Barriers to Breast and Cervical Cancer Screening in Singapore: a Mixed Methods Analysis

Malhotra, Chetna,Bilger, Marcel,Liu, Joy,Finkelstein, Eric Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.8

Background: In order to increase breast and cervical cancer screening uptake in Singapore, women's perceived barriers to screening need to be identified and overcome. Using data from both focus groups and surveys, we aimed to assess perceived barriers and motivations for breast and cervical cancer screening. Materials and Methods: We conducted 8 focus groups with 64 women, using thematic analysis to identify overarching themes related to women's attitudes towards screening. Based on recurring themes from focus groups, several hypotheses regarding potential barriers and motivations to screen were generated and tested through a national survey of 801 women aged 25-64. Results: Focus group participants had misconceptions related to screening, believing that the procedures were painful. Cost was an issue, as well as efficacy and fatalism. Conclusions: By identifying barriers to and motivators for screening through a mixed-method design that has both nuance and external validity, this study offers valuable suggestions to policymakers to improve breast and cervical cancer screening uptake in Singapore.

A Regression Test Selection and Prioritization Technique

Malhotra, Ruchika,Kaur, Arvinder,Singh, Yogesh Korea Information Processing Society 2010 Journal of information processing systems Vol.6 No.2

Regression testing is a very costly process performed primarily as a software maintenance activity. It is the process of retesting the modified parts of the software and ensuring that no new errors have been introduced into previously tested source code due to these modifications. A regression test selection technique selects an appropriate number of test cases from a test suite that might expose a fault in the modified program. In this paper, we propose both a regression test selection and prioritization technique. We implemented our regression test selection technique and demonstrated in two case studies that our technique is effective regarding selecting and prioritizing test cases. The results show that our technique may significantly reduce the number of test cases and thus the cost and resources for performing regression testing on modified software.

An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

Malhotra, Ruchika,Garg, Mohit Korea Information Processing Society 2011 Journal of information processing systems Vol.7 No.2

As the complexity of software is increasing, generating an effective test data has become a necessity. This necessity has increased the demand for techniques that can generate test data effectively. This paper proposes a test data generation technique based on adequacy based testing criteria. Adequacy based testing criteria uses the concept of mutation analysis to check the adequacy of test data. In general, mutation analysis is applied after the test data is generated. But, in this work, we propose a technique that applies mutation analysis at the time of test data generation only, rather than applying it after the test data has been generated. This saves significant amount of time (required to generate adequate test cases) as compared to the latter case as the total time in the latter case is the sum of the time to generate test data and the time to apply mutation analysis to the generated test data. We also use genetic algorithms that explore the complete domain of the program to provide near-global optimum solution. In this paper, we first define and explain the proposed technique. Then we validate the proposed technique using ten real time programs. The proposed technique is compared with path testing technique (that use reliability based testing criteria) for these ten programs. The results show that the adequacy based proposed technique is better than the reliability based path testing technique and there is a significant reduce in number of generated test cases and time taken to generate test cases.

Will psychological empowerment and role satisfaction influence motivation? Evidence from public sector organizations in India

Malhotra, Ruby Sengar,Vohra, P.S.,Rangnekar, Santosh KNU The Institute of Management Economy Research 2014 Asia-Pacific Journal of Business Vol.5 No.2

This paper aims to propose a conceptual model that empirically examines the relationship of psychological empowerment & role satisfaction and their dimensions with motivation in an Indian context. 176 executives/managers from many public sector organizations in India were approached. Cronbach alpha, correlation and regression analyses were applied to check the research hypotheses. Only meaning was found to be important predictor of motivation. Interestingly, achievement and extension were also observed to be the determinants of motivation. This paper would help researchers and practitioners to work on these variables in some other sectors also. Improvement in the psychological empowerment and role satisfaction will enhance the motivation among Indian business executives/managers which will improve the overall performance of the organization. It is an innovative attempt to utilize psychological empowerment and role satisfaction independently to improve motivation in an Indian framework.

Factors affecting adoption of Internet Banking: A case study from India

Malhotra, Pooja,Kassim, Normalini Md,Ramayah, T. KNU The Institute of Management Economy Research 2014 Asia-Pacific Journal of Business Vol.5 No.2

The objective of this research is to find out the factors affecting adoption of Internet banking in India. The data is based upon a survey of 150 bank customers using a convenience sampling technique with the aid of a structured self-administered questionnaire. The research model was analyzed using Partial Least Squares (PLS) analysis. The recommended procedures have been tested which is measurement model and structural model. Perceived Usefulness, Perceived Ease of Use, Perceived Risk, Image, Results Demonstrability, Perceived Behavioral Control and Subjective Norm were influence intention to use Internet banking. However, Perceived Ease of Use, Perceived Credibility and Computer Self Efficacy were not influence intention to use Internet banking. The findings of this study are expected to be of great use to the bank marketers. An understanding of the factors identified in this study allows bank managers to direct efforts and resources in the most effective and efficient way to increase bank business in the long run and encourage their bank customer's to adopt Internet banking. Moreover, this paper contributes to the empirical literature of diffusion of financial innovations, particularly Internet banking in a developing country, such as India.

Analyzing Machine Learning Techniques for Fault Prediction Using Web Applications

Malhotra, Ruchika,Sharma, Anjali Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.3

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

Prediction & Assessment of Change Prone Classes Using Statistical & Machine Learning Techniques

Malhotra, Ruchika,Jangra, Ravi Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.4

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

Cloning, Expression and Hormonal Regulation of Steroidogenic Acute Regulatory Protein Gene in Buffalo Ovary

Malhotra, Nupur,Singh, Dheer,Sharma, M.K. Asian Australasian Association of Animal Productio 2007 Animal Bioscience Vol.20 No.2

In mammalian ovary, steroidogenic acute regulatory (StAR) protein mediates the true rate-limiting step of transport of cholesterol from outer to inner mitochondrial membrane. Appropriate expression of StAR gene represents an indispensable component of steroidogenesis and its regulation has been found to be species specific. However, limited information is available regarding StAR gene expression during estrous cycle in buffalo ovary. In the present study, expression, localization and hormonal regulation of StAR mRNA were analyzed by semi-quantitative RT-PCR in buffalo ovary and partial cDNA was cloned. Total RNA was isolated from whole follicles of different sizes, granulosa cells from different size follicles and postovulatory structures like corpus luteum and Corpus albicans. Semi-quantitative RT-PCR analyses showed StAR mRNA expression in the postovulatory structure, corpus luteum. No StAR mRNA was detected in total RNA isolated from whole follicles of different size including the preovulatory follicle (>9 mm in diameter). However, granulosa cells isolated from preovulatory follicles showed the moderate expression of StAR mRNA. To assess the hormonal regulation of StAR mRNA, primary culture of buffalo granulosa cells were treated with FSH (100 ng/ml) alone or along with IGF-I (100 ng/ml) for 12 to 18 h. The abundance of StAR mRNA increased in cells treated with FSH alone or FSH with IGF-I. However, effect of FSH with IGF-I on mRNA expression was found highly significant (p<0.01). In conclusion, differential expression of StAR messages was observed during estrous cycle in buffalo ovary. Also, there was a synergistic action of IGF-I on FSH stimulation of StAR gene.

Genetic Symmetric Key Generation for IDEA

Malhotra, Nandini,Nagpal, Geeta Korea Information Processing Society 2015 Journal of information processing systems Vol.11 No.2

Cryptography aims at transmitting secure data over an unsecure network in coded version so that only the intended recipient can analyze it. Communication through messages, emails, or various other modes requires high security so as to maintain the confidentiality of the content. This paper deals with IDEA's shortcoming of generating weak keys. If these keys are used for encryption and decryption may result in the easy prediction of ciphertext corresponding to the plaintext. For applying genetic approach, which is well-known optimization technique, to the weak keys, we obtained a definite solution to convert the weaker keys to stronger ones. The chances of generating a weak key in IDEA are very rare, but if it is produced, it could lead to a huge risk of attacks being made on the key, as well as on the information. Hence, measures have been taken to safeguard the key and to ensure the privacy of information.

Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality

Malhotra, Ruchika,Jain, Ankita Korea Information Processing Society 2012 Journal of information processing systems Vol.8 No.2

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.