Online-Journals.org (International Association of Online Engineering)
Not a member yet
9805 research outputs found
Sort by
Digital Pathology in Healthcare: Current Trends and Future Perspective
Get PDFDiagnosing a disease requires observing the affected tissues and drawing conclusions based on specific known features. Conventionally, a pathologist would diagnose the sample manually by placing it on a glass slide and viewing it under the microscope. These microscopes existed 400 years ago, but over the years, there have been modifications aimed at digitizing every possible diagnostic test. One of the major advantages of digitizing the process is the reduced time consumption for acquiring, processing, and analyzing the slides. Another positive aspect is the reduction in subjectivity achieved by utilizing artificial intelligence (AI) algorithms to classify and diagnose specific diseases. This is achieved by attaching a digital camera to the microscope, which captures images of the glass slides for subsequent processing and diagnosis. There has been a lot of research in this field, but its implementation has been hindered by challenges such as interoperability and high-resolution data, resulting in large file sizes. Various applications for whole slide imaging, such as disease diagnosis techniques, whole slide imaging (WSI) scanners, digital slide scanners, the Internet of Things (IoT), and AI, have been explored in this study. This paper reviews the trends and evolution of microscopes leading to present-day digital pathology scanners, with a major focus on one of the digital techniques, which is whole slide imaging. It also explores various areas where AI has been integrated into whole-slide imaging
Delay of Transmitted Data in the Remote Patient Monitoring System through AMQP and CoAP
Get PDFRemote Patient Monitoring (RPM) is a healthcare solution that uses technology to monitor patients outside conventional healthcare settings. It is especially useful for people with chronic conditions or needing regular monitoring. One of the main reasons for the increase in the number of deaths each year is the increase in cardiovascular diseases, including hypertension. Online blood pressure monitoring offers many advantages but also potential challenges. This work reviews the key communication technologies and research challenges in the real-time transmission of measured blood pressure data. Delay in these systems is not tolerated as it involves human lives. To conduct the experimental studies, a prototype of an experimental intelligent system was created to study the delay and processor load of the RPI4 gateway. The measured blood pressure data is sent to the Things Board cloud using the AMQP and CoAP protocols. The experimental results are particularly useful for RPM system designers. The results of this research facilitate an informed decision on the choice of protocol that transmits the data from the gateway to the cloud in the process of designing remote patient monitoring systems
Unveiling the Landscape of Big Data Analytics in Healthcare: A Comprehensive Bibliometric Analysis
Get PDFIn the rapidly evolving landscape of healthcare, the digital transformation marked by healthcare 4.0 has spurred a surge in data generation, giving rise to ‘big data’. Big data analytics has become an effective tool in the healthcare industry, revolutionising medical research, patient care, and healthcare management. This study undertakes a meticulous bibliometric analysis, drawing upon a dataset of 2212 articles from the Scopus database spanning 2014 to 2023, to unravel the trajectory of big data analytics in healthcare. The research explores diverse dimensions, from the distribution of studies across years to the productivity rankings of journals, countries, and institutions, elucidating the evolving trends and key contributors. Co-authorship networks and keyword co-occurrence analysis reveal thematic clusters and intellectual structures, contributing to a nuanced understanding of the field. The results underscore the escalating global interest in the fusion of big data and healthcare, illuminating collaborations, and identifying influential players. Additionally, the study identifies pressing challenges, including security concerns and skill shortages, emphasizing the imperative of overcoming these barriers for effective big data applications in healthcare. Serving as a valuable resource for researchers, practitioners, and policymakers, this research not only captures the current landscape but also provides insights for future exploration, contributing to strategic planning in this dynamic domain
Deep Reinforcement Learning Approach for Cyberattack Detection
Get PDFRecently, there has been a growing concern regarding the detrimental effects of cyberattacks on both infrastructure and users. Conventional safety measures, such as encryption, firewalls, and intrusion detection, are inadequate to safeguard cyber systems against emerging and evolving threats. To address this issue, researchers have turned to reinforcement learning (RL) as a potential solution for complex decision-making problems in cybersecurity. However, the application of RL faces various obstacles, including a lack of suitable training data, dynamic attack scenarios, and challenges in modeling real-world complexities. This paper suggests applying deep reinforcement learning (DRL), a deep framework, to simulate malicious cyberattacks and enhance cybersecurity. Our framework utilizes an agent-based model that is capable of continuous learning and adaptation within a dynamic network security environment. The agent determines the most optimal course of action based on the network’s state and the corresponding rewards received for its decisions. We present the outcomes of our experimentation with the application of DRL on a specific model, double deep Q-network (DDQN), utilizing policy gradient (PG) on three distinct datasets: NSL-KDD, CIC-IDS-2018, and AWID. Our research demonstrates that DRL can effectively improve cyberattack detection outcomes through our model and specific parameter adjustments
e-LSTM: EfficientNet and Long Short-Term Memory Model for Detection of Glaucoma Diseases
Get PDFGlaucoma is an eye disease that often has no symptoms until it is advanced. According to the World Health Organization (WHO), after cataracts, glaucoma is the second-leading cause of permanent blindness globally and is expected to affect 111.8 million patients by 2040. Early detection of glaucoma is important to reduce the risk of permanent blindness. Detection is achieved by structural measurement of early thinning of the retinal nerve fiber layer (RNFL). The RNFL is the portion of the retina located outside the optic nerve head (ONH) and can be observed in fundus images of the retina. Analysis of retinal fundus images can be performed with computer assistance using machine learning, especially deep learning. This study proposes a deep learning-based model, a convolutional neural network (CNN) using the EfficientNet architecture combined with long short-term memory (LSTM), for laucoma detection. Using ACRIMA, DRISHTI-GS, and RIM-ONE DL datasets with k-fold cross-validation, the model achieved high performance on the ACRIMA dataset: accuracy 0.9799, loss 0.0596, precision 0.9802, sensitivity 0.9799, specificity 0.9771, and F1score 0.9799. This EfficientNet and LSTM combination (e-LSTM) outperformed previous studies, offering a promising alternative for evaluating retinal fundus images in glaucoma detection
Social Robots, Mindfulness, and Kindergarten
Get PDFThe following review examines the use of social robots in mindfulness practices, with a focus on their application in preschool settings. Additionally, it explores the key attributes of social robots that could enhance their effectiveness in achieving targeted outcomes. This study is the initial phase of a project that aims to investigate the advantages of technology and mindfulness in kindergarten. The selection of this age group is based on its significance in the comprehensive development of children, despite the lack of extensive study on mindfulness in this specific context. The objective of this paper is to present existing research on social robots and mindfulness, assess the potential benefits and challenges of integrating these two fields in kindergarten, and, most importantly, inspire future studies on the use of robots and mindfulness in early childhood education. A bibliographic review of articles was conducted. The findings of our study suggest that the use of robots and human-robot interactions can enhance self-development, well-being, and mindfulness. Robots have the capacity to capture attention and motivate young children, specifically. Both humanoid and non-humanoid robots seem suitable for facilitating mental well-being exercises. However, a well-designed social robot for children should incorporate both human-like and mechanical features. Our primary aim is to encourage further study on the integration of robots and mindfulness in preschool education, as there is still a vast unexplored territory in this rapidly advancing field
Professional Development for Primary School Teachers Intended to Promote Students’ Spatial Ability
Get PDFProfessional development (PD) plays a crucial role in equipping teachers with the necessary knowledge and skills to effectively foster spatial ability among their students. This study aimed to explore the potential of action research as a PD mode for primary school teachers to enhance students’ spatial ability. The findings of this study indicate the positive impact of PD on teachers’ practice, leading to significant improvements in student performance and engagement. The PD program effectively equipped teachers with new knowledge and skills, leading to noticeable progress in their instructional approaches and teaching practices. This research provides a foundation for future studies on PD programs aimed at enhancing students’ spatial ability
Intelligent Support for Low Literacy Adults: The European Portuguese iRead4Skills Corpus
Get PDFThis paper presents the Portuguese dataset of the iRead4Skills project (Dataset 1: corpora by complexity level for FR, PT, and SP – v.2.0), a representative sample of written European Portuguese for automatic complexity assessment that addresses a gap in existing resources for Portuguese. The corpus was created within the framework of the iRead4Skills project, which encompasses Portuguese, French, and Spanish. The project aims to develop an intelligent system to evaluate text complexity while recommending appropriate reading materials to native adult learners with low literacy skills. The corpus compilation involved a manual selection of text samples across various textual genres and document types, covering a wide range of existing written materials and focusing on the reading needs and reading habits of the target audience—low literacy adults enrolled in vocational education and training centres or adult learning (AL) centres. The collected texts were categorised into the three distinct levels of complexity targeted and defined by the project: very easy, easy, and plain levels. Texts of higher complexity were also included, resulting in the creation of four distinct sub-corpora. The resulting Portuguese dataset consists of 2,186 texts and 942,818 tokens and serves as the foundational source for training and testing the project’s complexity analysis systems. This paper presents a comprehensive overview of the compilation process of the corpus, encompassing its methodological design and the challenges faced. Although some existing Portuguese corpora were used for complexity studies and tool development, these primarily consist of texts classified according to CERF levels and retrieved from didactic materials designed for L2 teaching/learning or texts produced by L2 learners. The corpus presented in this paper introduces a new resource that addresses a significant gap in materials needed to inform and support studies and applications related to text complexity. The resulting dataset provides a novel and important language resource for European Portuguese, with several applications including research on linguistic complexity, development of automatic text complexity and readability assessment systems, and educational purposes
Augmented Reality in Early Childhood Education: Trends, Practices, and Insights from a Literature Review
Get PDFIn recent years, the use of augmented reality (AR) in early childhood education (ECE) has grown significantly. However, there remains a limited understanding of publication trends, research methodologies, and the overall impact of this technology. This study evaluates the evolution of AR in ECE from 2013 to 2023 through a comprehensive review of 49 articles published in journals indexed by Scopus. We employed VOS viewer for bibliometric analysis, alongside Google Scholar for database searches and Microsoft Excel for statistical evaluation. Three researchers conducted content analysis of the selected papers based on established criteria. Our findings reveal a consistent increase in AR-related publications in ECE over the past decade. In studies utilizing quantitative methods, participant numbers typically ranged from 30 to 100. Researchers developed AR applications using marker-based techniques and assessed their effectiveness through questionnaires. The results indicate that AR offers numerous advantages, including enhanced motivation, a supportive learning environment, improved academic performance, assistance for children with special needs, and promotion of social and emotional development. This study recommends that early childhood educators incorporate AR into their teaching practices as an innovative and engaging tool for future educational endeavors. This revision aims to improve readability while preserving the essential information and findings of your original abstract
Students Emotion and Distraction Detection While Adopting E-Learning Approach
Get PDFCurrently, e-learning has changed the way students’ study by providing high-quality education that is not restricted by place or time. Mobile phones, tablets, laptops, and desktop computers are some of the products that make online learning easier. These devices were used for mandatory online learning due to the COVID-19 pandemic. However, because the e-learning approach prevents an instructor from actively observing a group of students, they may become distracted for many reasons, significantly reducing their learning potential. This paper proposes an intelligent system called the Intelligent E-Learning Monitoring System (IELMS) that helps faculty members keep track of such students and supports them in improving their performance. Convolutional neural network (CNN) techniques are utilized to detect emotions, and once the optimum algorithm for detecting emotions has been identified, it is fused into the model that detects an online learner’s distraction. The fused model produces logs of distraction and emotion. These logs will assist the teaching community in identifying underperforming online learners and facilitating counseling