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Guidelines for the development of automated test case incident reporting tool s

Thesis Info

Author

Bushra Areeb Fatimah

Supervisor

Rizwan Bin Faiz

Department

Department of Computer Science

Program

MS

Institute

International Islamic University

Institute Type

Public

City

Islamabad

Province

Islamabad

Country

Pakistan

Thesis Completing Year

2015

Thesis Completion Status

Completed

Page

55

Subject

Computer Science

Language

English

Other

MS 005.276 FAG

Added

2021-02-17 19:49:13

Modified

2023-02-17 21:08:06

ARI ID

1676722089678

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۔غزل

غزل۔۔۔۔۔۔۔ اجمل اعجاز

بنا بولے ہوئے گفتار کرنا
سکوں دیتا ہے روٹھے کو منانا
جگایا ہے کسی غفلت زدہ کو
کیا مفلس مری دریا دلی نے
جہاں پاؤں رکھو، روشن زمیں ہو
تری باتیں رلائیں خوں کے آنسو
محبت کا صلہ اجرت نہیں ہے

 

اسے کہتے ہیں آنکھیں چار کرنا
منا کر پھر اسی کو پیار کرنا
غلط ہے کیوں اسے ہر بار کرنا
مجھے آتا نہیں انکار کرنا
دئے روشن یونہی دلدار کرنا
زباں کو اور کیا تلوار کرنا
مری جاں، ہے یہ کاروبار کرنا

TOWARDS INTEGRATING REHABILITATION INTO HEALTH SYSTEMS THROUGH PROFESSIONAL REGULATION

Strengthening rehabilitation in health systems and integrating rehabilitation across all levels of care depends on a mix of strategies, however all depend on an appropriately trained, resourced and organized workforce. Indeed, among the ten areas for action described in the World Health Organization 2030 initiative is developing a strong multidisciplinary rehabilitation workforce that is suitable for country context, and promoting rehabilitation concepts across all health workforce education.   The rehabilitation workforce is constantly evolving as it strives to provide safe practices and treatment choices based on the best available evidence to improve function, promote independence and help people reach their maximum potential. However, barriers to this evolution include a lack of well-resourced training programs, variations in the competencies expected within the standard entry-level curriculum, limited opportunities for continuing professional development, geopolitical instability, competing demands for limited health budgets and persistent de-prioritization of rehabilitation.

Time Series Models: Forecasting and Control for the Management of Computing Infrastructure and Resources

The existence of self-similar or fractal nature of network traffic has been proven by recent studies over a wide range of time scale. These properties are very different from the traditional Poisson processes/models. If one analyzes the network traffic by these traditional Poisson models then this leads toward wrong or incorrect decisions. The issues with Poisson models are overestimation of the performance of computer network and false allocation of data processing and network resources. Hence complete understanding of self-similar nature is required for fast and appropriate performance of the network. This thesis describes a number of problems arising from the experimental study of telecommunication networks. The work can be split into two main areas: full understanding of self-similarity within a network traffic and secondly focused over the comparison of two different networks i.e. searching similarity between two or more self- similar network traffic by using wavelet based time warping technique. Estimation techniques of self-similarity measure i.e. Hurst Index H is considered first the critical analysis of these methods is studied. There is a lack of GUI based tool for the estimation of Hurst Parameter we developed a new Statistical tool that can estimate the Hurst Parameter H by different methods along with their statistical analysis. Next we focused over the generation of self-similar traffic using simulation methods. A comparison of these methods was also studied. Main focus was on the Wavelet based estimation and generation methods for computer network traffic. Secondly concentration is made over filtering of temporal data to get the smooth form of the signal that may be used for further analysis. The main goal of second part is to explore new trends for filtering and smoothing of temporal data. A recently developed technique of wavelets for smoothing of temporal data is explored. New approach was developed using different features of wavelet transformation for dimensionality reduction and tested them for various decision making processes in business and computer science. The developed smoothing techniques are applied for forecasting, query processing of vsimilar sequences in huge databases and their clustering on the basis of membership value. The developed forecasting technique is based on Wavelet families and Seasonal Autoregressive integrated moving average model. In this thesis, two new approaches for smoothing of large temporal databases using the wavelet filtering theory for approximations of query procedures for decision support systems (DSS) are also proposed. For these smooth signals, a novel query processing algorithms was developed while selecting wavelet based features and time warping distance metric, called wavelet based features time warping. For first proposed algorithm we utilized the features extracted on the basis of minima, maxima and averages of wavelet based compressed signals and for second proposed algorithm local features of wavelet transformation using average of approximation coefficients at the coarsest scale and maxima of maxima and minima of minima of detail coefficients at all scales were used. Our both models support index based time warping distance. It is proved by carrying out extensive experiments with synthesized databases using different wavelet families that the proposed methods are very effective and ensure the nonoccurrence of false dismissals and minimal false alarms with least compromise over accuracy. The developed method gives extremely fast response times as our approximate query executes its maximum processing over synoptic set of wavelet features. A new measure of degree of similarity is introduced for clustering using time warping distance method which gives better control over the number of clusters.