity and market share competition, online retail sales prediction and online advertising have become very important to answer questions of pricing decisions, advertising responsiveness, and product demand. To make adequate investment in products and channels it is necessary to have a model that relates certain features of the product with the number of sales that will occur in the future. In this paper we describe a comparative analysis of machine learning techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). This method is a new type of machine learning that have proved to adapt very well to a wide spectrum of problems of regression and classification. Thus, we use artificial hydrocarbon networks for predicting the number of online sales, and then we compare their performance with other ten well-known methods of machine learning regression, obtaining promising results.

View
No Thumbnail Available
Publication

A 2020 perspective on “A novel methodology for optimizing display advertising campaigns using genetic algorithms”

2020 , Miralles-Pechuán, Luis , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Online advertising has become the most important area of publicity. From a post-2020 perspective, we identify three trends in online advertising comprising: the rapid evolution of online advertising mainly over mobile networks, how to cope with big companies leading digital marketing, and the exploration of new methods to handle the dynamics of the e-commerce ecosystem. We proposed a new methodology for online advertising in small ad networks using supervised machine learning and metaheuristic methods. Our research will be beneficial for addressing the above-mentioned trends in online advertising focusing on small ad networks. It contributes to the establishment of an information system technology and practice within the scope of the development of marketing business strategies in e-commerce. Currently, we are exploring how to improve the flexibility of our approach to make it easier to adapt to new ad campaigns, analyzing and comparing different computational methods, and how to increase the performance of presenting custom ads to users when dealing with small data sets. Online advertising in small ad networks will be very useful in the following years. Hence, there are still many challenges to be dealt with in order to implement it in the business strategies of the new digital marketing. © 2020 Elsevier B.V.

View
No Thumbnail Available
Publication

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

2016 , Ponce, Hiram , Miralles-Pechuán, Luis , Martinez-Villaseñor, Lourdes

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

View
No Thumbnail Available
Publication

Human Activity Recognition on Mobile Devices Using Artificial Hydrocarbon Networks

2018 , Ponce, Hiram , Miralles-Pechuán, Luis , González Mora, José Guillermo , Martinez-Villaseñor, Lourdes

Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. However, versatility of users, location of smartphones, battery, processing and storage limitations, among other issues have been identified. In that sense, this paper presents a human activity recognition system based on artificial hydrocarbon networks. This technique have been proved to be very effective on HAR systems using wearable sensors, so the present work proposes to use this learning method with the information provided by the in-sensors of mobile devices. Preliminary results proved that artificial hydrocarbon networks might be used as an alternative for human activity recognition on mobile devices. In addition, a real dataset created for this work has been published. © Springer Nature Switzerland AG 2018.

View
No Thumbnail Available
Publication

An Explainable Tool to Support Age-related Macular Degeneration Diagnosis

2022 , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis , Ponce, Hiram , Martínez Velasco, Antonieta Teodora

Artificial intelligence and deep learning, in particu-lar, have gained large attention in the ophthalmology community due to the possibility of processing large amounts of data and dig-itized ocular images. Intelligent systems are developed to support the diagnosis and treatment of a number of ophthalmic diseases such as age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity. Hence, explainability is necessary to gain trust and therefore the adoption of these critical decision support systems. Visual explanations have been proposed for AMD diagnosis only when optical coherence tomography (OCT) images are used, but interpretability using other inputs (i.e. data point-based features) for AMD diagnosis is rather limited. In this paper, we propose a practical tool to support AMD diagnosis based on Artificial Hydrocarbon Networks (AHN) with different kinds of input data such as demographic characteristics, features known as risk factors for AMD, and genetic variants obtained from DNA genotyping. The proposed explainer, namely eXplainable Artificial Hydrocarbon Networks (XAHN) is able to get global and local interpretations of the AHN model. An explainability assessment of the XAHN explainer was applied to clinicians for getting feedback from the tool. We consider the XAHN explainer tool will be beneficial to support expert clinicians in AMD diagnosis, especially where input data are not visual. © 2022 IEEE.

View
No Thumbnail Available
Publication

A novel methodology for optimizing display advertising campaigns using genetic algorithms

2018 , Miralles-Pechuán, Luis , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Online advertising campaigns have attracted the attention of many advertisers willing to promote their business on the Internet. One of the main problems faced by advertisers, especially by those who have little experience in Internet advertising, is configuring their campaigns in an efficient way. To configure a campaign properly it is required to select the appropriate target, so it is guaranteed a high acceptance of users to adverts. It is also required that the number of visits that satisfy the configuration requirements is high enough to cover the advertisers’ campaigns. Thus, this paper presents a novel methodology for optimizing the micro-targeting technique in direct response display advertising campaigns by using genetic algorithms as the basis optimization model and a machine-learning based click-through rate (CTR) model. We implement our methodology to optimize display advertising campaigns on mobile devices using a real dataset. Results show that our methodology is feasible to optimize the campaigns by selecting the set of the best features required. Also, customization of the advertising campaign selecting some features by an advertiser, e.g. applying micro-targeting, can be optimized efficiently. © 2017 Elsevier B.V.

View
No Thumbnail Available
Publication

A Methodology Based on Deep Q-Learning/Genetic Algorithms for Optimizing COVID-19 Pandemic Government Actions

2020 , Miralles-Pechuán, Luis , Jiménez, Fernando , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Whenever countries are threatened by a pandemic, as is the case with the COVID-19 virus, governments need help to take the right actions to safeguard public health as well as to mitigate the negative effects on the economy. A restrictive approach can seriously damage the economy. Conversely, a relaxed one may put at risk a high percentage of the population. Other investigations in this area are focused on modelling the spread of the virus or estimating the impact of the different measures on its propagation. However, in this paper, we propose a new methodology for helping governments in planning the phases to combat the pandemic based on their priorities. To this end, we implement the SEIR epidemiological model to represent the evolution of the COVID-19 virus on the population. To optimize the best sequences of actions governments can take, we propose a methodology with two approaches, one based on Deep Q-Learning and another one based on Genetic Algorithms. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system focused on meeting two objectives: firstly, getting few people infected so that hospitals are not overwhelmed, and secondly, avoiding taking drastic measures which could cause serious damage to the economy. The conducted experiments evaluate our methodology based on the accumulated rewards during the established period. The experiments also prove that it is a valid tool for governments to reduce the negative effects of a pandemic by optimizing the planning of the phases. According to our results, the approach based on Deep Q-Learning outperforms the one based on Genetic Algorithms. © 2020 ACM.

View
No Thumbnail Available
Publication

A Survey on Freezing of Gait Detection and Prediction in Parkinson’s Disease

2020 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Miralles-Pechuán, Luis

Most of Parkinson’s disease (PD) patients present a set of motor and non-motor symptoms and behaviors that vary during the day and from day-to-day. In particular, freezing of gait (FOG) impairs their quality of life and increases the risk of falling. Smart technology like mobile communication and wearable sensors can be used for detection and prediction of FOG, increasing the understanding of the complex PD. There are surveys reviewing works on Parkinson and/or technologies used to manage this disease. In this review, we summarize and analyze works addressing FOG detection and prediction based on wearable sensors, vision and other devices. We aim to identify trends, challenges and opportunities in the development of FOG detection and prediction systems. © 2020, Springer Nature Switzerland AG.

View
No Thumbnail Available
Publication

Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition

2015 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis

In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In context-aware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.

View
No Thumbnail Available
Publication

Feature Selection Methods Evaluation for CTR Estimation

2016 , Miralles-Pechuán, Luis , Ponce, Hiram , Martinez-Villaseñor, Lourdes

The most widespread payment model in online advertising is Cost-per-click (CPC). In this model the advertisers pay each time that a user generates a click. In order to enhance the income of CPC Advertising Networks, it is necessary to give priority to the most profitable adverts. The most important factor in the profitability of an advert is Click-through-rate (CTR), which is the probability that a user generates a click in a given advert. In this paper we find which feature selection method between PCA, RFE, Gain ratio and NSGA-II is better suited, or if otherwise, the machine learning classification methods work best without any feature selection method. ©2016 IEE

View