Online-Journals.org (International Association of Online Engineering)
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Teacher’s Perceptions of STEM Education at the Primary Level in Morocco
Get PDFSTEM education is critical in developing students' interest in science and technology. Primary schools establish the groundwork by teaching fundamental technology ideas. Digital programs are favored over old paper-based approaches in response to current demands. The essential objective of this research is to show how information and communication technology (ICT) can improve the quality of teaching, learning, and motivation among primary school students. A questionnaire has been sent to 80 teachers at elementary schools, and a quantitative methodology was utilized to examine their thoughts. As an evaluation instrument, an eight-question quiz has been utilized to assess the usefulness and significance of ICT in promoting active and autonomous learning, resulting in creating a student-centered education approach. According to the research, the majority of teachers support the use of ICT as an of use pedagogical tool that fosters a proactive learning culture in young learners
A Systematic Review of the Intelligent Digital Storytelling Process in Disseminating Health Information
Get PDFDigital storytelling is a new concept in education that involves creating meaning. It is a tool with great potential, but in Thailand, it is currently not very popular in terms of sharing stories about disease. This review analyzes the components and processes of intelligent digital storytelling to aid the development of an intelligent digital storytelling platform for disseminating health information. Based on the synthesis of relevant documents, the research process involves 9 main steps: 1) identifying the review objectives, 2) reviewing research questions, 3) determining inclusion criteria, 4) finding relevant studies, 5) selecting documents, 6) data extraction, 7) arriving at a conclusion, 8) document synthesis and 9) discussion of the results. A study of articles from the PRISMA Checklist published between 2017 and 2022 revealed that ultimately only 47 articles met the inclusion criteria. From the analysis of the data, it was found that there are four main elements and 16 sub-components of intelligent digital storytelling. There are 12 steps in the process of intelligent digital storytelling with regard to health information dissemination. The optimal length of an intelligent digital narrative video clip relating to health information dissemination is approximately 2-5 minutes when it comes to achieving the best knowledge of health information
COV-CTX: A Deep Learning Approach to Detect COVID-19 from Lung CT and X-Ray Images
Get PDFWith the massive outbreak of coronavirus (COVID-19) disease, the demand for automatic and quick detection of COVID-19 has become a crucial challenge for scientists around the world. Many researchers are working on finding an automated and effective system for detecting COVID-19. They have found that computed tomography (CT-scan) and X-ray images of COVID-19 infected patients can provide more accurate and faster results. In this paper, an automated system is proposed named as COV-CTX which can detect COVID-19 from CT-scan and X-ray images. The system consists of three different CNN models: VGG16, VGG16- InceptionV3-ResNet50, and Francois CNN. The models are trained with CT-scan and X-ray images individually to classify COVID-19 and non-COVID patients. Finally, the results of the models are combined to develop a voting ensemble of classifiers to ensure more accurate and precise results. The three models are trained and validated with 9412 CT-scan images (4756 numbers of COVID positive and 4656 numbers of non-COVID images) and 3257 X-ray images (1647 numbers of COVID positive and 1610 numbers of non-COVID images). The proposed system, COV-CTX provides up to 96.37% accuracy, 96.71% precision, 96.02% F1-score, 97.24% sensitivity, 95.35% specificity, 92.68% Cohens Kappa score for CT-scan image based COVID-19 detection and 99.23% accuracy, 99.37% precision, 99.22% F1-score, 99.39% sensitivity, 99.07% specificity, 98.46% Cohens Kappa score for X-ray image based COVID-19 detection
Towards Design and Implementation of an EEG-based BCI TV Remote Control
Get PDFBCI is a rapidly growing field within biomedical engineering as it enables a direct connection between the central nervous system and an external device. BCI detects brain signals using biosensors (electrodes) installed on the head’s scalp or implanted inside the brain. EEG is a non-invasive method for detecting and monitoring the brain’s activity. Using EEG-based BCI in the medical field can significantly help disabled people to perform daily activities. In this context, it is very important to support and enable paralysed people to interact with multimedia devices like televisions by developing suitable solutions. This paper proposes an EEG mind-controlled TV remote control system prototype. The proposed prototype uses affordable hardware components to perform its task. A quantitative questionnaire has been conducted to identify the system’s functional and non-functional requirements. The system can send four different signals to power on/off, change the channel, raise and reduce the volume of the TV. The system has been tested by 20 subjects. The testing results show that the accuracy of the system is 74.9%. Despite the system being able to control only four TV functions, the system is scalable, and more commands can be added in the future. Also, using Raspberry Pi in the system gives a great possibility to eliminate the computer and to use Raspberry Pi directly with the headset. This paper demonstrates the approach’s feasibility and opens the route for enhancing the system and using EEG-based BCI with more and different devices
Edge-Fog-Cloud Data Analysis for eHealth-IoT
Get PDFThanks to advancements in artificial intelligence and the Internet of Things (IoT), eHealth is becoming an increasingly attractive area for researchers. However, different challenges arise when sensor-generated information is stored and analyzed using cloud computing. Latency, response time, and security are critical concerns that require attention. Fog and Edge Computing technologies have emerged in response to the requirement for resources near the network edge where data is collected, to minimize cloud challenges. This paper aims to assess the effectiveness of Machine Learning (ML) and Deep Learning (DL) techniques when executed in Edge or Fog nodes within the eHealth data. We compared the most efficient baseline techniques from the state-of-the-art on three eHealth datasets: Human Activity Recognition (HAR), University of Milano Bicocca Smartphone-based Human Activity Recognition (UniMiB SHAR), and MIT-BIH Arrhythmia. The experiment showed that for the HAR dataset, the Support Vector Machines (SVM) model was the best performer among the ML techniques, with low processing time and an accuracy of 96%. In comparison, the K-Nearest Neighbors (KNN) performed 94.43, and 96%, respectively, for SHAR and MIT-BIH datasets. Among the DL techniques, the Convolutional Neural Network with Fourier (CNNF) model performed the best, with accuracies of 94.49% and 98.72% for HAR and MIT-BIH. In comparison, CNN achieved 96.90% for the SHAR dataset.
 
An Emulation Framework for Haptic Data Transmission Using Real-Time Transport Protocol
Get PDFThe Tactile Internet (TI) can be regarded as the next evolution in the world of communication. With its envisioned purpose and potential in shaping up the economy, industry and society, this paradigm aims to bring a new dimension to life by enabling humans to interact with machines remotely and in real-time with haptic and kinesthetic feedback. However, to translate this into reality, Tactile Internet will need to meet the stringent requirements of extremely low latency in conjunction with ultra-high reliability, availability, and security. This poses a challenge on the available communication systems to achieve a round-trip delay within 1 to 10 milliseconds time bound that enables the timely delivery of critical tactile and haptic sensations.
This paper aims to evaluate the Real-Time Transport Protocol (RTP) through an emulation framework. It integrates containerization using Linux-based Docker Containers with NS-3 Network Simulator to conceptualize a haptic teleoperation system. The framework is then used to test the protocol’s feasibility for delivering texture haptic data between master and slave domains in accordance with the end-to-end delay requirements specified by IEEE 1918.1 standards. The results have shown that the timely provision of haptic data is achievable by obtaining an average round-trip delay of 17.8493 ms from the emulation experiment. As such, the results satisfy the expected IEEE 1918.1 standards constraints for medium-dynamic environment use cases
Mobile-Based Applications: The Legal Challenges on Data Privacy
Get PDFThe mobile-based apps used is getting popular and continued to increased. Mobile user often downloaded the apps from various sources that provided from numerous of categorization of the application included health apps. Some of apps is optional to choose, but nevertheless, there are several apps is compulsory or must-action by citizens as instructed by the government or their agency. As for that, some issues of legal challenges on data privacy kin to data security have occurred. The issues on legal challenges is more intricate for non-legal educated users with non-awareness citizens while there are government involvements. Hence, in this paper, the issues and the legal challenges on the data privacy for mobile-based application are reviewed to give awareness for both side, the users (citizens) and apps provider (government or developer). Together with that, the idea of action, such as recommendation and option to react with the issues and challenges are also presented. Several Acts (legislation) are also proposed according to the legal issues and challenges that occurred, as showed the Personal Data Protection Act (PDPA) 2010 is became as the famous act used to confront with the existing privacy legislation in mobile-based application. The suggestions and recommendations might assist citizens to keep stand with their rights on data privacy issues in mobile apps, and to the other-side, it might provide some idea be more precise when create and develop the mobile apps
Revolutionizing Manufacturing with Blockchain Technology: Opportunities and Challenges
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A decentralised, tamper-proof ledger offered by blockchain technology has the potential to revolutionise the manufacturing sector by enhancing digital rights management, supply chain management, and product monitoring and tracking. Industrial supply chains may be made more transparent, secure, and efficient with the use of blockchain technology. This will save costs, boost quality control, and raise consumer confidence that the goods they buy are genuine and high calibre. However, there is a research gap in the implications of blockchain technology in the manufacturing sector. The aim of this research was to investigate the challenges and opportunities of blockchain technologies in the manufacturing sector. In order to accomplish the study’s goal, a two-stage systematic literature review technique was used, with the PRISMA framework being used to gather pertinent data from reliable sources like Scopus. The study contained 117 research papers, which were analysed using descriptive and scientometric methods and lysis to synthesise the literature and investigate important research clusters using the centrality and co-occurrence of keywords. The study’s conclusions point to the potential of blockchain technology to support decentralised manufacturing systems that provide risk-free and trustworthy cooperation among multiple stakeholders. The report also discusses the advantages and drawbacks of using blockchain in manufacturing and offers information on recent developments in the field of digital manufacturing that are related to blockchain technology. This study emphasises the value of blockchain technology for the industrial sector and the need for more research to fully understand its potential. Blockchain technology may help the manufacturing industry become more effective, transparent, and quality assured while also reducing costs and fostering better confidence among supply chain actors
Development of Teamwork Skills Using ICTs in Undergraduate Students of Food Industry Engineering Degree
Get PDFThe development of soft skills is considered vitally important for the professionals in the Food Industry Engineering field. Employers in the food industry seek graduates who are prepared for work and possess strong teamwork skills. One effective strategy for students to practice and enhance their teamwork skills is through team-based assignments. In this study, three different team-based tasks were assigned to students in the three Food Industry Engineering courses. These tasks included the preparation of a Kahoot! questionnaire, a video presentation, and a traditional written report, all related to specific topics covered in the courses. The teamwork skills of the students were evaluated using VALUE rubrics. The acceptability of these three learning methodologies was also measured. The results indicated that the use of Kahoot! and student video presentations resulted in higher teamwork scores compared to the traditional methodology (P<0.05). No significant differences were found between the uses of Kahoot! and video presentation in any of the courses (P>0.05). The level of student satisfaction, regarding the general acceptance of Kahoot! and video presentation tools, as well as the acquisition of teamwork skills, was very positive. These findings undoubtedly encourage faculty to incorporate these new teaching methodologies in Food Industry Engineering courses
MapReduce Solutions Classification by Their Implementation
Get PDFDistributed Systems are widely used in industrial projects and scientific research. The Apache Hadoop environment, which works on the MapReduce paradigm, lost popularity because new, modern tools were developed. For example, Apache Spark is preferred in some cases since it uses RAM resources to hold intermediate calculations; therefore, it works faster and is easier to use. In order to take full advantage of it, users must think about the MapReduce concept. In this paper, a usual solution and MapReduce solution of ten problems were compared by their pseudocodes and categorized into five groups. According to these groups’ descriptions and pseudocodes, readers can get a concept of MapReduce without taking specific courses. This paper proposes a five-category classification methodology to help distributed-system users learn the MapReduce paradigm fast. The proposed methodology is illustrated with ten tasks. Furthermore, statistical analysis is carried out to test if the proposed classification methodology affects learner performance. The results of this study indicate that the proposed model outperforms the traditional approach with statistical significance, as evidenced by a p-value of less than 0.05. The policy implication is that educational institutions and organizations could adopt the proposed classification methodology to help learners and employees acquire the necessary knowledge and skills to use distributed systems effectively