Efficacy Of Learning Research Articles

Comprehensive Performance Evaluation of Machine Learning Algorithms for detecting DDoS attacks in SDN

<div align="center">Software-Defined Networking (SDN) revolutionizes networking by separating control logic and data forwarding, enhancing security against threats like Distributed Denial of Service (DDoS) attacks. These attacks flood control plane bandwidth, causing SDN network failures. Recent studies emphasize the efficacy of machine learning and statistical approaches in identifying and mitigating these security risks. However, there has been a lack of focus on employing ensembling techniques, amalgamating diverse machine learning models, selecting pertinent features, and utilizing oversampling techniques to balance categorical data. Our study evaluates 20 machine-learning models, emphasizing feature engineering and addressing class imbalance using Synthetic Minority Oversampling TEchnique (SMOTE). The results indicate that Ensemble methods such as LGBM Classifier, Random Forest Classifier, XGB Classifier, Decision Tree Classifier attained near-perfect scores (almost 100%) across all metrics, suggesting potential overfitting. Conversely, models like AdaBoost Classifier, K-Neighbors Classifier, and SVC exhibited slightly lower (99%) but realistic performance, underscoring the intricacy of accurate prediction in cybersecurity. Simpler models, including Logistic Regression, Linear Discriminant Analysis, and Gaussian Naive Bayes, demonstrated moderate to low accuracy, approximately around 70%. These findings stress the imperative need for a nuanced approach in the selection and fine-tuning of machine learning models to ensure effective DDoS detection in SDN environments.</div>

Inhibition SNN: unveiling the efficacy of various lateral inhibition learning in image pattern recognition

Inhibition SNN: unveiling the efficacy of various lateral inhibition learning in image pattern recognition

Compositional pretraining improves computational efficiency and matches animal behavior on complex tasks.

1Recurrent neural networks (RNN) are ubiquitously used in neuroscience to capture both neural dynamics and behaviors of living systems. However, when it comes to complex cognitive tasks, training RNNs with traditional methods can prove difficult and fall short of capturing crucial aspects of animal behavior. Here we propose a principled approach for identifying and incorporating compositional tasks as part of RNN training. Taking as target a temporal wagering task previously studied in rats, we design a pretraining curriculum of simpler cognitive tasks that reflect relevant sub-computations. We show that this pretraining substantially improves learning efficacy and is critical for RNNs to adopt similar strategies as rats, including long-timescale inference of latent states, which conventional pretraining approaches fail to capture. Mechanistically, our pretraining supports the development of slow dynamical systems features needed for implementing both inference and value-based decision making. Overall, our approach is an important step for endowing RNNs with relevant inductive biases, which is important when modeling complex behaviors that rely on multiple cognitive computations.

Comparative Analysis of Machine Learning, Decision Trees, and K-Nearest Neighbors for Heart Disease Prediction

Abstract. This study investigates the efficacy of Machine Learning (ML), Decision Trees, and K-Nearest Neighbors (KNN) techniques in predicting heart disease, aiming to identify their strengths and limitations. ML models are effective in detecting complex patterns and delivering evolving predictions from large datasets but require high-quality data and can be challenging to interpret. Decision Trees provide clear, understandable decision rules, which is beneficial in clinical settings, yet they are susceptible to overfitting and instability. KNN, valued for its simplicity and flexibility, classifies heart disease based on similarity but struggles with high computational costs and sensitivity to noisy data. Experimental results indicate that each model has distinct advantages: ML excels in pattern recognition, Decision Trees offer interpretability, and KNN handles diverse data effectively. However, each also faces challenges that impact performance, such as data quality issues for ML, overfitting for Decision Trees, and computational demands for KNN. The study suggests that balancing these strengths and weaknesses is crucial for optimizing heart disease prediction models. Future research should explore hybrid approaches that combine these models' advantages while addressing their respective limitations to improve predictive accuracy and practical application in real-world scenarios.

The Application and Implications of Memetics Theory in Second Language Acquisition

Abstract: In recent years, with the continuous development of language learning theories, the traditional model of second language acquisition has gradually exposed its limitations and is unable to fully explain the complex language learning process. Meanwhile, Memetic Theory, as a theory that explains cultural transmission and imitation, has shown potential application in the field of language learning. As a result, this paper explores its application strategies and implications in second language learning. Specifically, through a literature review of traditional language learning theories (e.g., behaviorism, cognitivism, and social constructivism), it analyzes the shortcomings of these theories in language learning learning, and proposes modal theory as an alternative theory. In addition, it examines the impact of modality on the vocabulary retention, grammatical proficiency, and phonological imitation of language learners, and assesses the potential benefits of modal propagation in language teaching and learning by integrating it with actual teaching cases. In short, the study, which employs a systematic application of modality theory to the field of language learning, can provide new perspectives and methods for second language acquisition, thereby enhancing the efficacy of language learning.

Simplify, Consolidate, Intervene: Facilitating Institutional Support with Mental Models of Learning Management System Use

Measuring instructors' adoption of learning management system (LMS) tools is a critical first step in evaluating the efficacy of online teaching and learning at scale. Existing models for LMS adoption are often qualitative, learner-centered, and difficult to leverage towards institutional support. We propose depth-of-use (DOU): an intuitive measurement model for faculty's utilization of a university-wide LMS and their needs for institutional support. We hypothesis-test the relationship between DOU and course attributes like modality, participation, logistics, and outcomes. In a large-scale analysis of metadata from 30000+ courses offered at Virginia Tech over two years, we find that a pervasive need for scale, interoperability and ubiquitous access drives LMS adoption by university instructors. We then demonstrate how DOU can help faculty members identify the opportunity-cost of transition from legacy apps to LMS tools. We also describe how DOU can help instructional designers and IT organizational leadership evaluate the impact of their support allocation, faculty development and LMS evangelism initiatives.

TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS

In the digital banking landscape, the increasing volume of online transactions has heightened the risk of fraudulent activities, necessitating the development of more effective detection systems. This study investigates the efficacy of various machine learning and deep learning algorithms in identifying fraudulent transactions, emphasizing Long Short-Term Memory (LSTM) networks. We implemented and evaluated multiple algorithms, including Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost, on a large-scale credit card transaction dataset. Our results demonstrate that the LSTM model outperforms traditional machine learning algorithms, achieving an accuracy of 98.5%, precision of 87.2%, recall of 85.0%, and an Area Under the Curve (AUC) score of 0.94. These findings highlight the superior capability of LSTM networks to capture complex patterns in sequential transaction data, making them an asset for real-time fraud detection in banking. This research underscores the need for financial institutions to adopt advanced deep learning techniques to enhance their fraud detection systems, thereby minimizing financial losses and improving customer trust.

Gauging the effectiveness of a mobile application for learning English phrasal verbs

Abstract This study used a mixed-methods approach to evaluate the efficacy of mobile-assisted language learning (MALL) in teaching English phrasal verbs (PVs) in a 12-week study. The participants were 122 EFL college students divided equally into an experimental and a control group. The experimental group was assigned PV learning on an iOS-based application (henceforth referred to as “app”) for eight weeks; the control group learned the same PVs through paper-based material. Pre-tests, post-tests, and weekly class tests were conducted, and one-way ANOVAs were performed to evaluate the differences between the two groups using their pre-test and post-test scores, with repeated measures ANOVA used to analyse the learning gains in weekly tests. The results revealed that the experimental group significantly outperformed the control group in the post-test (F = 6.09, p = .015, Cohen’s d = 0.45) and weekly tests (F = 31.68, p = .000). A Likert-scale-based e-questionnaire consisting of 19 items was administered to the experimental group to obtain their perceptions of the app’s usefulness for learning English PVs. The overall results suggest that MALL, particularly with this specific mobile app, may enhance students’ ability to understand and use English PVs, a key aspect of vocabulary skills. The findings can be used to encourage instructors to employ MALL for teaching the English lexicon for better learning outcomes in EFL settings.

Effects of Frontal-Midline Theta Neurofeedback with Different Training Directions on Goal-Directed Attentional Control.

As a significant component of executive function, goal-directed attentional control is crucial for cognitive processing and is closely linked to frontal-midline theta (FMT) rhythms. However, how up-regulation and down-regulation of FMT through neurofeedback training (NFT) impact goal-directed attention control remains unclear, especially for both short-term and long-lasting effects. Therefore, this study employed a single-blind sham-controlled between-subject design to answer this question. Forty-seven healthy adults were randomly assigned to the up-regulation, down-regulation, or sham control groups. Each group underwent one NFT session per day at the Fz electrode site for four consecutive days. All participants completed a visual search task before, immediately after the first, after the final, and one week following the last NFT session. The down-regulation group significantly reduced FMT activity during NFT and in the resting state (p < = 0.038), while the up-regulation group only showed an upward trend during the training phase (r = 0.721, p = 0.002). The behavioral performance showed no significant improvement in any group (p > 0.05). Importantly, the FMT learning efficacy in the up-regulation group revealed a significantly negative correlation with the change in switch cost (r = -0.602, p = 0.046). These findings suggest a close link between the up-regulation efficacy of FMT rhythms and goal-directed attentional control. In educational or clinical settings, it would be desirable to improve goal-directed attention through enhancement of FMT up-regulation efficacy of NFT in future work.

Comparative analysis of machine learning techniques in predicting dielectric behavior of ternary chalcogenide thin films

Abstract The current research introduces a comparative analysis of the dielectric behavior exhibited by ternary chalcogenide thin films, including both experimental data and machine learning techniques. The study of temperature and frequency dependencies of dielectric parameters is crucial for assessing material losses, particularly focusing on the low-frequency range where dielectric dispersion occurs. The experimental results on the frequency (100–1000000 Hz)and the temperature(303–393 K) influences on the dielectric (constant ( ε 1 ) and loss ( ε 2 )) of A s 4 G e 24 T e 72 composition in thin film form have been discussed. Results demonstrate that ε 1 exhibits an increasing trend with temperature, attributed to heightened orientation polarization as dipoles gain mobility with elevated thermal energy. Conversely, ε 1 declines with rising frequency, reflecting diverse different polarization mechanisms. Understanding these behaviors is important for material characterization and applications in fields like electronics and solar cells. A comparative analysis of the dielectric behavior exhibited by ternary chalcogenide thin films, including both experimental data and machine learning techniques. Through the experimental approach, the dielectric properties of the films are investigated. Additionally, the study employs a range of machine learning algorithms, including Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Genetic Programming (GP), hybrid techniques ANFIS-GA, and ANFIS-PSO, to model and predict the dielectric behavior with remarkable accuracy. The paper presents a comparison between the performance of the proposed machine learning models, highlighting their strengths and limitations in capturing the complex dielectric phenomena observed in the ternary chalcogenide thin films. This comparative study not only provides valuable insights into the dielectric properties of these materials but also demonstrates the efficacy of machine learning in understanding and predicting their behavior, paving the way for further advancements in materials science and device development.

Machine learning adjusted sequential CUSUM-analyses are superior to cross-sectional analysis of excess mortality after surgery

BackgroundThe assessment of clinical outcome quality, particularly in surgery, is crucial for healthcare improvement. Traditional cross-sectional analyses often fall short in timely and systematic identification of clinical quality issues. This study explores the efficacy of machine learning adjusted sequential CUSUM (Cumulative Sum) analyses in monitoring post-surgical mortality. Material and methodsUtilizing the Global Open Source Severity of Illness Score (GOSSIS) dataset involving 91,714 patient records from 147 hospitals, this study involved the development of a machine learning model for mortality using a modified LightGBM algorithm. With this, sequential and cross sectional quality monitoring was simulated and compared. ResultsThe modified LightGBM model demonstrated superior predictive accuracy (ROC AUC of 0.88). Simulations revealed that the AI risk-adjusted CUSUM required fewer patient outcome alterations to detect atypical trends compared to standard methods. ConclusionThe AI risk-adjusted CUSUM analysis represents a significant advancement in monitoring clinical outcome quality in healthcare, especially in surgery. Its ability to detect minor discrepancies in mortality rates with greater sensitivity and specificity positions it as a valuable tool for healthcare providers. This approach could lead to earlier interventions and improved patient care.

A Systematic Literature Review of Synchronous E-Learning Challenges in Higher Education Institutions in Developing Nations during the COVID-19 Pandemic

Abstract Introduction: The COVID-19 pandemic has accelerated the transition to online learning, with synchronous e-learning becoming a critical modality in higher education institutions (HEIs) worldwide. This shift, while ensuring educational continuity, has unveiled numerous challenges, especially in developing nations where resources and infrastructure may not adequately support such a rapid transition. Purpose: This study aims to provide a systematic literature review of the challenges encountered by students in developing nations during the implementation of synchronous e-learning amid the COVID-19 pandemic. It focuses on identifying and analysing technological, pedagogical, infrastructural, socioeconomic, and health-related challenges that have impacted the efficacy of online learning. Methods: Through a systematic literature review of 43 peer-reviewed studies conducted in developing nations, dated between 2020 and 2022 sourced from the SCOPUS database, this article synthesises findings on the multifaceted challenges faced by HEIs students during the COVID-19 pandemic. The study employs a thematic analysis approach to categorise these challenges and discuss their implications for educational equity, student well-being, and the resilience of higher education systems in developing countries. Conclusions: The study underscores the need for comprehensive strategies to address the identified challenges, emphasising the importance of infrastructural improvements, pedagogical support, socioeconomic interventions, and health-focused measures. It calls for a collaborative effort among educational institutions, governments, and international organisations to enhance the resilience and inclusivity of e-learning environments, ensuring equitable access to quality education in developing nations during and beyond the pandemic.

Assessing the Efficacy of Artificial Intelligence-Enabled EFL Learning and Teaching in Saudi Arabia: Perceptions, Perspectives, and Prospects

Assessing the Efficacy of Artificial Intelligence-Enabled EFL Learning and Teaching in Saudi Arabia: Perceptions, Perspectives, and Prospects

Multi-agent reinforcement learning with synchronized and decomposed reward automaton synthesized from reactive temporal logic

Multi-agent systems (MAS) consist of multiple autonomous agents interacting to achieve collective objectives. Multi-agent reinforcement learning (MARL) enhances these systems by enabling agents to learn optimal behaviors through interaction, thus improving their coordination in dynamic environments. However, MARL faces significant challenges in adapting to complex dependencies on past states and actions, which are not adequately represented by the current state alone in reactive systems. This paper addresses these challenges by considering MAS operating under task specifications formulated as Generalized Reactivity of rank 1 (GR(1)). These synthesized strategies are used as a priori knowledge to guide the learning. To tackle the difficulties of handling non-Markovian tasks in reactive systems, we propose a novel synchronized decentralized training paradigm that guides agents to learn within the MARL framework using a reward structure constructed from decomposed synthesized strategies of GR(1). We initially formalize the synthesis of GR(1) strategies as a reachability problem of winning states of the system. Subsequently, we develop a decomposition mechanism that constructs individual reward structures for decentralized MARL, incorporating potential values calculated through value iteration. Theoretical proofs are provided to verify that the safety and liveness properties are preserved. We evaluate our approach against other state-of-the-art methods under various GR(1) specifications and scenario maps, demonstrating superior learning efficacy and optimal rewards per episode. Additionally, we show that the decentralized training paradigm outperforms the centralized training paradigm. The value iteration strategy used to calculate potential values for the reward structure is compared against two other strategies, showcasing its advantages.

Re-Creation of a Classic Novel through Transmedia Storytelling : Cross-Cultural Learning Focusing on Tess of the d&amp;apos;Urbervilles

This study explores the transmedia-storytelling model to induce EFL learners to participate in project-based learning while reading a classic novel, &lt;i&gt;Tess of the d'Urbervilles&lt;/i&gt;. The objectives are to encourage their participation and to design a class representing ‘limited selection and freedom’ for the twenties. In particular, when provided with subtitles through CCD (Cross-Cultural Dialogue) based inquiry, learners can experience student-centered creation activities by adding, reforming, and storytelling the content. In this study, the more the students learn about individual freedom and the ability to adapt, the higher consideration they will have for both their own and other cultures, which can improve their emotional resilience. They will also learn that studyng with people from different cultural backgrounds can promote their learning efficacy. Hopefully, this study can contribute to building a model of Liberal Arts to encourage further readings in the Classics and to activate learners’ convergence skills in English language classes.

Findings from the Mighty Girls Efficacy Trial: Changes in Acceptance of Dating Violence

Background/Objectives: Test efficacy of the social emotional learning (SEL)-based Mighty Girls program, a program culturally tailored for English-speaking Hispanic/Latino girls in seventh grade comprised of classroom sessions and a virtual reality computer game. We hypothesized that the curriculum would decrease risky sexual behaviors in a program that can be used as part of a comprehensive sex education curriculum or as a stand-alone program. Methods: A randomized group trial was conducted in which 22 low-income, predominately Hispanic schools within the Miami-Dade County Public School System were randomly assigned to intervention (consented n = 335) and control (consented n = 217) conditions. All study activities occurred after school. Primary outcome measures were resistance self-efficacy, acceptance of dating violence, sexual intentions, and sexual behavior. Assessments occurred at baseline, immediately post-intervention, 3-, 12-, and 24-months post-intervention. Changes in outcomes from baseline to 24 months were modeled using multi-level models to account for nesting of students within schools with full information maximum likelihood to account for missing data and baseline school attendance and enrollment in free and reduced lunch as covariates. Analyses are also controlled for multiple testing. Results: The program had a significant effect on reducing acceptance of dating violence at 24 months post-intervention (estimate = −0.083, p ≤ 0.05), but no effect on resistance self-efficacy, sexual intentions, or sexual behavior (p ≥ 0.58). Conclusion: Study findings demonstrate that a social emotional learning (SEL) curriculum can impact sexual behaviors such as susceptibility to dating violence. Low baseline levels for sexual intentions and behaviors as well as a high baseline of efficacy may have impacted findings for the other outcomes.

The effect of OMO learning on high school students' problem-solving skills in physics

Online-merge-offline (OMO) learning in a hybrid learning space is becoming a popular pedagogy to enhance students' blended learning using appropriate technology. This investigation delves into the impact of OMO learning on developing physics problem-solving skills among high school students. A ten-week intervention employing OMO learning is implemented with 34 participants in both the experimental and control groups at a high school in Hangzhou, China. The efficacy of OMO learning is assessed through a quasi-experimental design encompassing pre- and post-assessments. The findings of this inquiry reveal: (1) The experimental group's physics problem-solving abilities are significantly impacted by OMO learning; (2) the use of cognitive tools rationally, the integration of online and offline collaborative learning, rich learning resources, and heuristic teaching all help students become more adept at solving physics problems; and (3) the cognitive load, students' lack of prior knowledge, poor self-regulation, and lack of help-seeking techniques all work against the improvement of students' physics problem-solving abilities. Suggestions are made in line with the research results and aims.

Wheat Flour Discrimination Using Two-Dimensional Correlation Spectroscopy and Deep Learning.

The continuous evolution of deep learning has garnered significant attention in spectroscopy. This study focuses on identifying wheat flour, presenting a more efficient and accurate method by combining two-dimensional correlation spectroscopy (2D-COS) and deep learning techniques. A data set of 316 near-infrared (NIR) spectral samples of four types of wheat flour was collected. By applying three disparate 2D-COS techniques, i.e., synchronous, asynchronous, and integrated, we crafted 948 2D-COS images. These images, obtained by transforming the original one-dimensional spectra into 2D representations, offer richer information for deep learning analysis. The study introduced an 18-layer residual network incorporating a convolutional attention mechanism, specifically tailored for the 2D-COS analysis of wheat flour, aimed at enhancing the model's discriminative capabilities by refining the residual neural network's structure. Achieving an unprecedented recognition accuracy of 100% through methodical optimization and rigorous training on the synchronous 2D-COS data set of wheat flour, the proposed model is a testament to the efficacy of deep learning in spectroscopic analysis. To further exhibit the confluence of 2D-COS with deep learning, t-distributed stochastic neighbor embedding was employed to visualize the distinctive 2D-COS features within the deep learning architecture. Additionally, the model's performance was juxtaposed with prevailing NIR spectral recognition methods, including random forest, gradient boosting decision tree, and artificial neural network. This comparison cemented the proposed approach's superiority in wheat flour categorization. The findings of this study not only introduce a novel and efficient solution for wheat flour quality analysis but also underscore the significant potential of deep learning techniques in spectroscopy applications.

Efficacy of Jigsaw learning in teaching volleyball skills to grade 12 students

Playing volleyball is one of the Physical Education courses in the Philippines. However, playing it is not mastered due to a lack of research-based strategies. This study determined the efficacy of Jigsaw learning in teaching Volleyball in Grade 12 at Biliran Province State University-Main Campus, Naval, Biliran during the academic year 2022–2023 as a basis for a proposed skills enhancement activity. The study used a quantitative descriptive-correlation research design. There were one hundred (100) respondents. The skills performance of the students was good after using jigsaw learning in teaching volleyball as to, serving, passing, setting, spiking/attacking, and blocking. The students’ skills performance before and after using jigsaw learning in teaching volleyball were significantly related. Jigsaw learning was efficient in teaching volleyball. Thus, the Jigsaw model is used to make the students more skillful and competent.

Assessing interactional metadiscourse in EFL writing through intelligent data-driven learning: the Microsoft Copilot in the spotlight

The purpose of the current study was twofold: examining the efficacy of data-driven learning (DDL) (hands-on and hands-off approaches) in the realization of interactional metadiscourse markers (IMMs) among English as a foreign language (EFL) learners and analyzing the learners’ perceptions of DDL. The participants consisted of 93 male and female advanced language learners randomly assigned to one of the three groups: hands-on, hands-off, and control. Throughout the duration of treatment lasting for 10 sessions, the hands-on group employed the use of Microsoft Copilot, artificial intelligence (AI) chatbot, on a computer screen to discuss and explore IMMs, but the hands-off group was exposed to IMMs through written texts that were physically printed on paper and articles to be examined through AntConc concordancing program. The control group received conventional instructional techniques including reading assigned course materials. The findings from a one-way analysis of covariance (ANCOVA) procedure indicated that both experimental groups outperformed the control group in the posttest of realizing and identifying IMMs. However, the post hoc comparisons showed statistically significant differences between the hands-on and hands-off groups, with the hands-on group performing more successfully in identifying IMMs. The results of the questionnaire data revealed that all the learners had positive perception of DDL. The results of the current study suggest using both hands-on and hands-off DDL methods helps learners develop their writing performance through metadiscourse realization.