Research

17 pages, 2377 KiB

Open AccessArticle

Line Chart Understanding with Convolutional Neural Network

by Chanyoung Sohn, Heejong Choi, Kangil Kim, Jinwook Park and Junhyug Noh

Electronics 2021, 10(6), 749; https://doi.org/10.3390/electronics10060749 - 22 Mar 2021

Cited by 5 | Viewed by 4494

Visual understanding of the implied knowledge in line charts is an important task affecting many downstream tasks in information retrieval. Despite common use, clearly defining the knowledge is difficult because of ambiguity, so most methods used in research implicitly learn the knowledge. When [...] Read more.

Visual understanding of the implied knowledge in line charts is an important task affecting many downstream tasks in information retrieval. Despite common use, clearly defining the knowledge is difficult because of ambiguity, so most methods used in research implicitly learn the knowledge. When building a deep neural network, the integrated approach hides the properties of individual subtasks, which can hinder finding the optimal configurations for the understanding task in academia. In this paper, we propose a problem definition for explicitly understanding knowledge in a line chart and provide an algorithm for generating supervised data that are easy to share and scale-up. To introduce the properties of the definition and data, we set well-known and modified convolutional neural networks and evaluate their performance on real and synthetic datasets for qualitative and quantitative analyses. In the results, the knowledge is explicitly extracted and the generated synthetic data show patterns similar to human-labeled data. This work is expected to provide a separate and scalable environment to enhance research into technical document understanding. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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24 pages, 7409 KiB

Open AccessArticle

Robust Active Shape Model via Hierarchical Feature Extraction with SFS-Optimized Convolution Neural Network for Invariant Human Age Classification

by Syeda Amna Rizwan, Ahmad Jalal, Munkhjargal Gochoo and Kibum Kim

Electronics 2021, 10(4), 465; https://doi.org/10.3390/electronics10040465 - 14 Feb 2021

Cited by 31 | Viewed by 3527

Abstract

The features and appearance of the human face are affected greatly by aging. A human face is an important aspect for human age identification from childhood through adulthood. Although many traits are used in human age estimation, this article discusses age classification using [...] Read more.

The features and appearance of the human face are affected greatly by aging. A human face is an important aspect for human age identification from childhood through adulthood. Although many traits are used in human age estimation, this article discusses age classification using salient texture and facial landmark feature vectors. We propose a novel human age classification (HAC) model that can localize landmark points of the face. A robust multi-perspective view-based Active Shape Model (ASM) is generated and age classification is achieved using Convolution Neural Network (CNN). The HAC model is subdivided into the following steps: (1) at first, a face is detected using aYCbCr color segmentation model; (2) landmark localization is done on the face using a connected components approach and a ridge contour method; (3) an Active Shape Model (ASM) is generated on the face using three-sided polygon meshes and perpendicular bisection of a triangle; (4) feature extraction is achieved using anthropometric model, carnio-facial development, interior angle formulation, wrinkle detection and heat maps; (5) Sequential Forward Selection (SFS) is used to select the most ideal set of features; and (6) finally, the Convolution Neural Network (CNN) model is used to classify according to age in the correct age group. The proposed system outperforms existing statistical state-of-the-art HAC methods in terms of classification accuracy, achieving 91.58% with The Images of Groups dataset, 92.62% with the OUI Adience dataset and 94.59% with the FG-NET dataset. The system is applicable to many research areas including access control, surveillance monitoring, human–machine interaction and self-identification. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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17 pages, 1532 KiB

Open AccessArticle

A Modified Chaotic Binary Particle Swarm Optimization Scheme and Its Application in Face-Iris Multimodal Biometric Identification

by Qi Xiong, Xinman Zhang, Xuebin Xu and Shaobo He

Electronics 2021, 10(2), 217; https://doi.org/10.3390/electronics10020217 - 19 Jan 2021

Cited by 23 | Viewed by 3414

Abstract

In order to improve the recognition rate of the biometric identification system, the features of each unimodal biometric are often combined in a certain way. However, there are some mutually exclusive redundant features in those combined features, which will degrade the identification performance. [...] Read more.

In order to improve the recognition rate of the biometric identification system, the features of each unimodal biometric are often combined in a certain way. However, there are some mutually exclusive redundant features in those combined features, which will degrade the identification performance. To solve this problem, this paper proposes a novel multimodal biometric identification system for face-iris recognition.It is based on binary particle swarm optimization. The face features are extracted by 2D Log-Gabor and Curvelet transform, while iris features are extracted by Curvelet transform. In order to reduce the complexity of the feature-level fusion, we propose a modified chaotic binary particle swarm optimization (MCBPSO) algorithm to select features. It uses kernel extreme learning machine (KELM) as a fitness function and chaotic binary sequences to initialize particle swarms. After the global optimal position (Gbest) is generated in each iteration, the position of Gbest is varied by using chaotic binary sequences, which is useful to realize chaotic local search and avoid falling into the local optimal position. The experiments are conducted on CASIA multimodal iris and face dataset from Chinese Academy of Sciences.The experimental results demonstrate that the proposed system can not only reduce the number of features to one tenth of its original size, but also improve the recognition rate up to 99.78%. Compared with the unimodal iris and face system, the recognition rate of the proposed system are improved by 11.56% and 2% respectively. The experimental results reveal its performance in the verification mode compared with the existing state-of-the-art systems. The proposed system is satisfactory in addressing face-iris multimodal biometric identification. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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21 pages, 2521 KiB

Open AccessArticle

Data Analytics and Mathematical Modeling for Simulating the Dynamics of COVID-19 Epidemic—A Case Study of India

by Himanshu Gupta, Saurav Kumar, Drishti Yadav, Om Prakash Verma, Tarun Kumar Sharma, Chang Wook Ahn and Jong-Hyun Lee

Electronics 2021, 10(2), 127; https://doi.org/10.3390/electronics10020127 - 08 Jan 2021

Cited by 20 | Viewed by 3679

Abstract

The global explosion of the COVID-19 pandemic has created worldwide unprecedented health and economic challenges which stimulated one of the biggest annual migrations globally. In the Indian context, even after proactive decisions taken by the Government, the continual growth of COVID-19 raises questions [...] Read more.

The global explosion of the COVID-19 pandemic has created worldwide unprecedented health and economic challenges which stimulated one of the biggest annual migrations globally. In the Indian context, even after proactive decisions taken by the Government, the continual growth of COVID-19 raises questions regarding its extent and severity. The present work utilizes the susceptible-infected-recovered-death (SIRD) compartment model for parameter estimation and fruitful prediction of COVID-19. Further, various optimization techniques such as particle swarm optimization (PSO), gradient (G), pattern search (PS) and their hybrid are employed to solve the considered model. The simulation study endorse the efficiency of PSO (with or without G) and G+PS+G over other techniques for ongoing pandemic assessment. The key parametric values including characteristic time of infection and death and reproduction number have been estimated as 60 days, 67 days and 4.78 respectively by utilizing the optimum results. The model assessed that India has passed its peak duration of COVID-19 with more than 81% recovery and only a 1.59% death rate. The short duration analysis (15 days) of obtained results against reported data validates the effectiveness of the developed models for ongoing pandemic assessment. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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20 pages, 9428 KiB

Open AccessFeature PaperArticle

A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management

by Saurav Kumar, Drishti Yadav, Himanshu Gupta, Om Prakash Verma, Irshad Ahmad Ansari and Chang Wook Ahn

Electronics 2021, 10(1), 14; https://doi.org/10.3390/electronics10010014 - 24 Dec 2020

Cited by 62 | Viewed by 8378

Abstract

The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for [...] Read more.

The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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15 pages, 1662 KiB

Open AccessArticle

Evolutionary Machine Learning for Optimal Polar-Space Fuzzy Control of Cyber-Physical Mecanum Vehicles

by Hsu-Chih Huang and Jing-Jun Xu

Electronics 2020, 9(11), 1945; https://doi.org/10.3390/electronics9111945 - 18 Nov 2020

Cited by 2 | Viewed by 1595

Abstract

This paper contributes to the development of evolutionary machine learning (EML) for optimal polar-space fuzzy control of cyber-physical Mecanum vehicles using the flower pollination algorithm (FPA). The metaheuristic FPA is utilized to design optimal fuzzy systems, called FPA-fuzzy. In this hybrid computation, both [...] Read more.

This paper contributes to the development of evolutionary machine learning (EML) for optimal polar-space fuzzy control of cyber-physical Mecanum vehicles using the flower pollination algorithm (FPA). The metaheuristic FPA is utilized to design optimal fuzzy systems, called FPA-fuzzy. In this hybrid computation, both the fuzzy structure and the number of IF–THEN rules are optimized through the FPA evolutionary process. This approach overcomes the drawback of the structure tuning problem in conventional fuzzy systems. After deriving the polar-space kinematics model of Mecanum vehicles, an optimal EML FPA-fuzzy online control scheme is synthesized, and the global stability is proven via Lyapunov theory. An embedded cyber-physical robotic system is then constructed using the typical 5C strategy. The proposed FPA-fuzzy computation collaborates with the advanced sensors and actuators of Mecanum vehicles to design a cyber-physical robotic system. Compared with conventional Cartesian-space control methods, the proposed EML FPA-fuzzy has the advantages of metaheuristics, fuzzy online control, and cyber-physical system design in polar coordinates. Finally, the mechatronic design and experimental setup of a Mecanum vehicle cyber-physical system is constructed. Through experimental results and comparative works, the effectiveness and merit of the proposed methods are validated. The proposed EML FPA-fuzzy control approach has theoretical and practice significance in terms of its real-time capability, online parameter tuning, convergent behavior, and hybrid artificial intelligence. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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27 pages, 629 KiB

Open AccessArticle

An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings

by Kei Ohnishi, Kouta Hamano and Mario Koeppen

Electronics 2020, 9(11), 1866; https://doi.org/10.3390/electronics9111866 - 06 Nov 2020

Cited by 1 | Viewed by 1715

Abstract

Recently, evolutionary algorithms that can efficiently solve decomposable binary optimization problems have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by applying a machine learning technique to a population. Their central procedure is linkage detection [...] Read more.

Recently, evolutionary algorithms that can efficiently solve decomposable binary optimization problems have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by applying a machine learning technique to a population. Their central procedure is linkage detection that reveals a problem structure, that is, how the entire problem consists of sub-problems. However, the model-based evolutionary algorithms have been shown to be ineffective for problems that do not have relevant structures or those whose structures are hard to identify. Therefore, evolutionary algorithms that can solve both types of problems quickly, reliably, and accurately are required. The objective of the paper is to investigate whether the evolutionary algorithm evolving developmental timings (EDT) that we previously proposed can be the desired one. The EDT makes some variables values more quickly converge than the remains for any problems, and then, decides values of the remains to obtain a higher fitness value under the fixation of the variables values. In addition, factors to decide which variable values converge more quickly, that is, developmental timings are evolution targets. Simulation results reveal that the EDT has worse performance than the linkage tree genetic algorithm (LTGA), which is one of the state-of-the-art model-based evolutionary algorithms, for decomposable problems and also that the difference in the performance between them becomes smaller for problems with overlaps among linkages and also that the EDT has better performance than the LTGA for problems whose structures are hard to identify. Those results suggest that an appropriate search strategy is different between decomposable problems and those hard to decompose. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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21 pages, 4302 KiB

Open AccessArticle

A Multi-Objective Evolutionary Algorithm Based on KNN-Graph for Traffic Network Attack

by Junhui Li, Shuai Wang, Hu Zhang and Aimin Zhou

Electronics 2020, 9(10), 1589; https://doi.org/10.3390/electronics9101589 - 28 Sep 2020

Cited by 1 | Viewed by 2270

Abstract

The research of vulnerability in complex network plays a key role in many real-world applications. However, most of existing work focuses on some static topological indexes of vulnerability and ignores the network functions. This paper addresses the network attack problems by considering both [...] Read more.

The research of vulnerability in complex network plays a key role in many real-world applications. However, most of existing work focuses on some static topological indexes of vulnerability and ignores the network functions. This paper addresses the network attack problems by considering both the topological and the functional indexes. Firstly, a network attack problem is converted into a multi-objective optimization network vulnerability problem (MONVP). Secondly to deal with MONVPs, a multi-objective evolutionary algorithm is proposed. In the new approach, a k-nearest-neighbor graph method is used to extract the structure of the Pareto set. With the obtained structure, similar parent solutions are chosen to generate offspring solutions. The statistical experiments on some benchmark problems demonstrate that the new approach shows higher search efficiency than some compared algorithms. Furthermore, the experiments on a subway system also suggests that the multi-objective optimization model can help to achieve better attach plans than the model that only considers a single index. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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17 pages, 4476 KiB

Open AccessArticle

An Optimized Digital Watermarking Scheme Based on Invariant DC Coefficients in Spatial Domain

by Musrrat Ali, Chang Wook Ahn, Millie Pant, Sanoj Kumar, Manoj K. Singh and Deepika Saini

Electronics 2020, 9(9), 1428; https://doi.org/10.3390/electronics9091428 - 02 Sep 2020

Cited by 16 | Viewed by 3359

Abstract

Digital watermarking has become an essential and important tool for copyright protection, authentication, and security of multimedia contents. It is the process of embedding a watermark in the multimedia content and its extraction. Block-based discrete cosine transform (DCT) is a widely used method [...] Read more.

Digital watermarking has become an essential and important tool for copyright protection, authentication, and security of multimedia contents. It is the process of embedding a watermark in the multimedia content and its extraction. Block-based discrete cosine transform (DCT) is a widely used method in digital watermarking. This paper proposes a novel blind image watermarking scheme developed in the spatial domain by quantization of invariant direct current (DC) coefficients. The cover image is redistributed and divided into non-overlapped square blocks and then the DC coefficients invariant to rotation, row and column flip operations, without utilization of the DCT transform, are directly calculated in the spatial domain. Utilizing the quantization parameter and watermark information, the modified DC coefficients and the difference between DC and modified DC coefficients are calculated to directly modify the pixel values to embed watermark bits in the spatial domain instead of the DCT domain. Optimal values of the quantization parameter, which plays a significant role in controlling the tradeoff between robustness and invisibility, are calculated through differential evolution (DE), the optimization algorithm. Experimental results, compared with the latest similar schemes, demonstrate the advantages of the proposed scheme. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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11 pages, 2151 KiB

Open AccessArticle

Pixel-Based Approach for Generating Original and Imitating Evolutionary Art

by Yuchen Wang and Rong Xie

Electronics 2020, 9(8), 1311; https://doi.org/10.3390/electronics9081311 - 14 Aug 2020

Viewed by 2482

Abstract

We proposed a pixel-based evolution method to automatically generate evolutionary art. Our method can generate diverse artworks, including original artworks and imitating artworks, with different artistic styles and high visual complexity. The generation process is fully automated. In order to adapt to the [...] Read more.

We proposed a pixel-based evolution method to automatically generate evolutionary art. Our method can generate diverse artworks, including original artworks and imitating artworks, with different artistic styles and high visual complexity. The generation process is fully automated. In order to adapt to the pixel-based method, a von Neumann neighbor topology-modified particle swarm optimization (PSO) is employed to the proposed method. The fitness functions of PSO are well prepared. Firstly, we come up with a set of aesthetic fitness functions. Next, the imitating fitness function is designed. Finally, the aesthetic fitness functions and the imitating fitness function are weighted into one single object function, which is used in the modified PSO. Both the original outputs and imitating outputs are shown. A questionnaire is designed to investigate the subjective aesthetic feeling of proposed evolutionary art, and the statistics are shown. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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19 pages, 2831 KiB

Open AccessArticle

AntsOMG: A Framework Aiming to Automate Creativity and Intelligent Behavior with a Showcase on Cantus Firmus Composition and Style Development

by Chun-Yien Chang and Ying-Ping Chen

Electronics 2020, 9(8), 1212; https://doi.org/10.3390/electronics9081212 - 28 Jul 2020

Cited by 6 | Viewed by 2786

Abstract

Creative behavior is one of the most fascinating areas in intelligence. The development of specific styles is the most characteristic feature of creative behavior. All important creators, such as Picasso and Beethoven, have their own distinctive styles that even non-professional art lovers can [...] Read more.

Creative behavior is one of the most fascinating areas in intelligence. The development of specific styles is the most characteristic feature of creative behavior. All important creators, such as Picasso and Beethoven, have their own distinctive styles that even non-professional art lovers can easily recognize. Hence, in the present work, attempting to achieve cantus firmus composition and style development as well as inspired by the behavior of natural ants and the mechanism of ant colony optimization (ACO), this paper firstly proposes a meta-framework, called ants on multiple graphs (AntsOMG), mainly for roughly modeling creation activities and then presents an implementation derived from AntsOMG for composing cantus firmi, one of the essential genres in music. Although the mechanism in ACO is adopted for simulating ant behavior, AntsOMG is not designed as an optimization framework. Implementations can be built upon AntsOMG in order to automate creation behavior and realize autonomous development on different subjects in various disciplines. In particular, an implementation for composing cantus firmi is shown in this paper as a demonstration. Ants walk on multiple graphs to form certain trails that are composed of the interaction among the graph topology, the cost on edges, and the concentration of pheromone. The resultant graphs with the distribution of pheromone can be interpreted as a representation of cantus firmus style developed autonomously. Our obtained results indicate that the proposal has an intriguing effect, because significantly different styles may be autonomously developed from an identical initial configuration in separate runs, and cantus firmi of a certain style can be created in batch simply by using the corresponding outcome. The contribution of this paper is twofold. First, the presented implementation is immediately applicable to the creation of cantus firmi and possibly other music genres with slight modifications. Second, AntsOMG, as a meta-framework, may be employed for other kinds of autonomous development with appropriate implementations. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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12 pages, 11536 KiB

Open AccessArticle

Spellcaster Control Agent in StarCraft II Using Deep Reinforcement Learning

by Wooseok Song, Woong Hyun Suh and Chang Wook Ahn

Electronics 2020, 9(6), 996; https://doi.org/10.3390/electronics9060996 - 14 Jun 2020

Cited by 1 | Viewed by 3595

Abstract

This paper proposes a DRL -based training method for spellcaster units in StarCraft II, one of the most representative Real-Time Strategy (RTS) games. During combat situations in StarCraft II, micro-controlling various combat units is crucial in order to win the game. Among many [...] Read more.

This paper proposes a DRL -based training method for spellcaster units in StarCraft II, one of the most representative Real-Time Strategy (RTS) games. During combat situations in StarCraft II, micro-controlling various combat units is crucial in order to win the game. Among many other combat units, the spellcaster unit is one of the most significant components that greatly influences the combat results. Despite the importance of the spellcaster units in combat, training methods to carefully control spellcasters have not been thoroughly considered in related studies due to the complexity. Therefore, we suggest a training method for spellcaster units in StarCraft II by using the A3C algorithm. The main idea is to train two Protoss spellcaster units under three newly designed minigames, each representing a unique spell usage scenario, to use ‘Force Field’ and ‘Psionic Storm’ effectively. As a result, the trained agents show winning rates of more than 85% in each scenario. We present a new training method for spellcaster units that releases the limitation of StarCraft II AI research. We expect that our training method can be used for training other advanced and tactical units by applying transfer learning in more complex minigame scenarios or full game maps. Full article

(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)

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