Online-Journals.org (International Association of Online Engineering)
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Engineering Education, Innovation, and Economic Growth: A Case Study
Get PDFThe role of engineering education is pivotal in driving regional economic expansion, especially in the context of wider development strategies. The research investigates the connection between engineering-oriented tertiary education and technological advancements, utilizing panel data from Shanghai (2014–2023). Shanghai, as China’s economic center, serves as the focal point of this study. The study investigates the role of regional innovation in bridging the gap between education and economic expansion, serving as a crucial link. The study underscores the intricate interplay among education, innovation, and economic growth, underscoring the significance of strategic investments and partnerships between industry and universities. Research indicates that merging educational elements with innovative approaches promotes industrial advancement and enduring economic development, potentially extending its impact beyond Shanghai to influence worldwide regional economies
Study on the Purchase Intention and Influencing Factors of Internet Financial Products
Get PDFWith the rapid advancement of Internet finance, understanding the factors influencing consumers’ purchase intentions for financial products has become a critical area of focus for both academia and industry. This study investigates the key factors influencing consumers’ adoption of Internet financial products using a questionnaire survey. Data from 158 valid responses were analyzed through a structural equation model (SEM) to examine the relationships among perceived ease of use, perceived usefulness, usage attitudes, subjective norms, and usage behavior. The findings reveal the following: perceived ease of use significantly enhances perceived usefulness; both perceived ease of use and perceived usefulness positively influence usage attitudes; and perceived usefulness also exerts a significant positive impact on usage behavior. Additionally, subjective norms were found to significantly affect usage behavior. Importantly, usage attitudes mediate the relationships between perceived ease of use, perceived usefulness, and usage behavior. This study provides theoretical insights and practical recommendations for improving and promoting Internet financial products. It provides valuable reference for businesses to design marketable Internet financial products
Methodology for Monitoring and Control of Finger Deformation in Patients with Arthritis Using Fractal Dimension
Get PDFRegenerative diseases, as is the case of arthritis, generate a change in the morphology of the fingers and toes; thus, as time passes, the joints of the fingers begin to deform, which causes a change in the morphology of the joints and fingers. One of the effects that is noticeable by patients is the pain that these deformations cause in addition to the deterioration of the maneuverability of objects with hands. In this article we present a method for the analysis of the fingers of the hands, through the analysis of images obtained through the photographic registration of the hand, then the value of the fractal dimension (FD) is obtained, to know the objective index for the measurement of the morphology of the hand, in this way it is possible to evaluate the deformation of the joints of the fingers, In this way it is possible to analyze and monitor the level of deformation, thus being able to improve the treatment. Due to having a record of the evolution of the deformations, we present as a result a demonstration of the methodology by analyzing the FD of the hands of a patient. The calculation of the FD was developed using the Matlab tool, which can be replicated and scaled according to the need of each situation
Development of Linear Contrast Stretching (LCS) and Region of Interest (ROI) Method for Detecting Spinal Fractures Based on CT-Scan Images
Get PDFThis study aims to develop linear contrast stretching (LCS) and region of interest (ROI) methods to detect spinal fractures based on CT-scan images. LCS is applied to enhance image contrast by expanding the pixel intensity range, thereby clarifying bone structure. ROI is used to focus the analysis on relevant areas, such as sections of bone suspected to have fractures. The development of these LCS and ROI methods is expected to improve fracture detection visibility, as evidenced by more precise measurement of the detected fracture pixel area and increased detection accuracy, which is anticipated to facilitate the clinical diagnosis process. The study consists of several stages, including image preprocessing, edge detection, image sharpening, and the application of LCS + ROI on the targeted area. The study findings indicate that the proposed method is capable of detecting fractures with high accuracy, thereby assisting radiologists in diagnosing spinal fractures more quickly and accurately, achieving an accuracy rate of 95%. The implementation of this method is expected to make a significant contribution to CT-scan image-based medical diagnosis
Development of OCR Technology Application System for Health Data Recording
Get PDFThe shift to digital health records requires advanced technologies to transform medical device data into digital formats. This study created a way to digitize health data from devices that measure blood sugar, blood pressure, and pulse oximeters. It used YOLOv5 to find objects and optical character recognition (OCR) technologies to read text. The solution incorporates a MySQL database for effective data storage and a web application for intuitive data presentation. YOLOv5 was trained on 6,630 photos to effectively detect and evaluate seven-segment displays. A YOLOv5 confidence level of 80.75% and an OCR accuracy of 93.2% were found when testing at different distances (7 to 30 cm) and angles (0º, −35º, −30º left, −30º right) and with different lighting conditions. Well-lit settings yielded optimal performance; however, extreme tilts occasionally led to misreading’s. The technology processed photographs in 10 to 15 seconds, facilitating real-time data conversion and enhancing usability for senior individuals handling daily health information. Even though there were challenges, such as low light and differences between devices, the system showed that it could cut down on mistakes and make healthcare more efficient. Future enhancements will concentrate on sophisticated preparation methods and mistake correction algorithms to guarantee uniform performance. This system provides a strong and scalable solution for digitizing health data, facilitating enhanced electronic health records (EHRs) and individualized healthcare management
Development of GLCM Method in Calculate Entropy Value for Digital Visualization in Identifying Childhood Pneumonia Based on Chest X-Ray Images
Get PDFPneumonia can affect people of all ages, especially children. One way to identify pneumonia is by using medical equipment through radiological examinations such as chest X-rays. This study proposes the development of an entropy formula found in the gray level co-occurrence matrix (GLCM) texture extraction method to automatically detect pediatric chest X-ray results in identifying pneumonia. The pre-processing stage is tested with several steps, including converting RGB to grayscale, adaptive histogram equalization (AHE), filtering, Otsu thresholding, image inversion, and automatic image cropping. After preprocessing is the segmentation stage that conducted by processing the image from the cropping results. The testing process in the segmentation stage includes contrast enhancement, Otsu multi-thresholding, border clearing, and image segmentation. The results from the segmentation process are then followed by the extraction stage. The extraction stage focuses on developing the entropy value found in GLCM, referred to as the entropy value algorithm with gray level co-occurrence matrix (EVAGLCM). The key contributions of this study lie in the advancement of digital image processing methods for the accurate identification of childhood pneumonia through improved texture feature extraction. This study compares the developed entropy value with several previous studies. The development of this entropy value is then followed by the classification stage using a support vector machine (SVM). The accuracy achieved in this study was 97.5%, meaning it was able to accurately detect 390 images out of 400 images. This indicates that the entropy value calculation using the EVA-GLCM formula and classification using SVM can provide more accurate output with a higher accuracy rate
Study of AI-Based Solutions for Automatic Detection of Some Diseases Related to Red Blood Cells in West Africa
Get PDFThe majority of hematology laboratories in the West Africa does not have equipment dedicated to the automatic classification of blood cells. The integration of artificial intelligence (AI) in hematology improves diagnostic accuracy, reduces the burden on healthcare systems, and provide timely interventions in regions with limited access to medical resources. This paper discusses the development and implementation of AI-based tools designed to automatically detect diseases related to red blood cells (RBC) in West Africa. These tools leverage advanced machine learning algorithms to analyze blood cell morphology and identify abnormalities indicative of diseases such as sickle cell anemia, elliptocytosis and other blood disorders. An analysis of previous techniques shows that models based on artificial neural networks (ANNs) and convolutional neural networks (CNNs) are the best systems for automatically detecting pathologies, with performance over 80%. When these models are combined with classifiers such as support vector machine (SVM) and k-nearest neighbor (KNN), they achieve better performance, with values between 91% and 98%
Computational Evaluation of Dental Adhesive for Four Direct Restorative Procedures
Get PDFDirect restoration is recovering the damaged tooth within the mouth by filling the cavity on the tooth using filling material. Therefore, the filling material and location of a cavity are essential in determining the durability of the restored part. This study aims to determine the stress distribution in the teeth using finite element analysis (FEA) with lithium disilicate as a filling material. The binding strength created between tooth enamel and lithium disilicate is different for each restoration class with varying locations of a cavity. In this study, ANSYS Engineering Simulation Software was employed to analyze the stress distribution for four types of classes of direct restoration (class 1, 2, 5, and 6). The analyses were made by applying vertical force on the tooth crown with 600N magnitude. The results show class 1 was 121.2 MPa which is the lowest maximum von mises stress value. The results obtained are beneficial to increase the understanding of the behavior of lithium disilicate as a filling material and the quality of tooth restoration
Improving Engineering Students’ Motivation for Success in Statistics and Data Science: An Innovative Visualization of Four Mathematical Models in Higher Education in United Arab Emirates
Get PDFThis study aims to analyze the factors that improve engineering students’ motivation for success in statistics and data science courses at higher education institutions by using four mathematical models. The distinctiveness of this study was exemplified by the innovative graphical depiction of those models. The impact of certain factors, such as the importance of recognition and enjoyment of the course, students’ self-concept, and future aspirations, on engineering students’ motivation for achieving success in statistics and data science courses was examined. The proposed models are expected to provide beneficial academic insights to students, instructors, administrators of higher education, and societies worldwide. This paper employed a quantitative methodology, including a sample consisting of 144 female and 101 male engineering students enrolled in various statistics and data science courses at higher education institutions in the United Arab Emirates (UAE). A comprehensive survey questionnaire was developed to gather quantitative data, which were mathematically modeled via factor and regression analyses. The four mathematical models analyzed six variables derived from the survey items. According to the results, models IV, II, I, and III had the most significant influence on motivation, in decreasing order. Model IV explained 94.4% of the variation in the motivation for achieving success in statistics and data science courses, while models II and I explained 75.5% and 71.4%, respectively. The study’s limitations stem from the fact that its findings might not apply universally and are dependent on the specific educational settings or cultural contexts in which the study was conducted
Assessing the Efficacy of AI-Driven Corrective Feedback via WhatsApp Application to Improve ESL Learners’ Writing Skills: An Experimental Study
Get PDFWriting in English poses challenges due to the intricacy of its grammatical rules; however, these challenges may be effectively overcome through artificial intelligence (AI)-driven technologies. As there is insufficient research on AI-enabled WhatsApp for English language learning and instruction, this study examines how WhatsApp’s AI system enhances ESL learners’ writing skills. To deal with these gaps, the study aims to assess how AI-driven corrective feedback through the WhatsApp application enhances ESL learners’ writing skills by reducing grammatical errors within the framework of the interactionist theory of second language learning. The present research study utilized a quasi-experimental design involving two groups—an experimental group and a control group—to experiment. The experimental group was subjected to treatment involving AI-driven corrective feedback via WhatsApp application, whereas the control group did not receive such feedback. Data were collected from 112 undergraduate participants in India at two intervals: a pre-test and a post-test. The participants’ written submissions were assessed and scored using ChatGPT 4.0. The method of statistical analysis, repeated measures analysis of variance (RM-ANOVA), was applied to analyze the score. The results of the study showed that ESL learners who received AI-driven corrective feedback via WhatsApp performed better than those who received traditional feedback, particularly in correcting complex grammatical errors related to subject-verb agreement errors, tense errors, pronoun errors, fragment errors run-on sentences, comma splices, misplaced modifiers, apostrophe errors, capitalization errors, word choice errors/lexical errors, and preposition errors in their writing. The study found that augmented feedback systems and AI-driven corrective comments in ESL classrooms could enhance writing skills