Neural Network and Genetic Algorithm based Hybrid Data Mining Algorithm (Hybrid Data Mining Algorithm)

GPCR are the largest family of cell surface receptors; many of them still remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein, the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy (FES), data mining algorithm (DMA)). The authors propose also to use the BAT algorithm for extracting the pertinent features and the Genetic Algorithm to choose the best couple. They compared the results they we obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.

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Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020732093 ◽

2020 ◽

Vol 7 (3) ◽

pp. 443

Author(s):

Azahari Azahari ◽

Yulindawati Yulindawati ◽

Dewi Rosita ◽

Syamsuddin Mallala

Keyword(s):

Neural Network ◽

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Drop Out ◽

Training Data ◽

Data Mining Algorithm ◽

Mining Algorithm ◽

Testing Data ◽

Target Data

Prediksi kelulusan dibutuhkan oleh manajemen perguruan tinggi dalam menentukan kebijakan preventif terkait pencegahan dini kasus drop out. Lama masa studi setiap mahasiswa bisa disebabkan dengan berbagai faktor. Dengan menggunakan data mining algoritma naive bayes dan neural network dapat dilakukan prediksi kelulusan mahasiswa di STMIK Widya Cipta Dharma (WiCiDa) Samarinda . Atribut yang digunakan yaitu, umur saat masuk kuliah, klasifikasi kota asal Sekolah Menengah Atas, pekerjaan ayah, program studi, kelas, jumlah saudara, dan Indeks Prestasi Kumulatif (IPK). Sampel mahasiswa yang lulus dan drop-out pada tahun 2011 sampai 2019 dijadikan sebagai data training dan data testing. Sedangkan angkatan 2015–2018 digunakan sebagai data target yang akan diprediksi masa studinya. Sebanyak 3229 mahasiswa, 1769 sebagai data training, 321 sebagai data testing, dan 1139 sebagai data target. Semua data diambil dari data mahasiswa program strata 1, dan tidak mengikut sertakan data mahasiswa D3 dan alih jenjang/transfer. Dari data testing diperoleh tingkat akurasi hanya 57,63%. Hasil penelitian menunjukkan banyaknya kelemahan dari hasil prediksi naive bayes dikarenakan tingkat akurasi kevalidannya tergolong tidak terlalu tinggi. Sedangkan akurasi prediksi neural network adalah 72,58%, sehingga metode alternatif inilah yang lebih baik. Proses evaluasi dan analisis dilakukan untuk melihat dimana letak kesalahan dan kebenaran dalam hasil prediksi masa studi.<div><div>AbstractGraduation predictions are required by the higher education institution preventive policies related to the early prevention of drop-out cases. The duration of study, for each student can be caused by various factors. By using the data mining algorithm Naive bayes and neural network, the student graduation in STMIK Widya Cipta Dharma (WiCiDa) can be predicted. The attributes used are as follows: age at admission, classification of cities from high school, father’s occupation, study program, class, number of siblings, and grade point average (GPA). Samples of students who graduated and dropped out between year 2011 and 2019 were used as training data and testing data. While the year class of 2015to 2018 is used as the target data, which will be predicted during the study period. According to the data mining algorithm Naive bayes, there are 3229 students; 1769 as training data, 321 as testing data, and 1139 as target data. All data is taken from students enrolled in undergraduate program and does not include data on diploma students and transfer student. From the testing data, an accuracy rate only 57.63%. The other side, prediction accuracy of the neural network is 72.58%, so this alternative method is the best chosen. The research results show the many weaknesses of the results of prediction of Naive bayes because the level of accuracy of its validity is not high. The evaluation and analysis process are conducted to see where the errors and truths are in the results of the study period predictions. </div></div>

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

Computational Intelligence and Neuroscience ◽

10.1155/2021/1890120 ◽

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.

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