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119 results on '"Tushir, Meena"'

2. Hybrid KFCM-PSO Clustering Technique for Image Segmentation

Author

Arora, Jyoti, Tushir, Meena, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bansal, Poonam, editor, Tushir, Meena, editor, Balas, Valentina Emilia, editor, and Srivastava, Rajeev, editor

Published
2021
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3. Performance Analysis of Different Kernel Functions for MRI Image Segmentation

Author

Arora, Jyoti, Tushir, Meena, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bansal, Poonam, editor, Tushir, Meena, editor, Balas, Valentina Emilia, editor, and Srivastava, Rajeev, editor

Published
2021
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4. Linear Interpolation-Based Fuzzy Clustering Approach for Missing Data Handling

Author

Goel, Sonia, Tushir, Meena, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Hura, Gurdeep Singh, editor, Singh, Ashutosh Kumar, editor, and Siong Hoe, Lau, editor

Published
2021
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7. An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness.

Author

Goel, Sonia, Tushir, Meena, Arora, Jyoti, Sharma, Tripti, Gupta, Deepali, Nauman, Ali, and Muhammad, Ghulam

Subjects
CLASSIFICATION algorithms, MEDICAL coding, INTERPOLATION, CLASSIFICATION, MISSING data (Statistics), MEDICAL care
Abstract

In numerous real-world healthcare applications, handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks. Traditional approaches often rely on statistical methods for imputation, which may yield suboptimal results and be computationally intensive. This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy. Conventional classification methods are ill-suited for incomplete medical data. To enhance efficiency without compromising accuracy, this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data. Initially, the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification algorithm. The effectiveness of the proposed approach is evaluated using multiple performance metrics, including accuracy, precision, specificity, and sensitivity. The encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Fuzzy c-Means Clustering Strategies: A Review of Distance Measures

Author

Arora, Jyoti, Khatter, Kiran, Tushir, Meena, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hoda, M. N., editor, Chauhan, Naresh, editor, Quadri, S. M. K., editor, and Srivastava, Praveen Ranjan, editor

Published
2019
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10. A New Log Kernel-Based Possibilistic Clustering

Author

Tushir, Meena, Nigam, Jyotsna, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hoda, M. N., editor, Chauhan, Naresh, editor, Quadri, S. M. K., editor, and Srivastava, Praveen Ranjan, editor

Published
2019
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11. A Semi-supervised Clustering for Incomplete Data

Author

Goel, Sonia, Tushir, Meena, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Malik, Hasmat, editor, Srivastava, Smriti, editor, Sood, Yog Raj, editor, and Ahmad, Aamir, editor

Published
2019
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13. Conditional spatial biased intuitionistic clustering technique for brain MRI image segmentation.

Author

Arora, Jyoti, Altuwaijri, Ghadir, Nauman, Ali, Tushir, Meena, Sharma, Tripti, Gupta, Deepali, and Sung Won Kim

Subjects
MAGNETIC resonance imaging, BRAIN imaging, MAGNETIC resonance, MEDICAL research, DIAGNOSTIC imaging, IMAGE segmentation
Abstract

In clinical research, it is crucial to segment the magnetic resonance (MR) brain image for studying the internal tissues of the brain. To address this challenge in a sustainable manner, a novel approach has been proposed leveraging the power of unsupervised clustering while integrating conditional spatial properties of the image into intuitionistic clustering technique for segmenting MRI images of brain scans. In the proposed technique, an Intuitionistic-based clustering approach incorporates a nuanced understanding of uncertainty inherent in the image data. The measure of uncertainty is achieved through calculation of hesitation degree. The approach introduces a conditional spatial function alongside the intuitionistic membership matrix, enabling the consideration of spatial relationships within the image. Furthermore, by calculating weighted intuitionistic membership matrix, the algorithm gains the ability to adapt its smoothing behavior based on the local context. The main advantages are enhanced robustness with homogenous segments, lower sensitivity to noise, intensity inhomogeneity and accommodation of degree of hesitation or uncertainty that may exist in the real-world datasets. A comparative analysis of synthetic and real datasets of MR brain images proves the efficiency of the suggested approach over different algorithms. The paper investigates how the suggested research methodology performs in medical industry under different circumstances including both qualitative and quantitative parameters such as segmentation accuracy, similarity index, true positive ratio, false positive ratio. The experimental outcomes demonstrate that the suggested algorithm outperforms in retaining image details and achieving segmentation accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Performance Assessment for Clustering Techniques for Image Segmentation

Author

Arora, Jyoti, Khatter, Kiran, Tushir, Meena, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Urooj, Shabana, editor, and Virmani, Jitendra, editor

Published
2018
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21. Performance Assessment of Kernel-Based Clustering

Author

Tushir, Meena, Srivastava, Smriti, Kacprzyk, Janusz, Series editor, Krishnan, G. Sai Sundara, editor, Anitha, R., editor, Lekshmi, R. S., editor, Kumar, M. Senthil, editor, Bonato, Anthony, editor, and Graña, Manuel, editor

Published
2014
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26. Different Approaches for Missing Data Handling in Fuzzy Clustering: A Review

Author

Goel Sonia and Tushir Meena

Subjects
Fuzzy clustering, Computer science, computer.software_genre, Missing data, 01 natural sciences, Electronic, Optical and Magnetic Materials, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, 030212 general & internal medicine, Data mining, 0101 mathematics, Electrical and Electronic Engineering, computer
Abstract

Introduction: Incomplete data sets containing some missing attributes is a prevailing problem in many research areas. The reasons for the lack of missing attributes may be several; human error in tabulating/recording the data, machine failure, errors in data acquisition or refusal of a patient/customer to answer few questions in a questionnaire or survey. Further, clustering of such data sets becomes a challenge. Objective: In this paper, we presented a critical review of various methodologies proposed for handling missing data in clustering. The focus of this paper is the comparison of various imputation techniques based FCM clustering and the four clustering strategies proposed by Hathway and Bezdek. Methods: In this paper, we imputed the missing values in incomplete datasets by various imputation/ non-imputation techniques to complete the data set and then conventional fuzzy clustering algorithm is applied to get the clustering results. Results: Experiments on various synthetic data sets and real data sets from UCI repository are carried out. To evaluate the performance of the various imputation/ non-imputation based FCM clustering algorithm, several performance criteria and statistical tests are considered. Experimental results on various data sets show that the linear interpolation based FCM clustering performs significantly better than other imputation as well as non-imputation techniques. Conclusion: It is concluded that the clustering algorithm is data specific, no clustering technique can give good results on all data sets. It depends upon both the data type and the percentage of missing attributes in the dataset. Through this study, we have shown that the linear interpolation based FCM clustering algorithm can be used effectively for clustering of incomplete data set.

Published
2020
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29. MCBC-SMOTE: A Majority Clustering Model for Classification of Imbalanced Data.

Author

Arora, Jyoti, Tushir, Meena, Sharma, Keshav, Mohan, Lalit, Singh, Aman, Alharbi, Abdullah, and Alosaimi, Wael

Subjects
SUPERVISED learning, FUZZY algorithms, KEY performance indicators (Management), CLASSIFICATION, MACHINE learning, CLASSIFICATION algorithms
Abstract

Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms. In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research problem. Various machine learning techniques are designed to operate on balanced datasets; therefore, the state of the art, different undersampling, over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets, but highly skewed datasets still pose the problem of generalization and noise generation during resampling. To overcome these problems, this paper proposes amajority clusteringmodel for classification of imbalanced datasets known as MCBC-SMOTE (Majority Clustering for balanced Classification-SMOTE). The model provides a method to convert the problem of binary classification into a multi-class problem. In the proposed algorithm, the number of clusters for themajority class is calculated using the elbow method and the minority class is over-sampled as an average of clustered majority classes to generate a symmetrical class distribution. The proposed technique is cost-effective, reduces the problem of noise generation and successfully disables the imbalances present in between and within classes. The results of the evaluations on diverse real datasets proved to provide better classification results as compared to state of the art existing methodologies based on several performance metrics. [ABSTRACT FROM AUTHOR]

Published
2022
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33. A New semi-supervised clustering for incomplete data.

Author

Goel, Sonia, Tushir, Meena, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti

Subjects
*MISSING data (Statistics), *ERROR rates, *INTERPOLATION, *PRIOR learning
Abstract

Semi-supervised clustering technique partitions the unlabeled data based on prior knowledge of labeled data. Most of the semi-supervised clustering algorithms exist only for the clustering of complete data, i.e., the data sets with no missing features. In this paper, an effort has been made to check the effectiveness of semi-supervised clustering when applied to incomplete data sets. The novelty of this approach is that it considers the missing features along with available knowledge (labels) of the data set. The linear interpolation imputation technique initially imputes the missing features of the data set, thus completing the data set. A semi-supervised clustering is now employed on this complete data set, and missing features are regularly updated within the clustering process. In the proposed work, the labeled percentage range used is 30, 40, 50, and 60% of the total data. Data is further altered by arbitrarily eliminating certain features of its components, which makes the data incomplete with partial labeling. The proposed algorithm utilizes both labeled and unlabeled data, along with certain missing values in the data. The proposed algorithm is evaluated using three performance indices, namely the misclassification rate, random index metric, and error rate. Despite the additional missing features, the proposed algorithm has been successfully implemented on real data sets and showed better/competing results than well-known standard semi-supervised clustering methods. [ABSTRACT FROM AUTHOR]

Published
2022
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48. Image Enhancement Using Exposure and Standard Deviation-Based Sub-image Histogram Equalization for Night-time Images

Author

Acharya, Upendra Kumar, Kumar, Sandeep, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bansal, Poonam, editor, Tushir, Meena, editor, Balas, Valentina Emilia, editor, and Srivastava, Rajeev, editor

Published
2021
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49. A Novel Data Prediction Technique Based on Correlation for Data Reduction in Sensor Networks

Author

Jain, Khushboo, Agarwal, Arun, Kumar, Anoop, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bansal, Poonam, editor, Tushir, Meena, editor, Balas, Valentina Emilia, editor, and Srivastava, Rajeev, editor

Published
2021
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50. Design and Analysis of 2D Extended Reed–Solomon Code for OCDMA

Author

Bharti, Manisha, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bansal, Poonam, editor, Tushir, Meena, editor, Balas, Valentina Emilia, editor, and Srivastava, Rajeev, editor

Published
2021
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