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Electronics
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Deep Learning Techniques for Big Data Analysis
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Deep Learning Techniques for Big Data Analysis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 10712

Special Issue Editor

Prof. Prasan Kumar Sahoo

E-Mail Website1 Website2
Guest Editor
Department of Computer Science and Information Engineering, Chang Gung University, Guishan 33302, Taiwan
Interests: artificial intelligence; deep learning; big data analysis; medical image data analysis; prediction model design; IoT; fog and edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Deep learning (DL) is a subset of artificial intelligence (AI) which is applied to automatically dig through large volumes of data to identify patterns and extract features from complex unsupervised data without the involvement of humans. Classic neural networks, convolutional neural networks (CNN), recurrent neural networks (RNNs), generative adversarial networks, deep reinforcement learning, etc. are some of the important DL algorithms which could be used for image data analysis, pattern recognition, speech recognition, and to perform several computer vision tasks. Applications of DL for image, audio, video, and text data in unstructured form can help us to make smart decisions and build effective strategies by focusing on perspectives and needs of real-world buyers and users of technology. The exponential growth of big data would be meaningless unless technologies such as AI and DL are used for the anomaly detection, pattern recognition, and industrial fault detection. It is pivotal to design efficient algorithms, models, and methodologies of DL to analyze the big data generated from the industry, healthcare, smart cities, and the medical field.

The topics of interest in this SI include but are not limited to:

  • Deep learning algorithms
  • Deep learning for image data analysis
  • Deep learning in the Internet of Things (IoT)
  • Deep learning for industrial data analysis
  • Deep learning in natural language processing
  • Speech recognition with deep learning
  • Medical imaging data analysis with deep learning
  • Pattern recognition and applications of deep learning
  • Computer vision tasks and deep learning

Prof. Prasan Kumar Sahoo
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

  • artificial intelligence
  • deep learning
  • big data
  • image analysis
  • language processing
  • computer vision tasks

Published Papers (5 papers)

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Research

9 pages, 429 KiB  
Article
Incorporating Derivative-Free Convexity with Trigonometric Simplex Designs for Learning-Rate Estimation of Stochastic Gradient-Descent Method
by Emre Tokgoz, Hassan Musafer, Miad Faezipour and Ausif Mahmood
Electronics 2023, 12(2), 419; https://doi.org/10.3390/electronics12020419 - 13 Jan 2023
Viewed by 1278
Abstract
This paper proposes a novel mathematical theory of adaptation to convexity of loss functions based on the definition of the condense-discrete convexity (CDC) method. The developed theory is considered to be of immense value to stochastic settings and is used for developing the [...] Read more.
This paper proposes a novel mathematical theory of adaptation to convexity of loss functions based on the definition of the condense-discrete convexity (CDC) method. The developed theory is considered to be of immense value to stochastic settings and is used for developing the well-known stochastic gradient-descent (SGD) method. The successful contribution of change of the convexity definition impacts the exploration of the learning-rate scheduler used in the SGD method and therefore impacts the convergence rate of the solution that is used for measuring the effectiveness of deep networks. In our development of methodology, the convexity method CDC and learning rate are directly related to each other through the difference operator. In addition, we have incorporated the developed theory of adaptation with trigonometric simplex (TS) designs to explore different learning rate schedules for the weight and bias parameters within the network. Experiments confirm that by using the new definition of convexity to explore learning rate schedules, the optimization is more effective in practice and has a strong effect on the training of the deep neural network. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Big Data Analysis)
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16 pages, 976 KiB  
Article
Artificial-Intelligence-Assisted Activities of Daily Living Recognition for Elderly in Smart Home
by Djeane Debora Onthoni and Prasan Kumar Sahoo
Electronics 2022, 11(24), 4129; https://doi.org/10.3390/electronics11244129 - 11 Dec 2022
Cited by 4 | Viewed by 1879
Abstract
Activity Recognition (AR) is a method to identify a certain activity from the set of actions. It is commonly used to recognize a set of Activities of Daily Living (ADLs), which are performed by the elderly in a smart home environment. AR can [...] Read more.
Activity Recognition (AR) is a method to identify a certain activity from the set of actions. It is commonly used to recognize a set of Activities of Daily Living (ADLs), which are performed by the elderly in a smart home environment. AR can be beneficial for monitoring the elder’s health condition, where the information can be further shared with the family members, caretakers, or doctors. Due to the unpredictable behaviors of an elderly person, performance of ADLs can vary in day-to-day life. Each activity may perform differently, which can affect the sequence of the sensor’s raw data. Due to this issue, recognizing ADLs from the sensor’s raw data remains a challenge. In this paper, we proposed an Activity Recognition for the prediction of the Activities of Daily Living using Artificial Intelligence approach. Data acquisition techniques and modified Naive Bayes supervised learning algorithm are used to design the prediction model for ADL. Our experiment results establish that the proposed method can achieve high accuracy in comparison to other well-established supervised learning algorithms. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Big Data Analysis)
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17 pages, 3816 KiB  
Article
Computerized Prediction of Perovskite Performance Using Deep Learning
by Hanan A. Hosni Mahmoud
Electronics 2022, 11(22), 3759; https://doi.org/10.3390/electronics11223759 - 16 Nov 2022
Viewed by 1190
Abstract
Perovskites have exceptional physical and chemical features in different fields. Perovskites have an ABO3 formula with similar sizes of A-site and B-site cations. This research explores the challenges of developing new perovskite solar cells with high performance. Therefore, this article proposes a [...] Read more.
Perovskites have exceptional physical and chemical features in different fields. Perovskites have an ABO3 formula with similar sizes of A-site and B-site cations. This research explores the challenges of developing new perovskite solar cells with high performance. Therefore, this article proposes a deep learning model for the prediction of perovskites performance measures. The measures are: energy conversion performance, ABO3 stability, ion volume, and induced oxygen vacancy dimension. These performance measures are very crucial electrochemical reactions in energy conversion in fuel crystals. The challenges in any deep learning model are the lack of the presence of sufficient data and training time. Consequently, in this research, we propose a transfer learning perovskites model. Perovskite performance detection is critical to offer operative energy resources. In the proposed model, the constructed detection model uses a perovskites feature set. The transfer learning model utilizes other materials with large-sized datasets to predict the four performance measures with high accuracy. The output of the transfer learning is then utilized for the proposed deep learning model to predict perovskites performance measures with a small-sized dataset. A dataset of 8500 perovskite samples is utilized in the research. The results prove that a deep learning F2-Score with transfer learning attains high accuracy of 98.95%, recall of 96.91% and F2-score of 97.05%. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Big Data Analysis)
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15 pages, 9948 KiB  
Article
Utilizing Spatio Temporal Gait Pattern and Quadratic SVM for Gait Recognition
by Hajra Masood and Humera Farooq
Electronics 2022, 11(15), 2386; https://doi.org/10.3390/electronics11152386 - 30 Jul 2022
Cited by 6 | Viewed by 1649
Abstract
This study aimed to develop a vision-based gait recognition system for person identification. Gait is the soft biometric trait recognizable from low-resolution surveillance videos, where the face and other hard biometrics are not even extractable. The gait is a cycle pattern of human [...] Read more.
This study aimed to develop a vision-based gait recognition system for person identification. Gait is the soft biometric trait recognizable from low-resolution surveillance videos, where the face and other hard biometrics are not even extractable. The gait is a cycle pattern of human body locomotion that consists of two sequential phases: swing and stance. The gait features of the complete gait cycle, referred to as gait signature, can be used for person identification. The proposed work utilizes gait dynamics for gait feature extraction. For this purpose, the spatio temporal power spectral gait features are utilized for gait dynamics captured through sub-pixel motion estimation, and they are less affected by the subject’s appearance. The spatio temporal power spectral gait features are utilized for a quadratic support vector machine classifier for gait recognition aiming for person identification. Spatio temporal power spectral preserves the spatiotemporal gait features and is adaptable for a quadratic support vector machine classifier-based gait recognition across different views and appearances. We have evaluated the gait features and support vector machine classifier-based gait recognition on a locally collected gait dataset that captures the effect of view variance in high scene depth videos. The proposed gait recognition technique achieves significant accuracy across all appearances and views. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Big Data Analysis)
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17 pages, 5733 KiB  
Article
Deep-Learning-Assisted Multi-Dish Food Recognition Application for Dietary Intake Reporting
by Ying-Chieh Liu, Djeane Debora Onthoni, Sulagna Mohapatra, Denisa Irianti and Prasan Kumar Sahoo
Electronics 2022, 11(10), 1626; https://doi.org/10.3390/electronics11101626 - 19 May 2022
Cited by 12 | Viewed by 4142
Abstract
Artificial intelligence (AI) is among the major emerging research areas and industrial application fields. An important area of its application is in the preventive healthcare domain, in which appropriate dietary intake reporting is critical in assessing nutrient content. The traditional dietary assessment is [...] Read more.
Artificial intelligence (AI) is among the major emerging research areas and industrial application fields. An important area of its application is in the preventive healthcare domain, in which appropriate dietary intake reporting is critical in assessing nutrient content. The traditional dietary assessment is cumbersome in terms of dish accuracy and time-consuming. The recent technology in computer vision with automatic recognition of dishes has the potential to support better dietary assessment. However, due to the wide variety of available foods, especially local dishes, improvements in food recognition are needed. In this research, we proposed an AI-based multiple-dish food recognition model using the EfficientDet deep learning (DL) model. The designed model was developed taking into consideration three types of meals, namely single-dish, mixed-dish, and multiple-dish, from local Taiwanese cuisine. The results demonstrate high mean average precision (mAP) = 0.92 considering 87 types of dishes. With high recognition performance, the proposed model has the potential for a promising solution to enhancing dish reporting. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud-computing-based system to enhance the accuracy of current dietary intake reporting tasks. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Big Data Analysis)
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