Online-Journals.org (International Association of Online Engineering)
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Optimizing Broadcast Utilization for Efficient Disaster Management Using Wireless Ad Hoc Networks and Novel Energy-Saving Algorithms
Get PDFGlobalization, industry, and population growth is considered the main reasons for climate change, so when some natural calamity or disaster occurs in any area, it leads to isolating this area from the rest of the region. Wireless ad hoc networks are important in disaster management to reduce losses and costs as they reduce the response, aiding time to save lives. However, they suffer from limited energy, which controls the lifetime of the sensor. Different methods are applied to save energy and increase battery life, such as sleep-awake schedules which assume similar initial energy for all nodes. Domestic partition in a unit disk graph is the production of the maximum possible number of such disjoint dominants. However, it assumes different initial energy levels for all nodes, which is another problem. In this paper, a new broadcast approach based on minimum set data broadcasting is proposed to minimize the number of broadcasting messages to increase the battery life during disasters and enhance rescue operations. The proposed minimum rebroadcast algorithm (MSRA) method balances coverage and utilization efficiency by dynamically adjusting node selection based on network conditions. The results show that MSRA significantly reduces power consumption and transmission latency, opening the way to find a new solution for disaster management using wireless ad hoc networks
Combined Deep Learning Approaches for Intrusion Detection Systems
Get PDFCybersecurity has become increasingly important because of the widespread use of data and its enormous global storage. Hackers and other invaders always want to breach data security by interfering with network traffic. The breaches must be stopped by several tools, such as firewalls. Other solutions, such as intrusion detection systems (IDSs), may detect network intrusions effectively. In this paper, we introduce a hybrid technique (CNN-LSTM) that combines the convolutional neural network (CNN) with long short-term memory (LSTM), a modified version of the recurrent neural network (RNN). The model is tested using the CSE-CIC-IDS2018 dataset. Both CNN and LSTM were individually applied to the datasets, and the results are compared with our hybrid CNN-LSTM model. The hybrid CNN-LSTM model demonstrated higher accuracy (99%) during both training and validation processes compared to individual models; the accuracy of the CNN model is 92% and the accuracy of the LSTM is 93.5%. The outcomes validate the usefulness and effectiveness of the hybridizing model
Evaluation of Vocational Education and Training Outcomes Based on Mobile Learning
Get PDFWith the rapid advancement of information technology (IT), mobile learning has gradually become a significant approach in vocational education and training. University students utilize mobile applications for learning, which not only enhances the flexibility and efficiency of their studies but also promotes the equitable distribution of educational resources. However, effectively evaluating the impact of these applications in vocational education and training remains an urgent issue to be addressed. Current study methods predominantly focus on the analysis of static data, which inadequately captures the dynamic changes in students’ learning behaviors. Additionally, traditional predictive models exhibit low accuracy and poor generalization capabilities when handling high-dimensional, nonlinear time-series data. This study proposes an evaluation method for vocational education and training outcomes based on an improved gated recurrent unit (GRU) model, which comprises three main components: decomposition of university students’ mobile application time-series data based on the variable dependence model (VDM), preprocessing of the mobile application data, and outcome evaluation using the improved GRU model. Incorporation of an attention mechanism enhances the predictive performance of the model, providing data support and a decision-making basis for educators and developers
Evaluating the Effectiveness of Mobile Interactive Technology in University Physical Education Courses
Get PDFWith the rapid development of mobile interactive technology, a transformative shift in the pedagogical model of university sports courses has been initiated. Traditional teaching methods are simplistic and characterized by their lack of interactivity and personalization. However, the implementation of mobile interactive technology provides students with increased opportunities for participation and access to learning resources. Although existing research focuses on the integration of mobile interactive technology in university sports courses, a significant limitation remains in the evaluation methodologies employed, with a lack of systematic quantitative analysis and a comprehensive evaluation framework. This study proposes an effectiveness evaluation model comprising three primary components: construction of the evaluation model, calculation of indicator weights, and application of the fuzzy comprehensive evaluation method. The objective is to provide a comprehensive understanding of the effectiveness of mobile interactive technology in university sports courses through scientific evaluation methods, offering insights for future educational reforms and technological implementations
A Comprehensive Approach to Enhancing Doctor-Patient Interaction: Bridging the Gap for Better Healthcare
Get PDFEffective doctor-patient communication is a critical clinical function, and the resulting relationship is the heart and soul of medicine, as well as a critical component in healthcare delivery. Inefficient communication between doctors and patients is the leading cause of patient dissatisfaction, as it directly affects the health outcomes of the patient. The current study aimed to develop a holistic solution to bridge the doctor-patient communication gaps in the cardiology department and facilitate effective communication, ensuring better health outcomes. Desk study and empathy interviews highlighted the doctor-patient communication gaps and their effects on the overall health outcome and patient satisfaction. Various concepts were explored using the insights found using journey maps, causal loops, and affinity walls. User approval was determined by soliciting user feedback, and the System Usability Scale (SUS) was used to evaluate usability. A holistic solution utilizing educational material for quick understanding and recall of all issues reorganized the file, which provides structure to the interaction during the consultation and facilitates two-way interaction and appbased tracking of symptoms for informed decision-making, was developed. The solution was very effective in educating and preparing the patient, involving the patient for higher patient engagement, and assuring the patient for increasing patient adherence. It was found that the patients were able to understand and address their concerns effectively, and doctors were able to communicate more effectively. (SUS score: 78.12 and 75.62 for doctors and patients, respectively). As the solution is easy to adapt in the current system, it would help facilitate effective communication between doctor and patient
The Research on the Application of Deep Learning in Education
Get PDFWith the in-depth reform of education, deep learning is gradually being applied to education, which can stimulate students’ initiative in learning and improve their learning efficiency. This article explores some specific applications of deep learning in education, uses the deep learning model LSTM to predict students’ learning abilities and outcomes, and obtains high accuracy. Through deep learning models, we have abstracted the characteristics of students’ learning states and made relatively accurate predictions on their learning outcomes. This paper provides some useful explorations on the application of artificial intelligence and deep learning in education, which can provide valuable references for better research in the future
Linking Digital Technology, Omics and Education to Facilitate Global Equity
Get PDFThere are many challenges, not least health inequities, global warming, and a rush for growth and economic development. Personalized, precision, and preventative medicine, bringing the latest omics techniques—genomics, transcriptomics, and metabolomics—for individuals allied to personalized prescription and care should help health equity. Digital technologies and artificial intelligence (AI) can help in an understanding of disease processes and in drug development. A holistic approach to the relationship between technology and the environment and clarity about both the positive benefits and negative harms resulting from using digital tools is necessary. We need to focus on the complete human-environmental interface and not just on climate change and carbon. It will be a measure of collaborative civilization if digital technology, omics techniques, and education can be used to promote global equity. Education linking diversities and performance throughout the world will be crucial
Factors Influencing the Implementation of Data-Driven Techniques for Students’ Mental Health
Get PDFData-driven methods are being implemented in many schools around the world to improve education. In this study, two schools were studied to investigate how they implemented datadriven methods for the monitoring and improvement of the well-being of their students. These schools were part of a Swedish national program where 15 schools participated to use data on both classroom, school, and system levels for school improvement. We identified five factors that influenced the implementations, namely data collection and analysis, frequency, anonymity, involving students, and organizational changes. We conclude that continuous and frequent data collection provided insights on students´ well-being that cannot be achieved without systematic data collection. Since this kind of data collection can be time-consuming, dedicated digital tools can be used to automate data collection and analysis. These tools can also provide a better basis for decision-making since it is easier to connect and visualize data. We also conclude that the European Union’s (EU) General Data Protection Regulation (GDPR) is important when using student data, and there is a need for national guidelines on how to use data securely and efficiently in schools
Mobile Application Design Oriented to Students with Deaf Mute Disabilities
Get PDFDeaf-mute individuals have a right to inclusive education in today’s global context. The primary objective of this study is to design a mobile application that caters to the specific needs of students with hearing and speech disabilities, contributing to the promotion of inclusive and equitable education. To conduct this study coherently, the effective Design Sprint methodology was employed, consisting of five well-defined phases. Throughout this process, interviews and surveys were conducted with various stakeholders, including students, parents, and specialists in inclusive education. The results of these investigations formed a robust foundation for understanding user needs and perspectives, ensuring the optimal adaptation of the mobile application to the demands of this demographic. In conclusion, this study underscores the importance of creating inclusive solutions for students with hearing and speech disabilities, emphasizing the need for collaboration and commitment from educational authorities to successfully implement such solutions
A Study of Generative Artificial Intelligence on Mobile Learning Adoption Based on SEM Models
Get PDFWith the rapid development of information technology, the application of generative artificial intelligence (GAI) in the field of education is becoming more and more extensive, especially on contemporary college students’ mobile learning, which has a profound impact. However, the attitudes of contemporary college students towards using GAI for mobile learning are characterized by complexity and diversity, so it is necessary to explore the factors affecting college students’ willingness to use GAI. In view of this, this paper conducted a questionnaire survey with 1028 college students in China and adopted the structural equation modeling (SEM) model to identify and analyze the factors affecting college students’ behavior of using GAI for mobile learning. The results show that performance expectation, effort expectation, social influence, convenience conditions, and perceived fun of GAI significantly affect college students’ willingness to use GAI, while perceived risk and perceived learning resources have no significant direct influence effect on willingness to use. Based on the empirical results, future strategies for the advancement of GAI education are proposed to further optimize the application of GAI in m-learning