Online-Journals.org (International Association of Online Engineering)
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Cybersecurity Challenges and Solutions in the Fintech Mobile App Ecosystem
Get PDFThe rapid growth of the fintech industry, driven by the proliferation of mobile applications, has revolutionized financial services, providing unprecedented convenience to users. However, this innovation comes with inherent cybersecurity challenges that demand rigorous attention. This study delves into the complex and ever-evolving landscape of cybersecurity within the fintech mobile app ecosystem, aiming to identify challenges and present viable solutions. Cybersecurity threats in the fintech mobile app ecosystem encompass a broad spectrum, including data breaches, malware attacks, phishing schemes, and identity theft. As fintech apps handle sensitive financial data and transactions, they are prime targets for malicious actors seeking financial gain. To address these threats, this research examines current cybersecurity strategies and emerging technologies, such as advanced encryption, biometric authentication, and AI-driven anomaly detection. Furthermore, regulatory frameworks and industry standards play a crucial role in shaping cybersecurity practices within fintech. This study assesses the impact of compliance requirements on fintech companies and their ability to protect user data. Real-world case studies and incident analyses provide valuable insights into the consequences of cybersecurity breaches in this sector. Ultimately, this research aims to contribute to a comprehensive understanding of the multifaceted cybersecurity challenges faced by the fintech mobile app ecosystem and offers practical recommendations for fintech firms, regulators, and cybersecurity professionals to enhance security measures. Strengthening the security foundation is paramount to sustaining user trust, fostering continued innovation, and securing the future of mobile fintech
Improving Student Learning Outcomes Using Powtoon Media Apps
Get PDF21st-century learning requires us to utilize technology to enhance learning. Powtoon is one of the media that can be used. The aim of this research is to demonstrate that the utilization of Powtoon video media can enhance student learning outcomes regarding the excretory system. This research utilizes a pre-experimental method employing a one-group pretest-posttest design. This research involved 80 students who were divided into three groups: the upper group, the middle group, and the lower group. Data collection techniques include observation, tests, and questionnaires. The data analysis technique employs a quantitative analysis approach. The research results show that: 1). the learning outcomes in the top group showed an N-gain of 0.68. 2) The learning outcomes in the medium group had an N-gain of 0.57. 3) The learning outcomes in the top group showed an N-gain of 0.66. Thus, it can be concluded that the use of Powtoon video media can enhance student learning outcomes. We recommend that if Powtoon media is used and created with greater creativity, it can effectively support the learning process
Comparative Biomechanical Evaluation of Unilateral and Bilateral Cages in Posterior Lumbar Interbody Fusion: Endplates Subsidence, Pedicle Screw Loosening and Implant Stability
Get PDFImplant sinking, pedicle screw loosening, and instability are commonly observed complications in posterior lumbar interbody fusion (PLIF) surgeries, attributed to a range of mechanical, biomechanical, and environmental factors. To address these challenges, the utilization of unilateral cages positioned obliquely, along with bilateral posterior instrumentation, and employing a material with a comparable modulus to cortical bone, has shown promising efficacy. The present study employed image-based finite element analysis (FEA) to evaluate the influences on cage sinking, screw loosening, and construct stability. The outcomes revealed that obliquely positioned unilateral fusion cage constructs with posterior instrumentation yielded superior biomechanical outcomes, characterized by reduced range of motion and stress distortion at the cage-endplate and screw-bone junctions. Furthermore, these findings indicated a lower risk of fractures and diminished deformations in normal and traumatic events. Hence, the utilization of biocompatible cage materials and structural symmetry holds critical significance for achieving biomechanical success in PLIF surgery
Automated Detection of Dental Caries from Oral Images using Deep Convolutional Neural Networks
Get PDFThe urgent demand for accurate and efficient diagnostic methods to combat oral diseases, particularly dental caries, has led to the exploration of advanced techniques. Dental caries, caused by bacterial activities that weaken tooth enamel, can result in severe cavities and infections if not promptly treated. Despite existing imaging techniques, consistent and early diagnoses remain challenging. Traditional approaches, such as visual and tactile examinations, are prone to variations in expertise, necessitating more objective diagnostic tools. This study leverages deep learning to propose an explainable methodology for automated dental caries detection in images. Utilizing pre-trained convolutional neural networks (CNNs) including VGG-16, VGG-19, DenseNet-121, and Inception V3, we investigate different models and preprocessing techniques, such as histogram equalization and Sobel edge detection, to enhance the detection process. Our comprehensive experiments on a dataset of 884 oral images demonstrate the efficacy of the proposed approach in achieving accurate caries detection. Notably, the VGG-16 model achieves the best accuracy of 98.3% using the stochastic gradient descent (SGD) optimizer with Nesterov’s momentum. This research contributes to the field by introducing an interpretable deep learning-based solution for automated dental caries detection, enhancing diagnostic accuracy, and offering potential insights for dental health assessment
Prototype for Analyzing Instructor Profiles in Online Courses: A Fuzzy Logic-Based Approach
Get PDFOnline education has grown exponentially in recent years, becoming the main form of education in many countries due to the global pandemic. However, evaluating the performance of online instructors can be challenging as obtaining accurate feedback from students is not always easy. This research project proposes an intelligent evaluation model for analyzing the instructor’s profile in online courses using fuzzy deformable prototypes. By identifying the personality traits and emotions expressed by students in surveys, the model evaluates the instructor’s personality based on the five main traits of psychology: openness to experience, conscientiousness, agreeableness, extraversion, and neuroticism. The results obtained can help instructors improve their methodologies and communication, providing better quality online education. The proposed method involves data cleaning, natural language processing, and fuzzy logic analysis, and the results are presented in a user-friendly web interface. The approach has the potential to revolutionize instructor evaluation in online education
A Study of the Impact of the Application of Digital Technology-Integrated Entrepreneurship Education on Entrepreneurship and Entrepreneurial Performance
Get PDFUnder the condition of a perfect digital technology environment, teachers receive richer teaching resources to enhance their teaching ability, improve their teaching quality, and enrich their teaching content. Teachers teaching with computers could enhance students’ learning motivation, reinforce peer cooperation, train students’ autonomy, and enhance students’ subject learning ability. Delivering vivid sounds and pictures through multimedia- assisted teaching could induce students’ learning interests and further promote learning effectiveness. Using an experimental design model for the experimental research, 206 college students in Taiwan Province, as the experimental objects, are preceded by the 16-week (total 48 hours) digital technology-integrated entrepreneurship education experiment. Research findings show the following: 1. Teachers, according to students’ learning satisfaction, check students’ learning effectiveness to ensure teaching quality and timely modify teaching styles to better match students’ learning needs. The benign interaction could largely assist in the improvement of the entire educational environment. 2. The application of digital technology in integrated entrepreneurship education reveals significantly positive effects on entrepreneurship and entrepreneurial performance. According to the results of the proposed discussions, it is expected to acquire more information about entrepreneurship education and its relevance in order to provide entrepreneurship education and the related promotion units and people with practice directions and suggestions for practicable and proper entrepreneurship education in order to effectively implement the ideal and objective of entrepreneurship education
Classification and Retrieval of Multimedia Audio Learning Resources
Get PDFWith the development of the Internet and new media, multimedia and audio learning resources have been widely used in teaching and learning. However, their classification and retrieval have become important and urgent issues to be addressed. This study conducted in-depth research on the classification system, construction, and retrieval of multimedia audio learning resources, with the aim of solving several problems with existing research methods, such as timeconsuming manual labeling, inconsistent labeling, and traditional retrieval methods neglecting the correlation between audio and metadata. First, a classification model of audio learning resources was constructed. It processed single-mode data from audios and annotated texts and further abstracted the single-mode information into high-level feature vectors. Then the complementarity between multi-modalities was used to fuse the abstract features or decisionmaking results and eliminate information redundancy between modalities, thereby learning a better feature representation of multimedia audio learning resources. Second, a retrieval method for the resources based on self-similarity matrix filtering was proposed, which aimed to improve the accuracy and efficiency of retrieval. This study provides a new theoretical and practical perspective for classifying and retrieving multimedia audio learning resources
Influence of Teacher Intervention on College Students’ Metacognition in an Online Collaborative Learning Environment
Get PDFThe rapid development of the Internet, big data, and mobile Internet technology has resulted in the emergence of online collaborative learning as the primary mode of collaborative learning. Although online collaborative learning is flexible and autonomous, the learning performance of learners is not high. One of the main problems that perplexes teachers in online teaching is the high dropout rate but low completion rate. Online education has become increasingly important for improving the quality of students’ learning. Teachers’ rational use of resources, strategies, and other intervention techniques can significantly enhance the quality of online education. Exploring the effect of teacher-led classroom interventions on college students’ metacognition under the collaborative learning mode can further integrate information technology with classrooms and promote the improvement of classroom learning and teaching effectiveness. Two parallel classes, consisting of 80 students each, were selected as research subjects at Harbin University of Science and Technology in Heilongjiang Province, China. The aim of the study was to investigate the impact of teacher intervention on college students’ metacognition (specifically, metacognitive planning, metacognitive monitoring, metacognitive regulation, and metacognitive evaluation) in an online collaborative learning environment. Results show that there is no significant difference in the online collaborative learning experience between the experimental class and the control class (P = 0.3542 > 0.05). Nonetheless, the experimental and control groups differ in their metacognitive planning (t = 3.943, p = 0.000), metacognitive monitoring (t = 2.464, p = 0.016), metacognitive regulation (t = 2.024, p = 0.046), and metacognitive evaluation (t = 3.675, p = 0.000). These findings indicate that teacher intervention promotes metacognition among college students. The findings have significant reference value for the seamless integration of online teaching resources and teachers’ instruction, the customization of students’ individualized learning paths, and the enhancement of online collaborative learning performance
Improved Detection and Tracking of Objects Based on a Modified Deep Learning Model (YOLOv5)
Get PDFRecent years have seen advances in deep learning, including in the field of traffic management. Detecting distant objects that occupy a small number of pixels in the input image is one of the major challenges in computer vision for several reasons, including limited resolution. The challenges of detecting the rotation of objects may be attributed to the deflection of the camera when taking photographs. We recommend enhancing the features of the YOLOv5 network. The proposed method is to train a model on a traffic dataset, which achieves the best inference results through training, testing, and detection on a 1280 × 1280 image for 300 epochs. Moreover, modifications were made to some structural elements of the YOLOv5. In addition to detecting round objects by increasing degrees from 0 to 270, it also increases the probability of flipping in all directions: up, down, left, and right. In addition, the degree of rotation of the image was increased to 90 degrees. The results showed optimized accuracy in detecting distant and small objects, as 73 objects were detected compared to the original YOLOv5 23 objects. It achieved the best number of objects detected in the video (people, cars, and others), and detecting rotating objects increases the number of detected objects (32 objects). The inference time was (23 Ms.) this dataset can make excellent traffic monitoring applications. This model can be deployed on an Android mobile device to provide accurate data about current traffic at a specific location. This is because a mobile device can be used at any time and place. Therefore, in the future, we are working on designing models for object detection that can be operated on mobile devices
3D Pre-Processing Algorithm for MRI Images of Different Stages of AD
Get PDFAlzheimer’s disease (AD) is a degenerative neuronal brain disorder resulting in memory loss, skills, and cognitive changes. The disorder’s primary diagnostic tests are defined as total brain atrophy and hippocampal atrophy. Early diagnosis is significant, and automatic systems design is necessary for this disorder. Potential biomarkers for AD are described using a hippocampal magnetic resonance imaging volumetry system with certain limitations. For the definite identification of the hippocampus region, pre-processing of the 3D MRI images of AD is necessary. The filtering and histogram-based pre-processing techniques enhance the region of interest, which helps in effectively segmenting the biomarker, the hippocampus. The median and eight histogram clippings are defined to be 98% efficient pre-processing techniques with the comparison of image quality parameters and statistical analysis. Thus an algorithm for pre-processing of the 3D MRI images of stages of AD is designed for the further process of identification