Learning System Research Articles

Role of Artificial Intelligence in Endoscopic Intervention: A Clinical Review.

Gastrointestinal diseases are increasing in global prevalence. As a result, the contribution to both mortality and healthcare costs is increasing. While interventions utilizing scoping techniques or ultrasound are crucial to both the timely diagnosis and management of illness, a few limitations are associated with these techniques. Artificial intelligence, using computerized diagnoses, deep learning systems, or neural networks, is increasingly being employed in multiple aspects of medicine to improve the characteristics and outcomes of these tools. Therefore, this review aims to discuss applications of artificial intelligence in endoscopy, colonoscopy, and endoscopic ultrasound.

Integrating Artificial Intelligence (AI) Into Adult Education

This conceptual article provides a comprehensive overview of the current status of Artificial Intelligence (AI) integration and its influence on adult education. It discusses generative AI technologies and their potential applications in adult education settings, examines the opportunities and ethical challenges associated with integrating AI, and provides insights into emerging trends. The article consists of five sections. The introduction provides a rationale as to why AI should be integrated into adult education. Second, it describes evolving AI technologies such as Large Language Models (LLM) for personalized learning, Machine Learning Algorithms for adaptive learning systems, Virtual Reality (VR) and Augmented Reality (AR) for immersive learning experiences, Chatbots and virtual assistants for learner support and guidance, and Data Learning Analytics (DLA) for tracking learner progress and performance into adult education. Section three explores the ethical implications of AI in adult education, including academic honesty and integrity, data privacy, and algorithmic bias. In section four, emerging trends and future directions are discussed. The final section considers policy implications and makes recommendations for adult educators working to develop AI-enriched adult education.

MULTIMODAL SYSTEM FOR FACIAL EMOTION RECOGNITION BASED ON DEEP LEARNING

Emotions are one of the main ways of communication between people and of expressing attitudes towards objects, products, services, etc. They are divided to verbal and non-verbal classes. Human speech and intonation belong to the first class, and to the second (non-verbal) facial and body emotions, known as body language. The subject of this report is the development of multimodal deep learning system intended to recognize facial and body emotions and their relationship with the scene (weather) in which they occur. It is based on three deep learning neural networks (DNN) each one for recognition of facial emotion, body emotion and weather. Combining their results, we improve significantly the final facial emotion recognition (FER) results.

The Impact of Blockchain Technology Effectiveness in Indonesia's Learning System

This research investigates the integration of blockchain technology in vocational education in Indonesia, evaluating its impact on database system learning outcomes. Involving 86 Indonesian students, this study aims to assess the effectiveness of blockchain in improving students’ understanding, skills, and academic performance. Using a quasi-experimental design, the findings of this study showed a significant improvement in learning outcomes, as evidenced by the analysis of pre-test and post-test scores. The measure of the effectiveness of the improvement in learning outcomes, with a substantial increase of approximately 46.50%, emphasizes the positive influence of blockchain technology on student achievement in the database systems course. The findings of this study will contribute valuable information to educational institutions, policy makers, and educators who wish to incorporate new technologies such as blockchain into their learning systems. The implication of this research is to provide recommendations in optimizing the integration of blockchain technology, which significantly affects learning outcomes in database systems. This is realized through facilitating fast and efficient access to learning materials, fostering a more interactive and engaging learning environment, and enhancing students’ understanding of industry practices. The integration of blockchain into database systems learning not only improves the quality of learning but also equips students for a successful career in information technology.

Using the DigCompEdu Framework to analyse your institution’s support for teachers’ digital competencies development

Everyone agrees that teachers at all levels need to develop their digital competencies – but both the institution and the individual teachers need to take on this task – so this session asks: What can institutions do to take on some of the responsibility? Strategic goal setting is nice and helpful, but how can teaching and learning units, system owners and others improve the resources and help they provide to teachers to support digital competencies development? The University of Copenhagen have used the European framework DigCompEdu to analyse and evaluate how the university as an institution can better support the competence development needs of individual teachers. The purpose of this exercise was to avoid competence development becoming an individualized problem for the teacher. The participants have a choice in the workshop's hands-on part:Hands-on A: Plan an analysis of your own institution using the CUTE Canvas.Hands-on B: Analyse your own institution. This workshop is based on the work carried out in the ERASMUS+: Strategic Partnership CUTE (KA203-867FE04B) (2019-2022) and the CUTIE project (2022-2024)

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and proven patient benefit. Systems may be vulnerable to confounding and poor generalization to new patient populations or contemporary patterns of care. Typical electronic health records do not capture rich enough data, at sufficient temporal resolution, to produce systems that make actionable treatment suggestions. To resolve these issues, we recommend a simultaneous focus on technical challenges and removing barriers to translation. This will involve improving data quality, adopting causally grounded models, prioritizing safety assessment and integration into healthcare workflows, conducting randomized clinical trials and aligning with regulatory requirements.

Pros and cons of E- learning

This is the most important area where majority of the students get confused and have number of doubts. Therefore, it is advised that the student should discuss the topic with the concerned guidesA learning system based on formalised teaching but with the help of electronic resources is known as E-learning. While teaching can be based in or out of the classrooms, the use of A learning system based on formalised teaching but with the help of electronic resources is known as E-learning. While teaching can be based in or out of the classrooms, the use of computers and the Internet forms the major compon INTRODUCTION: E-learning, short for electronic learning, is the process of learning via electronic devices, typically over the internet. It encompasses various forms of educational activities conducted online, including: Online Courses: These are courses delivered entirely over the internet, often through platforms like Coursera, Udemy, or Khan Academy. They can cover a wide range of subjects and are usually self-paced. Virtual Classrooms: These simulate traditional classrooms but take place online. Students and teachers interact in real-time through video conferencing tools like Zoom or Google Meet. Webinars: These are seminars conducted over the web, usually in the form of presentations, workshops, or lectures. Participants can interact with the presenter through chat or Q&A sessions. Mobile Learning: Learning materials are delivered through mobile devices, such as smartphones and tablets, allowing learners to access content anytime, anywhere. Gamification: Educational content is presented in the form of games to engage learners and enhance retention.

Power load forecasting based on spatial–temporal fusion graph convolution network

The cyclical behavior of electricity users facilitates the prediction of future electricity loads through the analysis of historical data, in addition to considering the spatial correlation among various electricity units. Building on these observations, this study transforms the power loads generated by regional-level user units into graph signals for a more effective visualization of spatial–temporal electricity consumption patterns. Specifically, the graph-based model harnesses its unique spatial–temporal dependencies to deduce the dynamics of electricity consumption patterns and enhance the precision of electricity generation predictions. Introducing a novel deep learning model, the Spatial-Temporal Fusion Graph Convolutional Network (FSTGRNN), which integrates temporal and spatial correlations for the prediction of electricity load values at future time steps. FSTGRNN is an end-to-end model that, firstly, delineates the spatial and temporal patterns of electric loads by constructing spatial relationship graphs using the FC-LSTM and GC-GRU models designed within the spatial–temporal block (ST-Block). Secondly, the spatial–temporal fusion block (STF-Block) in FSTGRNN, equipped with a gating mechanism, encodes the graph signals to capture both long-term dependencies and short-term correlations. The resulting integrated representation is utilized for the forecasting process. Experimental validation is conducted on two electricity load datasets, and the forecasting results clearly demonstrate the superiority and efficiency of the proposed deep learning system.

Harnessing Traditional Business Culture Resources: Scalable Parameter Server Architecture with Distributed Machine Learning

This paper explores the development and utilization of Traditional business culture resources, focusing on Yan'an's rich historical significance and its potential political, cultural, educational, and economic value. Leveraging distributed machine learning systems within a parameter server architecture, the research addresses challenges related to scalability and robustness, particularly in the face of random node downtime and network interruptions. Through intelligent simulation and experimentation, the study demonstrates the effectiveness of employing machine learning techniques to achieve high accuracy in modeling Traditional business culture's development and utilization.

The microfoundations of a lean leadership: Monozukuri, Hitozukuri, Kotozukuri

This paper sets out to investigate the microfoundations of lean leadership. To do so, we explore the Japanese philosophical concepts of Monozukuri, Hitozukuri, and Kotozukuri, representing that passion and craftsmanship for making things, developing people, and making things happen. The paper delves into how to foster lean leadership as a pivotal success factor in adopting lean thinking and practices in a manufacturing firm. By addressing the research gap concerning leadership traits contributing to successful lean deployment, the study explores the microfoundations of lean leadership. The paper is based on a single, in-depth longitudinal case study that offers new insights into the microfoundations underpinning lean leadership at VELUX, a Danish rooftop window manufacturer. The research contributes valuable evidence to the existing lean leadership research discourse, where lean should be seen as a human learning system, illustrating how lean leaders can cultivate lean and how the Japanese philosophical concepts of Monozukuri, Hitozukuri, and Kotozukuri resonate in a Western context.

Integrating Cultural Values Into Buddhist Sunday School Learning System

This paper draws upon the possibility of integrating cultural values into Indonesia's Buddhist Sunday School (BSS) learning system. It employed George’s (2008) library research methods consisting of motivation or assignment, imagination, research questions (brainstorming), research plan (strategy), reference works and databases (tools and tactics), sources, evaluation, insight, thesis, argument and outline, and drafting and revising. By reviewing 25 journal articles and books regarding with culture and education, this paper proposed three things to establish cultural-based religious learning in BSS, which are strengthening cultural content and values in the learning process, building a community-based learning, and involving parents.

Multi-task Information Enhancement Recommendation model for educational Self-Directed Learning System

In Self-Directed Learning Systems (SDLS), suggesting specific exercises to students after viewing educational videos is crucial, which promotes the integration of learning resources and improves learning efficiency. Given that exercises and educational videos are distinct learning resources, suggesting exercises centered on educational videos containing knowledge concepts is referred to as cross-type recommendations. Traditional cross-type recommendation methods usually separate data representation and recommendation tasks, which hampers effective knowledge transfer and information fusion. Furthermore, educational resources frequently feature an irregular distribution of information, posing a significant challenge to existing models in extracting crucial information. Besides, due to the inadequate data, the cross-type recommendation system needs help in developing a robust model. To address these challenges, we propose a Multi-task Information Enhancement Recommendation (MIER) Model in this paper. This model provides a comprehensive recommendation capability by integrating resource representation and merging recommendations. Multi-task learning improves the capacity to integrate information and transfer knowledge. Then, we propose an Information Enhancement Encoder (IEE) module that utilizes the attention mechanism to enhance the model’s ability to extract essential information from educational videos and exercises. Finally, to address the problem of inadequate data, we design a fusion structure that incorporates the concept knowledge graph as expert information into the model. To evaluate the effectiveness of the MIER, we compare it with other models on both predicting concepts and recommending exercises task. The corresponding results demonstrate that the MIER outperforms all the other models. We comprehensively evaluated the MIER model’s performance across a range of datasets varying in labeled data quantity. The findings demonstrate that the model consistently achieves outstanding performance and exhibits robustness, even under conditions of limited label availability.

Tutor Competence in Online-Learning Service within Distance Education

The online learning landscape in the Elementary Teacher Education (PGSD) program at Universitas Terbuka Makassar poses significant challenges for tutors, requiring them to adapt to new pedagogical approaches and technologies. This study investigates the competence of tutors in facilitating synchronous online-learning tutoring (Tuweb UT) and asynchronous Learning System Management (LMS) through Silayar UT, which integrates an independent learning program into online platforms. The study examines the tutors' social, strategic, and ethical competence in managing online classes, with a focus on their preparation, planning, and interaction with students. The study uses an ex post facto design with descriptive statistics, analyzing data from 185 tutors in 340 online classes within the undergraduate PGSD program during the 2022 registration term. The findings highlight the tutors' ability to prepare and plan for online tutoring, deploy the tutoring unit (SAT) as a learning service scheme, and incorporate relevant competencies and module characteristics into their tutoring mode. The study also shows that tutoring assignments are given in accordance with competence and that active engagement in Webinar UT and LMS (Silayar UT) is high, with attendance averaging 93.03%. The results indicate that tutors are competent in preparing and planning for online tutoring, incorporating online platforms into their experiences, and fostering active interaction with students, which in turn supports the completion of students' academic trajectories. The study's implications for online learning practices are significant, highlighting the importance of tutor competence in navigating the challenges of online education. Recommendations for further research include exploring the impact of tutor competence on student outcomes and investigating the role of technology in enhancing tutor-student interaction.

A novel life prediction method of RF circuits based on the improved recurrent broad learning system

Life prediction of RF circuits can greatly improve the reliability of RF systems. But most literature has been focused on the life prediction of RF devices, which is not applicable to RF circuits. Therefore, a novel RF circuit life prediction method is proposed based on an improved recurrent broad learning system (RBLS). First, a new feature matrix is proposed to characterize a RF circuit at each moment. Then, an improved RBLS model is used to predict the life of the circuit, in which the RBLS model is used to predict the feature vector and then the extreme learning machine (ELM) is used to expand the feature vector into a feature matrix. The method is validated in a low-noise amplifier with classical GRU, BLS-ELM, BLS, RBLS, ELM, and LSTM as a control group. The analysis results show that RBLS-ELM has the highest prediction accuracy with an RMSE of only 2.5959, the smallest prediction uncertainty of 1.2767, and a very short prediction time of 0.4251 s.

Learning with Asynchronous Labels

Learning with data streams has attracted much attention in recent decades. Conventional approaches typically assume that the feature and label of a data item can be timely observed at each round. In many real-world tasks, however, it often occurs that either the feature or the label is observed firstly while the other arrives with delay. For instance, in distributed learning systems, a central processor collects training data from different sub-processors to train a learning model, whereas the feature and label of certain data items can arrive asynchronously due to network latency. The problem of learning with asynchronous feature or label in streams encompasses many applications but still lacks sound solutions. In this paper, we formulate the problem and propose a new approach to alleviate the negative effect of asynchronicity and mining asynchronous data streams. Our approach carefully exploits the timely arrived information and builds an online ensemble structure to adaptively reuse historical models and instances. We provide the theoretical guarantees of our approach and conduct extensive experiments to validate its effectiveness.

BFL-SA: Blockchain-based federated learning via enhanced secure aggregation

Federated learning, involving a central server and multiple clients, aims to keep data local but raises privacy concerns like data exposure and participation privacy. Secure aggregation, especially with pairwise masking, preserves privacy without accuracy loss. Yet, issues persist like security against malicious models, central server fault tolerance, and trust in decryption keys. Resolving these challenges is vital for advancing secure federated learning systems. In this paper, we present BFL-SA, a blockchain-based federated learning scheme via enhanced secure aggregation, which addresses key challenges by integrating blockchain consensus, publicly verifiable secret sharing, and an overdue gradients aggregation module. These enhancements significantly boost security and fault tolerance while improving the efficiency of data utilization in the secure aggregation process. After security analysis, we have demonstrated that BFL-SA achieves secure aggregation even in malicious models. Through experimental comparative analysis, BFL-SA exhibits rapid secure aggregation speed and achieves 100% model aggregation accuracy.

Abstract PO4-07-03: Machine learning breast cancer risk prediction using sequential past mammograms - a pilot study

Abstract Background and Aim Some studies have reported the use of mammogram AI deep learning algorithms to accurately predict the risk of future breast cancer development in women. In these studies, random and unrelated mammograms were independently used for training of the AI model. We propose a novel deep learning system using sequential past mammograms instead of random studies and aimed to determine if it can further improve risk prediction in a pilot study. Methods Pre-training of the backbone of a Swin-transformer machine learning model was performed by applying region-of-interest lesional masks of benign and malignant on a training set of 1644 mammograms derived from the open-access Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset. To further develop the model, deep learning training and validation were performed on the Cohort of Screen-age Women (CSAW) open-access screening mammogram set which was derived from the long term data of 873 women who developed breast cancer for the first time and 7850 non-cancer women. We then compared the diagnostic accuracy of a model using sequential past mammograms for training against the standard, non-sequential model. For the sequential model, imaging segmentation of the potential cancer site on mammography was performed on all past, pre-cancer mammograms obtained within 60 to 2555 days of cancer diagnosis in the pre-processing stage for breast cancer cases. AI training was then carried out by combining the pre-cancer mammograms belonging to the same woman in a temporal sequence using a transformer encoder. The learning rate started from 1e-6 and decayed 10 times every 10 epochs. For the non-sequential model, we removed the temporal encoder and used random, unrelated mammogram studies for training. Validation was performed on a dataset consisting of 75 pre-cancer mammograms from breast cancer cases and 69 mammograms from non-cancer cases. Results Sensitivity, specificity and overall accuracy on the validation set were 77.3% (58/75), 68.1% (47/69) and (105/144) 72.9% respectively for the sequential model compared to 69.3% (52/75), 69.6% (48/69) and 69.4% (100/144) respectively for the non-sequential model. Conclusion The study showed that deep learning breast cancer risk prediction can be further improved by using sequential past mammograms instead of random mammograms for AI model training. This can also potentially enhance other AI risk prediction models that employ combined mammogram and traditional breast cancer clinical risk factors. A larger validation study will be useful. Contact information: email Lester Leong at lester.leong.c.h@singhealth.com.sg ROC curve for breast cancer risk prediction using AI deep learning training with sequential past mammograms. Citation Format: Lester Leong, Mingjie Xu, Weimin Huang, Erli Zhang, Engracia Loh, Sze Yiun Teo, Geok Hoon Lim, Veronique Kiak Mien Tan, Yirong Sim, Fredrik Strand, Ryan Tan. Machine learning breast cancer risk prediction using sequential past mammograms - a pilot study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-03.

Deep learning system for screening AIDS-related cytomegalovirus retinitis with ultra-wide-field fundus images

BackgroundOphthalmological screening for cytomegalovirus retinitis (CMVR) for HIV/AIDS patients is important to prevent lifelong blindness. Previous studies have shown good properties of automated CMVR screening using digital fundus images. However, the application of a deep learning (DL) system to CMVR with ultra-wide-field (UWF) fundus images has not been studied, and the feasibility and efficiency of this method are uncertain. MethodsIn this study, we developed, internally validated, externally validated, and prospectively validated a DL system to detect AIDS-related from UWF fundus images from different clinical datasets. We independently used the InceptionResnetV2 network to develop and internally validate a DL system for identifying active CMVR, inactive CMVR, and non-CMVR in 6960 UWF fundus images from 862 AIDS patients and validated the system in a prospective and an external validation data set using the area under the curve (AUC), accuracy, sensitivity, and specificity. A heat map identified the most important area (lesions) used by the DL system for differentiating CMVR. ResultsThe DL system showed AUCs of 0.945 (95 % confidence interval [CI]: 0.929, 0.962), 0.964 (95 % CI: 0.870, 0.999) and 0.968 (95 % CI: 0.860, 1.000) for detecting active CMVR from non-CMVR and 0.923 (95 % CI: 0.908, 0.938), 0.902 (0.857, 0.948) and 0.884 (0.851, 0.917) for detecting active CMVR from non-CMVR in the internal cross-validation, external validation, and prospective validation, respectively. Deep learning performed promisingly in screening CMVR. It also showed the ability to differentiate active CMVR from non-CMVR and inactive CMVR as well as to identify active CMVR and inactive CMVR from non-CMVR (all AUCs in the three independent data sets >0.900). The heat maps successfully highlighted lesion locations. ConclusionsOur UWF fundus image-based DL system showed reliable performance for screening AIDS-related CMVR showing its potential for screening CMVR in HIV/AIDS patients, especially in the absence of ophthalmic resources.

Practical training of future teachers as prevention of psychological barriers in inclusive education

Introduction. Currently, international organizations pay special attention to the development of inclusive education system, which, taking into account the special educational needs of everyone, provides quality education at the place of residence and full socialization of people with disabilities. Inclusiveness has become one of the main components of the modern educational system, but at the moment there is a problem of occurrence of psychological barriers in teachers, which is poorly studied and requires detailed research. The aim of the article is to consider the problem of preventing the emergence of psychological barriers in inclusive education in students of pedagogical directions through practical professional training, including the development of their communicative and organizational abilities. Materials and Methods. The study was conducted on the basis of the North-Eastern Federal University named after M.K. Ammosov (NEFU) among students studying in the following fields of training: "Special (defectology) education", "Pedagogical education", "Psychological and pedagogical education", in the number of 122 people, aged from 18 to 23 years. The following methods were used: analysis of sources, regulatory and educational-methodical documents of the North-Eastern Federal University on practical training of students, observation and surveys using the method of E.S. Slyusareva "Psychological barriers of teachers in the process of interaction in an inclusive educational environment" and the method "Assessment of communicative and organizational abilities" (V.V. Sinyavsky, V.A. Fedoroshin), statistical processing of the results by U-Manna-Whitney test. Results of the research. The system of practical training of higher education students for pedagogical activity in inclusive education of future teachers, including practice-oriented educational process, socially significant public activity is developed. A separate aspect of this training is the prevention of psychological barriers in inclusive education in future teachers. The application of the developed system of training contributed to the effective development of communicative and organizational abilities of students, and also demonstrated reliably significant reductions in the criteria "barrier of mental tension (anxiety, inability to initiate contact with a child with disabilities, etc.)" Uemp=178,5; p<0,001, and "difficulties of choice and transfer of knowledge about the psychological characteristics of children with disabilities in the process of inclusive education", Uemp=248; p<0,043, which indicates that the conducted preventive and developmental work contributed to a decrease in the expression of the presence of difficulties of choice and transfer of knowledge about the psychological characteristics of children with disabilities in the process of inclusive education. Conclusion. The results of the study indicate the need to rethink the organization and content of the practical component of higher pedagogical education in the aspect of preventing the emergence of psychological barriers in inclusive education in future teachers through the development of their communicative and organizational abilities in the system of practice-oriented learning.

Incorporating mitigation strategies in machine learning for landslide susceptibility prediction

This study proposes an approach that considers mitigation strategies in predicting landslide susceptibility through machine learning (ML) and geographic information system (GIS) techniques. ML models, such as random forest (RF), logistic regression (LR), and support vector classification (SVC) are incorporated into GIS to predict landslide susceptibilities in Hong Kong. To consider the effect of mitigation strategies on landslide susceptibility, non-landslide samples were produced in the upgraded area and added to randomly created samples to serve as ML models in training datasets. Two scenarios were created to compare and demonstrate the efficiency of the proposed approach; Scenario I does not considering landslide control while Scenario II considers mitigation strategies for landslide control. The largest landslide susceptibilities are 0.967 (from RF), followed by 0.936 (from LR) and 0.902 (from SVC) in Scenario II; in Scenario I, they are 0.986 (from RF), 0.955 (from LR) and 0.947 (from SVC). This proves that the ML models considering mitigation strategies can decrease the current landslide susceptibilities. The comparison between the different ML models shows that RF performed better than LR and SVC, and provides the best prediction of the spatial distribution of landslide susceptibilities.