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Deep Learning and Symmetry

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A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (2 March 2023) | Viewed by 34317

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Prof. Dr. Chien-Hsing Chou

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Guest Editor

Department of Electrical and Computer Engineering, Tamkang University, New Taipei City, Taiwan
Interests: image analysis and recognition; mobile phone programming; machine learning; document analysis and recognition; clustering analysis

Prof. Dr. Yi-Zeng Hsieh

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Guest Editor

Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
Interests: embedded systems; machine learning; human-computer interaction; swarm intelligence; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Given the the recent advances in deep learning, related applications are growing at a fast pace with a plethora of research being conducted. Recently, some researchers have incorporated the symmetries concept into the deep learning model and architecture. Their method could reduce complexity, training time, and over-fitting in the training process. The objective of this Special Issue, “Deep Learning and Symmetry”, is to focus on all aspects of deep learning in the field of symmetry-based deep learning algorithms, multimedia enabled IoT, intelligent human–computer interaction for both theoretical and practical applications. Authors are invited to submit outstanding and original unpublished research manuscripts focused on the latest findings in this field. It will be of benefit to technicians in this field to exchange the latest technical developments. Topics of interest include but are not limited to the following:

Deep learning for symmetry;
New deep learning algorithms and architectures;
Deep learning for multimedia enabled IoT;
Deep learning for image/video recognition;
Deep learning for style transfer and generative adversarial network;
Deep learning for intelligent human computer interaction.

Prof. Dr. Chien-Hsing Chou
Prof. Dr. Yi-Zeng Hsieh
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. Symmetry 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 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

deep learning
multimedia
generative adversarial network
pattern recognition
intelligent human computer interaction

Published Papers (11 papers)

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Research

13 pages, 411 KiB

Open AccessArticle

BEM-SM: A BERT-Encoder Model with Symmetry Supervision Module for Solving Math Word Problem

by Yijia Zhang, Tiancheng Zhang, Peng Xie, Minghe Yu and Ge Yu

Symmetry 2023, 15(4), 916; https://doi.org/10.3390/sym15040916 - 14 Apr 2023

Cited by 1 | Viewed by 1224

Abstract

In order to find solutions to math word problems, some modules have been designed to check the generated expressions, but they neither take into account the symmetry between math word problems and their corresponding mathematical expressions, nor do they utilize the efficiency of pretrained language models in natural language understanding tasks. Anyway, designing fine-tuning tasks for pretrained language models that encourage cooperation with other modules to improve the performance of math word problem solvers is an unaddressed problem. To solve these problems, in this paper we propose a BERT-based model for solving math word problems with a supervision module. Based on pretrained language models, we present a fine-tuning task to predict the number of different operators in the expressions to learn the potential relationships between the problems and the expressions. Meanwhile, a supervision module is designed to check the incorrect expressions generated and improve the model’s performance by optimizing the encoder. A series of experiments are conducted on three datasets, and the experimental results demonstrate the effectiveness of our model and its component’s designs. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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24 pages, 2268 KiB

Open AccessArticle

Contextually Enriched Meta-Learning Ensemble Model for Urdu Sentiment Analysis

by Kanwal Ahmed, Muhammad Imran Nadeem, Dun Li, Zhiyun Zheng, Nouf Al-Kahtani, Hend Khalid Alkahtani, Samih M. Mostafa and Orken Mamyrbayev

Symmetry 2023, 15(3), 645; https://doi.org/10.3390/sym15030645 - 03 Mar 2023

Cited by 4 | Viewed by 2350

Abstract

The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study’s findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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13 pages, 4751 KiB

Open AccessArticle

Diabetic Retinopathy Classification Using CNN and Hybrid Deep Convolutional Neural Networks

by Yasashvini R., Vergin Raja Sarobin M., Rukmani Panjanathan, Graceline Jasmine S. and Jani Anbarasi L.

Symmetry 2022, 14(9), 1932; https://doi.org/10.3390/sym14091932 - 16 Sep 2022

Cited by 23 | Viewed by 9066

Abstract

Diabetic Retinopathy (DR) is an eye condition that mainly affects individuals who have diabetes and is one of the important causes of blindness in adults. As the infection progresses, it may lead to permanent loss of vision. Diagnosing diabetic retinopathy manually with the help of an ophthalmologist has been a tedious and a very laborious procedure. This paper not only focuses on diabetic retinopathy detection but also on the analysis of different DR stages, which is performed with the help of Deep Learning (DL) and transfer learning algorithms. CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet are used on a huge dataset with around 3662 train images to automatically detect which stage DR has progressed. Five DR stages, which are 0 (No DR), 1 (Mild DR), 2 (Moderate), 3 (Severe) and 4 (Proliferative DR) are processed in the proposed work. The patient’s eye images are fed as input to the model. The proposed deep learning architectures like CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet 2.1 are used to extract the features of the eye for effective classification. The models achieved an accuracy of 96.22%, 93.18% and 75.61% respectively. The paper concludes with a comparative study of the CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet architectures that highlights hybrid CNN with DenseNet as the perfect deep learning classification model for automated DR detection. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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22 pages, 6430 KiB

Open AccessArticle

Toward Efficient Intrusion Detection System Using Hybrid Deep Learning Approach

by Ammar Aldallal

Symmetry 2022, 14(9), 1916; https://doi.org/10.3390/sym14091916 - 13 Sep 2022

Cited by 11 | Viewed by 2860

Abstract

The increased adoption of cloud computing resources produces major loopholes in cloud computing for cybersecurity attacks. An intrusion detection system (IDS) is one of the vital defenses against threats and attacks to cloud computing. Current IDSs encounter two challenges, namely, low accuracy and a high false alarm rate. Due to these challenges, additional efforts are required by network experts to respond to abnormal traffic alerts. To improve IDS efficiency in detecting abnormal network traffic, this work develops an IDS using a recurrent neural network based on gated recurrent units (GRUs) and improved long short-term memory (LSTM) through a computing unit to form Cu-LSTMGRU. The proposed system efficiently classifies the network flow instances as benign or malevolent. This system is examined using the most up-to-date dataset CICIDS2018. To further optimize computational complexity, the dataset is optimized through the Pearson correlation feature selection algorithm. The proposed model is evaluated using several metrics. The results show that the proposed model remarkably outperforms benchmarks by up to 12.045%. Therefore, the Cu-LSTMGRU model provides a high level of symmetry between cloud computing security and the detection of intrusions and malicious attacks. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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17 pages, 3593 KiB

Open AccessArticle

A Study of Sentiment Analysis Algorithms for Agricultural Product Reviews Based on Improved BERT Model

by Ying Cao, Zhexing Sun, Ling Li and Weinan Mo

Symmetry 2022, 14(8), 1604; https://doi.org/10.3390/sym14081604 - 04 Aug 2022

Cited by 16 | Viewed by 2547

Abstract

With the rise of mobile social networks, an increasing number of consumers are shopping through Internet platforms. The information asymmetry between consumers and producers has caused producers to misjudge the positioning of agricultural products in the market and damaged the interests of consumers. This imbalance between supply and demand is detrimental to the development of the agricultural market. Sentiment tendency analysis of after-sale reviews of agricultural products on the Internet could effectively help consumers evaluate the quality of agricultural products and help enterprises optimize and upgrade their products. Targeting problems such as non-standard expressions and sparse features in agricultural product reviews, this paper proposes a sentiment analysis algorithm based on an improved Bidirectional Encoder Representations from Transformers (BERT) model with symmetrical structure to obtain sentence-level feature vectors of agricultural product evaluations containing complete semantic information. Specifically, we propose a recognition method based on speech rules to identify the emotional tendencies of consumers when evaluating agricultural products and extract consumer demand for agricultural product attributes from online reviews. Our results showed that the F1 value of the trained model reached 89.86% on the test set, which is an increase of 7.05 compared with that of the original BERT model. The agricultural evaluation classification algorithm proposed in this paper could efficiently determine the emotion expressed by the text, which helps to further analyze network evaluation data, extract effective information, and realize the visualization of emotion. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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17 pages, 3946 KiB

Open AccessArticle

Advanced Feature-Selection-Based Hybrid Ensemble Learning Algorithms for Network Intrusion Detection Systems

by Doaa N. Mhawi, Ammar Aldallal and Soukeana Hassan

Symmetry 2022, 14(7), 1461; https://doi.org/10.3390/sym14071461 - 17 Jul 2022

Cited by 32 | Viewed by 3391

Abstract

As cyber-attacks become remarkably sophisticated, effective Intrusion Detection Systems (IDSs) are needed to monitor computer resources and to provide alerts regarding unusual or suspicious behavior. Despite using several machine learning (ML) and data mining methods to achieve high effectiveness, these systems have not proven ideal. Current intrusion detection algorithms suffer from high dimensionality, redundancy, meaningless data, high error rate, false alarm rate, and false-negative rate. This paper proposes a novel Ensemble Learning (EL) algorithm-based network IDS model. The efficient feature selection is attained via a hybrid of Correlation Feature Selection coupled with Forest Panelized Attributes (CFS–FPA). The improved intrusion detection involves exploiting AdaBoosting and bagging ensemble learning algorithms to modify four classifiers: Support Vector Machine, Random Forest, Naïve Bayes, and K-Nearest Neighbor. These four enhanced classifiers have been applied first as AdaBoosting and then as bagging, using the aggregation technique through the voting average technique. To provide better benchmarking, both binary and multi-class classification forms are used to evaluate the model. The experimental results of applying the model to CICIDS2017 dataset achieved promising results of 99.7%accuracy, a 0.053 false-negative rate, and a 0.004 false alarm rate. This system will be effective for information technology-based organizations, as it is expected to provide a high level of symmetry between information security and detection of attacks and malicious intrusion. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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18 pages, 17883 KiB

Open AccessArticle

An Embedding Skeleton for Fish Detection and Marine Organisms Recognition

by Jinde Zhu, Wenwu He, Weidong Weng, Tao Zhang, Yuze Mao, Xiutang Yuan, Peizhen Ma and Guojun Mao

Symmetry 2022, 14(6), 1082; https://doi.org/10.3390/sym14061082 - 24 May 2022

Cited by 4 | Viewed by 1840

Abstract

The marine economy has become a new growth point of the national economy, and many countries have started to implement the marine ranch project and made the project a new strategic industry to support vigorously. In fact, with the continuous improvement of people’s living standards, the market demand for precious seafood such as fish, sea cucumbers, and sea urchins increases. Shallow sea aquaculture has extensively promoted the vigorous development of marine fisheries. However, traditional diving monitoring and fishing are not only time consuming but also labor intensive; moreover, the personal injury is significant and the risk factor is high. In recent years, underwater robots’ development has matured and has been applied in other technologies. Marine aquaculture energy and chemical construction is a new opportunity for growth. The detection of marine organisms is an essential part of the intelligent strategy in marine ranch, which requires an underwater robot to detect the marine organism quickly and accurately in the complex ocean environment. This paper proposes a method called YOLOv4-embedding, based on one-stage deep learning arithmetic to detect marine organisms, construct a real-time target detection system for marine organisms, extract the in-depth features, and improve the backbone’s architecture and the neck connection. Compared with other object detection arithmetics, the YOLOv4-embedding object detection arithmetic was better at detection accuracy—with higher detection confidence and higher detection ratio than other one-stage object detection arithmetics, such as EfficientDet-D3. The results show that the suggested method could quickly detect different varieties in marine organisms. Furthermore, compared to the original YOLOv4, the mAP75 of the proposed YOLOv4-embedding improves 2.92% for the marine organism dataset at a real-time speed of 51 FPS on an RTX 3090. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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19 pages, 25349 KiB

Open AccessArticle

RAISE: Rank-Aware Incremental Learning for Remote Sensing Object Detection

by Haifeng Li, Ye Chen, Zhenshi Zhang and Jian Peng

Symmetry 2022, 14(5), 1020; https://doi.org/10.3390/sym14051020 - 17 May 2022

Cited by 1 | Viewed by 1772

Abstract

The deep learning method is widely used in remote sensing object detection on the premise that the training data have complete features. However, when data with a fixed class are added continuously, the trained detector is less able to adapt to new instances, impelling it to carry out incremental learning (IL). IL has two tasks with knowledge-related symmetry: continuing to learn unknown knowledge and maintaining existing knowledge. Unknown knowledge is more likely to exist in these new instances, which have features dissimilar from those of the old instances and cannot be well adapted by the detector before IL. Discarding all the old instances leads to the catastrophic forgetting of existing knowledge, which can be alleviated by relearning old instances, while different subsets represent different existing knowledge ranges and have different memory-retention effects on IL. Due to the different IL values of the data, the existing methods without appropriate distinguishing treatment preclude the efficient absorption of useful knowledge. Therefore, a rank-aware instance-incremental learning (RAIIL) method is proposed in this article, which pays attention to the difference in learning values from the aspects of the data-learning order and training loss weight. Specifically, RAIIL first designs the rank-score according to inference results and the true labels to determine the learning order and then weights the training loss according to the rank-score to balance the learning contribution. Comparative and analytical experiments conducted on two public remote sensing datasets for object detection, DOTA and DIOR, verified the superiority and effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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13 pages, 558 KiB

Open AccessArticle

Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images

by Mehrun Nisa, Saeed Ahmad Buzdar, Khalil Khan and Muhammad Saeed Ahmad

Symmetry 2022, 14(2), 383; https://doi.org/10.3390/sym14020383 - 15 Feb 2022

Cited by 3 | Viewed by 2929

Abstract

Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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21 pages, 9975 KiB

Open AccessArticle

A Hybrid Vegetation Detection Framework: Integrating Vegetation Indices and Convolutional Neural Network

by Wahidah Hashim, Lim Soon Eng, Gamal Alkawsi, Rozita Ismail, Ammar Ahmed Alkahtani, Sumayyah Dzulkifly, Yahia Baashar and Azham Hussain

Symmetry 2021, 13(11), 2190; https://doi.org/10.3390/sym13112190 - 17 Nov 2021

Cited by 3 | Viewed by 2787

Abstract

Vegetation inspection and monitoring is a time-consuming task. In the era of industrial revolution 4.0 (IR 4.0), unmanned aerial vehicles (UAV), commercially known as drones, are in demand, being adopted for vegetation inspection and monitoring activities. However, most off-the-shelf drones are least favoured by vegetation maintenance departments for on-site inspection due to limited spectral bands camera restricting advanced vegetation analysis. Most of these drones are normally equipped with a normal red, green, and blue (RGB) camera. Additional spectral bands are found to produce more accurate analysis during vegetation inspection, but at the cost of advanced camera functionalities, such as multispectral camera. Vegetation indices (VI) is a technique to maximize detection sensitivity related to vegetation characteristics while minimizing other factors which are not categorised otherwise. The emergence of machine learning has slowly influenced the existing vegetation analysis technique in order to improve detection accuracy. This study focuses on exploring VI techniques in identifying vegetation objects. The selected VIs investigated are Visible Atmospheric Resistant Index (VARI), Green Leaf Index (GLI), and Vegetation Index Green (VIgreen). The chosen machine learning technique is You Only Look Once (YOLO), which is a clever convolutional neural network (CNN) offering object detection in real time. The CNN model has a symmetrical structure along the direction of the tensor flow. Several series of data collection have been conducted at identified locations to obtain aerial images. The proposed hybrid methods were tested on captured aerial images to observe vegetation detection performance. Segmentation in image analysis is a process to divide the targeted pixels for further detection testing. Based on our findings, more than 70% of the vegetation objects in the images were accurately detected, which reduces the misdetection issue faced by previous VI techniques. On the other hand, hybrid segmentation methods perform best with the combination of VARI and YOLO at 84% detection accuracy. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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16 pages, 12372 KiB

Open AccessArticle

Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat

by Jiameng Gao, Chengzhong Liu, Junying Han, Qinglin Lu, Hengxing Wang, Jianhua Zhang, Xuguang Bai and Jiake Luo

Symmetry 2021, 13(11), 2012; https://doi.org/10.3390/sym13112012 - 23 Oct 2021

Cited by 10 | Viewed by 1924

Abstract

Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production. Full article

(This article belongs to the Special Issue Deep Learning and Symmetry)

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