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Sharma, Pinki
- Gender Differences as per Child's Companion and Type of Activities Performed during Mother's Absence and Presence in Migrant Labour Families
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Authors
Pinki Sharma
1,
J. K. Gulati
2
Affiliations
1 Krishi Vigyan Kendra Jairampur, Changlang, Arunachal Pradesh, IN
2 Department of Human Development, Punjab Agricultural University, Ludhiana, Punjab, IN
1 Krishi Vigyan Kendra Jairampur, Changlang, Arunachal Pradesh, IN
2 Department of Human Development, Punjab Agricultural University, Ludhiana, Punjab, IN
Source
Asian Journal of Home Science, Vol 8, No 1 (2013), Pagination: 153-156Abstract
The aim of present study was to explore the gender wise distribution of children of migrant families as per activities during mother's absence as well as presence. A self-structured interview schedule was interviewed on mothers of the selected 120 children of age range of 6-8 years. In the total sample, children were composed of two groups on the basis of their school attendance. One group of children, who were attending school (n=60) and another group was composed of those children who were not attending school (n=60). Each of these two groups further included equal number of boys and girls (30 boys and 30 girls). Data were statistically analyzed by using Z-test. The study showed that majority of the male children stayed with the siblings and spent time while playing whereas, majority of the females stayed with the friends/neighbourhood and in home and spent time either in playing or doing household chores during their mother's absence. Among both the groups, school going as well as non-school going, significantly larger percentage of boys were involved in playing and roaming as compared to girls. Significantly larger percentage of girls as compared to boys was taking care of siblings during their mother's absence. During mother's presence, boys as well as girls spent longer time in or around the home where as during mother's absence children spent more time away from home.Keywords
Gender Differences,migrant Labour Families,migration, Sibling, Household ChoresReferences
- Bee, H. and Boyd, D. (2004) The developing child. Academic Press, New York, pp. 390-391.
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- Chatteerjee, S. (2012). Prefernce for migrant agricultural labour in Punjab. Economic &Political Weekly, Weekly, 41: 29
- Kaur, P. and Jaswal, I.J.S. (2009). Time use pattern and academic achievement. In: Jaswal, I.J.S. and Jaswal, S. (Ed.)Contemporary research designs in human development. pp. 111-17. Child Development Society, Punjab Agricultural University, Ludhiana (PUNJAB) INDIA
- Sharma, S. (2011). Dip in migration to Punjab: Census. Times of India, (Daily), April 9, 2011, p. 5.
- Associative Analysis between Mean Scores of Verbal, Perceptual and Quantitative Cognitive Abilities and Time Spent in Various Activities by School Going and Non-school Going Children of Migrant Labour Families
Abstract Views :174 |
PDF Views:0
Authors
Pinki Sharma
1,
J. K. Gulati
2
Affiliations
1 Krishi Vigyan Kendra Jairampur, Changlang, Aarunachal Pradesh, IN
2 Department of Human Development, Punjab Agricultural University, Ludhiana, Punjab, IN
1 Krishi Vigyan Kendra Jairampur, Changlang, Aarunachal Pradesh, IN
2 Department of Human Development, Punjab Agricultural University, Ludhiana, Punjab, IN
Source
Asian Journal of Home Science, Vol 8, No 1 (2013), Pagination: 212-216Abstract
This paper studied the gender differences in cognitive abilities (mean scores) among school going and non-school going children of migrant families. Six slum localities were selected from the selected zone and a sample of 120 mother-child dyads including children between 6-8 years of age and mothers working as domestic servants, were purposively selected from each chosen locality through snowball technique. Children were approached to assess their cognitive abilities. Results depicted that school going children of migrant families were superior in all cognitive abilities than their non-school counterparts. Results also revealed that school going females were superior in verbal, quantitative and general cognitive index whereas males possessed higher measures in perceptual, memory and motor abilities. Non-school going males performed significantly better in all cognitive domains as compared to non-school going females except for verbal ability.Keywords
Gender Differences, Mean Scores, Dyads, Cognitive Abilities, Migrant FamiliesReferences
- Benin, M.H. and Edwards, D.A. (1990). Adolescent chores: The difference between dual-earner and single-earner families. J. Marriage & Family, 52:361-73.
- Chatterjee, Shankar (2012). Prefernce for migrant agricultural labour in Punjab. Economic & Political Weekly, 47: 29.
- Cunha, F., Heckman, J.J. (2008). Formulating, identifying and estimating the technology of cognitive and non-cognitive skill formation. J. Hum. Resources, 43(4):739–782.
- Gill, P.P.S. (2002). Migrants vs local labour. The Tribune, 3rd September, pp.12-13.
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- Sharma, Sanjay (2011). Dip in migration to Punjab: Census. Times of India, Daily, April 9, 2011, p. 5.
- Smita, S. (2008). Distress seasonal migration and its impact on children’s education, Falmer, United Kingdom. Paper presented in consortium for research on educational access:Transitions and equity pathways to access research monograph No.28 inU.K.July,8-10, 2009.
- Timmer, S.G., Eccles, J. and O’brein, K. (1985) How children use time. In: Juster, F.T. and Stafford, F.P. (ed.) Time, goods and wellbeing (pp.-353-381). Institute for Social Research, Michigan, U.S.A.
- Woldehanna, Tassew (2011). The effects of early childhood education attendance on cognitive development: Evidence from urban Ethiopia. Paper presented in CSAE Conference on Economic Development in Africa at St. Catherine’s College, Oxford,March 20 to 22, 2011.
- WLI-FCM and Artificial Neural Network Based Cloud Intrusion Detection System
Abstract Views :180 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Punjabi University, Patiala, Punjab, IN
1 Department of Computer Science, Punjabi University, Patiala, Punjab, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No 1 (2018), Pagination: 3698-3703Abstract
Security and Performance aspects of cloud computing are the major issues which have to be tended to in Cloud Computing. Intrusion is one such basic and imperative security problem for Cloud Computing. Consequently, it is essential to create an Intrusion Detection System (IDS) to detect both inside and outside assaults with high detection precision in cloud environment. In this paper, cloud intrusion detection system at hypervisor layer is developed and assesses to detect the depraved activities in cloud computing environment. The cloud intrusion detection system uses a hybrid algorithm which is a fusion of WLI- FCM clustering algorithm and Back propagation artificial Neural Network to improve the detection accuracy of the cloud intrusion detection system. The proposed system is implemented and compared with K-means and classic FCM. The DARPA’s KDD cup dataset 1999 is used for simulation. From the detailed performance analysis, it is clear that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate.Keywords
Cloud Computing, Cloud Intrusion Detection System, Intrusion Detection System, IDS, Security.References
- H. Jin, G, Xiang, D. Zou, S. Wu, F. Zhao, M. Li, And W. Zheng, AVMM-based intrusion prevention system in cloud computing environment, Journal of Supercomputing Springer, 66(3), 2011, 1133–1151.
- F. Gens, New IDC IT Cloud Service Survey: Top Benefits and Challenges Exchange, 2009, online; http://www.blogs.idc.com/ie/p=730S. (Accessed 12 may 2017).
- L. Martin, White Paper, 2010, online: http://www.Lockheedmartin.com/data/assets/isgs/documents/CloudComputingWhitePaper.pdf.
- C. Modi ,D. Patel, B. Borisaniya, H. Patel, A. Patel and M. Rajarajan, A survey of intrusion detection techniques in Cloud, Journal of Network and Computer Applications, 36(1), 2013, 42-57.
- K. Vieira, A. Schulter, C.B. Westphall, and C. M. Westphall, Intrusion detection techniques in grid and cloud computing environment. IEEE IT Professional Magazine , 2010, 38–43
- S.Raja and S. Ramaiah, An Efficient Fuzzy-Based Hybrid System to Cloud Intrusion Detection, International Journal of Fuzzy Systems, 19(1), 2016, 116.
- N. Pandeeswari and Ganesh Kumar, Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN, Mobile Networks and Applications, 21(3), 2016, 494-505.
- C. N. Modi, D. R. Patel, A. Patel, and M. Rajarajan, Integrating Signature Apriori based Network Intrusion Detection system (NIDS) in Cloud Computing. In: Proceedings of 2nd International Conference on Communication, Computing & Security, Procedia Technology, 6:905–912. Doi: 10.1016/j.protcy.2012.10.110
- C. C. Lo, C. C. Huang, and J. Ku , A Cooperative Intrusion Detection System Framework for Cloud Computing Networks, 39th International Conference on Parallel Processing Workshops , 2010, 280-284.
- Z. Chiba, N. Abghour, K. Moussaid and M. Rida, A Cooperative and Hybrid Network Intrusion Detection Framework in Cloud Computing Based on Snory and Optimized back Propagation neural Network, International Workshop on Mobile Cloud Computing Systems, Management and Security, 83, 2016, 1200-1206.
- C. Wu, C. Ouyang, L. Chen, and L. Lu, A New Fuzzy Clustering Validity Index with a Median Factor for Centroid-based Clustering, IEEE Transactions on Fuzzy Systems, 23(3),2015, 701 – 718.
- KDD Cup 1999. Available online: http://www.kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, October 2007.
- R. kulhare and D. Singh, Intrusion Detection System based on Fuzzy C Means Clustering and Probabilistic Neural Network, International Journal of Computer Applications, 74, 2013, 30-33.
- K. Nalavade and B. B. Mehsram, Evaluation of KMeans Clustering for Effective Intrusion Detection and Prevention in Massive Network Traffic Data, International Journal of Computer Applications, 96, 2014, 9-14.
- Intrusion Detection Using Data Mining in Cloud Computing Environment
Abstract Views :246 |
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Authors
Affiliations
1 Department of Computer Science, Punjabi University, Patiala, Punjab, IN
2 Kurukshetra University, Kurukshetra, Haryana, IN
1 Department of Computer Science, Punjabi University, Patiala, Punjab, IN
2 Kurukshetra University, Kurukshetra, Haryana, IN
Source
International Journal of Distributed and Cloud Computing, Vol 6, No 2 (2018), Pagination: 19-23Abstract
Nowadays cloud computing is widely accepted a paradigm. At present large amount of data is transferred between cloud and user and vice-versa. That data in transient is exposed to various intrusions. Therefore, security is the primary concern of cloud computing environment. Firewall and other security techniques can act as first line of defence and cannot provide a robust security solution. Intrusion detection systems proved to be best solutions to various attacks. Data mining techniques have emerged to make it less vulnerable and thus to analyze data and to determine various kind of attack. Both signatures based and anomaly based techniques effectively and efficiently used data mining techniques for any kind of attack detection. This paper presents various data mining techniques used in intrusion detection. This paper also reviews various cloud intrusion detection systems that uses data mining techniques for attack detection.Keywords
Cloud Computing, Cloud Intrusion Detection Systems, Data Mining, IDS, Intrusion Detection.References
- I. Gul, and M. Hussain, “Distributed cloud intrusion detection model,” International Journal of Advanced Science and Technology, vol. 34, September 2011.
- U. Oktay, and O. K. Sahingoz, “Attack types and intrusion detection systems in cloud computing,” In 6th International Information Security & Cryptology Conference, Turkey, pp. 71-76, 20-21 September 2013.
- F. Gens, “New IDC IT cloud service survey: Top benefits and challenges, IDC exchange,” 2009. Available: http://blogs.idc.com/ie/?p=730S
- L. Martin, White Paper, 2010. Available: /http://www.lockheedmartin.com/data/assets/isgs/documents/CloudComputingWhitePaper.pdf
- J. Han, and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann, 2006.
- H. J. Patel, and R. Patel, “A Survey on intrusion detection system in cloud,” International Journal of Engineering and Technical Research (IJETR), vol. 2, no. 5, pp. 38-39, May 2014.
- P. Berkhin, “A survey of clustering data mining techniques,” Grouping Multidimensional Data, Springer Berlin Heidelberg, pp. 25-71, 2006.
- S. Agrawal, and J. Agrawal, “Survey on anomaly detection using data mining techniques,” Procedia Computer Science, vol. 60, pp. 708-713, 2015.
- P. Ganeshkumar, and N. Pandeeswari, “Adaptive neuro-fuzzy-based anomaly detection system in cloud,” International Journal of Fuzzy Systems, vol. 18, no. 3, pp. 367-378, June 2016.
- C. N. Modi, D. R. Patel, A. Patel, and R. Muttukrishnan, “Bayesian classifier and snort based network intrusion detection system in cloud computing,” Third International Conference on Computing, Communication and Networking Technologies, 26-28 July 2012.
- C. N. Modi, D. R. Patel, A. Patel, and R. Muttukrishnan, “Integrating signature apriori based Network Intrusion Detection System (NIDS) in cloud computing,” Procedia Technology, vol. 6 pp. 905-912, 2012.
- N. Pandeeswari, and G. Kumar, “Anomaly detection system in cloud environment using fuzzy clustering based ANN,” Mobile Networks and Applications, vol. 21, no. 3, pp. 494-505, June 2016.
- K. Vieira, A. Schulter, C. B. Westphall, and C. M. Westphall, “Intrusion detection for grid and cloud computing,” IT Professional, IEEE, vol. 12, no. 4, pp. 38-43, July-August 2010.
- Z. Chiba, N. Abghour, K. Moussaid, A. E. Omri, and M. Rida, “A cooperative and hybrid network intrusion detection framework in cloud computing based on snort and optimized back propagation neural network,” Procedia Computer Science, vol. 83, pp. 1200-1206, 2016.
- S. Raja, and S. Ramaiah, “An efficient fuzzy-based hybrid system to cloud intrusion detection,” International Journal of Fuzzy Systems, vol. 19, no. 1, pp. 62-77, February 2017.