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Mathematics
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Computational Intelligence in Computer Vision and Software Engineering
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Computational Intelligence in Computer Vision and Software Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: closed (1 August 2022) | Viewed by 22111

Special Issue Editors

Prof. Dr. George E. Tsekouras

E-Mail Website
Guest Editor
Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
Interests: computational intelligence and its applications in system modeling; statistical data analysis; image processing; software engineering
Prof. Dr. Christos Kalloniatis

E-Mail Website
Guest Editor
Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece
Interests: privacy requirement engineering; security requirement engineering; business modelling; security and privacy in cloud computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dimitrios Makris

E-Mail Website
Guest Editor
Department of Computer Science, Kingston University, London, UK
Interests: computer vision; machine learning; pattern recognition; video and motion analysis; human motion analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Computational intelligence includes algorithmic techniques that have established themselves as effective and viable mathematical tools for several research and application areas. In general, computational intelligence includes neural networks, fuzzy logic, neuro-fuzzy networks, evolutionary computation, and machine learning.

The implementation of computational intelligence in image processing and computer vision has been effectively studied by many scholars who are interested in developing new approaches, in both research and real-world problems.

Recently, driven by the success of computational intelligence in image processing and computer vision, there is an increasing interest among many researchers in integrating computational intelligence techniques in software engineering.

The aim of this Special Issue is to focus on combining the abovementioned frameworks into a very interesting and fruitful interdisciplinary research area with promising opportunities.

Prof. Dr. George E. Tsekouras
Prof. Dr. Christos Kalloniatis
Prof. Dr. Dimitrios Makris
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. Mathematics 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 2600 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

  • neural networks
  • fuzzy systems and neuro-fuzzy networks
  • evolutionary computing, swarm intelligence
  • intelligent systems
  • search and optimization
  • interpretability issues in computational intelligence models
  • image processing (edge detection and segmentation, image retrieval, compression, filtering, image enhancement and restoration, e.t.c.)
  • image classification, object recognition, and feature extraction
  • document processing and recognition
  • color image analysis
  • image recoloring for color vision deficiencies
  • computer vision
  • video and motion analysis, motion and tracking
  • human motion analysis
  • face and emotion recognition, affective computing
  • scene analysis and understanding
  • semantic Segmentation
  • medical Imaging
  • computational photography, photometry
  • intelligent software engineering
  • big data analytics and data mining
  • recommender systems
  • natural language processing
  • privacy, information security, and cybersecurity
  • intrusion detection and fault diagnosis
  • digital twin, internet of things (IoT), and cloud computing
  • software effort estimation and metrics for software development (software quality, reliability, e.t.c.)
  • software analytics, and decision analysis in software engineering
  • federated learning
  • web Mining and Semantic Web

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Published Papers (6 papers)

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Research

22 pages, 899 KiB  
Article
VID2META: Complementing Android Programming Screencasts with Code Elements and GUIs
by Mohammad D. Alahmadi
Mathematics 2022, 10(17), 3175; https://doi.org/10.3390/math10173175 - 3 Sep 2022
Cited by 6 | Viewed by 1688
Abstract
The complexity of software projects and the rapid technological evolution make it such that developers often need additional help and knowledge to tackle their daily tasks. For this purpose, they often refer to online resources, which are easy to access and contain a [...] Read more.
The complexity of software projects and the rapid technological evolution make it such that developers often need additional help and knowledge to tackle their daily tasks. For this purpose, they often refer to online resources, which are easy to access and contain a wealth of information in various formats. Programming screencasts hosted on platforms such as YouTube are one such online resource that has seen a growth in popularity and adoption over the past decade. These screencasts usually have some metadata such as a title, a short description, and a set of tags that should describe what the main concepts captured in the video are. Unfortunately, metadata are often generic and do not contain detailed information about the code showcased in the tutorial, such as the API calls or graphical user interface (GUI) elements employed, which could lead to developers missing useful tutorials. Having a quick overview of the main code elements and GUIs used in a video tutorial can be very helpful for developers looking for code examples involving specific API calls, or looking to design applications with a specific GUI in mind. The aim is to make this information easily available to developers, and propose VID2META, a technique that automatically extracts Java import statements, class names, method information, GUI elements, and GUI screens from videos and makes them available to developers as metadata. VID2META is currently designed to work with Android screencasts. It analyzes video frames using a combination of computer vision, deep learning, optical character recognition, and heuristic-based approaches to identify the needed information in a frame, extract it, and present it to the developer. VID2META has been evaluated in an empirical study on 70 Android programming videos collected from YouTube. The results revealed that VID2META can accurately detect and extract Java and GUI elements from Android programming videos with an average accuracy of 90%. Full article
(This article belongs to the Special Issue Computational Intelligence in Computer Vision and Software Engineering)
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26 pages, 1676 KiB  
Article
Deep Learning-Based Software Defect Prediction via Semantic Key Features of Source Code—Systematic Survey
by Ahmed Abdu, Zhengjun Zhai, Redhwan Algabri, Hakim A. Abdo, Kotiba Hamad and Mugahed A. Al-antari
Mathematics 2022, 10(17), 3120; https://doi.org/10.3390/math10173120 - 31 Aug 2022
Cited by 22 | Viewed by 5705
Abstract
Software defect prediction (SDP) methodology could enhance software’s reliability through predicting any suspicious defects in its source code. However, developing defect prediction models is a difficult task, as has been demonstrated recently. Several research techniques have been proposed over time to predict source [...] Read more.
Software defect prediction (SDP) methodology could enhance software’s reliability through predicting any suspicious defects in its source code. However, developing defect prediction models is a difficult task, as has been demonstrated recently. Several research techniques have been proposed over time to predict source code defects. However, most of the previous studies focus on conventional feature extraction and modeling. Such traditional methodologies often fail to find the contextual information of the source code files, which is necessary for building reliable prediction deep learning models. Alternatively, the semantic feature strategies of defect prediction have recently evolved and developed. Such strategies could automatically extract the contextual information from the source code files and use them to directly predict the suspicious defects. In this study, a comprehensive survey is conducted to systematically show recent software defect prediction techniques based on the source code’s key features. The most recent studies on this topic are critically reviewed through analyzing the semantic feature methods based on the source codes, the domain’s critical problems and challenges are described, and the recent and current progress in this domain are discussed. Such a comprehensive survey could enable research communities to identify the current challenges and future research directions. An in-depth literature review of 283 articles on software defect prediction and related work was performed, of which 90 are referenced. Full article
(This article belongs to the Special Issue Computational Intelligence in Computer Vision and Software Engineering)
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24 pages, 34718 KiB  
Article
S3D: Squeeze and Excitation 3D Convolutional Neural Networks for a Fall Detection System
by Seung Baek Hong, Yu Hwan Kim, Se Hyun Nam and Kang Ryoung Park
Mathematics 2022, 10(3), 328; https://doi.org/10.3390/math10030328 - 21 Jan 2022
Cited by 6 | Viewed by 4548
Abstract
Because of the limitations of previous studies on a fall detection system (FDS) based on wearable and ambient devices and visible light and depth cameras, the research using thermal cameras has recently been conducted. However, they also have the problem of deteriorating the [...] Read more.
Because of the limitations of previous studies on a fall detection system (FDS) based on wearable and ambient devices and visible light and depth cameras, the research using thermal cameras has recently been conducted. However, they also have the problem of deteriorating the accuracy of FDS depending on various environmental changes. Given these facts, in this study, we newly propose an FDS method based on the squeeze and excitation (SE) 3D convolutional neural networks (S3D). In our method, keyframes are extracted from input thermal videos using the optical flow vectors, and the fall detection is carried out based on the output of the proposed S3D, using the extracted keyframes as input. Comparative experiments were carried out on three open databases of thermal videos with different image resolutions, and our proposed method obtained F1 scores of 97.14%, 95.30%, and 98.89% in the Thermal Simulated Fall, Telerobotics and Control Lab fall detection, and eHomeSeniors datasets, respectively (the F1 score is a harmonic mean of recall and precision; it was confirmed that these are superior results to those obtained using the state-of-the-art methods of a thermal camera-based FDS. Full article
(This article belongs to the Special Issue Computational Intelligence in Computer Vision and Software Engineering)
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22 pages, 5779 KiB  
Article
Induction Motor Fault Classification Based on Combined Genetic Algorithm with Symmetrical Uncertainty Method for Feature Selection Task
by Chun-Yao Lee, Yun-Jhan Hsieh and Truong-An Le
Mathematics 2022, 10(2), 230; https://doi.org/10.3390/math10020230 - 12 Jan 2022
Cited by 8 | Viewed by 2111
Abstract
This research proposes a method to improve the capability of a genetic algorithm (GA) to choose the best feature subset by incorporating symmetrical uncertainty (SU) to rank the features and remove redundant features. The proposed method is a combination of symmetrical [...] Read more.
This research proposes a method to improve the capability of a genetic algorithm (GA) to choose the best feature subset by incorporating symmetrical uncertainty (SU) to rank the features and remove redundant features. The proposed method is a combination of symmetrical uncertainty and a genetic algorithm (SU-GA). In this study, feature selection is implemented on four different conditions of an induction motor: normal, broken bearings, a broken rotor bar, and a stator winding short circuit. The Hilbert-Huang transform (HHT) is then used to analyze the current signal in these four motor conditions. After that, the feature selection is used to find the best feature subset for the classification task. A support vector machine (SVM) was used for the feature classification. Three feature selection methods were implemented: SU, GA, and SU-GA. The results show that SU-GA obtained better accuracy with fewer selected features. In addition, to simulate and analyze the actual operating situation of the induction motors, three different magnitudes of white noise were added with the following signal-to-noise ratios (SNR): 40 dB, 30 dB, and 20 dB. Finally, the results show that the proposed method has a better classification capability. Full article
(This article belongs to the Special Issue Computational Intelligence in Computer Vision and Software Engineering)
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17 pages, 5606 KiB  
Article
Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks
by Gyanendra Prasad Joshi, Fayadh Alenezi, Gopalakrishnan Thirumoorthy, Ashit Kumar Dutta and Jinsang You
Mathematics 2021, 9(22), 2984; https://doi.org/10.3390/math9222984 - 22 Nov 2021
Cited by 38 | Viewed by 3972
Abstract
Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, [...] Read more.
Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches. Full article
(This article belongs to the Special Issue Computational Intelligence in Computer Vision and Software Engineering)
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32 pages, 8093 KiB  
Article
Pedestrian Gender Recognition by Style Transfer of Visible-Light Image to Infrared-Light Image Based on an Attention-Guided Generative Adversarial Network
by Na Rae Baek, Se Woon Cho, Ja Hyung Koo and Kang Ryoung Park
Mathematics 2021, 9(20), 2535; https://doi.org/10.3390/math9202535 - 9 Oct 2021
Cited by 4 | Viewed by 2566
Abstract
Gender recognition of pedestrians in uncontrolled outdoor environments, such as intelligent surveillance scenarios, involves various problems in terms of performance degradation. Most previous studies on gender recognition examined recognition methods involving faces, full body images, or gaits. However, the recognition performance is degraded [...] Read more.
Gender recognition of pedestrians in uncontrolled outdoor environments, such as intelligent surveillance scenarios, involves various problems in terms of performance degradation. Most previous studies on gender recognition examined recognition methods involving faces, full body images, or gaits. However, the recognition performance is degraded in uncontrolled outdoor environments due to various factors, including motion and optical blur, low image resolution, occlusion, pose variation, and changes in lighting. In previous studies, a visible-light image in which image restoration was performed and infrared-light (IR) image, which is robust to the type of clothes, accessories, and lighting changes, were combined to improve recognition performance. However, a near-IR (NIR) image requires a separate NIR camera and NIR illuminator, because of which challenges are faced in providing uniform illumination to the object depending on the distance to the object. A thermal camera, which is also called far-IR (FIR), is not widely used in a surveillance camera environment because of expensive equipment. Therefore, this study proposes an attention-guided GAN for synthesizing infrared image (SI-AGAN) for style transfer of visible-light image to IR image. Gender recognition performance was improved by using only a visible-light camera without an additional IR camera by combining the synthesized IR image obtained by the proposed method with the visible-light image. In the experiments conducted using open databases—RegDB database and SYSU-MM01 database—the equal error rate (EER) of gender recognition of the proposed method in each database was 9.05 and 12.95%, which is higher than that of state-of-the-art methods. Full article
(This article belongs to the Special Issue Computational Intelligence in Computer Vision and Software Engineering)
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