Intelligence Learning Research Articles

Development of a Self-Evaluation Model Based on an Intelligent Tutoring System to Increase Student Learning Motivation in Basic Programming Subjects

Learning evaluation is a series of efforts to improve the quality of learning. Evaluation activities will have a good impact on students to encourage themselves in making improvements that affect their learning achievement. However, the implementation of learning evaluation in the field is not always easy. As the results of field studies conducted at SMK BPI Bandung, there are obstacles in the continuity of learning evaluations which the implementation has not been optimal. This study aims to develop a self-evaluation model based on an intelligent learning system (ITS) as a support in the learning process, where students can actively and independently evaluate their abilities, thereby increasing learning motivation which has an impact on their learning achievement. From the results of the research conducted using a nonequivalent pretest-posttest control group design, it was concluded that: 1) The evaluation media based on the ITS got a percentage value of 97.78% in the "very good" category and was feasible to use. 2) ITS evaluation media proved to increase learning motivation in the experimental class, marked by an increase in the average value of pre-test to post-test of 0.83% and showed differences in motivation with the control class. 3) Students respond to the ITS evaluation media, namely the media has a "very good" category with a percentage of 83.58%.

A STUDY ON THE EFFECT OF AEROBIC FITNESS ON THE SYNCHRONICITY OF BRAIN NERVE–LIMB MOVEMENT IN HUMANS

In order to analyze the synchronization ability of human brain nerve–limb movement, a research framework based on the gray wolf algorithm was constructed. The response timestamp data of human brain nerves was obtained under command excitation using a near-infrared brain functional imaging testing system as input for the gray wolf algorithm. The body motion response timestamp data was used as the output of the gray wolf algorithm. By using the gray wolf algorithm for swarm intelligence learning and training, the synchronization relationship between brain nerves and limb movements can be obtained. Taking aerobic energy as the key factor, we set up groups with higher aerobic energy and groups with lower aerobic energy, and then conduct the experimental research. The experimental results showed that the synchronization ability of “brain nerve–limb movement” was significantly stronger in the group with higher aerobic fitness than in the group with lower aerobic fitness, and this advantage increased with the increase of testing time interval.

Development of a New Technology Compound Talent Intelligent Learning Platform Based on Personalized Learning

With the continuous deepening of educational digital reform, the demands for digital empowerment of modern education and technology-driven educational transformation have become particularly urgent. Smart learning platforms and intelligent learning cloud services have emerged in this context. The JinYu project focuses on the cultivation of talents in the new era of the Internet and the construction of smart education. It is dedicated to providing an online learning platform and practical opportunities for programming learners, cultivating university students innovation and entrepreneurship capabilities. The project centers around the construction of high-quality courses in programming languages, emphasizing video recording and textbook writing. This initiative aims to expose a wider audience to courses related to programming and big data development, thereby creating a new technology compound talent intelligent learning platform based on personalized learning. The platform effectively promotes the transformation of smart learning forms and teaching modes in universities.

Intelligent learning approach for transition control and protection of solar PV integrated electric vehicle charging station

Recently, the control of distributed generations (DGs) with electric vehicles (EVs) parking lot charging systems has received much attention for supporting emergency local loads during grid outages. To achieve optimal transition control of grid-connected (GC) and standalone (SA) modes, an intelligent learning-based algorithm is implemented for optimal handling of the disturbance in the system by injecting a fault condition over certain cycles during a weak/strong grid operation. Here, a fuzzy Q-learning (FQL) based algorithm is implemented for detecting and monitoring the distributed grid condition during different scenarios and a seamless transitioning control in GC and SA modes of operation. In this context, the implementation of designed FQL algorithm enables dynamic adjustments of control signals in response to real-time uncertainties in two major applications (grid synchronization and islanding detection mode). The FQL algorithm focuses on real-time decision-making in response to sudden fault scenarios, addressing the problem under various modes of operation. To validate the proposed FQL controller accuracy, a real-time hardware-in-loop (HIL) simulation and their experimental validation is carried out of a 16kWp grid integrated PV sub-system with different load profiles. The performance evaluation of the FQL controller is investigated under linear and nonlinear local load scenarios.

A Study on the Design and Production of 3d Traditional Stone Pagoda Dataset for Artificial Intelligence Learning

A Study on the Design and Production of 3d Traditional Stone Pagoda Dataset for Artificial Intelligence Learning

ВЕБ-ОРІЄНТОВАНЕ НАВЧАННЯ ТА УСПІШНІСТЬ СТУДЕНТІВ У НАЦІОНАЛЬНОМУ ВІДКРИТОМУ УНІВЕРСИТЕТІ НІГЕРІЇ

The study examined the impact of Web based learning structures (Moodle, Blackboard, Online Library, Online Course Module/Study Guide, Internet Learning for coach student and Student - Student Connection and Times of Access of Internet Learning Applications) on students' scholarly accomplishment at the National Open University of Nigeria. The descriptive survey research design was used with a population of 100 to 400 students and a sample size of 150 students from the National Open University of Nigeria's schools of law, arts and social science, business and human resources, education, science and technology, and center for continuing education. A questionnaire with 53 questions titled: Experts validated the Online Learning and Learners' Academic Achievement in National Open University of Nigeria Scale (OLLAAS), and Cronbach's alpha yielded a reliability coefficient of 0.76. To test the hypotheses at the 0.05 level of significance, data were analyzed using Multiple Regressions and the Pearson Product Moment Correlation Coefficient. The discoveries of the review uncovered that the Internet learning structures (Moodle, Blackboard, Online Library, Online Course Module/Study Guide, Web based Learning for mentor student and Student - Student Connection and Times of Access of Web based Learning Applications) essentially impacted students' scholarly accomplishment (F (6, 143) = 24.907; R2 = 0.511), and mutually represented 49.1% of its difference. Moodle (r =.206, P0.001), Blackboard (r =.417, P0.001), the Online Library (r =.594, P0.001), the Online Course Module/Study Guide (r =.395, P0.001), online learning for tutor-learner and learner-learner interaction (r =.442, P0.001), and periods of access to online learning applications (r =.625, P0.001) all had positive correlations with students. In light of the discoveries, it was suggested that administration ought to put more in training of this season of computerized age through satisfactory financing to empower schools obtain and introduce ICT foundation. The government and all relevant stakeholders ought to address the problem of incessant power outages. Mechanical gadgets ought to likewise be made accessible to students at sponsored rates to empower everybody partake in remote learning. Web based learning meetings ought to be planned with significant and profoundly captivating and intelligent learning exercises to lessen fatigue among detached students during learning.

Multi-modal Emotion Recognition Using Neural Networks: Deploy on Cloud

This paper explores recent developments in emotion recognition, focusing on methodologies employing Multilayer Perceptron (MLP) classifiers. Emotion recognition is crucial for human-machine interaction across various domains, including healthcare, surveillance systems, and intelligent learning environments. There are many deep learning models that have been employed for emotion detection, this emphasizes the effectiveness of MLP classifiers. The project at hand centers on predicting emotions such as happiness, calmness, fear, and disgust solely utilizing MLP classifiers. The system is trained on audio data, with recordings matched in frequency, time, and speed to the raw data used for training. Additionally, Convolutional Neural Network (CNN) models are used here to analyze facial expressions in real-time video streams by providing neat and complete approach to emotion recognition. Upon deployment on the AWS cloud platform, the system provides real-time emotion predictions in textual format via a web interface created using Flask. Furthermore, the system offers the capability to analyze emotions from live video streams, presenting continuous feedback on emotions through a face-outlined box updated every three seconds. This functionality serves to confirm individuals' emotions and provide self-awareness regarding their emotional states. This project focuses on giving efficiency by using MLP classifiers in conjunction with CNN models for emotion recognition tasks, thereby contributing to the advancement of human-machine interaction. Key Words: MLP Classifier, CNN, AWS Cloud, Flask, emotion, audio, video

Effects of colored lighting on learning processes: Towards a smart classroom

Colour in the classroom, either in isolation or in conjunction with other environmental factors, is an element that has been widely discussed in the scientific literature, albeit not systematically. On the other hand, its evolution towards coloured light, made possible in recent years by light emitting diode (LED) technology, has hardly found a place among researchers. Despite the progressive rise of smart classrooms, intelligent learning environments or references by some authors to "dynamic lighting", its analogous concept, "dynamic colour", hardly appears in reviews of the scientific literature. This exploratory, quasi-experimental study shows how coloured light affects the learning process in primary school classroom environments, integrating cognitive processes, instrumental learning and affective processes, and helping to define the dynamic potential of its use. The experimental part has been carried out for four weeks, collecting data that are analysed descriptively, comparatively with the control group and in the internal variance of the experimental group. The results show positive influence tendencies in the cognitive and affective processes of the students, although not so much in instrumental learning.

Design and Development of Intelligent Learning System for University Innovation and Entrepreneurship Based on Knowledge Visualisation

To build a comprehensive learning resource system covering learning process, learning users, learning resources and other aspects, an intelligent learning system for innovation and entrepreneurship in universities was constructed and designed combined with knowledge visualisation theory. The system includes student information record module, system recommendation calculation module, learning material module, and knowledge visualisation module. The research focuses on the system recommendation calculation module, including the User-based Collaborative Filtering Algorithm (UCF) and the Joint Recommendation (JR) algorithm of the application content recommendation algorithm. The best neighbour number of JR is 20, and the best transmission path length is 2. The recommendation effect is optimal under this setting. The convergence iterations of the training and test set of the joint recommendation algorithm are 80 times and 100 times, respectively. For the training set and test set, the accuracy, precision, sensitivity, recall, running time, error, and other performance evaluation indicators of the joint recommendation algorithm are better than the corresponding values of other recommendation algorithms. The system has a good design effect and experience effect in the application of the online learning platform.

Research on Application Authorization Control Based on Business Center Services

This article analyzes the limitations of traditional authorization in the current situation of insufficient multi-level authorization management capabilities for end consumer applications in business middleware services. Taking the authorization system of business middleware services as a reference, multi-level authorization roles are introduced, and a fine-grained authorization management and authorization management model is proposed. And based on this authorization management model, a risk authorization identification method based on authorization behavior characteristics was explored. An intelligent learning risk authorization control model was proposed, which uses authorization behavior characteristics at all levels as the analysis basis. This enables the system to identify risk authorization actions at all levels, thereby improving the authorization system of business middleware services and achieving full supervision of hierarchical authorization allocation and use. The actual operational data proves that the risk authorization control model proposed in this article can accurately evaluate the risk nature of authorization behavior, and has broad application prospects.

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

With the continuous promotion of the unified electricity spot market in the southern region, the formation mechanism of spot market price and its forecast will become one of the core elements for the healthy development of the market. Effective spot market price prediction, on one hand, can respond to the spot power market supply and demand relationship; on the other hand, market players can develop reasonable trading strategies based on the results of the power market price prediction. The methods adopted in this paper include: Analyzing the principle and mechanism of spot market price formation. Identifying relevant factors for electricity price prediction in the spot market. Utilizing a clustering model and Spearman’s correlation to classify diverse information on electricity prices and extracting data that aligns with the demand for electricity price prediction. Leveraging complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to disassemble the electricity price curve, forming a multilevel electricity price sequence. Using an XGT model to match information across different levels of the electricity price sequence. Employing the ocean trapping algorithm-optimized Bidirectional Long Short-Term Memory (MPA-CNN-BiLSTM) to forecast spot market electricity prices. Through a comparative analysis of different models, this study validates the effectiveness of the proposed MPA-CNN-BiLSTM model. The model provides valuable insights for market players, aiding in the formulation of reasonable strategies based on the market's supply and demand dynamics. The findings underscore the importance of accurate spot market price prediction in navigating the complexities of the electricity market. This research contributes to the discourse on intelligent forecasting models in electricity markets, supporting the sustainable development of the unified spot market in the southern region.

Empowerment of Artificial Intelligence in Teaching Reform of Ideological and Political Courses in Universities

The rapid development of artificial intelligence (AI) technology has brought new opportunities and challenges to the field of education. As an important link in cultivating students’ comprehensive quality and socialist core values, it is necessary to carry out continuous teaching reform and innovation in ideological and political courses in colleges and universities. Based on the concept of AI empowering the teaching reform of ideological and political courses, this study aims to explore how to use artificial intelligence technology to improve the teaching effect and learning experience of ideological and political courses. The research first analyzes the application status of artificial intelligence technology in education, and then discusses the application potential of artificial intelligence in ideological and political courses. Subsequently, the teaching reform strategy of ideological and political courses based on artificial intelligence is proposed, including the use of virtual reality technology, the application of intelligent auxiliary teaching tools to enhance personalized teaching, and the construction of an intelligent learning management system. Lastly, a case analysis is conducted to explore the implementation effect of the teaching reform of ideological and political courses in universities. The results showed that the application of artificial intelligence technology can effectively improve the teaching effect and learning experience of ideological and political courses, and provide new ideas and methods for the teaching reform of ideological and political courses in universities.

Optimal Transport-Based One-Shot Federated Learning for Artificial Intelligence of Things

Optimal Transport-Based One-Shot Federated Learning for Artificial Intelligence of Things

Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence

INTRODUCTION: As the essential part of intelligent learning, innovative learning model construction is conducive to improving the quality of intelligent new teaching models, thus leading the deep integration of teaching and artificial intelligence and accelerating the change and development of teaching supported by artificial intelligence.OBJECTIVES: Aiming at the current intelligent teaching evaluation design method, there are problems such as more objectivity, poor precision, and a single method of evaluation indexes.METHODS: his paper proposes an intelligent learning construction method based on cluster analysis and deep learning algorithms. First of all, the intelligent learning model construction process is sorted out by clarifying the idea of clever learning model construction and extracting model elements; then, the intelligent learning model is constructed through a K-means clustering algorithm and deep compression sparse self-encoder; finally, the effectiveness and high efficiency of the proposed method is verified through simulation experiment analysis.RESULTS: Solved the problem that the intelligent learning model construction method is not objective enough, has poor accuracy and is not efficient enough.CONCLUSION: The results show that the proposed method improves the model’s accuracy.

Artificial intelligence (AI) learning tools in K-12 education: A scoping review

AbstractArtificial intelligence (AI) literacy is a global strategic objective in education. However, little is known about how AI should be taught. In this paper, 46 studies in academic conferences and journals are reviewed to investigate pedagogical strategies, learning tools, assessment methods in AI literacy education in K-12 contexts, and students’ learning outcomes. The investigation reveals that the promotion of AI literacy education has seen significant progress in the past two decades. This highlights that intelligent agents, including Google’s Teachable Machine, Learning ML, and Machine Learning for Kids, are age-appropriate tools for AI literacy education in K-12 contexts. Kindergarten students can benefit from learning tools such as PopBots, while software devices, such as Scratch and Python, which help to develop the computational thinking of AI algorithms, can be introduced to both primary and secondary schools. The research shows that project-based, human–computer collaborative learning and play- and game-based approaches, with constructivist methodologies, have been applied frequently in AI literacy education. Cognitive, affective, and behavioral learning outcomes, course satisfaction and soft skills acquisition have been reported. The paper informs educators of appropriate learning tools, pedagogical strategies, assessment methodologies in AI literacy education, and students’ learning outcomes. Research implications and future research directions within the K-12 context are also discussed.

Comparative analysis of bidding strategies for auction-based local energy markets

In view of the ongoing integration of distributed energy resources (DERs) and energy storage into the energy system, conventional consumers are transitioning into prosumers and flexumers. Local energy markets (LEMs) enables these end users to trade electricity directly with each other in order to obtain lower energy prices and to increase the local self-consumption. Since bidding strategies have a decisive impact on efficient trading in auction-based LEMs, the comparison and consistent evaluation of bidding strategies is an important research area. As novelty we propose an uniform evaluation methodology keeping all market features equal except the used bidding strategy. For the first time we evaluate a device-oriented (DO) strategy including an incentive price signal with a zero-intelligence (ZI) strategy, a learning-intelligence (LI) strategy employing the modified Roth–Erev learning algorithm. As evaluation we analyze the resulting market trading with various key performance indicators (KPIs), which consider both cost savings and local energy supply. Our findings reveal that the chosen bidding strategy has a decisive impact on cost savings and the distribution of gains between the buyer and seller side. In a photovoltaic (PV) and combined heat and power (CHP) scenario with different technology penetrations, the end users’ gains attain the highest values for the DO bidding strategy with 7909 € and 16454 €. Also the average market clearing prices are for the DO bidding strategy the highest with 0.269 €/kWh for the and 0.3032 €/kWh, which implies that the seller side predominantly obtains the gains.

Research on Multimodal English Teaching Methods and Practices Leading to Intelligent Generation

Abstract In this paper, we first establish a multimodal knowledge graph and process the course teaching video using by PaddleOCR algorithm to distinguish and understand the teaching text. This paper also improves the traditional classroom behavior recognition method by using the skeletal covariance matrix extraction strategy to aggregate the skeletal sequence features while combining the courseware information features to jointly achieve the recognition of classroom behavior. Based on the above algorithms, this paper summarizes the English multimodal teaching system based on intelligent learning, which improves traditional English teaching methods in terms of learning environment, teaching method, and mode. Finally, this paper conducted an experimental test on the effects of a multimodal English teaching method. The results show that the class scores of the class applying the multimodal English teaching method have been cumulatively improved by 22.06 points, and the rankings have been improved from 4 or 5 to 1 or 2 among the six tested classes. Students’ satisfaction with the pedagogy increased from an initial score of 3.4 to 4.5. The results show that multimodal English teaching promotes the change of English classrooms in the smart era.

Tensor Representation Based Multi-View Graph Contrastive Learning for IoE Intelligence

Tensor Representation Based Multi-View Graph Contrastive Learning for IoE Intelligence

Federated Split Learning for Edge Intelligence in Resource-Constrained Wireless Networks

Federated Split Learning for Edge Intelligence in Resource-Constrained Wireless Networks

Research Hot Spots and Development Trends of Deep Learning in Artificial Intelligence—Visualization and Analysis Based on CiteSpace

Research Hot Spots and Development Trends of Deep Learning in Artificial Intelligence—Visualization and Analysis Based on CiteSpace