Examinando por Autor "Herrera Salazar, Jose Luis"
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Publicación Acceso abierto Cybersecurity in health sector: a systematic review of the literature(Institute of Advanced Engineering and Science, 2023-04-02) Peve Herrera, Catherine Vanessa; Mendoza Valcarcel, Jonathan Steve; Díaz, Mónica; Herrera Salazar, Jose Luis; Andrade-Arenas, LaberianoCurrently, health centers are being affected by various cyberattacks putting at risk the confidential information of their patients and the organization because they do not have a plan or tools to help them mitigate these cyberattacks, which is important to know what measures to take to protect the privacy of personal data. The present work was carried out under a systematic literature review, which aims to show the importance of cybersecurity in the health sector knowing which tools are the most used and efficient to prevent a cyberattack. A systematic review of 301 articles was carried out, 79 of which are aligned with the objective set, fulfilling the inclusion and exclusion criteria. The search for information was carried out in the Scopus and Dimensions databases. The analysis carried out has resulted in good information that was compiled for the development of this topic, being favorable thanks to the different research of different authors.Publicación Acceso abierto Mobile App Prototype for Course Management in Secondary Education(International Association of Online Engineering, 2023-06-07) Joo Aquije, Eric Valdez; Palacios Vergara, Greyh; Herrera Salazar, Jose Luis; Andrade-Arenas, LaberianoIn the last decade, mobile applications have shown an expansion to a great diversity of uses that can be made, which has influenced at different levels (economic, cultural, political), as in the area of learning where it has played a very important role in learning, since now with the development of technologies, children and adolescents are very interested in these new technologies and it is easier for them to use them, which also supports them in learning a great diversity of subjects of their own interest. That is why in this work a prototype of a mobile application for the management of mobile education using the Scrum methodology at the Norbert Wiener University of Lima-Peru, knowing that there are learning problems in students in the classroom, for this, want to provide a mobile application that supports the student to improve their learning. As a result, obtained from the study, was a prototype of a mobile application to improve learning in the Monte Carmelo private school in Lima-Peru, where a virtual model of the mobile application is shown, register the user, login where you can view their classes and share their class assignments, facilitating the user to enter their class and study material, this was achieved using balsamiq, the Scrum methodology with its four phases. The results of the survey conducted to the experts show that in most of the questions they gave a high scale, with the highest average of 4.91 with a standard deviation of 0.302 and only one low scale obtained of 1.45 on average with a standard deviation of 0.52.Publicación Acceso abierto “Search and classify topics in a corpus of text using the latent dirichlet allocation model“(Institute of Advanced Engineering and Science, 2022-11-18) Iparraguirre-Villanueva, Orlando; Sierra-Liñan, Fernando; Herrera Salazar, Jose Luis; Beltozar-Clemente, Saul; Pucuhuayla-Revatta, Félix; Zapata-Paulin, Joselyn; Cabanillas-Carbonell, Michael“This work aims at discovering topics in a text corpus and classifying the most relevant terms for each of the discovered topics. The process was performed in four steps: first, document extraction and data processing; second, labeling and training of the data; third, labeling of the unseen data; and fourth, evaluation of the model performance. For processing, a total of 10,322 ““curriculum““ documents related to data science were collected from the web during 2018-2022. The latent dirichlet allocation (LDA) model was used for the analysis and structure of the subjects. After processing, 12 themes were generated, which allowed ranking the most relevant terms to identify the skills of each of the candidates. This work concludes that candidates interested in data science must have skills in the following topics: first, they must be technical, they must have mastery of structured query language, mastery of programming languages such as R, Python, java, and data management, among other tools associated with the technology.“Publicación Acceso abierto “The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“(MDPI, 2023-01-31) Iparraguirre-Villanueva, Orlando; Alvarez-Risco, Aldo; Herrera Salazar, Jose Luis; Beltozar-Clemente, Saul; Zapata-Paulini, Joselyn; Yáñez, Jaime A.; Cabanillas-Carbonell, Michael“Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus. “Publicación Acceso abierto “The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“(MDPI, 2023-01-31) Iparraguirre-Villanueva, Orlando; Alvarez-Risco, Aldo; Herrera Salazar, Jose Luis; Beltozar-Clemente, Saul; Zapata-Paulini, Joselyn; Yáñez, Jaime A.; Cabanillas-Carbonell, Michael“Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus. “