Online-Journals.org (International Association of Online Engineering)
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A Visual Computing Unified Application Using Deep Learning and Computer Vision Techniques
Get PDFVision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. Computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models
Using the Podcast for Feedback: A Qualitative Study
Get PDFIn virtual environments, it is essential for the teacher to structure the learning experience and guide knowledge using various feedback tools. Therefore, the objective of the present study was to analyze the effectiveness of using podcasts as a formative evaluation tool. A phenomenological-qualitative methodological strategy was proposed to report the process of a learning experience through the design of cases. To achieve this goal, data collection techniques such as observation, semi-structured interviews, and documentary analysis were employed. The qualitative analysis from the teacher’s perspective revealed the primary areas for improvement in academic writing. In response to this, a process for providing corrective and suggestive feedback was proposed, utilizing comments and podcasts. In particular, the best-performing groups were those that most frequently utilized the multimodal resources for preparing and providing feedback in newsrooms. From the students’ perspective, they valued the feedback process in virtual environments because it allowed them to identify mistakes made in the writing process and improve their writing by considering the teacher’s comments and the criteria of the rubric
Development of an Artificial Nose Using 3D Printing Technology for Patients with Tumors
Get PDFA prosthetic nose is an option where the patient can gain an artificial organ for aesthetics. Thus, this study includes the 3D scan and reconstruction of the 3D model of an artificial nose and analyzes different material properties, which are thermoplastic polyurethane (TPU), thermoplastic elastomer (TPE), and nylon 6 (PA). This research aims to fabricate an artificial nose model using 3D printing technology. A 3D scanner can scan the human head before reconstructing the nose. The analysis of different materials on the artificial nose model is to determine the appropriate material for the artificial nose to fit perfectly into the patient’s face. The artificial nose will use TPU material with a deformation value of 0.00413mm and a maximum stress value of 0.4930 MPa. This prosthesis nose enables people to interact in their social and family lives, making them happier and more comfortable
Mobile Application with Augmented Reality as a Support Tool for Learning Human Anatomy
Get PDFLearning the anatomy of the human skeletal system presents several challenges in understanding the complexity of the human body. One of the most common issues is the absence of effective and accessible learning methods that enable students to gain comprehensive knowledge. Therefore, the use of technologies such as augmented reality (AR) aims to address this issue and facilitate its resolution by enabling students to engage with three-dimensional anatomical models, fostering hands-on, visualization-based learning. The aim of this study is to enhance the learning of human skeletal anatomy through the use of AR technology. The study employed a quantitative approach and a pre-experimental design, in which the experiment was conducted according to the research plan and involved 60 students. Mobile-D was used to develop the mobile application. The findings revealed that 93.3% of participants agreed that the use of augmented reality is a valuable for learning human anatomy, as it enables interactive visualization of various parts of the human body. The study also indicated that 28.3% of the students scored “Outstanding,” while 68.3% scored “Predicted.” In addition, 65% of students expressed interest in using augmented reality technology to learn anatomy
Integrating Escape Rooms and E-Learning Technologies for Smart Cities Development: A Simulation Study
Get PDFThe paper gives an intensive review of the current state of escape rooms and e-learning innovations within smart city instruction. It consolidates existing information, making it available to teachers, analysts, and policymakers. The paper bridges two distinct but complementary areas by combining insights from escape rooms and e-learning innovations. The paper distinguishes holes within the current investigation and hones, indicating ranges requiring assist examination. It also highlights openings for future advancement and development within the utilization of escape rooms and e-learning advances. This study examines 13 review papers related to each innovation. The strategy proposes a system that gives commonsense rules and contemplations for teachers and engineers. The paper outlines how escape rooms and e-learning advances have been effectively coordinated and utilized in keen city instruction in Oman. The paper finds and examines the potential effect of escape rooms and e-learning advances on learning results, emphasizing their part in improving engagement, inspiration, and critical thinking abilities among learners. The study reveals the center on innovative educational innovations; the paper contributes to forming more intelligent and feasible urban situations
The Impact of Augmented Reality Storybooks on Children’s Reading Comprehension and Motivation
Get PDFWhile existing literature extensively explores the application of augmented reality (AR) technology in science, technology, engineering, and mathematics (STEM) fields and higher education, research on its use in children’s reading remains relatively limited. This research gap urgently needs to be addressed, particularly in understanding how AR can enhance children’s reading comprehension, story retelling, and reading motivation. To address this gap, this study examined the effects of AR Chinese picture storybooks on second-grade students’ reading comprehension, story retelling, and reading motivation. A quasi-experimental design was employed with 60 second-grade students from a primary school in central China, where 30 students used AR storybooks and 30 used traditional paper storybooks. The results indicated that students who used AR storybooks demonstrated better reading comprehension, particularly implicit comprehension, than those who used paper storybooks. However, there was no significant difference between the two groups in explicit comprehension. Additionally, the AR storybook group outperformed the paper storybook group in both story retelling and reading motivation. These findings suggest that AR technology can create more interactive and immersive reading experiences, positioning AR as a promising tool for enhancing literacy development in young learners
IoT-Enabled Smart Greenhouse for Robotic Enhancement of Tomato Production: Leveraging 5G and Edge Computing for Advanced Data-Driven Automation, Precision Irrigation, and Scalable Zoning Principles
Get PDFThere are many difficulties in growing tomatoes, such as erratic weather patterns, variable yields, and short shelf lives. Tomatoes are a staple food that is grown all throughout the world, particularly in climate-friendly areas such as Morocco. These areas are not free from the difficulties that present major obstacles for farmers, though. Modern agricultural techniques are increasingly using cutting-edge technologies, such as Internet of Things (IoT)-enabled smart greenhouses, to address these problems. In order to improve tomato output, this study presents an enhanced smart greenhouse model that incorporates the zoning principle. The method provides optimal plant growth and resource efficiency by separating the greenhouse into various zones, each tailored for specific growth stages and environmental circumstances. Precision farming and sustainable practices are made possible by the suggested systems real-time monitoring and control, which integrates sensors, actuators, and data analytics. The automatic irrigation system of the intelligent greenhouse prototype is additionally set up to offer the best possible support for the growth of tomato plants. The functionality and efficacy of the prototype are investigated through extensive laboratory testing and experimentation, providing insight into its potential influence on agricultural practices and practical practicality. The greenhouse that has been created with the help of IoT capabilities and the zoning concept has proven to be an effective instrument in assisting farmers in their attempts to grow tomatoes in an efficient and sustainable manner. Our comprehensive testing yielded data that demonstrate the zoning principle-integrated IoT-enabled smart greenhouse greatly improves tomato quality and production. This system demonstrates the benefits and practical applicability of sophisticated data-driven automation in agriculture by providing a scalable and sustainable solution to the problems encountered by tomato producers
Optimizing Electric Vehicle Charging Infrastructure through Machine Learning: A Study of Charging Patterns and Energy Consumption
Get PDFThe rapid adoption of electric vehicles (EVs) has created a pressing need for efficient charging infrastructure. but challenges such as inconsistent demand and poor placement remain. An effective distribution of sufficient EV charging stations (CSs) is one of the major obstacles preventing the market penetration of EVs and the realization of a sustainable transportation system in urban areas. In this paper, a new machine learning technique is proposed in order to optimize the placement of EV charging stations (EVCSs) in metropolitan areas based on an energy consumption prediction model. A dataset from 148,136 charging transactions in Boulder, Colorado, is used with the proposed model. Key algorithms such as KNeighborsRegressor and RandomForestRegressor were incorporated to solve the placement problem. The analysis revealed significant demand fluctuations during peak commute hours, with the KNeighborsRegressor model demonstrating superior prediction accuracy. These insights can guide more effective infrastructure planning and resource allocation, ultimately enhancing the efficiency and user experience of EV charging networks and promoting sustainable urban transportation
Design and Development of Multimodal Biometric System Using Finger Veins and Iris by CNN Integrated with Hybrid SIO and Whale Optimization Techniques
Get PDFBiometrics encompasses technological and scientific advancements in monitoring and interpreting biological data from the human body to enhance system security by offering precise and dependable patterns and techniques for person authentication and recognition. Its solutions are widely used by governments, the armed forces, and enterprises. Single sources of data in biometric systems, referred to as unimodal systems, are effective but frequently struggle with noisy data. Many of these challenges can be solved with multimodal biometric systems, which incorporate two or more biometric modalities. In this study, we utilized an optimized Convolutional Neural Network (CNN) for multimodal biometric recognition. For optimization, a hybrid of Swarm Intelligence (SI) and Whale Optimization (WO) algorithms was employed. The Finger Vein (FV) and iris modalities were chosen for biometric recognition. Data for both modalities were collected from the SDUMLA-HMT database and preprocessed before being fed into the CNN model for feature extraction and selection. Following the CNN modeling, both feature- and score-level fusion techniques were applied for individual recognition. The developed hybrid SI-WO-CNN model was evaluated against two other optimized models, namely the SI-CNN and WO-CNN. Experimental results show that the proposed hybrid CNN model achieves the highest accuracy, reaching 99% through the score-level fusion technique. Furthermore, the proposed model was compared with recent research works, demonstrating its effectiveness in biometric recognition
Predictive Model for Physical Performance in Athletics: Correlation between Anthropometric Data and Cardiorespiratory Capacity in Students from a Private School
Get PDFIn the current educational context, physical education and student sports development face challenges marked by continuous technological evolution. This study proposes a predictive model supported by machine learning and artificial intelligence (AI), establishing a connection between cardiorespiratory capacity (VO2max) and student anthropometric data. With a sample of 179 students aged 13 to 18, the model-building process included preparing and partitioning a dataset, training, and evaluation under the CRISP-DM methodology. A multiple linear regression model was applied, incorporating weight, age, height, sex, and body mass index (BMI) to analyze their relationship with the dependent variable (VO2max). Performance metrics revealed a significant correlation between anthropometric measurements and cardiorespiratory fitness (CRF), with a 24% improvement in training, although test accuracy was -0.8%. Including additional variables, such as sex and age, they have improved the predictive equations. However, the ability of the model to predict VO2max was limited, suggesting the complexity of the relationship between these factors. In a comprehensive evaluation, five linear regression models achieved a correlation accuracy of 22% with the complete data set