Online-Journals.org (International Association of Online Engineering)
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Exploring the Impact of AI on Education: Implications and Future Trends
Get PDFArtificial intelligence (AI) has emerged as a fundamental component of contemporary society, significantly transforming various sectors, including education. Its application within educational contexts is transforming education by offering new approaches to teaching and learning. This paper explores the impact of AI on education, focusing on its potential to personalize learning, improve teaching efficiency, and support administrative tasks. AI applications such as machine learning, natural language processing, and data analytics are used to create intelligent tutoring systems, virtual assistants, and adaptive learning platforms that cater to individual student needs. While AI enhances student engagement and learning outcomes, challenges such as privacy concerns, data security, and algorithmic biases must be addressed. The paper also discusses the ethical implications of AI in education and highlights future trends, including the integration of AI-driven educational games, virtual reality, and augmented reality. Overall, this paper aims to provide a comprehensive overview of AI’s role in shaping the future of education while emphasizing the need for responsible implementation
IoT-Based Real-Time Water Quality Monitoring and Sensor Calibration for Enhanced Accuracy and Reliability
Get PDFThe increasing water pollution levels and the growing demand for clean water necessitate advanced monitoring solutions. This study aims to develop an Internet of Things (IoT)-based real-time water quality monitoring system that addresses the limitations of traditional methods, which are often time-consuming and lack continuous monitoring capabilities. The system integrates pH, temperature, and turbidity sensors with an Arduino microcontroller and NodeMCU for Wi-Fi connectivity, transmitting data to a cloud platform for real-time access and analysis. The methodology involved interfacing these sensors with the microcontroller, displaying LCD data, and providing LED alerts. The findings indicate that the prototype’s readings closely align with laboratory measurements, with minor deviations in pH and temperature values and slightly more significant differences in turbidity readings. This demonstrates the prototype’s reliability and accuracy in real-time environmental monitoring. The conclusion emphasises the system’s potential to enhance water resource management through continuous and accurate tracking, enabling timely interventions. Future recommendations include improving sensor calibration, expanding the range of monitored parameters, and integrating advanced data analytics for predictive capabilities. The system’s real-time data logging capabilities significantly enhance water management by providing timely alerts and enabling proactive responses to water quality issues. The impact of this system is substantial, offering a scalable and cost-effective solution for continuous monitoring, thus contributing to improved water resource management and public health protection
Predicting Heart Failure: A Comparative Approach between Artificial Neural Networks and Support Vector Machines
Get PDFIn recent years, cardiovascular diseases have become increasingly important as a leading cause of death globally. heart failure (HF), a chronic disease affecting some 26 million people worldwide, has become a growing pandemic. Its prevention is a national and global emergency. In India, between 1.3 and 4.6 million adults suffer from HF, and despite advances in therapy and prevention, mortality and morbidity remain high, with significant costs to the healthcare system. The purpose of this study is to conduct a comparative evaluation of ML models for predicting HF. The support vector machine (SVM) and artificial neural network (ANN) models were analyzed to determine which model offers superior accuracy. A dataset from the Kaggle platform with x records and x features was used to train the models. The results indicated that the SVM model is the best predictor of HF with an accuracy of 79%, which far exceeds the ANNs with 77%. It is concluded that the machine learning (ML) method known as SVM shows outstanding and effective performance in the task of predicting heart failure
An Integrated AI Specification to Improve Distance Learning
Get PDFThe distance learning domain has undergone an increasing interest in recent artificial intelligence (AI) technological innovations, aiming to improve the quality of learning while saving time, energy, and cost. Nevertheless, despite using these technologies, during the COVID-19 pandemic, distance learning actors, including tutors, content producers, and learners, encountered difficulties in learning through online sessions and virtual classrooms. They suffer from issues related to the availability of tutors and teachers, reliability of knowledge, restricted learner behavior, limited human interaction, and learners’ dropout. To address these challenges, this paper proposes the “PIKU” specification, focusing on four main requirements, particularly, 1) pedagogy, 2) inclusivity, 3) knowledge management, and 4) user-centricity. This specification aims to support learners, promote interaction, and foster collaboration while enhancing learners’ engagement. We propose providing reliable knowledge while ensuring equitable learning and prioritizing learners’ preferences, improving the overall learning experience. Furthermore, we illustrate the feasibility of the “PIKU” specification by proposing an educational system capable of automatically supporting learners. This system not only meets the “PIKU” requirements but also demonstrates its ability to promote an engaging and rich learning experience
Implementation of IEEE 802.11p for Vehicular Communication: Utilizing NI USRP N321 to Advance Mobile Interactive Technologies
Get PDFThis paper aims to comprehensively implement the Institute of Electrical and Electronics Engineers (IEEE) 802.11p transceiver using the Universal Software Radio Peripheral (USRP) device N321 series for teaching undergraduates. It is a standard in the 802.11 family designed explicitly for wireless access in vehicular environments (WAVE). This process involves configuring the USRP N321 device with IEEE standards, such as adjusting the frequency, amplitude, transmitter gain, direction of the radiation pattern, receiver gain, etc. This transceiver uses an orthogonal frequency division multiplexing (OFDM) modulation scheme. This IEEE 802.11p transceiver provides a practical approach for students and researchers in the realworld platform to analyze wireless communication technologies such as OFDM transceiver, peak-to-average power ratio (PAPR) values, and constellation diagrams for various modulation schemes such as binary phase shift keying (BPSK), quadrature amplitude modulation (QAM), and quadrature phase shift keying (QPSK) with different code rates. The bit error rate values were calculated for the modulation coding schemes index, and data rates of 24 Mbps were achieved for this transceiver
Agricultural Transformation: IoT Technology in the Controlled Cultivation of Oryza Sativa Seedlings in Greenhouse
Get PDFThis paper offers a current review of the application of Internet of Things (IoT) technology for the cultivation of Oryza sativa (rice) in greenhouses through the incorporation of climate and soil sensors. It is not intended to cover all aspects of IoT, but rather to provide a detailed understanding of its application in this context. Through the analysis of published works, the applications developed, the approaches adopted, and the proposals of experts in the field are investigated. Therefore, real data are examined in the cultivation of Oryza sativa seedlings that we consolidated with Power BI, managing to compare the traditional crop with that carried out with IoT technology, highlighting the real-time monitoring of critical soil variables such as humidity, temperature, electroconductivity, pH, nitrogen, phosphorus, and potassium (NPK). Likewise, climate variables such as humidity and relative temperature. This monitoring facilitates the optimization of irrigation and fertilization, promoting efficient and sustainable agriculture. The implementation of IoT in greenhouses not only provides improvements in productivity and product quality but also boosts competitiveness and innovation in the agrifood sector
Enhancing Translation Teaching Efficiency and Learning Experience through Mobile Technology Applications
Get PDFWith the advancement of globalization, the significance of English translation in cross-cultural communication has been increasingly emphasized. Traditional methods of translation teaching are becoming insufficient to meet the demands of modern learners, particularly in the context of the rapid development of mobile internet and intelligent devices. Consequently, there is an urgent need to introduce new technological approaches to improve teaching efficiency and the learner experience in translation education. The widespread use of mobile technology presents new opportunities for translation teaching, especially in the areas of real-time feedback, personalized learning, and translation aids. However, existing translation tools and models are often too complex and unsuitable for lightweight devices, and they exhibit certain limitations in enhancing learning efficiency and translation quality. Thus, the design of lightweight translation models suited for mobile devices has become a critical issue in translation education. This study aims to explore the application of optimized translation model designs on mobile devices in translation teaching. The study focuses on two core components: first, the design of a lightweight translation model based on attention mechanisms to improve computational efficiency and translation quality; second, the investigation of encoder and decoder designs suitable for mobile devices to enhance model performance. Through these optimizations, the study aims to provide a more efficient and personalized tool for translation teaching, thereby improving teaching outcomes and the learning experience, while offering theoretical support and practical guidance for the innovation of translation education models
Design Principles and Practices for Mobile User Interfaces in High-Fidelity Virtual Reality Environments
Get PDFWith the continuous development of virtual reality (VR) technology, high-fidelity VR environments are increasingly applied in fields such as education, healthcare, and entertainment. As the primary mode of interaction between users and virtual environments, mobile user interfaces (MUI) play a crucial role, where the design’s effectiveness directly impacts user immersion and operational efficiency. However, most current MUI designs rely on 2D interface design principles, lacking a tailored design framework for 3D VR environments. In particular, in high-fidelity VR environments, user interaction demands exhibit high dynamics and individuality, which presents challenges for traditional design methods that struggle with adaptability. To address this, this paper first proposes design principles for MUI in high-fidelity VR environments, aiming to provide theoretical guidance and a practical framework for interface design in this field. Secondly, the paper investigates an anchor-point matching method based on simultaneous localization and mapping (SLAM) to solve the dynamic matching problem between user position and interface elements in VR environments. The study shows that by integrating SLAM technology, the accuracy and smoothness of user interaction can be significantly improved. This study enriches the theoretical foundation of user interface design in VR and offers developers more efficient and flexible interaction approaches
Let Computing Be a Powerful Tool for Teaching Conceptual Biostatistics to Public Health Students
Get PDFOver a decade ago some statistics educators were excited at the prospect of using computing as an effective tool for teaching conceptual knowledge of statistics, but few concrete examples of this novel teaching approach have emerged. Here I describe a novel method of treating computing as a tool for teaching conceptual biostatistical knowledge to public health students. By this new approach instructors translate the task of learning conceptual biostatistical knowledge into a process of doing carefully designed computing exercises that are reliant on elementary mathematics and first principles thinking. Infusing basic computer coding knowledge into the task of learning biostatistics, instructors succeed in bringing conceptual biostatistical knowledge into the zone of proximal development for public health students, catalyzing a beneficial synergistic effect in teaching biostatistics and computing. I demonstrate the feasibility of the new approach by citing unedited student work examples drawn from an undergraduate biostatistics course where students used the command-line R computing environment as their course software
The Design of a Core Biopsy Needle with a Larger Sample Extraction Size
Get PDFAcquiring core biopsy samples requires a minimally invasive procedure in which tissue samples are collected from internal organs for histologic diagnosis. Usually, a clinician requests and extracts core biopsy samples when there is a suspicious lump in the body. Samples are used to diagnose various forms of cancer and other diseases. However, to have enough sample size to perform histological tests, clinicians must insert/extract the core biopsy needle a couple of times. These multiple insertions/extractions might reduce the healing process and increase the patient’s risk of infection, internal organ damage, and unwanted bleeding. To address such an issue, it is recommended to obtain the required amount of tissue sample in a single insertion/extraction. In this study, a 14-gauge core biopsy needle (CBN) design, with four sample notches, was designed instead of the conventional one-sample notch to maximize the size of the sample being collected. Furthermore, sample notches were redesigned in a plus (+) cross-sectional pattern to improve the overall strength of the core biopsy needle at the sample notch area. This plus-shaped design of the sample notch enables the decrease of the thickness of the metal the biopsy needle is made from, increasing the sample notch size and thus increasing the total sample size extracted. In this design, each sample notch can collect 13.14 mm3 of sample. Hence, with four sample notches, the total sample being collected is 52.56 mm3 compared to conventional CBNs that can collect a sample size of 30.94 mm3 only. A significant increase of up to 69.88% in the sample size being collected was calculated compared to 14-gauge conventional CBNs, such as Achieve® biopsy needles