Опубликована: Дек. 13, 2024
Язык: Английский
Applied Mathematics and Nonlinear Sciences, Год журнала: 2025, Номер 10(1)
Опубликована: Янв. 1, 2025
Abstract This paper completes the intelligent construction of Civics teaching resources for ancient literature course based on natural language processing technology. The automatic filtering elements and Politics is realized by using TF-IDF statistical method cosine similarity, combined with improved BERT model. Based knowledge graph introduction collaborative algorithms, we construct a recommendation Ancient Literature course. experimental results show that combination model in this can effectively complete comprehensive scoring screening Politics, provide high-quality content resources. outperforms other algorithms resource coverage, P/N measure, AUC capable comprehensively accurately recommending to students.
Язык: Английский
Процитировано
0
Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)
Опубликована: Янв. 1, 2024
Abstract In the era of informationization, inheritance and development traditional music are facing challenges opportunities. This study analyzes problems faced by teaching basic theory proposes a coping strategy for constructing an interactive model platform informatization. model, P2P technology is used to design construct behavior interest analysis module, fuzzy precisely set key information weights. The informationized designed with AI technology, adopting recommendation hybrid combined collaborative filtering, using cosine similarity calculate content similarity, combining artificial intelligence realize quality assessment. practice, S High School as practice site, experimental control classes up conduct teaching. class perceived negative behaviors such “harshness” “dissatisfaction” lower than class, pass rate excellence post-test reached 92.5% 36.9%, respectively. mean values all other core literacy dimensions except knowledge opera general musical instruments were higher those showing significant differences (P<0.05).
Язык: Английский
Процитировано
1
Modern Humanities Success, Год журнала: 2024, Номер 4, С. 84 - 90
Опубликована: Апрель 30, 2024
статья посвящена исследованию возможностей применения алгоритмов кластеризации и обработки естественного языка при изучении вариативности языковой картины мира носителей русского арабского языков. Эта актуальна, поскольку сегодня необходимо изучить возможности в рамках корпусной лингвистики, особенно исследовании мира. Новизна этого исследования заключается том, что впервые алгоритм k-means используется для анализа Автор использует из-за его простоты, масштабируемости, скорости универсальности. Задачи являются как теоретическими, так практическими. Теоретические включают объяснение механизмов работы алгоритма описание корпусного подхода исследования. Практические задачи себя сбор наборов данных формате JSON арабского, языков, проведение экспериментов оценки точности с использованием моделей TF-IDF, а затем визуализацию лучших результатов. Это исследование демонстрирует потенциал понимании мира, контексте Оно предоставляет доказательства показывая, алгоритмы работают по-разному разными языками. Полученные результаты имеют практическое применение таких областях, лингвистика, межкультурное общение, преподавание машинный перевод программирование. the article is devoted to researching possibilities of using clustering algorithms and natural language processing in studying variabilities linguistic picture world for native speakers both Russian Arabic. This paper relevant because there a need nowadays explore capabilities within corpus linguistics, particularly investigating variability picture. The novelty this research that, first time, algorithm utilized analyzing Arabic pictures world. author utilizes due its simplicity, scalability, speed, versatility. tasks study are theoretical practical. ones include explaining working mechanisms describing corpus-based approach study. practical encompass gathering datasets format Russian, followed by experimentation with TF-IDF models assess accuracy. Subsequently, most effective outcomes visualized. investigation showcases utility comprehending worldviews, specifically By demonstrating that exhibit distinct behavior across languages, offers insights into pictures. findings have applications fields such as cross-cultural communication, teaching, machine translation, programming.
Язык: Русский
Процитировано
0
Urban Climate, Год журнала: 2024, Номер 56, С. 102015 - 102015
Опубликована: Июнь 20, 2024
Язык: Английский
Процитировано
0
Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)
Опубликована: Янв. 1, 2024
Abstract English teaching materials serve as a critical vehicle for instruction, with well-designed fostering positive learning habits and interests among students. This study employs an ecological philosophy approach multimodal discourse analysis to examine the modal shifts in college textbooks. It utilizes BiFPN network model capture image features within these materials. Furthermore, TF-IDF method extracts key terms from textbook text, while integration of CNN-GRU facilitates classification terms. Additionally, this research introduces relevant computational formulas text readability theory evaluate difficulty levels The focuses on “New Vision College Textbook” series, volumes Compulsory 1 through 4. explores semantic relationships between graphics, chapter-specific reading challenges, overall indices. Findings indicate that average proportion graphic-text equality relations stands at 58.30%, highest occurrence images depicting detailed totaling 217. Grade Level index 4 reaches 1.61, signifying high complexity, whereas Flesch Reading Ease (FRE) score peaks 75.42, suggesting easier comprehension. In contrast, 2 exhibit lower scores. Through analysis, delineates varying across textbooks, advocating graded development aligns students’ evolving competencies. strategy is poised significantly boost engagement facilitate more effective learning.
Язык: Английский
Процитировано
0
Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)
Опубликована: Янв. 1, 2024
Abstract Vocabulary acquisition is pivotal in enhancing English writing proficiency. Effective integration of output vocabulary into written essential for improving students’ compositional skills. This study proposes a methodology extracting from textual materials and subsequently applying it to student endeavors. To ensure the integrity accuracy text utilized, this research employs Long Short-Term Memory (LSTM) algorithm perform comprehensive spelling check on corpus prior extraction. Further, paper adopts high-frequency word list Term Frequency-Inverse Document Frequency (TF-IDF) techniques identify evaluate significance within texts. Key that significantly impacts importance classification preliminarily identified using Graph Convolutional Network-K Nearest Neighbor (GCKN) algorithm. These words, termed ‘key nodes, ’ form basis constructing network Utilizing message-passing mechanism, information associated nodes aggregated at central node, facilitating vocabulary. The findings indicate students, after learning acquired vocabulary, demonstrate considerable improvements their capabilities. They exhibit broader more sophisticated use leading marked enhancements performance overall
Язык: Английский
Процитировано
0
Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)
Опубликована: Янв. 1, 2024
Abstract Cultural construction is an essential and increasingly important part of urban construction. Today’s document heavily influenced by ancient poetry, which one the best parts Chinese culture. Based on realistic value poetry in culture construction, study constructs text mining model using Word2Vec model, keyword word extraction algorithm, classification algorithm. Taking Hangzhou as example, conducts data analysis poems related to Hangzhou, extracts elements that are useful for construct evaluation index system finally puts forward corresponding development suggestions based results evaluation. The majority landscape words natural, with 7591 natural appearing 4516 humanistic appearing. Excluding indicator ecological greening facilities, Hangzhou’s highest score landscape, a 0.858184, lowest 0.643769. This shows still insufficient, there room improvement, it necessary focus enhancement improve overall level There improvement.
Язык: Английский
Процитировано
0
Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)
Опубликована: Янв. 1, 2024
Abstract The urbanization process is accelerating, the competition between cities getting more and intense, problem of urban image management attention from researchers city managers. Taking Nanchang City as case study, this paper selects government, economy, tourism, people image, well three data platforms: official website, news positioning, tourists’ diary, crawls comments four types elements on platforms, carries out fusion pre-processing to get comment dataset. Subsequently, TF-IDF algorithm used extract high-frequency words it combined with SnowNLP model LDA theme analysis analyze supplement overall City. Finally, after studying mechanism under (media) fusion, communication effect Nanchang’s explored in terms heat, recognition, participation. Government tourism aspects appear most often, highest lowest percentage positive emotions are city’s economic government respectively, which should integrate existing resources, improve level service, further create characteristics, expand visibility effect.
Язык: Английский
Процитировано
0
Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)
Опубликована: Янв. 1, 2024
Abstract The development of social media has brought many tests to the mental health education college students, and some students have fallen into network addiction dependence, which greatly affects their physical health. article uses microblogging as source students’ data preprocesses using de-emphasis Chinese word separation. It also analyzes problematic manifestations in colleges universities, extracts indicators by TF-IDF algorithm, realizes recognition topics BTM model. CNN-LSTM-ATT model was established introducing attention mechanism LSTM assess status students. analyzed terms characteristics predictive validation used develop intervention strategies for text length is [1,22], occupies 86.98% all sentences, AUC value corresponding 0.946, prediction accuracy CNN-LSTMATT assessment universities can reach up 97.62%. clarify realize construction from dimensions literacy regulatory mechanisms.
Язык: Английский
Процитировано
0
Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)
Опубликована: Янв. 1, 2024
Abstract The cultivation of talents in interdisciplinary education mode is increasingly important, both terms policy support and industry demand. In this paper, we identify the core requirements for talent establish structure course system. implementation path higher vocational divided into four directions: building mode, strengthening teacher team, innovating school-enterprise cooperation, capacity mechanisms. For personalized students, XDeepFM algorithm used to classification model realize resource integration recommendation. Evaluating effect under model, there a significant difference learning psychology between students with different development plans. evaluation activities, results second round action research are than those first round, two actions 7.515. designed paper has shown its initial success.
Язык: Английский
Процитировано
0