scholarly journals SentiHotel: a sentiment analysis application of hotel services using an optimized neural network

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Dyah Apriliani ◽  
Taufiq Abidin ◽  
Edhy Sutanta ◽  
Amir Hamzah ◽  
Oman Somantri
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Software Validation ◽  
Application Development ◽  
Validation Test ◽  
Proposed Model ◽  
Hotel Service ◽  
Analysis Application ◽  
Evaluation Stage ◽  
The Right

An assessed hotel service is necessary for tourists and everyone who is traveling, however currently it is still difficult to find recommended hotel information. The solution provided in this research is to propose a smart application that has been developed by implementing machine learning in it. The purpose is to build a sentiment review smart application by applying the sentiment analysis hybrid model of the best neural network (NN) algorithm model that has been optimized using genetic algorithms. To get the right model, the research method was carried out with experiments starting from the initial stages of conducting data preprocessing, tokenization, weighting, modeling experiments, and conducting the system evaluation stage to determine the success of the proposed model. The progress of the application development system is by using the prototyping model. SentiHotel is a sentiment application that was successfully built to provide a solution for tourists in assessing a hotel service. The software validation test is carried out using the blackbox method and the results show that the SentHotel application is in accordance with the expected result; all system functions can run properly.

DEVELOPMENT OF A TEACHING BOOK BASED ON LOCAL WISDOM OF JEPARA DISTRICT FOR CLASS V ELEMENTARY SCHOOL STUDENTSDEVELOPMENT OF A TEACHING BOOK BASED ON LOCAL WISDOM OF JEPARA DISTRICT FOR CLASS V ELEMENTARY SCHOOL STUDENTS

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Zaenal Arifin
Keyword(s):  
Elementary School ◽  
Data Collection ◽  
Learning Outcomes ◽  
Elementary School Students ◽  
Descriptive Analysis ◽  
School Students ◽  
Initial Product ◽  
Class V ◽  
Validation Test ◽  
Analysis Technique

This study aims to: (1) develop thematic textbooks based on local wisdom of our best friend's environmental theme material for grade V elementary school students, (2) Know the validity, practicality and effectiveness of thematic textbooks based on local wisdom of our best friend's environmental theme material for grade students V elementary school. The development of thematic textbook learning media based on local wisdom uses the Research and Development (R & D) method. The development stages include: (1) Potentials and problems, (2) Data collection, (3) Product design, (4) Design validation, (5) Design revision, (6) Product testing, (7) Product revision (Sugiyono, 2016). The research was conducted with 33 students from class V SDN 2 Sendang Jepara Regency. Initial product development is a process of making media based on needs analysis. Expert testing or validation was carried out by 2 experts, namely media experts and material experts. The data collection method in this research is observation, interview, and questionnaire. The data analysis technique used descriptive analysis and comparison test of two independent groups, namely using the independent t-test. Based on the results of the trial, this thematic teaching material is very valid, very interesting, effective and can be applied in learning. This can be seen from the results of the validation test from three experts which show that the product is good. The results of the effectiveness test showed that the experimental class students had better learning outcomes than the control class. This means that textbooks based on local wisdom of Jepara Regency are developed effectively

Validation Test Report for OCEANS 1.0: The 1/40 Global, Reduced Gravity NRL Layered Ocean Model

1998 ◽  
Author(s):  
E. J. Metzger ◽  
Robert C. Rhodes ◽  
Dong S. Ko ◽  
Harley E. Hurlburt
Keyword(s):  
Ocean Model ◽  
Reduced Gravity ◽  
Test Report ◽  
Validation Test

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

2020 ◽  
Vol 25 (40) ◽  
pp. 4296-4302 ◽  
Author(s):  
Yuan Zhang ◽  
Zhenyan Han ◽  
Qian Gao ◽  
Xiaoyi Bai ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inclusion Bodies ◽  
Cross Validation ◽  
Independent Set ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Validation Test ◽  
Excess Number ◽  
Fold Cross Validation

Background: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen. Methods: In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors. Results: The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging. Conclusion: This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

scholarly journals Algorithm selection for software validation based on graph kernels

2020 ◽  
Vol 27 (1-2) ◽  
pp. 153-186
Author(s):  
Cedric Richter ◽  
Eyke Hüllermeier ◽  
Marie-Christine Jakobs ◽  
Heike Wehrheim
Keyword(s):  
Software Validation ◽  
Algorithm Selection ◽  
Graph Kernels ◽  
Selection For

scholarly journals 4mCPred-CNN—Prediction of DNA N4-Methylcytosine in the Mouse Genome Using a Convolutional Neural Network

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 296
Author(s):  
Zeeshan Abbas ◽  
Hilal Tayara ◽  
Kil To Chong
Keyword(s):  
Mouse Genome ◽  
Matthews Correlation Coefficient ◽  
Biological Functions ◽  
Independent Dataset ◽  
Validation Test ◽  
Single Feature ◽  
Proposed Model ◽  
Dna Modifications ◽  
Feature Encoding ◽  
Feature Encoding Scheme

Among DNA modifications, N4-methylcytosine (4mC) is one of the most significant ones, and it is linked to the development of cell proliferation and gene expression. To know different its biological functions, the accurate detection of 4mC sites is required. Although we have several techniques for the prediction of 4mC sites in different genomes based on both machine learning (ML) and convolutional neural networks (CNNs), there is no CNN-based tool for the identification of 4mC sites in the mouse genome. In this article, a CNN-based model named 4mCPred-CNN was developed to classify 4mC locations in the mouse genome. Until now, we had only two ML-based models for this purpose; they utilized several feature encoding schemes, and thus still had a lot of space available to improve the prediction accuracy. Utilizing only a single feature encoding scheme—one-hot encoding—we outperformed both of the previous ML-based techniques. In a ten-fold validation test, the proposed model, 4mCPred-CNN, achieved an accuracy of 85.71% and Matthews correlation coefficient (MCC) of 0.717. On an independent dataset, the achieved accuracy was 87.50% with an MCC value of 0.750. The attained results exhibit that the proposed model can be of great use for researchers in the fields of biology and bioinformatics.

New assertion concepts for self-metric software validation

1975 ◽  
Vol 10 (6) ◽  
pp. 59-71 ◽  
Author(s):  
Leon G. Stucki ◽  
Gary L. Foshee
Keyword(s):  
Software Validation
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