Online-Journals.org (International Association of Online Engineering)
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Artificial Intelligence and M-Learning in Arabic Countries: Innovations, Trends, and Regional Perspectives
Get PDFThis study addresses the significant knowledge gap in understanding the regional dynamics of artificial intelligence (AI) in education within the Arabic countries. It applied a bibliometric analysis of research published on the Scopus database from 2003 to 2024 to map publication trends, collaborative structure, and emerging research themes. The findings indicate that there has been a significant increase in research output since 2018, with a notable shift from general eLearning applications to specialized AI applications, specifically in mobile learning (M-learning) environments. These applications enhance adaptive, personalized, and resilient educational practices. Key findings include the anticipation of ChatGPT’s prominence in 2024 as a research focus, strong cross-country collaborations, especially with Saudi Arabia, and a growing interest in AI applications in higher education. Additionally, the study highlights the research advancements in AI ethics, natural language processing (NLP) in education, and AI-powered M-learning platforms. The study applied Bradford’s and Lotka’s laws to uncover patterns in journal distribution and author productivity, indicating a concentration of research among a core set of journals and authors. However, challenges remain, including the need for increased international collaboration and addressing ethical considerations in AI implementation. The in-depth study provides useful information for researchers, teachers, and policymakers, focusing on how AI can change M-Learning and the need for culturally aware and morally sound methods for incorporating AI into Arabic school systems
Career Paths and Talent Management: A Study on Postgraduate Studies of Finnish Engineers
Get PDFTight competition for jobs and skilled professionals has increased the need for talent development among both employees and employers. Employees must take care of their own skills to secure their position in the labor market and companies, in turn, must develop both the skills of their current employees and to attract new professionals. This paper focuses on the talent development of Finnish engineers, and it especially analyzes when and to what kind of master’s programs they apply to during their careers. First, it explores the potential differences at the application ages of the undergraduates applying to master’s programs offered by universities and polytechnics (UAS). Second, it investigates variations between the engineering management programs and discipline-specific programs of the UASs. Findings of the study indicate that the age distributions for the master’s programs in universities and UASs differ significantly and the UASs programs seem to be an important form of education for engineers in employment. Results on different kinds of programs indicate that both engineering management and discipline-specific master’s programs will be needed also in the future to fulfill the different kinds of career paths of the engineers
Efficient and Parallel Medical Image Segmentation Model (EPSM) Based on Brink-MCET Using Heterogeneous Distributions
Get PDFMedical image segmentation is becoming increasingly popular in the field of image analysis. In computer vision, medical image segmentation is a challenging task but is crucial for identifying and analyzing diseased areas in the body. It is especially crucial for detecting conditions such as brain tumors, skin cancer, and other serious illnesses. This paper presents a novel thresholding technique based on minimum cross-entropy thresholding (MCET), specifically designed for precise segmentation of dermoscopy images. The suggested bimodal technique was evaluated using three benchmark datasets from PH2, HAM10000, and ISIC 2017. To determine the MCET of each input image, three different combinations of statistical distributions— Gaussian, Gamma, and Lognormal—were employed. To further raise the effectiveness of the effective and parallel segmentation model (EPSM) model, a novel parallel boosting segmentation technique was created and applied. By comparing the proposed image segmentation method’s output with that of the Entropy-Li approach, its effectiveness was assessed. Both supervised and unsupervised evaluation methods were used. Based on the obtained outcomes, it can be inferred that the EPSM segmentation model is a reliable, accurate, and consistent method with outstanding performance characteristics
An IoT Monitoring System Based on Artificial Intelligence Image Recognition and EMG Signal Processing for Abdominal Exercise Performance
Get PDFCorrectly executing exercises during training is of vital importance to ensure adequate athletic performance. Sit-ups are among the most frequently performed exercises requiring proper evaluation. This exercise contributes to increasing abdomen strength, having better posture to reduce back problems, and improving overall physical condition and appearance, among other benefits. Existing methods for evaluating the correct execution of sit-ups are manual, subjective, and inefficient in terms of time, cost, and precision. Therefore, there is a need to have technological tools that measure and monitor core abdominal strength while simultaneously verifying, through image processing, the correct execution of the exercise. Since no solutions with these capabilities have been found in the literature, this work proposes a system that performs these functions using electromyographic (EMG) sensors, force signal processing, and biomechanical monitoring based on image processing and the BlazePose algorithm. The results obtained show a very satisfactory performance of the biomechanical monitoring method, where an accuracy of over 95% is obtained in the identification of the correct body posture, while for the estimation of abdominal strength, a sensitivity of over 90% is achieved during the execution of sit-ups
Evaluation and Detection of Cyberattack in IoT-Based Smart City Networks Using Machine Learning on the UNSW-NB15 Dataset
Get PDFWith the proliferation of Internet of Things (IoT) devices across various applications, for example, smart homes, drones, and healthcare, the security vulnerabilities have also increased, necessitating robust network intrusion detection systems (NIDS). This study focuses on the classification of cyberattacks, including denial of service (DoS), worms, and backdoor attacks, from normal network traffic using the UNSW-NB15 dataset. machine learning (ML) and deep learning (DL) models, such as decision tree (DT) classifier, K-nearest-neighbor (KNN) classifier, linear regression, linear support vector machine, logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF), were employed for both binary and multi-class classification. Data preprocessing involved handling null values, one-hot encoding categorical variables, and normalizing numerical features. Feature selection was performed using the Pearson correlation coefficient method, reducing the dataset attributes significantly. The models demonstrated high accuracy in detecting anomalies, with the RF classifier achieving the highest accuracy of 98.64% for binary classification and notable performance across multi-class classifications. This study underscores the effectiveness of ML techniques in enhancing IoT network security and offers comprehensive insights
Collaborative Skills Developed in Collaborative Online International Learning as a Multivariate Research Subject
Get PDFWhen attending Collaborative Online International Learning (COIL), professors and students engage in synchronous teaching-oriented activities with telecommunication technologies to foster knowledge exchange and cross-cultural professional skills. COIL is crucial in promoting student collaboration, making it a desirable research subject that matches quality education as a sustainable goal. Here, we illustrate some challenges for applied researchers who promote COIL-based innovations. We examined the psychometric structure of a collaboration scale in a COIL experience with a small sample of 65 attendees from Colombia and Ecuador, two Latin American developing countries. The study found that COIL experiences effectively foster collaboration skills, measured through the Transactive Memory System (TMS) framework, emphasizing expertise, credibility, and coordination. In a COIL environment between Colombia and Ecuador, they learned collaboratively in an interdisciplinary formative research educational experience. The results of the collaboration measurement demonstrated the potential to integrate research-based learning with collaborative learning methodologies in global educational contexts. We discuss the implications for educators and policymakers seeking to enhance student collaboration and foster meaningful interactions in online learning environments
IoT Challenges and Issues: A Comprehensive Review of Software Defined Networking and Network Function Virtualization Solutions
Get PDFThe idea behind the Internet of Things (IoT) is to connect everything, including laptops, smartphones, sensors, and other devices, to the Internet. To build an autonomous environment without human intervention. This novel network was used in several industries, including smart homes, smart cities, healthcare, etc. For this reason, IoT networks are growing in infrastructure. As a result, the administration of this vast array of linked devices and produced data becomes more complicated. Thus, a new elastic mechanism is required for this dynamic and rapid evolution in configuration, control, management, etc. Network Function Virtualization (NFV) and Software Defined Networking (SDN) have become essential points in scientific research to overcome IoT challenges such as security, heterogeneity, energy efficiency, interoperability, and more. These two approaches have proven their efficiency in adapting to dynamic and evolving networks. SDN reduces network latency by up to 30% and increases device scalability by 40%. At the same time, NFV optimizes resource allocation, achieving up to a 35% reduction in energy consumption and a 20% decrease in operational costs through virtualized infrastructure. In this review, we systematically analyze solutions designed for IoT systems by developing a state-of-the-art for NFV and SDN and thoroughly researching the various problems that IoT will face. Thus, we compare SDN- and NFV-based IoT solutions to overcome these challenges. Lastly, we will discuss the different obstacles that can lower the performance of SDN/NFV applications on the IoT. The contribution of this review lies in its systematic evaluation and comparison of current NFV and SDN approaches, providing valuable insights and paving the way for future research to enhance the integration and management of IoT systems
Design and Development of Mobile Teaching Aids Using Go-Based Electronic Games for Teaching Digital Electronics in Higher Education
Get PDFThe ongoing evolution of technology in education has highlighted the need for modernized instructional methods. This study explores the development of the Go Electronic game, designed to enhance teaching and learning in digital electronics through gamification. Students often face difficulties in understanding digital electronics, a complex subject that is difficult to learn through traditional teaching methods. This gap highlights the need for teaching aids that simplify concepts and maintain student focus through interactive learning approaches. Using the Waterfall model, a systematic development methodology that progresses through sequential phases: requirements analysis, system design, implementation, integration, testing, and maintenance. This approach ensures that each stage is thoroughly completed before moving to the next, allowing for a structured and efficient development process. Go Electronic was developed to address the confusion often faced by university students in this subject area. The research focuses on the integration of new media resources to make the learning process more interactive and engaging. Data collected from interviews with educators at Universiti Pendidikan Sultan Idris (UPSI) confirmed the tool’s effectiveness, emphasizing its potential to improve student focus and engagement. The research findings indicate that the Go Electronic game, developed using this model, effectively enhances student engagement and focus on digital electronics topics. Feedback from educators highlights that the gamified approach not only captures student interest but also supports a more interactive and productive learning experience. The study suggests that expanding the game’s question bank could further improve students’ comprehension, thus advancing the integration of gamification in higher education course delivery
Designing Interactive Mobile Learning Applications for Visually Impaired Children: A User Experience Evaluation Based on Affective Design Principles Using Hypothetical Datasets and Heatmap Analysis
Get PDFThis study explores the development and user experience testing of an interactive mobile learning application designed for children with visual impairments integrated with affective design principles. Despite the availability of various assistive technology (AT) solutions, existing mobile applications often fail to address the specific needs of visually impaired children, particularly regarding emotional engagement and usability. Affective design principles are essential in developing functional, emotionally resonant, and engaging products for users. Therefore, this study aims (i) to develop an interactive mobile learning application tailored for visually impaired children based on affective design principles and (ii) to validate the application through comprehensive user experience testing by utilizing a hypothetical dataset and heatmap overlay. Employing iterative triangulation methodology (ITM), the study demonstrates that integrating affective design principles significantly enhances the usability and engagement of mobile learning applications for visually impaired children. The findings highlight the critical role of affective design in promoting equitable and effective learning experiences for this demographic, contributing both theoretically and practically to the field of assistive technology
Mobile Application with Augmented Reality and its Impact on Anatomy Learning in Human Medicine Students
Get PDFThis paper presents the development of an augmented reality (AR) mobile application to display 3D structures of the human body and evaluate its impact on anatomy learning in students of the Faculty of Human Medicine of a university in northern Peru. The need to improve educational quality is addressed, given that traditional methods often lack adequate resources and effective motivation for students. Through the application, which uses the Mobile-D methodology, 3D anatomical models, a database, and a supporting web page were created. The results indicate that the AR mobile application had a positive impact on the learning of first-year Human Medicine students with a significant improvement of 0.88 in the averages, since it provides them with the basic knowledge for the introduction to this subject, in addition to improving their motivation and being considered as a learning and feedback tool