Integration of Emerging Technologies in Teaching And Learning Process in Nigeria: the challenges

The evolution of Emerging Technologies (ETs) is changing all facets of educational process ranging from; the nature of classrooms, quality of content, methodologies, mode of students’ engagement, and evaluation. The integration of emerging technologies in teaching and learning process increase the interest of learners, and the quality of outcome in educational process. It brings about innovations, creativity, and flexibility to learning, thereby equipping both the educators and the learners with necessary problem solving and survival skills in a digital world. However, despite the enormous benefits of emerging technologies, its integration in teaching and learning process is often hampered by number of factors which directly or indirectly affects the integration process. The study examines the various challenges that obstruct the integration of emerging technologies in teaching and learning process in Nigeria. Data were collected through structured questionnaires, in addition to secondary data generated for review of literature. A total of two hundred (200) questionnaires were administered to respondents that consist of educators and students selected from both public and private secondary schools and tertiary institutions with similar level of infrastructures in Southwestern Nigeria. The collected data were later analyzed using descriptive statistics. The results show that majority of the respondents agrees that the integration of emerging technologies in teaching and learning process brings inspiration and modernization to education, enhance inclusiveness, and promotes the achievement of teaching and learning objectives. In addition, the findings proved that the integration of ETs in teaching learning process are often constrained by number of challenges which includes: epileptic power supply, insufficient skills, availability and accessibility issues, funding, inadequate professional development, and poor internet connectivity. The study concluded that educators at all levels of education should continue to update their knowledge and skills on how best to integrate emerging technologies in the teaching and learning proces

Impact of Coronavirus Pandemic on Education

Coronavirus Disease (COVID-19) outbreak poses serious concerns to global education systems. Efforts to contain COVID-19 prompted unscheduled closure of schools in more than 100 countries worldwide. COVID-19 school closures left over one billion learners out of school. The study investigates the impact of COVID-19 on education.  Data were collected through structured questionnaires administered to 200 respondents that consist of teachers, students, parents, and policy makers selected from different countries. The collected data were analyzed using STATA/Regression. The results show that COVID-19 has adverse effects on education including, learning disruptions, and decreased access to education and research facilities, Job losses and increased student debts. The findings also show that many educators and students relied on technology to ensure continued learning online during the Coronavirus pandemic. However, online education was hindered by poor infrastructures including, network, power, inaccessibility and unavailability issues and poor digital skills. The study underscores the damaging effects of COVID-19 on education sector and the need for all educational institutions, educators, and learners to adopt technology, and improve their digital skills in line with the emerging global trends and realities in education. Keywords: Coronavirus, Education, School closure, Technology, Virtual learning, Covidiot. DOI: 10.7176/JEP/11-13-12 Publication date:May 31st 202

Human emotions recognition, analysis and transformation by bioenergy field in smart grid using image processing

The passage of electric signals throughout the human body produces an electromagnetic field, known as the human-biofield, carries information about a person's psychological health. The human biofield can be rehabilitated by using healing techniques like sound therapy, and many others in smart grid. However, psychiatrists, and psychologists often face difficulties in clarifying the mental state of a patient in a quantifiable form. Therefore, the objective of this research work was to transform human emotions using sound healing therapy and produce visible results as a novel. The present research is based on the amalgamation of image processing and machine learning techniques, including a real-time aura-visualization-interpretation and an emotion-detection classifier. The experimental results highlight the effectiveness of healing emotions through the aforementioned techniques. The accuracy of the proposed method, specifically the module combining both emotion and aura, was determined to be ~88%. Additionally, the participants’ feedbacks were recorded and analyzed based on prediction and overall satisfaction. The participants were strongly satisfied with the prediction level (~81%) and future recommendation level (~84%). The results indicate the positive impact of sound therapy on emotions and the biofield. In future, experimentation using different therapies, and integrating more advanced techniques are anticipated to open a new gateways in healthcare

Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition

Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. This study presents Kubernetes Container Scheduling Strategy (KCSS) based on Artificial Intelligence (AI) that can assist in decision making to control the scheduling and shifting of load to nodes. The aim is to improve the container’s schedule requested digitally from users to enhance the efficiency in scheduling and reduce cost. The constraints associated with the existing container scheduling techniques which often assign a node to every new container based on a personal criterion by relying on individual terms has been greatly improved by the new system presented in this study. The KCSS presented in this study provides multicriteria node selection based on artificial intelligence in terms of decision making systems thereby giving the scheduler a broad picture of the cloud's condition and the user's requirements. AI Scheduler allows users to easily make use of fractional Graphics Processing Units (GPUs), integer GPUs, and multiple-nodes of GPUs, for distributed training on Kubernetes. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden

Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications

Remote monitoring is the process that monitors and observes information from a distance utilizing sensors or electronic types of equipment. Remote monitoring is used in real-time applications like traffic, forest, military, shops, and hospitals to determine abnormal activities. Earlier research has done video processing methods based on computer vision techniques, but the computational complexity regarding time and memory is high. This paper designs and implements a novel Slow-Fast Convolution Neural Network (SF–CNN) to identify, detect, and classify abnormal behaviours from a surveillance video. The proposed CNN architecture learns the video frames automatically, obtains the most appropriate properties about various objects' behaviour from a large set of videos. The learning process of SF-CNN is carried out in two ways, such as slow learning and fast learning. The slow learning process is enabled when the frame rate is less, and the rapid learning process is enabled when the frame rate is high. Both the learning processes learn spatial and temporal information from the input video. Different objects, such as humans, vehicles, and animals, are detected and recognized according to their actions. All the videos have normal and abnormal activities that vary in various contexts. The proposed SF-CNN architecture provides an end-to-end solution to dealing with multiple constraints abnormal movements. The experiment is carried out on several benchmark datasets, and the performance of the SF-CNN architecture is evaluated. The proposed approach obtained 99.6% of accuracy, which is higher than the other existing techniques

Management and prediction of navigation of industrial robots based on neural network

In the past, a robotic arm needed to be taught to carry out certain tasks, such as selecting a single object type from a fixed location and orientation. Neural networks have autonomous abilities that are being deployed to aid the development of robots and also improve their navigation accuracy. Maximizing the potentials of neural network as shown in this study enhances the positioning and movement targets of industrial robots. The study adopted an architecture called XBNet (Extremely Boosted Neural Network) trained using a unique optimization approach (Boosted Gradient Descent for Tabular Data (BGDTD) that improves both its interpretability and performance. Based on the analysis of the simulations, the result demonstrates accuracy and precision. The study would contribute significantly to the advancement of robotics and its efficiency

Content Coverage and Readability of Science Textbooks in Use in Nigerian Secondary Schools

This study evaluated the content coverage and readability of science textbooks in use in Nigerian secondary schools. The study utilized an evaluation design. The study was limited to the core sciences studied at both the junior and senior secondary schools. They are Physics, Chemistry, Biology and Basic Science.  A total of one thousand eight hundred and forty-eight research subjects comprising one thousand eight hundred students and forty-eight science teachers were used for the study. Two research questions and one null hypothesis guided the study. The research questions were answered using the quantitative model for textbook evaluation developed by Emerole (2008) while the hypothesis was tested at 0.05 level of significance using Chi-square test of goodness of fit. The finding revealed that all the science textbooks evaluated covered the contents of the core curriculum. The result of data analysis revealed that three out of four evaluated textbooks in biology are readable.  Modern Biology for Senior Secondary Schools had readability mean score of 38.41%, Essential Biology for Senior Secondary Schools had 72.4%, College Biology for Senior Secondary Schools had 66.29% while Comprehensive Biology for Senior Secondary Schools had readability index of 60.1%. The data on readability for physics, chemistry and basic science indicate that they are readable. Based on the findings the researchers made specific recommendations with respect to the textbooks recommended for use in the four subject areas in Nigerian secondary schools. This will provide a template and guide for ministry of education in recommendation of science textbooks for secondary schools. In addition, it will serve as a basis and guide for review of science textbooks in use in both junior and senior secondary schools. Keywords: content coverage, readability, science textbooks, secondary schools DOI: 10.7176/JEP/13-7-06 Publication date:March 31st 202

Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications

Remote monitoring is the process that monitors and observes information from a distance utilizing sensors or electronic types of equipment. Remote monitoring is used in real-time applications like traffic, forest, military, shops, and hospitals to determine abnormal activities. Earlier research has done video processing methods based on computer vision techniques, but the computational complexity regarding time and memory is high. This paper designs and implements a novel Slow-Fast Convolution Neural Network (SF–CNN) to identify, detect, and classify abnormal behaviours from a surveillance video. The proposed CNN architecture learns the video frames automatically, obtains the most appropriate properties about various objects' behaviour from a large set of videos. The learning process of SF-CNN is carried out in two ways, such as slow learning and fast learning. The slow learning process is enabled when the frame rate is less, and the rapid learning process is enabled when the frame rate is high. Both the learning processes learn spatial and temporal information from the input video. Different objects, such as humans, vehicles, and animals, are detected and recognized according to their actions. All the videos have normal and abnormal activities that vary in various contexts. The proposed SF-CNN architecture provides an end-to-end solution to dealing with multiple constraints abnormal movements. The experiment is carried out on several benchmark datasets, and the performance of the SF-CNN architecture is evaluated. The proposed approach obtained 99.6% of accuracy, which is higher than the other existing techniques

Management and prediction of navigation of industrial robots based on neural network

In the past, a robotic arm needed to be taught to carry out certain tasks, such as selecting a single object type from a fixed location and orientation. Neural networks have autonomous abilities that are being deployed to aid the development of robots and also improve their navigation accuracy. Maximizing the potentials of neural network as shown in this study enhances the positioning and movement targets of industrial robots. The study adopted an architecture called XBNet (Extremely Boosted Neural Network) trained using a unique optimization approach (Boosted Gradient Descent for Tabular Data (BGDTD) that improves both its interpretability and performance. Based on the analysis of the simulations, the result demonstrates accuracy and precision. The study would contribute significantly to the advancement of robotics and its efficiency

Human emotions recognition, analysis and transformation by bioenergy field in smart grid using image processing

The passage of electric signals throughout the human body produces an electromagnetic field, known as the human-biofield, carries information about a person's psychological health. The human biofield can be rehabilitated by using healing techniques like sound therapy, and many others in smart grid. However, psychiatrists, and psychologists often face difficulties in clarifying the mental state of a patient in a quantifiable form. Therefore, the objective of this research work was to transform human emotions using sound healing therapy and produce visible results as a novel. The present research is based on the amalgamation of image processing and machine learning techniques, including a real-time aura-visualization-interpretation and an emotion-detection classifier. The experimental results highlight the effectiveness of healing emotions through the aforementioned techniques. The accuracy of the proposed method, specifically the module combining both emotion and aura, was determined to be ~88%. Additionally, the participants’ feedbacks were recorded and analyzed based on prediction and overall satisfaction. The participants were strongly satisfied with the prediction level (~81%) and future recommendation level (~84%). The results indicate the positive impact of sound therapy on emotions and the biofield. In future, experimentation using different therapies, and integrating more advanced techniques are anticipated to open a new gateways in healthcare