Sherry Chalotra ¹, S. K. Sehra ¹, Y. S. Brar ², Navdeep Kaur ³

Affiliations
1 CSE Department, Guru Nanak Dev Engineering College, Ludhiana - 141006, Punjab, IN
2 EE Department, Guru Nanak Dev Engineering College, Ludhiana - 141006, Punjab, IN
3 CSE Department, Sri Guru Granth Sahib World University, Fatehgarh Sahib - 140407, Punjab, IN

Abstract

Constructive Cost Model (COCOMO) used parameters for software effort estimation, which were calculated in 1981 by regression analysis of 63 types of project data; therefore applying these parameters to current project development will not generate accurate results. The objective of current research is applying Bee Colony Optimization (BCO) metaheuristic approach to optimize the parameters of COCOMO model for improving software cost estimation. The Bee Colony Optimization (BCO) is a new branch of Swarm Intelligence and has been applied successfully to various engineering disciplines. BCO approach is a “bottom-up” approach to modeling where special kinds of artificial agents are created by analogy with bees. These artificial agents or bees are used to solve complex combinatorial optimization problems. The proposed model validation is carried out using Interactive Voice Response software project dataset of a company. The results generated by the proposed model are compared to those obtained by methods proposed in the literature using Walston-Felix, SEL, Bailey-Basil, COCOMO II and Halstead models. The BCO approach generates various partial solutions and best solution is selected based on Mean Magnitude of Relative Error. The results obtained show that the proposed BCO based model is able to improve the accuracy of cost estimation and also outperform other models.

Keywords

Bee Colony Optimization, Constructive Cost Model, Optimization, Software Cost Estimation

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Navdeep Kaur ¹, Williamjeet Singh ¹

Affiliations
1 Punjabi University Patiala, Punjab, IN

Abstract

Objectives/Background: Addiction of alcohol is a complex disease which results from diversity of social, genetic and environmental influences. A report by World Health Organization, WHO (2014) estimates that most of the deaths are from alcohol related causes.The objective of this study is to analyze the alcoholic behavior of different age group people on the basis of risk factors. In this paper, we construct a comparative model of different classification techniques to analyze the best algorithm for predicting the alcoholic behavior of a person. Methods: Under this context, random tree and J48 that are decision tree algorithms have been exercised on the dataset of 600 people that is collected through a structured questionnaire by visiting de addicted centers, colleges, villages, government offices, old age homes of Patiala, Punjab. Findings: Results conclude that the random tree provides more precise results than J48 for all the age group people. Risk factors that come out to be most effective are impulsive nature, sensation seeking nature, financial loss, family conflict, depression, child abuse, alcoholic shop near home distance.The overall accuracy of random tree is 75.94% and for J48 is 71.26%. Applications/Improvement: There is a need to develop some intelligent tools in this area and the rules extracted from this analysis can be further used for designing the tool. More attributescan be incorporated to achieve the optimal results for predicting the behavior of an alcoholic person.

Keywords

Addiction, Classification, Data Mining, Prediction.

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Baljit Singh Saini ¹, Navdeep Kaur ², Kamaljit Singh Bhatia ²

Affiliations
1 CSE, Sri Guru Granth Sahib World University, Fatehgarh Sahib - 140407,CSE, Lovely Professional University, Phagwara, Punjab, IN
2 CSE, Sri Guru Granth Sahib World University, Fatehgarh Sahib - 140407, Punjab, IN

Abstract

Biometric is the science of authenticating a user based on his physical or behavioral attributes. Keystroke dynamics is behavioral study which analyses the typing rhythm of the user. We adopted a systematic procedure for studying the state of the art in keystroke dynamics in mobile phones. We analyzed the features extracted, the classification techniques, the input text, length of the input text, number of users, hardware used and the results that each study got. We included research articles that focused on keystroke dynamics for mobile devices only. It was found that majority of the research used latency as the prominent feature. Hold time and pressure are also used in combination with latency to get improved results. The most popular classification techniques are either statistical or neural network based, although it is difficult to say which is better since the users, testing conditions and features used are different in all researches. Also the number of users that are used for taking the input are generally less than 100 which is not a good representation sample. The application of this technique is very cost effective as it does not require any extra hardware. Hence there is a need to share the datasets by researchers and develop a standard against which every researcher can compare his results. Also the environment in which the tests are performed should be uncontrolled which will give results that are more realistic and close to real deployment environment.

Keywords

Keystroke Dynamics, Mobile, Survey

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