The enormous amount of data contained in the video image has grown rapidly along with surveillance, greatly outpacing the capacity of human resources to handle it effectively. Smart surveillance retrieval is an essential component of any modern video surveillance system, considerably boosting the effectiveness, precision, and interoperability of the system. The use of face recognition and other cutting-edge technology in the security surveillance system is rapidly rising. Therefore, in this article, the distributed deep convolutional neural network (DCNN) and distributed deep BiLSTM is proposed to efficiently detect the face from the video. One of the major contributions involved in this research relies on the key frame selection, where four unique distance measurement techniques are fused, and is named hybrid distance- key frame selection. The Tri birds optimization (TBO) technique selects the best solution from a large number of solutions for the ensemble model classifier engaged in face recognition. The ensemble model classifier incorporates various hyper-parameters that are optimally trained. Multiple test videos with 401 and 802 test videos are used as the input for the TBO-ensemble model that attains 97% accuracy, 98.33% precision, recall, and f-measure for epoch 50 and the 500 number of retrievals, respectively.
{"title":"Effective Face Recognition with Hybrid Distance-Key Frame Selection Using TBO-Ensemble Model","authors":"Jitendra Chandrakant Musale, Anujkumar Singh, Swati Shirke","doi":"10.1142/s0219691323500443","DOIUrl":"https://doi.org/10.1142/s0219691323500443","url":null,"abstract":"The enormous amount of data contained in the video image has grown rapidly along with surveillance, greatly outpacing the capacity of human resources to handle it effectively. Smart surveillance retrieval is an essential component of any modern video surveillance system, considerably boosting the effectiveness, precision, and interoperability of the system. The use of face recognition and other cutting-edge technology in the security surveillance system is rapidly rising. Therefore, in this article, the distributed deep convolutional neural network (DCNN) and distributed deep BiLSTM is proposed to efficiently detect the face from the video. One of the major contributions involved in this research relies on the key frame selection, where four unique distance measurement techniques are fused, and is named hybrid distance- key frame selection. The Tri birds optimization (TBO) technique selects the best solution from a large number of solutions for the ensemble model classifier engaged in face recognition. The ensemble model classifier incorporates various hyper-parameters that are optimally trained. Multiple test videos with 401 and 802 test videos are used as the input for the TBO-ensemble model that attains 97% accuracy, 98.33% precision, recall, and f-measure for epoch 50 and the 500 number of retrievals, respectively.","PeriodicalId":50282,"journal":{"name":"International Journal of Wavelets Multiresolution and Information Processing","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135944597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1142/s0219691323500509
Fang Yang, Min Chen, Pengtao Li, Wei Qu, Jiecheng Chen, Tao Qian
{"title":"Sparse Series Solutions of Random Boundary and Initial Value Problems","authors":"Fang Yang, Min Chen, Pengtao Li, Wei Qu, Jiecheng Chen, Tao Qian","doi":"10.1142/s0219691323500509","DOIUrl":"https://doi.org/10.1142/s0219691323500509","url":null,"abstract":"","PeriodicalId":50282,"journal":{"name":"International Journal of Wavelets Multiresolution and Information Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136014721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-07DOI: 10.1142/s0219691323500479
Chi Xiao, Xiaoyu Xia, Shunhao Xu, Qilin Huang, Hao Xiao, Jingdong Song
Due to the advantages of direct visualization and high resolution, transmission electron microscopy (TEM) technology has been widely used in the morphological identification of viruses. With the development of artificial intelligence (AI), there have been some studies on automated TEM virus identification using deep learning. However, to achieve effective virus identification results, a large number of high-quality labeled images are required for network training. In this work, we propose an automatic virus segmentation method based on few-shot learning. We use the Chikungunya virus, Parapoxvirus and Marburg virus, etc. to construct a pre-training virus dataset and train an attention U-Net-like network with an encoder module, relationship module, attention module and decoding module to realize severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) segmentation using few-shot learning. The experiment shows that the proposed few-shot learning methods yield 0.900 Dice and 0.828 Jaccard in 1-shot, 0.903 Dice and 0.832 Jaccard in 5-shot, which demonstrates the effectiveness of our method and outperforms other promising methods. Our fully automated method contributes to the development of medical virology by providing virologists with a low-cost and accurate approach to identify SARS-CoV-2 in TEM.
{"title":"Automatic SARS-CoV-2 Segmentation in Electron Microscopy Based on Few-shot Learning","authors":"Chi Xiao, Xiaoyu Xia, Shunhao Xu, Qilin Huang, Hao Xiao, Jingdong Song","doi":"10.1142/s0219691323500479","DOIUrl":"https://doi.org/10.1142/s0219691323500479","url":null,"abstract":"Due to the advantages of direct visualization and high resolution, transmission electron microscopy (TEM) technology has been widely used in the morphological identification of viruses. With the development of artificial intelligence (AI), there have been some studies on automated TEM virus identification using deep learning. However, to achieve effective virus identification results, a large number of high-quality labeled images are required for network training. In this work, we propose an automatic virus segmentation method based on few-shot learning. We use the Chikungunya virus, Parapoxvirus and Marburg virus, etc. to construct a pre-training virus dataset and train an attention U-Net-like network with an encoder module, relationship module, attention module and decoding module to realize severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) segmentation using few-shot learning. The experiment shows that the proposed few-shot learning methods yield 0.900 Dice and 0.828 Jaccard in 1-shot, 0.903 Dice and 0.832 Jaccard in 5-shot, which demonstrates the effectiveness of our method and outperforms other promising methods. Our fully automated method contributes to the development of medical virology by providing virologists with a low-cost and accurate approach to identify SARS-CoV-2 in TEM.","PeriodicalId":50282,"journal":{"name":"International Journal of Wavelets Multiresolution and Information Processing","volume":"38 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135251916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-23DOI: 10.1142/s0219691323500455
Huimin Li, Youming Liu
Lower bound estimation plays an important role for establishing the minimax risk. A key step in lower bound estimation is deriving a lower bound of the affinity between two probability measures. This paper provides a simple method to estimate the affinity between mixture probability measures. Then we apply the lower bound of the affinity to establish the minimax lower bound for a family of sparse covariance matrices, which contains Cai–Ren–Zhou’s theorem in [T. Cai, Z. Ren and H. Zhou, Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation, Electron. J. Stat. 10(1) (2016) 1–59] as a special example.
{"title":"Lower Bound Estimation for A Family of High-dimensional Sparse Covariance Matrices","authors":"Huimin Li, Youming Liu","doi":"10.1142/s0219691323500455","DOIUrl":"https://doi.org/10.1142/s0219691323500455","url":null,"abstract":"Lower bound estimation plays an important role for establishing the minimax risk. A key step in lower bound estimation is deriving a lower bound of the affinity between two probability measures. This paper provides a simple method to estimate the affinity between mixture probability measures. Then we apply the lower bound of the affinity to establish the minimax lower bound for a family of sparse covariance matrices, which contains Cai–Ren–Zhou’s theorem in [T. Cai, Z. Ren and H. Zhou, Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation, Electron. J. Stat. 10(1) (2016) 1–59] as a special example.","PeriodicalId":50282,"journal":{"name":"International Journal of Wavelets Multiresolution and Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135959567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.1142/s0219691323500467
Junying Hu, Peiju Chang, Fang Du, Rongrong Fei, Kai Sun, Jiangshe Zhang, Hai Zhang
{"title":"Back-Propagating Errors Through Artificial Neural Networks for Variable Selection","authors":"Junying Hu, Peiju Chang, Fang Du, Rongrong Fei, Kai Sun, Jiangshe Zhang, Hai Zhang","doi":"10.1142/s0219691323500467","DOIUrl":"https://doi.org/10.1142/s0219691323500467","url":null,"abstract":"","PeriodicalId":50282,"journal":{"name":"International Journal of Wavelets Multiresolution and Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-08DOI: 10.1142/s0219691323500431
Giriprasad Manoharan
{"title":"IntelligentFaceNet: Designing a Multi-Cascaded Attentive and Adaptive Deep Learning Network for Facial Recognition using Heuristic Approach","authors":"Giriprasad Manoharan","doi":"10.1142/s0219691323500431","DOIUrl":"https://doi.org/10.1142/s0219691323500431","url":null,"abstract":"","PeriodicalId":50282,"journal":{"name":"International Journal of Wavelets Multiresolution and Information Processing","volume":"379 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136363005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.1142/s021969132350042x
Xiaoke Jia, Xingya Fan
{"title":"The Plancherel Formula of Fourier Integral Operators: the Case of Sp(1, 1)","authors":"Xiaoke Jia, Xingya Fan","doi":"10.1142/s021969132350042x","DOIUrl":"https://doi.org/10.1142/s021969132350042x","url":null,"abstract":"","PeriodicalId":50282,"journal":{"name":"International Journal of Wavelets Multiresolution and Information Processing","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43274424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, there has been an increasing interest in multi-view subspace clustering (MSC). However, existing MSC methods fail to take full advantage of the local geometric structure in each manifold throughout the data flow, which is essential for clustering. To remedy this drawback, in this paper, a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method is proposed, which aims to harness both global and local structural information of multi-view data in a unified framework. Specifically, DGRMSC first learns a latent representation to exploit the global complementary information of multiple views. Based on the learned latent representation, we learn a self-representation to explore its global cluster structure. Further, Double Graphs Regularization (DGR) is performed on both latent representation and self-representation to take advantage of their local manifold structures simultaneously. Then, we design an iterative algorithm to solve the optimization problem effectively. Comprehensive experiments on several popular multi-view datasets demonstrate the effectiveness of the proposed method.
{"title":"Double graphs regularized multi-view subspace clustering","authors":"Longlong Chen, Yulong Wang, Youheng Liu, Yutao Hu, Libin Wang, Huiwu Luo, Yuan Yan Tang","doi":"10.1142/s0219691323500327","DOIUrl":"https://doi.org/10.1142/s0219691323500327","url":null,"abstract":"In recent years, there has been an increasing interest in multi-view subspace clustering (MSC). However, existing MSC methods fail to take full advantage of the local geometric structure in each manifold throughout the data flow, which is essential for clustering. To remedy this drawback, in this paper, a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method is proposed, which aims to harness both global and local structural information of multi-view data in a unified framework. Specifically, DGRMSC first learns a latent representation to exploit the global complementary information of multiple views. Based on the learned latent representation, we learn a self-representation to explore its global cluster structure. Further, Double Graphs Regularization (DGR) is performed on both latent representation and self-representation to take advantage of their local manifold structures simultaneously. Then, we design an iterative algorithm to solve the optimization problem effectively. Comprehensive experiments on several popular multi-view datasets demonstrate the effectiveness of the proposed method.","PeriodicalId":50282,"journal":{"name":"International Journal of Wavelets Multiresolution and Information Processing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136119846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-04DOI: 10.1142/s0219691323500418
Zhihua Zhang
{"title":"Dual Wavelet Frame Packets with Non-square Iterative Matrices","authors":"Zhihua Zhang","doi":"10.1142/s0219691323500418","DOIUrl":"https://doi.org/10.1142/s0219691323500418","url":null,"abstract":"","PeriodicalId":50282,"journal":{"name":"International Journal of Wavelets Multiresolution and Information Processing","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42399993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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