scholarly journals Ensemble Learning Models for Classification and Selection of Web Services: A Review

2022 ◽  
Vol 40 (1) ◽  
pp. 327-339
Author(s):  
Muhammad Hasnain ◽  
Imran Ghani ◽  
Seung Ryul Jeong ◽  
Aitizaz Ali
2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.


2021 ◽  
Vol 23 (4) ◽  
pp. 2742-2752
Author(s):  
Tamar L. Greaves ◽  
Karin S. Schaffarczyk McHale ◽  
Raphael F. Burkart-Radke ◽  
Jason B. Harper ◽  
Tu C. Le

Machine learning models were developed for an organic reaction in ionic liquids and validated on a selection of ionic liquids.


2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.


2020 ◽  
Vol 2 (2) ◽  
pp. 94-104
Author(s):  
Yusuf Budi Prasetya Santosa ◽  
Fahmi Hidayat

The use of varied learning models by history teachers will facilitate teachers and students in implementing and following the learning process. This study aims to determine the learning process and the use of learning models used by history teachers. This study uses a qualitative methodology with an observation and interview approach conducted at two high schools, Dian Didaktika High School and SMA Negeri 2 Depok. From the results of the study it can be seen, that the history teacher at the two schools has carried out the learning process using a scientific approach. There is no difference in the selection of learning strategies, both of them use the contextual teaching learning model. The difference between the two is in the selection of learning methods, where the history teacher Dian Didaktika uses the method of learning project base learning and the history teacher of SMA Negeri 2 Depok uses a method of learning outside the classroom by visiting museums.


2021 ◽  
Author(s):  
Ayu Amelia Aprilia ◽  
Nana

The purpose of this writing is to analyze the hypothetical learning model as a progressive education on physics learning. This writing is based on the lack of precise and less varied selection of learning models in learning activities in the classroom, especially physics lessons. The method used is the study of literature by reviewing some literature for analysis and then drawing conclusions. The Deductive Hypothesis Learning Model is a learning model that in its activities begins by exploring the general knowledge or initial knowledge of the student on what to learn. The deductive hypothesis learning model is process-oriented that can develop students' basic skills, especially students' science process skills. The results of literature studies from several reliable references, that hypothetical learning models included in education are progressive in physics learning where students experience and discover for themselves material concepts as well as hooking in social life


Sign in / Sign up

Export Citation Format

Share Document