scholarly journals Storage and Allocation of English Teaching Resources Based on k-Nearest Neighbor Algorithm

2019 ◽  
Vol 14 (17) ◽  
pp. 102 ◽  
Author(s):  
Yi Lou
Keyword(s):  
Nearest Neighbor ◽  
Internet Technology ◽  
K Nearest Neighbor ◽  
Teaching Resources ◽  
English Teaching ◽  
Internet Resources ◽  
Document Frequency ◽  
Teachers And Students ◽  
Research Findings ◽  
Weight Method

The boom of Internet technology gives a boost to the informatization of education in China. Internet resources serve as a new carrier of knowledge, offering teachers and students an alternative to books. However, the exponential growth of Internet resources has greatly complicated the storage and allocation of resources. This paper attempts to fully utilize English teaching resources through effective resource management and allocation. Specifically, the features of English teaching resources were analyzed, and then the term frequency-inverse document frequency (TF-IDF) weight method and k-nearest neighbor (kNN) algorithm were improved to make resource allocation more efficient and effective. The improved methods were then verified through a case analysis. The results show that the improved kNN provides a feasible way to allocate English teaching resources. The research findings provide reference to the storage and allocation of teaching resources.

Feature Selection Algorithm for Hyperlipidemia Classification

2014 ◽  
Vol 701-702 ◽  
pp. 110-113
Author(s):  
Qi Rui Zhang ◽  
He Xian Wang ◽  
Jiang Wei Qin
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Information Gain ◽  
Classification Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Document Frequency ◽  
Selection Algorithms ◽  
Term Weights

This paper reports a comparative study of feature selection algorithms on a hyperlipimedia data set. Three methods of feature selection were evaluated, including document frequency (DF), information gain (IG) and aχ2 statistic (CHI). The classification systems use a vector to represent a document and use tfidfie (term frequency, inverted document frequency, and inverted entropy) to compute term weights. In order to compare the effectives of feature selection, we used three classification methods: Naïve Bayes (NB), k Nearest Neighbor (kNN) and Support Vector Machines (SVM). The experimental results show that IG and CHI outperform significantly DF, and SVM and NB is more effective than KNN when macro-averagingF1 measure is used. DF is suitable for the task of large text classification.

scholarly journals New Multi-Keyword Ciphertext Search Method for Sensor Network Cloud Platforms

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3047
Author(s):  
Lixia Xie ◽  
Ziying Wang ◽  
Yue Wang ◽  
Hongyu Yang ◽  
Jiyong Zhang
Keyword(s):  
Sensor Network ◽  
Hierarchical Clustering ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
K Nearest Neighbor Algorithm ◽  
Series Of Experiments ◽  
Sensor Cloud ◽  
Cloud Network

This paper proposed a multi-keyword ciphertext search, based on an improved-quality hierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to work with encrypted data. It has improved search accuracy and can self-adapt when performing multi-keyword ciphertext searches on privacy-protected sensor network cloud platforms. Document vectors are first generated by combining the term frequency-inverse document frequency (TF-IDF) weight factor and the vector space model (VSM). The improved quality hierarchical clustering (IQHC) algorithm then generates document vectors, document indices, and cluster indices, which are encrypted via the k-nearest neighbor algorithm (KNN). MCS-IQHC then returns the top-k search result. A series of experiments proved that the proposed method had better searching efficiency and accuracy in high-privacy sensor cloud network environments, compared to other state-of-the-art methods.

scholarly journals Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jingjing Li
Keyword(s):  
Decision Tree ◽  
Nearest Neighbor ◽  
Decision Tree Algorithm ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Tree Algorithm ◽  
English Teaching ◽  
K Nearest Neighbor Algorithm

As the number of students in universities continues to grow, the university academic management system has a large amount of data on student performance. However, the utilization of these data is only limited to simple query and statistical work, and there is no precedent of using these data for improving English teaching mode. With the application of fuzzy theory in machine learning and artificial intelligence, the fuzzy decision tree algorithm was born by integrating fuzzy set theory with decision tree algorithm. In this paper, we propose a way to obtain the centroids of continuous attribute clustering by K-means algorithm and combine the triangular fuzzy number to fuzzy the continuous data. In addition, this paper analyzes the influence of nearest neighbor distance on classification, introduces Gaussian weight function, gives different voting weights to the neighborhood according to the distance, and establishes a weighted K-nearest neighbor classification algorithm. To address the problem of low classification efficiency of K-nearest neighbor algorithm when the dataset is large, this paper further improves the algorithm and establishes the partitioned weighted K-nearest neighbor algorithm. The classification time was shortened from 11.39 seconds to 5.22 seconds, and the classification efficiency greatly improved.

scholarly journals Knowledge Transfer About Earthquake Disaster Mitigation To Children Through TF-IDF

Elkawnie ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 165
Author(s):  
Maria Umran ◽  
Hafiz Mohd. Sarim
Keyword(s):  
Artificial Intelligence ◽  
Nearest Neighbor ◽  
Large Body ◽  
Disaster Mitigation ◽  
K Nearest Neighbor ◽  
Inverse Document Frequency ◽  
Earthquake Disaster ◽  
Body Of Knowledge ◽  
Document Frequency ◽  
Banda Aceh

Abstract: Past observations during a disaster identify that when children are separated from parents, they suffer due to the inability to comprehend disaster mitigation concepts. This study proposes a process from the existing framework K-Nearest Neighbor (KNN) and Term Frequency - Inverse Document Frequency (TF-IDF) for extracting a large body of knowledge in the form of documents into simple words. Those simple words can be arranged into contextual lyrics utilizing an Artificial Intelligence lyrics generator and then orchestrated into a song using a music generator. The piece, which is the output of the proposed process, is utilized to transfer the knowledge about earthquake disaster mitigation to children. A quantitative analysis of questionnaires on students aged 9-10 in Banda Aceh shows the song's highly significant effect in transferring the knowledge about earthquake disaster mitigation to children.

scholarly journals Construction of Online English Offline Teaching Mode in the Era of Internet Plus

2021 ◽  
pp. 25-36
Author(s):  
Zhang Mei
Keyword(s):  
Online Teaching ◽  
Rapid Development ◽  
Internet Technology ◽  
Teaching Resources ◽  
English Teaching ◽  
Grammar Teaching ◽  
English Grammar ◽  
Learning Interest ◽  
Teaching Grammar

With the rapid development of Internet technology, its application in the field of education is more extensive and diversified, and plays a greater role in teaching. Grammar teaching is an important part of English teaching. The complexity of grammar in forestry English leads to the common problems of English grammar among students. Relying on the Internet technology to establish the cloud classroom intelligent platform, online and offline interactive English teaching can fully play the role of online teaching resources and information technology by combining online and offline. This method can activate the classroom and improve students' language application ability and learning interest. Based on this, this paper analyzes the application of cloud classroom intelligent platform in English teaching. Then this paper discusses its construction method and application strategy, aiming to provide reference for relevant researchers.

scholarly journals Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF dan K-Nearest Neighbor

2019 ◽  
Vol 1 (1) ◽  
pp. 43-49
Author(s):  
Jeremy Andre Septian ◽  
Tresna Maulana Fachrudin ◽  
Aryo Nugroho
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Term Frequency ◽  
Document Frequency ◽  
Fold Cross Validation

Persepakbolaan Indonesia belakangan ini memiliki banyak polemik mulai dari kasus pengaturan skor, pergantian pelatih timnas senior hingga pergantian ketua umum Persatuan Sepak bola Seluruh Indonesia (PSSI). Polemik ini menimbulkan banyaknya opini maupun pendapat dari pengguna twitter terhadap persepakbolaan di Indonesia sehingga diperlukan sebuah sistem untuk memudahkan dalam mengetahui sentimen pada setiap kalimat. Tujuan dari penelitian ini adalah untuk menganalisis sentimen pada setiap kalimat dari pengguna twitter terhadap persepakbolaan Indonesia apakah memiliki sentimen negatif atau positif. Data yang digunakan dalam penelitian ini didapatkan dari hasil crawling dari media sosial twitter terkait persepakbolaan di Indonesia yang diambil dari akun twitter resmi PSSI. Setelah data dikumpulkan kemudian akan dilakukan beberapa tahapan yaitu preprocessing yang terdiri dari cleansing, tokenizing, stopword removal, dan stemming.  Pembobotan kata menggunakan Term Frequency-Invers Document Frequency (TF-IDF). Pada tahap validasi data dilakukan pengujian silang sebanyak 10 kali menggunakan k-fold cross validation, kemudian diklasifikasikan dengan metode K-Nearest Neighbor dapat menghasilkan akurasi yang cukup baik. Dari 2000 data tweet berbahasa indonesia didapatkan hasil akurasi optimal pada nilai k=23 sebanyak 79.9%

scholarly journals Classification Method of Teaching Resources Based on Improved KNN Algorithm

2019 ◽  
Vol 14 (04) ◽  
pp. 73
Author(s):  
Yingbo An ◽  
Meiling Xu ◽  
Chen Shen
Keyword(s):  
Nearest Neighbor ◽  
Classification Method ◽  
K Nearest Neighbor ◽  
Teaching Resources ◽  
Network Teaching ◽  
Resource Classification ◽  
School Teaching ◽  
K Nearest Neighbor Algorithm ◽  
Multiple Class ◽  
Primary And Secondary School

In order to effectively utilize the network teaching resources, a teaching resource classification method based on the improved KNN (K-Nearest Neighbor) algorithm was proposed. Taking the text class primary and secondary school teaching resources as the research object, combined with the domain characteristics, the KNN algorithm was improved. By measuring the sample space density, the text of the high-density area was found. Different clipping methods were proposed for both intra-class and inter-class regions. The problem of cropping in the space of multiple class boundaries was considered. Results showed that the method ensured uniform distribution of samples and reduced the time of classification. Therefore, under the Weka platform, the improved KNN algorithm is effective.

scholarly journals Sentiment analysis on film review in Gujarati language using machine learning

2022 ◽  
Vol 12 (1) ◽  
pp. 1030
Author(s):  
Parita Shah ◽  
Priya Swaminarayan ◽  
Maitri Patel
Keyword(s):  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Word Level ◽  
Document Frequency ◽  
Accuracy Result ◽  
Gujarati Language

<span>Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer.</span>

scholarly journals A Study of a Multiple Evaluation System of College English Online Teaching in China

2021 ◽  
Vol 4 (4) ◽  
pp. p27
Author(s):  
Fan Zhang ◽  
Shuxiong Feng
Keyword(s):  
Evaluation System ◽  
Online Teaching ◽  
Analytic Hierarchy ◽  
Teaching Resources ◽  
College English ◽  
English Teaching ◽  
Online Mode ◽  
Teacher Student ◽  
Teachers And Students ◽  
Practice Teaching

The comprehensive and scientific evaluation of college English teaching in online mode is an important basis for further promoting and optimizing the pragmatic reform of college English teaching. Based on the operational principle, the principle of service to students, and the feasibility principle, this study aims to construct a multiple evaluation system of online college English teaching based on three aspects: online interaction, online autonomous learning, and English online practice. A questionnaire has been conducted among undergraduates, teachers, and experts. The Analytic Hierarchy Process (AHP) has been used to analyze the relative importance of the indicators at the first two levels in the multiple evaluation system. The results revealed that the weight coefficients of teacher-student interaction, learning resources, and English listening practice are higher, while those of learning freedom and oral English practice are lower. Therefore, the online college English teaching reform should take the following measures: equipment input of college English practice teaching should be reinforced, communication channels between teachers and students should be strengthened, and online teaching resources should be enriched.

scholarly journals KLASIFIKASI SENTIMENT ANALYSIS PADA KOMENTAR PESERTA DIKLAT MENGGUNAKAN METODE K-NEAREST NEIGHBOR

Kilat ◽  
2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Riki Ruli A. Siregar ◽  
Zuhdiyyah Ulfah Siregar ◽  
Rakhmat Arianto
Keyword(s):  
Sentiment Analysis ◽  
Test Data ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Term Frequency ◽  
Document Frequency ◽  
Negative Comments

The process of analyzing and classifying comment data done by reading and sorting one by one negative comments and classifying them one by one using Ms. Excel not effective if the data to be processed in large quantities. Therefore, this study aims to apply sentiment analysis on comment data using K-Nearest Neighbor (KNN) method. The comment data used is the comments of the participants of the training on Udiklat Jakarta filled by each participant who followed the training. Furthermore, the comment data is processed by pre-processing, weighting the word using Term Frequency-Invers Document Frequency, calculating the similarity level between the training data and test data with cosine similarity. The process of applying sentiment analysis is done to determine whether the comment is positive or negative. Furthermore, these comments will be classified into four categories, namely: instructors, materials, facilities and infrastructure. The results of this study resulted in a system that can classify comment data automatically with an accuracy of 94.23%

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