A Web-Based Computerized System for Effective Baby Gender Validation
International Journal of Computer Science and Engineering |
© 2022 by SSRG - IJCSE Journal |
Volume 9 Issue 6 |
Year of Publication : 2022 |
Authors : Ezikwa Tenas God'swill, Maxwell Ibe Leo |
How to Cite?
Ezikwa Tenas God'swill, Maxwell Ibe Leo, "A Web-Based Computerized System for Effective Baby Gender Validation," SSRG International Journal of Computer Science and Engineering , vol. 9, no. 6, pp. 1-9, 2022. Crossref, https://doi.org/10.14445/23488387/IJCSE-V9I6P101
Abstract:
This research focuses on: "A Web-Based Computerized System for Effective Baby Gender Validation." To most couples, the gender of the baby is paramount to them. Frequently, not having the desired gender of the baby in the family is associated with using an inappropriate baby gender validation system, which can result in malice, infidelity, abortion, polygamy, divorce, and untimely death. These problems, as mentioned above, can be solved by adopting the computerized baby gender validation system such as Tenas Baby Gender (TBG) web-based computerized validation system proposed for this research. This study aims to develop and implement a Web-Based Computerized System for Effective Baby Gender Validation; whereas the study's objectives include; designing the Tenas Baby Gender Guide system using Object-Oriented Analysis and Design (OOAD) methodology. The system's backend was implemented using Hypertext Preprocessor (PHP) programming language and my structured query language (MySQL) as the database software. The result of the system indicates that the accuracy of the gender of the baby's validation before and within the first trimester of conception had a higher degree of accuracy of 92%. At the same time, the existing system had an accuracy of 54%, thereby showing higher accuracy of the baby gender validation. Therefore, by implication, the inappropriate baby gender validation system results not only in invalid baby gender authentication but can also exacerbate lingering crises among expectant couples.
Keywords:
Baby gender, Computerized system, Effective, Validation and web-based.
References:
[1] G. Anant, and B. K. Tripathy, "A Generic Hybrid Recommender System Based on Neural Networks," International Journal of Advance Computing Technology, Vol. 12, No. 4, Pp. 123-125, 2014.
[2] P. Avar, "Modeling of Users Age and Gender, Adaptation and Personalization", Journal of Computer Science and Information Technology, Vol. 3, No. 1, Pp. 25-30, 2017.
[3] E. S. Bauman, L. N. Hollander, D. E. V. Fauchon, A. J. Eggink, F. K. Lotgering, and R. J. Benzie, "Factors Associated with Parental Desire to Find Out the Sex of Their Baby" International Journal of Ultrasonography, Vol. 11, No. 2, Pp. 119–123, 2008.
[4] D. Chelli, A. Methni, K. Dimassi, F. Boudaya, E. Safar, B. Zouaoui, H. Chelli, and M. B. Chennoufi, "Foetal Sex Assignment by FirstTrimester Ultrasound: A Tunisian Experience", Journal of Population Health Metrics, Vol. 29, No. 3, Pp. 145–148, 2009.
[5] M. R. Dileepa, and D. Ajit, "Human Age and Gender Prediction Based on Neural Networks and Three Sigma Control Limits," Journal of Computing and Applied Artificial Intelligence, Vol. 32 No. 3, Pp. 281–289, 2018.
[6] In the International Journal of Novel Research in Computer, C. Jianle, X. Tianqi, S. Jie, and T. Ankur, "Gender Prediction on A Real Life Blog Data Set Using LSI and K-NN Algorithm," International Journal of Novel Research in Science and Software Engineering, Vol. 3, No. 1, Pp. 67-72, 2017.
[7] L. Jie, W. Dianshuang, M. Mingsong, W. Wei and Z. Guangquan, "Recommender System Application Developments For Gender Decision Support System," 2nd International Conference on Software Eng. and Data Mining, Pp. 47-51, 2015.
[8] T. Kamil, and O. Gultekin, "Emotional Classification and Visualization of Movies Using Decision Support Systems" International Journal of Computing and Information Technology, Vol. 45, No. 3, Pp. 149-158, 2017.
[9] G. Farideh, "the Ultrasound Identification of Foetal Gender At the Gestational Age of 11–12 Weeks", International Journal of Family Medicine and Primary Health Care, Vol. 7, No. 4, Pp. 210-2, 2018.
[10] Y. Lingling, L. Chuting, and L. Yao, "Detection of Human Movement Intention Based on A Multilayer Feed-Forward Neural Network with Dictionary Learning" International Journal of Image and Signal Processing-Biomedical Engineering and Informatics, Vol. 8 No. 4, Pp. 1-6, 2017. Https://Doi.Org/10.1109/CISP-BMEI.2017.8302265
[11] K., Manette, P. Karen, and G. Ian, "Accuracy of Sonographic Foetal Gender Determination: Predictions Made by Sonographers During Routine Obstetric Ultrasound Scans," Australian Journal of Ultrasound Medicine, Vol. 17 No. 3, Pp. 80-83, 2019.
[12] M., Mebarki, R. Kaidi, A. Azizi, and M. Basbaci, "Comparative Efficacy of Two-Dimensional Mode and Color Doppler Sonography in Predicting Gender of the Equine Foetus," Veterinary World, Vol. 12, No. 2, Pp. 325-330, 2019.
[13] J. Njoku, "Personality and Its Theories in Gender Variance Prediction" Aduwoyuyo and Sons Ltd, Enugu, Nigeri, 2010.
[14] M. Rashma, and B. K. R. Remesh, "Recommendation System: A Big Data Application," International Journal of Emerging Trends in Science and Technology Impact Factor, Vol. 3, No. 9, Pp. 39-46, 2016.
[15] S. Ricardo, G. Isabel, C. Ana, L. Diego, P. Pilar, M. Paola, and L. Juan, "Ultrasound Measurement Learning of Foetal Sex During the First Trimester: Does the Experience Matter?", Dove Press Journal Research and Reports in Focused Ultrasound, Vol. 15, No. 4, Pp. 201-205, 2015.
[16] V. H. Tim, G. Giuseppe, A. R. Enrique, P. Davy, and J. Wouter, "A Systematic Comparison of Age and Gender Prediction on IMU Sensor-Based Gait Traces," PMICID, Vol. 19, No.13, Pp. 294-295, 2019.
[17] K. Adhatrao, A. Gaykar, A. Dhawan, R. Jaha, and V. Honrao, "Predicting Students' Performance Using ID3 and C45 Classification Algorithms", International Journal of Data Mining and Knowledge Management Process, Vol. 3, No. 5, Pp. 39-52, 2013.
[18] M. Antonio, V. Aida, I. David, M. Lucas, and B. Joan, "Ontology-Based Personalized Recommendation of Tourism and Leisure Activities," Journal of Engineering Applications of Artificial Intelligence, Vol. 26 No. 9, Pp. 633-638, 2013.
[19] C. Colmant, M. Morin-Surroca, F. Fuchs, H. Fernandez, and M. V. Senat, "Non-Invasive Prenatal Testing For Fetal Sex Determination: Relevance of Ultrasound," European Journal of Obstetrics and Gynecology, Vol. 17, No. 1, Pp. 197-202, 2013.
[20] P. D. R. Dixon, and P. S. Rangaraja, "A Latent Factor Model-Based Movie Recommender Using Smartphone Browsing History," International Journal of Research and Innovation in Information Systems, Vol. 8, No. 1, Pp. 1-6, 2017. Https://Doi.Org/10.1109/ICRIIS.2017.8002510
[21] A. Georgios, S. Georgios, and S. Andreas, "Applying K-Separability to Collaborative Recommender Systems," International Journal of Computer and Artificial Intelligence, Vol. 21, No. 8, Pp. 21-24.
[22] A. Karahoca, "Advances in Data Mining Knowledge Discovery and Applications," Turkey Journal of Information Technology and Computing; Vol. 57, No. 5, Pp. 8–13, 2012.
[23] P. N. V. Kumar, and V. R. Reddy, "A Survey on Recommender Systems (RSS) and Its Applications," International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, No. 8, Pp.201-204, 2014.
[24] M. Mebarki, R. Kaidi, A. Azizi, and M. Basbaci, "Comparative Efficacy of Two-Dimensional Mode and Color Doppler Sonography in Predicting Gender of the Equine Foetus," Veterinary World, Vol. 12, No. 2, Pp. 325-330, 2019.
[25] N. Mehrbakhsh, B. I. Othman, I. Norafida, and Z. Rozana, "A Multi-Criteria Recommendation System Using Dimensionality Reduction and Neuro-Fuzzy Techniques," International Journal of Basic and Applied Sciences, Vol. 19, No. 11, 317-320, 2015.
[26] S. Ricardo, G. Isabel, C. Ana, L. Diego, P. Pilar, M. Paola, and L. Juan, "Ultrasound Measurement Learning of Foetal Sex During the First Trimester: Does the Experience Matter?", Dove Press Journal Research and Reports in Focused Ultrasound. Vol. 15, No. 4, Pp. 201-205, 2015