Journal articles on the topic 'Sequence Feature'
To see the other types of publications on this topic, follow the link: Sequence Feature.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Sequence Feature.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Liu, Zhi-Hua, Dian Jiao, and Xiao Sun. "Classifying Genomic Sequences by Sequence Feature Analysis." Genomics, Proteomics & Bioinformatics 3, no. 4 (2005): 201–5. http://dx.doi.org/10.1016/s1672-0229(05)03027-5.
Full text
2
Caspi, Yaron, Denis Simakov, and Michal Irani. "Feature-Based Sequence-to-Sequence Matching." International Journal of Computer Vision 68, no. 1 (March 1, 2006): 53–64. http://dx.doi.org/10.1007/s11263-005-4842-z.
Full text
3
Zhang, Tianjiao, Rongjie Wang, Qinghua Jiang, and Yadong Wang. "An Information Gain-based Method for Evaluating the Classification Power of Features Towards Identifying Enhancers." Current Bioinformatics 15, no. 6 (November 11, 2020): 574–80. http://dx.doi.org/10.2174/1574893614666191120141032.
Full textAbstract:
Background: Enhancers are cis-regulatory elements that enhance gene expression on DNA sequences. Since most of enhancers are located far from transcription start sites, it is difficult to identify them. As other regulatory elements, the regions around enhancers contain a variety of features, which can help in enhancer recognition. Objective: The classification power of features differs significantly, the performances of existing methods that use one or a few features for identifying enhancer vary greatly. Therefore, evaluating the classification power of each feature can improve the predictive performance of enhancers. Methods: We present an evaluation method based on Information Gain (IG) that captures the entropy change of enhancer recognition according to features. To validate the performance of our method, experiments using the Single Feature Prediction Accuracy (SFPA) were conducted on each feature. Results: The average IG values of the sequence feature, transcriptional feature and epigenetic feature are 0.068, 0.213, and 0.299, respectively. Through SFPA, the average AUC values of the sequence feature, transcriptional feature and epigenetic feature are 0.534, 0.605, and 0.647, respectively. The verification results are consistent with our evaluation results. Conclusion: This IG-based method can effectively evaluate the classification power of features for identifying enhancers. Compared with sequence features, epigenetic features are more effective for recognizing enhancers.
4
SHAN, YING, HARPREET S. SAWHNEY, and ART POPE. "CLUSTERING MULTIPLE IMAGE SEQUENCES WITH A SEQUENCE-TO-SEQUENCE SIMILARITY MEASURE." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 04 (June 2005): 551–64. http://dx.doi.org/10.1142/s0218001405004149.
Full textAbstract:
We propose a novel similarity measure of two image sequences based on shapeme histograms. The idea of shapeme histogram has been used for single image/texture recognition, but is used here to solve the sequence-to-sequence matching problem. We develop techniques to represent each sequence as a set of shapeme histograms, which captures different variations of the object appearances within the sequence. These shapeme histograms are computed from the set of 2D invariant features that are stable across multiple images in the sequence, and therefore minimizes the effect of both background clutter, and 2D pose variations. We define sequence similarity measure as the similarity of the most similar pair of images from both sequences. This definition maximizes the chance of matching between two sequences of the same object, because it requires only part of the sequences being similar. We also introduce a weighting scheme to conduct an implicit feature selection process during the matching of two shapeme histograms. Experiments on clustering image sequences of tracked objects demonstrate the efficacy of the proposed method.
5
Pugin, E. V., and A. L. Zhiznyakov. "CLASSIFICATION OF FEATURES OF IMAGE SEQUENCES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W6 (May 18, 2015): 79–81. http://dx.doi.org/10.5194/isprsarchives-xl-5-w6-79-2015.
Full textAbstract:
Processing of image sequences is a very actual trend now. This is confirmed with a vast amount of researches in that area. The possibility of an image sequence processing and pattern recognition became available because of increased computer capabilities and better photo and video cameras. The feature extraction is one of the main steps during image processing and pattern recognition. This paper presents a novel classification of features of image sequences. The proposed classification has three groups: 1) features of a single image, 2) features of an image sequence, 3) semantic features of an observed scene. The first group includes features extracted from a single image. The second group consists of features of any kinds of image sequences. The third group contains semantic features. Reverse feature clarification method is the iterative method when on each iteration we use higher level features to extract lower level features more precisely. The proposed classification of features of image sequences solves a problem of decomposition of the source feature space into several groups. Reverse feature clarification method allows to increase the quality of image processing during iterative process.
6
Vezzi, Francesco, Giuseppe Narzisi, and Bud Mishra. "Feature-by-Feature – Evaluating De Novo Sequence Assembly." PLoS ONE 7, no. 2 (February 3, 2012): e31002. http://dx.doi.org/10.1371/journal.pone.0031002.
Full text
7
Tian, Huixin, and Qiangqiang Xu. "Time Series Prediction Method Based on E-CRBM." Electronics 10, no. 4 (February 8, 2021): 416. http://dx.doi.org/10.3390/electronics10040416.
Full textAbstract:
To solve the problems of delayed prediction results and large prediction errors in one-dimensional time series prediction, a time series prediction method based on Error-Continuous Restricted Boltzmann Machines (E-CRBM) is proposed in this paper. This method constructs a deep conversion prediction framework, which is composed of two E-CRBMs and a neural network (NN). Firstly, the E-CRBM models of the original input sequence and the target prediction sequence are trained, respectively, to extract the time features of the two sequences. Then the NN model is used to connect and transform the time features. Secondly, the feature sequence H1 is extracted from the original input sequence of test data through E-CRBM1, which is used as input of NN to obtain feature transformation sequence H2. Finally, the target prediction sequence is obtained by reverse reconstruction of feature transformation sequence H2 through E-CRBM2. The E-CRBM in this paper introduces the residual sequence of NN feature transformation in the hidden layer of CRBM, which increases the robustness of CRBM and improves the overall prediction accuracy. The classical time series data (sunspot time series) and the actual operation data of reciprocating compressor are selected in the experiment. Compared with the traditional time series prediction method, the results verify the effectiveness of the proposed method in single-step prediction and multi-step prediction.
8
AL-SHAHIB, ALI, RAINER BREITLING, and DAVID GILBERT. "FRANKSUM: NEW FEATURE SELECTION METHOD FOR PROTEIN FUNCTION PREDICTION." International Journal of Neural Systems 15, no. 04 (August 2005): 259–75. http://dx.doi.org/10.1142/s0129065705000281.
Full textAbstract:
In the study of in silico functional genomics, improving the performance of protein function prediction is the ultimate goal for identifying proteins associated with defined cellular functions. The classical prediction approach is to employ pairwise sequence alignments. However this method often faces difficulties when no statistically significant homologous sequences are identified. An alternative way is to predict protein function from sequence-derived features using machine learning. In this case the choice of possible features which can be derived from the sequence is of vital importance to ensure adequate discrimination to predict function. In this paper we have successfully selected biologically significant features for protein function prediction. This was performed using a new feature selection method (FrankSum) that avoids data distribution assumptions, uses a data independent measurement (p-value) within the feature, identifies redundancy between features and uses an appropiate ranking criterion for feature selection. We have shown that classifiers generated from features selected by FrankSum outperforms classifiers generated from full feature sets, randomly selected features and features selected from the Wrapper method. We have also shown the features are concordant across all species and top ranking features are biologically informative. We conclude that feature selection is vital for successful protein function prediction and FrankSum is one of the feature selection methods that can be applied successfully to such a domain.
9
Et. al., Muthulakshmi M,. "A Novel Feature Extraction from Genome Sequences For Taxonomic Classification Of Living Organisms." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 1436–51. http://dx.doi.org/10.17762/turcomat.v12i2.1364.
Full textAbstract:
Genome sequencing aids in understanding the nature, characteristics, habitat and evolutionary history of all living organisms. Apart from sequencing, the more important task is to correctly place the sequenced genome in the taxonomy. Generally, the taxonomic classification of the living organisms is done by observing their morphological, behavioral, genetic and biochemical characteristics. Among them, taxonomic classification using genetic observation is more accurate scientifically as the Genome sequence analysis exploits the complete characteristics of the organism. In this paper, we developed a novel Frequency based Feature Extraction Technique (FFET) which extracts 120 features and helps to analyze the genome sequence of the organism and to classify them in the taxonomy accordingly. We performed a kingdom level taxonomic classification using the proposed FFET. The proposed FFET extracts features based on storage, frequency of nucleotide bases, pattern arrangement and amino acid composition of genome sequences. The feature extraction technique is applied to 150 samples of genome sequences of various organisms which were downloaded from National Centre for Biotechnology and Information (NCBI) database. The extracted features are classified using various Machine learning and Deep learning classifiers. From the results, it is evident that FFET performs well for classification with Convolutional Neural Network (CNN) classifier with an accuracy of 96.73 %.
10
Huang, Wen-Lin, Chun-Wei Tung, Chyn Liaw, Hui-Ling Huang, and Shinn-Ying Ho. "Rule-Based Knowledge Acquisition Method for Promoter Prediction in Human andDrosophilaSpecies." Scientific World Journal 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/327306.
Full textAbstract:
The rapid and reliable identification of promoter regions is important when the number of genomes to be sequenced is increasing very speedily. Various methods have been developed but few methods investigate the effectiveness of sequence-based features in promoter prediction. This study proposes a knowledge acquisition method (named PromHD) based on if-then rules for promoter prediction in human andDrosophilaspecies. PromHD utilizes an effective feature-mining algorithm and a reference feature set of 167 DNA sequence descriptors (DNASDs), comprising three descriptors of physicochemical properties (absorption maxima, molecular weight, and molar absorption coefficient), 128 top-ranked descriptors of 4-mer motifs, and 36 global sequence descriptors. PromHD identifies two feature subsets with 99 and 74 DNASDs and yields test accuracies of 96.4% and 97.5% in human andDrosophilaspecies, respectively. Based on the 99- and 74-dimensional feature vectors, PromHD generates several if-then rules by using the decision tree mechanism for promoter prediction. The top-ranked informative rules with high certainty grades reveal that the global sequence descriptor, the length of nucleotide A at the first position of the sequence, and two physicochemical properties, absorption maxima and molecular weight, are effective in distinguishing promoters from non-promoters in human andDrosophilaspecies, respectively.
11
Cao, Like, Jie Ling, and Xiaohui Xiao. "Study on the Influence of Image Noise on Monocular Feature-Based Visual SLAM Based on FFDNet." Sensors 20, no. 17 (August 31, 2020): 4922. http://dx.doi.org/10.3390/s20174922.
Full textAbstract:
Noise appears in images captured by real cameras. This paper studies the influence of noise on monocular feature-based visual Simultaneous Localization and Mapping (SLAM). First, an open-source synthetic dataset with different noise levels is introduced in this paper. Then the images in the dataset are denoised using the Fast and Flexible Denoising convolutional neural Network (FFDNet); the matching performances of Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF) and Oriented FAST and Rotated BRIEF (ORB) which are commonly used in feature-based SLAM are analyzed in comparison and the results show that ORB has a higher correct matching rate than that of SIFT and SURF, the denoised images have a higher correct matching rate than noisy images. Next, the Absolute Trajectory Error (ATE) of noisy and denoised sequences are evaluated on ORB-SLAM2 and the results show that the denoised sequences perform better than the noisy sequences at any noise level. Finally, the completely clean sequence in the dataset and the sequences in the KITTI dataset are denoised and compared with the original sequence through comprehensive experiments. For the clean sequence, the Root-Mean-Square Error (RMSE) of ATE after denoising has decreased by 16.75%; for KITTI sequences, 7 out of 10 sequences have lower RMSE than the original sequences. The results show that the denoised image can achieve higher accuracy in the monocular feature-based visual SLAM under certain conditions.
12
Caragea, Cornelia, Adrian Silvescu, and Prasenjit Mitra. "Protein sequence classification using feature hashing." Proteome Science 10, Suppl 1 (2012): S14. http://dx.doi.org/10.1186/1477-5956-10-s1-s14.
Full text
13
Chuzhanova, N. A., A. J. Jones, and S. Margetts. "Feature selection for genetic sequence classification." Bioinformatics 14, no. 2 (March 1, 1998): 139–43. http://dx.doi.org/10.1093/bioinformatics/14.2.139.
Full text
14
Liang, Yigao, Shaohua Jiang, Min Gao, Fengjiao Jia, Zaoyang Wu, and Zhijian Lyu. "GLSTM-DTA: Application of Prediction Improvement Model Based on GNN and LSTM." Journal of Physics: Conference Series 2219, no. 1 (April 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2219/1/012008.
Full textAbstract:
Abstract Most prediction models of drug-target binding affinity (DTA) treated drugs and targets as sequences, and feature extraction networks could not sufficiently extract features. Inspired by DeepDTA and GraphDTA, we proposed an improved model named GLSTM-DTA for DTA prediction, which combined Graph Neural Network (GNN) and Long Short-Term Memory Network (LSTM). The feature extraction block consists of two parts: GNN block and LSTM block, which extract drug features and protein features respectively. The novelty of our work is using LSTM, instead of Convolutional neural network (CNN) to extract protein sequence features, which is facilitating to capture long-term dependencies in sequence. In addition, we also converted drugs into graph structures and use GNN for feature extraction. The improved model performs better than DeepDTA and GraphDTA. The comprehensive results prove the advantages of our model in accurately predicting the binding affinity of drug-targets.
15
Wang, Yilin, and Baokuan Chang. "Extraction of Human Motion Information from Digital Video Based on 3D Poisson Equation." Advances in Mathematical Physics 2021 (December 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/1268747.
Full textAbstract:
Based on the 3D Poisson equation, this paper extracts the features of the digital video human body action sequence. By solving the Poisson equation on the silhouette sequence, the time and space features, time and space structure features, shape features, and orientation features can be obtained. First, we use the silhouette structure features in three-dimensional space-time and the orientation features of the silhouette in three-dimensional space-time to represent the local features of the silhouette sequence and use the 3D Zernike moment feature to represent the overall features of the silhouette sequence. Secondly, we combine the Bayesian classifier and AdaBoost classifier to learn and classify the features of human action sequences, conduct experiments on the Weizmann video database, and conduct multiple experiments using the method of classifying samples and selecting partial combinations for training. Then, using the recognition algorithm of motion capture, after the above process, the three-dimensional model is obtained and matched with the model in the three-dimensional model database, the sequence with the smallest distance is calculated, and the corresponding skeleton is outputted as the results of action capture. During the experiment, the human motion tracking method based on the university matching kernel (EMK) image kernel descriptor was used; that is, the scale invariant operator was used to count the characteristics of multiple training images, and finally, the high-dimensional feature space was mapped into the low-dimensional to obtain the feature space approximating the Gaussian kernel. Based on the above analysis, the main user has prior knowledge of the network environment. The experimental results show that the method in this paper can effectively extract the characteristics of human body movements and has a good classification effect for bending, one-foot jumping, vertical jumping, waving, and other movements. Due to the linear separability of the data in the kernel space, fast linear interpolation regression is performed on the features in the feature space, which significantly improves the robustness and accuracy of the estimation of the human motion pose in the image sequence.
16
Peng, He. "CFSP: a collaborative frequent sequence pattern discovery algorithm for nucleic acid sequence classification." PeerJ 8 (April 20, 2020): e8965. http://dx.doi.org/10.7717/peerj.8965.
Full textAbstract:
Background Conserved nucleic acid sequences play an essential role in transcriptional regulation. The motifs/templates derived from nucleic acid sequence datasets are usually used as biomarkers to predict biochemical properties such as protein binding sites or to identify specific non-coding RNAs. In many cases, template-based nucleic acid sequence classification performs better than some feature extraction methods, such as N-gram and k-spaced pairs classification. The availability of large-scale experimental data provides an unprecedented opportunity to improve motif extraction methods. The process for pattern extraction from large-scale data is crucial for the creation of predictive models. Methods In this article, a Teiresias-like feature extraction algorithm to discover frequent sub-sequences (CFSP) is proposed. Although gaps are allowed in some motif discovery algorithms, the distance and number of gaps are limited. The proposed algorithm can find frequent sequence pairs with a larger gap. The combinations of frequent sub-sequences in given protracted sequences capture the long-distance correlation, which implies a specific molecular biological property. Hence, the proposed algorithm intends to discover the combinations. A set of frequent sub-sequences derived from nucleic acid sequences with order is used as a base frequent sub-sequence array. The mutation information is attached to each sub-sequence array to implement fuzzy matching. Thus, a mutate records a single nucleotide variant or nucleotides insertion/deletion (indel) to encode a slight difference between frequent sequences and a matched subsequence of a sequence under investigation. Conclusions The proposed algorithm has been validated with several nucleic acid sequence prediction case studies. These data demonstrate better results than the recently available feature descriptors based methods based on experimental data sets such as miRNA, piRNA, and Sigma 54 promoters. CFSP is implemented in C++ and shell script; the source code and related data are available at https://github.com/HePeng2016/CFSP.
17
Xing, Chenjie, Yuan Zhou, Yinan Peng, Jieke Hao, and Shuoshi Li. "Specific Emitter Identification Based on Ensemble Neural Network and Signal Graph." Applied Sciences 12, no. 11 (May 28, 2022): 5496. http://dx.doi.org/10.3390/app12115496.
Full textAbstract:
Specific emitter identification (SEI) is a technology for extracting fingerprint features from a signal and identifying the emitter. In this paper, the author proposes an SEI method based on ensemble neural networks (ENN) and signal graphs, with the following innovations: First, a signal graph is used to show signal data in a non-Euclidean space. Namely, sequence signal data is constructed into a signal graph to transform the sequence signal from a Euclidian space to a non-Euclidean space. Hence, the graph feature (the feature of the non-Euclidean space) of the signal can be extracted from the signal graph. Second, the ensemble neural network is integrated with a graph feature extractor and a sequence feature extractor, making it available to extract both graph and sequence simultaneously. This ensemble neural network also fuses graph features with sequence features, obtaining an ensemble feature that has both features in Euclidean space and non-Euclidean space. Therefore, the ensemble feature contains more effective information for the identification of the emitter. The study results demonstrate that this SEI method has higher SEI accuracy and robustness than traditional machine learning methods and common deep learning methods.
18
Kinsner, Witold, and Hong Zhang. "Multi-Fractal Analysis for Feature Extraction from DNA Sequences." International Journal of Software Science and Computational Intelligence 2, no. 2 (April 2010): 1–18. http://dx.doi.org/10.4018/jssci.2010040101.
Full textAbstract:
This paper presents estimations of multi-scale (multi-fractal) measures for feature extraction from deoxyribonucleic acid (DNA) sequences, and demonstrates the intriguing possibility of identifying biological functionality using information contained within the DNA sequence. We have developed a technique that seeks patterns or correlations in the DNA sequence at a higher level than the local base-pair structure. The technique has three main steps: (i) transforms the DNA sequence symbols into a modified Lévy walk, (ii) transforms the Lévy walk into a signal spectrum, and (iii) breaks the spectrum into sub-spectra and treats each of these as an attractor from which the multi-fractal dimension spectrum is estimated. An optimal minimum window size and volume element size are found for estimation of the multi-fractal measures. Experimental results show that DNA is multi-fractal, and that the multi-fractality changes depending upon the location (coding or non-coding region) in the sequence.
19
Priya, Sarv, Yanan Liu, Caitlin Ward, Nam H. Le, Neetu Soni, Ravishankar Pillenahalli Maheshwarappa, Varun Monga, Honghai Zhang, Milan Sonka, and Girish Bathla. "Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?" Cancers 13, no. 11 (May 24, 2021): 2568. http://dx.doi.org/10.3390/cancers13112568.
Full textAbstract:
Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311–0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods.
20
McHugh, Damien J., Nuria Porta, Ross A. Little, Susan Cheung, Yvonne Watson, Geoff J. M. Parker, Gordon C. Jayson, and James P. B. O’Connor. "Image Contrast, Image Pre-Processing, and T1 Mapping Affect MRI Radiomic Feature Repeatability in Patients with Colorectal Cancer Liver Metastases." Cancers 13, no. 2 (January 11, 2021): 240. http://dx.doi.org/10.3390/cancers13020240.
Full textAbstract:
Imaging biomarkers require technical, biological, and clinical validation to be translated into robust tools in research or clinical settings. This study contributes to the technical validation of radiomic features from magnetic resonance imaging (MRI) by evaluating the repeatability of features from four MR sequences: pre-contrast T1- and T2-weighted images, pre-contrast quantitative T1 maps (qT1), and contrast-enhanced T1-weighted images. Fifty-one patients with colorectal cancer liver metastases were scanned twice, up to 7 days apart. Repeatability was quantified using the intraclass correlation coefficient (ICC) and repeatability coefficient (RC), and the impact of non-Gaussian feature distributions and image normalisation was evaluated. Most radiomic features had non-Gaussian distributions, but Box–Cox transformations enabled ICCs and RCs to be calculated appropriately for an average of 97% of features across sequences. ICCs ranged from 0.30 to 0.99, with volume and other shape features tending to be most repeatable; volume ICC > 0.98 for all sequences. 19% of features from non-normalised images exhibited significantly different ICCs in pair-wise sequence comparisons. Normalisation tended to increase ICCs for pre-contrast T1- and T2-weighted images, and decrease ICCs for qT1 maps. RCs tended to vary more between sequences than ICCs, showing that evaluations of feature performance depend on the chosen metric. This work suggests that feature-specific repeatability, from specific combinations of MR sequence and pre-processing steps, should be evaluated to select robust radiomic features as biomarkers in specific studies. In addition, as different repeatability metrics can provide different insights into a specific feature, consideration of the appropriate metric should be taken in a study-specific context.
21
McHugh, Damien J., Nuria Porta, Ross A. Little, Susan Cheung, Yvonne Watson, Geoff J. M. Parker, Gordon C. Jayson, and James P. B. O’Connor. "Image Contrast, Image Pre-Processing, and T1 Mapping Affect MRI Radiomic Feature Repeatability in Patients with Colorectal Cancer Liver Metastases." Cancers 13, no. 2 (January 11, 2021): 240. http://dx.doi.org/10.3390/cancers13020240.
Full textAbstract:
Imaging biomarkers require technical, biological, and clinical validation to be translated into robust tools in research or clinical settings. This study contributes to the technical validation of radiomic features from magnetic resonance imaging (MRI) by evaluating the repeatability of features from four MR sequences: pre-contrast T1- and T2-weighted images, pre-contrast quantitative T1 maps (qT1), and contrast-enhanced T1-weighted images. Fifty-one patients with colorectal cancer liver metastases were scanned twice, up to 7 days apart. Repeatability was quantified using the intraclass correlation coefficient (ICC) and repeatability coefficient (RC), and the impact of non-Gaussian feature distributions and image normalisation was evaluated. Most radiomic features had non-Gaussian distributions, but Box–Cox transformations enabled ICCs and RCs to be calculated appropriately for an average of 97% of features across sequences. ICCs ranged from 0.30 to 0.99, with volume and other shape features tending to be most repeatable; volume ICC > 0.98 for all sequences. 19% of features from non-normalised images exhibited significantly different ICCs in pair-wise sequence comparisons. Normalisation tended to increase ICCs for pre-contrast T1- and T2-weighted images, and decrease ICCs for qT1 maps. RCs tended to vary more between sequences than ICCs, showing that evaluations of feature performance depend on the chosen metric. This work suggests that feature-specific repeatability, from specific combinations of MR sequence and pre-processing steps, should be evaluated to select robust radiomic features as biomarkers in specific studies. In addition, as different repeatability metrics can provide different insights into a specific feature, consideration of the appropriate metric should be taken in a study-specific context.
22
Zhang, Jun, and Bin Liu. "A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods." Current Bioinformatics 14, no. 3 (March 7, 2019): 190–99. http://dx.doi.org/10.2174/1574893614666181212102749.
Full textAbstract:
Background:Proteins play a crucial role in life activities, such as catalyzing metabolic reactions, DNA replication, responding to stimuli, etc. Identification of protein structures and functions are critical for both basic research and applications. Because the traditional experiments for studying the structures and functions of proteins are expensive and time consuming, computational approaches are highly desired. In key for computational methods is how to efficiently extract the features from the protein sequences. During the last decade, many powerful feature extraction algorithms have been proposed, significantly promoting the development of the studies of protein structures and functions.Objective:To help the researchers to catch up the recent developments in this important field, in this study, an updated review is given, focusing on the sequence-based feature extractions of protein sequences.Method:These sequence-based features of proteins were grouped into three categories, including composition-based features, autocorrelation-based features and profile-based features. The detailed information of features in each group was introduced, and their advantages and disadvantages were discussed. Besides, some useful tools for generating these features will also be introduced.Results:Generally, autocorrelation-based features outperform composition-based features, and profile-based features outperform autocorrelation-based features. The reason is that profile-based features consider the evolutionary information, which is useful for identification of protein structures and functions. However, profile-based features are more time consuming, because the multiple sequence alignment process is required.Conclusion:In this study, some recently proposed sequence-based features were introduced and discussed, such as basic k-mers, PseAAC, auto-cross covariance, top-n-gram etc. These features did make great contributions to the developments of protein sequence analysis. Future studies can be focus on exploring the combinations of these features. Besides, techniques from other fields, such as signal processing, natural language process (NLP), image processing etc., would also contribute to this important field, because natural languages (such as English) and protein sequences share some similarities. Therefore, the proteins can be treated as documents, and the features, such as k-mers, top-n-grams, motifs, can be treated as the words in the languages. Techniques from these filed will give some new ideas and strategies for extracting the features from proteins.
23
Lin, Brian Y., Patricia P. Chan, and Todd M. Lowe. "tRNAviz: explore and visualize tRNA sequence features." Nucleic Acids Research 47, W1 (May 25, 2019): W542—W547. http://dx.doi.org/10.1093/nar/gkz438.
Full textAbstract:
Abstract Transfer RNAs (tRNAs) are ubiquitous across the tree of life. Although tRNA structure is highly conserved, there is still significant variation in sequence features between clades, isotypes and even isodecoders. This variation not only impacts translation, but as shown by a variety of recent studies, nontranslation-associated functions are also sensitive to small changes in tRNA sequence. Despite the rapidly growing number of sequenced genomes, there is a lack of tools for both small- and large-scale comparative genomics analysis of tRNA sequence features. Here, we have integrated over 150 000 tRNAs spanning all domains of life into tRNAviz, a web application for exploring and visualizing tRNA sequence features. tRNAviz implements a framework for determining consensus sequence features and can generate sequence feature distributions by isotypes, clades and anticodons, among other tRNA properties such as score. All visualizations are interactive and exportable. The web server is publicly available at http://trna.ucsc.edu/tRNAviz/.
24
Guo, Xiao Ran, Shao Hui Cui, and Fang Dan. "Robust Feature Points Extraction Based on Harris and SIFT." Applied Mechanics and Materials 347-350 (August 2013): 3500–3504. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3500.
Full textAbstract:
This article presents a novel approach to extract robust local feature points of video sequence in digital image stabilization system. Robust Harris-SIFT detector is proposed to select the most stable SIFT key points in the video sequence where image motion is happened due to vehicle or platform vibration. Experimental results show that the proposed scheme is robust to various transformations of video sequences, such as translation, rotation and scaling, as well as blurring. Compared with the current state-of-the-art schemes, the proposed scheme yields better performances.
25
Wang, Weiping, Congmin Ren, Hong Song, Shigeng Zhang, and Pengfei Liu. "FGL_Droid: An Efficient Android Malware Detection Method Based on Hybrid Analysis." Security and Communication Networks 2022 (April 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/8398591.
Full textAbstract:
With the popularity of Android intelligent terminals, malicious applications targeting Android platform are growing rapidly. Therefore, efficient and accurate detection of Android malicious software becomes particularly important. Dynamic API call sequences are widely used in Android malware detection because they can reflect the behaviours of applications accurately. However, the raw dynamic API call sequences are very usually too long to be directly used, and most existing works just use a truncated segment of the sequence or statistical features of the sequence to perform malware detection, which loses the execution order information of applications and consequently results in high false alarm rate. In this work, we propose a method that transforms the dynamic API call sequence into a function call graph, which retains most of the application execution order information with significantly reduced sequence size. To compensate for the missed behaviour information during the transformation, the advanced features of permission requests extracted from the application are utilized. We then propose FGL_Droid, which fusions the transformed function call graph feature and the extracted permission request feature to perform accurate malware detection. Experiments on benchmark dataset show that FGL_Droid achieves a high detection accuracy of 0.975 and a high F-score of 0.978, which are better than the existing methods.
26
Li, Xing, Qian Huang, Yunfei Zhang, Tianjin Yang, and Zhijian Wang. "PointMapNet: Point Cloud Feature Map Network for 3D Human Action Recognition." Symmetry 15, no. 2 (January 30, 2023): 363. http://dx.doi.org/10.3390/sym15020363.
Full textAbstract:
3D human action recognition is crucial in broad industrial application scenarios such as robotics, video surveillance, autonomous driving, or intellectual education, etc. In this paper, we present a new point cloud sequence network called PointMapNet for 3D human action recognition. In PointMapNet, two point cloud feature maps symmetrical to depth feature maps are proposed to summarize appearance and motion representations from point cloud sequences. Specifically, we first convert the point cloud frames to virtual action frames using static point cloud techniques. The virtual action frame is a 1D vector used to characterize the structural details in the point cloud frame. Then, inspired by feature map-based human action recognition on depth sequences, two point cloud feature maps are symmetrically constructed to recognize human action from the point cloud sequence, i.e., Point Cloud Appearance Map (PCAM) and Point Cloud Motion Map (PCMM). To construct PCAM, an MLP-like network architecture is designed and used to capture the spatio-temporal appearance feature of the human action in a virtual action sequence. To construct PCMM, the MLP-like network architecture is used to capture the motion feature of the human action in a virtual action difference sequence. Finally, the two point cloud feature map descriptors are concatenated and fed to a fully connected classifier for human action recognition. In order to evaluate the performance of the proposed approach, extensive experiments are conducted. The proposed method achieves impressive results on three benchmark datasets, namely NTU RGB+D 60 (89.4% cross-subject and 96.7% cross-view), UTD-MHAD (91.61%), and MSR Action3D (91.91%). The experimental results outperform existing state-of-the-art point cloud sequence classification networks, demonstrating the effectiveness of our method.
27
Wei, Dan, Qingshan Jiang, and Sheng Li. "A New Approach for DNA Sequence Similarity Analysis based on Triplets of Nucleic Acid Bases." International Journal of Nanotechnology and Molecular Computation 2, no. 4 (October 2010): 1–11. http://dx.doi.org/10.4018/978-1-60960-064-8.ch006.
Full textAbstract:
Similarity analysis of DNA sequences is a fundamental research area in Bioinformatics. The characteristic distribution of L-tuple, which is the tuple of length L, reflects the valuable information contained in a biological sequence and thus may be used in DNA sequence similarity analysis. However, similarity analysis based on characteristic distribution of L-tuple is not effective for the comparison of highly conservative sequences. In this paper, a new similarity measurement approach based on Triplets of Nucleic Acid Bases (TNAB) is introduced for DNA sequence similarity analysis. The new approach characterizes both the content feature and position feature of a DNA sequence using the frequency and position of occurrence of TNAB in the sequence. The experimental results show that the approach based on TNAB is effective for analysing DNA sequence similarity.
28
Zhang, Shu Lu, Dong Sheng Zhou, and Qiang Zhang. "Human Motion Capture Data Segmentation Based on LLE Algorithm." Applied Mechanics and Materials 538 (April 2014): 481–85. http://dx.doi.org/10.4028/www.scientific.net/amm.538.481.
Full textAbstract:
In this paper, we propose the motion sequence segmentation based on LLE (Locally Linear Embedding) algorithm. The method is to reduce the dimension of the high dimension motion sequence to obtain one-dimension feature curve. Then we use the feature curve to achieve motion sequence segmentation. Simulation results demonstrate that this method can achieve motion sequences segmentation and improve the accuracy rate greatly compared with the traditional algorithm.
29
Tcheng, David K., and Shankar Subramaniam. "MACHINE APPROACHES TO PROTEIN FEATURE PREDICTION." International Journal of Neural Systems 03, supp01 (January 1992): 183–93. http://dx.doi.org/10.1142/s0129065792000516.
Full textAbstract:
Knowledge-based approaches are being increasingly used in predicting protein structure and motifs. Machine learning techniques such as neural networks and decision-trees have become invaluable tools for these approaches. This paper describes the use of machine learning in predicting sequence-based motifs in antibody fragments. Given the limited number of three dimensional structures and the plethora of sequences, this technique is useful for homology modeling of three dimensional structures of antibody fragments.
30
Nikam, Rahul, and M. Michael Gromiha. "Seq2Feature: a comprehensive web-based feature extraction tool." Bioinformatics 35, no. 22 (May 28, 2019): 4797–99. http://dx.doi.org/10.1093/bioinformatics/btz432.
Full textAbstract:
Abstract Motivation Machine learning techniques require various descriptors from protein and nucleic acid sequences to understand/predict their structure and function as well as distinguishing between disease and neutral mutations. Hence, availability of a feature extraction tool is necessary to bridge the gap. Results We developed a comprehensive web-based tool, Seq2Feature, which computes 252 protein and 41 DNA sequence-based descriptors. These features include physicochemical, energetic and conformational properties of proteins, mutation matrices and contact potentials as well as nucleotide composition, physicochemical and conformational properties of DNA. We propose that Seq2Feature could serve as an effective tool for extracting protein and DNA sequence-based features as applicable inputs to machine learning algorithms. Availability and implementation https://www.iitm.ac.in/bioinfo/SBFE/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
31
Li, Ying Jie, and Mongi A. Abidi. "The Comparative Study between Difference Actions and Full Actions." Applied Mechanics and Materials 373-375 (August 2013): 500–503. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.500.
Full textAbstract:
An appearance-based feature set is proposed. With Hidden Markov Model (HMM) handling any temporal variance, the contributions of features, which are from full foreground sequence and from temporal difference sequence, are compared in details by methods which are based on feature selecting and feature voting. The experimental analysis shows that the comparative contributions can be achieved for human action identifying by the two data sources. This introduces the opportunity to analyze human behavior based on temporal difference sequence instead of full foreground sequence, and validates the far-reaching significance of this work.
32
Rakun, Erdefi, and Noer FP Setyono. "Improving Recognition of SIBI Gesture by Combining Skeleton and Hand Shape Features." Jurnal Ilmu Komputer dan Informasi 15, no. 2 (July 2, 2022): 69–79. http://dx.doi.org/10.21609/jiki.v15i2.1014.
Full textAbstract:
SIBI (Sign System for Indonesian Language) is an official sign language system used in school for hearing impairment students in Indonesia. This work uses the skeleton and hand shape features to classify SIBI gestures. In order to improve the performance of the gesture classification system, we tried to fuse the features in several different ways. The accuracy results achieved by the feature fusion methods are, in descending order of accuracy: 88.016%, when using sequence-feature-vector concatenation, 85.448% when using Conneau feature vector concatenation, 83.723% when using feature-vector concatenation, and 49.618% when using simple feature concatenation. The sequence-feature-vector concatenation techniques yield noticeably better results than those achieved using single features (82.849% with skeleton feature only, 55.530% for the hand shape feature only). The experiment results show that the combined features of the whole gesture sequence can better distinguish one gesture from another in SIBI than the combined features of each gesture frame. In addition to finding the best feature combination technique, this study also found the most suitable Recurrent Neural Network (RNN) model for recognizing SIBI. The models tested are 1-layer, 2-layer LSTM, and GRU. The experimental results show that the 2-layer bidirectional LSTM has the best performance.
33
Zhou, Aijun, Nurbol Luktarhan, and Zhuang Ai. "Research on WebShell Detection Method Based on Regularized Neighborhood Component Analysis (RNCA)." Symmetry 13, no. 7 (July 4, 2021): 1202. http://dx.doi.org/10.3390/sym13071202.
Full textAbstract:
The variant, encryption, and confusion of WebShell results in problems in the detection method based on feature selection, such as poor detection effect and weak generalization ability. In order to solve this problem, a method of WebShell detection based on regularized neighborhood component analysis (RNCA) is proposed. The RNCA algorithm can effectively reduce the dimension of data while ensuring the accuracy of classification. In this paper, it is innovatively applied to a WebShell detection neighborhood, taking opcode behavior sequence features as the main research object, constructing vocabulary by using opcode sequence features with variable length, and effectively reducing the dimension of WebShell features from the perspective of feature selection. The opcode sequence selected by the algorithm is symmetrical with the source code file, which has great reference value for WebShell classification. On the issue of the single feature, this paper uses the fusion of behavior sequence features and text static features to construct a feature combination with stronger representation ability, which effectively improves the recognition rate of WebShell to a certain extent.
34
IRANI, S. A., H. Y. KOO, and S. RAMAN. "Feature-based operation sequence generation in CAPP." International Journal of Production Research 33, no. 1 (January 1995): 17–39. http://dx.doi.org/10.1080/00207549508930135.
Full text
35
Fristensky, Brian. "Feature expressions: creating and manipulating sequence datasets." Nucleic Acids Research 21, no. 25 (1993): 5997–6003. http://dx.doi.org/10.1093/nar/21.25.5997.
Full text
36
Eng, Tiam-Hock, Zhi-Kui Ling, Walter Olson, and Chuck McLean. "Feature-based assembly modeling and sequence generation." Computers & Industrial Engineering 36, no. 1 (January 1999): 17–33. http://dx.doi.org/10.1016/s0360-8352(98)00106-5.
Full text
37
Kim, Jinsang, and Tom Chen. "Multiple feature clustering for image sequence segmentation." Pattern Recognition Letters 22, no. 11 (September 2001): 1207–17. http://dx.doi.org/10.1016/s0167-8655(01)00053-8.
Full text
38
Yahyaoui, Hamdi, and Aisha Al-Mutairi. "A feature-based trust sequence classification algorithm." Information Sciences 328 (January 2016): 455–84. http://dx.doi.org/10.1016/j.ins.2015.08.008.
Full text
39
Chalk, A. M., M. Wennerberg, and E. L. L. Sonnhammer. "Sfixem--graphical sequence feature display in Java." Bioinformatics 20, no. 15 (April 15, 2004): 2488–90. http://dx.doi.org/10.1093/bioinformatics/bth265.
Full text
40
PUDIMAT, RAINER, ROLF BACKOFEN, and ERNST G. SCHUKAT-TALAMAZZINI. "FAST FEATURE SUBSET SELECTION IN BIOLOGICAL SEQUENCE ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 02 (March 2009): 191–207. http://dx.doi.org/10.1142/s0218001409007107.
Full textAbstract:
Biological research produces a wealth of measured data. Neither it is easy for biologists to postulate hypotheses about the behavior or structure of the observed entity because the relevant properties measured are not seen in the ocean of measurements. Nor is it easy to design machine learning algorithms to classify or cluster the data items for the same reason. Algorithms for automatically selecting a highly predictive subset of the measured features can help to overcome these difficulties. We present an efficient feature selection strategy which can be applied to arbitrary feature selection problems. The core technique is a new method for estimating the quality of subsets from previously calculated qualities for smaller subsets by minimizing the mean standard error of estimated values with an approach common to support vector machines. This method can be integrated in many feature subset search algorithms. We have applied it with sequential search algorithms and have been able to reduce the number of quality calculations for finding accurate feature subsets by about 70%. We show these improvements by applying our approach to the problem of finding highly predictive feature subsets for transcription factor binding sites.
41
Pang, Yajun. "Design of National Sports Action Feature Extraction System Based on Convolutional Neural Network." Scientific Programming 2022 (February 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/5747647.
Full textAbstract:
Human action recognition is one of the hotspots in computer vision research. Its purpose is to detect and recognize target actions from videos, so that computer systems can understand human actions, and thus it has great research significance. Based on the action features of famous sports, this paper proposes an action recognition scheme based on RGB-D video compression to establish action features and deep learning as a means of recognition. By establishing the connection between the bone data of the three-dimensional data type and the depth image data, the depth sequence is analyzed and expressed as a three-level structure diagram sequence, which is the overall figure sequence, partial figure sequence, and joint point figure sequence, and then passes through the two-way pool. The sorting algorithm extracts the action features in the three picture sequences and compresses and generates three types of structured images of the corresponding picture sequences, and these three types of structured images are used as the feature expression of the video. When constructing a three-level structure diagram sequence, the innovation of this paper is to splice the extracted key unit image blocks to obtain a three-level structured moving image based on the three-key unit splicing, so that the image is not only retained. In addition to time-space information, the structure information of the depth image is also strengthened, and the amount of calculation is reduced at the same time. Finally, the three types of structured images are input into the convolutional neural network, respectively, and the judgment and recognition results obtained are multiplicatively fused to obtain the final recognition rate of the action.
42
Li, Bo, Lijun Cai, Bo Liao, Xiangzheng Fu, Pingping Bing, and Jialiang Yang. "Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features." Molecules 24, no. 5 (March 6, 2019): 919. http://dx.doi.org/10.3390/molecules24050919.
Full textAbstract:
The prediction of protein subcellular localization is critical for inferring protein functions, gene regulations and protein-protein interactions. With the advances of high-throughput sequencing technologies and proteomic methods, the protein sequences of numerous yeasts have become publicly available, which enables us to computationally predict yeast protein subcellular localization. However, widely-used protein sequence representation techniques, such as amino acid composition and the Chou’s pseudo amino acid composition (PseAAC), are difficult in extracting adequate information about the interactions between residues and position distribution of each residue. Therefore, it is still urgent to develop novel sequence representations. In this study, we have presented two novel protein sequence representation techniques including Generalized Chaos Game Representation (GCGR) based on the frequency and distributions of the residues in the protein primary sequence, and novel statistics and information theory (NSI) reflecting local position information of the sequence. In the GCGR + NSI representation, a protein primary sequence is simply represented by a 5-dimensional feature vector, while other popular methods like PseAAC and dipeptide adopt features of more than hundreds of dimensions. In practice, the feature representation is highly efficient in predicting protein subcellular localization. Even without using machine learning-based classifiers, a simple model based on the feature vector can achieve prediction accuracies of 0.8825 and 0.7736 respectively for the CL317 and ZW225 datasets. To further evaluate the effectiveness of the proposed encoding schemes, we introduce a multi-view features-based method to combine the two above-mentioned features with other well-known features including PseAAC and dipeptide composition, and use support vector machine as the classifier to predict protein subcellular localization. This novel model achieves prediction accuracies of 0.927 and 0.871 respectively for the CL317 and ZW225 datasets, better than other existing methods in the jackknife tests. The results suggest that the GCGR and NSI features are useful complements to popular protein sequence representations in predicting yeast protein subcellular localization. Finally, we validate a few newly predicted protein subcellular localizations by evidences from some published articles in authority journals and books.
43
Zhang, Zhenhao, Fan Feng, and Jie Liu. "Characterizing collaborative transcription regulation with a graph-based deep learning approach." PLOS Computational Biology 18, no. 6 (June 6, 2022): e1010162. http://dx.doi.org/10.1371/journal.pcbi.1010162.
Full textAbstract:
Human epigenome and transcription activities have been characterized by a number of sequence-based deep learning approaches which only utilize the DNA sequences. However, transcription factors interact with each other, and their collaborative regulatory activities go beyond the linear DNA sequence. Therefore leveraging the informative 3D chromatin organization to investigate the collaborations among transcription factors is critical. We developed ECHO, a graph-based neural network, to predict chromatin features and characterize the collaboration among them by incorporating 3D chromatin organization from 200-bp high-resolution Micro-C contact maps. ECHO predicted 2,583 chromatin features with significantly higher average AUROC and AUPR than the best sequence-based model. We observed that chromatin contacts of different distances affected different types of chromatin features’ prediction in diverse ways, suggesting complex and divergent collaborative regulatory mechanisms. Moreover, ECHO was interpretable via gradient-based attribution methods. The attributions on chromatin contacts identify important contacts relevant to chromatin features. The attributions on DNA sequences identify TF binding motifs and TF collaborative binding. Furthermore, combining the attributions on contacts and sequences reveals important sequence patterns in the neighborhood which are relevant to a target sequence’s chromatin feature prediction.
44
Rai, Ankush, and Jagadeesh Kannan R. "FUSION OF THERMAL AND RGB IMAGES FOR BORDER SECURITY SURVEILLANCE." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 279. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19685.
Full textAbstract:
Border security surveillance in remote areas are often suffer from low visibility and poor lighting conditions Thus, tracking and recognizing the intruder in such a situation is a daunting task. In this study a fusion technique is presented for enhancing the visibility by merging feature sets from thermal and RGB image sequence. The technique also solves the problem of computing the feature sets in massive sequences o images by carry-forwarding the past feature sets for consequent image sequence.
45
Chen, Fei, and Yuan-Ting Zhang. "A DNA Structure-Based Bionic Wavelet Transform and Its Application to DNA Sequence Analysis." Applied Bionics and Biomechanics 1, no. 1 (2003): 3–9. http://dx.doi.org/10.1155/2003/675645.
Full textAbstract:
DNA sequence analysis is of great significance for increasing our understanding of genomic functions. An important task facing us is the exploration of hidden structural information stored in the DNA sequence. This paper introduces a DNA structure-based adaptive wavelet transform (WT) – the bionic wavelet transform (BWT) – for DNA sequence analysis. The symbolic DNA sequence can be separated into four channels of indicator sequences. An adaptive symbol-to-number mapping, determined from the structural feature of the DNA sequence, was introduced into WT. It can adjust the weight value of each channel to maximise the useful energy distribution of the whole BWT output. The performance of the proposed BWT was examined by analysing synthetic and real DNA sequences. Results show that BWT performs better than traditional WT in presenting greater energy distribution. This new BWT method should be useful for the detection of the latent structural features in future DNA sequence analysis.
46
Pan, Zhen, Zhenya Huang, Defu Lian, and Enhong Chen. "A Variational Point Process Model for Social Event Sequences." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 173–80. http://dx.doi.org/10.1609/aaai.v34i01.5348.
Full textAbstract:
Many events occur in real-world and social networks. Events are related to the past and there are patterns in the evolution of event sequences. Understanding the patterns can help us better predict the type and arriving time of the next event. In the literature, both feature-based approaches and generative approaches are utilized to model the event sequence. Feature-based approaches extract a variety of features, and train a regression or classification model to make a prediction. Yet, their performance is dependent on the experience-based feature exaction. Generative approaches usually assume the evolution of events follow a stochastic point process (e.g., Poisson process or its complexer variants). However, the true distribution of events is never known and the performance depends on the design of stochastic process in practice. To solve the above challenges, in this paper, we present a novel probabilistic generative model for event sequences. The model is termed Variational Event Point Process (VEPP). Our model introduces variational auto-encoder to event sequence modeling that can better use the latent information and capture the distribution over inter-arrival time and types of event sequences. Experiments on real-world datasets prove effectiveness of our proposed model.
47
Ranjan, Chitta, Samaneh Ebrahimi, and Kamran Paynabar. "Sequence graph transform (SGT): a feature embedding function for sequence data mining." Data Mining and Knowledge Discovery 36, no. 2 (January 4, 2022): 668–708. http://dx.doi.org/10.1007/s10618-021-00813-0.
Full text
48
Fan, Yong Hui, Yu Wei Xing, and Hua Long Yang. "Prediction of Baltic Capesize Freight Index Based on GARCH Model." Applied Mechanics and Materials 488-489 (January 2014): 1494–97. http://dx.doi.org/10.4028/www.scientific.net/amm.488-489.1494.
Full textAbstract:
This paper analyzed the statistical data of the Baltic Capesize Freight Index (BCI) and the daily return rate sequences to improve forecast reliability of the international dry bulk shipping market. It used the first-order logarithmic difference method to get the BCI daily return rate sequence, showing that the BCI daily return sequence had a leptokurtosis and fat-tail fluctuation feature and other features such as integration. To analyze volatility persistence, sensitivity and hysteresis of the sequence, a GARCH (1, 1) model was introduced. The GARCH (1, 1) model constructed the forecast method of BCI sequence, and it predicted the BCI daily return rate by optimizing lag phases. The logarithm sequences of BCI daily return rate finally reverted to BCI, which was forecasted at last. The conclusion is supposed to improve the international dry bulk shipping market forecasting method.
49
Pei, Cong, Feng Jiang, and Mao Li. "Fusing appearance and motion information for action recognition on depth sequences." Journal of Intelligent & Fuzzy Systems 40, no. 3 (March 2, 2021): 4287–99. http://dx.doi.org/10.3233/jifs-200954.
Full textAbstract:
With the advent of cost-efficient depth cameras, many effective feature descriptors have been proposed for action recognition from depth sequences. However, most of them are based on single feature and thus unable to extract the action information comprehensively, e.g., some kinds of feature descriptors can represent the area where the motion occurs while they lack the ability of describing the order in which the action is performed. In this paper, a new feature representation scheme combining different feature descriptors is proposed to capture various aspects of action cues simultaneously. First of all, a depth sequence is divided into a series of sub-sequences using motion energy based spatial-temporal pyramid. For each sub-sequence, on the one hand, the depth motion maps (DMMs) based completed local binary pattern (CLBP) descriptors are calculated through a patch-based strategy. On the other hand, each sub-sequence is partitioned into spatial grids and the polynormals descriptors are obtained for each of the grid sequences. Then, the sparse representation vectors of the DMMs based CLBP and the polynormals are calculated separately. After pooling, the ultimate representation vector of the sample is generated as the input of the classifier. Finally, two different fusion strategies are applied to conduct fusion. Through extensive experiments on two benchmark datasets, the performance of the proposed method is proved better than that of each single feature based recognition method.
50
Sung, Raymond C. W., Jonathan R. Corney, and Doug E. R. Clark. "Automatic Assembly Feature Recognition and Disassembly Sequence Generation." Journal of Computing and Information Science in Engineering 1, no. 4 (October 1, 2001): 291–99. http://dx.doi.org/10.1115/1.1429931.
Full textAbstract:
This paper describes a system for the automatic recognition of assembly features and the generation of disassembly sequences. The paper starts by reviewing the nature and use of assembly features. One of the conclusions drawn from this survey is that the majority of assembly features involve sets of spatially adjacent faces. Two principle types of adjacency relationships are identified and an algorithm is presented for identifying assembly features which arise from “spatial” and “contact” face adjacency relationships (known as s-adjacency and c-adjacency respectively). The algorithm uses an octree representation of a B-rep model to support the geometric reasoning required to locate assembly features on disjoint bodies. A pointerless octree representation is generated by recursively sub-dividing the assembly model’s bounding box into octants which are used to locate: 1. Those portions of faces which are c-adjacent (i.e. they effectively touch within the tolerance of the octree). 2. Those portions of faces which are s-adjacent to a nominated face. The resulting system can locate and partition spatially adjacent faces in a wide range of situations and at different resolutions. The assembly features located are recorded as attributes in the B-rep model and are then used to generate a disassembly sequence plan for the assembly. This sequence plan is represented by a transition state tree which incorporates knowledge of the availability of feasible gripping features. By way of illustration, the algorithm is applied to several trial components