Tópicos Sugeridos dentro de su búsqueda.
Tópicos Sugeridos dentro de su búsqueda.
1
artículo
Publicado 2023
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Machine learning is a branch of artificial intelligence that uses scientific computing, mathematics and statistics through automated techniques to solve problems based on classification, regression and clustering. Social demand refers to the need for service and product of the professional training process, expressed by interest groups, aimed at contributing to national development, as established by the quality assurance policy of university higher education and national licensing and accreditation models. In this context, this paper conducts research based on job positions of IT professionals posted n web portals, designs a machine learning process with an unsupervised approach, extracts occupational profiles, designs a multidimensional model, applies k-means clustering when determining clusters of job positions by similarity, and reports the results obtained.
2
artículo
Publicado 2023
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With the increase of the university population, the individual psychological care service by psychologists in universities has been affected. Which has caused discomfort among students to access the psychological consulting service. Therefore, this project aims to implement a data analysis system to control the psychological variables that affect university students, improving attention to them through the use of artificial intelligence (AI). We present a system that allows the visualization of data related to the mental health of the students who developed a psychological test, with which the psychologist will be able to diagnose the student's mental state and determine if he or she requires personalized attention. Finally, with this research, we achieved an improvement in the speed of attention and quality of service for the student.
3
artículo
Publicado 2024
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This study explores the literature that evaluates how artificial intelligence (AI) and machine learning (ML) can affect the optimization of sales processes, using a scientometric and bibliometric approach. Through keyword co-occurrence analysis in the scientific literature, the main trends and patterns in AI and ML research applied to sales were identified. VOSviewer software was used to map the relationships between key terms and visualize the predominant focus areas in the field. The results reveal that the adoption of AI and ML technologies is highly correlated with improvements in the efficiency of sales processes, highlighting the growing importance of these technologies in the development of business strategies. However, limited participation of researchers from developing countries was observed in this cutting-edge field, underscoring the need for greater inclusion and internation...
4
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 ...
5
artículo
Publicado 2022
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In this research satellite image classification for environmental change prediction using image processing and machine learning methods is used. As we know satellite images is one of the important sources of collecting information for all area and region of interest which is suitable for any difficult situation around the world. The satellite image helps in collecting information on areas which is unpredictable and unreachable through digital cameras. In this research work, an advanced study on environmental change perdition has been examined using three classes’ ice land area, cropland area, and forest area. This research help in characterizing the type of satellite image classification for the particular three classes. The following stages have been considered are preprocessing, segmentation, and classification methods using K- Nearest Neighbor classifier. The present investigation r...
6
artículo
Publicado 2023
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Currently, type 2 diabetes mellitus is one of the world's most prevalent diseases and has claimed millions of people's lives. The present research aims to know the impact of the use of machine learning in the diagnostic process of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of the dis-ease quickly and easily. Different machine learning models were designed and compared, being random forest was the algorithm that generated the model with the best performance (90.43% accuracy), which was integrated into a web platform, working with the PIMA dataset, which was validated by specialists from the Peruvian League for the Fight against Diabetes organization. The result was a decrease of (A) 88.28% in the information collection time, (B) 99.99% in the diagnosis time, (C) 44.42% in the diagnosis cost, and (D) 100% in the level of difficulty, concluding that the appl...
7
artículo
Publicado 2023
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Currently, type 2 diabetes mellitus is one of the world's most prevalent diseases and has claimed millions of people's lives. The present research aims to know the impact of the use of machine learning in the diagnostic process of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of the dis-ease quickly and easily. Different machine learning models were designed and compared, being random forest was the algorithm that generated the model with the best performance (90.43% accuracy), which was integrated into a web platform, working with the PIMA dataset, which was validated by specialists from the Peruvian League for the Fight against Diabetes organization. The result was a decrease of (A) 88.28% in the information collection time, (B) 99.99% in the diagnosis time, (C) 44.42% in the diagnosis cost, and (D) 100% in the level of difficulty, concluding that the appl...
8
tesis de grado
Publicado 2023
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In this work, it is necessary to analyze the increase of Back Order in the attention of crossdocking orders in the attention of Homecenter customers due to the lack of definition of purchase planning processes, resulting in logistics costs, fill rate charges and low service level. Thus, it is intended the companies that handle high volumes of inventory and constant orders should have a forecast plan to cover possible stock-outs. The main purpose of the research is to explain a way to prevent stock-outs using an artificial intelligence model, based on historical sales data of a medium-sized company that manages inventories, as well as to determine the machine earning model to predict and reduce backorders. For the data analysis, the Orange software was used, where the data was trained with different artificial intelligence models such as Decision Tree, Support Vector Machine, Random Fores...
9
artículo
Publicado 2023
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Identifying and classifying text extracted from social networks, following the traditional method, is very complex. In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work aims to identify, classify and analyze tweets related to real natural disasters through tweets with the hashtag #NaturalDisasters, using Machine learning (ML) algorithms, such as Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF). First, tweets related to natural disasters were identified, creating a dataset of 122k geolocated tweets for training. Secondly, the data-cleaning process was carried out by applying stemming and lemmatization techniques. Third, exploratory data analysis (EDA) was performed...
10
artículo
Publicado 2023
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A dissatisfied customer with a product and/or service is motivated to express a complaint. Classifying complaints manually is a process that represents high costs in human and material resources. Artificial Intelligence (AI) allows the use of various algorithms to perform tasks that can simulate human intelligence, a branch of this is Natural Language Processing (NLP), its objective is that machines have the capacity to understand human language, allowing, for example, to classify and categorize data automatically. This article provides a systematic review of the literature addressing challenges in the classification of complaint texts, such as the lack of class balance, the presence of unlabeled data, and the interpretation of model results. Preprocessing techniques are explored, such as tokenization, stopword removal, and lemmatization, which influence model performance. Additionally, ...
11
artículo
Publicado 2025
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The emergence of Machine Learning (ML) technologies and their integration into agriculture has demonstrated a significant impact on disease detection in crops, enabling continuous monitoring and enhancing risk planning and management. This study applied image processing techniques such as thresholding, gamma correction, and the Stretched Neighborhood Effect Color to Grayscale (SNECG) method, alongside ML, to develop a predictive model for identifying five types of rice diseases. The ML techniques used included Logistic Regression, Multilayer Perceptron, Support Vector Machines, Decision Trees, and Random Forests (RF). Hyperparameters were optimized and evaluated through 5-fold cross-validation. In the results, the SNECG method successfully converted images to grayscale, capturing essential features of lesions on rice leaves. The ML models developed with these techniques showed evaluation...
12
artículo
Publicado 2022
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so, machine learning techniques are being developed to improve performance and maintenance prediction. Increasing our knowledge of the relationship between humans and algorithms, Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. Numerous researchers recently developed numerous computer-aided diagnostic algorithms employing various supervised learning approaches. Early identification of sickness may help to reduce the number of people who die as a result of these illnesses. Using machine learning techniques, this research creates an efficient automated illness diagnostic algorithm. We chose three key disorders in this paper: coronavirus, cardiovascular diseases, and diabetes. The data are inputted into a mobile application in the suggested m...
13
tesis de grado
Publicado 2023
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The document presents the results of the evaluation of the classification process of milk samples through the modeling of machine learning techniques. The objective of this research was to discriminate the presence or absence of adulterants, which allowed obtaining adequate damages for human consumption. Also, speed up and specify the inspection process of said samples. The relevance of this study can be understood from the product under analysis: milk. This is for mass consumption, especially among children. Due to the above, it is considered relevant to efficiently demonstrate that quality products are provided to the population and this document is a contribution to the reliability of the integrity of dairy products.
14
objeto de conferencia
Today's business landscape is characterized by competition and dynamism, which has transformed human resource management into an essential strategic partner for organizations. Employee turnover poses risks that affect productivity and knowledge management. This study focuses on predicting employee turnover using machine learning (ML) models. For the training process, a dataset composed of 4410 records and 29 variables was used, in the process of training and evaluation of the ten models, the artificial intelligence (AI) method was followed. The findings showed that the XG Boost Classifier (XGBC) and Random Forest (RF) models achieved the best accuracy and performance rates, with 98.8% and 98.7%. Followed by Decision Tree Classifier (DT) with 97.6%, and the other models, such as Gradient Boosting Classifier (GBC), Ada boost Classifier (AC), Logistic Regression (LR), KN Classifier (K-NNC),...
15
tesis de grado
One of the main causes of low crop efficiency in Peru is poor management of water resources; that is why the main objective of this article is to estimate the amount of irrigation water required in quinoa crops through a comparison between the machine learning and Aquacrop models. For the development of this study, meteorological data from the province of Jauja and descriptive data of quinoa crops were processed and a simulation period was established from June to December. From the simulation carried out, it was determined that the best model to predict the required irrigation water is the Ada Boost model in which it was observed that the mean and standard deviation of the Ada Boost models (Mean = 19.681 and Std. Dev. = 4.665) behave similarly to AquaCrop (Mean = 19.838 and Std. Dev. = 5.04). In addition, the result of the analysis of variance (ANOVA) was that the AdaBoost model has the...
16
artículo
Overweight is a serious problem in Peru, and compliance with a healthy and balanced diet is essential for its management. Despite the existence of different types of diets and meal plans, people still find it difficult to comply with the treatment. Also, lack of access to adequate nutritional information and lack of follow-up in implementing a low-calorie diet can demotivate people and reduce the effectiveness of the process. To reduce this problem, a mobile application is presented that allows the control of the caloric intake of food, based on food suggestions using Machine Learning. For this purpose, the Support Vector Machine algorithm is applied to train a model that recommends personalized food to users according to their preferences. This is how the application allows users to easily access personalized food recommendations by analyzing their preferences and caloric needs individu...
17
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...
18
artículo
One of the main causes of having low crop efficiency in Peru is the poor management of water resources; which is why the main objective of this article is to estimate the amount of irrigation water required in quinoa crops through a comparison between the machine learning and AquaCrop models. For the development of this study, meteorological data from the province of Jauja and descriptive data of quinoa crops were processed and a simulation period was established from June to December 2020. From the simulation carried out, it was determined that the best model to predict the required irrigation water is the Adaptive Boosting (AdaBoost) model in which it was observed that the mean and standard deviation of the AdaBoost models (mean = 19.681 and SD = 4.665) behave similarly to AquaCrop (mean = 19.838 and SD = 5.04). In addition, the result of ANOVA was that the AdaBoost model has the best ...
19
artículo
The COVID-19 health crisis has led to unprecedented changes in consumer behavior, as consumers now purchase differently and use different means. Consumers are checking and judging products via electronic devices, shaping trends in consumer segments. This research study aimed to use the clustering model with Machine Learning resources in the analysis of clusters as a resource for consumer segmentation, a major component in business marketing management. A 6-question questionnaire was administered to 506 people ranging from 18 to 65 years old to gauge their opinions about going shopping. A dataset was organized using the data collected and processed using RapidMiner Studio 9.10 software. The optimal number of clusters and their components were obtained from the performance indicator provided by Machine Learning.
20
artículo
Publicado 2017
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Peruvian retail market today, more than ever, has turned the phrase “everything goes through the eyes” into a competitive tool. The design and optimization of space, as well as visual merchandising, are techniques that impact the sale new concepts such as omnicanality and buying experience are fed by data analytics in order to describe the commercial mode; and new qualitative sources of information, among them color theory, specially help to understand and predict the impact of future decisions on the point of sale. This paper describes the utility of image processing techniques to innovate the retail market in the effort to extract useful information from advertising pieces frequently used in this sector.
