Online-Journals.org (International Association of Online Engineering)
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Collaborative Learning & Collective Sensemaking on Generative AI & Its Impacts on Adult Learning
Get PDFThis paper details our reflections on experiencing collaborative learning and collective sensemaking through a discussion-focused event aimed at exploring generative artificial intelligence (AI) and its impacts on adult learning. As generative AI becomes increasingly relevant in educational contexts, adult learning practitioners must effectively navigate the challenges and opportunities presented by these technologies. Through a facilitated discussion session with graduate students studying adult learning, we explored many of the impacts and viewpoints of generative AI, in relation to adult learning. In this paper, we provide a brief overview of adult learning and generative AI and offer a reflection on our experience facilitating an event with graduate students and faculty in an adult learning graduate program
Institutional Factors and Student Satisfaction of Post-graduate Adult Learners
Get PDF In recent years, various stakeholders, including researchers, policymakers, and students, have displayed a growing interest in assessing the effectiveness and performance of higher education institutions. Although no single indicator is sufficient to describe the organizational quality of the university, student satisfaction is one of the most often used indicators. Extensive prior research has consistently demonstrated that high levels of student satisfaction positively influence crucial performance measures such as student retention and institutional graduation rates. Although student satisfaction has been extensively studied, particularly among undergraduate students, much less research has been done among adult learners returning to college. This paper aims to identify the key institutional factors that significantly impact the satisfaction of part-time postgraduate students. We hope that the insights of the study can help universities to allocate their limited resources and ultimately to enhance the well-being of the students
The Coherence between Innovative Teaching Methods and Formative Assessment in Higher Education
Get PDFUnder the umbrella of VUCA-world and growing international competition, the human factors are playing more important role in higher education [9], [18]. Innovative teaching methods and formative assessment are significant transformational part of this process [24]. No doubt, teaching and learning methodology and assessment has strong coherency [19]. This presentation focuses on the relationship between innovative teaching methods and formative assessment. In the first part of the presentation, the philosophical phenomena of this process comes from John Dewey ‘learning by doing’ principle [8]. Thus, innovative teaching methods have strong impact of different types of interactions and broader meaning of learning, especially problem-, project- and inquiry-based learning [15]. Formative assessment focuses on following students’ progression and continuous feedback changing feedback culture in the teaching and learning process [29], [30]. Obviously, there are strong coherence between innovative teaching methods and formative assessment. In the second part of the presentation, the case study from Budapest Metropolitan University gives evidences to this required relationship giving best practices on innovation and formative assessment. Finally, at the end of the presentation, opened conclusion has dilemmas and questions
Human Activity Recognition Using Convolutional Autoencoder and Advanced Preprocessing
Get PDFE-health systems rely on information and communication technology to support and improve various aspects of health services, delivery, and management. The success of artificial intelligence techniques has led to the emergence of a variety of systems designed to address a wide range of healthcare issues. In particular, gathering data on patient activity and behavior has enabled the development of reliable predictive systems for detecting chronic diseases and forecasting their progression. Human activity detection is a vast and emerging field, and various datasets have been collected for training different machine learning and deep learning (DL) models. The University of Milano Bicocca smartphone-based human activity recognition (UniMiB-SHAR) dataset is widely used for analyzing and recognizing human actions, including walking, running, and other daily activities. However, the autoencoder (AE) technique trained on this dataset yields poor performance. This paper aims to enhance the performance of AEs on the challenging UniMiB-SHAR dataset by introducing a convolutional AE model and employing novel preprocessing techniques, including normalization, magnitude, principal component analysis (PCA), and balancing methods such as SMOTEEN and ADASYNE. The experimental results demonstrate that the proposed AE model achieved successful performance, surpassing the state-of-the-art methods, with accuracies of 96.56% for activities of daily living (ADL), 98.86% for Fall, and 88.47% for the full dataset
AT: Asynchronous Teleconsultation for Health Centers in Rural Areas of Peru
No full textCurrently, telehealth services in rural regions of Peru primarily rely on telephone and text message communication between rural physicians and specialists based in cities, leading to delays in accessing specialized healthcare services. To overcome this limitation, we propose an information and communication technology (ICT) model for asynchronous teleconsultation in rural areas of Peru. This model, implemented through a system called SITEA, coordinates city-based specialists with treating physicians in rural areas and integrates care phases along with electronic clinical records. A case study conducted in a rural Peruvian healthcare facility, which had limited Internet connectivity and lacked teleconsultation services, revealed significant outcomes. Within 23 days of implementing SITEA, the facility began offering specialized care services, leading to a 60% reduction in patient transfers to specialized urban healthcare facilities. Furthermore, a satisfaction survey conducted with 50 patients resulted in overwhelmingly positive feedback regarding the quality of medical care and future expectations for healthcare services. These positive outcomes can be attributed to the implementation of specialized services, the shift from physical to electronic records, and improved diagnostic accuracy. Importantly, healthcare personnel found the system easy to navigate and highly beneficial, despite the area’s connectivity limitations
Post-Operative Brain MRI Resection Cavity Segmentation Model and Follow-Up Treatment Assistance
Get PDFPost-operative brain magnetic resonance imaging (MRI) segmentation is inherently challenging due to the diverse patterns in brain tissue, which makes it difficult to accurately identify resected areas. Therefore, there is a crucial need for a precise segmentation model. Due to the scarcity of post-operative brain MRI scans, it is not feasible to use complex models that require a large amount of training data. This paper introduces an innovative approach for accurately segmenting and quantifying post-operative brain resection cavities in MRI scans. The proposed model, named Attention-Enhanced VGG-U-Net, integrates VGG16 initial weights in the encoder section and incorporates a self-attention module in the decoder, offering improved accuracy for postoperative brain MRI segmentation. The attention mechanism enhances its accuracy by concentrating on a specific area of interest. The VGG16 model is comparatively lightweight, has pre-trained weights, and allows the model to extract incredibly detailed information from the input. The model is trained on publicly available post-operative brain MRI data and achieved a Dice coefficient value of 0.893. The model is then assessed using a clinical dataset of postoperative brain MRIs. The model facilitates the quantification of the resected regions and enables comparisons with each brain region based on pre-operative images. The capabilities of the model assist radiologists in evaluating surgical success and directing follow-up procedures
Design of a Sign Language-to-Natural Language Translator Using Artificial Intelligence
Get PDFThis paper describes the results obtained from the design and validation of translation gloves for Colombian sign language (LSC) to natural language. The MPU6050 sensors capture finger movements, and the TCA9548a card enables data multiplexing. Additionally, an Arduino Uno board preprocesses the data, and the Raspberry Pi interprets it using central tendency statistics, principal component analysis (PCA), and a neural network structure for pattern recognition. Finally, the sign is reproduced in audio format. The methodology developed below focuses on translating specific preselected words, achieving an average classification accuracy of 88.97%
Combining IoMT and XAI for Enhanced Triage Optimization: An MQTT Broker Approach with Contextual Recommendations for Improved Patient Priority Management in Healthcare
Get PDFThe widespread adoption of the Internet of Things has significantly enhanced our daily lives across various dimensions. E-health has significantly benefited from advancements in the Internet of Things (IoT), particularly with the emergence of the Internet of Medical Things (IoMT). A sophisticated wireless sensor network produces a huge amount of data, requiring robust cloud-based hardware for precise processing and categorization. The IoMT allows for the extensive gathering of medical data from incoming hospital patients, enabling real-time monitoring of vital signs and health statuses. Nevertheless, effectively prioritizing patients in emergencies is challenging due to the importance and complicatedness of the data. To tackle this issue, an innovative solution involves integrating Explainable Artificial Intelligence into the IoMT ecosystem. By incorporating Explainable AI, the system enhances explainability, fostering trust and reliability in patient prioritization. This provides healthcare providers a more reliable prioritization mechanism that aligns with established medical guidelines. The study explores IoMT devices for collecting medical data from incoming patients, focusing on the MQTT protocol for lightweight devices, aiming to guide patients to the right department and prioritize emergency management through IoMT data analysis
Proposed Feature Selection Technique for Pattern Detection in Patients with Pneumonia Records
Get PDFPneumonia in Peru is a very serious problem. Its impact in recent years has been aggravated due to the Covid-19 pandemic, generating an increase in infections and deaths without distinguishing the age range, which placed this country on the mortality list due to the pandemic. That is why this research seeks the causes of this problem and evaluates what patterns were detected between the years 2019–2022 in patients with pneumonia in Peru from data set from the Comprehensive Health Insurance (SIS). The data presented values related to age, gender, medication and other significant values to understand the disease. The results of the research were achieved by using the PCA technique where the dimensionality of the data was reduced from 28 to 4 main features (Patient’s year of health care, Age, BMI, Department). Finally, with this processed data set, the K-Means algorithm was used, where it was determined that patients in the 60 to 85 years range are the most affected by J189 pneumonia. In addition, an environmental pattern was found in J189 pneumonia. J128, resulting in a focus on patients on the Peruvian coast in places like Lima or La Libertad
A New Approach for Cluster Head Selection in Wireless Sensor Networks
Get PDFThe proliferation of mobile devices and the spread of IoT devices have increased the tendency to use wireless sensor networks (WSNs), especially since the implementation of the 5G communication system has begun in most countries. This type of network does not require any infrastructure or additional cost, making it a good alternative for use in disasters, environmental monitoring, military, and rescue operations. However, WSNs suffer from some limitations, such as mobility and battery lifetime. Significant research has been conducted to overcome the limitation of battery lifetime by developing routing methods and reducing the required communications among wireless mobile nodes. In this research, we utilize the low-energy adaptive clustering hierarchy (LEACH) concept to minimize communication between the nodes and the base station. A new approach has been developed to form clusters in WSN nodes and select the optimal cluster head by facilitating the election of a new cluster head (CH). When the current cluster head’s energy depletes, a new one is selected, ensuring continuous operation. The simulation results demonstrate that the proposed algorithm outperforms the existing LEACH clustering algorithm in terms of energy consumption, packet delivery ratio (PDR), and latency time