Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
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COMPUTER SCIENCE (1305 journals)            First | 1 2 3 4 5 6 7 | Last

Showing 601 - 800 of 872 Journals sorted alphabetically
International Journal of Digital Enterprise Technology     Hybrid Journal   (Followers: 1)
International Journal of Digital Literacy and Digital Competence     Full-text available via subscription   (Followers: 6)
International Journal of Digital Signals and Smart Systems     Hybrid Journal   (Followers: 4)
International Journal of Education and Development using Information and Communication Technology     Open Access   (Followers: 9)
International Journal of Electrical and Computer Engineering     Open Access   (Followers: 8)
International Journal of Electronic Banking     Hybrid Journal   (Followers: 3)
International Journal of Electronic Business     Hybrid Journal   (Followers: 2)
International Journal of Electronic Commerce     Full-text available via subscription   (Followers: 10)
International Journal of Electronic Government Research     Full-text available via subscription   (Followers: 3)
International Journal of Embedded and Real-Time Communication Systems     Full-text available via subscription   (Followers: 9)
International Journal of Engineering and Manufacturing     Open Access   (Followers: 3)
International Journal of Engineering Science     Hybrid Journal   (Followers: 5)
International Journal of Entertainment Technology and Management     Hybrid Journal   (Followers: 1)
International Journal of Experimental Design and Process Optimisation     Hybrid Journal   (Followers: 5)
International Journal of Foundations of Computer Science     Hybrid Journal   (Followers: 3)
International Journal of Fuzzy Computation and Modelling     Hybrid Journal   (Followers: 2)
International Journal of Fuzzy System Applications     Full-text available via subscription   (Followers: 3)
International Journal of General Systems     Hybrid Journal   (Followers: 1)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of Green Computing     Full-text available via subscription  
International Journal of Grid and High Performance Computing     Full-text available via subscription   (Followers: 2)
International Journal of Grid and Utility Computing     Hybrid Journal  
International Journal of Handheld Computing Research     Full-text available via subscription  
International Journal of Heritage in the Digital Era     Full-text available via subscription   (Followers: 7)
International Journal of High Performance Computing and Networking     Hybrid Journal   (Followers: 4)
International Journal of High Performance Computing Applications     Hybrid Journal   (Followers: 4)
International Journal of High Performance Systems Architecture     Hybrid Journal   (Followers: 6)
International Journal of Human Capital and Information Technology Professionals     Full-text available via subscription   (Followers: 3)
International Journal of Human-Computer Interaction     Hybrid Journal   (Followers: 22)
International Journal of Human-Computer Studies     Hybrid Journal   (Followers: 20)
International Journal of Humanitarian Technology     Hybrid Journal   (Followers: 1)
International Journal of Humanities and Arts Computing     Hybrid Journal   (Followers: 11)
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
International Journal of ICT Research and Development in Africa     Full-text available via subscription   (Followers: 4)
International Journal of Imaging Systems and Technology     Hybrid Journal   (Followers: 1)
International Journal of Impact Engineering     Hybrid Journal   (Followers: 9)
International Journal of Industrial and Systems Engineering     Hybrid Journal   (Followers: 7)
International Journal of Industrial Electronics and Drives     Hybrid Journal   (Followers: 3)
International Journal of Information and Coding Theory     Hybrid Journal   (Followers: 6)
International Journal of Information and Communication Technology Education     Full-text available via subscription   (Followers: 13)
International Journal of Information Communication Technologies and Human Development     Full-text available via subscription   (Followers: 4)
International Journal of Information Quality     Hybrid Journal   (Followers: 3)
International Journal of Information Retrieval Research     Full-text available via subscription   (Followers: 28)
International Journal of Information Science and Management     Open Access   (Followers: 5)
International Journal of Information Science and Technology     Open Access  
International Journal of Information Systems and Management     Hybrid Journal   (Followers: 2)
International Journal of Information Systems and Project Management     Free   (Followers: 12)
International Journal of Information Systems and Software Engineering for Big Companies     Open Access   (Followers: 2)
International Journal of Information Technology and Computer Science     Open Access   (Followers: 3)
International Journal of Information Technology and Web Engineering     Hybrid Journal   (Followers: 2)
International Journal of Information Technology Project Management     Full-text available via subscription   (Followers: 9)
International Journal of Information Technology, Communications and Convergence     Hybrid Journal   (Followers: 14)
International Journal of Innovation in the Digital Economy     Full-text available via subscription   (Followers: 5)
International Journal of Innovative Computing and Applications     Hybrid Journal   (Followers: 3)
International Journal of Innovative Technology and Research     Open Access   (Followers: 1)
International Journal of Intelligence and Sustainable Computing     Hybrid Journal  
International Journal of Intelligence Science     Open Access   (Followers: 3)
International Journal of Intelligent Engineering Informatics     Hybrid Journal  
International Journal of Intelligent Enterprise     Hybrid Journal   (Followers: 1)
International Journal of Intelligent Information and Database Systems     Hybrid Journal   (Followers: 3)
International Journal of Intelligent Internet of Things Computing     Hybrid Journal   (Followers: 2)
International Journal of Intelligent Networks     Open Access  
International Journal of Intelligent Systems Technologies and Applications     Hybrid Journal   (Followers: 2)
International Journal of Intercultural Relations     Hybrid Journal   (Followers: 16)
International Journal of IT Standards and Standardization Research     Full-text available via subscription  
International Journal of IT/Business Alignment and Governance     Full-text available via subscription  
International Journal of Knowledge and Systems Science     Full-text available via subscription   (Followers: 1)
International Journal of Knowledge Engineering and Soft Data Paradigms     Hybrid Journal   (Followers: 1)
International Journal of Knowledge Society Research     Full-text available via subscription  
International Journal of Leadership in Education: Theory and Practice     Hybrid Journal   (Followers: 23)
International Journal of Logistics Research and Applications : A Leading Journal of Supply Chain Management     Hybrid Journal   (Followers: 16)
International Journal of Management & Information Technology     Open Access   (Followers: 2)
International Journal of Management Innovation Systems     Open Access  
International Journal of Mathematical Modelling & Computations     Open Access   (Followers: 3)
International Journal of Mathematical Sciences and Computing     Open Access  
International Journal of Mathematics & Computation     Full-text available via subscription  
International Journal of Mathematics in Operational Research     Hybrid Journal   (Followers: 2)
International Journal of Medical Engineering and Informatics     Hybrid Journal   (Followers: 4)
International Journal of Medical Informatics     Hybrid Journal   (Followers: 10)
International Journal of Metadata, Semantics and Ontologies     Hybrid Journal   (Followers: 9)
International Journal of Metaheuristics     Hybrid Journal   (Followers: 1)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 8)
International Journal of Mobile Computing and Multimedia Communications     Full-text available via subscription   (Followers: 2)
International Journal of Mobile Network Design and Innovation     Hybrid Journal   (Followers: 1)
International Journal of Modeling, Simulation, and Scientific Computing     Hybrid Journal   (Followers: 3)
International Journal of Modelling, Identification and Control     Hybrid Journal   (Followers: 1)
International Journal of Modern Education and Computer Science     Open Access   (Followers: 2)
International Journal of Multimedia Data Engineering and Management     Full-text available via subscription   (Followers: 2)
International Journal of Multimedia Information Retrieval     Partially Free   (Followers: 8)
International Journal of Nanotechnology and Molecular Computation     Full-text available via subscription   (Followers: 4)
International Journal of Natural Computing Research     Hybrid Journal  
International Journal of Neural Systems     Hybrid Journal   (Followers: 4)
International Journal of Online Marketing     Full-text available via subscription   (Followers: 5)
International Journal of Organizational and Collective Intelligence     Hybrid Journal   (Followers: 1)
International Journal of Parallel, Emergent and Distributed Systems     Hybrid Journal   (Followers: 3)
International Journal of Pattern Recognition and Artificial Intelligence     Hybrid Journal   (Followers: 12)
International Journal of Performance Arts and Digital Media     Hybrid Journal   (Followers: 12)
International Journal of Pervasive Computing and Communications     Hybrid Journal   (Followers: 3)
International Journal of Polymer Science     Open Access   (Followers: 25)
International Journal of Process Systems Engineering     Hybrid Journal   (Followers: 1)
International Journal of Quantum Information     Hybrid Journal   (Followers: 6)
International Journal of Reasoning-based Intelligent Systems     Hybrid Journal  
International Journal of Reconfigurable and Embedded Systems     Open Access   (Followers: 6)
International Journal of Reconfigurable Computing     Open Access  
International Journal of Refractory Metals and Hard Materials     Hybrid Journal   (Followers: 5)
International Journal of Reliability, Quality and Safety Engineering     Hybrid Journal   (Followers: 14)
International Journal of Reliable and Quality E-Healthcare     Full-text available via subscription   (Followers: 2)
International Journal of Research Studies in Computing     Open Access  
International Journal of RF and Microwave Computer-Aided Engineering     Hybrid Journal   (Followers: 26)
International Journal of Sediment Research     Full-text available via subscription   (Followers: 2)
International Journal of Sensor Networks     Hybrid Journal   (Followers: 2)
International Journal of Service and Computing Oriented Manufacturing     Hybrid Journal   (Followers: 2)
International Journal of Shape Modeling     Hybrid Journal   (Followers: 1)
International Journal of Signs and Semiotic Systems     Full-text available via subscription  
International Journal of Smart Grid and Green Communications     Hybrid Journal   (Followers: 2)
International Journal of Social and Organizational Dynamics in IT     Full-text available via subscription   (Followers: 1)
International Journal of Sociotechnology and Knowledge Development     Full-text available via subscription   (Followers: 1)
International Journal of Soft Computing and Networking     Hybrid Journal   (Followers: 2)
International Journal of Soft Computing and Software Engineering     Open Access   (Followers: 13)
International Journal of Software Engineering and Knowledge Engineering     Hybrid Journal   (Followers: 6)
International Journal of Spatio-Temporal Data Science     Hybrid Journal  
International Journal of Speech Technology     Hybrid Journal   (Followers: 7)
International Journal of Strategic Change Management     Hybrid Journal   (Followers: 7)
International Journal of Strategic Communication     Hybrid Journal   (Followers: 5)
International Journal of Strategic Information Technology and Applications     Full-text available via subscription   (Followers: 1)
International Journal of Stress Management     Full-text available via subscription   (Followers: 6)
International Journal of Student Project Reporting     Hybrid Journal   (Followers: 4)
International Journal of Swarm Intelligence     Hybrid Journal   (Followers: 2)
International Journal of Swarm Intelligence Research     Full-text available via subscription   (Followers: 3)
International Journal of System Dynamics Applications     Full-text available via subscription  
International Journal of Systems Science     Hybrid Journal   (Followers: 2)
International Journal of Systems Science : Operations & Logistics     Hybrid Journal  
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 6)
International Journal of Technoethics     Full-text available via subscription   (Followers: 2)
International Journal of Technology and Educational Marketing     Full-text available via subscription   (Followers: 2)
International Journal of Technology and Human Interaction     Full-text available via subscription   (Followers: 2)
International Journal of Technology Diffusion     Full-text available via subscription   (Followers: 1)
International Journal of Technology Marketing     Hybrid Journal   (Followers: 3)
International Journal of Telecommunications & Emerging Technologies     Full-text available via subscription   (Followers: 1)
International Journal of the Digital Human     Hybrid Journal   (Followers: 2)
International Journal of Trust Management in Computing and Communications     Hybrid Journal   (Followers: 1)
International Journal of Ultra Wideband Communications and Systems     Hybrid Journal  
International Journal of Virtual Reality     Open Access   (Followers: 1)
International Journal of Virtual Technology and Multimedia     Hybrid Journal   (Followers: 2)
International Journal of Web Services Research     Full-text available via subscription  
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 12)
International Journal of Wireless Information Networks     Hybrid Journal   (Followers: 2)
International Journal on Advances in ICT for Emerging Regions (ICTer)     Open Access   (Followers: 2)
International Journal on Artificial Intelligence Tools     Hybrid Journal   (Followers: 9)
International Journal on Digital Libraries     Hybrid Journal   (Followers: 544)
International Journal on Document Analysis and Recognition (IJDAR)     Hybrid Journal   (Followers: 2)
International Journal on Smart Sensing and Intelligent Systems     Open Access  
International Journal on Software Tools for Technology Transfer (STTT)     Hybrid Journal   (Followers: 4)
International Review of Law, Computers & Technology     Hybrid Journal   (Followers: 3)
International Review of Research in Open and Distance Learning     Open Access   (Followers: 24)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Internet of Things     Hybrid Journal   (Followers: 2)
Internet of Things and Cyber-Physical Systems     Open Access   (Followers: 1)
Internet Technology Letters     Hybrid Journal  
IoT     Open Access  
IPSJ Transactions on Computer Vision and Applications     Open Access   (Followers: 1)
Iran Journal of Computer Science     Hybrid Journal  
ISPRS Open Journal of Photogrammetry and Remote Sensing     Open Access   (Followers: 3)
ISSS Journal of Micro and Smart Systems     Hybrid Journal   (Followers: 3)
Issues in Informing Science and Information Technology     Open Access   (Followers: 2)
IT Journal Research and Development     Open Access  
ITM Web of Conferences     Open Access  
ITNOW     Hybrid Journal   (Followers: 1)
J-ENSITEC : Journal Of Engineering and Sustainable Technology     Open Access   (Followers: 4)
JISTEM : Journal of Information Systems and Technology Management     Open Access   (Followers: 6)
JMIR mHealth and uHealth     Open Access   (Followers: 3)
Johnson Matthey Technology Review     Open Access  
Jornal Brasileiro de TeleSSaúde     Open Access  
Journal of Computer Science & Systems Biology     Open Access   (Followers: 3)
Journal of 3D Printing in Medicine     Hybrid Journal  
Journal of Advanced Computer Science & Technology     Open Access   (Followers: 3)
Journal of Advances in Information Systems and Technology     Open Access  
Journal of Advances in Mathematics and Computer Science     Open Access  
Journal of Aggression Maltreatment & Trauma     Hybrid Journal   (Followers: 5)
Journal of Algorithms & Computational Technology     Open Access  
Journal of Altmetrics     Open Access   (Followers: 7)
Journal of Ambient Intelligence and Humanized Computing     Hybrid Journal   (Followers: 1)
Journal of Applied & Computational Mathematics     Open Access  
Journal of Applied and Computational Topology     Hybrid Journal  
Journal of Applied Bioinformatics & Computational Biology     Hybrid Journal   (Followers: 4)
Journal of Applied Communication Research     Hybrid Journal   (Followers: 10)
Journal of Applied Informatics and Technology     Open Access  
Journal of Applied Intelligent System     Open Access  
Journal of Approximation Theory     Hybrid Journal   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 18)
Journal of Automated Reasoning     Hybrid Journal  
Journal of Automation and Control     Open Access   (Followers: 9)
Journal of Banking and Financial Technology     Hybrid Journal   (Followers: 1)
Journal of Big Data     Open Access   (Followers: 16)
Journal of Bioinformatics and Computational Biology     Hybrid Journal   (Followers: 19)
Journal of Biomedical Informatics     Partially Free   (Followers: 9)
Journal of Cases on Information Technology     Full-text available via subscription   (Followers: 3)
Journal of Chemical Information and Modeling     Hybrid Journal   (Followers: 18)
Journal of Chemical Theory and Computation     Hybrid Journal   (Followers: 21)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)

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Similar Journals
Journal Cover
International Journal of Pattern Recognition and Artificial Intelligence
Journal Prestige (SJR): 0.315
Citation Impact (citeScore): 1
Number of Followers: 12  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0218-0014 - ISSN (Online) 1793-6381
Published by World Scientific Homepage  [120 journals]
  • Deep Recurrent Encoder Network and Spark Model for Angiographic Disease
           Risk Classification

    • Free pre-print version: Loading...

      Authors: R. Vinoth, J. P. Ananth
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      Analysis and extraction of effective results from the medical big data is very complex due to the existence of large volume of data and it is also very difficult to classify the risk of angiographic diseases. The challenges faced by the conventional methods are complexity in classifying the data and performance degradation for a large-sized dataset. Hence, a hybrid approach named Deep Recurrent Encoder Network and Spark Model for Angiographic Disease Risk Classification (DRE-NET) is developed in this research to achieve accurate results for classifying the risk of angiographic diseases using Spark architecture. The developed Adaptive RCOA integrates Rider Optimization Algorithm (ROA) and Chicken Swarm Optimization (CSO) with the adaptive concept. The disease risk classification process is obtained by the Recurrent Neural Network (RNN) and Deep Stacked Autoencoder (DSAE) in such a way that the weights are optimally trained by the developed Adaptive RCOA. The optimal solution is obtained by evaluating the fitness function such that the fitness with minimal error value is considered the best solution. Moreover, the developed Adaptive RCOA-based RNN+DSAE attained better result with the metrics such as specificity, accuracy, and sensitivity with the values of 100%, 97.69%, and 99.19%, respectively, using the KDD Cup dataset.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-30T07:00:00Z
      DOI: 10.1142/S0218001422500100
      Issue No: Vol. 36, No. 04 (2022)
       
  • CPRO: Competitive Poor and Rich Optimizer-Enabled Deep Learning Model and
           Holoentropy Weighted-Power K-Means Clustering for Brain Tumor
           Classification Using MRI

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      Authors: V. Agalya, Manivel Kandasamy, Ellappan Venugopal, Balajee Maram
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      A brain tumor is a collection of irregular and needless cell development in the brain region, and it is considered a life-threatening disease. Therefore, early level segmentation and brain tumor detection with Magnetic Resonance Imaging (MRI) is more important to save the patient’s life. Moreover, MRI is more effective in identifying patients with brain tumors since the recognition of this modality is moderately larger than considering other imaging modalities. The classification of brain tumors is the most important, difficult task in medical imaging systems because of size, appearance and shape variations. In this paper, Competitive Poor and Rich Optimization (CPRO)-based Deep Quantum Neural Network (Deep QNN) is proposed for brain tumor classification. Additionally, the pre-processing process assists in eradicating noises and uses image intensity to eliminate the artifacts. The significant features are extracted from pre-processed image to perform a productive classification process. The Deep QNN classifier is employed for classifying the brain tumor regions. Besides, the Deep QNN classifier is trained by the developed CPRO approach, which is newly designed by integrating Poor and Rich Optimization (PRO) and Competitive Swarm Optimizer (CSO). The developed brain tumor detection model outperformed other existing models with accuracy, sensitivity and specificity of 94.44%, 97.60% and 93.78%.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-30T07:00:00Z
      DOI: 10.1142/S0218001422520085
      Issue No: Vol. 36, No. 04 (2022)
       
  • DWT Lifting Scheme for Image Compression with Cordic-Enhanced Operation

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      Authors: M. I. Anju, J. Mohan
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      This paper proposes an innovative image compression scheme by utilizing the Adaptive Discrete Wavelet Transform-based Lifting Scheme (ADWT-LS). The most important feature of the proposed DWT lifting method is splitting the low-pass and high-pass filters into upper and lower triangular matrices. It also converts the filter execution into banded matrix multiplications with an innovative lifting factorization presented with fine-tuned parameters. Further, optimal tuning is the most important contribution that is achieved via a new hybrid algorithm known as Lioness-Integrated Whale Optimization Algorithm (LI-WOA). The proposed algorithm hybridizes the concepts of both the Lion Algorithm (LA) and Whale Optimization Algorithm (WOA). In addition, innovative cosine evaluation is initiated in this work under the CORDIC algorithm. Also, this paper defines a single objective function that relates multi-constraints like the Peak Signal-to-Noise Ratio (PSNR) as well as Compression Ratio (CR). Finally, the performance of the proposed work is compared over other conventional models regarding certain performance measures.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-30T07:00:00Z
      DOI: 10.1142/S0218001422540064
      Issue No: Vol. 36, No. 04 (2022)
       
  • Soft Margin Triplet-Center Loss for Multi-View 3D Shape Retrieval

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      Authors: Ruting Cheng, Fuzhou Wang, Tianmeng Zhao, Hongmin Liu, Hui Zeng
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      Obtaining discriminative features is one of the key problems in three-dimensional (3D) shape retrieval. Recently, deep metric learning-based 3D shape retrieval methods have attracted the researchers’ attention and have achieved better performance. The triplet-center loss can learn more discriminative features than traditional classification loss, and it has been successfully used in deep metric learning-based 3D shape retrieval task. However, it has a hard margin parameter that only leverages part of the training data in each mini-batch. Moreover, the margin parameter is often determined by experience and remains unchanged during the training process. To overcome the above limitations, we propose the soft margin triplet-center loss, which replaces the margin with the nonparametric soft margin. Furthermore, we combined the proposed soft margin triplet-center loss with the softmax loss to improve the training efficiency and the retrieval performance. Extensive experimental results on two popular 3D shape retrieval datasets have validated the effectiveness of the soft margin triplet-center loss, and our proposed 3D shape retrieval method has achieved better performance than other state-of-the-art method.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-25T07:00:00Z
      DOI: 10.1142/S0218001422500173
      Issue No: Vol. 36, No. 04 (2022)
       
  • SentiNet: A Nonverbal Facial Sentiment Analysis Using Convolutional Neural
           Network

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      Authors: Md Abu Rumman Refat, Bikash Chandra Singh, Mohammad Muntasir Rahman
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      Human facial expressions are an essential and fundamental component for expressing the state of the human mind. The automatic analysis of these nonverbal facial expressions has become a fascinating and quite challenging problem in computer vision, with its application in different areas, such as psychology, human–machine interaction, health, and augmented reality. Recently, deep learning (DL) has become a widespread technique for studying human nonverbal facial sentiment expressions, and some research attempts have been made to propose a certain model on this topic. The purpose of this paper is to apply the appropriate convolutional neural network (CNN) approach by adding several layers of different dimensions, which allows the CNN approach to efficiently classify human facial sentiment expressions with data augmentation capable of recognizing seven basic human facial expressions: anger, sadness, fear, disgust, happiness, surprise, and neutral. In particular, this study mainly proposes a convolution neural network architecture, as well as learning factors that minimize the memory space and total training time of the proposed network due to the shallow architecture of the model. Following that, we demonstrated our proposed model’s network complexity, computational cost, and classification accuracy on the three benchmark datasets: FER2013, KDEF, and JAFFE. As a result, our proposed approach achieves accuracy of [math], [math], [math] in the FER2013, KDEF, and JAFFE, respectively, which is better compared to other state-of-the-art approaches.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-25T07:00:00Z
      DOI: 10.1142/S0218001422560079
      Issue No: Vol. 36, No. 04 (2022)
       
  • Multimodal Medical Image Fusion Using Nonsubsampled Shearlet Transform and
           Smallest Uni-Value Segment Assimilating Nucleus

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      Authors: Sharma Dileepkumar Ramlal, Jainy Sachdeva, Chirag Kamal Ahuja, Niranjan Khandelwal
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      This paper presents a new fusion scheme for medical (CT-MRI) images which is based on the nonsubsampled shearlet transform (NSST). The various image pairs to be fused are obtained from primary and internet sources. Initially, the images are decomposed through NSST into general and detailed features. The smallest uni-value segment assimilating nucleus (SUSAN) and local sum of Gaussian weighted pixel intensities-based activity measures are proposed to fuse the detailed sub-bands and low-frequency sub-band of NSST, respectively, for faster execution of the algorithm. Visual and parametric comparison of the proposed scheme is done through five traditional fusion algorithms using nine fusion performance parameters. In addition, Wilcoxon signed ranks test is also applied to compare different methods scientifically with the proposed fusion scheme. It is observed that the presented method is better in retaining bone, calcification, cerebrospinal fluid (CSF), edema and tumor details of the source images and is faster than other classical fusion schemes. The fused images of the proposed method are suitable for locating the site of biopsy externally or incision location in the bone of the brain skull with minimum diagnostic time.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-25T07:00:00Z
      DOI: 10.1142/S0218001422570014
      Issue No: Vol. 36, No. 04 (2022)
       
  • A Rectal CT Tumor Segmentation Method Based on Improved U-Net

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      Authors: Haowei Dong, Haifei Zhang, Fang Wu, Jianlin Qiu, Jian Zhang, Haoyu Wang
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      Automatic and accurate segmentation of tumor area from rectal CT image plays an extremely key role in the treatment and diagnosis of rectal cancer. This paper proposes the MR-U-Net network model. The improvement is that a pair of encoder and decoder is added longitudinally to the U-shaped structure, which is the network structure of the fifth layer, and a residual module is added horizontally to the encoder and decoder of each layer. This model is used to conduct targeted research on the automatic segmentation method of rectal cancer. [H. Gao et al., Rectal tumor segmentation method based on U-Net improved model, J. Comput. Appl.40(8) (2020) 2392–2397] also improved U-Net and used the same dataset as this paper, but the Dice coefficient of all targets was only 83.15%, and the Dice coefficient of small targets was only 87.17%. This paper evaluates the improved MR-U-Net network model with the three indicators of precision, recall and Dice coefficient, and finds that in comparison to Ref.  the precision is 95.13%, 2.29% higher than the former work, recall is 94.28%, higher than the former work by 0.34%, Dice coefficient of all targets is 88.45%, increased by 5.3% compared with the former work, and the small targets Dice coefficient is increased by 1.28%, which is the best optimization state of this paper. Experiments show that for datasets with extremely skewed positive and negative samples, the MR-U-Net network structure after improving the hyperparameters in the optimizer can more accurately segment the rectal CT tumor lesion area.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-19T07:00:00Z
      DOI: 10.1142/S0218001422500069
      Issue No: Vol. 36, No. 04 (2022)
       
  • Protein Structure Prediction Using Quantile Dragonfly and Structural
           Class-Based Deep Learning

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      Authors: Varanavasi Nallasamy, Malarvizhi Seshiah
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      Predicting three-dimensional structure of a protein in the field of computational molecular biology has received greater attention. Most of the recent research works aimed at exploring search space, however with the increasing nature and size of data, protein structure identification and prediction are still in the preliminary stage. This work is aimed at exploring search space to tackle protein structure prediction with minimum execution time and maximum accuracy by means of quantile regressive dragonfly and structural class homolog-based deep learning (QRD-SCHDL). The proposed QRD-SCHDL method consists of two distinct steps. They are protein structure identification and prediction. In the first step, protein structure identification is performed by means of QRD optimization model to identify protein structure with minimum error. Here the protein structure identification is first performed as the raw database contains sequence information and does not contain structural information. An optimization model is designed to obtain the structural information from the database. However, protein structure gives much more insight than its sequence. Therefore, to perform computational prediction of protein structure from its sequence, actual protein structure prediction is made. The second step involves the actual protein structure prediction via structural class and homolog-based deep learning. For each protein structure prediction, a scoring matrix is obtained by utilizing structural class maximum correlation coefficient. Finally, the proposed method is tested on a set of different unique numbers of protein data and compared to the state-of-the-art methods. The obtained results showed the potentiality of the proposed method in terms of metrics, error rate, protein structure prediction time, protein structure prediction accuracy, precision, specificity, recall, ROC, Kappa coefficient and [math]-measure, respectively. It also shows that the proposed QRD-SCHDL method attains comparable results and outperformed in certain cases, thereby signifying the efficiency of the proposed work.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-14T07:00:00Z
      DOI: 10.1142/S021800142250015X
      Issue No: Vol. 36, No. 04 (2022)
       
  • Ensemble of the Deep Convolutional Network for Multiclass of Plant Disease
           Classification Using Leaf Images

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      Authors: Bo Li, Jinhong Tang, Yuejing Zhang, Xin Xie
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      Plant diseases are a major threat to agricultural production. Reduced yield due to plant diseases can lead to immeasurable economic losses. Therefore, the detection and classification of crop diseases are of great significance. Current existing classification methods based on the single convolutional neural network (CNN) are not satisfactory for plant disease classification performance in a large number of classes. In this case, a CNN-based approach named multiclass plant EnsembleNet (MCPE) is proposed to address these problems. MCFN firstly adopts a data augmentation strategy-based AutoAugment enhance dataset. Next, an EnsembleNet including four CNNs is employed to classify plant species, with a new activation function, concatenated dynamic ReLU, which has better performance than conventional ReLU in the multiclass plant disease dataset. Then, training a diseases classifier for each plant, which is used to identify the types and severity of plant diseases. Experimental results on 61 plant diseases from 10 different plant species, over 40 000 images, show that MCFN outperforms the state-of-the-art methods in multiclass plant disease recognition and achieves a good identification accuracy of 97.5%. We believe that the method described in this paper can further improve the identification efficiency of plant diseases, thus providing a basis for the identification of other plant leaf diseases.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-14T07:00:00Z
      DOI: 10.1142/S0218001422500161
      Issue No: Vol. 36, No. 04 (2022)
       
  • Improved Exploration-Enhanced Gray Wolf Optimizer for a Mechanical Model
           of Braided Bicomponent Ureteral Stents

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      Authors: Zhikai Sun, Xiaoyan Liu, Lihong Ren, Kuangrong Hao
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      Ureteral stent tubes are important medical devices used to repair ureteral obstruction or injury. However, relevant experiments of ureteral stent tubes are usually time-consuming and expensive. This research introduces a mechanical model that can simulate the force and deformation of ureteral stents. In addition, a novel optimization algorithm called improved exploration-enhanced gray wolf optimizer (IEE-GWO) is proposed to optimize parameters of the model. In order to balance exploration and exploitation of gray wolf optimizer (GWO), a dimension learning-based hunting (DLH) search strategy and a nonlinear control parameter strategy are integrated into the IEE-GWO. The experimental results show that the proposed IEE-GWO has better performance, such as fast convergence speed and high solution quality. Furthermore, the novel approach can improve the accuracy of the mechanical modal.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-14T07:00:00Z
      DOI: 10.1142/S0218001422590108
      Issue No: Vol. 36, No. 04 (2022)
       
  • Facial Makeup Detection Using Multi-Scale Local Binary Patterns and
           Convolutional Neural Network Fusion

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      Authors: Maryam Eskandari, Omid Sharifi
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      This study presents a novel facial makeup detection scheme using fusion of multi-scale Local Binary Patterns (ms-LBP) texture methods and convolutional neural network (CNN) deep learning extractor. Facial makeups affect the accuracy of face recognition systems due to appearance alteration of individuals. In order to fuse the facial features, the proposed scheme first considers concatenation of extracted global histogram of a whole image using three different LBP operators. The LBP scales are used to extract and then concatenate the local histogram of overlapping and nonoverlapping blocks of images. This way utilizes the advantages of microtexton information of local primitives besides the extracted global textures. Finally, the extracted features using CNN feature extractor are combined with global and local multi-scale textures. In general, CNN deep learning extractor learns high-level discriminative characteristics of images and therefore the proposed system improves the facial makeup detection rate by involving both microtexton and discriminative information of facial images. In addition, the proposed method attempts to select the optimized subset of facial features to increase the detection performance of system by applying Particle Swarm Optimization (PSO) technique after feature level fusion. The paper uses Support Vector Machine (SVM) classifier for classifying the facial vectors into makeup or no-makeup classes. The proposed scheme is then evaluated using YMU, VMU and MIW facial makeup databases with consideration of light, medium and heavy makeups on several datasets. Experimental results analysis clarifies the effectiveness of proposed facial makeup detection framework of this study.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-02-26T08:00:00Z
      DOI: 10.1142/S0218001422560080
      Issue No: Vol. 36, No. 04 (2022)
       
  • Adaptive Hardness Indicator Softmax for Deep Face Recognition

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      Authors: Mao Cai, Ning Cheng, Chunzheng Cao, Jianwei Yang, Yunjie Chen
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      Due to their simplicity and efficiency, the margin-based Softmax losses are proposed to enhance feature discrimination in face recognition. Recently, the strategy of hard sample mining is incorporated to the margin-based Softmax losses for focusing the misclassified samples and achieves superior performance. However, the current mining-based Softmax losses indicate the sample difficultness only from the perspective of the negative cosine similarity, which is local and not robust. To obtain more discriminative deep face features, a novel adaptive hardness indicator Softmax (AHI-Softmax) loss is proposed in this paper to fully exploit the hardness information of samples. Our AHI-Softmax firstly defines a global sample hardness indicator function that integrates three difficultness factors to robustly indicate the level of “hardness” in numerical form. Then, a training stage indicator is incorporated to avoid the convergence issue. Finally, a novel sample-related modulation coefficient of the negative cosine similarity which combines the global and local hardness indicator will be defined to further enhance the differentiation of constraints imposed on samples. The experimental results on general face datasets, including LFW, AgeDB-30, CFP-FP, CALFW, CPLFW, MegaFace, IJB-B and IJB-C, show that our method can obtain more discriminative features and achieve superior verification and recognition results.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-02-26T08:00:00Z
      DOI: 10.1142/S0218001422560092
      Issue No: Vol. 36, No. 04 (2022)
       
  • Improving Utterance Rewriter Based on MMI and Text Data Augmentation

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      Authors: Lina Yang, Hai Lin, Wei Li, Zuqiang Meng, Patrick Shen-Pei Wang, Xichun Li, Huiwu Luo
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Volume 36, Issue 04, 30 March 2022.
      In multi-round dialogue tasks, how to maintain the consistency of model answers is a major research challenge. Every answer to the model should be time dependent, causal, and logical. In order to maintain the consistency of the personality, dialogue style, and context of the model, it is necessary to retain the key information in the historical dialogue as much as possible so that the model can generate more accurate answers. Utterance rewriting is a technique that replenishes the information of the current sentence by analyzing the historical dialogue, so as to retain the key information. This paper mainly uses text augmentation, Maximum Mutual Information (MMI) method and character correction method based on Knuth–Morria–Pratt (KMP) algorithm to improve the effect of utterance rewriting generation. The number of original statement rewriting datasets is limited, and the cost of manual manufacturing is too high. By using the method of text data augmentation based on coreference resolution, the positive dataset that is missing from the statement rewriting dataset is repaired. At the same time, the existing datasets are expanded to increase the number of data. The generated results are optimized by using the MMI method, and the KMP character correction method is used to modify the wrong characters to improve the overall accuracy.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-02-26T08:00:00Z
      DOI: 10.1142/S021800142259011X
      Issue No: Vol. 36, No. 04 (2022)
       
  • Lightweight Convolution Neural Network Based on Multi-Scale Parallel
           Fusion for Weed Identification

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      Authors: Zhen Wang, Jianxin Guo, Shanwen Zhang
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Accurate identification of weed species is the premise for controlling weeds in field. But it is a challenging task due to the complexity and high-dimensional nonlinearity of the weed images in natural field. Convolutional neural networks (CNNs) model has been widely applied to image identification, but most of the CNNs models have the problems of large parameters, low identification accuracy, and single feature scale. This paper presents a novel deep neural network structure, named as MPF-Net for weed species identification. In MPF-Net, firstly, the weed images is sent into two different scales of depthwise separable convolution layers; secondly, the parallel output feature information is cross-fused, and uses the residual learning structure to increase the network model depth and feature extraction ability; finally the lightweight model PL-Model and the scale reduction module SR-Model are stacked together to construct the lightweight network. We have performed extensive experiments on real weed datasets, and compared the proposed MPF-Net against several variations of lightweight networks. The experimental results on the weed image dataset show that the proposed method is effective and feasible for weed species identification.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-13T07:00:00Z
      DOI: 10.1142/S0218001422500288
       
  • End-to-End Multi-Resolution 3D Capsule Network for People Action Detection

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      Authors: Mohamad Ivan Fanany, Ahmad Arinaldi
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      In this paper, we propose an end-to-end multi-resolution three-dimensional (3D) capsule network for detecting actions of multiple actors in a video scene. Unlike previous capsule, network-based action recognition does not specifically concern with the individual action of multiple actors in a single scene, our 3D capsule network takes advantage of multi-resolution technique to detect different actions of multiple actors that have different sizes, scales, and aspect ratios. Our 3D capsule network is built on top of 3D convolutional neural network (3DCNN) that extracts spatio-temporal features from video frames inside regions of interest generated by Faster RCNN object detection. We first apply our method to the problem of detecting illegal cheating activities in a classroom examination scene with multiple subjects involved. Second, we test our system on the publicly available and extensively studied UCF-101 dataset. We compare our method with several state-of-the-art 3DCNN-based methods, first the multi-resolution 3DCNN, the single-resolution 3D capsule network, and a combination of both these models. We show that models containing 3D capsule networks have a slight advantage over the conventional 3DCNN and multi-resolution 3DCNN. Our 3D capsule networks not only perform a classification of said actions but also generate videos of single actions. Our experimental results show that the use of multi-resolution pathways in the 3D capsule networks make the result even better. Such findings also hold even when we use pre-trained C3D (convolutional 3D) features to train these networks. We believe that the multiple resolutions capture lower-level features at different scales. At the same time, the 3D capsule layers combine these features in more complex ways than conventional convolutional models.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-13T07:00:00Z
      DOI: 10.1142/S0218001422550151
       
  • Attention Mechanism Based on Improved Spatial-Temporal Convolutional
           Neural Networks for Traffic Police Gesture Recognition

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      Authors: Zhixuan Wu, Nan Ma, Yue Gao, Jiahong Li, Xinkai Xu, Yongqiang Yao, Li Chen
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Human action recognition has attracted extensive research efforts in recent years, in which traffic police gesture recognition is important for self-driving vehicles. One of the crucial challenges in this task is how to find a representation method based on spatial-temporal features. However, existing methods performed poorly in spatial and temporal information fusion, and how to extract features of traffic police gestures has not been well researched. This paper proposes an attention mechanism based on the improved spatial-temporal convolutional neural network (AMSTCNN) for traffic police gesture recognition. This method focuses on the action part of traffic police and uses the correlation between spatial and temporal features to recognize traffic police gestures, so as to ensure that traffic police gesture information is not lost. Specifically, AMSTCNN integrates spatial and temporal information, uses weight matching to pay more attention to the region where human action occurs, and extracts region proposals of the image. Finally, we use Softmax to classify actions after spatial-temporal feature fusion. AMSTCNN can strongly make use of the spatial-temporal information of videos and select effective features to reduce computation. Experiments on AVA and the Chinese traffic police gesture datasets show that our method is superior to several state-of-the-art methods.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-13T07:00:00Z
      DOI: 10.1142/S0218001422560018
       
  • Automatic Object Detection and Direction Prediction of Unmanned Vessels
           Based on Multiple Convolutional Neural Network Technology

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      Authors: Chuen-Horng Lin, Xin-Cheng Wang
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      This study aims to detect objects quickly and accurately and then start the necessary obstacle avoidance procedure when the uncrewed vessel is at sea. This study uses a multivariate convolutional neural network (CNN) to perform automatic object detection and direction prediction for uncrewed vessels. This study is divided into three parts for processing. The first part of this process uses camera calibration technology to correct the image. Discrete cosine transform (DCT) is then used to detect sea level. Finally, this study uses Kalman filtering and affine transformation to stabilize images taken by uncrewed vessels at sea. The second part of the processing system uses a CNN to detect sea objects automatically. The third part of the process uses the dual-lens camera installed on the vessel to detect the distance and direction of objects at sea. In the experiment, the detection rate can reach more than 91% in this study method. In the experiment on image stabilization, this study’s method can also effectively improve video instability. In the experiment involving the distance and direction of the object, the experimental results show that the distance and direction of the object obtained by this study method have a distance error value of less than 10%, and the prediction results have a good effect no matter whether the object is at a short or long distance. It is hoped that this paper’s method can be applied to the automatic obstacle avoidance of unmanned vessels.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-12T07:00:00Z
      DOI: 10.1142/S0218001422500021
       
  • Attribute-Guided Global and Part-Level Identity Network for Person
           Re-Identification

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      Authors: Shaoming Pan, Wenqiang Feng, Yanwen Chong
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Most of the person re-identification (re-ID) algorithms based on deep learning mainly learn the global feature representation of pedestrians, while ignoring the important role of fine-grained pedestrian attribute features on re-ID tasks. Pedestrian attributes are middle-level semantic features, which have invariance in different poses, camera views, and illumination conditions. Considering the robustness and promotion of pedestrian attributes for person re-ID task, we propose an Attribute-guided Global and Part-level identity Network (AGPNet), which consists of a global identity task, a part-level identity task, and a pedestrian attributes learning task. AGPNet takes advantage of perceived semantic information of pedestrian attributes and deploys them as guidance to attend to human body regions and learn robust feature representation in the feature representation construction stage. Extensive experiments on two large-scale person re-ID datasets (Market-1501 and DukeMTMC-reID) show the effectiveness of our method, which is competitive with the state-of-the-art algorithms.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-12T07:00:00Z
      DOI: 10.1142/S0218001422500112
       
  • A Hybrid Random Forest Classifier for Chronic Kidney Disease Prediction
           from 2D Ultrasound Kidney Images

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      Authors: Deepthy Mary Alex, D. Abraham Chandy, A. Hepzibah Christinal, Arvinder Singh, M. Pushkaran
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Chronic kidney disease (CKD) is one of the causes of mortality in almost all countries across the globe and the notable thing is its asymptomatic nature in the early stages. This disease is characterized by the gradual loss of kidney function in an individual. Frequently chronic kidney disease is diagnosed based on the Estimated Glomerular Filtration Rate (eGFR) determined from blood and urine tests. In order to reduce the risk factors arising due to chronic kidney disease, it is essential to be diagnosed in the earlier stages itself. This work proposes an automated chronic kidney disease detection based on the textural features of the kidney using a hybrid random forest classifier from 2D ultrasound kidney images. The proposed classifier is compared with the other competing machine learning classifiers through experimenting on a dataset of 150 images and gives a better accuracy of [math] with [math] of recall and precision, thus proving it to be promising in detecting CKD noninvasively in the early stages.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-12T07:00:00Z
      DOI: 10.1142/S0218001422560109
       
  • Small-Footprint Keyword Spotting Based on Gated Channel Transformation
           Sandglass Residual Neural Network

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      Authors: Ying Zhang, Shirong Zhu, Chao Yu, Lasheng Zhao
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Keyword spotting plays a crucial role in realizing voice-based user interaction on intelligent equipment terminals and service robots. In this task, it remains challenging to achieve the balance between low memory and high precision. To better satisfy this requirement, we propose an end-to-end neural architecture with sandglass residual blocks embedded with the gated channel-wise attention mechanism. The sandglass residual blocks utilize 1D separable convolutions to extract bottleneck temporal features, which can effectively drive the model to focus more on the speech segment with lower parameters. Especially, the gated attention mechanism helps the model enhance the critical speech temporal features and suppress the useless ones and further focus on the most important part of the human speech region for keyword spotting. The experimental results on Google Speech Commands Dataset show that our proposed model has an accuracy of 97.4[math] with only 46K parameters. Compared with the baseline method with the highest accuracy, our model parameters are decreased by 54[math] and accuracy is increased by 0.8[math]. That makes us take further step in achieving the goal of low memory and high precision.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-12T07:00:00Z
      DOI: 10.1142/S0218001422580034
       
  • DMS-SK/BLSTM-CTC Hybrid Network for Gesture/Speech Fusion and Its
           Application in Lunar Robot–Astronauts Interaction

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      Authors: Jian Ding, Jin Liu, Xiaolin Ning, Zhiwei Kang
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      In the future manned lunar exploration mission, astronauts would work with the lunar robots, which has a high requirement for human–robot interaction (HRI). As the accuracy of gesture recognition interaction does not fulfill the requirement for human–robot joint exploration missions, we propose the DMS-SK/BLSTM-CTC hybrid network to improve the performance of HRI. For gesture recognition, considering VGG-SK has low accuracy and complex architecture, we delete the fourth convolution module, optimize the last global pooling layer, introduce dilated convolution block and multiscale convolution block in VGG-SK, and get the DMS-SK-based gesture recognition sub-network. Compared with the traditional recognition methods, the accuracy and performance of DMS-SK improve. For speech recognition, considering that Bidirectional long–short-term memory unit (BLSTM) has the advantages of processing temporal information, and the Connectionist Temporal Classification (CTC) algorithm can simplify speech data preprocessing, we use BLSTM based on CTC as the speech recognition sub-network. Finally, we combine DMS-SK with BLSTM-CTC, and propose the DMS-SK/BLSTM-CTC hybrid network as the gesture/speech hybrid network. In addition, we use 10 gestures in the American Sign Language (ASL) dataset and 10 speech commands to construct the gesture/speech hybrid dataset. Experimental results show that compared with the pure gesture or pure speech networks, the recognition accuracy of the gesture-speech hybrid network improves by 2% and 12%, respectively, its accuracy reaches 97.38%, which fulfills the requirement of astronauts for HRI.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-12T07:00:00Z
      DOI: 10.1142/S0218001422580058
       
  • Four-Stream Network and Nonsignificant Feature Learning for
           Visible–Infrared Person Re-Identification

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      Authors: Yilei Liang, Hua Han, Li Huang, Chunyuan Wang
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Visible–infrared person re-identification (VI-ReID) is a current focused area in the field of re-identification. In order to reduce the gap between two modalities in VI-ReID and improve recognition accuracy, this paper proposes a four-stream network and nonsignificant feature learning (FS-NSF) method for VI-ReID. First, the dual-intermediate modality images of visible and infrared modalities are generated by two lightweight networks, and the labels are inherited from the visible and infrared images. Second, the ResNet50 backbone network is split in order to reconstruct the network adapted to shared feature learning of the four modalities. Finally, a multi-branch, multi-scale and multi-granularity feature extraction strategy is used to extract both significant and nonsignificant features. The comparison experiments are conducted on SYSU-MM01 dataset and RegDB dataset. The experimental results show that, compared with state-of-the-arts, our method has excellent performance on both datasets, especially on the SYSU-MM01 dataset, with an increase in performance of 1.9–6.28% for each index.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-06T07:00:00Z
      DOI: 10.1142/S021800142250029X
       
  • Multi-Attention Residual Network for Image Super Resolution

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      Authors: Qing Chang, Xiaotian Jia, Chenhao Lu, Jian Ye
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Recently, many studies have shown that deep convolutional neural network can achieve superior performance in image super resolution (SR). The majority of current CNN-based SR methods tend to use deeper architecture to get excellent performance. However, with the growing depth and width of network, the hierarchical features from low-resolution (LR) images cannot be exploited effectively. On the other hand, most models lack the ability of discriminating different types of information and treating them equally, which results in limiting the representational capacity of the models. In this study, we propose the multi-attention residual network (MARN) to address these problems. Specifically, we propose a new multi-attention residual block (MARB), which is composed of attention mechanism and multi-scale residual network. At the beginning of each residual block, the channel importance of image features is adaptively recalibrated by attention mechanism. Then, we utilize convolutional kernels of different sizes to adaptively extract the multi-attention features on different scales. At the end of blocks, local multi-attention features fusion is applied to get more effective hierarchical features. After obtaining the outputs of each MARB, global hierarchical feature fusion jointly fuses all hierarchical features for reconstructing images. Our extensive experiments show that our model outperforms most of the state-of-the-art methods.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-06T07:00:00Z
      DOI: 10.1142/S021800142254009X
       
  • A_CenterNet: Object as a Point by Attention

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      Authors: Xianrang Shi, Yang Su, Yan Ti, Tinglun Song
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      In this paper, in view of the insufficiency of the CenterNet detector’s inability to achieve high real-time performance with high accuracy when performing object detection, we designed a new detector called A_CenterNet. In the detector, we use our newly designed lightweight Hourglass-256 model, and we also use the feature map fusion method we designed, as well as our improved attention mechanism. Through the experimental results on multiple datasets, it can be known that the A_CenterNet proposed in this paper has a competitive advantage compared with some existing classic detectors. A_CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 44.6 AP at 36 FPS. Compared with CenterNet, A_CenterNet greatly improves the detection speed without loss of detection accuracy.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-05-06T07:00:00Z
      DOI: 10.1142/S0218001422550114
       
  • Conformance Between Choreography and Collaboration in BPMN Involving
           Multi-Instance Participants

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      Authors: Tianhong xiong, Maolin Pan, Yang Yu, Dingjun Lou
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      AI-based process model analysis has attracted more and more interest. Model quality is crucial for such research. At present, inter-organizational business process (IOBP) has been widely used in the model design and development of the distributed system. Before implementing the intelligent analysis of the IOBP model, conformance as a foundation for model quality checking plays a key role because it ensures in advance that the participants can successfully interact without violating the global communication constraints imposed by the choreography. In fact, the multi-instance participant is a common requirement in IOBP. This paper provides a formal approach and framework supporting the conformance between BPMN choreography and collaboration while considering multi-instance participants and message communication modes. As a core, the formalization proposed is based on BNF syntax and structured CSP# processes. It can well support multi-instance features and multiple communication modes. Combined with CSP#, the formal definitions of communication modes and verification properties are given. On this basis, an integrated framework is provided to support automated formal verification referring to multiple communication modes. Finally, a set of experiments is conducted to demonstrate the effectiveness of the proposal.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-28T07:00:00Z
      DOI: 10.1142/S0218001422590133
       
  • Inverse Representation Inspired Multi-Resolution Dictionary Learning
           Method for Face Recognition

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      Authors: Chunman Yan, Yuyao Zhang
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Face recognition is widely used and is one of the most challenging tasks in computer vision. In recent years, many face recognition methods based on dictionary learning have been proposed. However, most methods only focus on the resolution of the original image, and the change of resolution may affect the recognition results when dealing with practical problems. Aiming at the above problems, a method of multi-resolution dictionary learning combined with sample reverse representation is proposed and applied to face recognition. First, the dictionaries associated with multiple resolution images are learnt to obtain the first representation error. Then different auxiliary samples are generated for each test sample, and a dictionary consisted of test sample, auxiliary samples, and other classes of training samples is established to sequentially represent all training samples at this resolution, and to obtain the second representation error. Finally, a weighted fusion scheme is used to obtain the ultimate classification result. Experimental results on four widely used face datasets show that the proposed method achieves better performance and is effective for resolution change.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-25T07:00:00Z
      DOI: 10.1142/S0218001422560122
       
  • Phylogenetic Analysis: A Novel Method of Protein Sequence Similarity
           Analysis

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      Authors: Wei Li, Lina Yang, Zuqiang Meng, Yu Qiu, Patrick Shen-Pei Wang, Xichun Li
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Protein sequence similarity analysis (PSSA) is a significant task in bioinformatics, which can obtain information about unknown sequences such as protein structures and homology relationships. Protein sequence refers to the series of amino acids with rich physical and chemical properties, namely the basic structure of proteins. However, sequence similarity analysis and phylogenetic analysis between different species which have complex amino acid sequences is a challenging problem. In this paper, nine properties of amino acids were considered and the sequence was converted into numerical values by principal component analysis (PCA); with Haar Wavelet Transform, and Higuchi fractal dimension (HFD), a new feature vector is constructed to represent the sequence; Spearman distance was selected to calculate the distance matrix and the phylogenetic tree was constructed. In this paper, two representative protein sequences (9 ND5 (NADH dehydrogenase 5) and 8 ND6 (NADH dehydrogenase 6)) were selected for similarity analysis and phylogenetic analysis, and compared with MEGA software and other existing methods. The extensive results show that our method is outperforming and results consistent with the known facts.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-25T07:00:00Z
      DOI: 10.1142/S0218001422580071
       
  • Multi-Content Merging Network Based on Focal Loss and Convolutional Block
           Attention in Hyperspectral Image Classification

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      Authors: Lina Yang, Fengqi Zhang, Patrick Shen-Pei Wang, Xichun Li, Huiwu Luo
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Simultaneous extraction of spectral and spatial features and their fusion is currently a popular solution in hyperspectral image (HSI) classification. It has achieved satisfactory results in some research. Because the scales of objects are often different in HSI, it is necessary to extract multi-scale features. However, this aspect was not taken into account in many spectral-spatial feature fusion methods. This causes the model to be unable to get sufficient features on scales with a large difference range. The model (MCMN: Multi-Content Merging Network) proposed in this paper designs a multi-branch fusion structure to extract multi-scale spatial features by using multiple dilated convolution kernels. Considering the interference of the surrounding heterogeneous objects, the useful information from different directions is also fused together to realize the merging of multiple regional features. MCMN introduces a convolution block attention mechanism, which fully extracts attention features in both spatial and spectral directions, so that the network can focus on more useful parts, which can effectively improve the performance of the model. In addition, since the number of objects in each class is often discrepant, it will have some impact on the training process. We apply the focal loss function to eliminate the negative factor. The experimental results of MCMN on three data sets have a breakthrough compared with the other comparison models, which highlights the role of MCMN structure.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-23T07:00:00Z
      DOI: 10.1142/S0218001422500185
       
  • Region-Based Split Octonion Networks with Channel Attention Module for
           Tuna Classification

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      Authors: Jisha Anu Jose, C. Kumar, S. Sureshkumar
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Tuna fish is a popular food because of its nutritional value and taste. Demand for various species of tuna increases over time, necessitating the development of a system to sort tuna fish into distinct species in export sectors in order to accelerate the process. The work proposes an automated tuna classification system based on split octonion network. The images are initially preprocessed and divided into region images. Each region image is applied to a split octonion network with eleven layers. In addition, a split octonion channel attention module is presented, which is fed to the last two convolutional layers. The features from the three octonion networks are fused and applied to a series of dense layers. In the last layer, a softmax classifier is utilized for final classification. Results show that the proposed region-based split octonion network with attention module gives an accuracy of 98.01% on tuna database. The region-based tuna classification model is fine-tuned for the categorization of six species from QUT-FishBase dataset and Fish-Pak dataset. The system shows accuracies of 97.83% and 98.17% on QUT-FishBase and Fish-Pak datasets, respectively. The proposed methodology is also compared with existing approaches using a variety of evaluation criteria.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-23T07:00:00Z
      DOI: 10.1142/S0218001422500306
       
  • Mutual Information Variational Autoencoders and Its Application to Feature
           Extraction of Multivariate Time Series

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      Authors: Junying Li, Weijie Ren, Min Han
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      The application of deep learning in time-series prediction has developed gradually. In this paper, we propose a deep generative network model for feature extraction of multivariate time series, namely, mutual information variational autoencoders (MI-VAE). In the architecture of the proposed model, we use the latent space of VAE for feature learning, which can extract the essential features of multivariate time-series data effectively. The latent space employed directly as a feature extractor can avoid poor interpretability of model. In addition, we introduce a mutual information term into the loss function, which improves the expression capability and accuracy of model. The proposed model, combining the merits of VAE and mutual information, extracts features for multivariate time-series data from a new perspective. The Lorenz system and Beijing air quality time series are used to test performance of the proposed model and comparative models. Results show that the proposed model is superior to other similar models in terms of accuracy and expression capability of latent space.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-23T07:00:00Z
      DOI: 10.1142/S0218001422550059
       
  • Accurate Locating and Recognizing of ID Card Information

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      Authors: Jingting Zhong, Cihui Yang, Xingmiao Xu, Wuzhida Bao, Jianyong Guo
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      As an important identification certificate for citizens, ID card plays a significant role in daily life and its information has found its way into almost every aspect. However, traditional ways tend to adopt manual input, which is not only time-consuming and labor-intensive, but also expensive as well as inaccuracy. In this paper, we proposed a novel algorithm to locate and recognize ID card information, in which several fresh strategies are presented to rectify image, detect boundary, and locate information, respectively. To solve the problem of image rotating, the image is rectified by searching the best rotating angle that can lead to the maximum corner point projection peak. Meanwhile, the boundary of ID card is detected by finding the best lines in the predicted boundary area based on the deviation between the predicted boundary and the detected boundary, and the position of information is located by incorporating the prior information and the location relation between the key information. Experimental results show that the proposed algorithm can achieve a state-of-the-art effect for recognizing ID card’s information.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-22T07:00:00Z
      DOI: 10.1142/S0218001422500215
       
  • Optimized Convolutional Neural Network for Road Detection with Structured
           Contour and Spatial Information for Intelligent Vehicle System

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      Authors: Deepak Kumar Dewangan, Satya Prakash Sahu
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      “Road detection is said to be a major research area in remote sensing analysis and it is usually complex due to the data complexities as it gets varied in appearance with minor inter-class and huge intra-class variations that often cause errors and gaps in the extraction of the road”. Moreover, the majority of supervised learning techniques endure from the high price of manual annotation or inadequate training data. Thereby, this paper intends to introduce a new model for road detection. This work exploits a siamesed fully convolutional network (named as “s-FCN-loc”) based on VGG-net architecture that considers semantic contour, RGB channel and location prior for segmenting road regions precisely. As a major contribution, super pixel segmentation was carried out, where the RGB images are given as input to the FCN network and the road regions of images are set as a target. Further, the segmented outputs are fused using AND operation to attain the final segmented output that detects the road regions accurately. To make the detection more accurate, the convolutional layers of FCN are optimally chosen by a new improved model termed as distance oriented sea lion algorithm (DSLnO) model. The presented [math] model has achieved a minimal value of negative measures and accuracy is 8.2% higher than traditional methods. Finally, the presented method is evaluated on the KITTI road detection dataset, and achieves a better result. The analysis was done with respect to positive measures and negative measures.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-22T07:00:00Z
      DOI: 10.1142/S0218001422520024
       
  • LWRN: Light-Weight Residual Network for Edge Detection

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      Authors: Chen Han, Dingyu Li, Xuanyin Wang
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Edge detection is one of the most fundamental fields in computer vision. With the rapid development of the combination of Convolutional Neural Network and Multi-Scale Representation of image, significant progress has been made in this field. However, most of them have a huge size, which makes it hard to apply in reality, and a huge number of parameters may lead to waste of computing resources. In this paper, we focus on qualitative analysis of the role of each part in the network, and propose a modified light-weight architecture based on our result and the study of former works. Our new architecture is composed of residual-blocks, max-pooling layers and batch normalization layers. Compared with the previous models, the new architecture performs better in memory, convergence and computation efficiency with similar model size. Moreover, the new architecture can achieve better accuracy with smaller model size. When evaluating our model on the well-known BSDS500 benchmark, we achieve ODS F-measure of 0.769 with parameters less than 0.3[math]M, which shows a better property than the state-of-the-art result 0.766 at this level.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-22T07:00:00Z
      DOI: 10.1142/S0218001422540076
       
  • Multi-Modal Sparse Tracking by Jointing Timing and Modal Consistency

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      Authors: Jiajun Li, Bin Fang, Mingliang Zhou
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      In this paper, we propose a multi-modal sparse tracking by jointing timing and modal consistency to locate the target location with the similarity of multiple local appearances. First, we propose an alignable patching strategy for red-green-blue (RGB) color mode and thermal infrared mode to adapt to the local changes of the target. Second, we propose a consistency expression of the corresponding aligned patches between the modes and the correlation of the gaussian mapping within mode to reconstruct the target judgment likelihood function. Finally, we propose an updating scenario based on timing correlation and mode sparsity to fit with the target changes. According to the experimental results, significant improvement in terms of tracking accuracy can be achieved on average compared with the state-of-the-art algorithms. The source code of our algorithm is available on https://github.com/Liincq/tracker.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-20T07:00:00Z
      DOI: 10.1142/S0218001422510089
       
  • Construction of English Pronunciation Judgment and Detection Model Based
           on Deep Learning Neural Networks Data Stream Fusion

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      Authors: Yi Shi, Young Chun Ko
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Aiming at the defects of pronunciation errors and limited collection of pronunciation data resources in traditional artificial neural networks, an English pronunciation judgment and detection model based on deep learning neural networks data stream fusion is proposed. Taking Chinese English pronunciation as the research object, three groups of phonetic data were selected as experimental auxiliary data, based on the convolutional neural network, through the preset reset of the pronunciation detection system of the model, the sampling and recognition extraction of the speech system, the wrong speech detection and the feature analysis of the multi-level data stream tandem, the experiments are carried out with CU-CHLOE language learning database, WSJ1 database and 863 Mandarin database. The experimental results show that the recognition accuracy of this model is higher than that of the traditional neural network model, the accuracy of error type diagnosis is significantly improved, and its noise robustness is the best.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-20T07:00:00Z
      DOI: 10.1142/S0218001422520115
       
  • AFFNet: Attention Mechanism Network Based on Fusion Feature for Image
           Cloud Removal

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      Authors: Runhan Shen, Xiaofeng Zhang, Yonggang Xiang
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Clouds frequently affect optical remote sensing pictures throughout the gathering process, resulting in low-resolution images that affect judgment and subsequent use of ground data. Because of the thick cloud cover, the ground surface information below is entirely incorrect. This kind of end-to-end image problem should not be dismissed as a simple task of image inpainting or image translation. Therefore, this paper proposes a multi-head self-attention module based on the encoding–decoding generative adversarial network, considering the redundant information of the deep network, furthermore this paper introduces Ghost convolution to effectively solve the influence of redundant feature maps in the network on the increase of time consumption and parameters. The method in this paper can solve the problem of cloud occlusion. By considering spatial information, it can better complete the prediction of cloud removal. It can reduce the amount of network calculations and parameters while maintaining the effect. In addition, Feature Fusion Module is proposed to integrate high-level features with low-level features, so that the network can extract enough feature information and better supplement the details to complete the cloud removal. The method in this paper has achieved excellent results on the RICE1 and RICE2 datasets.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-20T07:00:00Z
      DOI: 10.1142/S0218001422540143
       
  • Proximity-Distance Mapping and Jaya Optimization Algorithm Based on
           Localization for Wireless Sensor Network

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      Authors: Peng Duo, Gao Yuwei
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Aiming at the problem of large location errors of traditional ranging-free algorithms in Wireless Sensor Network (WSN), a novel node location algorithm based on proximity-distance mapping (PDM) and Jaya optimization was proposed. In this algorithm, proximity and Euclidean distance are extracted from the relationship of anchor nodes to construct a mapping matrix by using the idea of PDM. It is calculated by using the mapping matrix that the estimated distance from the unknown node to the anchor node can be used for the subsequent calculations. After the estimated distance is obtained, the Jaya optimization algorithm is imported to calculate the location of the unknown one. To accelerate the convergence and enhance the accuracy of the algorithm, the idea of a boundary box is used to limit the initial feasible region of unknown nodes. The experiment results show that the PDM–Jaya algorithm has better positioning accuracy than the original PDM in the same condition.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-20T07:00:00Z
      DOI: 10.1142/S0218001422550084
       
  • POGT: A Peking Opera Gesture Training System Using Infrared Sensors

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      Authors: Xingquan Cai, Tong Wang, Xin Bai, Zaichao Lin, Yakun Ge, Haiyan Sun
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Peking opera is one of the national cultural heritages in China. However, it is difficult for people to learn the gestures in Peking opera performance, which limits the spread of this traditional culture. To address this issue, we propose a Peking opera gesture training system using infrared sensors. Specifically, we build a character avatar for demonstrating the gestures in Peking opera in the proposed system. Based on the data collected by infrared sensors, a method for calculating gesture similarity is proposed and is applied for the training of Peking opera gestures, which allows natural interactions and provides interactive feedback for user gestures. We conducted multiple experiments to verify the feasibility and effectiveness of the training system. The experimental results showed that the proposed system can overcome the difficulties in the traditional learning process of Peking opera gestures, which helps users to achieve the goal of learning standard Peking opera gestures. The proposed training system greatly eases the learning of Peking opera gestures, adding vitality into the culture of traditional Peking opera.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-20T07:00:00Z
      DOI: 10.1142/S0218001422560110
       
  • Semi-Automatic Ontology Matching Based on Interactive Compact Genetic
           Algorithm

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      Authors: Xingsi Xue, Chaofan Yang, Guojun Mao, Hai Zhu
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automatic ontology matching techniques suffer from the limitation of similarity measure, whose alignment’s quality cannot be ensured. To overcome this drawback, in this work, an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique is proposed, which utilizes both the compact encoding mechanism and expert interacting mechanism to improve the algorithm’s performance and the alignment’s quality. In addition, an optimization model is established to formally define the ontology entity matching problem, and an efficient interacting strategy is proposed, which is able to reduce the expert’s workload and maximize his working value. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test our proposal’s performance. The experimental results show that our approach is able to make use of the expert knowledge to improve the alignment’s quality, and it also outperforms OAEI’s participants.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-20T07:00:00Z
      DOI: 10.1142/S0218001422570026
       
  • Newton Algorithm Based DELM for Enhancing Offline Tamil Handwritten
           Character Recognition

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      Authors: K. Shanmugam, B. Vanathi
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer’s complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-18T07:00:00Z
      DOI: 10.1142/S0218001422500203
       
  • Multi-Extended Target Tracking Algorithm Based on VBEM-CPHD

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      Authors: Yawen Li, Bo Wang
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Considering that the extended targets tracking problem of the measurement is a glint noise with an unknown inverse covariance, a new algorithm from a multi-extended target tracking based on the Variational Bayesian Expectation Maximization (VBEM) is proposed. To improve the variational Bayesian technique, a modeled Student’s [math] distribution is proposed based on multiple Gaussian mixture terms in order to replace the probability hypothesis density (PHD) intensity. The extended target was modeled with a Student’s [math] distribution with the glint noise, using Gauss-Gamma distribution combining the variational Bayesian technique to obtain an approximate distribution and applying the expectation-maximization algorithm for iterative estimation. The two experiments are compared and analyzed with the VBEM-CPHD algorithm and the traditional extended target tracking algorithm. Experiment 1 estimated the trajectory of the target, compared algorithms of VBEM-CPHD and GM-CPHD with OSPA distance, varied glint noise with the three different measurement noise standard deviations. Experiment 2 completed the examination of the tracking performance and stability of the proposed method and performed to compare the VBEM-CPHD algorithm proposed with the VB-based GM-CPHD (GM-VBCPHD) algorithm under an unknown measurement noise covariance. The experimental results indicate that the algorithm of VBEM-CPHD has high tracking accuracy, good adaptability, and a strong antijamming ability for multiple extended targets under glint noise conditions.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-18T07:00:00Z
      DOI: 10.1142/S0218001422500264
       
  • Deep Learning for Covid-19 Screening Using Chest X-Rays in 2020: A
           Systematic Review

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      Authors: KC Santosh, Supriti Ghosh, Debasmita GhoshRoy
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Artificial Intelligence (AI) has promoted countless contributions in the field of healthcare and medical imaging. In this paper, we thoroughly analyze peer-reviewed research findings/articles on AI-guided tools for Covid-19 analysis/screening using chest X-ray images in the year 2020. We discuss on how far deep learning algorithms help in decision-making. We identify/address data collections, methodical contributions, promising methods, and challenges. However, a fair comparison is not trivial as dataset sizes vary over time, throughout the year 2020. Even though their unprecedented efforts in building AI-guided tools to detect, localize, and segment Covid-19 cases are limited to education and training, we elaborate on their strengths and possible weaknesses when we consider the need of cross-population train/test models. In total, with search keywords: (Covid-19 OR Coronavirus) AND chest x-ray AND deep learning AND artificial intelligence AND medical imaging in both PubMed Central Repository and Web of Science, we systematically reviewed 58 research articles and performed meta-analysis.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-18T07:00:00Z
      DOI: 10.1142/S0218001422520103
       
  • Image Attribute Migration Based on Decoupling and Adaptive Layer Instance
           Normalization

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      Authors: Xingquan Cai, Fajian Li, Keng Chen, Yuechao Wei, Haiyan Sun
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      The issue of image attribute migration is one of the hot research topics in the field of computer vision, which has received extensive research interest. However, current unsupervised image attribute migration models using symmetric generative adversarial network structure do not work well on datasets with large geometric variations, where the results lack diversity and are of low quality. To address these problems, we present an image attribute migration model based on decoupling and adaptive layer instance normalization. First, a codec structure based on a decoupled representation is constructed as the generator, and an adaptive layer instance normalization operation is used in the decoder. Then, the iterations of the model are constrained by various improved loss functions. We conducted controlled experiments and compared the results of our method with other methods using several datasets with large geometric variations. The experimental results demonstrate that the proposed method can achieve high quality and diverse image attribute migration.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-18T07:00:00Z
      DOI: 10.1142/S0218001422540118
       
  • Automatic Twitter Crime Prediction Using Hybrid Wavelet Convolutional
           Neural Network with World Cup Optimization

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      Authors: Monika, Aruna Bhat
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Social media are digitally mediated platforms that allow people to create and exchange content, professional interests, ideas, and other forms of expression through virtual networks. Users often utilize web-based programs on their PCs and laptops to visit social media sites, or they download programs that provide their devices social media capabilities. As users connect with these platforms, groups, organizations, and individuals can upload, co-create, discuss, engage in, and update self-curated or user-generated information. Although the platforms such as Facebook, Twitter, Instagram, etc., aid in the communication purposes, it also has some demerits like cyber-crime, hacking, etc. The growing number of crimes through these platforms needs to be deducted by predicting the crimes. For the crime prediction, the data acquired from Twitter is pre-processed for the data cleansing process. Later the features are extracted using various techniques like bag of words (BoW), Glove, term frequency-inverse document frequency (TF-IDF), and feature hashing. The feature selection is done using a modified tree growth algorithm (MTGA) and clustering is performed using the fuzzy manta ray foraging (FMRF). Finally, the crime detection is done using hybrid wavelet convolutional neural network with world cup organization (WCNN-WCO). The PYTHON tool is used for the implementation and the Twitter user dataset is used for analysis. The results showed that the proposed method outperforms the existing method in terms of precision, accuracy, [math]1 measure, and recall.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-18T07:00:00Z
      DOI: 10.1142/S0218001422590054
       
  • Link Connectivity-Based Access Selection Method for Multi-UAV
           Heterogeneous Networks

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      Authors: Shaojie Wen, Lianbing Deng, Zengliang Liu
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      While UAV ad hoc networks have made significant progress, implementing it in most of real-application scenarios is challenging due to data explosion. Instead of only ad hoc mode, this work simultaneously considers non-orthogonal multiple access technique (NOMA) and ad hoc mode to handle the problem. We further propose an effective framework to seamlessly integrate these two techniques, namely Multi-UAV heterogeneous networks. Specifically, we formulate the framework as a mixed integer nonlinear programming, in which original problem can be decomposed into two sub-problems, e.g., access mode selection for UAV to UAV mode (U2U) and UAV to Ground Base mode (U2G), and transmission rate optimization. For the access mode selection, to reduce the computational complexity at each UAV, a candidate set is constructed based on the connection time and link quality. After that, the component in candidate set that maximizes the objective function is selected as the access point. Due to the different communication techniques in U2U mode and U2G mode, we can obtain the optimal rate for each UAV by using the NOMA technique in U2G mode and channel prediction method with local information in U2U mode, respectively. For the transmission rate optimization, an effective algorithm is proposed for U2G mode and U2U mode, which considers the effects of the network connectivity and link quality on the optimization performance. Simulation results show that our method can reduce the outage rate and improve the network throughput effectively.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-18T07:00:00Z
      DOI: 10.1142/S0218001422590121
       
  • Human Body Pose Distance Image Analysis for Action Recognition

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      Authors: Amit Verma, Toshanlal Meenpal, Bibhudendra Acharya
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Body pose analysis is an important factor of human action recognition. Recently, the proposed Recurrent Neural Networks (RNNs) and deep ConvNets-based methods are showing good performances in learning sequential information. Despite these good performances, RNN lacks to efficiently learn spatial relation between body parts while deep ConvNets require a huge amount of data for training. We propose a Distance-based Neural Network (DNN) for action recognition in static images. We compute effective distances between a set of body part pairs for a given image and feed to DNN to learn effective representation of complex actions. We also propose Distance-based Convolutional Neural Network (DCNN) to learn representations from 2D images. The distances are rearranged in 2D grayscale image called as a Distance Image. This 2D representation allows the network to learn specific discriminative information between adjacent pixel distance values corresponding to different body part pairs. We evaluate our method on two real-world datasets i.e. UT-Interaction and SBU Kinect Interaction. Results show that our proposed method achieves better performance compared to the state-of-the-art approaches.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-13T07:00:00Z
      DOI: 10.1142/S0218001422550126
       
  • Indoor Positioning and Navigation Model Based on Semantic Grid

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      Authors: Cuncun Wei, Qianqian Ge
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Traditional GPS positioning technology cannot be used in indoor space. With the development of the new positioning technology and the Internet of things, the indoor mobile object positioning and navigation model have been the focus of the relevant research institutions at home and abroad. Based on this, indoor positioning technology was studied starting from Wi-Fi, RFID, and iBeacon technology in this paper. However, the accuracy of indoor positioning and navigation needs to be further improved. This paper presents a semantic space model based on artificial intelligence technology, through semantic pattern matching, semantic concept extension, semantic reasoning and semantic mapping, and interior semantic localization is realized. The indoor semantic network and indoor grid navigation model are constructed, and the indoor semantic path is modeled from time, location, user, and congestion. At the same time, the improved Term Frequency-Inverse Document Frequency is combined with the Hidden Markov Model to improve the accuracy of matching the stay area with the most likely location to visit and improve the accuracy of semantic annotation. It was found that the research on the indoor positioning and navigation model based on the semantic grid can realize the uniform expression of the complex spatial semantics of the theme, geometry, connectivity, and distance, which can promote the development of indoor positioning and navigation.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-11T07:00:00Z
      DOI: 10.1142/S0218001422550096
       
  • Machine Reading Comprehension with Rich Knowledge

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      Authors: Jun He, Li Peng, Yinghui Zhang, Bo Sun, Rong Xiao, Yongkang Xiao
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Machine reading comprehension (MRC) is a crucial and challenging task in natural language processing (NLP). With the development of deep learning, language models have achieved excellent results. However, these models still cannot answer complex questions. Currently, researchers often utilize structured knowledge, such as knowledge bases (KBs), as external knowledge by directly extracting triples to enhance the results of machine reading. Although they can support certain background knowledge, the triples are limited to the interrelationships among entities or words. Unlike structured knowledge, unstructured knowledge is rich and extensive. However, these methods ignore unstructured knowledge resources, such as Wikipedia. In addition, the effect of combining the two types of knowledge is still not known. In this study, we first attempt to explore the usefulness of combining them. We introduce a fusion mechanism into a rich knowledge fusion layer (RKF) to obtain more useful and relevant knowledge from different external knowledge resources. Further to promote interaction among different types of knowledge, a bi-matching layer is added. We propose the RKF-NET framework based on BERT, and our experimental results demonstrate the effectiveness of two classic datasets: SQuAD1.1 and the Easy-Challenge (ARC).
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-09T07:00:00Z
      DOI: 10.1142/S0218001422510041
       
  • An Efficient Detection and Recognition System for Multiple Motorcycle
           License Plates Based on Decision Tree

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      Authors: Chun-Ming Tsai, Frank Y. Shih
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      The automatic detection and recognition for motorcycle license plates present a very challenging task since they appear more compact and versatile than vehicle license plates. In this paper, we present an efficient detection and recognition system for motorcycle license plates based on decision tree and deep learning. It can be successfully carried out under various conditions, such as frontal, horizontally or vertically skewed, blurry, poor illumination, large viewing distances or angles, distortions, multiple license plates in an image, at night or interfered with brake lights, and headlights. Experimental results show that our system performs the best when testing with multiple license plates images under different conditions as compared against six state-of-the-art methods. Furthermore, our detection and recognition system have shown more accurate results than three commercial automatic license plate recognition systems in evaluation using accuracy, precision, recall, and F1 rates.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-08T07:00:00Z
      DOI: 10.1142/S0218001422500227
       
  • Enhanced Edge Detection for 3D Crack Segmentation and Depth Measurement
           with Laser Data

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      Authors: Ting Cao, Jinyuan Hu, Sheng Liu
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      With the usage of computer visual technology in civil engineering, pavement crack survey with imaging sensor gained wide attention in the past years. Unfortunately, it is still a challenge to achieve the satisfying results. This paper presents a pavement crack survey approach based on edge detection for laser data. At first, the LS-40 line-laser scanner is implemented to achieve 3D pavement surface data. With the advantage of the various depth information exhibited in the pavement data, an enhanced edge detection based on fractional differential is proposed for 3D crack segmentation. The proposed method could effectively enhance the crack boundary and maintain texture details, which can guarantee the high accuracy in crack segmentation. Moreover, a novel plane fitting method based on dynamic threshold is studied to calculate crack depth information. It can not only identify and remove invalid points effectively, but also accomplish plane calculation with errors in three directions. Experiments verify that the proposed approach can achieve the satisfying result and can work well in F-measure system.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-08T07:00:00Z
      DOI: 10.1142/S0218001422550060
       
  • SMOTE-LMKNN: A Synthetic Minority Oversampling Technique Based on Local
           Means-Based [math]-Nearest Neighbor

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      Authors: Shuang Liu
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Traditional classifiers are trapped by the class-imbalanced problem due to the fact that they are biased toward the majority class. Oversampling methods can improve imbalanced classification by creating synthetic minority class samples. Noise generation has been a great challenge in oversampling methods. Filtering-based and direction-change methods are proposed against noise generation. Yet, the adopted noise filters in filtering-based methods are biased to the majority class. Besides, the [math]-nearest neighbor (KNN)-based interpolation in filtering-based and direction-change methods is susceptible to abnormal samples (e.g. outliers, noise or unsafe borderline samples). To overcome noise generation while solving the above shortcomings of filtering-based and direction-change methods, this work presents a new synthetic minority oversampling technique based on local means-based KNN (SMOTE-LMKNN). In SMOTE-LMKNN, the local mean-based KNN (LMKNN) is first introduced to describe the local characteristic of imbalanced data. Second, a new LMKNN-based noise filter is proposed to remove noise and unsafe borderline samples. Third, the interpolation between a base sample and its LMKNN is proposed to create synthetic minority class samples. Empirical results of extensive experiments with 18 data sets show that SMOTE-LMKNN is competent compared with seven popular oversampling methods in training KNN classifier and classification and regression tree (CART).
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-06T07:00:00Z
      DOI: 10.1142/S0218001422500197
       
  • Optical Film Damage Classification Based on Neural Network

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      Authors: Guoliang Yang, Junhong Su, Wenbo Huang, Gaohan Zhou, Yuan Li
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Traditional machine learning requires users to have a strong ability to control features and distance calculation formulas, especially in the use of support vector machine SVM and nearest neighbor KNN. Traditional machine learning uses PCA in feature extraction will actually lead to Information is lost. In order to solve the problem of low optical film damage detection rate of traditional methods, a new method is proposed in this paper based on a convolutional neural network instead of traditional machine learning to classify CCD images with different damage degrees of SiO2 film and K9 glass. First, film images are collected by online CCD, and the proposed algorithm is designed to extract the image characteristic parameters of the film microscopic images, filter denoising, and run binarization to analyze film images. Second, gray values of images are extracted and classified by unsupervised learning. Finally, the film microscopic images under the microscope are analyzed. The experimental results show that the defect positions on the images can be detected after the images are detected and processed by a convolution neural network, binarization, and connected domains. The defective parts can be intercepted from the images, and the data related is saved for damage type determination. The average classification rate is over 99%, which is better than the traditional method by 9.1%. Therefore, it has a high application value.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-06T07:00:00Z
      DOI: 10.1142/S0218001422500240
       
  • Lightweight Hardware Architecture for Object Detection in Driver
           Assistance Systems

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      Authors: Bhaumik Vaidya, Chirag Paunwala
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Object detection on hardware platforms plays a very significant role in developing driver assistance systems (DASs) with limited computational resources. Object detection for DAS is a multiclass detection problem that involves detecting various objects like cars, auto, traffic lights, bicycles, pedestrians, etc. DAS also requires accuracy, speed, and sensitivity for detecting these objects in various challenging conditions. The lighting and weather conditions pose a serious challenge for accurate object detection for DAS. This paper proposes a speed-efficient and lightweight fully convolutional neural network (CNN) architecture for object detection in adverse rainy conditions. The proposed architecture uses a CNN-based deraining network with a custom SSIM loss function in the object detection pipeline, which can give an accurate performance using limited computational and memory resources. The object detection architecture contains some architectural modifications to the existing single shot multibox detector (SSD) architecture to make it more hardware efficient and improve accuracy on small objects. It uses a trainable color transformation module using [math] convolutions for handling the adverse lighting conditions encountered in DAS. The architecture uses feature fusion and the dilated convolution approach to enhance the accuracy of the proposed architecture on small objects. The datasets available for object detection in DAS are very imbalanced with cars as a predominant object. The class weight penalization technique is used to improve the performance of the architecture on scarcely present objects. The performance of the architecture is evaluated on well-known datasets like Kitti, Udacity, Indian Driving Dataset (IDD), and DAWN. The architecture achieves satisfactory performance in terms of mean average precision (mAP) and detection time on all these datasets. It requires three times fewer hardware resources compared to existing architectures. The lightweight nature of the proposed architecture and modification of CNN architecture with TensorRT allow the efficient implementation on the jetson nanohardware platform for prototyping, which can be integrated with other intelligent transportation systems.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-06T07:00:00Z
      DOI: 10.1142/S0218001422500276
       
  • Network Intrusion Logit Detection Model with IO Port Cross-Classification

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      Authors: Jingchun Sun, Fei Deng, Qin Su
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Recently, information networks are becoming a significant part of daily life, so keeping the system’s security is necessary for security tools, such as firewalls and encryption. However, because of the weaknesses of the existing tools, the Intrusion Detection System (IDS) has been implemented to solve the problem. In the application of IDS, feature classification and data analysis are the two most important steps. In this paper, by using the Logit regression model, we attempt to search for the optimal cutting value based on the relationship between cutting value and accuracy index and put forward an input-output port crossed (IOPC) classification for IDS to distinguish the new intrusion features. First, we discuss whole features and propose a taxonomy of IOPC classification for CIC-IDS2017 that is different from other former studies, which can reduce the data space. Second, we compute the distribution curve of cutting values varied with the accuracy index, the purpose of which is to search for the optimal cutting values. Finally, utilizing IOPC classification, the difference between the distribution of the cutting values under the attacks of distributed denial of service (DDoS) and PortScan in CIC-IDS2017 is discussed, which highlights the characteristic that cutting values besieged the attack by PortScan has a conditional distribution compared with DDoS.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-04-01T07:00:00Z
      DOI: 10.1142/S0218001422500239
       
  • User-Specified Image Color Transfer

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      Authors: Hsiau-Wen Lin, Yen-Yu Chen, Yoshimasa Tokuyama, Hwei Jen Lin
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      This paper proposes a flexible example-based color transfer system, providing an automatic mode and an advanced mode, for novices and expert users, respectively. The experimental results show that the proposed color transfer system not only can introduce natural results with simple operation by novices in the first use, but also can produce various desired results by users with short-term learning.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-31T07:00:00Z
      DOI: 10.1142/S0218001422540106
       
  • Neighborhood Learning-Based Cuckoo Search Algorithm for Global
           Optimization

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      Authors: Yan Xiong, Jiatang Cheng, Lieping Zhang
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      This paper presents a new variant of cuckoo search (CS) algorithm named neighborhood learning-based CS (NLCS) to address global optimization problems. Specifically, in this modified version, each individual learns from the personal best solution rather than the best solution found so far in the entire population to discourage premature convergence. To further enhance the performance of CS on complex multimode problems, each individual is allowed to learn from different learning exemplars on different dimensions. Moreover, the exemplar individual is chosen from a predefined neighborhood to further maintain the population diversity. This scheme enables each individual to interact with the historical experience of its own or its neighbors, which is controlled by using a learning probability. Extensive comparative experiments are conducted on 39 benchmark functions and two application problems of neural network training. Comparison results indicate that the proposed NLCS algorithm exhibits competitive convergence performance.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-28T07:00:00Z
      DOI: 10.1142/S0218001422510065
       
  • Method of Short-Circuit Fault Diagnosis in Transmission Line Based on Deep
           Learning

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      Authors: Li Tong, Zhao Hai, Zhou Xiaoming, Zhu Shidong, Yang Zheng, Yang Hongping, Liu Wei, Zhou Zhenliu
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      It is important to locate the fault distance and identify the fault types quickly, take effective measures to maintain line stability, and minimize the losses timely when there are short-circuit faults in transmission lines. For this purpose, a method based on deep learning is proposed for short-circuit faults identification in the transmission line. According to the similarity of samples in the reconstruction phase, a minimum neighborhood sample set is selected from the massive samples firstly, and then, the samples are trained using the back propagation algorithm along time in a recurrent neural network (RNN) with long-short term memory (LSTM) units. Compared with existing algorithms, the experimental results show that this algorithm meets the requirements of rapid fault diagnosis in the case of variable parameters, and higher fault type recognition accuracy and lower fault distance error can be obtained.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-28T07:00:00Z
      DOI: 10.1142/S0218001422520097
       
  • An Accelerated and Flexible SIFT Parallel-Computing Approach Based on the
           General Multi-Core Platform

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      Authors: Gang Wang, Mingliang Zhou, Bin Fang, Haichao Huang, Zhenyu Shu, Xueshu Chen
      Abstract: International Journal of Pattern Recognition and Artificial Intelligence, Ahead of Print.
      Visual retrieval has been a significant technology in the computer vision task. Visual feature descriptors are the key to the visual retrieval. The famous local feature descriptor is called the Scale Invariant Feature Transform (SIFT), which can keep invariant mapping for the scale, rotate and simulate images. To utilize effectively the SIFT feature descriptor for visual matching on different hardware platforms, this paper proposes an accelerated SIFT algorithm based on the SIFT feature computing principle of the general multi-core platform. First, our multi-core task allocation method introduces the WFM theory into task assignment for each core to improve the core computing resource utilization for high-efficient parallel computing. Then, to improve the efficiency of picture matching, we introduce global geometric constraints condition to optimal picture matching for the multi-core parallelization approach. Experimental results show that the proposed approach can save on average 87.31% on the Intel X86 platform, compared to the single-core time. Also, our approach can save on average 33.79% on the Raspberry Pi platform, compared to the single-core time.
      Citation: International Journal of Pattern Recognition and Artificial Intelligence
      PubDate: 2022-03-28T07:00:00Z
      DOI: 10.1142/S0218001422550102
       
 
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