Online-Journals.org (International Association of Online Engineering)
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Advancing Enterprise Financial Control and Risk Management through Mobile Interactive Technologies
Get PDFWith the deep integration of the digital economy and enterprise operations, mobile interactive technologies—characterized by real-time connectivity and ubiquitous access—have emerged as a critical solution to overcoming the spatial and temporal limitations inherent in traditional financial monitoring. However, existing research predominantly relies on conventional information technology frameworks, which exhibit substantial limitations in multidimensional financial data correlation mining, lightweight deployment on mobile terminals, and the integration and analysis of heterogeneous data. These limitations hinder the timely identification of risks in dynamic business environments. In this study, a financial monitoring algorithmic framework was proposed for enterprise environments under mobile interactive networks, integrating self-attention mechanisms and graph convolutional networks (GCNs). A formal definition of financial data monitoring was established, and a hierarchical algorithmic architecture was developed. Attribute-wise and relation-wise self-attention mechanisms were employed to capture the multidimensional characteristics of financial entities and the dynamic associations among transaction participants. GCNs were utilized to extract deep semantic representations from heterogeneous graph data. In addition, a domain-specific loss function and anomaly detection mechanism were designed based on financial business rules. The proposed framework addresses the limitations of traditional monitoring methods reliant on structured data and offers a robust, real-time solution for dynamic financial surveillance across regions and systems. The findings contribute both theoretical and practical value by advancing the precision of enterprise financial control and supporting the development of intelligent risk management systems
Exploring the Impact of Artificial Intelligence on University Students' Perception of Slow Employment: A Psychological and Behavioral Analysis
Get PDFThis study explores the psychological effects of artificial intelligence (AI) tools on university students’ perceptions and experiences of slow employment. The aim is to understand how the use of AI tools influences students’ anxiety, stress, motivation, and decision-making confidence, as well as their coping strategies in the context of job search challenges. Using a mixed-methods approach, data were collected through quantitative surveys (n = 200) and qualitative interviews (n = 10), followed by behavioral and emotional analysis using AI-based sentiment and emotion recognition systems. Moreover, for quantitative data, regression analysis was performed to identify the factors that impacted the students’ psychological well-being. In contrast, for qualitative data, thematic analysis was employed to identify the psychological and emotional outcomes associated with slow employment. The findings show that AI tool usage significantly reduced anxiety, increased confidence, and enhanced students’ self-efficacy and motivation, while stress remained positively correlated with anxiety levels. The emotional analysis revealed dynamic shifts in emotional states, with anxiety decreasing and confidence increasing after the AI interaction. The study concludes that AI tools offer significant psychological support but require refinement in terms of personalization and transparency. The integration of AI tools with human support systems is essential for practical career guidance. This study offers novel insights into the impact of AI on emotional well-being and provides implications for enhancing career support systems for students
The Impact of Smartwatches on Health and Lifestyle: A Study on Jordanian Society
Get PDFThe widespread adoption of smartwatches plays a crucial role in promoting healthy behaviors by tracking vital signs, monitoring daily activities, and encouraging more health-conscious living. Smartwatch technology is significant in its ability to raise awareness and motivate users, helping mitigate health risks associated with inactivity and unhealthy habits. This study surveyed 384 smartwatch users to examine their usage patterns and the impact of smartwatches on maintaining a healthy lifestyle. The findings reveal that integrating smartwatches into daily routines enhances health behaviors including improved sleep, physical activity, and cardiovascular health. However, certain challenges, such as discomfort during sleep, sensor inaccuracies, high costs, and privacy concerns, may limit user engagement and device effectiveness. The study underscores the role of smartwatches as effective preventive health tools but also emphasizes the need for improvements in sensor accuracy and privacy policies. Future research should explore the long-term effects of smartwatch usage on physical and neurological health, with a focus on sustained health outcomes and user satisfaction. This study contributes to the growing body of evidence on the potential of wearable to support chronic disease prevention and health promotion
Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications
Get PDFComputational neuroscience (CNS) has enabled significant advances in the understanding of cognitive processes through mathematical models and computational simulations, providing a more precise understanding of brain activity. However, its application in higher education remains limited, which restricts its potential to optimize teaching, cognitive and emotional regulation, and personalized learning. This study aims to examine the problems addressed by CNS, the methods used, and their implications for higher education, analyzing scientific articles from the ScienceDirect, PubMed, and Scopus databases through a systematic review study following the PRISMA guidelines. The results show that the application of methods such as EEG, BCI, neurofeedback, fNIRS, tDCS, and computational models has facilitated the adaptation of content and the assessment of cognitive load in students. However, its implementation still faces methodological, economic, and technological barriers, such as variability in neural responses and limited accessibility. It is concluded that CNS has a high potential to transform higher education, but its effective integration requires the adoption of regulatory and standardized frameworks, which promote the creation of specialized areas in CNS within their departments of psychopedagogy or neuroeducation, in order to promote its development, accessibility, and ethical application in educational environments
Improving 3D Animation Education: A Case Study of Curriculum Development in Jordan
Get PDFThis study explores the various factors that influence the development of 3D animation skills, focusing on the role of acting skills, instructor feedback, and the understanding of emotions in improving students’ animation outcomes. The primary goal is to propose methods for enhancing the pedagogy of 3D animation subjects. This study adopts a quantitative research design, analyzing the grades of 97 students and the responses to a survey administered to 50 students. Descriptive, correlation, and regression analyses are employed. Findings from our quantitative analysis indicate a strong positive correlation between students’ acting skills and the produced quality of animation and a moderate positive correlation between observing human emotion and improved acting skills. These findings highlight the importance of integrating acting techniques into the animation curricula. The study offers actionable insights to improve 3D animation curricula and enhance the educational practices in 3D animation, leading to the development of professional animators
Deep Learning for Cultural Heritage: A Mobile App for Monument Recognition Using Convolutional Neural Networks
Get PDFConvolutional neural networks (CNN) has multi-dimensional features that are inextricably interrelated to the extraordinary identification of visionary objects. Humans have a thirst of eagerness to know about ancient monuments. CNN can be a suitable tool for this purpose, in addition to some image processing approaches. This study has developed a mobile application for monument identification in two classes. The prominent contributions have been performed in the data preprocessing and feature extraction requirement using the Canny approach. In addition, CNN has been applied for the utmost level of recognition of the inputted visionary image to the mobile application using CNN. The proposed mechanism has obtained 90.68% accuracy in the testing period as its utmost accuracy result. The coagulation of CNN and other cutting-edge technology has enhanced the performance of the developed application. Moreover, these features have introduced this implementation as a comparatively desired and required app in daily utilization
Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback
Get PDFThe gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews
Enhancing Real-Time Data Analysis through Advanced Machine Learning and Data Analytics Algorithms
Get PDFThis paper investigates the amalgamation of sophisticated machine learning and data analytics algorithms to enhance real-time data analysis across diverse domains. Specifically, it concentrates on the utilization of machine learning methods for real-time data analysis, encompassing supervised, unsupervised, and reinforcement learning algorithms. The research underscores the significance of instantaneous processing, analysis, and decision-making in contemporary data-centric environments spanning industries like defense, exploration, public policy, and mathematical science. The paper explores data analytics strategies for real-time data analysis, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive analytics techniques are explored for summarizing and visualizing extensive sensor data, while diagnostic analytics methodologies focus on detecting anomalies and irregular patterns in real-time data streams. Predictive analytics endeavors to predict forthcoming events based on historical data trends, thereby enabling proactive decision-making. Lastly, prescriptive analytics provides decision recommendations and optimization tactics grounded in predictive models and constraint logic. By offering a comprehensive examination of machine learning techniques and data analytics methodologies, the paper furnishes insights into augmenting real-time data analysis capabilities across various sectors. Additionally, it presents a case study on processing real-time data from an environmental monitoring system, illustrating the practical application of advanced machine learning and data analytics algorithms for proactive decision-making and environmental management
Enhancing the Industrial and Systems Engineering Capstone Design Course through a Collaborative Approach
Get PDFThis paper explores the current strengths and challenges of an Industrial and Systems Engineering (ISE) senior capstone design course in an ABET-accredited program. We aim to propose changes to improve the course through collaborative discussions with the course instructor. The major contribution of this consultation work is that—by leveraging the educational expertise in the field of engineering education research—it serves as a promising testament to the transformative potential of collaboration efforts in driving educational excellence in course design, delivery, and assessment of a senior capstone design course in a traditional engineering department. Consultations with the Capstone course instructor uncovered some gaps. In particular, we propose the inclusion of learning outcomes that focus on Fink’s Taxonomy of Significant Learning dimensions that have not been sufficiently addressed in the current version of the course to extend beyond the cognitive domain to encompass social and emotional aspects of learning, namely the Human Dimension, Caring, and Learning How to Learn. Recommendations were shared with the Capstone design instructor for potential future implementation through the engineering education faculty overseeing the engineering course improvement initiative
Research on the Incentive Model of Express Packaging Recycling Based on the Three-Party Game Model
Get PDFReverse logistics, a new logistics model with the advantages of resource conservation and environmental protection, has emerged in response to the strong negative externalities of traditional logistics. However, in a fully competitive market environment, enterprises generally do not actively implement reverse logistics based on their own profitability and competitiveness considerations. In order to explore the impact of various factors on the implementation of reverse logistics by enterprises, an incentive model based on the three-party game model for express packaging recycling is constructed from the perspective of the participating subjects in the process of reverse logistics. This is a three-party evolutionary game model that includes the government, express packaging recycling enterprises, and consumers, then analyzes the stability of strategic choices and equilibrium points of the game system of the three-party subjects in the implementation of the reverse logistics. The results show that when the incentives from the central government can fully cover the cost of the local government’s choice of active regulation strategy, it will lead the system to stabilize at the strategy combination of implementing reverse logistics, actively regulating, and participating in reverse logistics