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Electronics
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Machine Learning: System and Application Perspective
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Machine Learning: System and Application Perspective

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 8667

Special Issue Editor

Prof. Dr. Tae-Sun Chung

E-Mail Website
Guest Editor
Department of Software, Ajou University, Suwon 16499, Republic of Korea
Interests: database systems; data mining; machine learning; VR/AR system; flash memory storage; embedded systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning techniques are widely used in many types of computing problems. For example, many types of problems related to computer vision, natural language processing, big data analysis, etc., are solved using machine learning techniques. There are many challenging issues associated with machine learning technologies. As machine learning technologies are based on massive datasets and require a lot of computing resources, they need to be designed from a systems perspective. There are also many new machine learning applications such as VR/AR applications and virtual humans.

In this Special Issue, we invite original research articles and review articles dealing with machine learning from a systems or application perspective. A new system architecture for machine learning applications will be addressed from a systems perspective. In particular, how to perform machine learning algorithms in limited resource environments such as cell phones is a challenging problem. From an application point of view, it can cover many types of new applications. For example, a virtual human or virtual robot can become intelligent based on machine learning technology.

Potential topics include, but are not limited to, the following: 

  • Machine learning systems;
  • New machine learning applications;
  • VR/AR applications using machine learning;
  • System support for machine learning applications;
  • Performance analysis for machine learning systems;
  • Natural language processing based on machine learning;
  • Speech recognition systems based on machine learning;
  • Intelligent virtual human/robot systems.

Prof. Dr. Tae-Sun Chung
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • VR/AR
  • virtual human/robot
  • future memory
  • natural language processing
  • speech recognition

Published Papers (2 papers)

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Research

17 pages, 689 KiB  
Article
MTDOT: A Multilingual Translation-Based Data Augmentation Technique for Offensive Content Identification in Tamil Text Data
by Vaishali Ganganwar and Ratnavel Rajalakshmi
Electronics 2022, 11(21), 3574; https://doi.org/10.3390/electronics11213574 - 1 Nov 2022
Cited by 6 | Viewed by 1797
Abstract
The posting of offensive content in regional languages has increased as a result of the accessibility of low-cost internet and the widespread use of online social media. Despite the large number of comments available online, only a small percentage of them are offensive, [...] Read more.
The posting of offensive content in regional languages has increased as a result of the accessibility of low-cost internet and the widespread use of online social media. Despite the large number of comments available online, only a small percentage of them are offensive, resulting in an unequal distribution of offensive and non-offensive comments. Due to this class imbalance, classifiers may be biased toward the class with the most samples, i.e., the non-offensive class. To address class imbalance, a Multilingual Translation-based Data augmentation technique for Offensive content identification in Tamil text data (MTDOT) is proposed in this work. The proposed MTDOT method is applied to HASOC’21, which is the Tamil offensive content dataset. To obtain a balanced dataset, each offensive comment is augmented using multi-level back translation with English and Malayalam as intermediate languages. Another balanced dataset is generated by employing single-level back translation with Malayalam, Kannada, and Telugu as intermediate languages. While both approaches are equally effective, the proposed multi-level back-translation data augmentation approach produces more diverse data, which is evident from the BLEU score. The MTDOT technique proposed in this work achieved a promising improvement in F1-score over the widely used SMOTE class balancing method by 65%. Full article
(This article belongs to the Special Issue Machine Learning: System and Application Perspective)
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29 pages, 1635 KiB  
Article
A Survey on Different Plant Diseases Detection Using Machine Learning Techniques
by Sk Mahmudul Hassan, Khwairakpam Amitab, Michal Jasinski, Zbigniew Leonowicz, Elzbieta Jasinska, Tomas Novak and Arnab Kumar Maji
Electronics 2022, 11(17), 2641; https://doi.org/10.3390/electronics11172641 - 24 Aug 2022
Cited by 7 | Viewed by 6288
Abstract
Early detection and identification of plant diseases from leaf images using machine learning is an important and challenging research area in the field of agriculture. There is a need for such kinds of research studies in India because agriculture is one of the [...] Read more.
Early detection and identification of plant diseases from leaf images using machine learning is an important and challenging research area in the field of agriculture. There is a need for such kinds of research studies in India because agriculture is one of the main sources of income which contributes seventeen percent of the total gross domestic product (GDP). Effective and improved crop products can increase the farmer’s profit as well as the economy of the country. In this paper, a comprehensive review of the different research works carried out in the field of plant disease detection using both state-of-art, handcrafted-features- and deep-learning-based techniques are presented. We address the challenges faced in the identification of plant diseases using handcrafted-features-based approaches. The application of deep-learning-based approaches overcomes the challenges faced in handcrafted-features-based approaches. This survey provides the research improvement in the identification of plant diseases from handcrafted-features-based to deep-learning-based models. We report that deep-learning-based approaches achieve significant accuracy rates on a particular dataset, but the performance of the model may be decreased significantly when the system is tested on field image condition or on different datasets. Among the deep learning models, deep learning with an inception layer such as GoogleNet and InceptionV3 have better ability to extract the features and produce higher performance results. We also address some of the challenges that are needed to be solved to identify the plant diseases effectively. Full article
(This article belongs to the Special Issue Machine Learning: System and Application Perspective)
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