Online-Journals.org (International Association of Online Engineering)
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Iris Recognition Approach for Preserving Privacy in Cloud Computing
Get PDFBiometric identification systems involve securing biometric traits by encrypting them using an encryption algorithm and storing them in the cloud. In recent decades, iris recognition schemes have been considered one of the most effective biometric models for identifying humans based on iris texture, due to their relevance and distinctiveness. The proposed system focuses on encrypting biometric traits. The user’s iris feature vector is encrypted and stored in the cloud. During the matching process, the user’s iris feature vector is compared with the one stored in the cloud. If it meets the threshold conditions, the user is authenticated. Iris identification in cloud computing involves several steps. First, the iris image is pre-processed to remove noise using the Hough transform. Then, the pixel values are normalized, Gabor filters are applied to extract iris features. The features are then encrypted using the AES 128-bit algorithm. Finally, the features of the test image are matched with the stored features on the cloud to verify authenticity. The process ensures the privacy and security of the iris data in cloud storage by utilizing encryption and efficient image processing techniques. The matching is performed by setting an appropriate threshold for comparison. Overall, the approach offers a significant level of safety, effectiveness, and accuracy
Computer Vision-Based Approach for Automated Monitoring and Assessment of Gait Rehabilitation at Home
Get PDFThis study presents a markerless video-based human gait analysis system for automatic assessment of at-home rehabilitation. A marker-based MoCap system (Vicon) is used to evaluate the accuracy of the proposed approach. Additionally, a novel gait rehabilitation score based on the Dynamic Time Warping (DTW) algorithm is introduced, enabling quantification of rehabilitation progress. The accuracy of the proposed approach is assessed by comparing it to a marker-based MoCap system (Vicon), which is used to evaluate the proposed approach. This evaluation results in mean absolute errors (MAE) of 4.8° and 5.2° for the left knee, and 5.9° and 5.7° for the right knee, demonstrating an acceptable accuracy in knee angle measurements. The obtained scores effectively distinguish between normal and abnormal gait patterns. Subjects with normal gait exhibit scores around 97.5%, 98.8%, while those with abnormal gait display scores around 30%, 29%, respectively. Furthermore, a subject at an advanced stage of rehabilitation achieved a score of 65%. These scores provide valuable insights for patients, allowing them to assess their rehabilitation progress and distinguish between different levels of gait recovery. The proposed markerless approach demonstrates acceptable accuracy in measuring knee joint angles during a sagittal walk and provides a reliable rehabilitation score, making it a convenient and cost-effective alternative for automatic at-home rehabilitation monitoring
Predicting Amyloid-β Positivity in Alzheimer’s Disease: Comprehensive Analysis of Feature Selection and Machine Learning Models for Accurate Identification
Get PDFTo accurately identify individuals at risk of Alzheimer’s disease (AD), it is crucial to develop precise tools for predicting amyloid-β (Aβ) positivity in the brain. We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to analyze 1,377 human subjects. These participants were divided into five groups: cognitive normal (CN), subjective memory complaints (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and confirmed AD. Each group was further divided into ten subgroups based on sex, resulting in a comprehensive analysis. The dataset was used to create and evaluate the performance of 15 machine learning (ML) models. A set of 17 potential predictors was generated by combining variables from different categories, including six demographic factors (such as age), ten measurements (such as ADAS13), and APOE4 status. Through ML-based predictive modeling, several cognitive assessment measures, including ADAS13, demonstrate significant importance in multiple ML models. The highest accuracies in the 10 subgroups were 0.875, 0.892, 0.778, 0.850, 0.771, 0.739, 0.781, 0.791, 0.879, and 0.903, respectively. The collection of ML models consists of practical and valuable risk feature scores that can significantly enhance the identification of individuals who are likely to test positive for Aβ
Business Engineering 4.0: The Transformation of a University Course in Response to Industry 4.0, Sustainable Development Goals, & Covid-19 in South Africa
Get PDFThe rising waves of Industry 4.0, Sustainable Development Goals and COVID-19 have resulted in repercussions that have challenged the status quo. In preparation, Action Design Research (ADR) was used in Engineering Education as an adaptive mechanism. This research paper follows a 5-year development journey of a Business Engineering course at a South African university. The paper discusses the adaptations and refinements in response to the waves above and how they were integrated in the content, presentation and assessments of the course. Furthermore, this paper extracts generalizable findings for Engineering Education and reflects on the design of the next cycle of the course in anticipation of further waves
Digital Gaming and Autistic Spectrum Disorder
No full textThe use of digital games in the educational process promotes interactivity and de-escalation of tensions, promotes active educational models, and offers new possibilities for communication, collaboration, and learning, giving the possibility of introducing the student with autism in a controlled environment that is predictable and without social stimuli to manage the concerned person’s attention and concentration in an activity. This paper aims to present the findings for the use of educational digital games in the field of special education and specifically in the education of people with autism spectrum disorder (ASD)
Teaching Effect Evaluation of Flipped Classroom in Engineering Management Colleges: A Multivariate Ordered Logit Model
Get PDFAdapting traditional classroom teaching methods to students’ personalized learning styles and needs can be challenging, resulting in passive learning, low teaching quality, and insufficient support for students’ learning and development. The flipped classroom teaching model integrates information technology (IT) into education, enriches teaching approaches, and emphasizes a student-oriented concept. The flipped classroom can help students deeply understand the content by building upon shallow learning guided by effective teaching strategies. In turn, it facilitates the development of higher-order thinking skills and promotes students’ physical and mental well-being. In this study, five engineering management colleges in Henan Province, China, were surveyed using a questionnaire distributed during the fall semester of 2022–2023. The factors influencing the teaching effect of the flipped classroom were analyzed using a multivariate ordered logit model. Results demonstrated that the questionnaire had a reliability coefficient of 0.844, a KMO value of 0.788, and a Bartlett sphericity test with a corresponding P value of less than 0.01. Several factors, including gender (P = 0.054), self-study ability (P = 0.064), campus network (P = 0.000), teaching resources (P = 0.085), and classroom interaction (P = 0.036), significantly influence the teaching effect of the flipped classroom. The findings of this study provide valuable insights for enriching the teaching management theory of the flipped classroom, helping teachers enhance their management abilities in implementing the flipped classroom, and facilitating convenient teaching and learning experiences for both teachers and students in information technology
A Neural Networks Based Model to Predict the Interest of College Students in Sports Activities
Get PDFThe widespread application of big data technology in various fields, including research in education and sports in colleges, has also been deeply influenced. College students are the future strength of a country, and their habits and interests in sports activities have profound significance for their physical and mental health, teamwork, and outlook on life. However, traditional research methods, such as questionnaire surveys, observations, or interviews, have obvious limitations when dealing with large amounts of complex high-dimensional data. This study aimed to extract the interesting features of college students regarding sports activities using graph neural network (GNN) technology. Then, the labels of those interest features were further predicted, and a feature matrix was constructed. Finally, the K-means clustering method was used to achieve accurate feature clustering. This study presents a novel idea and approach for physical education and event planning in colleges, offering both practical value and theoretical significance
Impact of Peer Review on Learning Performance in a Smart Classroom Teaching Environment
Get PDFWith the rapid progress of educational technology, smart classrooms have gradually been widely applied, aiming to provide students with more efficient and innovative learning experiences. As a non-traditional assessment method, peer review has attracted widespread attention in this context, and its role in the learning process of students is increasingly prominent. However, there are still disputes and deficiencies regarding its specific applications and benefits in smart classroom environments. This study aimed to dig into the peer review standard and its score prediction in a smart classroom environment and evaluate the specific impact of peer review on learning performance. It is expected that this study can provide educators with a more accurate and practical peer review method, thereby optimizing the teaching and assessment modes of smart classrooms
An Information Service Platform for Decision Support in Academic Admissions Using Data Fabrics and Artificial Intelligence
Get PDFThis paper presents the architecture of an information service platform for decision support in academic admissions, utilizing data fabrics and artificial intelligence. The factors affecting students’ further education can be classified into four main types: (1) the course, (2) image, (3) personal reasoning of the student, and (4) public relations. The process of providing information for decision support in academic admissions can be divided into six stages: (1) collecting information, (2) matching study guidance, (3) recommending appropriate education, (4) confirming information, (5) assessing student admissions, and (6) providing feedback. Data fabric is an increasingly popular technology application for data management. The data fabric architecture consists of six layers: (1) an augmented data catalog, (2) a knowledge graph enriched with semantics, (3) metadata activation, (4) a recommendation engine for active metadata, (5) data preparation and integration, and (6) orchestration and data operations. A smart decision support system (DSS) technology is used to assist in decision-making. The results showed that this architecture has an excellent level of suitability (mean = 4.56, standard deviation = 0.35). It can be applied to a university to help it become a digital university and align with its mission
What My Friends Are Up To? The Relationship between Social Media Usage and Fear of Missing Out among Undergraduates
Get PDFThe social media platform is extremely popular among undergraduate students. Along with the increase in social media usage, phenomena such as the fear of missing out are also becoming more prevalent among this group. It is hypothesized that excessive usage of social media contributes to the psychological decline of undergraduate students. This study aimed to examine the relationship between social media usage and the fear of missing out among undergraduate students. In addition, this study also examined the level of social media usage and the fear of missing out. The study adopted a quantitative approach, specifically a correlational research design. Data were collected from 306 undergraduates at Universiti Teknologi Malaysia using the social networking time use scale and fear of missing out (FOMO) scale instruments to measure social media usage and fear of missing out, respectively. Descriptive analysis revealed that undergraduates have a moderate level of social media usage and a moderate level of fear of missing out. Meanwhile, the inferential analysis revealed a significant positive relationship between the usage of social media and the level of fear of missing out among undergraduates. In conclusion, the study results indicate that social media usage does influence the fear of missing out among undergraduates, although the coefficient was weak