Editors Choice – Hybrid Model-Based and Data-Driven Solution for Uncertainty Quantification at the Microscale

Author(s):Jose Pablo Quesada-Molina and Stefano Mariani*

Volume 14, Issue 4, 2022

Published on: 17 May, 2022

Page: [281 – 286]

Pages: 6

DOI: 10.2174/1876402914666220328123601

Abstract

Background: Due to their size, microelectromechanical systems (MEMS) display performance indices affected by uncertainties linked to the mechanical properties and to the geometry of the films constituting their movable parts.

Objective: In this perspective, a recently proposed multiscale and hybrid solution for uncertainty quantification is discussed.

Methods: The proposed method is based on the (deep) learning of the morphology-affected elasticity of the polycrystalline films and of the microfabrication-induced defective geometry of the devices. The results at the material and at the device levels are linked through a reduced-order representation of the response of the entire device to the external stimuli, foreseen to finally feed a Monte Carlo uncertainty quantification engine.

Results: Preliminary results relevant to a single-axis resonant Lorentz force micro-magnetometer have shown a noteworthy capability of the proposed multiscale deep learning method to account for the mentioned uncertainty sources at the microscale.

Conclusion: A promising two-scale deep learning approach has been proposed for polysilicon MEMS sensors to account for both materials- and geometry-governed uncertainties and to properly describe the scale-dependent response of MEMS devices. Read now: https://bit.ly/3QyIXDi

Most Cited Article – Email Fraud Attack Detection Using Hybrid Machine Learning Approach

Author(s):Yousef A. YaseenMalik Qasaimeh*Raad S. Al-Qassas and Mustafa Al-Fayoumi

Volume 14, Issue 5, 2021

Published on: 17 June, 2019

Page: [1370 – 1380]

Pages: 11

DOI: 10.2174/2213275912666190617162707

Abstract

Background: E-mail is an efficient way to communicate. It is one of the most commonly used communication methods, and it can be used for achieving legitimate and illegitimate activities. Many features that can be effective in detecting email fraud attacks, are still under investigation.

Methods: This paper proposes an improved classification accuracy for fraudulent emails that is implemented through feature extraction and hybrid Machine Learning (ML) classifier that combines Adaboost and Majority Voting. Eleven ML classifiers are evaluated experimentally within the hybrid classifier, and the performance of the email fraud filtering is evaluated by using WEKA and R tool on a data set of 9298 email messages.

Results: The performance evaluation shows that the hybrid model of Voting using Adaboost outperforms all other classifiers, with the lowest Error Rate of 0.6991%, highest f1-measure of 99.30%, and highest Area Under the Curve (AUC) of 99.9%.

Conclusion: The utilized proposed email features with the combination of Adaboost and Voting algorithms prove the efficiency of fraud email detection. Read now: https://bit.ly/3cSwzz1

Most Cited Article – A Review on Machine-learning Based Code Smell Detection Techniques in Object-oriented Software System(s)

Author(s):Amandeep KaurSushma JainShivani Goel and Gaurav Dhiman*

Volume 14, Issue 3, 2021

Published on: 22 September, 2020

Page: [290 – 303]

Pages: 14

DOI: 10.2174/2352096513999200922125839

Abstract

Background: Code smells are symptoms that something may be wrong in software systems that can cause complications in maintaining software quality. In literature, there exist many code smells and their identification is far from trivial. Thus, several techniques have also been proposed to automate code smell detection in order to improve software quality.

Objective: This paper presents an up-to-date review of simple and hybrid machine learning-based code smell detection techniques and tools.

Methods: We collected all the relevant research published in this field till 2020. We extracted the data from those articles and classified them into two major categories. In addition, we compared the selected studies based on several aspects like code smells, machine learning techniques, datasets, programming languages used by datasets, dataset size, evaluation approach, and statistical testing.

Results: A majority of empirical studies have proposed machine-learning based code smell detection tools. Support vector machine and decision tree algorithms are frequently used by the researchers. Along with this, a major proportion of research is conducted on Open Source Softwares (OSS) such as Xerces, Gantt Project and ArgoUml. Furthermore, researchers pay more attention to Feature Envy and Long Method code smells.

Conclusion: We identified several areas of open research like the need for code smell detection techniques using hybrid approaches, the need for employing valid industrial datasets, etc. Read now: https://bit.ly/3ogFvAH

Most Cited Article – Android Malware Detection Techniques: A Literature Review

Author(s):Meghna Dhalaria and Ekta Gandotra*

Volume 15, Issue 2, 2021

Published on: 10 July, 2020

Page: [225 – 245]Pages: 21

DOI: 10.2174/1872212114999200710143847

Abstract

Objective: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for the classification of Android malware.

Methods: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms.

Results: The number of Android users is increasing at an exponential rate due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware is complex and sophisticated, earlier approaches like signature-based and machine learning-based approaches are not able to identify it timely and accurately. The findings from the review show various limitations of earlier techniques, i.e. requirement of more detection time, high false-positive and false-negative rates, low accuracy in detecting sophisticated malware and less flexibility.

Conclusion: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights that could help researchers to come up with innovative and robust techniques for detecting and classifying Android malware. Read now: https://bit.ly/3RbpysZ

Join Editorial Board | Current Chinese Computer Science

 

join as Editorial Board Member

 

JOIN EDITORIAL BOARD FOR THE  JOURNAL

CURRENT CHINESE COMPUTER SCIENCE      

“NEW IN 2020”

 

Bentham Science is interested in appointing active Editorial Board Members for  the journal; Current Chinese Computer Science. If you are working in the related field as of the mentioned journals and are interested in becoming an Editorial Board Member, please send us your CV and a list of publications. You can also recommend suitable colleagues for the same, and, if possible, send their CV along with their list of publications. 

 

MENTION IN THE SUBJECT LINE, THE FIELD OF INTEREST AND SEND YOUR CV AT: HERMAIN@BENTHAMSCIENCE.NET  CC: FAIZAN@BENTHAMSCIENCE.NET

 

AIMS & SCOPE

Current Chinese Computer Science publishes original research articles, letters, reviews/mini-reviews and guest edited thematic issues dealing with various topics related to computer science in China, while contributions from abroad are also welcome.

Current Chinese Computer Science is not limited to a specific aspect of the field but is instead devoted to a wide range of sub fields in the field. Articles of interdisciplinary nature are particularly welcome. Submission in the following areas are of special interest to the readers of this journal:

  • Computer Engineering
  • Theoretical Computer Science
  • Machine Learning
  • Communication and Security
  • Telecommunications Engineering
  • Artificial Intelligence
  • Evolutionary Computing/Quantum Computing
  • Relational Databases
  • Software Engineering
  • Computer Vision

 

To know more about the journal, please visit: https://benthamscience.com/journals/current-chinese-computer-science/

Join as Executive Guest Editor | Current Chinese Computer Science

 

Current-Chinese-Computer-Science- Executive Guest Editor

 

BECOME AN EXECUTIVE GUEST EDITOR FOR THE  JOURNALS

CURRENT CHINESE COMPUTER SCIENCE 

“NEW IN 2020”

 

Bentham Science is interested in appointing active Executive Guest Editors for the journal; Current Chinese Computer Science. If you are working in the related field as of the mentioned journals and are interested in becoming an Executive Guest Editor, please send us your CV and a list of publications. You can also recommend suitable colleagues for the same, and, if possible, send their CV along with their list of publications. 

 

Mention in the subject line, the field of interest and send your CV at: hermain@benthamscience.net and CC: faizan@benthamscience.net

 

AIMS & SCOPE

Current Chinese Computer Science publishes original research articles, letters, reviews/mini-reviews and guest edited thematic issues dealing with various topics related to computer science in China, while contributions from abroad are also welcome.

Current Chinese Computer Science is not limited to a specific aspect of the field but is instead devoted to a wide range of sub fields in the field. Articles of interdisciplinary nature are particularly welcome. Submission in the following areas are of special interest to the readers of this journal:

  • Computer Engineering
  • Theoretical Computer Science
  • Machine Learning
  • Communication and Security
  • Telecommunications Engineering
  • Artificial Intelligence
  • Evolutionary Computing/Quantum Computing
  • Relational Databases
  • Software Engineering
  • Computer Vision

 

To know more about the journal, please visit: https://benthamscience.com/journals/current-chinese-computer-science/

Join as Executive Guest Editor | Current Chinese Computer Science

 

Current-Chinese-Computer-Science- Executive Guest Editor

 

BECOME AN EXECUTIVE GUEST EDITOR FOR THE  JOURNALS 

CURRENT CHINESE COMPUTER SCIENCE 

“NEW IN 2020”

 

Bentham Science is interested in appointing active Executive Guest Editors for the journal; Current Chinese Computer Science. If you are working in the related field as of the mentioned journals and are interested in becoming an Executive Guest Editor, please send us your CV and a list of publications. You can also recommend suitable colleagues for the same, and, if possible, send their CV along with their list of publications. 

Mention in the subject line, the field of interest and send your CV at: hermain@benthamscience.net and CC: faizan@benthamscience.net

AIMS & SCOPE

Current Chinese Computer Science publishes original research articles, letters, reviews/mini-reviews and guest edited thematic issues dealing with various topics related to computer science in China, while contributions from abroad are also welcome.

Current Chinese Computer Science is not limited to a specific aspect of the field but is instead devoted to a wide range of sub fields in the field. Articles of interdisciplinary nature are particularly welcome. Submission in the following areas are of special interest to the readers of this journal:

  • Computer Engineering
  • Theoretical Computer Science
  • Machine Learning
  • Communication and Security
  • Telecommunications Engineering
  • Artificial Intelligence
  • Evolutionary Computing/Quantum Computing
  • Relational Databases
  • Software Engineering
  • Computer Vision

 

 

To know more about the journal, please visit: https://benthamscience.com/journals/current-chinese-computer-science/

Publish your Medical Research for FREE | Current Chinese Computer Science

 

Chinese Computer Science.

 

CALL FOR PAPERS! 💡

Publish your Medical Research for FREE in our Journal

 Current Chinese Computer Science

 

★ All articles published in the first year would be free! ★

 

Submission Deadline: December 31st, 2019

 

To submit your paper, email at: hermain@benthamscience.net and CC: faizan@benthamscience.net

Become a Bentham Ambassador for the Journal | Current Chinese Computer Science “NEW IN 2020”

Become an Ambassador

BECOME A BENTHAM AMBASSADOR FOR THE  JOURNAL

 CURRENT CHINESE COMPUTER SCIENCE      

“NEW IN 2020”

Bentham Science is interested in appointing active Bentham Ambassadors for the journal; Current Chinese Computer Science. If you are working in the related field as of the mentioned journal and are interested in becoming an Ambassador, please send us your CV and a list of publications. You can also recommend suitable colleagues for the same, and, if possible, send their CV along with their list of publications. To know more about the journal, please visit: https://benthamscience.com/journals/current-chinese-computer-science/

 

Mention in the subject line, the field of interest and send your CV at: hermain@benthamscience.net and CC: faizan@benthamscience.net

BENEFITS AVAILABLE TO BENTHAM SCIENCE AMBASSADORS:

Bentham Science has developed a comprehensive system of rewards and benefits for Bentham Science Ambassadors, some of which are mentioned below:

  1. Your name, photograph and brief biography will be published on a dedicated Bentham Science Ambassadors web-page, which will help you in your various promotional activities since your position will be visible to a large number of visitors on the website.
  2. As a Bentham Science Ambassador, you would have the possibility of having your first article published, in any Bentham Science journal of your choice, as Open Access, at a 50% discount on our standard publishing rates. For the list of journals, please refer to our website http://benthamscience.com/
  3. You will get an online Certificate of acknowledgement for your services as a Bentham Science Ambassador.
  4. The most active Ambassadors can avail discounts on any conferences that are supported by Bentham Science, in respect of registration fee.
  5. Other benefits can be viewed on the Bentham Science Ambassadors web-page http://www.benthamambassadors.com/

The responsibilities of Bentham Science Ambassadors are to:

  1. Introduce and promote Bentham Science journals among librarians, colleagues and researchers in your region.
  2. Encourage submission of articles to relevant Bentham Science journals, in your field, from eminent scientists.
  3. You may, if you wish, also appoint some young researchers as “Associate Bentham Science Ambassadors” to magnify this effort.

Become a Reviewer | Current Chinese Computer Science

 

Become a Reviewer

BECOME A REVIEWER FOR THE  JOURNAL

CURRENT CHINESE COMPUTER SCIENCE     

“NEW IN 2020”

 

Bentham Science is interested in appointing active Reviewers for the journal; Current Chinese Computer Science. If you are working in the related field as of the mentioned journals and are interested in becoming a Reviewer, please send us your CV and a list of publications. You can also recommend suitable colleagues for the same, and, if possible, send their CV along with their list of publications. 

 

Mention in the subject line, the field of interest and send your CV at: hermain@benthamscience.net and CC: faizan@benthamscience.net

 

AIMS & SCOPE

Current Chinese Computer Science publishes original research articles, letters, reviews/mini-reviews and guest edited thematic issues dealing with various topics related to computer science in China, while contributions from abroad are also welcome.

Current Chinese Computer Science is not limited to a specific aspect of the field but is instead devoted to a wide range of sub fields in the field. Articles of interdisciplinary nature are particularly welcome. Submission in the following areas are of special interest to the readers of this journal:

  • Computer Engineering
  • Theoretical Computer Science
  • Machine Learning
  • Communication and Security
  • Telecommunications Engineering
  • Artificial Intelligence
  • Evolutionary Computing/Quantum Computing
  • Relational Databases
  • Software Engineering
  • Computer Vision

 

To know more about the journal, please visit: https://benthamscience.com/journals/current-chinese-computer-science/

%d bloggers like this: