Online-Journals.org (International Association of Online Engineering)
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Machine Learning Classification Algorithms for Traffic Stops—A Comparative Study
Get PDFThe application of machine learning algorithms across various fields is gaining momentum, and the results increasingly emphasize the need for further testing and implementation. This is driven by the potential to streamline and expedite numerous processes. In this paper, we have employed five algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes, and these algorithms have been tested in three large datasets. On average, their performance ranges from a minimum of 80% to a maximum of 90%. Data preprocessing has been completed, and concurrently, we have implemented the SMOTE algorithm to address the challenge of unbalanced data in this research. Simultaneously, the Naïve Bayes algorithm yields the most favorable results of Accuracy, Precision, Recall, and F1 Score, for the “is_arrested” class. Furthermore, to assess the performance of each algorithm, we employed metrics including Accuracy, Precision, Recall, and F1 Score. These metrics allowed us to decide which algorithm achieved the most effective classification
Estimate the Region of Interest, Movement and Magnitude of Ciliary Beat with Dense Optical Flow
Get PDFIn this study, we analyze mucociliary transport (MCT) by measuring the magnitude and identifying regions of ciliary beats using high-frame-rate microscopic videos. Our methodology, integrating dense optical flow (DOF), connected component labeling (CCL), Butterworth filter, and Fast Fourier Transform (FFT), captures ciliary movement and magnitude. We focus on region extraction, quantification of ciliary activity, and classification of power and recovery strokes in ciliary beat frequency (CBF), which are crucial for evaluating MCT efficiency. Our approach was able to extract the ciliary region semi-automatically, obtain the CBF, and visualize the ciliary movement in each frame. Despite dataset challenges and limited ground truth, our approach shows a promising result for ciliary dynamics research and medical diagnostics. We hope for future open-source datasets with ground-truth ciliary beat patterns to enable developing and evaluating automated ciliary analysis techniques, leading to improved assessment
Enhanced Water Quality Prediction in the Yellow River Basin: The Application of the HHO-LSTM Model
Get PDFIn the pivotal water resource region of the Yellow River Basin in China, precise prediction of water resources is essential for their effective and rational management. This study introduces a novel approach to water resource prediction by employing the Harris Hawks Optimization-Long Short-Term Memory (HHO-LSTM) model. This method overcomes the constraints faced by traditional techniques in processing time series data and various variable factors. It encompasses a comprehensive description of the multi-source hydrological data collection process within the Yellow River Basin, followed by meticulous data preprocessing. The data set for this study includes estimates of four critical water quality parameters, and the efficacy of the model is gauged through the mean squared error (MSE) and root mean squared error (RMSE) metrics. This facilitates the projection of future water quality trends in specific areas by leveraging historical water quality data. The HHO-LSTM model has demonstrated outstanding accuracy and robustness in predicting water quality across diverse temporal scales and water resource variables, marking a significant advancement in water resource management within the Yellow River Basin. This approach not only enhances current management strategies but also contributes valuable insights for ongoing water resource research and decision-making processes
EEG-Based Control of a 3D-Printed Upper Limb Exoskeleton for Stroke Rehabilitation
Get PDFBrain-computer interfaces (BCIs) have emerged as transformative tools for translating users’ neural signals into commands for external devices. The urgent need for innovative treatments to enhance upper limb motor function in stroke survivors is underscored by the limitations of traditional rehabilitation methods. The development of communication and control technology for individuals with severe neuromuscular diseases, particularly stroke patients, is centered on utilizing electroencephalographic (EEG) signals to accurately decode users’ intentions and operate external devices. Two healthy subjects and a stroke patient were enrolled to acquire EEG signals using the EMOTIV EPOC+ sensor. The experimental procedure involved recording five actions for both motor imagery and facial expression signals to control the 3D-printed upper limb exoskeleton. EEGLAB and BCILAB software were used for preprocessing and classification. The results showed successful EEG-based control of the exoskeleton, representing a significant advancement in assistive technology for individuals with motor impairments. The support vector machine (SVM) classifier achieved higher accuracy in both offline and online modes for both motor imaginary and facial expression tasks. The conclusion highlights the appropriateness of using EEGLAB for offline EEG data analysis and BCILAB for both offline and online analysis and classification. The integration of servo motors in the exoskeleton, allowing movements in five Degrees of Freedom (DOF), positions it as an effective rehabilitation solution for individuals with upper limb impairments
Trustworthy Verification of Academic Credentials through Blockchain Technology
Get PDFThe increasing demand for a secure and transparent mechanism for verifying academic credentials has led to the exploration of blockchain technology (BT) as a viable solution. This paper presents a three-layered application for the reliable verification of academic credentials, leveraging the immutable and decentralized nature of BT. The system includes a data layer (DL), a blockchain layer (BL), and an application layer (AL). The DL records student records, faculty staff records, and credentials. The BL utilizes smart contracts to record and verify academic records, ensuring their authenticity and integrity. The AL provides a userfriendly interface for various stakeholders, such as students, educational institutions, and employers, to communicate with the system. This effort aims to determine how BT can be utilized to enhance the security, transparency, and efficiency of academic credential verification processes. Efficiency in academic credential verification processes ultimately contributes to a more reliable and effective educational ecosystem
From Doubt to Drive: How Instructional Modality and Self-Efficacy Shape Motivation in Remedial Spatial Visualization Courses
Get PDFSpatial thinking is the foundation for successful problem-solving and critical thinking. Scholars have confirmed that spatial skills are essential tools for problem solving in fields such as engineering, design, physics, and mathematics. Drawing on Bandura’s self-efficacy theory, this study investigates the impact of instructional modality, self-efficacy, and attitudes toward a spatial visualization app on student motivation in the context of an engineering remedial spatial visualization course. Our study focused on undergraduate engineering students from two cohorts with different instructional modalities, one in 2019 and the other in 2020. This study employs a quantitative approach, gathering data through questionnaires to measure student motivation, self-efficacy, attitudes toward the app, computer-aided design (CAD) experience, gender, and instructional modality. Our findings indicate that instructional modality significantly influenced student motivation, with online instruction during the pandemic being associated with lower motivation. Furthermore, significant predictors of student motivation were identified as self-efficacy and attitudes towards the app, independent of instructional modality. The findings provide insights into strategies for educators to implement educational technology in their courses while also remaining committed to nurturing student self-efficacy in online and in-person learning
Games and Arts as Tools for Developing Generic Skills in Engineering Students
Get PDFIn today’s educational landscape, the integration of innovative pedagogical approaches is essential for improving student learning. In this respect, game-based learning appears to be a strategy that not only actively engages students—who often struggle to understand scientific concepts, leading to negative emotions and discouragement—but also helps them acquire transferable skills. Particularly in the field of telecommunications engineering, where the practical application of knowledge is essential, the use of educational games offers a unique opportunity to explore and reinforce specific and general skills. In this study, a cross-sectional quantitative methodology was implemented using a representative sample of 64 undergraduate students in the sixth semester of the telecommunications engineering program at a public university. In general, educational games are primarily intended to evoke emotions in participants, such as enjoyment, to help them cultivate a “receptive learning mindset” and to effectively teach a specific subject. In contrast to these approaches, the game proposed in this study incorporates humanistic skills and artistic tools to construct antennas within a specific context. This enabled students to engage in a practical, applied learning experience in their field of study, focusing on three key aspects: appropriating knowledge, building values, and learning about life, as well as recognizing others in society and interacting with the environment. The results obtained using the proposed game-based strategy were compared with those obtained using conventional teaching tools. The results showed that play not only imparted knowledge but also contributed to students’ overall development by fostering the values and skills essential to their personal and professional success. The results underscore the significance of developing games that authentically simulate project processes and activities. This emphasizes the importance of aligning game dynamics with real-world challenges and situations in the field of telecommunications engineering. This simulation-based approach may be the key to maximizing the impact of game-based learning on the training of future professionals in this field. This comparative study helps fill a gap in knowledge about game-based learning in engineering education by providing valuable insights. It is also important to comprehend the impact of game-based learning on an antenna course and the students’ willingness to engage with this innovative teaching style in the classroom
Predicting Global Education Quality: A Comprehensive Machine Learning Approach Using World Bank Data
Get PDFThis paper introduces an innovative approach to predicting the quality of education on a global scale. It leverages a comprehensive dataset spanning multiple years and countries from the World Bank. Our methodology involves two key steps: first, unsupervised clustering using the K-means model to categorize countries based on their educational quality levels; and second, employing supervised classification techniques to develop a predictive model. Through training and optimizing various machine learning (ML) algorithms, we aim to identify the most accurate model for predicting education quality. The outcomes highlight the efficacy of our approach, with the KNN algorithm demonstrating superior performance after hyperparameter optimization. It achieved precision, recall, accuracy, and AUC values of 0.9740, 0.9721, 0.9711, and 0.9959, respectively. These findings provide valuable insights for policymakers, educational institutions, and researchers, helping to identify areas that require attention and to design targeted interventions
The Educational Recommendation System with Artificial Intelligence Chatbot: A Case Study in Thailand
Get PDFThe educational recommendation system with an artificial intelligence (AI) chatbot is a system designed to operate on smartphones and is intended for use at the College of Industrial Technology in Thailand. Compatible with both iOS and Android operating systems, the educational recommendation system in this research is capable of providing educational recommendations and valuable information about the engineering degree program. It includes information about the Electronics Engineering Technology Department, relevant bodies of knowledge that can enhance awareness and public relations, and a proactive introduction to information. The main objectives of this study are to develop the capacity to analyze educational issues and provide useful and accurate information for educational recommendations, as well as to explore the possibilities for enhancing this system. The results of the study address the three research questions and demonstrate that the process and structure of the educational recommendation system with an AI chatbot can be utilized to create educational recommendation tools in higher education. These tools are accessible anywhere and at any time through small mobile devices. The system is also compatible with popular social media applications and is capable of analyzing user questions precisely and accurately to find the optimal answers that can meet the user’s needs. However, there are still gaps in the research that need to be investigated in the future. These studies should include exploring a broader range of applications among the general population and educational settings to assess the effectiveness of the educational recommendation system with an AI chatbot. Additionally, they should examine the skills that impact the utilization of educational technology in various contexts
Metaverse-Based Activities for Enhancing Communicative Competencies among Young Learners
Get PDFThe transformative potential of the metaverse in English language learning, particularly in rural areas such as the Felda (Federal Land Development) Malaysian communities, has gained popularity following the COVID-19 outbreak. This study explores the use of metaverse-based activities to enhance communicative competencies among young learners in these rural areas. Employing a quantitative methodology, the research involves 60 participants divided evenly into two groups: an experimental group and a control group. The former received metaverse-based English language instruction, while the latter followed traditional classroom teaching. Results from pre- and post-tests, as well as structured questionnaires, indicate that the metaverse significantly improves communication competencies, including speaking, listening, and social interaction skills. The data show that metaverse-based activities provide a more engaging and immersive learning environment, promoting natural and authentic improvements in communicative abilities for young children in rural primary schools. These findings hold significant implications for English language instructors and curriculum designers, highlighting the potential of integrating modern technology into early childhood education