Analysis of stress, attention, interest, and engagement in onsite and online higher education: A neurotechnological study

Este trabajo tiene como objetivo registrar y analizar, mediante el uso de neurotecnología, en un contexto formativo universitario presencial y online, el efecto que tiene en variables relevantes en el proceso de aprendizaje, lo cual supone una innovación en la literatura. En este estudio se ha empleado tecnología de neurociencia para medir el procesamiento cognitivo de los estímulos diseñados para una experiencia académica de una clase de máster universitario. Las neurotecnologías empleadas han sido la respuesta galvánica de la piel (GSR), la electroencefalografía (EEG) y el seguimiento ocular. Tras el análisis de los registros cerebrales, basados en la atención, interés, estrés y conexión emocional (engagement), en un contexto educativo presencial y su análisis comparativo con el seguimiento online, los resultados indicaron que los niveles de intensidad emocional de los alumnos que siguieron la clase de forma presencial son más elevados que aquellos que asistieron de forma online. A su vez, los valores de actividad cerebral positiva (atención, interés y engagement) son superiores en el grupo de asistencia presencial, siendo la variable negativa estrés también superior, pudiendo justificarse debido a que los alumnos conectados online no activaban la cámara. Los registros cerebrales de los alumnos que asisten a distancia muestran menor interés y atención, así como una menor intensidad emocional, por lo que el aprendizaje a distancia (online) es menos efectivo, a efectos de señales cerebrales, que la enseñanza en el aula, para una clase teórica de máster universitario.Alternate :The aim of this work is to register and analyse, using neurotechnology, in onsite onsite and online university educational context, the effect on relevant variables in the learning process. This represents an innovation in the current academic literature in this field. In this study, neuroscience technology has been used to measure the cognitive processing of stimuli designed for an academic experience in a university master's degree class. The neurotechnologies employed were galvanic skin response (GSR), electroencephalography (EEG) and eye tracking. After the analysis of the brain recordings, based on attention, interest, stress and engagement in an onsite educational context and their comparative analysis with the online monitoring, the results indicated that the levels of emotional intensity of the students who followed the class in person were higher than those who attended online. At the same time, the values of positive brain activity (attention, interest and engagement) were higher in the onsite group, and the negative variable stress was also higher, which could be explained by the fact that the online students did not activate the camera. The brain recordings of students who were distance learning show less interest and attention, as well as less emotional intensity, demonstrating that distance (online) learning is less effective than classroom learning, in terms of brain signals, for a theoretical university master's degree class.

Analysis of retail sector research evolution and trends during COVID-19.

The purpose of this study is to analysis the evolution of the retail sector during the COVID-19 period and to identify future research issues. Scopus databases were searched for articles published in English between 2020 and 2022 to discover current trends and concerns in the retail industry. A total of 1071 empirical and nonempirical studies were compiled as a result of the evaluation process. During the study period, the number of articles published in scientific journals increased exponentially, indicating that the research topic is still in the developmental phase. It also highlights the most important research trends, allowing numerous new research lines to be proposed via visual mapping of Thematic Maps. This study makes an important contribution to the field of the retail sector, providing a comprehensive overview of the field's evolution and current status, as well as a comprehensive, synthesized, and organized summary of the various perspectives, definitions, and trends in the field.

A comprehensive survey on DDoS attacks on various intelligent systems and it's defense techniques

The purpose of this study is to provide an overview of distributed denial of service (DDoS) attack detection in intelligent systems. In recent times, due to the endemic COVID-19, the use of intelligent systems has increased. However, these systems are easily affected by DDoS attacks. A DDoS attack is a reliable tool for cyber-attackers because there is no efficient method which can detect or filter it properly. In this context, we analyze different types of DDoS attacks and defense techniques for intelligent systems. For the analysis, we used Scopus databases to collect relevant papers in English between 2014 and 2022. This study makes an important contribution to the field of DDoS attack detection for intelligent systems, providing a comprehensive overview of the field's evolution and current status, as well as a comprehensive, synthesized, and organized summary of various perspectives, definitions, and trends in the field.

A novel approach for DDoS attacks detection in COVID-19 scenario for small entrepreneurs.

The current COVID-19 issue has altered the way of doing business. Now that most customers prefer to do business online, many companies are shifting their business models, which attracts cyber attackers to launch several kinds of cyberattacks against commercial companies simultaneously. The most common and lethal DDoS attack disables the victim's online resources. While large businesses can afford defensive measures against DDoS assaults, the situation is different for new entrepreneurs. Their lack of security resources restricts their ability to ward off DDoS attacks. Here, we aim to highlight the problems that prospective entrepreneurs should be aware of before joining the business, followed by a filtering mechanism that efficiently identifies DDoS assaults in the COVID-19 scenario, which is the subject of our research. The suggested approach employs statistical and machine learning techniques to discriminate between DDoS attack data and regular communication. Our suggested framework is cost-effective and identifies DDoS attack traffic with a 92.8% accuracy rate.

Efficient deep learning approach for augmented detection of Coronavirus disease.

The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.