Learning Methodology Research Articles

Artificial intelligence and active learning methodologies in the training of future general surgeons: A comprehensive review

Artificial intelligence and active learning methodologies in the training of future general surgeons: A comprehensive review

Synergizing teacher collaboration to foster active learning strategies, student engagement and achievement in physics: a unified analysis across nine countries

This investigation explores the nexus between professional teacher collaboration, active learning methodologies, teaching promoting student engagement in physics classes and physics achievement. Leveraging Trends in International Mathematics and Science Study Advanced 2015 data from 1729 physics instructors across nine countries, it probes the extent to which collaborative practices among teachers influence their teaching to enhance active learning and engagement. Analytical techniques include Spearman correlations, confirmatory factor analysis, and structural equation modeling. Results reveal a robust model, confirmed by a Comparative Fit Index of 0.989 and Tucker–Lewis Index of 0.988, suggesting a good fit with the empirical data, alongside a Root Mean Square Error of Approximation of 0.053. The analysis demonstrates that teacher collaboration (‘Collab’) significantly predicts active learning (‘Active’) with a notable effect (β = 0.344, p < 0.001), which in turn significantly enhances student engagement (‘Engage’) (β = 0.438, p < 0.001). Additionally, ‘Engage’ is also directly influenced by ‘Collab’ (β = 0.174, p < 0.001). Although the relationship between ‘Collab’ and physics achievement (‘Physics’) is weaker, it remains significant (β = 0.063, p = 0.019). These findings support the enhancement of teacher collaboration as a catalyst for active learning and student engagement, which could indirectly foster improvements in physics achievement globally.

Human monkeypox disease prediction using novel modified restricted Boltzmann machine-based equilibrium optimizer

While the globe continues to struggle to recover from the devastation brought on by the COVID-19 virus's extensive distribution, the recent worrying rise in human monkeypox outbreaks in several nations raises the possibility of a novel worldwide pandemic. The symptoms of human monkeypox resemble those of chickenpox and traditional measles, with a few subtle variations like the various kinds of skin blisters. A range of deep learning techniques have demonstrated encouraging results in image-oriented tumor cell, Covid-19 diagnosis, and skin disease prediction tasks. Hence, it becomes necessary to perform the prediction of the new monkeypox disease using deep learning techniques. In this paper, an image-oriented human monkeypox disease prediction is performed with the help of novel deep learning methodology. Initially, the data is gathered from the standard benchmark dataset called Monkeypox Skin Lesion Dataset. From the collected data, the pre-processing is accomplished using image resizing and image normalization as well as data augmentation techniques. These pre-processed images undergo the feature extraction that is performed by the Convolutional Block Attention Module (CBAM) approach. The extracted features undergo the final prediction phase using the Modified Restricted Boltzmann Machine (MRBM), where the parameter tuning in RBM is accomplished by the nature inspired optimization algorithm referred to as Equilibrium Optimizer (EO), with the consideration of error minimization as the major objective function. Simulation findings demonstrate that the proposed model performed better than the remaining models at monkeypox prediction. The proposed MRBM-EO for the suggested human monkeypox disease prediction model in terms of RMSE is 75.68%, 70%, 60.87%, and 43.75% better than PSO-SVM, Xception-CBAM-Dense, ShuffleNet, and RBM respectively. Similarly, the proposed MRBM-EO for the suggested human monkeypox disease prediction model with respect to accuracy is 9.22%, 7.75%, 3.77%, and 10.90% better than PSO-SVM, Xception-CBAM-Dense, ShuffleNet, and RBM respectively.

Enhancing Project Management Education: Investigating the Effectiveness of Flipped Learning and Active Experiential Learning Approaches

This study proposes an in-depth investigation into the efficacy of combining flipped learning and active experiential learning methodologies within the context of project management education at Open University Malaysia. The primary aim is to overcome the shortcomings of the traditional lecture-based teaching approach, which often falls short in engaging students and equipping them with the practical skills required for effective project management in real-world scenarios. By transitioning towards a more student-centered model through the implementation of flipped classrooms, this research seeks to enhance student engagement, learning outcomes, and overall satisfaction with the course. Employing a mixed-methods approach, the study will involve quantitative and qualitative techniques to gather comprehensive data. This includes pre- and post-assessment of students' knowledge and understanding of project management principles, alongside surveys and reflective journals to capture students' perceptions of the learning experience. The participant base will consist of students enrolled in a project management course, allowing for direct observation of the pedagogical strategies' impacts. The anticipated outcomes of this research are twofold. Firstly, it aims to provide empirical evidence on the effectiveness of integrating flipped learning and active experiential learning in project management education. Secondly, it seeks to offer actionable insights for educators and educational institutions on how to design and implement more engaging and effective instructional strategies. Ultimately, this study aspires to contribute significantly to the literature on educational methodologies in project management, paving the way for enhanced educational practices and student preparedness for professional success.

3D Features Fusion for Automated Segmentation of Fluid Regions in CSCR Patients: An OCT-based Photodynamic Therapy Response Analysis.

Central Serous Chorioretinopathy (CSCR) is a significant cause of vision impairment worldwide, with Photodynamic Therapy (PDT) emerging as a promising treatment strategy. The capability to precisely segment fluid regions in Optical Coherence Tomography (OCT) scans and predict the response to PDT treatment can substantially augment patient outcomes. This paper introduces a novel deep learning (DL) methodology for automated 3D segmentation of fluid regions in OCT scans, followed by a subsequent PDT response analysis for CSCR patients. Our approach utilizes the rich 3D contextual information from OCT scans to train a model that accurately delineates fluid regions. This model not only substantially reduces the time and effort required for segmentation but also offers a standardized technique, fostering further large-scale research studies. Additionally, by incorporating pre- and post-treatment OCT scans, our model is capable of predicting PDT response, hence enabling the formulation of personalized treatment strategies and optimized patient management. To validate our approach, we employed a robust dataset comprising 2,769 OCT scans (124 3D volumes), and the results obtained were significantly satisfactory, outperforming the current state-of-the-art methods. This research signifies an important milestone in the integration of DL advancements with practical clinical applications, propelling us a step closer towards improved management of CSCR. Furthermore, the methodologies and systems developed can be adapted and extrapolated to tackle similar challenges in the diagnosis and treatment of other retinal pathologies, favoring more comprehensive and personalized patient care.

Trajectory-based fish event classification through pre-training with diffusion models

This study contributes to advancing the field of automatic fish event recognition in natural underwater videos, addressing the current gap in studying fish interaction and competition, including predator-prey relationships and mating behaviors. We used the corkwing wrasse (Symphodus melops) as a model, a marine species of commercial importance that reproduces in sea-weed nests built and cared for by a single male. These nests attract a wide range of visitors and are the focal point for behavior such as spawning, chasing, and maintenance. We propose a deep learning methodology to analyze the movement trajectories of the nesting male and classify the associated events observed in their natural habitat. Our approach leverages unsupervised pre-training based on diffusion models, leading to improved feature learning. Additionally, we introduce a dataset comprising 16,937 trajectories across 12 event classes, making it the largest in terms of event class diversity. Our results demonstrate the superior performance of our method compared to several deep architectures. The code for the proposed method and the trajectories can be found at https://github.com/NoeCanovi/Fish_Behaviors_Generative_Models.

Optimization of swimming mode for elongated undulating fin using multi-agent deep deterministic policy gradient

Optimizing speed and propulsive efficiency are the most crucial survival skills for biomimetic robots. This paper investigates a swimming mode controller inspired by the black Knifefish to govern the fast-swimming gait with high propulsive efficiency for an elongated undulating fin robot. The proposed swimming mode controller is composed of a couple of Hopf oscillator-based central pattern generators (CPG) to generate the moving gait of robotic fish and a novel variant of Reinforcement Learning (RL) known as Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) for optimizing the propulsive efficiency. The proposed swimming controller facilitates the autonomous optimization of the oscillatory amplitude of the robotic fish to improve its propulsive efficiency. The proposed MA-DDPG demonstrates an aptitude for functioning within mixed cooperative-competitive environments. Furthermore, it effectively mitigates the drawback of zero amplitude in the updating process of conventional reinforcement learning (RL) methodologies. These findings highlight the potential utility of the MA-DDPG in optimizing the performance of multi-agent systems in dynamic, real-world scenarios. The simulation results show that the undulating fin robot reaches a maximum thrust of 0.9 N with a propulsive efficiency of 12.48 %, which is higher than that of traditional reinforcement learning methods.

Enhancing radio signal classification under low SNR conditions using deep residual networks with channel attention mechanisms

This paper delves into the advancement of deep residual networks (ResNets) integrated with channel attention mechanisms for the classification of radio signals under conditions of low Signal-to-Noise Ratio (SNR). Utilizing an expansive dataset of radio signals, this paper introduces a novel architecture, MyResNet1, that combines residual learning with channel-wise attention, allowing the model to concentrate on essential features for precise classification. My, investigations exhibit notable improvements in classification accuracy, especially in challenging low SNR scenarios, highlighting the potential of attention-augmented deep residual networks in radio signal processing. Furthermore, this studyexplores various optimization strategies, including data augmentation and regularization techniques, to enhance the models performance and robustness. My findings contribute significantly to cognitive radio technologies and illuminate the potential of deep learning in sophisticated signal classification tasks, aligned with recent explorations in automatic modulation recognition (AMR) through deep learning and autoencoder-based methodologies for enhancing I/Q channel interactions.

Machine learning-driven mast cell gene signatures for prognostic and therapeutic prediction in prostate cancer

BackgroundThe role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy. MethodsThis study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS. ResultsMCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and β-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors. ConclusionThe combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.

Speaker Recognition: A Comparative analysis Between Deep Learning and Non-Deep Learning Methodologies

This work carries out a comparative study of two methodologies for speaker recognition. It is Deep Learning (DL) and Vector Quantization (VQ). The key area in biometric au-thentication systems involves speaker recognition. This requires durable and efficient algorithms. The aim is to ensure a high accuracy and reliability. The study delves into a deep neural network (DNN) model implementation. It leverages advanced feature extraction. It uses pattern recognition capabilities which are in DL. The study also examines a traditional VQ approach. This method makes use of codebook generation and quantization for speaker ID. Extensive experimentation was done on standard datasets. The project evaluates the performance of two methods. It compares accuracy. It assesses computational complexity. It does so for noise and for variations in speech. The findings of this analysis reveal the strengths and limitations of each technique. Looking at their practical applicability in real-world scenarios provides insights. The comparative results of these techniques aim to guide future developments. This concerns speaker recognition systems - particularly their potential for enhanced performance.

An Investigation of Machine Learning Applications in the Financial Fraud Detection

Financial fraud presents itself in various forms, and it often involves intricate financial transaction networks, making it challenging to detect the perpetrators and identify the characteristics of the fraud. In recent years, machine learning has gained widespread application within the financial sector. Therefore, various financial fraud detection models have been developed based on diverse machine learning methodologies. In the method part, this paper provides an overview of the machine learning process and then discusses the application of machine learning models in various financial fraud scenarios. Financial fraud in the insurance field has been further refined into automobile and medical insurance fraud. In automobile insurance fraud detection, some studies applied implicit Naive Bayes Model to analyze observable features and estimate hidden variables, and some studies used resampler to solve data imbalance and adopted 7 kinds of machine learning models for analysis. In health insurance fraud detection, many studies train medical data with multiple models, including AdaBoost, Logistic Regression and Support Vector Machine. In credit card fraud detection, studies use five algorithms, including random forest and decision tree et al., and some construct a model combining Decision Tree (DT) and Logistic Regression (LR). In bank fraud detection, some studies introduce Value-at-Risk to combine it with machine learning algorithms, and some studies propose a decentralized model training method based on federated learning. In the discussion section, this paper addresses the current limitations of the research, such as poor interpretability, uneven distribution of data sets, and issues related to customer privacy, and proposes corresponding solutions.

AI Based Urban Resilience Planning: Opportunities and Challenges

Urban areas across the globe are confronting escalating flood risks, a crisis exacerbated by the effects of climate change, which necessitates precise hyper-localized risk assessments. This research introduces the application of the AI for resilient cities model for flood risk assessment, focusing specifically on the vulnerable area of Penthakata, located in the coastal city of Puri, Odisha. The hyper-local evaluation of associated flood risks is carried out, particularly at the building level. Leveraging cutting-edge geospatial technology, deep learning methodologies, and multi-parameter analysis, this study offers valuable insights into the flood vulnerabilities in the region. Additionally, the research emphasizes the integration of technology with community volunteers and local knowledge, highlighting the essential role of grassroots-level efforts in effective disaster management. The model is one of its kind combining advanced AI technology with community engagement, the study contributes to a holistic and localized approach to strengthen adaptive capacity in the face of increasing flood risks. The findings offer a compelling case for the adoption of hyperlocal risk assessments for urban and rural areas to be better informed of flood risks, prepared for potential disasters, and implement more effective mitigative measures. Ultimately, it aims to safeguard the lives and livelihoods of vulnerable communities of various regions, offering a model for spatial environmental and community resilience.

Digital Twin and federated learning enabled cyberthreat detection system for IoT networks

The widespread deployment of Internet of Things (IoT) devices across various smart city applications presents significant security challenges, increased by the rapidly evolving landscape of cyber threats. Traditional security solutions, including those using Federated Learning with federated averaging, suffer from inefficiencies due to random node selection and partial data sampling, which can hinder the detection of comprehensive network-wide attacks. This paper introduces a novel Cyberthreat Detection System for IoT networks that leverages Digital Twin technology and an optimized Federated Learning approach. Our hypothesis implies integrating Digital Twin models within an IoT security framework to improve real-time cyberthreat detection capabilities. We implement a 'Adaptive Thresholding with Early Stopping method' based methodology in Federated Learning to systematically train and aggregate local models based on predefined training rounds, thereby ensuring that all local models contribute to the global model until a target accuracy is achieved. This method significantly improves the detection of zero-day attacks by reducing dependency on random selections and partial data. The system architecture features Digital Twins of IoT medical infrastructure components—such as radiology, intensive care, and outpatient care—positioned at the network edge to minimize latency and bandwidth usage. Comparative evaluations of our model against traditional federated averaging methods demonstrate superior performance, with enhancements in model aggregation efficiency evidenced by higher F1 scores and reduced CPU usage. Specifically, our distributed digital twin environment at the edge layer shows 14% and 33% latency reductions compared to fog and cloud-based implementations, respectively. This study highlights the potential of Digital Twin and advanced Federated Learning methodologies to secure IoT networks against evolving and growing cyber threats.

Development of a teacher training program for autism spectrum disorders

Objective: Continuing education focused on the inclusion of children with autism spectrum disorder (ASD) can contribute to better implementation of educational practices, and the use of online technologies is an alternative to increasing the number of specialized professionals. Methods: This study develops a distance teacher training program (DTTP) based on the previous research’s identification of the essential requirements for training education professionals caring for children with ASD within a collaborative digital learning environment are used to facilitate learning techniques, strategies, and resources with the goal of fostering innovation; and tests and discusses the program’s feasibility. Results: A total of 187 education professionals from a municipality in Brazil participated in this study and answered a structured questionnaire to collect information on practices, attitudes, and knowledge. Data were analyzed quantitatively to characterize the sample, and the answers were analyzed qualitatively using Nvivo coding assistance software and manual coding; key concepts were isolated, themes developed, and quotes were identified within the interview transcripts to illustrate data analysis. Data saturation was achieved through interviews to identify themes for the syllabus of the course. Conclusion: From the results and based on active learning methodologies to address real-world problems and promote practical and collaborative solutions to daily challenges with theoretical support, the DTTP was designed, and its application was conducted and evaluated.

Scholar-Administrators as Leaders

Community engagement has seen many changes over the years as scholars studied the field and learned more about its application as a methodology for teaching and learning, as well as conducting research. Henry R. Cunningham, the 2023 Barbara A. Holland Scholar-Administrator Award recipient, discusses some of the evolution in community engagement and identified community impact as an area of great importance to change the public’s negative perception of higher education. The transformation led to the institutionalization of community engagement on campuses where it is integrated into the fabric of the institution. Numerous studies conducted show the benefit to students when they are engaged in the community, and the enhancement to teaching and learning with policies and practices such as faculty professional development to enhance this work. However, we need to look closer at the impact community engagement is having on communities with which we are collaborating and at how this work is helping to improve communities. Demonstrating that higher education is changing communities and improving the lives of individuals through educational attainment, economic development, and healthier communities, as well as in other areas, can change the negative public perception of higher education.

Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease

To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINNs) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatio-temporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.

Влияние современных симуляционных тренажеров на развитие навыков командной работы: согласованных действий и коммуникации

&lt;p style="text-align: justify;"&gt;The article presents a review of a number of modern foreign studies of simulators and their role in the development of teamwork skills. The analysis of some trending learning theories and methodologies, which are used by both national and foreign researchers in the implementation of training programs on simulators of professional activity is carried out. The results of studies demonstrating the influence of computer simulators on the development of teamwork skills, including aspects of coordinated actions and effective communication are presented. Various methods and means used by foreign researchers to improve the effectiveness of simulation-based training are described. The importance of using simulation-based training in various professional fields, such as medicine, aviation, space programs and information technology, to ensure a high level of training of specialists is substantiated. It is shown that modern simulation-based simulators offer training methods that are maximally close to real professional activity, which contributes to the formation of highly qualified specialists in professional fields that require high development of teamwork skills.&lt;/p&gt;

Porosity Prediction Based on Ensemble Learning for Feature Selection and an Optimized GRU Improved by the PSO Algorithm

Accurate and reliable prediction of porosity forms the foundational basis for evaluating reservoir quality, which is essential for the systematic deployment of oil and gas exploration and development plans. When data quality of samples is low, and critical model parameters are typically determined through subjective experience, resulting in diminished accuracy and reliability of porosity prediction methods utilizing gated recurrent units (GRU), a committee-voting ensemble learning (EL) method, and an enhanced particle swarm optimization (PSO) algorithm are proposed to optimize the GRU-based porosity prediction model. Initially, outliers are eliminated through box plots and the min–max normalization is applied to enhance data quality. To address issues related to model accuracy and high training costs arising from dimensional complexity, substantial noise, and redundant information in logging data, a committee-voting EL strategy based on four feature selection algorithms is introduced. Following data preprocessing, this approach is employed to identify logging parameters highly correlated with porosity, thereby furnishing the most pertinent data samples for the GRU model, mitigating constraints imposed by single-feature selection methods. Second, an improved PSO algorithm is suggested to tackle challenges associated with low convergence accuracy stemming from random population initialization, alongside the absence of global optimal solutions due to overly rapid particle movement during iteration. This algorithm uses a good-point set for population initialization and incorporates a compression factor to devise an adaptive velocity updating strategy, thereby enhancing search efficacy. The enhanced PSO algorithm’s superiority is substantiated through comparison with four alternative swarm intelligent algorithms across 10 benchmark test functions. Ultimately, optimal hyper-parameters for the GRU model are determined using the improved PSO algorithm, thereby minimizing the influence of human factors. Experimental findings based on approximately 15,000 logging data points from well A01 in an operational field validate that, relative to three other deep learning methodologies, the proposed model proficiently extracts spatiotemporal features from logging data, yielding enhanced accuracy in porosity prediction. The mean squared error on the test set was 7.19 × 10–6, the mean absolute error stood at 0.0082, and coefficient of determination reached 0.99, offering novel insights for predicting reservoir porosity.

Identification and Localization of Wind Turbine Blade Faults Using Deep Learning

This study addresses the challenges inherent in the maintenance and inspection of wind turbines through the application of deep learning methodologies for fault detection on Wind Turbine Blades (WTBs). Specifically, this research focuses on defect detection on the blades of small-scale WTBs due to the unavailability of commercial wind turbines. This research compared popular object localization architectures, YOLO and Mask R-CNN, to identify the most effective model to detect common WTB defects, including cracks, holes, and erosion. YOLOv9 C emerged as the most effective model, with the highest scores of mAP50 and mAP50-95 of 0.849 and 0.539, respectively. Modifications to Mask R-CNN, specifically integrating a ResNet18-FPN network, reduced computational complexity by 32 layers and achieved a mAP50 of 0.8415. The findings highlight the potential of deep learning and computer vision in improving WTB fault analysis and inspection.

Influence of the problem-based learning methodology on the intrapreneurial intentions of university students

This paper analyzes the impact of the Problem-Based Learning (PBL) methodology, a teaching method that involves working with real-world problems and developing skills for their resolution, on university students' Intrapreneurial Intentions (IIN). A sample of 267 students participated in the study, and their responses were collected at two different times. The data were analyzed using statistical regression analysis and the fs-QCA technique. The results revealed that the PBL methodology significantly improved the behavior of variables associated with potentially intrapreneurial behaviors. Specifically, three components, namely Subjective Norms, Perceived Behavioral Control, and Risk-Taking Capacity, positively influenced undergraduate students' IIN. These findings suggest that the PBL methodology can be a valuable tool for universities in developing initiatives to improve graduate employability.