Navdeep Kaur

Multi Criteria Decision Making Approach for Selecting Effort Estimation Model

Effort Estimation has always been a challenging task for the Project managers. Many researchers have tried to help them by creating different types of models. This has been already proved that none is successful for all types of projects and every type of environment. Analytic Hierarchy Process (AHP) has been identified as the tool that would help in Multi Criteria Decision Making. Researchers have identified that AHP can be used for the comparison of effort estimation of different models and techniques. But the problem with traditional AHP is its inability to deal with the imprecision and subjectivity in the pairwise comparison process. The motive of this paper is to propose Fuzzy Analytic Hierarchy Process, which can be used to rectify the subjectivity and imprecision of AHP and can be used for selecting the type of Model best suited for estimating the effort for a given problem type or environment. Instead of single crisp value, Fuzzy AHP uses a range of values to incorporate decision maker‟s uncertainty. From this range, decision maker can select the value that reflects his confidence and also he can specify his attitude like optimistic, pessimistic or moderate. In this work, the comparison of AHP and Fuzzy AHP is concluded using a case study of selection of effort estimation model.

Research patterns and trends in software effort estimation

Abstract Context Software effort estimationxa0(SEE) is most crucial activity in the field of software engineering. Vast research has been conducted in SEE resulting into a tremendous increase in literature. Thus it is of utmost importance to identify the core research areas and trends in SEE which may lead the researchers to understand and discern the research patterns in large literature dataset. Objective To identify unobserved research patterns through natural language processing from a large set of research articles on SEE published during the period 1996 to 2016. Method A generative statistical method, called Latent Dirichlet Allocationxa0(LDA), applied on a literature dataset of 1178 articles published on SEE. Results As many as twelve core research areas and sixty research trends have been revealed; and the identified research trends have been semantically mapped to associate core research areas. Conclusions This study summarises the research trends in SEE based upon a corpus of 1178 articles. The patterns and trends identified through this research can help in finding the potential research areas.

Analysis of Data Mining Techniques for Software Effort Estimation

Software effort estimation requires high accuracy, but accurate estimations are difficult to achieve. Increasingly, datamining is used to improve an organizations software process quality, e.g. the accuracy of effort estimations. There are a large number of different method combination exists for software effort estimation, selecting the most suitable combination becomes the subject of research in this paper. In this study data preprocessing is implemented and effort is calculated using COCOMO Model. Then data mining techniques OLS Regression and K Means Clustering are implemented on preprocessed data and results obtained are compared and data mining techniques when implemented on preprocessed data proves to be more accurate then OLS Regression Technique.

Keystroke dynamics based user authentication using numeric keypad

Keystroke dynamics is the study to identify/authenticate a person based on his/her typing rhythms, which are inferred from keystroke events like key-press and key-release. A lot of research work has been done in this field where the researchers have used either only alphabetic or alphanumeric or only numeric inputs. In this paper we address the question — What is the best possible numeric input for authentication using keystroke dynamics. We accomplished this by making the users enter four different numbers. Each number consisted of 8-digits. Out of these four numbers two were random numbers while the other two were formed using digits which had some pattern to them. Random Forest and Naive Bayes were used as classifiers. The results showed that using Random Forest classifier yielded best results when a random number is taken as input. The study also proved that a combination of hold time and latency as features yielded improved results. We achieved an average false acceptance rate of 2.7% and false rejection rate of 35.9%.

Synthesis of Iron Based Biocompatible Metal Organic Frameworks for Microencapsulation of Docetaxel

Tunable properties of porous metal organic frameworks (MOFs) make them a potential candidate for sustained release of functionally active biomolecules. Current study describes in situ encapsulation of anti-cancer drug, docetaxel in iron based MOFs for drug delivery applications. MOFs were synthesized using emulsification approach by mixing FeCl3.4H2Oand benzene tricarboxylate (BTC) in 1:1 molar ratio in the presence of cetyl trimethyl ammonium bromide (CTAB). Optical characterization of the NMOFs was done using UV-visible and FTIR spectroscopy. The peak obtained at 265 nm in the UV-visible spectrum indicated the formation of iron-based NMOF. The peaks obtained at 3416, 1623 and 3343cm-1 corresponding to C=O, C=C and C=O groups in FTIR spectroscopy further supported our observations. Microencapsulation of docetaxel was achieved by mixing the drug with the iron salt during the synthesis of MOF. Drug encapsulation was affirmed by Transmission Electron Microscopy. Current study is an attempt in exploring the microencapsulation properties of MOFs.

Authenticating Mobile Phone Users Based on Their Typing Position Using Keystroke Dynamics

Keystroke dynamics is an emerging biometric method for user authentication. With the increased use of mobile phones, the use of keystroke dynamics for mobiles has been evaluated by many researchers. Mostly, the research has been carried upon data which was collected from users when they were in sitting position only. This study was conducted by collecting user data in two positions, sitting and walking, and using the phone in landscape and portrait mode. The results were positive with best EER of 3.69% achieved in walking-landscape position by using standard keystroke features and random forest algorithm. The results were also better as compared to making a single user profile by combining the data from all different positions where the achieved EER was 4.12%.

Batch Mode Scheduling- Mid_Max Algorithm

In Desktop grid computing environment, range of computing devices coexists starting from personal computers to supercomputers. These devices are interconnected to provide a variety of computational capabilities in order to execute applications that have diverse requirements. An important decision for such computing infrastructure is how to optimally allocate computational and communication resources to these applications and to schedule their execution in order to maximize performance benefits. In order to utilize the power of desktop grid completely, we need an efficient task scheduling algorithm to assign tasks to resources in a desktop grid. In this paper, we propose a Batch Mode Scheduling (Mid_Max algorithm) for the desktop grid environment. Compared to other methods, it performs well.