Intelligence Learning Research Articles

Intelligent aeration amount prediction control for wastewater treatment process based on recurrent neural network

The use of machine learning in artificial intelligence to solve industrial problems has been a current trend. Predictions can be made by machine learning algorithms. This greatly improves the efficiency and also the accuracy. With the development of industrialization, the pollution of water resources is becoming more and more serious. How to treat wastewater more cost-effectively to meet the discharge standards has become one of the most urgent problems in the world. In the Anaerobic-Anoxic-Aerobic (AAO) process, the key to reach the standard of sewage treatment with low cost and high efficiency lies in the aeration link. In this study, multiple machine learning methods are used for modeling, and proposed a method employing the long short-term memory (LSTM) neural network model for regression prediction addresses the need for a more accurate prediction approach. By utilizing data from the preceding 100 days, this model replaces manual, experience-based prediction methods, thereby mitigating energy consumption. This model is optimized by training and testing with real wastewater treatment plant data and continuously adjusting the parameters through multiple sets of experiments. This can make the real recorded aeration amount and the actual aeration amount infinitely close. Through the experimental comparison, it is found that the performance of the LSTM neural network model is better, with about 14% higher accuracy than the benchmark model.

Parameters prediction for Low-Plasticity ultrasonic rolling strengthening process of blades based on Few-Shot Genetic Bayesian-Back Propagation intelligent learning

This study aims to reduce titanium alloy blade surface roughness and enhance compressive residual stress (CRS) using the low-plasticity ultrasonic rolling strengthening process (URSP). Due to the URSP’s multifactorial complexity and characteristics of few and irregular test samples, this study proposes a Genetic Bayesian-Back Propagation neural network (GB-BP) for a few sample parameters optimization for low-plasticity URSP of the blade, which combines a back propagation (BP) neural network with genetic algorithm (GA) and Bayesian Optimization (BO). The proposed approach establishes a robust correlation between processing parameters, blade surface roughness, and CRS. Firstly, an orthogonal test with three factors, rolling depth, feeding speed, and rolling distance, is designed. The signal-to-noise ratio (S/N) analysis and mean value analysis identify rolling distance and depth as the primary factors influencing surface roughness and CRS. Furthermore, BP complement with BO is employed to predict blade surface roughness and CRS after URSP. Finally, GB-BP is proposed to predict parameters, and experimental validation demonstrates the superior predictive accuracy of the GB-BP network. Compared to traditional BP models, the mean error percentages for GB-BP prediction dropped by 51.286% for surface roughness and 69.818% for CRS. The root mean square error (RMSE) decreased by 51.864% for surface roughness and 71.982% for CRS. This network model can provide accurate parameters optimization for low-plasticity URSP of blades.

The relationship of emotional intelligence to burnout and related factors in healthcare profession students

BackgroundHigh prevalence of burnout amongst healthcare profession students results in detrimental effects on academic performance, mental health, and quality of life. Emotional intelligence is a trainable skillset demonstrated to protect against burnout, improve psychological well-being, and decrease anxiety and stress, yet it lacks standardized inclusion in many healthcare profession program curricula. ObjectivesTo explore the utility of emotional intelligence as a tool for burnout mitigation, this study aimed to determine the relationship between emotional intelligence and burnout in an interprofessional sample of healthcare profession students and identify related variables. DesignCross-sectional survey study. SettingsData was collected over 14 weeks in the fall of 2022 via a nationally distributed online survey. Participants147 healthcare profession students from the following professional or doctoral programs: Master of Science in Athletic Training (ATC), Doctor of Physical Therapy (DPT), Master of Science in or Doctor of Occupational Therapy (OT/OTD), Master of Science in Speech-Language Pathology (SLP), Doctor of Medicine (MD), Physician Assistant Studies (PA-S), Bachelor of Science in Nursing (RN), or Nurse Practitioner Studies (NP). MethodsParticipants completed a demographics form (personal/school related variables including prior education and mindfulness habits), the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), Oldenberg Burnout Inventory-Student (OLBI-S), and RU-SATED sleep health scale. ResultsA large negative correlation was noted between emotional intelligence and burnout (r = −0.591, p < .001). Emotional intelligence and age were significant predictors of burnout. Previous emotional intelligence learning and mindfulness practice also demonstrated significant differences in emotional intelligence. ConclusionsThese findings suggest that greater emotional intelligence may have a positive impact on burnout and wellbeing in healthcare profession students. Educational interventions aimed to improve emotional intelligence should be explored for inclusion in healthcare profession educational program curricula.

Graph neural network based intelligent tutoring system: A survey

Online education is developing rapidly driven by artificial intelligence technology. The massive learning resources lead to information overload and low resource utilization. Intelligent tutoring system (ITS) plays a vital role in the education platform, providing personalized learning services for students. The data obtained from the online education platform has complex correlations, which can be potentially transformed into multi-level graph structures. In recent years, graph neural networks (GNNs) have been tried to be introduced into intelligent learning services due to their superior performance in processing graph-structured data. This paper aims to provide researchers and engineers with a general overview of modeling processes and techniques for intelligent learning services based on GNNs. Through a careful review of the advanced models published between 2019 and 2023, existing research primarily focuses on four detailed areas within the smart services scenario. The GNN models involved are systematically classified, and the principles, pioneers and variants of various models are summarized in detail. Simultaneously, this paper analyzes the applications, the specific problems to be solved, and the technologies and innovations of graph-based models in the four key areas. In addition, we examine the commonly used datasets and evaluation metrics in the field of education. Finally, the current challenges and future development trends are summarized to provide comprehensive and in-depth guidance for research in related fields.

From Sequence to Solution: Intelligent Learning Engine Optimization in Drug Discovery and Protein Analysis.

This study introduces the intelligent learning engine (ILE) optimization technology, a novel approach designed to revolutionize screening processes in bioinformatics, cheminformatics, and a range of other scientific fields. By focusing on the efficient and precise identification of candidates with desirable characteristics, the ILE technology marks a significant leap forward in addressing the complexities of candidate selection in drug discovery, protein classification, and beyond. The study's primary objective is to address the challenges associated with optimizing screening processes to efficiently select candidates across various fields, including drug discovery and protein classification. The methodology employed involves a detailed algorithmic process that includes dataset preparation, encoding of protein sequences, sensor nucleation, and optimization, culminating in the empirical evaluation of molecular activity indexing, homology-based modeling, and classification of proteins such as G-protein-coupled receptors. This process showcases the method's success in multiple sequence alignment, protein identification, and classification. Key results demonstrate the ILE's superior accuracy in protein classification and virtual high-throughput screening, with a notable breakthrough in drug development for assessing drug-induced long QT syndrome risks through hERG potassium channel interaction analysis. The technology showcased exceptional results in the formulation and evaluation of novel cancer drug candidates, highlighting its potential for significant advancements in pharmaceutical innovations. The findings underline the ILE optimization technology as a transformative tool in screening processes due to its proven effectiveness and broad applicability across various domains. This breakthrough contributes substantially to the fields of systems optimization and holds promise for diverse applications, enhancing the process of selecting candidate molecules with target properties and advancing drug discovery, protein classification, and modeling.

Inteligencia Artificial en educación matemática: Una revisión sistemática

[Objective] This study aims to analyze the current state of research on artificial intelligence (AI) in mathematics education, its applications, and its role in teaching and learning processes. [Methodology] A systematic review of the literature was conducted in three stages: identification, selection, and inclusion of articles from three recognized databases, resulting in 29 articles. These articles were thoroughly analyzed to identify participants, instruments used, the country of the authors' affiliation, year of publication, type of research, methodological approach, and the role of AI in these studies. [Results] There is a noticeable increase in research related to AI in mathematics education, with most studies being empirical and quantitative. The most frequently used instruments are questionnaires and interviews, with half of the studies employing at least two data collection instruments. Additionally, most studies focused on intelligent learning systems to enhance learning and support teaching, particularly for online assessment. [Conclusions] The reviewed articles show no evidence of research at the early childhood education level and very little related to teacher training. Few studies demonstrate the use of theoretical frameworks or approaches from the Didactics of Mathematics.

Artificial intelligence-driven real-world battery diagnostics

Addressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances in data assimilation techniques, the overwhelming volume and diversity of data, coupled with the lack of universally accepted models, underscore the limitations of these traditional approaches. Recently, deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate, multidimensional correlations. This approach resolves challenges previously deemed insurmountable, especially with lost, irregular, or noisy data through the design of specialized network architectures that adhere to physical invariants. However, gaps remain between academic advancements and their practical applications, including challenges in explainability and the computational costs associated with AI-driven solutions. Emerging technologies such as explainable artificial intelligence (XAI), AI for IT operations (AIOps), lifelong machine learning to mitigate catastrophic forgetting, and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment. In this perspective, we outline these challenges and opportunities, emphasizing the potential of innovative technologies to transform battery diagnostics, as demonstrated by our recent practice and the progress made in the field. This includes promising achievements in both academic and industry field demonstrations in modeling and forecasting the dynamics of multiphysics and multiscale battery systems. These systems feature inhomogeneous cascades of scales, informed by our physical, electrochemical, observational, empirical, and/or mathematical understanding of the battery system. Through data assimilation efforts, meticulous craftsmanship, and elaborate implementations—and by considering the wealth and spatio-temporal heterogeneity of available data—such AI-based intelligent learning philosophies have great potential to achieve better accuracy, faster training, and improved generalization.

Strategies and Implementation of Exploring the Integration of Artificial Intelligence in Ideological and Political Education

The rapid evolution of artificial intelligence (AI) technology heralds significant transformations in education, especially in ideological and political education, where traditional teaching methods are being reassessed due to mounting challenges. This paper embarks on a mission to investigate how AI technology can revolutionize the delivery of political science courses by adopting a “human-computer collaboration” model. The central aim is to enhance the effectiveness and quality of teaching while stimulating students' critical thinking skills. It delves into the landscape and potential applications of AI technology in this field, emphasizing the role of intelligent teaching assistants in augmenting teachers' capabilities for lesson planning and delivery. This study not only outlines the design and implementation of AI tools but also emphasizes their potential to foster a more engaging and intelligent learning environment. By doing so, it contributes to the discourse on leveraging AI to advance teaching platforms and classroom practices, thereby enhancing the educational experience.

L2 writer engagement with automated written corrective feedback provided by ChatGPT: A mixed-method multiple case study

Automated written corrective feedback (AWCF) has been widely applied in second language (L2) writing classrooms in the past few decades. Recently, the introduction of tools based on generative artificial intelligence (GAI) such as ChatGPT heralds groundbreaking changes in the conceptualization and practice of AWCF in L2 pedagogy. However, students’ engagement in such an interactive and intelligent learning environment remains unstudied. The present study aims to investigate L2 writers’ behavioral, cognitive, and affective engagement with ChatGPT as an AWCF provider for writing products. This mixed-method multiple case study explored four L2 writers’ behavioral, cognitive, and affective engagement with AWCF provided by ChatGPT. Bearing the conversational and generative mechanisms of ChatGPT in mind, data on students’ engagement were collected from various sources: prompt writing techniques, revision operations, utilization of metacognitive and cognitive strategies, and attitudinal responses to the feedback. The results indicated that: 1) behavioral engagement was related to their individual differences in language proficiencies and technological competencies; 2) the participants have failed to metacognitively regulate the learning processes in an effective manner; and 3) ChatGPT ushered in an affectively engaging, albeit competence-demanding and time-consuming, learning environment for L2 writers. The study delivers conceptual and pedagogical implications for educators and researchers poised to incorporate GAI-based technologies in language education.

Future-proofing quality education using integrated assessment systems

Purpose Current assessment models in education have focused solely on measuring knowledge and fail to address the goals of Sustainable Development Goal 4 (SDG4) for a well-rounded, future-proof education. While SDG4 emphasizes quality education, traditional assessments do not account for the diverse skills and intelligence learners possess. This gap between assessment and the needs of SDG4 presents a conundrum for educators: How can we develop assessment strategies that encompass multiple intelligences and prepare learners for the future while ensuring the delivery of quality education as outlined by SDG4? This paper aims to propose integrated assessment strategies as a solution, examining their effectiveness in assessing multiple intelligences and supporting the future-proofing agenda within quality education. Design/methodology/approach The study used a qualitative research design. Interviews were held up to saturation point with 60 teachers and students purposively selected from schools in ten provinces across the country. Data from interviews were analysed using thematic network analysis. The data were complemented by documentary analysis from the Ministry of Primary and Secondary Education, Zimbabwe documents which included Curriculum Frameworks and policy documents as well as a systematic literature review. Findings Results indicated that integrated assessment systems provide an avenue for testing deeper learning and help students acquire competencies needed in the world of work, such as problem-solving and teamwork. However, certain conditions mitigate against the effective implementation of integrated assessment in schools. Research limitations/implications This study uses the use of a qualitative research methodology, hence the generalizability of results in other settings may not be possible. The data collected from the research findings was manually coded and analysed. However, coding the data manually allowed the researchers to be fully immersed in the emerging themes enriching the study with additional data. This means that in-depth data engagement was ensured. Practical implications The paper concludes that integrated assessment provides authentic assessment which prepares learners for the future. The study recommends that the government should redress the teaching-learning environment in schools for effective implementation of integrated assessment systems so that not only one regime of intelligence is tested and future-proofing of quality is guaranteed. Originality/value The research contributes to increasing the motivation to deliver quality education by investing in integrated evaluation systems.

Research on Online Vocal Music Smart Classroom-Assisted Teaching Based on Wireless Network Combined With Artificial Intelligence

With the rapid advancement of information technology, online education based on big data and artificial intelligence is a hot research topic in education. This study focuses on applying big data and AI in online vocal wisdom classes to enhance personalized teaching and effectiveness. It aims to address issues in traditional vocal education like lack of personalized guidance and poor outcomes. The research proposes an intelligent learning system for vocal education, featuring modules for data collection, analysis, and intelligent recommendations. Functions include personalized learning paths, teaching assistance, real-time feedback, and assessment. Experiments show significant improvements in student outcomes and satisfaction. This innovative approach contributes to enhancing vocal education through personalized and intelligent teaching, offering valuable insights for future education development. Online vocal wisdom classrooms leveraging big data and AI represent a crucial direction with broad application prospects.

Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search

Unsupervised learning is a type of machine learning that learns from data without human supervision. Unsupervised feature selection (UFS) is crucial in data analytics, which plays a vital role in enhancing the quality of results and reducing computational complexity in huge feature spaces. The UFS problem has been addressed in several research efforts. Recent studies have witnessed a surge in innovative techniques like nature-inspired algorithms for clustering and UFS problems. However, very few studies consider the UFS problem as a multi-objective problem to find the optimal trade-off between the number of selected features and model accuracy. This paper proposes a multi-objective symbiotic organism search algorithm for unsupervised feature selection (SOSUFS) and a symbiotic organism search-based clustering (SOSC) algorithm to generate the optimal feature subset for more accurate clustering. The efficiency and robustness of the proposed algorithm are investigated on benchmark datasets. The SOSUFS method, combined with SOSC, demonstrated the highest f-measure, whereas the KHCluster method resulted in the lowest f-measure. SOSFS effectively reduced the number of features by more than half. The proposed symbiotic organisms search-based optimal unsupervised feature-selection (SOSUFS) method, along with search-based optimal clustering (SOSC), was identified as the top-performing clustering approach. Following this, the SOSUFS method demonstrated strong performance. In summary, this empirical study indicates that the proposed algorithm significantly surpasses state-of-the-art algorithms in both efficiency and effectiveness. Unsupervised learning in artificial intelligence involves machine-learning techniques that learn from data without human supervision. Unlike supervised learning, unsupervised machine-learning models work with unlabeled data to uncover patterns and insights independently, without explicit guidance or instruction.

Effectiveness of Using Artificial Intelligence Learning Tools and Customized Curriculum on Improving Students' Critical Thinking Skills in Indonesia

This study investigates the effectiveness of using artificial intelligence (AI) learning tools and customized curricula in enhancing the critical thinking skills of students in Indonesia. A quantitative research design was employed, involving a sample of 250 students. Data were collected using a Likert scale ranging from 1 to 5 and analyzed using Structural Equation Modeling - Partial Least Squares (SEM-PLS 3). The findings indicate that all relationships between AI learning tools, customized curricula, and critical thinking skills are positive and significant. The results suggest that the integration of AI in education, combined with tailored educational content, significantly improves students' critical thinking abilities. These insights provide valuable guidance for educators and policymakers aiming to foster higher-order thinking skills in the educational system.d distance learning, guiding future studies and practical applications.n.

Design and Research of an Intelligent Learning System for University Physics

In order to break through the limitations of traditional teaching, realize the integration of online and offline teaching, and optimize the intelligent learning experience of university physics, this paper proposes the design of an intelligent learning system for university physics based on cloud computing platforms, and applies this system to teaching environment of university physics. It successfully integrates emerging technologies such as cloud computing, machine learning, and situational awareness, integrates learning context awareness, intelligent recording and broadcasting, resource sharing, learning performance prediction, and content planning and recommendation, and comprehensively improves the quality of university physics teaching. It can optimize the teaching process and deepen intelligent teaching reform, aiming at providing references for the teaching practice of university physics.

Research on the Application of Artificial Intelligence in The 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 to cultivate students' comprehensive quality and socialist core values, it is necessary to carry out continuous teaching reform and innovation. Based on the idea of AI-enabled ideological and political course teaching reform, this study aims to explore how to use artificial intelligence technology to improve the teaching effect and learning experience of ideological and political course. This study 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. Then, it puts forward the teaching reform strategy of ideological and political course based on artificial intelligence, including the use of virtual reality technology, the application of intelligent auxiliary teaching tools to enhance personalized teaching, and the construction of intelligent learning management system. Finally, through case analysis, this paper discusses the effect of ideological and political teaching reform in colleges and universities. The results show 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 colleges and universities.

Intelligent Learning Platform with Deep Neural Network for Korean Language Teaching in Universities

Intelligent learning represents a dynamic approach to education that provides innovative technologies and personalized methodologies to enhance learning outcomes. Intelligent teaching adapts instruction to the individual needs, preferences, and progress of each student. This approach enables educators to tailor curriculum delivery, identify areas for improvement, and provide timely feedback, fostering a more engaging and effective learning environment. Moreover, intelligent teaching promotes collaborative learning experiences and encourages critical thinking skills, preparing students for success in an increasingly digital and interconnected world. This paper proposed a framework of Generative Platform-Oriented Intelligent Deep Neural Network (GPoIDNN) for Korean language teaching in Universities. The proposed GPoIDNN network comprises a social media platform for the promotion of Korean language teaching among students. With the GPoIDNN platform, a Generative network is implemented for the analysis of the factors involved in Language teaching in universities. The platform considered for the proposed model is Weibo for acquiring in-depth information about the language learning process. Upon the estimated features GPoIDNN uses the Generative Deep Neural Network platform for the classification and examination of the student performance. With the Weibo platform in social media, the Generative network constructs the intelligent teaching system for the Korean language teaching process in University students. The examination of student performance demonstrated that the proposed GPoIDNN model improves the student learning of Korean language with improved by 73% through the intelligent model. Further, the keywords and opinions classified with the GPoIDNN model exhibits a higher classification rate of 0.98 based on the opinion of the students in the universities.

Fault analysis in clustered microgrids utilizing SVM-CNN and differential protection

The integration of distributed generation, microgrids, and renewable energy sources has significantly enhanced the resilience of modern electrical grids. However, this transition presents challenges in control, stability, safety, and protection due to low fault currents from renewables. This paper addresses these challenges by proposing novel methodologies to enhance fault detection, classification, and localization in microgrids. The literature review highlights a shift towards intelligent learning methods in microgrid protection systems, improving fault response times and identifying electrical faults, including high impedance faults. Nonetheless, existing methods often neglect high impedance fault detection and the integration of differential protection in clustered microgrids. To fill these gaps, this study presents a methodology combining support vector machines and convolutional neural networks for fault detection in microgrids, integrating differential protection for high impedance fault detection. The paper also proposes approaches to optimize protection in clustered microgrid systems. The effectiveness of the methodology is validated using Opal-RT through comparative analyses of signal decomposition techniques, performance and accuracy of support vector machines and convolutional neural networks, K-Fold validation, and sensitivity analysis. Results demonstrate robustness and high performance, achieving up to 100 % accuracy in fault detection and classification.

Effects of artificial intelligence learning environments on engineers’ intercultural communication competence: mediating role of learning adaptability

ABSTRACT The purpose of this study is to investigate the impact of artificial intelligence learning environments on the intercultural communication competence of engineers. Engineering students at Chinese higher education institutions were invited to participate, and 990 students (510 males and 480 females) from different school types, majors, and grades completed an online survey. After analyses with multivariate linear regression and structural equation modelling, the results show that different dimensions of AI learning environments – including personalized learning, use of technology, and cooperation and interaction – have significant positive impacts on engineers’ intercultural communication competence and learning adaptability. At the same time, learning adaptability plays a mediating role between AI learning environments and intercultural communication competence. This study suggests that AI learning environments may have the potential to (i) enhance engineers’ intercultural communication competence and learning adaptability, an (ii) indirectly improve engineers’ intercultural communication competence through enhancements in their learning adaptability.

Federated Reinforcement Learning for Collaborative Intelligence in UAV-Assisted C-V2X Communications

This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial vehicle (UAV). Different sensors in vehicles capture different types of data, contributing to data heterogeneity. C-V2X communication networks impose additional communication overhead in order to converge to a global model when the sensor data are not independent-and-identically-distributed (non-i.i.d.). Consequently, the training time for local model updates also varies considerably. Using FRL, we accelerated this convergence by minimizing communication rounds, and we delayed it by exploring the correlation between the data captured by various vehicles in subsequent time steps. Additionally, as UAVs have limited battery power, processing of the collected information locally at the vehicles and then transmitting the model hyper-parameters to the UAVs can optimize the available power consumption pattern. The proposed FRL algorithm updates the global model through adaptive weighing of Q-values at each training round. By measuring the local gradients at the vehicle and the global gradient at the UAV, the contribution of the local models is determined. We quantify these Q-values using nonlinear mappings to reinforce positive rewards such that the contribution of local models is dynamically measured. Moreover, minimizing the number of communication rounds between the UAVs and vehicles is investigated as a viable approach for minimizing delay. A performance evaluation revealed that the FRL approach can yield up to a 40% reduction in the number of communication rounds between vehicles and UAVs when compared to gross data offloading.

TECHNOLOGICAL SOLUTIONS BASED ON ARTIFICIAL INTELLIGENCE IN TEACHING FOREIGN LANGUAGES: AN ANALYTICAL REVIEW

AI technologies are already beginning to change the learning process, they contribute to the creation of a personalized learning path; help to analyze educational metadata almost instantly; provide access to big data, helping in solving various professional tasks and problems. The purpose of this article is to provide an analytical review of examples of the use of artificial intelligence technologies in language teaching and to create a typology of AI-based technological solutions that have been actively used in the educational process over the past five years. The proposed typology of AI-based technological solutions used in teaching a foreign language is based on areas of didactic activity and didactic tasks that can be effectively solved with the help of AI. Under the directions of didactic activity, we mean: teaching and learning of foreign languages; management of the educational process, including the analysis of educational analytics, the development of an individual learning path, the creation of recommender systems; organization of the educational process, that is, control and assessment, providing feedback and reflection, developing didactic materials for adaptive online courses and intelligent learning systems.