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Item type:Publication, Depression Detection Using Audio-Visual Data and Artificial Intelligence: A Systematic Mapping Study(Springer, 2020-09-30)Major depression disorder is a mental issue that has been increasing in the last decade, in consequence, prediction or detection of this mental disorder in early stages is necessary. Artificial intelligence techniques have been developed in order to ease the diagnosis of different illnesses, including depression, using audio-visual information such as voice or video recordings and medical images. This research field is growing, and some organizations and descriptions are required. In the present work, a systematic mapping study was conducted in order to summarize the factors involved in depression detection such as artificial intelligence techniques, source of information, and depression scales. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Assessment of supervised classifiers for the task of detecting messages with suicidal ideation(Elsevier, 2020-08-01)According to the World Health Organization (WHO) close to 800,000 people worldwide die by suicide each year, and many more attempts to do it. In consequence, the WHO recognizes suicide as a global public health priority, which affects not only rich countries but poor and middle-income countries as well. This study makes a systematic analysis of 28 supervised classifiers using different features of the corpus Life to detect messages with suicidal ideation and depression to know if these can be used in an automatic prevention online system. The Life Corpus, used in this research, is a bilingual text corpus (English and Spanish) oriented to the detection of suicide ideation. This corpus was constructed retrieving texts from several social networks and its quality was measured using mutual annotation agreement. The different experiments determined that the classifier with the best performance was KStar, with the corpus features POS-SYNSETS-NUM, achieving the best results with the ROC Area metrics of 0,81036 and F-measure of 0,7148. The present research fulfilled the objective of discovering which supervised classifiers and which features are the most suitable for the automatic classification of messages with suicidal ideation using the Life Corpus. Also, given the imbalance of the results, a new precision measure was developed called the Two-dimensional Accuracy and Recovery Index (GDP), which can provide better results, in unbalanced systems, than the usual measures to assess the quality of the results (measure F, Area ROC), and thus increase the number of messages at risk of suicidal ideation, detected at the cost of receiving more messages that are not related to suicide or vice versa. Computer Science; Suicidal ideation; supervised classifiers; Machine Learning; Social networks; automatic classification; suicid10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms(Wiley, 2021-12-11)With the substantial industrial growth, the industrial internet of things (IIoT) and many IoT avenues have emerged. However, the existing industrial architectures are still inefficient to deal with advanced security issues due to the distributed and distensible nature of the network IIoT communication networks. Therefore, solutions for improving intelligent decision‐making actions to the IIoT are sorely necessary. Thus, in this paper, the main cybersecurity attacks are predicted by applying a deep learning model. The various security and integrity features such as the DoS, malevolent operation, data type probing, spying, scanning, intrusion detection, brute force, web attacks, and wrong setup is analysed and detected by a novel sparse evolutionary training (SET) based prediction model. To scrutinize the conduct of the proposed SET‐based prediction model, evaluation parameters, such as, precision, accuracy, recall, and F1 score are measured and compared to other state‐of‐the‐art algorithms, in which the proposed SET‐based model achieved an average accuracy of 0.99% for an average testing time of 2.29 ms. Results reveal that the proposed model improved the attack detection accuracy by an average of 6.25% when compared with the other state‐of‐the‐art machine learning models in a real scenario of IoT security in Industry 4.0. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An air quality monitoring and forecasting system for Lima city with low-cost sensors and artificial intelligence models(Frontiers Media S.A., 2022-07-07)Monitoring air quality is very important in urban areas to alert the citizens about the risks posed by the air they breathe. However, implementing conventional monitoring networks may be unfeasible in developing countries due to its high costs. In addition, it is important for the citizen to have current and future air information in the place where he is, to avoid overexposure. In the present work, we describe a low-cost solution deployed in Lima city that is composed of low-cost IoT stations, Artificial Intelligence models, and a web application that can deliver predicted air quality information in a graphical way (pollution maps). In a series of experiments, we assessed the quality of the temporal and spatial prediction. The error levels were satisfactory when compared to reference methods. Our proposal is a cost-effective solution that can help identify high-risk areas of exposure to airborne pollutants and can be replicated in places where there are no resources to implement reference networks. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fault detection and isolation for UAVs using neural ordinary differential equations(Elsevier B.V., 2022-01-01)In recent years, the increasing complexity and diversity of data-based fault detection and isolation (FDI) methods usually require high computational efforts in the pre-processing stage, large amounts of data, and, most of the time, some feature extraction to obtain relevant information for the data-based algorithms. This paper proposes using the Neural Ordinary Differential Equations (NODE) framework to represent the dynamics of the studied plant and later employ such representation in FDI system design. Such an approach enables loss optimization to be performed jointly in the plant dynamics and external inputs without previous use of complex pre-processing and is useful for working with nonlinear systems. The approach is first validated using a simulated Unmanned Aerial Vehicle (UAV) and later applied to a data-set that contains actuators and sensors faults. Ultimately, the proposed approach is compared with other usual machine learning techniques, showing better performance metrics. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Implementation of machine learning in health management to improve the process of medical appointmentsin Perú(IJETAE Publication House, 2022-02-01)The Peruvian health system has presented various deficiencies, one in particular is the difficulty and time for a user to schedule a medical appointment, in various health centers nationwide it is a problem for many citizens to easily access the health service. This research proposes developing a mobile app that allows automating this process by streamlining the procedures that lead to good health management to optimize both financial and human resources for better performance, quality and user experience that is insured at the service of ESSALUD. The results show that digital transformation and modernization with the support of technology significantly improve health management and therefore the medical appointment process, as well as other aspects that reduce the time of care and guarantee a reduction in the administrative work of the personnel of health allowing them to spend more time in patient care. Keywords—artificial intelligence; health; health management; machine learning; medical appointment - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Use of machine learning to predict the occurrence of deaths in the departments most affected by COVID-19 in Peru(Seventh Sense Research Group, 2022-03-01)This article shows the use of machine learning to predict the occurrence of deaths in the areas most affected by covid-19 in Peru, where the records of deaths during the pandemic are found reflecting the damage caused by this pandemic, according to a MINSA report in a standard format for analysis that contains all the detailed information of each person. The machine learning procedure is a method of data analysis that automates the construction of analytical models in which we will apply the decision tree where we will use the Python programming language to make the predictions of the deaths caused by covid-19 in the departments, and it will also help us to train the model for greater accuracy in obtaining expected results. In such a way, it can elaborate scenario predictions or initiate operations that are the solution for a specific task. As a case study, it was carried out in the 25 departments of Peru to analyze the departments with the highest mortality rates in our country. As a result of the study were that the departments of Lima, Piura, Huánuco, Ica have the highest rate of deaths by covid-19; this may be due to the biosecurity measures and social distancing; it is worth mentioning that to date they are the departments that have had more policy interventions in recent years. The results of this study may help the authorities to create prevention and sanitary control strategies by implementing rigorous measures in Peru. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analyze the spread of coronavirus in the world to predict new cases under machine learning techniques(Seventh Sense Research Group, 2022-07-01)Coronavirus is a worldwide pandemic disease. At the same time, it is making unexpected changes in different countries of the World, with new variants in each region due to their autonomous climates. That is why the coronavirus is mutating, and there is a massive contagion that causes death. Consequently, it is necessary to analyze and identify where the new cases occurred and where is the possible area of attack of the new variant of covid 19. It is also necessary to know the characteristics and the stage of infection of patients with covid 19. This research method is based on a branch of artificial intelligence, machine learning; the idea is to use artificial intelligence techniques to analyze and predict new coronavirus cases, using classification models, decision trees, and the Bernoulli model. The case study was used to input a real-time database with a systematic record of covid-19 from 2020 to the present. Accordingly, the data and properties for implementing the model and training were defined to make the corresponding predictions of new cases of covid 19. Finally, as a final result, predictions of the number of new cases and total deaths of covid 19 in the World were made. Finally, this research aims to analyze the data on the spread of Covid-19 in the World to predict new cases and help society prevent new variants of Covid 19 by using artificial intelligence to provide various related solutions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Survey of text mining techniques applied to judicial decisions prediction(MDPI, 2022-10-01)This paper reviews the most recent literature on experiments with different Machine Learning, Deep Learning and Natural Language Processing techniques applied to predict judicial and administrative decisions. Among the most outstanding findings, we have that the most used data mining techniques are Support Vector Machine (SVM), K Nearest Neighbours (K-NN) and Random Forest (RF), and in terms of the most used deep learning techniques, we found Long-Term Memory (LSTM) and transformers such as BERT. An important finding in the papers reviewed was that the use of machine learning techniques has prevailed over those of deep learning. Regarding the place of origin of the research carried out, we found that 64% of the works belong to studies carried out in English-speaking countries, 8% in Portuguese and 28% in other languages (such as German, Chinese, Turkish, Spanish, etc.). Very few works of this type have been carried out in Spanish-speaking countries. The classification criteria of the works have been based, on the one hand, on the identification of the classifiers used to predict situations (or events with legal interference) or judicial decisions and, on the other hand, on the application of classifiers to the phenomena regulated by the different branches of law: criminal, constitutional, human rights, administrative, intellectual property, family law, tax law and others. The corpus size analyzed in the reviewed works reached 100,000 documents in 2020. Finally, another important finding lies in the accuracy of these predictive techniques, reaching predictions of over 60% in different branches of law. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Big data analytics for critical information classification in online social networks using classifier chains(Springer, 2022-01-01)Industrial and academic organizations are using online social network (OSN) for different purposes, such as social and economic aspects. Now, OSN is a new mean of obtaining information from people about their preferences, and interests. Due to the large volume of user-generated content, researchers use various techniques, such as sentiment analysis or data mining to evaluate this information automatically. However, the sentiment analysis of OSN content is performed by different methods, but there are some problems to obtain highly reliable results, mainly because of the lack of user profile information, such as gender and age. In this work, a novel dataset is built, which contains the writing characteristics of 160,000 users of the Twitter OSN. Before creating classification models with Machine Learning (ML) techniques, feature transformation and feature selection methods are applied to determine the most relevant set of characteristics. To create the models, the Classifier Chain (CC) transformation technique and different machine learning algorithms are applied to the training set. Simulation results show that the Random Forest, XGBoost and Decision Tree algorithms obtain the best performance results. In the testing phase, these algorithms reached Hamming Loss values of 0.033, 0.033, and 0.034, respectively, and all of them reached the same F1 micro-average value equal to 0.976. Therefore, our proposal based on a multidimensional learning technique using CC transformation overcomes other similar proposals.
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