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Sarkar A, Hossain SKS, Sarkar R. Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm. Neural Comput Appl 2023; 35:5165-5191. [PMID: 36311167 PMCID: PMC9596348 DOI: 10.1007/s00521-022-07911-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 09/29/2022] [Indexed: 12/01/2022]
Abstract
Capturing time and frequency relationships of time series signals offers an inherent barrier for automatic human activity recognition (HAR) from wearable sensor data. Extracting spatiotemporal context from the feature space of the sensor reading sequence is challenging for the current recurrent, convolutional, or hybrid activity recognition models. The overall classification accuracy also gets affected by large size feature maps that these models generate. To this end, in this work, we have put forth a hybrid architecture for wearable sensor data-based HAR. We initially use Continuous Wavelet Transform to encode the time series of sensor data as multi-channel images. Then, we utilize a Spatial Attention-aided Convolutional Neural Network (CNN) to extract higher-dimensional features. To find the most essential features for recognizing human activities, we develop a novel feature selection (FS) method. In order to identify the fitness of the features for the FS, we first employ three filter-based methods: Mutual Information (MI), Relief-F, and minimum redundancy maximum relevance (mRMR). The best set of features is then chosen by removing the lower-ranked features using a modified version of the Genetic Algorithm (GA). The K-Nearest Neighbors (KNN) classifier is then used to categorize human activities. We conduct comprehensive experiments on five well-known, publicly accessible HAR datasets, namely UCI-HAR, WISDM, MHEALTH, PAMAP2, and HHAR. Our model significantly outperforms the state-of-the-art models in terms of classification performance. We also observe an improvement in overall recognition accuracy with the use of GA-based FS technique with a lower number of features. The source code of the paper is publicly available here https://github.com/apusarkar2195/HAR_WaveletTransform_SpatialAttention_FeatureSelection.
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Affiliation(s)
- Apu Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - S. K. Sabbir Hossain
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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Bijalwan V, Semwal VB, Singh G, Mandal TK. HDL-PSR: Modelling Spatio-Temporal Features Using Hybrid Deep Learning Approach for Post-Stroke Rehabilitation. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10744-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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3
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Tajanpure R, Muddana A. Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets. JOURNAL OF INTELLIGENT SYSTEMS 2021. [DOI: 10.1515/jisys-2020-0064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm accuracy is tested using the decision tree (DT), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy (Δ) is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy.
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4
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Classification of immature white blood cells in acute lymphoblastic leukemia L1 using neural networks particle swarm optimization. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06245-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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5
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Deep representation-based packetized predictive compensation for networked nonlinear systems. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05346-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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7
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Semwal VB, Gaud N, Lalwani P, Bijalwan V, Alok AK. Pattern identification of different human joints for different human walking styles using inertial measurement unit (IMU) sensor. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-09979-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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8
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Sang B, Chen H, Yang L, Zhou D, Li T, Xu W. Incremental attribute reduction approaches for ordered data with time-evolving objects. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106583] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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9
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Nasser M, Salim N, Hamza H, Saeed F, Rabiu I. Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief Networks. Molecules 2020; 26:E128. [PMID: 33383976 PMCID: PMC7795308 DOI: 10.3390/molecules26010128] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/24/2020] [Accepted: 12/25/2020] [Indexed: 11/24/2022] Open
Abstract
Virtual screening (VS) is a computational practice applied in drug discovery research. VS is popularly applied in a computer-based search for new lead molecules based on molecular similarity searching. In chemical databases similarity searching is used to identify molecules that have similarities to a user-defined reference structure and is evaluated by quantitative measures of intermolecular structural similarity. Among existing approaches, 2D fingerprints are widely used. The similarity of a reference structure and a database structure is measured by the computation of association coefficients. In most classical similarity approaches, it is assumed that the molecular features in both biological and non-biologically-related activity carry the same weight. However, based on the chemical structure, it has been found that some distinguishable features are more important than others. Hence, this difference should be taken consideration by placing more weight on each important fragment. The main aim of this research is to enhance the performance of similarity searching by using multiple descriptors. In this paper, a deep learning method known as deep belief networks (DBN) has been used to reweight the molecule features. Several descriptors have been used for the MDL Drug Data Report (MDDR) dataset each of which represents different important features. The proposed method has been implemented with each descriptor individually to select the important features based on a new weight, with a lower error rate, and merging together all new features from all descriptors to produce a new descriptor for similarity searching. Based on the extensive experiments conducted, the results show that the proposed method outperformed several existing benchmark similarity methods, including Bayesian inference networks (BIN), the Tanimoto similarity method (TAN), adapted similarity measure of text processing (ASMTP) and the quantum-based similarity method (SQB). The results of this proposed multi-descriptor-based on Stack of deep belief networks method (SDBN) demonstrated a higher accuracy compared to existing methods on structurally heterogeneous datasets.
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Affiliation(s)
- Maged Nasser
- School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; (H.H.); (I.R.)
| | - Naomie Salim
- School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; (H.H.); (I.R.)
| | - Hentabli Hamza
- School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; (H.H.); (I.R.)
| | - Faisal Saeed
- College of Computer Science and Engineering, Taibah University, Medina 344, Saudi Arabia
| | - Idris Rabiu
- School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; (H.H.); (I.R.)
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Li X, Li H, Lin Y, Guo J, Yang J, Yue H, Li K, Li C, Cheng Z, Hu H, Liu T. Learning-based denoising for polarimetric images. OPTICS EXPRESS 2020; 28:16309-16321. [PMID: 32549456 DOI: 10.1364/oe.391017] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 05/03/2020] [Indexed: 05/27/2023]
Abstract
Based on measuring the polarimetric parameters which contain specific physical information, polarimetric imaging has been widely applied to various fields. However, in practice, the noise during image acquisition could lead to the output of noisy polarimetric images. In this paper, we propose, for the first time to our knowledge, a learning-based method for polarimetric image denoising. This method is based on the residual dense network and can significantly suppress the noise in polarimetric images. The experimental results show that the proposed method has an evident performance on the noise suppression and outperforms other existing methods. Especially for the images of the degree of polarization and the angle of polarization, which are quite sensitive to the noise, the proposed learning-based method can well reconstruct the details flooded in strong noise.
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Semwal R, Aier I, Tyagi P, Varadwaj PK. DeEPn: a deep neural network based tool for enzyme functional annotation. J Biomol Struct Dyn 2020; 39:2733-2743. [PMID: 32274968 DOI: 10.1080/07391102.2020.1754292] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
With the advancement of high throughput techniques, the discovery rate of enzyme sequences has increased significantly in the recent past. All of these raw sequences are required to be precisely mapped to their respective functional attributes, which helps in deciphering their biological role. In the recent past, various prediction models have been proposed to predict the enzyme functional class; however, all of these models were able to quantify at most six functional enzyme classes (EC1 to EC6) out of existing seven functional classes, making these approaches inappropriate for handling enzymes corresponding to the seventh functional class (EC7). In this study, a Deep Neural Network-based approach, DeEPn, has been proposed, which can quantify enzymes corresponding to all seven functional classes with high precision and accuracy. The proposed model was compared with two recently developed tools, ECPred and SVM-Prot. The result demonstrated that DeEPn outperformed ECPred and SVM-Prot in terms of predictive quality. The DeEPn tool has been hosted as a web-based tool at https://bioserver.iiita.ac.in/DeEPn/.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rahul Semwal
- Department of Information Technology (Bioinformatics), Indian Institute of Information Technology Allahabad, Allahabad, Uttar Pradesh, India
| | - Imlimaong Aier
- Department of Bioinformatics and Applied Science, Indian Institute of Information Technology, Allahabad, Allahabad, Uttar Pradesh, India
| | - Pankaj Tyagi
- Department of Information Technology (Bioinformatics), Indian Institute of Information Technology Allahabad, Allahabad, Uttar Pradesh, India
| | - Pritish Kumar Varadwaj
- Department of Bioinformatics and Applied Science, Indian Institute of Information Technology, Allahabad, Allahabad, Uttar Pradesh, India
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Korashy A, Kamel S, Nasrat L, Jurado F. Developed multi-objective grey wolf optimizer with fuzzy logic decision-making tool for direction overcurrent relays coordination. Soft comput 2020. [DOI: 10.1007/s00500-020-04745-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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14
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Jaiswal S, Nandi GC. Robust real-time emotion detection system using CNN architecture. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04564-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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15
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Kim BW, Park Y, Suh IH. Integration of top-down and bottom-up visual processing using a recurrent convolutional–deconvolutional neural network for semantic segmentation. INTEL SERV ROBOT 2019. [DOI: 10.1007/s11370-019-00296-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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16
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Yang Y, Zhang W, He Z, Chen D. Locator slope calculation via deep representations based on monocular vision. Neural Comput Appl 2019. [DOI: 10.1007/s00521-017-3229-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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17
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Foroughi Nematollahi A, Rahiminejad A, Vahidi B. A novel multi-objective optimization algorithm based on Lightning Attachment Procedure Optimization algorithm. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.11.032] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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18
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Reversible data hiding using Lagrange interpolation for prediction-error expansion embedding. Soft comput 2018. [DOI: 10.1007/s00500-018-3537-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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19
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Evolutionary intelligence for brain tumor recognition from MRI images: a critical study and review. EVOLUTIONARY INTELLIGENCE 2018. [DOI: 10.1007/s12065-018-0156-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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20
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Naveena A, Narayanan N. Improving Image Search through MKFCM Clustering Strategy-Based Re-ranking Measure. JOURNAL OF INTELLIGENT SYSTEMS 2018. [DOI: 10.1515/jisys-2017-0227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
The main intention of this research is to develop a novel ranking measure for content-based image retrieval system. Owing to the achievement of data retrieval, most commercial search engines still utilize a text-based search approach for image search by utilizing encompassing textual information. As the text information is, in some cases, noisy and even inaccessible, the drawback of such a recovery strategy is to the extent that it cannot depict the contents of images precisely, subsequently hampering the execution of image search. In order to improve the performance of image search, we propose in this work a novel algorithm for improving image search through a multi-kernel fuzzy c-means (MKFCM) algorithm. In the initial step of our method, images are retrieved using four-level discrete wavelet transform-based features and the MKFCM clustering algorithm. Next, the retrieved images are analyzed using fuzzy c-means clustering methods, and the rank of the results is adjusted according to the distance of a cluster from a query. To improve the ranking performance, we combine the retrieved result and ranking result. At last, we obtain the ranked retrieved images. In addition, we analyze the effects of different clustering methods. The effectiveness of the proposed methodology is analyzed with the help of precision, recall, and F-measures.
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Affiliation(s)
- A.K. Naveena
- Department of Computer Science and Engineering, College of Engineering Trikaripur, Trikaripur, India
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21
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Yang Z, Yu W, Liang P, Guo H, Xia L, Zhang F, Ma Y, Ma J. Deep transfer learning for military object recognition under small training set condition. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3468-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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22
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Ferreira JP, Vieira A, Ferreira P, Crisóstomo M, Coimbra AP. Human knee joint walking pattern generation using computational intelligence techniques. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3458-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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23
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Pu H, Lian J, Fan M. Automatic Recognition of Flock Behavior of Chickens with Convolutional Neural Network and Kinect Sensor. INT J PATTERN RECOGN 2018. [DOI: 10.1142/s0218001418500234] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, we propose an automatic convolutional neural network (CNN)-based method to recognize the chicken behavior within a poultry farm using a Kinect sensor. It resolves the hardships in flock behavior image classification by leveraging a data-driven mechanism and exploiting non-manually extracted multi-scale image features which combine both the local and global characteristics of the image. To our best knowledge, this is probably the first attempt of deep learning strategy in the field of domestic animal behavior recognition. To testify the performance of our proposed method, we conducted experiments between state-of-the-art methods and our method. Experimental results witness that our proposed approach outperforms the state-of-the-art methods both in effectiveness and efficiency. Our proposed CNN architecture for recognizing flock behavior of chickens produces an extremely impressive accuracy of 99.17%.
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Affiliation(s)
- Haitao Pu
- Department of Electronic Engineering and Information Technology, Shandong University of Science and Technology, Jinan, P. R. China
| | - Jian Lian
- Department of Electronic Engineering and Information Technology, Shandong University of Science and Technology, Jinan, P. R. China
| | - Mingqu Fan
- Department of Electronic Engineering and Information Technology, Shandong University of Science and Technology, Jinan, P. R. China
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A Comparative Analysis of Metaheuristic Approaches for Multidimensional Two-Way Number Partitioning Problem. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018. [DOI: 10.1007/s13369-018-3155-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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25
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Villafáñez F, Poza D, López-Paredes A, Pajares J, Olmo RD. A generic heuristic for multi-project scheduling problems with global and local resource constraints (RCMPSP). Soft comput 2018. [DOI: 10.1007/s00500-017-3003-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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26
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27
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Simultaneous selection of material and supplier under uncertainty in carton box industries: a fuzzy possibilistic multi-criteria approach. Soft comput 2017. [DOI: 10.1007/s00500-017-2542-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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28
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Devi SS, Laskar RH, Sheikh SA. Hybrid classifier based life cycle stages analysis for malaria-infected erythrocyte using thin blood smear images. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-2937-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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29
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Dynamic hand gesture recognition using vision-based approach for human–computer interaction. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2525-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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30
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