Int. J. Wavelets Multiresolution Inf. Process.最新文献_第2页

Calibrating the classifier for protein family prediction with protein sequence using machine learning techniques: An empirical investigation 使用机器学习技术校准蛋白质序列预测分类器:一项实证研究

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2023-01-25 DOI: 10.1142/s021969132250045x

T. Idhaya, A. Suruliandi, D. Calitoiu, S. Raja

{"title":"Calibrating the classifier for protein family prediction with protein sequence using machine learning techniques: An empirical investigation","authors":"T. Idhaya, A. Suruliandi, D. Calitoiu, S. Raja","doi":"10.1142/s021969132250045x","DOIUrl":"https://doi.org/10.1142/s021969132250045x","url":null,"abstract":"A gene is a basic unit of congenital traits and a sequence of nucleotides in deoxyribonucleic acid that encrypts protein synthesis. Proteins are made up of amino acid residue and are classified for use in protein-related research, which includes identifying changes in genes, finding associations with diseases and phenotypes, and identifying potential drug targets. To this end, proteins are studied and classified, based on the family. For family prediction, however, a computational rather than an experimental approach is introduced, owing to the time involved in the latter process. Computational approaches to protein family prediction involve two important processes, feature selection and classification. Existing approaches to protein family prediction are alignment-based and alignment-free. The drawback of the former is that it searches for protein signatures by aligning every available sequence. Consequently, the latter alignment-free approach is taken for study, given that it only needs sequence-based features to predict the protein family and is far more efficient than the former. Nevertheless, the sequence-based characteristics taken for study have additional features to offer. There is, thus, a need to select the best features of all. When comes to classification still there is no perfection in classifying the protein. So, a comparison of different approaches is done to find the best feature selection technique and classification technique for protein family prediction. From the study, the feature subset selected provides the best classification accuracy of 96% for filter-based feature selection technique and the random forest classifier.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125550182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Rational Franklin MRA and its Wavelets 理性富兰克林MRA及其小波

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2023-01-19 DOI: 10.1142/s0219691323500066

Siva Prasad Murugan, G. P. Youvaraj

引用次数: 0

Object recognition from enhanced underwater image using optimized deep-CNN 基于优化深度cnn的增强水下图像目标识别

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2023-01-19 DOI: 10.1142/s0219691323500078

S. R. Lyernisha, C. Christopher, S. R. Fernisha

引用次数: 0

Accurate object tracking by aligning and refining multiple predictions in Siamese networks 通过在Siamese网络中对齐和精炼多个预测来精确跟踪目标

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2023-01-17 DOI: 10.1142/s0219691323500054

Xiao Lin, Zhaohua Hu, Haonan Liu

引用次数: 0

3D residual attention network for hyperspectral image classification 高光谱图像分类的三维残差关注网络

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2023-01-06 DOI: 10.1142/s0219691323500042

Huizhen Li, Kanghui Wei, Bengong Zhang

引用次数: 0

Middle frequency band and remark on Koch-Tataru's iteration space 中频带和Koch-Tataru迭代空间的注释

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2023-01-06 DOI: 10.1142/s0219691323500030

Haibo Yang, Qixiang Yang, Huo-xiong Wu

引用次数: 1

A robust method for coherent and non-coherent source number detection using a special Hankel-based covariance matrix 一种鲁棒的相干和非相干源数检测方法，使用特殊的基于汉克尔的协方差矩阵

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-12-29 DOI: 10.1142/s0219691323500029

R. Fazli, Hadi Owlia, R. Sheikhpour

引用次数: 0

Rational wavelet filter banks from Blaschke product 基于Blaschke产品的合理小波滤波器组

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-12-29 DOI: 10.1142/s0219691322500424

Xuefeng Wang

引用次数: 0

Estimation of misclassification rate in the Asymptotic Rare and Weak model with sub-Gaussian noises 含亚高斯噪声的渐近稀有弱模型的误分类率估计

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-12-22 DOI: 10.1142/s0219691323500017

Youming Liu, Zhentao Zhang

引用次数: 0

3D Image reconstruction using C-dual attention network from multi-view images 基于c -双注意网络的多视点三维图像重建

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-12-13 DOI: 10.1142/s0219691322500448

T. U. Kamble, S. Mahajan

{"title":"3D Image reconstruction using C-dual attention network from multi-view images","authors":"T. U. Kamble, S. Mahajan","doi":"10.1142/s0219691322500448","DOIUrl":"https://doi.org/10.1142/s0219691322500448","url":null,"abstract":"3D image reconstruction using multi-view imaging is widely utilized in several application domains: construction field, disaster management, urban planning, etc. The 3D reconstruction from the multi-view image is still challenging due to the high freedom and inaccurate reconstruction. This research introduces the hybrid deep learning technique for reconstructing the 3D image, in which the C-dual attention layer is proposed for generating the feature map to support the image reconstruction. The proposed 3D image reconstruction uses the encoder–decoder–refiner which is utilized for reconstruction. Initially, the features are extracted from the AlexNet and ResNet-50 features automatically. Then, the proposed C-dual attention layer is utilized for generating the inter-channel and inter-spatial relationship among the features to obtain enhanced reconstruction accuracy. The inter-channel relationship is evaluated using the channel attention layer, and the inter-spatial relationship is evaluated using the spatial attention layer of the encoder module. Here, the features generated by the spatial attention layer are combined to form the feature map in a 2D map. The proposed C-dual attention encoder provides enhanced features that help to acquire enhanced 3D image reconstruction. The proposed method is evaluated based on loss, IoU_3D, and IoU_2D, and acquired the values of 0.0721, 1.25 and 1.37, respectively.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134152783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1