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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (24 April 2023) | Viewed by 27580

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Dr. Abeer Alsadoon

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Guest Editor

School of Computing, Mathematics and Engineering, Charles Sturt University, 250 Boorooma St, Wagga Wagga, NSW 2678, Australia
Interests: software engineering; artificial intelligence and machine learning; neural network; mixed reality (MR); augmented reality (AR)

Dr. Luis Coelho

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Guest Editor

ISEP-School of Engineering, Polytechnic of Porto, Porto, Portugal
Interests: physiological signal processing; image processing; machine learning; neurodegenerative diseases; virtual reality
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

This leading special session promotes and stimulates research in the field of computational intelligence (CI) and image processing (IP). Covering a wide range of issues, from the tools and languages of CI to its philosophical implications, this special session of Computational Intelligent and Image Processing (CIAIP) provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The special session is designed to meet the needs of a wide range of CI workers in academic and industrial research and in different fields. Please see the scope to learn about the focal topics in computational intelligence. We would like to offer an opportunity to researchers and practitioners to identify new promising research directions as well as to publish recent advances in this area.

Dr. Abeer Alsadoon
Dr. Luis Coelho
Guest Editors

Manuscript Submission Information

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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.

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Keywords

The foundations of computational intelligence (CI)
CI in cognitive algorithms, the mind and the brain
CI in data mining
CI in financial engineering and economics
CI and intelligent agents
CI in robotic rehabilitation
Model-based evolutionary algorithms
CI in feature analysis, selection, and learning in image and pattern recognition
CI in E-government
CI in big data
CI applications in smart grids
CI in wireless systems
CI in healthcare and E-health
CI for multimedia, signal, and vision processing
Object detection and tracking
Activity detection and analysis
Image and video indexing and retrieval
2D/3D object detection and recognition
Pattern recognition and machine learning
Machine learning and data mining
Mathematical approaches to image processing
Computer vision and pattern recognition with applications
Artificial neural networks
Image, speech, signal, and video processing
Algorithm performance

Published Papers (12 papers)

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Research

15 pages, 3551 KiB

Open AccessArticle

A Semantics-Based Clustering Approach for Online Laboratories Using K-Means and HAC Algorithms

by Saad Hikmat Haji, Karwan Jacksi and Razwan Mohmed Salah

Mathematics 2023, 11(3), 548; https://0-doi-org.brum.beds.ac.uk/10.3390/math11030548 - 19 Jan 2023

Cited by 3 | Viewed by 3143

Abstract

Due to the availability of a vast amount of unstructured data in various forms (e.g., the web, social networks, etc.), the clustering of text documents has become increasingly important. Traditional clustering algorithms have not been able to solve this problem because the semantic relationships between words could not accurately represent the meaning of the documents. Thus, semantic document clustering has been extensively utilized to enhance the quality of text clustering. This method is called unsupervised learning and it involves grouping documents based on their meaning, not on common keywords. This paper introduces a new method that groups documents from online laboratory repositories based on the semantic similarity approach. In this work, the dataset is collected first by crawling the short real-time descriptions of the online laboratories’ repositories from the Web. A vector space is created using frequency-inverse document frequency (TF-IDF) and clustering is done using the K-Means and Hierarchical Agglomerative Clustering (HAC) algorithms with different linkages. Three scenarios are considered: without preprocessing (WoPP); preprocessing with steaming (PPwS); and preprocessing without steaming (PPWoS). Several metrics have been used for evaluating experiments: Silhouette average, purity, V-measure, F1-measure, accuracy score, homogeneity score, completeness and NMI score (consisting of five datasets: online labs, 20 NewsGroups, Txt_sentoken, NLTK_Brown and NLTK_Reuters). Finally, by creating an interactive webpage, the results of the proposed work are contrasted and visualized. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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42 pages, 14904 KiB

Open AccessArticle

RMOBF-Net: Network for the Restoration of Motion and Optical Blurred Finger-Vein Images for Improving Recognition Accuracy

by Jiho Choi, Jin Seong Hong, Seung Gu Kim, Chanhum Park, Se Hyun Nam and Kang Ryoung Park

Mathematics 2022, 10(21), 3948; https://0-doi-org.brum.beds.ac.uk/10.3390/math10213948 - 24 Oct 2022

Cited by 2 | Viewed by 1092

Abstract

Biometrics is a method of recognizing a person based on one or more unique physical and behavioral characteristics. Since each person has a different structure and shape, it is highly secure and more convenient than the existing security system. Among various biometric authentication methods, finger-vein recognition has advantages in that it is difficult to forge because a finger-vein exists inside one’s finger and high user convenience because it uses a non-invasive device. However, motion and optical blur may occur for some reasons such as finger movement and camera defocusing during finger-vein recognition, and such blurring occurrences may increase finger-vein recognition error. However, there has been no research on finger-vein recognition considering both motion and optical blur. Therefore, in this study, we propose a new method for increasing finger-vein recognition accuracy based on a network for the restoration of motion and optical blurring in a finger-vein image (RMOBF-Net). Our proposed network continuously maintains features that can be utilized during motion and optical blur restoration by actively using residual blocks and feature concatenation. Also, the architecture RMOBF-Net is optimized to the finger-vein image domain. Experimental results are based on two open datasets, the Shandong University homologous multi-modal traits finger-vein database and the Hong Kong Polytechnic University finger-image database version 1, from which equal error rates of finger-vein recognition accuracy of 4.290–5.779% and 2.465–6.663% were obtained, respectively. Higher performance was obtained from the proposed method compared with that of state-of-the-art methods. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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15 pages, 38182 KiB

Open AccessArticle

Three-Dimensional Reconstruction of Shoe Soles via Binocular Vision Based on Improved Matching Cost

by Rui Wang, Lisheng Wei, Zhengyan Gu and Xiaohui Liu

Mathematics 2022, 10(19), 3548; https://0-doi-org.brum.beds.ac.uk/10.3390/math10193548 - 28 Sep 2022

Cited by 1 | Viewed by 1269

Abstract

Aiming at the problem that the toe cap and upper part of the sole of a shoe easily appear missing when using binocular vision to reconstruct the shoe sole in the industrial production process, an improved matching cost calculation method is proposed to reconstruct shoe soles in three dimensions. Firstly, a binocular vision platform is built, and Zhang’s calibration method is used to obtain the calibration parameters. Secondly, the method of fusing Census and BT costs is used to calculate the matching cost of the image, so that the matching cost calculation result is more accurate. On this basis, 4-path aggregation is performed on the obtained cost, and the optimal matching cost is selected in combination with the WTA algorithm. Finally, left–right consistency detection and median filtering are used to optimize the disparity map and combine the camera calibration parameters to reconstruct the shoe sole in three dimensions. The experimental results show that the average mismatch rate of the four images on the Middlebury website in this method is about 6.57%, the reconstructed sole point cloud contour information is complete, and there is no material missing at the toe and heel. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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22 pages, 3615 KiB

Open AccessArticle

ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification

by Hussam J. Mohammed, Shumoos Al-Fahdawi, Alaa S. Al-Waisy, Dilovan Asaad Zebari, Dheyaa Ahmed Ibrahim, Mazin Abed Mohammed, Seifedine Kadry and Jungeun Kim

Mathematics 2022, 10(19), 3530; https://0-doi-org.brum.beds.ac.uk/10.3390/math10193530 - 28 Sep 2022

Cited by 8 | Viewed by 2511

Abstract

Person re-identification has become an essential application within computer vision due to its ability to match the same person over non-overlapping cameras. However, it is a challenging task because of the broad view of cameras with a large number of pedestrians appearing with various poses. As a result, various approaches of supervised model learning have been utilized to locate and identify a person based on the given input. Nevertheless, several of these approaches perform worse than expected in retrieving the right person in real-time over multiple CCTVs/camera views. This is due to inaccurate segmentation of the person, leading to incorrect classification. This paper proposes an efficient and real-time person re-identification system, named ReID-DeePNet system. It is based on fusing the matching scores generated by two different deep learning models, convolutional neural network and deep belief network, to extract discriminative feature representations from the pedestrian image. Initially, a segmentation procedure was developed based on merging the advantages of the Mask R-CNN and GrabCut algorithm to tackle the adverse effects caused by background clutter. Afterward, the two different deep learning models extracted discriminative feature representations from the pedestrian segmented image, and their matching scores were fused to make the final decision. Several extensive experiments were conducted, using three large-scale and challenging person re-identification datasets: Market-1501, CUHK03, and P-DESTRE. The ReID-DeePNet system achieved new state-of-the-art Rank-1 and mAP values on these three challenging ReID datasets. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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26 pages, 8582 KiB

Open AccessArticle

OADE-Net: Original and Attention-Guided DenseNet-Based Ensemble Network for Person Re-Identification Using Infrared Light Images

by Min Su Jeong, Seong In Jeong, Seon Jong Kang, Kyung Bong Ryu and Kang Ryoung Park

Mathematics 2022, 10(19), 3503; https://0-doi-org.brum.beds.ac.uk/10.3390/math10193503 - 26 Sep 2022

Cited by 1 | Viewed by 1202

Abstract

Recently, research on the methods that use images captured during day and night times has been actively conducted in the field of person re-identification (ReID). In particular, ReID has been increasingly performed using infrared (IR) images captured at night and red-green-blue (RGB) images, in addition to ReID, which only uses RGB images captured during the daytime. However, insufficient research has been conducted on ReID that only uses IR images because their color and texture information cannot be identified easily. This study thus proposes an original and attention-guided DenseNet-based ensemble network (OADE-Net)—a ReID model that can recognize pedestrians using only IR images captured during the day and night times. The OADE-Net consists of the original and attention-guided DenseNets and a shallow convolutional neural network for the ensemble network (SCE-Net), which is a model used for combining the two models. Owing to the lack of existing open datasets that only consist of IR images, the experiments are conducted by creating a new dataset that only consists of IR images retrieved from two open databases (DBPerson-Recog-DB1 and SYSU-MM01). The experimental results of the OADE-Net showed that the achieved ReID accuracy of the DBPerson-Recog-DB1 is 79.71% in rank 1, while the mean average precision (mAP) is 78.17%. Furthermore, an accuracy of 57.30% is achieved in rank 1 in the SYSU-MM01 case, whereas the accuracy of the mAP was 41.50%. Furthermore, the accuracy of the OADE-Net in both datasets is higher than that of the existing score-level fusion and state-of-the-art methods. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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20 pages, 6148 KiB

Open AccessArticle

Thyroid Nodule Segmentation in Ultrasound Image Based on Information Fusion of Suggestion and Enhancement Networks

by Dat Tien Nguyen, Jiho Choi and Kang Ryoung Park

Mathematics 2022, 10(19), 3484; https://0-doi-org.brum.beds.ac.uk/10.3390/math10193484 - 23 Sep 2022

Cited by 6 | Viewed by 1500

Abstract

Computer-aided diagnosis/detection (CADx) systems have been used to help doctors in improving the quality of diagnosis and treatment processes in many serious diseases such as breast cancer, brain stroke, lung cancer, and bone fracture. However, the performance of such systems has not been completely accurate. The key factor in CADx systems is to localize positive disease lesions from the captured medical images. This step is important as it is used not only to localize lesions but also to reduce the effect of noise and normal regions on the overall CADx system. In this research, we proposed a method to enhance the segmentation performance of thyroid nodules in ultrasound images based on information fusion of suggestion and enhancement segmentation networks. Experimental results with two open databases of thyroid digital image databases and 3DThyroid databases showed that our method resulted in a higher performance compared to current up-to-date methods. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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26 pages, 4146 KiB

Open AccessArticle

Deep Learning-Based Plant-Image Classification Using a Small Training Dataset

by Ganbayar Batchuluun, Se Hyun Nam and Kang Ryoung Park

Mathematics 2022, 10(17), 3091; https://0-doi-org.brum.beds.ac.uk/10.3390/math10173091 - 28 Aug 2022

Cited by 9 | Viewed by 4268

Abstract

Extensive research has been conducted on image augmentation, segmentation, detection, and classification based on plant images. Specifically, previous studies on plant image classification have used various plant datasets (fruits, vegetables, flowers, trees, etc., and their leaves). However, existing plant-based image datasets are generally small. Furthermore, there are limitations in the construction of large-scale datasets. Consequently, previous research on plant classification using small training datasets encountered difficulties in achieving high accuracy. However, research on plant image classification based on small training datasets is insufficient. Accordingly, this study performed classification by reducing the number of training images of plant-image datasets by 70%, 50%, 30%, and 10%, respectively. Then, the number of images was increased back through augmentation methods for training. This ultimately improved the plant-image classification performance. Based on the respective preliminary experimental results, this study proposed a plant-image classification convolutional neural network (PI-CNN) based on plant image augmentation using a plant-image generative adversarial network (PI-GAN). Our proposed method showed the higher classification accuracies compared to the state-of-the-art methods when the experiments were conducted using four open datasets of PlantVillage, PlantDoc, Fruits-360, and Plants. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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18 pages, 1512 KiB

Open AccessArticle

AI-Empowered Attack Detection and Prevention Scheme for Smart Grid System

by Aparna Kumari, Rushil Kaushikkumar Patel, Urvi Chintukumar Sukharamwala, Sudeep Tanwar, Maria Simona Raboaca, Aldosary Saad and Amr Tolba

Mathematics 2022, 10(16), 2852; https://0-doi-org.brum.beds.ac.uk/10.3390/math10162852 - 10 Aug 2022

Cited by 5 | Viewed by 1895

Abstract

The existing grid infrastructure has already begun transforming into the next-generation cyber-physical smart grid (SG) system. This transformation has improved the grid’s reliability and efficiency but has exposed severe vulnerabilities due to growing cyberattacks and threats. For example, malicious actors may be able to tamper with system readings, parameters, and energy prices and penetrate to get direct access to the data. Several works exist to handle the aforementioned issues, but they have not been fully explored. Consequently, this paper proposes an AI-ADP scheme for the SG system, which is an artificial intelligence (AI)-based attack-detection and prevention (ADP) mechanism by using a cryptography-driven recommender system to ensure data security and integrity. The proposed AI-ADP scheme is divided into two phases: (i) attack detection and (ii) attack prevention. We employed the extreme gradient-boosting (XGBoost) mechanism for attack detection and classification. It is a new ensemble learning methodology that offers many advantages over similar methods, including built-in features, etc. Then, SHA-512 is used to secure the communication that employs faster performance, allowing the transmission of more data with the same security level. The performance of the proposed AI-ADP scheme is evaluated based on various parameters, such as attack-detection accuracy, cycles used per byte, and total cycles used. The proposed AI-ADP scheme outperformed the existing approaches and obtained 99.12% accuracy, which is relatively high compared to the pre-existing methods. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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20 pages, 2369 KiB

Open AccessArticle

Recent Iris and Ocular Recognition Methods in High- and Low-Resolution Images: A Survey

by Young Won Lee and Kang Ryoung Park

Mathematics 2022, 10(12), 2063; https://0-doi-org.brum.beds.ac.uk/10.3390/math10122063 - 15 Jun 2022

Cited by 7 | Viewed by 2588

Abstract

Among biometrics, iris and ocular recognition systems are the methods that recognize eye features in an image. Such iris and ocular regions must have a certain image resolution to achieve a high recognition performance; otherwise, the risk of performance degradation arises. This is even more critical in the case of iris recognition where detailed patterns are used. In cases where such low-resolution images are acquired and the acquisition apparatus and environment cannot be improved, recognition performance can be enhanced by obtaining high-resolution images with methods such as super-resolution reconstruction. However, previous survey papers have mainly summarized studies on high-resolution iris and ocular recognition, but do not provide detailed summaries of studies on low-resolution iris and ocular recognition. Therefore, we investigated high-resolution iris and ocular recognition methods and introduced in detail the low-resolution iris and ocular recognition methods and methods of solving the low-resolution problem. Furthermore, since existing survey papers have focused on and summarized studies on traditional handcrafted feature-based iris and ocular recognition, this survey paper also introduced the latest deep learning-based methods in detail. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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16 pages, 4702 KiB

Open AccessArticle

Computational Intelligence-Based Harmony Search Algorithm for Real-Time Object Detection and Tracking in Video Surveillance Systems

by Maged Faihan Alotaibi, Mohamed Omri, Sayed Abdel-Khalek, Eied Khalil and Romany F. Mansour

Mathematics 2022, 10(5), 733; https://0-doi-org.brum.beds.ac.uk/10.3390/math10050733 - 25 Feb 2022

Cited by 20 | Viewed by 2109

Abstract

Recently, video surveillance systems have gained significant interest in several application areas. The examination of video sequences for the detection and tracking of objects remains a major issue in the field of image processing and computer vision. The object detection and tracking process includes the extraction of moving objects from the frames and continual tracking over time. The latest advances in computation intelligence (CI) techniques have become popular in the field of image processing and computer vision. In this aspect, this study introduces a novel computational intelligence-based harmony search algorithm for real-time object detection and tracking (CIHSA-RTODT) technique on video surveillance systems. The CIHSA-RTODT technique mainly focuses on detecting and tracking the objects that exist in the video frame. The CIHSA-RTODT technique incorporates an improved RefineDet-based object detection module, which can effectually recognize multiple objects in the video frame. In addition, the hyperparameter values of the improved RefineDet model are adjusted by the use of the Adagrad optimizer. Moreover, a harmony search algorithm (HSA) with a twin support vector machine (TWSVM) model is employed for object classification. The design of optimal RefineDet feature extraction with the application of HSA to appropriately adjust the parameters involved in the TWSVM model for object detection and tracking shows the novelty of the work. A wide range of experimental analyses are carried out on an open access dataset, and the results are inspected in several ways. The simulation outcome reported the superiority of the CIHSA-RTODT technique over the other existing techniques. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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18 pages, 4366 KiB

Open AccessArticle

Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification

by Altyeb Taha, Omar Barukab and Sharaf Malebary

Mathematics 2021, 9(22), 2880; https://0-doi-org.brum.beds.ac.uk/10.3390/math9222880 - 12 Nov 2021

Cited by 5 | Viewed by 1725

Abstract

One of the most commonly used operating systems for smartphones is Android. The open-source nature of the Android operating system and the ability to include third-party Android apps from various markets has led to potential threats to user privacy. Malware developers use sophisticated methods that are intentionally designed to bypass the security checks currently used in smartphones. This makes effective detection of Android malware apps a difficult problem and important issue. This paper proposes a novel fuzzy integral-based multi-classifier ensemble to improve the accuracy of Android malware classification. The proposed approach utilizes the Choquet fuzzy integral as an aggregation function for the purpose of combining and integrating the classification results of several classifiers such as XGBoost, Random Forest, Decision Tree, AdaBoost, and LightGBM. Moreover, the proposed approach utilizes an adaptive fuzzy measure to consider the dynamic nature of the data in each classifier and the consistency and coalescence between each possible subset of classifiers. This enables the proposed approach to aggregate the classification results from the multiple classifiers. The experimental results using the dataset, consisting of 9476 Android goodware apps and 5560 malware Android apps, show that the proposed approach for Android malware classification based on the Choquet fuzzy integral technique outperforms the single classifiers and achieves the highest accuracy of 95.08%. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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13 pages, 2267 KiB

Open AccessArticle

Intelligent Ensemble Learning Approach for Phishing Website Detection Based on Weighted Soft Voting

by Altyeb Taha

Mathematics 2021, 9(21), 2799; https://0-doi-org.brum.beds.ac.uk/10.3390/math9212799 - 04 Nov 2021

Cited by 16 | Viewed by 2800

Abstract

The continuous development of network technologies plays a major role in increasing the utilization of these technologies in many aspects of our lives, including e-commerce, electronic banking, social media, e-health, and e-learning. In recent times, phishing websites have emerged as a major cybersecurity threat. Phishing websites are fake web pages that are created by hackers to mimic the web pages of real websites to deceive people and steal their private information, such as account usernames and passwords. Accurate detection of phishing websites is a challenging problem because it depends on several dynamic factors. Ensemble methods are considered the state-of-the-art solution for many classification tasks. Ensemble learning combines the predictions of several separate classifiers to obtain a higher performance than a single classifier. This paper proposes an intelligent ensemble learning approach for phishing website detection based on weighted soft voting to enhance the detection of phishing websites. First, a base classifier consisting of four heterogeneous machine-learning algorithms was utilized to classify the websites as phishing or legitimate websites. Second, a novel weighted soft voting method based on Kappa statistics was employed to assign greater weights of influence to stronger base learners and lower weights of influence to weaker base learners, and then integrate the results of each classifier based on the soft weighted voting to differentiate between phishing websites and legitimate websites. The experiments were conducted using the publicly available phishing website dataset from the UCI Machine Learning Repository, which consists of 4898 phishing websites and 6157 legitimate websites. The experimental results showed that the suggested intelligent approach for phishing website detection outperformed the base classifiers and soft voting method and achieved the highest accuracy of 95% and an Area Under the Curve (AUC) of 98.8%. Full article

(This article belongs to the Special Issue Computational Intelligent and Image Processing)

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