Online-Journals.org (International Association of Online Engineering)
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On-Device Neural Network for Object Train and Recognition using Mobile
Get PDFA neural network is a machine learning (ML) program or model that processes information and recognizes patterns, similar to the human brain. The neural network algorithm operates by training on data to learn and enhance its accuracy. If there is any mistake in the learning process, the machine will react unexpectedly and produce incorrect information. So, whenever a neural network model is developed, it is mandatory to evaluate its performance and ensure its output is accurate. Cameras, touchscreens, internet connectivity, and powerful CPUs have contributed to the popularity of smartphones. Mobile apps are software applications that run on smartphones. ML enhances mobile app functionality by offering features such as voice recognition, image analysis, natural language processing, personalization, and recommendations. Training ML models using mobile apps is challenging due to limited resources, data, and privacy concerns. Object recognition is a neural network-based technique that enables the identification and localization of objects in an image or video. This technique is used in driverless cars, disease identification, industrial inspection, robotics, and more. In this paper, the author introduces a new neural network-based algorithm that utilizes mobile devices for training and recognizing images. Although mobile devices have some technological limitations for training, well-established guidelines for systematically mapping verification and validation techniques have been used in the proposed neural network to ensure performance and correctness in on-device object training and recognition
Analysis of Learners' Emotions in E-Learning Environments Based on Cognitive Sciences
Get PDFThe present study aimed to examine students’ emotions in e-learning classes through facial expressions and investigate the influence of different instructional methods on students’ emotional responses. In this study, we examined the facial expressions of 17 undergraduate students using three different methods of presenting educational content (PowerPoint, video, and Kahoot) in online classes and analyzed the data with face reader software. The findings demonstrated that students experienced various positive and negative emotions with different methods of content delivery. Furthermore, comparing the three methods revealed that the Kahoot method elicited the highest average of positive emotions among students compared to the other two methods. This difference can be attributed to the visual attractiveness and interactive nature of the Kahoot environment. Additionally, this study highlights that simply incorporating multimedia materials, such as PowerPoint presentations and videos, is not sufficient to enhance effectiveness and cultivate positive emotions in e-learning. While multimedia materials serve as supportive tools and enhance visualization, interaction at various levels (content, teacher, peers, etc.) is necessary. Nevertheless, the significance of this research lies in the innovative application of a tool for analyzing emotions in online learning classrooms, thereby enhancing the measurement of genuine and objective emotional responses in e-learning environments
Enhancing Scientific Knowledge Depth in Middle School Students through the Woods Model of Science Instruction using Mobile Technology
Get PDFThis study aims to investigate the impact of teaching science using the Woods model incorporated into mobile technology on developing the depth of scientific knowledge (DSK) among middle school students. To achieve this goal, a teacher’s guide and student worksheets were prepared for teaching and learning the “Chemistry of Matter” unit from the science book using the Woods model. Additionally, a test was developed to measure the DSK at three levels (recall, concepts and skills, and strategic thinking). The quasi-experimental pre-posttest design was used for two groups, one experimental and the other control. The study sample consisted of 62 students from the third grade of an intermediate school in the Abha region for the academic year 2022–2023, with 30 students in the control group and 32 students in the experimental group. The independent sample t-test, Cohen’s d effect size, and etasquared were used to analyze the research data. The results of the study showed a significant effect size for the utilization of the Woods model in developing the DSK at its three levels. The study recommends the importance of using the Woods model in teaching science at various educational levels
Design and Application of Scenario-Based Perception of Smart Wearable Device Interaction Method
Get PDFAt the moment, Internet technology is still in a rapid development phase, with more highperformance, low-power processor chips entering the market and the commercialization of the smart wearable device industry accelerating. Virtual reality, intelligent clouds, and other technologies are at the heart of today’s wearable technologies. It is a design product that employs cutting-edge technology to provide sturdy functioning and superior safety. However, the current study on smart wearable devices is more related to engineering technology, and there is relatively little research on the interaction behavior and user experience among users, devices, and environments. Incorporating the design object, application context, user experience, and other factors into the design of smart wearable devices can help promote the transformation of smart wearable devices from technology-driven to user experience-driven. Therefore, it is important to introduce the concepts of context-awareness and interaction design in the design of smart wearable devices. Through contextual cognitive theory, the study studies the contextual cognitive features of users, deconstructs and analyzes the interaction and experience of smart wearable devices, and discovers the relevant elements influencing user experience. The theoretical research results are then integrated with the interaction design system to connect the relationships among users, behaviors, scenes, and technologies, and a context-aware system for smart wearable devices based on scene perception is constructed. The influence of different aspects on the recognition accuracy of the system is analyzed, and the results show that the system proposed in this paper has very superior performance in gesture recognition
Decision Analysis and Practical Pathways for Innovative Teaching Methods in Information Technology Education
Get PDFWith the rapid advancement of information technology, its integration into modern education has become essential, with a focus on enhancing teaching outcomes and the student learning experiences through innovative teaching methods. However, existing studies predominantly suffer from a lack of systematic evaluation indicators and scientific decision models. The selection of evaluation indicators is often subjective, incomplete, and primarily qualitative, lacking quantitative data support. A systematic decision analysis method was proposed in this study to address these issues. This study comprises three components: the construction of an evaluation indicator system for innovative teaching methods in information technology education, a scale analysis based on gray relational degree, and the computational steps for the decision model concerning innovative teaching methods. This study provides scientific bases and practical pathways for the selection of innovative teaching methods in information technology education, aiming to enhance teaching effectiveness and educational quality
Intelligent Tourism: Innovative Applications of Mobile Technology in Personalized Travel Planning
Get PDFWith the rapid development of the global tourism industry and the widespread adoption of mobile internet technologies, personalized travel itinerary planning has emerged as a crucial method to meet the diverse needs of tourists. Traditional planning approaches, relying on fixed routes and preset attractions, often fail to provide flexible and personalized services. In this context, the application of intelligent tourism technologies has garnered increasing attention, facilitating the provision of customized itinerary planning services through mobile technologies and intelligent algorithms. Although existing study has enhanced the intelligence level of itinerary planning to some extent, it still falls short in considering the comprehensive dimensions of time and space, as well as in responding to the dynamic demands of tourists in real time. This study introduces a mobile predictive model tailored for personalized travel itinerary planning, incorporating the components of a spatio-temporal graph convolutional network (STGCN) with an attention mechanism and spatial node embedding vector components. This model effectively captures the dynamic characteristics of time and space in travel itineraries, achieving personalized recommendations and optimizations. It significantly improves the accuracy and response speed of itinerary planning, providing important support for the digital transformation and innovative development of the tourism industry
A Characteristic Curve Remote Laboratory for School and University
Get PDFRemote laboratories play a pivotal role in facilitating teaching and learning experiences, offering unique opportunities beyond traditional classroom settings. The project OnLabEdu (Online Laboratories for School Education) aims the development of remote laboratories for both, schools and university-level students, complete with the development of appropriate accompanying educational resources. This paper introduces a characteristic curve remote laboratory with a first-developed learning arrangement on RGB LEDs and the interplay of energy, forward voltage, and wavelength of light. Drawing from the model of educational reconstruction, the learning arrangement is based on a profound clarification of the scientific content and considering students’ perspectives. Against the background of a design-based research approach, the learning arrangement was already evaluated with high school students through probing acceptance interviews. Now we are interested if the learning arrangement also fits to tertiary level students. Therefore, we conducted six probing acceptance interviews with pre-service teacher students, hailing from physics (n=3) and non-physics (n=3) backgrounds. The primary objective of the evaluation was to identify potential barriers to learning and elements that promote a stimulating learning environment along with the operation of the remote laboratory for the tertiary level. The findings revealed similar challenges of tertiary-level students and high school students. We detected students’ challenges with technical terminology as well as explaining the term forward voltage and its connection to the concept of energy. These results give initial ideas for a re-design of the learning arrangement to address the identified issues and make the learning arrangement suitable for tertiary level students
Electronic Health Record Interoperability System in Peru Using Blockchain
Get PDFIn Peru, there is currently no integrated electronic health record (EHR) system that can be automatically shared between healthcare facilities. This leads to increased service costs due to duplicated examinations and records, as well as additional time required to manage patients’ clinical information. One alternative for ensuring the secure interoperability of EHRs while preserving data privacy is the use of blockchain technology. However, existing works consider a pre-established format for exchanging EHRs, which is not applicable when systems have different formats, as is the case in Peru. This work proposes an architecture and a web application for exchanging EHRs in heterogeneous systems. The proposed system includes the homologation of an EHR with rapid interoperability resources for medical attention using FHIR HL7, and vice versa, to achieve interoperability. Additionally, it utilizes blockchain technology to ensure data security and privacy. The web application was tested using a case simulation to demonstrate EHR interoperability between clinics in a clear, secure, and efficient manner. In addition, a survey was conducted with 30 patients regarding adoption, and another survey was conducted with 10 doctors from a public hospital in Peru regarding usability. The results demonstrate a very high level of adoption and usability for them all. Unlike other studies, the proposal does not necessitate alterations to existing EHR systems for interoperability. In other words, the proposal presents a feasible and cost-effective alternative to addressing the EHR interoperability issue in clinics and hospitals in Peru
The Role of Artificial Intelligence in the Diagnosis of Neoplastic Diseases: A Systematic and Bibliometric Review
Get PDFArtificial intelligence (AI) has significantly transformed the medical field, especially in the diagnosis, treatment, and management of oncological diseases. It has had a profound impact on clinical decision-making and has enhanced the quality of life for various populations. This study aims to comprehensively assess the inherent relationship between AI and medicine and to uncover both its positive and negative implications. To achieve a comprehensive understanding, a thorough systematic review of articles was conducted, examining a total of 80 papers published between 2017 and 2023. These articles were carefully selected from well-known open-access databases, such as Scopus, IOPscience, IEEE Xplore, Google Scholar, ResearchGate, and ProQuest. A key finding from this review is that the majority of research on this topic has been published in scientific journals ranked in the first-quartile (Q1), underscoring the importance and high quality of research in this field. The United States, China, India, the United Kingdom, and Canada are the foremost countries in publishing on this topic. Most of the research is published in first-quartile (Q1) journals, representing 51% of the studies. Only 1% of articles appear in third-quartile (Q3) journals. IEEE Xplore is renowned as the primary database for accessing high-impact studies in this field. Future research should prioritize investigating the long-term impact of AI on patient clinical outcomes. International collaborative research could promote innovation and fairness in the implementation of artificial intelligence (AI) in oncology
A Robust Approach for Ulcer Classification/Detection in WCE Images
Get PDFWireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier