i-manager's Journal on Software Engineering (JSE)


Volume 12 Issue 2 October - December 2017

Research Paper

Software Architecture Understandability in Object-Oriented Systems

Turki F. Alshammary * , Mamdouh Alenezi**
* College of Computer & Information Sciences, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia.
** Chief Information and Technology Officer (CITO), Prince Sultan University, Riyadh, Kingdom of Saudi Arabia.
Alshammary, F., and Alenezi, M. (2017). Software Architecture Understandability in Object-Oriented Systems. i-manager’s Journal on Software Engineering, 12(2), 1-14. https://doi.org/10.26634/jse.12.2.14062

Abstract

Software Architecture plays a vital role in the success or failure of software systems. Architecture understandability is a very important factor for managing and improving the system architecture. In this work, understandability of software architectures at the component-level will be explored. This study examines software structural properties of size, coupling, stability, and complexity against the effort spent by a developer to study a component. Number of software design metrics have been explored in the same context in the literature before, however, this work would explore a different combination of design metrics. A case study has been adopted from the literature that used an open source software system, which comprises of seven components. Analyses of Correlation, Collinearity, and Multivariate regression have been performed. The results of the statistical analyses indicate a correlation between most of the metrics used and the required effort needed to understand a component.

Research Paper

CAD based Medical Image Processing: Emphasis to Breast Cancer Detection

G. R. Sinha*
Professor, Myanmar Institute of Information Technology, Mandalay, Myanmar.
Sinha, G. R. (2017). Cad Based Medical Image Processing: Emphasis to Breast Cancer Detection. i-manager’s Journal on Software Engineering, 12(2), 15-24. https://doi.org/10.26634/jse.12.2.14063

Abstract

Medical Image Processing exploits the use of signal processing concept when applied to medical images. The medical images may be X-rays, Computed Tomographic (CT) images, or Mammograms. This paper gives an overview of image processing for the application areas of medical science that covers the concepts of Computer-Aided Diagnosis (CAD) system used in medical images and diagnosis system for segmentation, detection, and classification of cancer stages by post-processing the medical images. Medical Image Processing has brilliant research scope in understanding physical, mathematical, and engineering avenues of medical image uses in various disease diagnosis methods. This enables to “see” inside the human body to diagnose the disease and monitor treatment; an overview of recent developments in the field of medical imaging along with prominent challenges that radiologists and physicians come across while scanning, interpretation, and diagnosis processes. A practical approach and experimental results in some cases of segmentation with a review of a specific algorithm for medical image processing or analysis, along with the concept of CAD system and its evaluation criteria are discussed.

Research Paper

Nature-Inspired Evolutionary Computation For Optimization

B.V. Babu*
Former Vice Chancellor, Graphic Era University, Dehradun, India & Galgotias University, Uttar Pradesh, India and Former Professor & Dean, BITS-Pilani, Rajastan, India.
Babu, B.V. (2017). Nature-Inspired Evolutionary Computation for Optimization. i-manager’s Journal on Software Engineering, 12(2), 25-36. https://doi.org/10.26634/jse.12.2.14064

Abstract

As and when the conventional analytical and empirical approaches fail to optimize a given system, we need to look for alternatives. The emerging trend is to get inspiration from nature to handle such complex situations and systems. Traditionally, the gradient based optimization techniques are used for finding an optimal solution to a given problem. However, due to the inability of these techniques in terms of handling multi-variable multi-constrained complex systems, the nature inspired evolutionary algorithms (population based search algorithms) have been developed over the past few years. This paper focuses on nature- or bio-inspired evolutionary computation technique called Differential Evolution (DE) (an improved version of Generic Algorithm), its working principle, and demonstration with a numerical example using step-by-step procedure. Various DE strategies are discussed and applied to many engineering and management problems. DE is extended to multi-objective optimization problems as Multi-Objective Differential Evolution (MODE) and its variants, that can handle the limitations of traditional optimization techniques in addressing complex engineering problems in terms of constraints, objectives, etc. are demonstrated. The working principles of these Evolutionary Algorithms are demonstrated with examples and industrial applications.

Research Paper

Prediction Of Lower Cardiovascular Risk For Pet Holders (Data Mining)

R. Sivasharmili*
PG Scholar, Department of Computer Science and Technology, Women's Christian College, Chennai, India.
Sivasharmili, R. (2017). Prediction of Lower Cardiovascular Risk for Pet Owners (Data Mining). i-manager’s Journal on Software Engineering, 12(2), 37-43. https://doi.org/10.26634/jse.12.2.14065

Abstract

Heart Diseases have been the leading cause of death for decades. The proportion of deaths caused by heart disease is nearly 25% per year around the world. Therefore, there is a need for new potential strategies to reduce the risk factors of Cardiovascular disease (CVD). The objective of the research is, linking heart health and owning a pet, as holding a pet is probably associated with a lower risk of heart disease for those without a history of heart problems, and with the greater survival rates among heart disease patients. For the risk factors, medical dataset that has attributes such as age, sex, chest pain type, cholesterol level, blood pressure, blood sugar, heart rate, obesity and hereditary are collected. Finally, the review of the available data suggests that, owning a pet likely reduces the risk of developing heart disease or worsening it. The research is implemented in WEKA tool, using Association rule along with the Apriori algorithm.

Research Paper

Human Tracking Using Weighted Running Window Background Model Based GMM

Harihara Santosh Dadi* , Gopala Krishna Mohan Pillutla**, Madhavilatha Makkena***
* PhD Scholar, Department of Electronics and Communication Engineering, JNT University, Hyderabad, India.
** Professor, Institute of Aeronautical College of Engineering, Hyderabad, India.
*** Professor, Department of Electronics and Communication Engineering, JNT University, Hyderabad, India.
Dadi., Pillutla., and Makkena. (2017). Human Tracking Using Weighted Running Window Background Model Based GMM. i-manager’s Journal on Software Engineering, 12(2), 44-54. https://doi.org/10.26634/jse.12.2.14066

Abstract

Tracking of humans in video streams is important for many applications. For tracking purposes, many algorithms have come up in the recent years. The most prominent one among all of them is Gaussian Mixture Model (GMM). This algorithm is basically employed for tracking the objects in the Video scene. Later, the algorithm has been modified for the purpose of tracking humans. GMM uses only single rectangular template for tracking an object. In order to track humans specifically, the template has been divided into four regions. The top region is for the head and the remaining regions are for the chest, waist and legs respectively. All the regions are of rectangle shape. Connection has been established among all the regions assuming that all four regions will move at a time for humans. There is only 10% horizontal variation allowed between the regions. The proposed algorithm could handle both partial occlusion and full occlusion. The new algorithm is compared with the tracking system of GMM algorithm. The precision, recall, false alarm per frame, false negatives, false positives and mostly lost are compared with the existing GMM. The time taken for processing a single frame is reduced by using new algorithm when compared with the existing algorithm. Performance metrics show that the Weighted Running Window Background (WRWB) Model Based GMM algorithm out performs when compared with GMM algorithm in terms of time taking.