scholarly journals Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiujin Yu ◽  
Shengfu Liu ◽  
Hui Zhang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Feature Recognition ◽  
High Efficiency ◽  
Chinese Language ◽  
Strong Support ◽  
Superior Performance ◽  
Feature Analysis ◽  
Data Mining Techniques ◽  
Self Organizing

As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural network algorithms. The results show that the multilayer self-organizing neural network can make the accuracy, recall, and F1 score of feature recognition reach 68.69%, 80.21%, and 70.19%, respectively, when there are many samples. Under the influence of strong noise, it keeps high efficiency of feature analysis. This shows that the multilayer self-organizing neural network has superior performance and can provide strong support for Chinese language feature analysis.

Using data mining techniques to predict user’s behavior and create recommender systems in the libraries and information centers

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasim Ansari ◽  
Hossein Vakilimofrad ◽  
Muharram Mansoorizadeh ◽  
Mohamad Reza Amiri
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Recommender Systems ◽  
High Efficiency ◽  
Good Opportunity ◽  
Content Type ◽  
Data Mining Techniques ◽  
The Real ◽  
Using Data ◽  
Delay Prediction

Purpose This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques. Design/methodology/approach The present study is an analytical survey study of cross-sectional type. The required data for this study were collected from the transactions of the users of libraries and information centers in Hamadan University of Medical Sciences. Using data mining techniques, the existing patterns were investigated, and users’ loan transactions were analyzed. Findings The findings showed that the association rules with the degree of confidence above 0.50 were able to determine user access patterns. Furthermore, among the decision tree algorithms, the C.05 predicted the loan period, referrals and users’ delay with the highest accuracy (i.e. 90.1). The other findings on feedforward neural network with R = 0.99 showed that the predicted results of neural network computation were very close to the real situation and had a proper estimation of user’s delay prediction. Finally, the clustering technique with the k-means algorithm predicted users’ behavior model regarding their loyalty. Practical implications The results of this study can lead to providing effective services and improve the quality of interaction between librarians and users and provide a good opportunity for managers to align supply of information resources with the real needs of users. Originality/value The results of the study showed that various data mining techniques are applicable with high efficiency and accuracy in analyzing library and information centers data and can be used to predict a user’s behavior and create recommendation systems.

scholarly journals Data Mining Techniques for Identification and Classification of Various Diseases in Plants

2019 ◽  
Vol 9 (2S) ◽  
pp. 676-680
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Nearest Neighbors ◽  
Crop Productivity ◽  
Vital Role ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Data Mining Techniques

Data mining is currently being used in various applications; In research community it plays a vital role. This paper specify about data mining techniques for the preprocessing and classification of various disease in plants. Since various plants has different diseases based on that each of them has different data sets and different objectives for knowledge discovery. Data Mining Techniques applied on plants that it helps in segmentation and classification of diseased plants, it avoids Oral Inspection and helps to increase in crop productivity. This paper provides various classification techniques Such as K-Nearest Neighbors, Support Vector Machine, Principle component Analysis, Neural Network. Thus among various techniques neural network is effective for disease detection in plants.

Ketepatan Klasifikasi Penerima Beasiswa STMIK STIKOM Bali dengan Hybrid Self Organizing Maps dan Algoritma K-Mean

Jurnal Varian ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Ni Putu Nanik Hendayanti ◽  
Gusti Ayu Made Arna Putri ◽  
Maulida Nurhidayati
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Self Organizing Maps ◽  
Cluster Group ◽  
Artificial Neural ◽  
Clustering Data ◽  
Self Organizing

Data Mining adalah penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah data yang sangat besar. Salah satu teknik yang dikenal dalam Data Mining yaitu clustering. Pengertian clustering dalam Data Mining adalah pengelompokan sejumlah data atau objek ke dalam cluster (group) sehingga setiap di lama cluster tersebut akan berisi data yang semirip mungkin dan berbeda dengan objek dalam cluster yang lain. Salah satu metode klasifiaksi atau clustering adalah Self Organizing Maps (SOM). SOM merupakan metode artificial neural network yang digunakan untuk mengelompokkan (clustering) data berdasarkan karakteristik/fitur-fitur data. Metode pengelompokan yang menggunakan konsep jarak dan memiliki karakteristik yang hampir sama dengan SOM yaitu metode K-means. Penelitian ini bertujuan untuk mengembangkan suatu metode yang merupakan hybrid dari SOM dan K-means yang digunakan untuk menentukan ketepatan suatu klasifikasi. Sebelum diujikan pada data asli, metode hybrid SOM dan K-Means diujikan lebih dulu pada data benchmark sehingga dapat diketahui berapa persen ketepan yang dihasilkan. Kemudian dilanjutkan dengan penerapan metode hybrid SOM dan K-means pada data penerimaan beasiswa di STMIK STIKOM Bali. Penelitian ini bertujuan untuk menentukan ketepatan klasifikasi penerima beasiswa STMIK STIKOM Bali dengan metode hybrid SOM dan K-means. Hasil penelitian menunjukkan bahwa metode Kmeans dan SOM memberikan hasil yang sama yang akibatnya metode SOM-Kmeans juga memberikan hasil yang sama. Alasannya, metode SOM-Kmeans menggunakan nilai centroid dari hasil SOM, dan hasil yang diperoleh pada metode Kmean memiliki hasil yang sama dengan SOM akibatnya metode SOM-Kmeans menghasilkan hasil yang sama dengan kedua metode sebelumnya.

scholarly journals English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dan Wu ◽  
Yuanjun Shen
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Feature Recognition ◽  
Good Effect ◽  
Data Mining Algorithm ◽  
Main Function ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor-intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA-BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA-BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition.

scholarly journals Applying Data Mining Techniques on Continuous Sensed Data for Daily Living Activity Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 191-196
Author(s):  
Yun Jie Li ◽  
Hui Song
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Daily Living ◽  
Tree Model ◽  
Data Set ◽  
Data Mining Techniques ◽  
Daily Living Activity ◽  
The Neural Network ◽  
Better Than

In this paper, several data mining techniques were discussed and analyzed in order to achieve the objective of human daily activities recognition based on a continuous sensing data set. The data mining techniques of decision tree, Naïve Bayes and Neural Network were successfully applied to the data set. The paper also proposed an idea of combining the Neural Network with the Decision Tree, the result shows that it works much better than the typical Neural Network and the typical Decision Tree model.

scholarly journals Application of intellectual data analysis methods for digital educational platform

2020 ◽  
pp. 1-5
Author(s):  
Kristina Zhatkina ◽  
Oksana Kreider
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Services ◽  
Data Mining Techniques ◽  
Analysis Methods ◽  
The Neural Network ◽  
Competency Based ◽  
Using Data ◽  
Data Analysis Methods

This article describes the possibility of using data mining techniques. In order to join new carpet participants, it is necessary to understand that the system of interaction with them is public educational services. To implement digital educational platforms, it is proposed to create an agent that collects information about sites, and also selects and tests the architecture of the neural network to build an individual trajectory that is trained using the competency-based model.

scholarly journals Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

2021 ◽  
Vol 13 (17) ◽  
pp. 9775
Author(s):  
Bashir Khan Yousafzai ◽  
Sher Afzal ◽  
Taj Rahman ◽  
Inayat Khan ◽  
Inam Ullah ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Student Performance ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Educational Data Mining ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Superior Performance

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

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