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1
artículo
Publicado 2020
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ABSTRACT With the development of information and communication technologies, new opportunities and applications of many technologies are emerging that before could not be thought to be used, in this sense artificial intelligence is the technology that has gained greater strength, accompanied by the development of hardware that makes its execution possible and of software tools that make its implementation possible. The neural network is one of the most used techniques in the field of artificial intelligence. This work is based on analyzing possible cases of labor judicial problems, when workers who have suffered an abuse by employers are faced with. The success of the case according to the model presented, is based on being able to have the majority of documentation that evidences both the employment relationship, responsibilities of the employees, documents that support the payment of r...
2
artículo
Publicado 2018
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Different Machine Learning techniques have been used in order to identify the wishes of patients with neurodegenerative diseases. For this purpose, a database of electroencephalographic (EEG) signals was used, which were filtered and processed. The determination of the wills of patients was achieved through the identification of brain waves P300, these signals are presented in the brain in response to an unexpected stimulus and among its many applications is the implementation of the so-called Brain-Computer Interface .
3
artículo
Publicado 2018
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Different Machine Learning techniques have been used in order to identify the wishes of patients with neurodegenerative diseases. For this purpose, a database of electroencephalographic (EEG) signals was used, which were filtered and processed. The determination of the wills of patients was achieved through the identification of brain waves P300, these signals are presented in the brain in response to an unexpected stimulus and among its many applications is the implementation of the so-called Brain-Computer Interface .
4
artículo
Attention deficit hyperactivity disorder (ADHD) represents a medical condition characterized by the presence of inattention, hyperactivity, and impulsivity, which affects the academic development of students globally. In Peru, it affects a proportion of the pediatric population ranging from 2% to 12%, with a prevalence of 12.1% in South Lima, particularly in public schools. This research presents an online application with machine learning to improve the detection of ADHD in elementary school children. Several machine learning algorithms were reviewed and Random Forest was selected as the best-performing model with an accuracy of 96.08%. The model uses 27 selected variables, optimizing data collection and training. The child answers the questionnaire within the app and psychologists can access the app to visualize the results, aiding in the early detection of ADHD. The experiment involve...
5
objeto de conferencia
Publicado 2017
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Motor Imagery based BCIs (MI-BCIs) allow the control of devices and communication by imagining different mental tasks. Despite many years of research, BCIs are still not the most accurate systems to control applications, due to two main factors: signal processing with classification, and users. It is admitted that BCI control involves certain characteristics and abilities in its users for optimal results. In this study, spatial abilities are evaluated in relation to MI-BCI control regarding flexion and extension mental tasks. Results show considerable correlation (r=0.49) between block design test (visual motor execution and spatial visualization) and extension-rest tasks. Additionally, rotation test (mental rotation task) presents significant correlation (r=0.56) to flexion-rest tasks.
6
artículo
Publicado 2020
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Commonly the searching and identification of new particles, requires to reach highest efficiencies and purities as well. It demands to apply a chain of cuts that reject the background substantially. In most cases the processes to extract signal from the background is carried out by hand with some assistance of well designed and intelligent codes that save time and resources in high energy physics experiments. In this paper we present one application of the Mitchell’s criteria to extract efficiently beyond Standard Model signal events yielding an error of order of 1.22%. The usage of Machine Learning schemes appears to be advantageous when large volumes of data need to be scrutinized.
7
tesis de grado
Publicado 2021
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ABSTRACT In this paper a nonlinear mathematical model based at convolution theory and translated in terms of Machine Learning philosophy is presented. In essence, peaks functions are assumed as the pattern of rate of infections at large cities. In this manner, once the free parameters of theses patterns are identified then one proceeds to engage to the well-known Mitchell's criteria in order to construct the algorithm that would yield the best estimates as to carry out social intervention as well as to predict dates about the main characteristics of infection's distributions. The distributions are modeled by the Dirac-Delta function whose spike property is used to make the numerical convolutions. In this manner the parameters of Dirac-Delta function's argument are interpreted as the model parameters that determine the dates of social regulation such as quarantine as well as the possible ...
8
artículo
Publicado 2020
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In this paper, a surveillance system expected to run in the prospective technology called Internet of Bio-Nano Things is presented. For this end the theory of Cognitive Radio as well as the Machine Learning criteria based on the hypothesis of Tom Mitchell are employed. In addition the Feynman's propagator model is also used. Essentially this paper focuses on the events where diabetes patients might have initialized a stroke event, so that the necessity to make the best decision is critic in order to guarantee a fast recover in the short term. Therefore this paper is focused on the following clinic variables: (i) cardiac pulse, (ii) blood pressure, (iii) glucose, and (iv) cholesterol. When all these variables are fully interconnected among them the full response might very encouraging in those cases where critic and non-critic patients might to anticipate unexpected events against their w...
9
artículo
Publicado 2024
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Making raw material purchase forecasts for companies is very difficult and, if inadequately controlled, can affect the company's decision making and profitability. Currently, there are optimized systems or mathematical models to try to predict the demands and solve this problem. In this study, a raw material purchase prediction model is proposed that uses the Elastic Net algorithm to analyze historical sales and inventory data. The model is used to improve prediction accuracy, allowing SMEs to optimize inventories, reduce costs and improve efficiency. Experimental results indicate that the proposed model obtains better results in the MAE, RMSE and R2 indicators.
10
artículo
Publicado 2023
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The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indi...
11
capítulo de libro
Publicado 2019
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This work proposes a semi-automated analysis and modeling package for Machine Learning related problems. The library goal is to reduce the steps involved in a traditional data science roadmap. To do so, Sparkmach takes advantage of Machine Learning techniques to build base models for both classification and regression problems. These models include exploratory data analysis, data preprocessing, feature engineering and modeling. The project has its basis in Pymach, a similar library that faces those steps for small and medium-sized datasets (about ten millions of rows and a few columns). Sparkmach central labor is to scale Pymach to overcome big datasets by using Apache Spark distributed computing, a distributed engine for large-scale data processing, that tackle several data science related problems in a cluster environment. Despite the software nature, Sparkmach can be of use for local ...
12
artículo
Publicado 2019
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Advanced Brain-Computer Interface (BCI) paradigms aim to solve some problems as BCI illiteracy and unfamiliarity of the subjects to be able to control their elicited motor imagery (MI) successfully, hence improving training time and performance of BCI systems. This work evaluates the effect and performance of an Implicit BCI supported by the Gaze Monitoring (IBCI-GM) paradigm for virtual rehabilitation therapy of patients suffering from partial or total paralysis of their upper limbs; this paradigm also was compared with alternative forms of advanced BCI methods such as Virtual Reality-based BCI (VR-BCI) with a head-mounted display (HMD) and a computer screen (CS). Eight subjects participated in the experiments; four subjects tested the VR-BCI with a CS, and the rest of them tested both BCI advanced methods (IBCI-GM and VR-BCI with an HMD). The subjects were asked to control a virtual ar...
13
objeto de conferencia
The diagnostic process of respiratory diseases requires experience and skills to assess the different pathologies that patients may develop. Unfortunately, the lack of qualified radiologists is a global problem that limits respiratory diseases diagnosis. Therefore, it will be useful to have a tool that minimizes errors and workload, improves efficiency, and speeds up the diagnostic process in order to provide a better healthcare service to the community. This research proposes a methodology to detect pathologies by using deep learning architectures. The present proposal is divided into three types of experiments. The first one evaluates the performance of feature descriptors such as SIFT, SURF, and ORB in medical images with machine learning models as an introduction to the last experiment. The second one evaluates the performance of deep learning architectures such as ResNet50, Alexnet,...
14
objeto de conferencia
Publicado 2019
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This work was supported by FONDECYT from CONCYTEC, Peru. Contract 112-2017 and project grant J004-2016.
15
artículo
Publicado 2024
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In the context of IT incident management, the prioritization and automation of tickets can be a challenge for companies that lack advanced technologies. However, these difficulties can be overcome today by applying machine learning algorithms and techniques that use historical data to train predictive models, which allows for more efficient and effective IT incident management. The article proposes the implementation of a predictive model that uses machine learning to prioritize IT incidents in these companies. The goal of this proposal is to allow small and medium-sized enterprises to prioritize their incidents automatically, using a model that has been previously trained with a supervised multi-label classification algorithm technique to achieve high accuracy. Experimental results show that the Mean Absolute Error (MAE) is 2.79 and a Mean Squared Error (MSE) of 8.21, using the metrics ...
16
tesis de grado
Publicado 2025
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En el presente trabajo de investigación se implementan sistemas de control re-alimentados para regular la velocidad y posición de un motor brushless DC, así como la temperatura generada por una resistencia calefactora. Las implementaciones emplean técnicas de Machine Learning, específicamente el Proceso Gaussiano y una Red Neuronal Anticipativa (RNA) con el fin de predecir la respuesta de las variables físicas del sistema. Posteriormente, se determinan los parámetros óptimos del controlador PID (Proportional Integral Derivative) mediante un enfoque basado en descenso de gradiente. Con fines comparativos, se desarrollan también sistemas de control empleando un PID convencional. Las simulaciones de los sistemas se realizan en MATLAB/Simulink y las implementaciones se llevan a cabo en la Maleta de Entrenamiento Siemens del laboratorio de Sistemas de Control de la Universidad de Ing...
17
artículo
Publicado 2020
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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
18
artículo
Publicado 2022
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In the last decades, the accumulation of municipal solid waste in urban areas has become a latent concern in our society due to its implications for the exposed population and the possible health and environmental issues it may cause. In this sense, this research study contributes to the timely identification of these sectors according to the anthropogenic characteristics of their residents as dictated by 10 social indicators (i.e., age, education, income, among others) sorted into three assessment categories (sociodemographic, sociocultural, and socioeconomic). Then, the data collected was processed and analyzed using two machine learning algorithms (random forest (RF) and logistic regression (LR)). The primary information that fed the machine learning model was collected through field visits and local/national reports. For this research, the Puente Piedra and Chaclacayo districts, both...
19
tesis de grado
Publicado 2023
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This paper presents alternatives for the implementation of a Market Place called “Clic”, considering the technological infrastructure necessary for its operation based on Machine Learning algorithms. During the elaboration of this paper, surveys were conducted to both users qualified as "Clients" and "Professionals" about the intentionality of use and the probability of payment for the publication of their services in the platform. This paper presents the recommended steps for the composition of the Clic company, the way the information is collected for Machine Learning and the data obtained from the users, the recommended environments for the development of the application, the results of the surveys carried out, the feasibility of the Machine Learning algorithm, numerical data of financial feasibility for the execution of the project.
20
tesis de grado
Publicado 2020
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Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, u...
