EAI Endorsed Transactions on e-Learning is open access, a peer-reviewed scholarly journal focused on topics belonging to the variegated and engaging e-Learning landscape, ranging from various types of distance learning (e.g., online, mobile, cloud, hybrid) to virtual laboratory environments supported by sound pedagogies, cutting-edge technologies and much more. The journal publishes research, review, commentaries, editorials, technical articles, and short communications with a triannual frequency. Authors are not charged for article submission and processing. INDEXING: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
Exploring generative AI in foreign language education: Insights from a student survey
INTRODUCTION: Generative artificial intelligence (AI) has implications for foreign language education, necessitating careful consideration of how the technology should be addressed. This should be based on the perspectives of different stakeholders, not least the students. OBJECTIVES: The paper explores foreign language students’ use of and perspectives on generative AI in foreign language education. METHODS: The paper employs a survey design and analyses responses from 106 students collected over a three-year period. RESULTS: The study documents increasing, frequent and varied use of generative AI. The students’ evaluation of AI output quality has grown more moderate over the years. The study suggests benefits may arise from even limited integration of the technology into language education. CONCLUSION: The study has highlighted a need for integrating generative AI into foreign language education. We need to continue the dialogue with students to inform future pedagogical choices.
Copyright (c) 2024 Kristine Bundgaard, Anders Kalsgaard Møller https://creativecommons.org/licenses/by-nc-sa/4.0
A Review of Deep Learning Methods for Brain Tumor Detection
A brain tumor is a serious neurological condition caused by the growth of abnormal cells in various regions of the brain, leading to a variety of health issues. Although the specific causes of brain tumors are not yet fully understood, known risk factors include genetic predisposition, ionizing radiation, viral infections, and exposure to certain chemicals. With the advancement of deep learning technology, computer-aided diagnosis systems can offer crucial support for the early diagnosis of brain tumors. Brain tumor image classification using deep learning has emerged as a prominent area of research. This article begins by summarizing the publicly available datasets frequently utilized in brain tumor classification tasks. It then provides an overview of the models commonly applied for diagnosing brain tumors. Following this, the paper reviews the advancements made in the field of brain tumor classification research to date. Finally, it highlights the future trends and challenges in brain tumor classification.
Examining Thinking Skills and Higher Education Using Educational Technologies-Teaching
Copyright (c) 2024 Shuaichao Wen https://creativecommons.org/licenses/by-nc-sa/4.0
A Review of Real-Time Semantic Segmentation Methods for 2D Data in the Context of Deep Learning
Semantic segmentation is a key research topic in the field of computer vision, aiming to assign each pixel to the corresponding category based on the semantic information in the image. This technology has significant application value in fields such as virtual reality and autonomous driving.With the rapid development of deep learning, particularly with the advent of FCN, image semantic segmentation has made substantial progress. Fully supervised learning, which trains deep learning models using labeled data, has demonstrated excellent performance in semantic segmentation tasks. This paper provides a comprehensive discussion and analysis of fully supervised semantic segmentation algorithms for 2D data in deep learning. First, it introduces the concept of semantic segmentation, its development, and its application scenarios. Next, it systematically reviews and categorizes current real-time semantic segmentation algorithms, analyzing the characteristics and limitations of each. Additionally, this paper presents a complete evaluation framework for real-time semantic segmentation, including relevant datasets and evaluation metrics. Based on this foundation, it identifies several challenges currently facing the field and suggests potential directions for future research. Through this summary and analysis, the paper aims to provide valuable insights for researchers conducting studies on image semantic segmentation.
Copyright (c) 2024 Meng Gao, Haifeng Sima https://creativecommons.org/licenses/by-nc-sa/4.0
In recent years, Graph Neural Networks (GNNs) have seen notable success in fields such as recommendation systems and natural language processing, largely due to the availability of vast amounts of data and powerful computational resources. GNNs are primarily designed to work with graph data that involve pairwise relationships. However, in many real-world networks, the relationships between entities are complex and go beyond simple pairwise connections, as seen in scientific collaboration networks, protein networks, and similar domains. If these complex relationships are directly represented as pairwise relationships using graph structures, it can lead to information loss. A hypergraph, as a special kind of graph-structured data, can represent higher-order relationships that cannot be fully captured by graphs, thereby addressing the limitations of graphs. In light of this, researchers have begun to focus on how to design neural networks on hypergraphs, leading to the proposal of hypergraph neural network (HGNN) models for downstream tasks. Therefore, this paper reviews the existing hypergraph neural network models. The review is conducted from two perspectives: spectral analysis methods and neural network methods on hypergraphs, discussing both unfolded and non-unfolded methods, and further subdividing them based on their algorithm characteristics and application scenarios. Subsequently, the design concepts of various algorithms are analyzed and compared, and the advantages and disadvantages of each type of algorithm are summarized based on experimental results. Finally, potential future research directions in hypergraph learning are discussed.
Copyright (c) 2024 EAI Endorsed Transactions on e-Learning https://creativecommons.org/licenses/by/3.0/
Improvements in Brain Tumor Segmentation Methods Based on Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have emerged as a prominent research area in deep learning in recent years. U-Net, an essential model within CNNs, has gradually become a research focus in the field of medical image segmentation due to its remarkable segmentation performance. This paper presents a comprehensive overview of brain tumor segmentation methods based on CNNs. Firstly, it introduces common medical image datasets in the field of brain tumor segmentation. Secondly, it offers detailed reviews on the common improvements to 2D U-Net, 3D U-Net, and improvements based on other CNNs for brain tumor segmentation. Finally, it discusses the future development directions of CNNs for brain tumor segmentation.
Copyright (c) 2024 Yuzhuo Li, Lihong Zhang, Yingbo Liang, Chongxin Xu, Tong Liu https://creativecommons.org/licenses/by-nc-sa/4.0
ARM-Net: Improved MRI brain tumor segmentation method based on attentional mechanism and residual module
INTRODUCTION: Accurate tumor segmentation is a prerequisite for reliable diagnosis and treatment of brain cancer. Gliomas, a highly prevalent and life-threatening type of brain tumor, pose a challenge for segmentation due to the intricate nature of brain structures and unpredictable appearances on brain MRI images. OBJECTIVES: Current methods for brain tumor segmentation mostly rely on deep convolutional neural networks, which suffer from significant loss of feature information during encoding and decoding and the inability to capture tumor contours in detail. METHODS: To address these challenges, this study rethinks the network architecture for MRI brain tumor segmentation. It proposes ARM-Net: an improved method for MRI brain tumor segmentation based on attention mechanisms and residual modules. Firstly, inverted external attention and dilated gated attention are employed in the last two layers of the encoder to enable the network to interact with both lesion areas and global information, facilitating better interaction among the four modalities. Secondly, different numbers of Res-Paths are added in the encoder's first two layers and the decoder's last two layers to effectively mitigate the semantic gap issues caused by traditional skip connections. RESULTS: Experiments on the BraTS 2019 dataset demonstrate that ARM-Net outperforms other similar models in terms of segmentation performance. CONCLUSION: The experiment showed that the ARM-Net model could segment the contour structure of the tumor better than other methods.
A Community Detection Algorithm Based on Balanced Label Propagation
OBJECTIVES: In conventional label propagation algorithms, the randomness inherent in the selection order of nodes and subsequent label propagation frequently leads to instability and reduces the accuracy of community detection outcomes. METHODS: First, select the initial node according to the node importance and assign different labels to each initial node, aiming to reduce the number of iterations of the algorithm and improve the efficiency and stability of the algorithm; second, identify the neighbor node with the largest connection to each initial node for the pre-propagation of the labels; then, the algorithm traverses the nodes in descending order of the node importance for the propagation of labels to reduce the randomness of the label propagation process; finally, the final community is formed through the rapid merging of small communities. RESULTS: The experimental results on multiple real datasets and artificially generated networks show that the stability and accuracy are all improved. CONCLUSION: The proposed community detection algorithm based on balanced label propagation is better than the other four advanced algorithms on Q and NMI values of community division results.
Infusing Aboriginal Perspectives in Cyber Education
While human factors are important in cyber security, the discipline has largely not explored incorporating indigenous perspectives—or, more specifically, in an Australian context, Aboriginal perspectives—in its curricula. In this paper, we introduce a promising approach for aligning Aboriginal perspectives with the needs of cyber security graduates and incorporating diverse perspectives into cyber degrees. The approach advocates for the centrality of good curriculum design fundamentals: backward design, constructive alignment, and student outcomes. The paper ends by reflecting on challenges and lessons from the first implementation and review of the material. It provides recommendations for other cyber practitioners exploring ways of incorporating indigenous perspectives in their teaching.
Copyright (c) 2024 John Shannahan, Mohiuddin Ahmed https://creativecommons.org/licenses/by-nc-sa/4.0
Liver tumor segmentation method based on U-Net architecture: a review
Liver cancer is a disease with a high incidence and high probability of deterioration, and for the rapid diagnosis of liver disease, CT scans must be used to segment the liver tumors. For the past few years, with the rapid development of deep learning, many deep learning methods for liver tumor segmentation using abdominal computed tomography (CT) images have appeared, and the clinical application of these methods is of important significance for computer-aided diagnosis of liver tumors. The U-Net, with its unique U-shape network structure, exhibits excellent performance in medical image segmentation field and has been extensively utilized in various medical image segmentation applications. In this paper, we summarize the researches of U-Net and its improved networks in CT image segmentation of liver tumors by deep learning methods and classify various U-Net-based convolutional neural networks (CNNs) into 2D (two-dimensional), 3D (three-dimensional), and 2.5D (2.5-dimensional). In this paper, 2D, 3D, and 2.5D convolutional neural networks are summarized. In addition, this paper summarizes the advantages and disadvantages as well as the improvement methods of each type of network, which provides a useful reference for the studies of deep learning based on liver tumor segmentation field. Finally, this paper envisions future research trends for deep learning segmentation methods in the context of liver tumors.
Artificial Intelligence in Mathematical Modeling of Complex Systems
This article introduces artificial intelligence techniques in mathematical modelling of complex systems and their applications. Mathematical modelling of complex systems is a method of studying the structure and behaviour of complex systems, aiming to understand interactions and nonlinear effects in the system. Commonly used modelling methods include system dynamics, network theory, and algebraic methods. Artificial intelligence technologies include machine learning and deep learning, which can be used for tasks such as prediction and classification, anomaly detection, optimization and decision-making. In mathematical modelling of complex systems, artificial intelligence technology can learn system patterns and laws from large amounts of data, and can be applied to image and speech recognition, time series analysis and other fields. Deep learning and machine learning are important branches of artificial intelligence. They realize the modelling and analysis of complex systems by building neural network models. Data-driven modelling is a modelling method based on actual data that, combined with traditional theoretical modelling, can better describe and predict the behaviour of complex systems. Self-control of complex systems means that the system realizes its own optimization and adjustment through adaptive control algorithms and feedback mechanisms. In summary, artificial intelligence technology has broad application prospects in mathematical modelling of complex systems and will provide new tools and methods for in-depth understanding and solving problems in complex systems.
Mathematical Modeling of Complex Systems
Gesture Recognition Based on Deep Learning: A Review
Gesture recognition is an important and inevitable technology in modern times, its appearance and improvement greatly improve the convenience of people's lives, but also enrich people's lives. It has a wide range of applications in various fields. In daily life, it can carry out human-computer interaction and the use of smart home. In terms of medical treatment, it can help patients to recover and assist doctors to carry out experiments. In terms of entertainment, it allows users to interact with the game in an immersive manner. This paper chooses three technologies that deep learning plays a more prominent role in gesture recognition, namely CNNs, LSTM and transfer learning based on deep learning. They each have their own advantages and disadvantages. Because of the different principles of use, different techniques have different roles, such as CNNs can carry out feature extraction, LSTM can deal with long time series, transfer learning can transfer what is learned from another task to this task. Select different practical technologies according to different application scenarios, and make improvements in real time in practical applications. Gesture recognition based on deep learning has the advantages of good accuracy, robustness and real-time implementation, but it also bears the disadvantages of huge economic and time costs and high hardware requirements. Despite some challenges, researchers continue to optimize and improve the technology, and believe that in the future, gesture recognition technology will be more mature and valuable.
EEG Emotion Recognition Based on Self-Distillation Convolutional Graph Attention Network
A convolution graph attention model based on self-distillation convolutional graph attention network (SDC-GAT) is proposed for multi-channel EEG emotion recognition. Firstly, two-dimensional feature matrix based on EEG time-domain features are constructed, and the matrix is fed into the graph attention neural network to learn the internal connections between electrical brain channels located in different brain regions. Meanwhile, the three-dimensional feature matrix is constructed according to the relative positions of the electrode channels, and the self-distillation network is employed to extract local high-level abstract features containing electrode spatial position information from the three-dimensional feature matrix. Finally, outputs of the two networks are integrated to determine the emotional states. Experiments were performed on the DEAP dataset. The experimental results show that the spatial domain information of the electrode channel and the internal connection relationship between different channels are beneficial for emotion recognition. In addition, the proposed model can effectively fuse these information to improve the performance of multi-channel EEG emotion recognition.
Ethic wars: student and educator attitudes in the context of ChatGPT
Technologists and educators have been both fascinated and frightened since the publication of ChatGPT. ChatGPT has both supporters and detractors, but it is informative for individuals in the education community to look at the educational research on AI in education in order to gain understanding and establish objective judgments about the importance of ChatGPT in education. In this paper, we first present the journey of OpenAI GPT models, then give the implications of ChatGPT for education. Then, we list works for detection ChatGPT based texts and other precautions. Finally, an example of an exam with ChatGPT answers is given.
Copyright (c) 2023 EAI Endorsed Transactions on e-Learning https://creativecommons.org/licenses/by/3.0/
A review of research and development of semi-supervised learning strategies for medical image processing
Accurate and robust segmentation of organs or lesions from medical images plays a vital role in many clinical applications such as diagnosis and treatment planning. With the massive increase in labeled data, deep learning has achieved great success in image segmentation. However, for medical images, the acquisition of labeled data is usually expensive because generating accurate annotations requires expertise and time, especially in 3D images. To reduce the cost of labeling, many approaches have been proposed in recent years to develop a high-performance medical image segmentation model to reduce the labeling data. For example, combining user interaction with deep neural networks to interactively perform image segmentation can reduce the labeling effort. Self-supervised learning methods utilize unlabeled data to train the model in a supervised manner, learn the basics and then perform knowledge transfer. Semi-supervised learning frameworks learn directly from a limited amount of labeled data and a large amount of unlabeled data to get high quality segmentation results. Weakly supervised learning approaches learn image segmentation from borders, graffiti, or image-level labels instead of using pixel-level labeling, which reduces the burden of labeling. However, the performance of weakly supervised learning and self-supervised learning is still limited on medical image segmentation tasks, especially on 3D medical images. In addition to this, a small amount of labeled data and a large amount of unlabeled data are more in line with actual clinical scenarios. Therefore, semi-supervised learning strategies become very important in the field of medical image processing.
A Review of Deep Learning Approaches for Early Diagnosis of Alzheimer's Disease
Alzheimer's disease (AD), one of the major neurodegenerative diseases, has become the most common cause of dementia problems. Up to now, there is a lack of effective targeted therapeutic drugs and effective treatment modalities to stop the progression of the disease. With the continuous development of computer technology, the use of computer-aided diagnostic technology tools for AD early classification studies will provide clinicians with important assistance. Deep learning-based Alzheimer's disease (AD) imaging classification has become a current research hotspot. In this paper, we first describe the commonly used publicly available datasets in the AD imaging classification task; then introduce the commonly used deep learning classification models for AD diagnosis; secondly, we compare the studies that target different biomarkers of the subjects and the use of unimodal or a combination of different modalities for the early classification of AD; and finally, The challenges of AD classification are summarized and future research directions are proposed.
Deep learning-based lung nodule detection: a review
CT scan acquisition is fast and cost-effective and has become the main lung imaging tool. However, the increase in large numbers of CT scans has placed a heavy burden on radiologists; therefore, automated lung nodule detection techniques are needed to reduce the workload of radiologists and computer-aided detection systems are proposed for further accurate diagnosis of the condition. This review provides a comprehensive overview of recent automated lung nodule detection techniques and challenges, etc., as well as a detailed overview and discussion of current research gaps, future developments, and research trends. Relevant articles published in databases such as IEEE Xplore, Science Direct, PubMed, and Web of Science cover research algorithms published from 2014 to 2023, mainly discussing deep learning-based techniques. The schemes presented in these articles, the databases used, the experimental results, and the performance of the algorithms are compared and discussed. This work aims to introduce researchers and readers to the latest techniques and their advances in the detection of lung nodules in the last decade, which will help researchers and radiologists to further understand the latest techniques in this field.
ChatGpt impact on Student Educational Performance: a conceptual analysis
This research examines ChatGPT, a sophisticated AI language model created by OpenAI, and its effect on learning outcomes. The research explores the impact of implementing ChatGPT into different learning contexts with a mixed methods approach, combining quantitative data from educational performance measures with qualitative findings gathered from student surveys. Based on these early results, it seems that ChatGPT helps students learn more because it simplifies down challenging subject matter, provides immediate feedback, and encourages collaborative learning. The technology also looks to play a major role in boosting educational accessibility, closing the knowledge gap for student in underserved areas or with restricted access to teachers. The study also highlights the significance of using ChatGPT to supplement more conventional teaching strategies rather than as a replacement for them.
Development and Perceived Usability Evaluation of a Mobile application for Notetaking
INTRODUCTION: Notetaking is considered, by many educators, as one of the critical actions of learning. There are several note-taking methods and approaches. Based on these methods and approaches, various applications, whether mobile, desktop or -Web-based, were developed. OBJECTIVES: In this paper, a novel note-taking application based on Cornell Technique, is presented. Its development process and user acceptance trend are exhibited and results for user evaluation based on user satisfaction are presented. METHODS: For the software development process, Incremental Model was adopted. Requirement Analysis included, aside from examining principles and related note-taking structure of Cornell Technique, investigating (i) how to perform notetaking as an activity of learning, (ii) its product and (iii) relationship of notes for the purpose of storage. Models containing sub-activities, such as reviewing note have been identified and some were selectively adopted and related functions such as review alert (tickler) and collaboration on notetaking have been implemented. To the purpose of storage, a tree-based scheme called collection was modelled. User interfaces were first designed as mockups and click-through pro-totype using Adobe XD. The mobile application was implemented in Dart programming language. Google’s Firebase Service and Flutter Framework was adopted. The mobile application was compared with its equivalents in the Google Play Store and user statistics were investigated. To evaluate perceived usability, the System Usability Scale is adopted and applied to 14 university students conforming to determined persona. RESULTS: The application has been published in Google Play Store for users to install for free on 18th March 2022. As of 10th September 2023, total number of downloads is 5K and the Cornell Note mobile app is currently installed on 1108 devices. For the last three-month period (from 11th June to 10th September 2023), the active users per month changed in an increasing trend from 450 to 589. The average engagement time on 11th of April 2023 was 28 minutes 00 seconds. As the number of monthly active users increased, the average engagement time measured on 10th September 2023 decreased to 23 minutes 31 seconds. However, engagement rates measured were 76.91% and 77.19%, respectively. The mean SUS score was found to be equal to 79.5. CONCLUSION: The user statistics and comparison with equivalent mobile applications reveal that Cornell Note has potential to grow as a mobile application for notetaking since it has a good perceived usability, however, there is room for improvement. Considering any extra marketing effort was not spent for the application such as application store optimization, the statistics are another evidence for user appeal and acceptance. However, it is important to add new functionality without complicating the user experience so that user appeal and acceptance boosts.
Empowering Young Athletes: Elevating Anti-Doping Education with Virtual Reality
In recent times, doping's prevalence in sports has gained substantial recognition, sparking a concerted effort from researchers, policymakers, and sports bodies to underscore the critical role of impactful anti-doping education initiatives. An exhaustive examination of current literature underscores a critical requirement for advanced educational interventions that can effectively combat the multifaceted challenges presented by doping across the spectrum of competitive and recreational athletes. In response to this exigency, this paper introduces an innovative paradigm to redefine anti-doping education through the fusion of virtual reality (VR) technology. This proposed approach seeks to leverage VR's immersive potential, offering dynamic and interactive learning experiences that authentically mirror the complexities surrounding doping decisions. By immersing athletes within lifelike scenarios, VR education aims to provide a nuanced understanding of the psychological and emotional facets associated with doping, all within a secure and controlled environment. However, while the potential of VR in anti-doping education is promising, it also necessitates addressing technical, ethical, and usability considerations, an aspect that this paper further explores.
Blended Learning for Machine Learning-based Image Classification
The paper commences with an introduction to blended learning, an educational approach that amalgamates traditional face-to-face instruction with online learning, aiming to capitalize on the advantages of conventional classroom instruction and digital resources in order to enhance the overall learning experience. The incorporation of diverse technologies facilitates a personalized learning experience that caters to the needs and learning styles of individual students. Image classification entails training machine learning models to categorize or label images into predetermined classes or categories, empowering machines to recognize and comprehend crucial components of visual information, emulating humans' classification of objects in the real world. The crux of image classification relies on extracting meaningful features from images and distinguishing different categories by associating specific features with distinct classes through iterative optimization learning. Machine learning significantly aids image classification by endowing automated systems with the capability to discern patterns, features, and distinctions within datasets, ultimately achieving accurate image classification. The integration of hybrid learning methods can augment the training process for machine learning models used in image classification by providing a flexible and adaptive learning environment.
Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model
The exponential development of online learning resources has led to an information overload problem. Therefore, recommender systems play a crucial role in E-learning to provide learners with personalised course recommendations by automatically identifying their preferences. In addition, e-Learning platforms such as MOOCs and LMS have been criticised for their low course completion rates, and one of the primary reasons is that they do not provide personalised course recommendations for users with varying interests. Rapidly locating the courses that users are interested in on enormous e-Learning platforms can have a significant impact on the quality of learning and the dissemination of knowledge to the learner. This paper examines the most prevalent recommendation techniques utilised in E-learning. We examined how to apply Deep Transformer based Ensembled Attention Model (DTEAM) on e-Learning system in order to achieve personalized course recommendations. The proposed recommendation model uses BERT as its foundation integrated MLM and Transformers. Predicted course recommendations are more aligned with the interests of users. Our experimental results proved that traditional recommendation algorithms, such as collaborative filtering and item-based filtering are incapable of producing superior results. The consequence of the research can assist students in selecting courses according to their preferences and improve their learning caliber
Deep Transformer based Ensembled Attention Model
A K-Anonymous Location Privacy-Preserving Scheme for Mobile Terminals
Mobile terminals boost the prosperity of location-based service (LBS) which have already involved in every aspect of People's daily life and are increasingly used in various industries. Aimed at solving the security and efficiency problem in the existing location privacy protection schemes, a K-anonymity location privacy preservation scheme based on mobile terminal is proposed. Firstly, number of rational dummy locations is selected from the cloaking region, from which more favorable locations are further filtered according to location entropy, so a better anonymity effect can be achieved. Secondly, the secure and efficient m-out-of-n oblivious transfer protocol is adopted, which not only avoids the dependency on the trusted anonymity center in existing schemes to improve the efficiency, but also meets the requirements for querying multiple interest points at one time. Security analyses demonstrate that this scheme satisfies such security properties as anonymity, non-forgeability and resistance to replay attack, and simulation results show that this scheme has higher execution efficiency and privacy level, while is low in communications costs.
Applications of Image Segmentation Techniques in Medical Images
Image segmentation is an important research direction in medical image processing tasks, and it is also a challenging task in the field of computer vision. At present, there have been many image segmentation methods, including traditional segmentation methods and deep learning-based segmentation methods. Through the understanding and learning of the current situation in the field of medical image segmentation, this paper systematically combs it. Firstly, it briefly introduces the traditional image segmentation methods such as threshold method, region method and graph cut method, and focuses on the commonly used network architectures based on deep learning such as CNN, FCN, U-Net, SegNet, PSPNet, Mask R-CNN. At the same time, the application in medical image segmentation is expounded. Finally, the challenges and development opportunities of medical image segmentation technology based on deep learning are discussed.
Controllable Privacy-Preserving Online Diagnosis with Outsourced SVM over Encrypted Medical Data
With the widespread application of online diagnosis systems, users can upload their physical characteristics anytime and from anywhere to receive clinical diagnoses. However, for privacy and intellectual property considerations, users' physical characteristics, diagnosis results, and the medical diagnosis model should be protected. To achieve an efficient and secure online diagnosis, secure outsourcing and low burden become research objectives. However, few of the existing privacy-preserving schemes focus on the secure outsourcing of the training process, and few consider the supervision of the hospital for the online diagnosis process. By introducing a four-party architecture with two non-colluding servers, a hospital and users, in this paper, we propose a controllable privacy-preserving online diagnosis scheme (CPPOD) with outsourced SVM over encrypted medical data. Concretely, an integer vector homomorphic encryption is employed to protect medical data and user requests. In the encrypted domain, a series of collaborative protocols including data collection, sequence minimum optimization solver, SVM model building, and online diagnosis are constructed and take place between different participants, while no significant increase in computation on either the hospital or user side. CPPOD enables the hospital to delegate online diagnosis services to a cloud server while ensuring that its regulatory capabilities cannot be bypassed unauthorized. Security analysis and performance evaluation suggest that CPPOD performs well regarding security and efficiency.
In recent years, due to the explosion of COVID-19, people's expectation for accessing personalized learning resources anytime and anywhere has become stronger. The features of m-learning such as accessibility and personalization greatly satisfy people's needs and are therefore widely used. In this paper, a study was conducted to investigate and analyze how m-learning can help prevent COVID-19. The study shows that m-learning can help disseminate outbreak-related messages and provide people with personalized knowledge, so that it can enhance public health and community safety. While there are still many challenges, m-learning remains a valuable tool for preventing and mitigating the spread of COVID-19 globally, and provides a solid reference for deepening m-learning development in the future.
Use MOOC to learn image denoising techniques
This article focuses on using MOOCs to learn image denoising techniques. It begins with an introduction to the concept of MOOCs - these innovative online learning platforms that offer a wide range of courses across disciplines, providing convenient and affordable learning opportunities for a global audience. It then explains the characteristics of MOOC's wide coverage, high flexibility, and different from traditional education models. It then introduces the advantages of MOOCs: accessibility and inclusiveness (open to anyone with an Internet connection), cost-effectiveness (a cost-effective alternative, many courses available for free), flexibility and self-paced learning (the ability to learn at your own pace), a diverse curriculum and global expertise. Then the concept of image denoising is introduced - image denoising is a basic process of digital image processing, and the common denoising methods are described: filter method and the applicable range of various filters, the advantages and disadvantages of wavelet change, the advantages of deep learning method and the principle of non-local mean denoising technology. It then describes how MOOCs can help learn image denoising: integrating course content, getting expert guidance, hands-on exercises and projects, and community and peer communication. In addition, it introduces the challenges encountered by MOOCs: high dropout rate, quality and credibility of MOOCs, lack of interaction and humanization in traditional classrooms, accessibility. The relationship between E-learning and MOOC is also introduced – E-learning and MOOC play complementary roles in modern education. MOOC provide a structured, flexible, cost-effective environment and a transformative educational experience for learning about biological image denoising.
Review of AlexNet for Medical Image Classification
In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification. The variants of neural network models with ever-increasing performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and exploding, etc. AlexNet first utilizes the dropout technique to mitigate overfitting and the ReLU activation function to avoid gradient vanishing. Therefore, we focus our discussion on AlexNet, which has contributed greatly to the development of CNNs in 2012. After reviewing over 40 papers, including journal papers and conference papers, we give a narrative on the technical details, advantages, and application areas of AlexNet.
Review of Graph Neural Networks for Medical Image
As deep learning continues to evolve, more and more applications are generating data from non-Euclidean domains and representing them as graphs with complex relationships and interdependencies between objects. This poses a significant challenge to deep learning algorithms. Because, due to the uniqueness of graphs, applying deep learning to ubiquitous graph data is not an easy task. To solve the problem in non-Euclidean domains, graph Neural Networks (GNNs) have emerged. A graph neural network (GNN) is a neural model that captures dependencies between graphs by passing messages between graph nodes. With the continuous development of medical image technology, medical image diagnosis plays a crucial role in clinical practice. However, in practice, medical images are often affected by noise, artifacts, and other interfering factors, which may lead to inaccurate and unstable diagnostic results. Therefore, image-denoising techniques become especially critical in medical image processing. Therefore, researchers have proposed innovative methods based on graph neural networks for effective noise removal, preserving the key features of the image and improving the quality and usability of medical images. This paper reviews the research progress of graph neural networks in the field of medical image denoising. It also summarises the problems and challenges of current research and looks at the future direction of medical image-denoising research.
Microlearning helps Alzheimer’s Disease Patients
Alzheimer's disease is one of the most common diseases in older adults, and as the disease progresses, the need for daily care increases. Caregivers of Alzheimer's Disease patients face a variety of stresses and work pressures. Receiving professional and continuous training is one of the effective ways to improve their skills and competencies. A new approach to education is microlearning, where microeducational content is provided to learners. Microlearning as a pedagogical technique focuses on designing learning modules through micro-steps in a digital media environment. These activities can be integrated into learners' daily lives and tasks. Unlike "traditional" e-learning methods, microlearning often favours technology delivered through push media, thus reducing the cognitive load on the learner. Microlearning educational methods have been shown to be effective and efficient in educating and delivering materials to caregivers of older adults with Alzheimer's disease. This paper begins with a brief introduction to microlearning. And it details the key features and benefits of microlearning. Microlearning offers potential benefits to Alzheimer's Disease patients and their caregivers with its concise and focused approach. Secondly, machine learning enhances the design and delivery of microlearning, helping to provide a more personalised and effective learning experience. Machine learning plays a role in the design of microlearning. To conclude, microlearning offers a promising avenue of support and care for Alzheimer's Disease patients. Microlearning also provides a valuable resource for carers and healthcare professionals to gain the knowledge and skills needed to provide effective care.
Discrete Wavelet Analysis: A Mighty Approach for Image Segmentation
This paper explores the application of Discrete Wavelet Analysis, a mathematical and signal processing technique, in the context of image segmentation, which provide a pixel-level or region-level decomposition of the image, enabling the extraction of relevant information for subsequent analysis and interpretation. Introducing the basic image segmentation techniques and the DWA, this paper discovers that DWA has found widespread application in fields such as signal processing, image analysis, and data compression. Compared with Fourier Transform, DWA is more suitable for image segmentation, having unique advantages and characteristics. Among the procedures of image segmentation, the most important point is feature selection, which determine the criteria for distinguishing different regions within the image. Despite DWA has many advantages, this technology also owns many challenges and limitations, which may be solved by lasting academic research to refine and extend Discrete Wavelet Analysis methodologies for image segmentation. In short, this research highlights the promise of Discrete Wavelet Analysis, emphasizing the use of high- quality image processing.