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11. Probing the linkages between educational level and occupational choices of women working in urban informal sector of India.

Author

Jit, Param, Singh, Sumanjeet, Goel, Sonia, and Gupta, Apoorva

Subjects
FAMILIES & psychology, WOMEN'S education, INDIAN women (Asians), EMIGRATION & immigration, RESEARCH funding, UNEMPLOYMENT, DESCRIPTIVE statistics, WOMEN employees, METROPOLITAN areas, SOCIAL networks, CONCEPTUAL structures, VOCATIONAL guidance
Abstract

BACKGROUND: Education is crucial for empowering women in the labour market. However, the choice of occupation may not always necessarily be determined by their level of education. OBJECTIVE: The present study seeks to answer two important questions: (1) Does education help in determining occupational choices of women working in the informal sector? (2) How do factors like number of dependents in the family, woman staying with family, migration status of a woman and the use of networks in the job-search process, affect the occupational choices of women working in the informal sector? METHOD: The study utilised the data collected from 476 women, working in the urban informal sector of three northern States of India. We have estimated multinomial Probit model to analyse the data. RESULTS: This paper argues that education level cannot be the sole premise for a woman to have a better occupational choice within the informal sector. Other factors play a relatively significant role in determining the choice of occupation by a woman. CONCLUSION: Working women in the urban informal sector have less autonomy in the choice of their occupation and the education levels alone cannot help in providing access to more hygienic, career enriching and well-paying options to them. [ABSTRACT FROM AUTHOR]

Published
2024
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12. 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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18. Contributors

Author

Abbas, Ghulam, primary, Adeel, Muhammad, additional, Aftab, Tariq, additional, Afza, Nishat, additional, Afzal, Shadma, additional, Ahmad, Shakeel, additional, Ahmad, Zahoor, additional, Akhter, Sohail, additional, Ali, Mohammad Eyakub, additional, Ali, Sahin, additional, Ali, Zulfiqar, additional, Amin, Huma, additional, Anil, Ajin S., additional, Anjum, Mohsina, additional, Banerjee, Aditya, additional, Banerjee, Amrita, additional, Bansal, K.C., additional, Bansal, Radhika, additional, Bashir, Muhammad Amjad, additional, Behera, Laxmipreeya, additional, Bhadra, Preetha, additional, Bhatta, Bishal Binaya, additional, Bhattacharyya, Samrat, additional, Bhutia, Karma L., additional, Brestic, Marian, additional, Chandan, Kumari, additional, Chandra, Kailash, additional, Chaudhary, Nidhi, additional, Chauhan, Jyoti, additional, Choudhury, Sourav, additional, Dash, Deepali, additional, Datta, Diptanu, additional, Devedee, Anoop Kumar, additional, Dey, Prajjal, additional, Dutta, Asik, additional, Goel, Sonia, additional, Gorovtsov, Andrey, additional, Hassan, Mohamed M., additional, Hassan, Zeshan, additional, Hidangmayum, Akash, additional, Hossain, Akbar, additional, Hussain, Mubshar, additional, Ijaz, Muhammad, additional, Iqbal, Muhammad Munir, additional, Islam, Tanjina, additional, Islam, Tofazzal, additional, Jamil, Shakra, additional, Jatav, Hanuman Singh, additional, Kanwal, Shamsa, additional, Khan, Asif Ali, additional, Khan, Azhar Abbas, additional, Khan, Zulqurnain, additional, Kumari, Aradhna, additional, Kumari, Asha, additional, Kundu, Arnab, additional, Maitra, Sagar, additional, Mallick, Payel, additional, Manzoor, Zahid, additional, Mishra, Udit Nandan, additional, Mohapatra, Kiran Kumar, additional, Moulik, Debjyoti, additional, Nisar, Amina, additional, Panda, Darshan, additional, Pandey, Himanshu, additional, Pandita, Deepu, additional, Parida, Selukash, additional, Patra, Abhik, additional, Patra, Kiranmay, additional, Pattnaik, Debasish, additional, Praharaj, Subhashisa, additional, Pramanick, Biswajit, additional, Rahman, Md. Muzahid E., additional, Rajput, Vishnu D., additional, Raza, Hafiz Muhammad Ali, additional, Raza, Qurat-Ul-Ain, additional, Rehim, Abdur, additional, Rekwar, Ravindra Kumar, additional, Roy, Somnath, additional, Roychoudhury, Aryadeep, additional, Sadhukhan, Rahul, additional, Saha, Debanjana, additional, Sahoo, Sanjib Kumar, additional, Sarwat, Maryam, additional, Sathyanarayana, Eetela, additional, Sattar, Abdul, additional, Sen, Suman, additional, Shahzad, Rahil, additional, Shahzad, Umbreen, additional, Shankar, Tanmoy, additional, Sharma, Binny, additional, Sharma, Deepa, additional, Sharma, Laimayum Devarishi, additional, Sher, Ahmad, additional, Singh, Ankur, additional, Singh, Bansh Narayan, additional, Singh, Manish P., additional, Singh, Nand K., additional, Singh, Santosh Kumar, additional, Singh, Satish Kumar, additional, Singhal, Rajesh Kumar, additional, Skalicky, Milan, additional, Syed, M. Abu, additional, Syed, Tahira, additional, Tabassum, Javaria, additional, Thakkar, Shalini, additional, Thangjam, Rojeet, additional, Trivedi, Ankita, additional, Ul-Allah, Sami, additional, Yousaf, Muhammad Irfan, additional, and Zafar, Syed Adeel, additional

Published
2022
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20. 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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21. 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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40. 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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43. Abscisic Acid: Role in Fruit Development and Ripening

Author

Gupta, Kapil, primary, Wani, Shabir H., additional, Razzaq, Ali, additional, Skalicky, Milan, additional, Samantara, Kajal, additional, Gupta, Shubhra, additional, Pandita, Deepu, additional, Goel, Sonia, additional, Grewal, Sapna, additional, Hejnak, Vaclav, additional, Shiv, Aalok, additional, El-Sabrout, Ahmed M., additional, Elansary, Hosam O., additional, Alaklabi, Abdullah, additional, and Brestic, Marian, additional

Published
2022
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