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Electronics
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Deep Neural Networks and Their Applications, Volume II
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Deep Neural Networks and Their Applications, Volume II

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 31719

Special Issue Editor

Dr. Shyam Prasad Adhikari

E-Mail Website
Guest Editor
Department of Electronics Engineering, Jeonbuk National University, Jeonju 567-54896, Korea
Interests: neural networks; computer vision; deep learning; deep learning applications; memristor; memristor applications; hardware neural networks

Special Issue Information

Dear Colleagues,

By virtue of the success of recent deep neural network technologies, Artificial Intelligence has recently received great attention from almost all fields of academia and industries. Though the current success of Artificial Intelligence arose with the software version of neural networks, it is gradually extending to hardware implementations and human–computer interfaces. This Special Issue aims to provide a platform to researchers from both software and hardware of Artificial Intelligence to share cutting-edge developments in the field. The scope of this Special Issue is deep learning, neuromorphics, and brain–computer interfaces.

We solicit original research papers as well as review articles, including but not limited to the following key words:

  • Artificial Intelligence
  • Brain–computer interface (BCI)
  • Brain signal processing for BCI
  • Deep learning (AI) algorithm
  • Deep learning (AI) architecture
  • Deep learning applications
  • Intelligent bioinformatics
  • Intelligent robots
  • Intelligent systems
  • Machine learning
  • Memristors
  • Neural networks
  • Neural rehabilitation engineering
  • Neuromorphics
  • Parallel processing
  • Web intelligence applications and search

Dr. Shyam Prasad Adhikari
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
  • Brain–computer interface (BCI)
  • Brain signal processing for BCI
  • Deep learning (AI) algorithm
  • Deep learning (AI) architecture
  • Deep learning applications
  • Intelligent bioinformatics
  • Intelligent robots
  • Intelligent systems
  • Machine learning
  • Memristors
  • Neural networks
  • Neural rehabilitation engineering
  • Neuromorphics
  • Parallel processing
  • Web intelligence applications and search

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

22 pages, 802 KiB  
Article
Time-Lag Selection for Time-Series Forecasting Using Neural Network and Heuristic Algorithm
by Ola Surakhi, Martha A. Zaidan, Pak Lun Fung, Naser Hossein Motlagh, Sami Serhan, Mohammad AlKhanafseh, Rania M. Ghoniem and Tareq Hussein
Electronics 2021, 10(20), 2518; https://doi.org/10.3390/electronics10202518 - 15 Oct 2021
Cited by 40 | Viewed by 13919
Abstract
The time-series forecasting is a vital area that motivates continuous investigate areas of intrigued for different applications. A critical step for the time-series forecasting is the right determination of the number of past observations (lags). This paper investigates the forecasting accuracy based on [...] Read more.
The time-series forecasting is a vital area that motivates continuous investigate areas of intrigued for different applications. A critical step for the time-series forecasting is the right determination of the number of past observations (lags). This paper investigates the forecasting accuracy based on the selection of an appropriate time-lag value by applying a comparative study between three methods. These methods include a statistical approach using auto correlation function, a well-known machine learning technique namely Long Short-Term Memory (LSTM) along with a heuristic algorithm to optimize the choosing of time-lag value, and a parallel implementation of LSTM that dynamically choose the best prediction based on the optimal time-lag value. The methods were applied to an experimental data set, which consists of five meteorological parameters and aerosol particle number concentration. The performance metrics were: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared. The investigation demonstrated that the proposed LSTM model with heuristic algorithm is the superior method in identifying the best time-lag value. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications, Volume II)
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13 pages, 2239 KiB  
Article
Automatic Estimation of Age Distributions from the First Ottoman Empire Population Register Series by Using Deep Learning
by Yekta Said Can and M. Erdem Kabadayı
Electronics 2021, 10(18), 2253; https://doi.org/10.3390/electronics10182253 - 13 Sep 2021
Viewed by 1812
Abstract
Recently, an increasing number of studies have applied deep learning algorithms for extracting information from handwritten historical documents. In order to accomplish that, documents must be divided into smaller parts. Page and line segmentation are vital stages in the Handwritten Text Recognition systems; [...] Read more.
Recently, an increasing number of studies have applied deep learning algorithms for extracting information from handwritten historical documents. In order to accomplish that, documents must be divided into smaller parts. Page and line segmentation are vital stages in the Handwritten Text Recognition systems; it directly affects the character segmentation stage, which in turn determines the recognition success. In this study, we first applied deep learning-based layout analysis techniques to detect individuals in the first Ottoman population register series collected between the 1840s and the 1860s. Then, we employed horizontal projection profile-based line segmentation to the demographic information of these detected individuals in these registers. We further trained a CNN model to recognize automatically detected ages of individuals and estimated age distributions of people from these historical documents. Extracting age information from these historical registers is significant because it has enormous potential to revolutionize historical demography of around 20 successor states of the Ottoman Empire or countries of today. We achieved approximately 60% digit accuracy for recognizing the numbers in these registers and estimated the age distribution with Root Mean Square Error 23.61. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications, Volume II)
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16 pages, 1266 KiB  
Article
Diagnosis of Alzheimer’s Disease Based on the Modified Tresnet
by Zelin Xu, Hongmin Deng, Jin Liu and Yang Yang
Electronics 2021, 10(16), 1908; https://doi.org/10.3390/electronics10161908 - 9 Aug 2021
Cited by 12 | Viewed by 2535
Abstract
In the medical field, Alzheimer’s disease (AD), as a neurodegenerative brain disease which is very difficult to diagnose, can cause cognitive impairment and memory decline. Many existing works include a variety of clinical neurological and psychological examinations, especially computer-aided diagnosis (CAD) methods based [...] Read more.
In the medical field, Alzheimer’s disease (AD), as a neurodegenerative brain disease which is very difficult to diagnose, can cause cognitive impairment and memory decline. Many existing works include a variety of clinical neurological and psychological examinations, especially computer-aided diagnosis (CAD) methods based on electroencephalographic (EEG) recording or MRI images by using machine learning (ML) combined with different preprocessing steps such as hippocampus shape analysis, fusion of embedded features, and so on, where EEG dataset used for AD diagnosis is usually is large and complex, requiring extraction of a series of features like entropy features, spectral feature, etc., and it has seldom been applied in the AD detection based on deep learning (DL), while MRI images were suitable for both ML and DL. In terms of the structural MRI brain images, few differences could be found in brain atrophy among the three situations: AD, mild cognitive impairment (MCI), and Normal Control (NC). On the other hand, DL methods have been used to diagnose AD incorporating MRI images in recent years, but there have not yet been many selective models with very deep layers. In this article, the Gray Matter (GM) Magnetic Resonance Imaging (MRI) is automatically extracted, which could better distinguish among the three types of situations like AD, MCI, and NC, compared with Cerebro Spinal Fluid (CSF) and White Matter (WM). Firstly, FMRIB Software Library (FSL) software is utilized for batch processing to remove the skull, cerebellum and register the heterogeneous images, and the SPM + cat12 tool kits in MATLAB is used to segment MRI images for obtaining the standard GM MRI images. Next, the GM MRI images are trained by some new neural networks. The characteristics of the training process are as follows: (1) The Tresnet, as the network that achieves the best classification effect among several new networks in the experiment, is selected as the basic network. (2) A multi-receptive-field mechanism is integrated into the network, which is inspired by neurons that can dynamically adjust the receptive fields according to different stimuli. (3) The whole network is realized by adding multiple channels to the convolutional layer, and the size of the convolution kernel of each channel can be dynamically adjusted. (4) Transfer learning method is used to train the model for speeding up the learning and optimizing the learning efficiency. Finally, we achieve the accuracies of 86.9% for AD vs. NC, 63.2% for AD vs. MCI vs. NC respectively, which outperform the previous approaches. The results demonstrate the effectiveness of our approach. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications, Volume II)
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15 pages, 4738 KiB  
Article
Entity–Relation Extraction—A Novel and Lightweight Method Based on a Gate Linear Mechanism
by Guangming Peng and Xiong Chen
Electronics 2020, 9(10), 1637; https://doi.org/10.3390/electronics9101637 - 4 Oct 2020
Cited by 4 | Viewed by 3006
Abstract
Entity–relation extraction has attracted considerable attention in recent years as a fundamental task in natural language processing. The goal of entity–relation extraction is to discover the relation structures of entities from a natural language sentence. Most existing models approach this task using recurrent [...] Read more.
Entity–relation extraction has attracted considerable attention in recent years as a fundamental task in natural language processing. The goal of entity–relation extraction is to discover the relation structures of entities from a natural language sentence. Most existing models approach this task using recurrent neural nets (RNNs); however, given the sequential nature of RNNs, the states cannot be computed in parallel, which slows the machine comprehension. In this paper, we propose a new end-to-end model based on dilated convolutional units and the gate linear mechanism as an alternative to those recurrent models. We find that relation extraction becomes more difficult as the sentence length increases. In this paper, we introduce dynamic convolutions based on lightweight convolutions to process long sequences, which thus reduces the number of parameters to a low level. Another challenge in relation extraction is relation spans potentially overlapping in a sentence, representing a bottleneck for the detection of multiple relational triplets. To alleviate this problem, we design an entirely new prediction scheme to extract relational pairs and additionally boost performance. We conduct experiments on two widely used datasets, and the results show that our model outperforms the baselines by a large margin. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications, Volume II)
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16 pages, 4225 KiB  
Article
CED-Net: Crops and Weeds Segmentation for Smart Farming Using a Small Cascaded Encoder-Decoder Architecture
by Abbas Khan, Talha Ilyas, Muhammad Umraiz, Zubaer Ibna Mannan and Hyongsuk Kim
Electronics 2020, 9(10), 1602; https://doi.org/10.3390/electronics9101602 - 1 Oct 2020
Cited by 72 | Viewed by 6121
Abstract
Convolutional neural networks (CNNs) have achieved state-of-the-art performance in numerous aspects of human life and the agricultural sector is no exception. One of the main objectives of deep learning for smart farming is to identify the precise location of weeds and crops on [...] Read more.
Convolutional neural networks (CNNs) have achieved state-of-the-art performance in numerous aspects of human life and the agricultural sector is no exception. One of the main objectives of deep learning for smart farming is to identify the precise location of weeds and crops on farmland. In this paper, we propose a semantic segmentation method based on a cascaded encoder-decoder network, namely CED-Net, to differentiate weeds from crops. The existing architectures for weeds and crops segmentation are quite deep, with millions of parameters that require longer training time. To overcome such limitations, we propose an idea of training small networks in cascade to obtain coarse-to-fine predictions, which are then combined to produce the final results. Evaluation of the proposed network and comparison with other state-of-the-art networks are conducted using four publicly available datasets: rice seeding and weed dataset, BoniRob dataset, carrot crop vs. weed dataset, and a paddy–millet dataset. The experimental results and their comparisons proclaim that the proposed network outperforms state-of-the-art architectures, such as U-Net, SegNet, FCN-8s, and DeepLabv3, over intersection over union (IoU), F1-score, sensitivity, true detection rate, and average precision comparison metrics by utilizing only (1/5.74 × U-Net), (1/5.77 × SegNet), (1/3.04 × FCN-8s), and (1/3.24 × DeepLabv3) fractions of total parameters. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications, Volume II)
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14 pages, 1346 KiB  
Article
MDEAN: Multi-View Disparity Estimation with an Asymmetric Network
by Zhao Pei, Deqiang Wen, Yanning Zhang, Miao Ma, Min Guo, Xiuwei Zhang and Yee-Hong Yang
Electronics 2020, 9(6), 924; https://doi.org/10.3390/electronics9060924 - 2 Jun 2020
Cited by 2 | Viewed by 3374
Abstract
In recent years, disparity estimation of a scene based on deep learning methods has been extensively studied and significant progress has been made. In contrast, a traditional image disparity estimation method requires considerable resources and consumes much time in processes such as stereo [...] Read more.
In recent years, disparity estimation of a scene based on deep learning methods has been extensively studied and significant progress has been made. In contrast, a traditional image disparity estimation method requires considerable resources and consumes much time in processes such as stereo matching and 3D reconstruction. At present, most deep learning based disparity estimation methods focus on estimating disparity based on monocular images. Motivated by the results of traditional methods that multi-view methods are more accurate than monocular methods, especially for scenes that are textureless and have thin structures, in this paper, we present MDEAN, a new deep convolutional neural network to estimate disparity using multi-view images with an asymmetric encoder–decoder network structure. First, our method takes an arbitrary number of multi-view images as input. Next, we use these images to produce a set of plane-sweep cost volumes, which are combined to compute a high quality disparity map using an end-to-end asymmetric network. The results show that our method performs better than state-of-the-art methods, in particular, for outdoor scenes with the sky, flat surfaces and buildings. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications, Volume II)
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