Since November 2019, the COVID-19 Pandemic produced by Severe Acute Respiratory Syndrome Severe Coronavirus 2 (hereafter COVID-19) has caused approximately seven million deaths globally. Several studies have been conducted using technological tools to prevent infection, to prevent spread, to detect, to vaccinate, and to treat patients with COVID-19. This work focuses on identifying and analyzing machine learning (ML) algorithms used for detection (prediction and diagnosis), monitoring (treatment, hospitalization), and control (vaccination, medical prescription) of COVID-19 and its variants. This study is based on PRISMA methodology and combined bibliometric analysis through VOSviewer with a sample of 925 articles between 2019 and 2022 derived in the prioritization of 32 papers for analysis. Finally, this paper discusses the study’s findings, which are directions for applying ML to address COVID-19 and its variants.
There are different tools to estimate the end to end available bandwidth (AB). These tools use techniques which send pairs of packets to the network and observe changes in dispersion or propagation delays to infer the value of the AB. Given the fractal nature of Internet traffic, these observations are prompt to errors affecting the accuracy of the estimation. This article presents the application of a clustering technique to reduce the estimation error due to wrong observations of the available bandwidth in the network. The clustering technique used is K-means which is applied to a tool called Traceband that is originally based on a Hidden Markov Model to perform the estimation. It is shown that using K-means in Traceband can improve its accuracy in 67.45% when the cross traffic is about 70% of the end-to-end capacity.
The estimation of the available bandwidth (AB) in an end-to-end manner can be used in several network applications to improve their performance. Several tools send pairs of packets from one end to the other and measure the packets' dispersion to infer the value of the AB. Given the fractal nature of Internet traffic, these measurements have significant errors that affect the accuracy of the estimation. This article presents the application of a clustering technique to reduce the estimation error of the available bandwidth in and end-to-end path. The clustering technique used is K-means which is applied to a tool called Traceband that is originally based on a Hidden Markov Model to perform the estimation. It is shown that using K-means in Traceband can improve its accuracy in 67.45% when the cross traffic is about 70% of the end-to-end capacity. keywords Available Bandwidth Estimation; Clustering; K-Means; Traceband.
Este articulo presenta el diseño y prototipado de un sistema inteligente, que sirve para la gestión automática de un generador eléctrico, basado en la arquitectura del IoT, a través del protocolo de comunicación MQTT.El prototipo, permite automatizar diversas funciones de un generador eléctrico ante la interrupción del fluido eléctrico; tales como: encendido y apagado de forma automática, teniendo en cuenta las condiciones eléctricas. También, controlar variables como el nivel de combustible, temperatura, horas de uso del equipo; que facilita cambios de aceite, y mantenimientos preventivos. Adicionalmente, cuenta con un gestor automático de carga de potencia, que evita que el generador inicie con una potencia máxima desde el arranque; logrando de esta forma ampliar el margen de vida útil de los circuitos electrónico de potencia. Finalmente, para controlar de manera remota las funciones mencionadas, se presenta una aplicación móvil para que el usuario final pueda monitorear en tiempo real el funcionamiento del generador, mediante la implementación del protocolo de comunicación de Message Queue Telemetry Transport (MQTT).
The current Available Bandwidth Estimation Tools (ABET's) to perform an estimation, using probes packets are inserted into the network. The utilization These packages, makes ABET's are intrusive and consumes part of which is measuring bandwidth to noise known as "Overhead Estimation Tools" (OET); it’s can produce negative effects on measurements performed by the ABET. This paper presents a complete and comparative analysis of behavior of Available Bandwidth (av_bw), of the ABET's most representative, as well as: Abing, Diettopp, Pathload, PathChirp, Traceband, IGI, PTR, Assolo and Wbest. The study with real Internet traffic, shows the percentage of test that is a factor packets affecting two main aspects of the estimation. The first, the accuracy, and increased indicating that EOT is directly proportional to the percentage of RE, reaching up to 70% in the tool evaluated with most of 30% of Cross-Traffic (CT). And second, the techniques used to send probes packets highly influences the Estimation Time (ET), where some tools that use slops spend up to 240s to converge when there is 60% CT in the network, ensuring that the estimate this technique av_bw highly congested channel, OET as much is used, resulting in inaccuracies in measurement.
Actualmente el acceso a Internet se ha convertido en un factor indispensable para el desarrollo de la humanidad. En consecuencia, organizaciones y personas acceden a diferentes servicios vía Internet, desde cualquier lugar y dispositivo. Adicionalmente, la tecnología inalámbrica (WiFi) se ha convertido en la más utilizada en los servicios de telecomunicaciones, por todas las ventajas que ofrece; respecto a movilidad, accesibilidad y disponibilidad constante a los usuarios. Sin embargo, hay varios riesgos informáticos asociados a las conexiones inalámbricas; y uno de los riesgos más importantes se origina por el desconocimiento de los niveles de seguridad en las redes inalámbricas donde se conectan ocasionalmente los usuarios; convirtiéndolos en vulnerables a atacantes que se aprovechan de la tecnología para acceder sin autorización a sus dispositivos, y modificar parámetros de configuración, robar contraseñas, información privada, entre otras acciones maliciosas. Por lo tanto, este trabajo presenta una metodología de pentesting para realizar pruebas de vulnerabilidad de dispositivos y sistemas informáticos utilizando técnicas de Ethical hacking. Esta metodología se implementó usando la herramienta llamada Metasploit Framework, que funciona sobre plataforma de hardware (Raspberry Pi) y software libre (Kali-Linux). Las pruebas ejecutadas en escenarios reales permitieron comprobar que se pueden desarrollar e implementar sistemas robustos utilizando plataformas de hardware y software abierto de bajo costo; que pueden ser utilizados en entornos productivos para evaluar la vulnerabilidad en aspectos de seguridad en dispositivos móviles y sistemas informáticos.
The estimation of the available bandwidth (av bw) between two end nodes through the Internet, is an area that has motivated researchers around the world in the last twenty years, to have faster and more accurate tools; Due to the utility it has in various network applications; Such as routing management, intrusion detection systems and the performance of transport protocols. Different tools use different estimation techniques but generally only analyze the three most used metrics as av bw, relative error and estimation time. This work expands the information regarding the evaluation literature of the current Available Bandwidth Estimation Tools (ABET’s), where they analyze the estimation techniques, metrics, different generation tools of cross-traf?c and evaluation testbed; Concentrating on the techniques and estimation methodologies used, as well as the challenges faced by open-source tools in high-performance networks of 10Gbps or higher.
The number of AI applications in education is growing every day. One recent AI application in the educational sector is Chatbot technology, which is used to support teaching and administrative tasks. This document presents the design and implementation of a Chatbot called Tashi-Bot that helps applicants and university students to obtain information from an educational institution about certain academic and administrative processes. Among these are processes related to well-being, tuition, costs, admission, and other services. In order to design the Chatbot, an analysis of the state of the art, methodologies, and suitable tools was carried out, and a survey was conducted to discover the needs of users and their preferences in the use of a Chatbot for this specific purpose. Tashi-Bot was implemented on the SnatchBot platform and later deployed on a Telegram channel. In its evaluation, a final survey was carried out to check on the satisfaction of the users. The results suggest that Tashi-Bot could help applicants and university students to find information on academic and administrative processes with great certainty and without the need for human interaction. Tashi-Bot can be found at: https://web.telegram.org/#/im?p=@TashiE_Bot..
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