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Advances and Applications of Deep Learning Methods and Image Processing
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Advances and Applications of Deep Learning Methods and Image Processing

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 30 June 2024 | Viewed by 9245

Special Issue Editors

Dr. Robail Yasrab

E-Mail Website
Guest Editor
MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB2 1TN, UK
Interests: machine learning; AI
Dr. Md Mostafa Kamal Sarker

E-Mail Website
Guest Editor
Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
Interests: machine learning; AI

Special Issue Information

Dear Colleagues,

Deep learning has revolutionized the field of image processing since its emergence. As a result, manual feature extraction techniques have become obsolete. The field of deep learning (DL) has been one of the fastest-growing areas in artificial intelligence and data science, with rapidly emerging applications spanning read-scene understanding, medical image analysis, plant phenotyping, textual data modalities, etc. This allows researchers to analyze a variety of signal and information-processing tasks by automatically identifying features. This Special Issue explores the application of deep learning and artificial neural networks to analyze data and make predictions across a wide range of industries and fields and will present a wide variety of deep learning applications, from solving complex data science problems to developing automatic user controls.

The main objective is to bring together deep learning researchers from different disciplines to discuss new ideas, research questions, recent results, and challenges in this emerging area.

Potential topics include but are not limited to:

  • Deep learning models for medical image analysis (healthcare);
  • Deep learning models for plant phenotyping;
  • Deep learning models for road scene understanding (self-driving);
  • Deep learning models for fighting deepfakes;
  • Deep learning models for pixel restoration;
  • Deep learning models for video gaming;
  • Deep learning models for online marketing support;
  • Deep learning models for user behavior analysis.

In this Special Issue, original research articles and reviews are welcome.

We look forward to receiving your contributions.

Dr. Robail Yasrab
Dr. Md Mostafa Kamal Sarker
Guest Editors

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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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

  • deep learning
  • image analysis
  • scene understanding

Published Papers (3 papers)

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Research

17 pages, 33366 KiB  
Article
Sign-to-Text Translation from Panamanian Sign Language to Spanish in Continuous Capture Mode with Deep Neural Networks
by Alvaro A. Teran-Quezada, Victor Lopez-Cabrera, Jose Carlos Rangel and Javier E. Sanchez-Galan
Big Data Cogn. Comput. 2024, 8(3), 25; https://doi.org/10.3390/bdcc8030025 - 26 Feb 2024
Viewed by 1178
Abstract
Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long–short-term memory (LSTM) have become a means for providing solutions to problems involving sequential data. This research proposes [...] Read more.
Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long–short-term memory (LSTM) have become a means for providing solutions to problems involving sequential data. This research proposes the development of a sign language translation system that converts Panamanian Sign Language (PSL) signs into text in Spanish using an LSTM model that, among many things, makes it possible to work with non-static signs (as sequential data). The deep learning model presented focuses on action detection, in this case, the execution of the signs. This involves processing in a precise manner the frames in which a sign language gesture is made. The proposal is a holistic solution that considers, in addition to the seeking of the hands of the speaker, the face and pose determinants. These were added due to the fact that when communicating through sign languages, other visual characteristics matter beyond hand gestures. For the training of this system, a data set of 330 videos (of 30 frames each) for five possible classes (different signs considered) was created. The model was tested having an accuracy of 98.8%, making this a valuable base system for effective communication between PSL users and Spanish speakers. In conclusion, this work provides an improvement of the state of the art for PSL–Spanish translation by using the possibilities of translatable signs via deep learning. Full article
(This article belongs to the Special Issue Advances and Applications of Deep Learning Methods and Image Processing)
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14 pages, 3229 KiB  
Article
Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory
by Rubén E. Nogales and Marco E. Benalcázar
Big Data Cogn. Comput. 2023, 7(2), 102; https://doi.org/10.3390/bdcc7020102 - 23 May 2023
Cited by 3 | Viewed by 4293
Abstract
Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensionality of the problem is directly [...] Read more.
Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensionality of the problem is directly related to the performance of machine learning models. The dimensionality problem can be addressed through feature selection and feature extraction. In this sense, the evaluation of a model with manual feature extraction and automatic feature extraction was proposed. The manual feature extraction was performed using the statistical functions of central tendency, while the automatic extraction was performed by means of a CNN and BiLSTM. These features were also evaluated in classifiers such as Softmax, ANN, and SVM. The best-performing model was the combination of BiLSTM and ANN (BiLSTM-ANN), with an accuracy of 99.9912%. Full article
(This article belongs to the Special Issue Advances and Applications of Deep Learning Methods and Image Processing)
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12 pages, 2067 KiB  
Article
Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition
by Juliane Pfeil, Julienne Siptroth, Heike Pospisil, Marcus Frohme, Frank T. Hufert, Olga Moskalenko, Murad Yateem and Alina Nechyporenko
Big Data Cogn. Comput. 2023, 7(1), 51; https://doi.org/10.3390/bdcc7010051 - 17 Mar 2023
Viewed by 2566
Abstract
Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. [...] Read more.
Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera. Full article
(This article belongs to the Special Issue Advances and Applications of Deep Learning Methods and Image Processing)
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