آلودہ پانی کی مروجہ تحلیل و تطہیر کا سائنسی و شرعی جائزہ

Water symbolizes life on the earth. All kinds of existence depend on water. Allah has created great reservoirs of water on this earth which cater to the needs of all living beings, but human negligence is making these resources polluted. Pollution not only poses threats to human life, it also endangers marine life and resources. These threats have been increased manifold by industrial revolution and increasing human population. The same factors are also responsible for the increased use of water. Many countries have employed distillation processes to provide clean water for the increasing demands of population, agriculture and industries. Islamic jurisprudence also takes into account different types of water, some of which can be used in cleansing rituals for worship, while some cannot be used. The question arises whether the purified water by scientific process will be "Ṭahir & Muṭṭahir" and can be used for ablution and other Islamic rituals? Will Islamic Jurist accept it as "Istiḥalah"? The following study will outline the details of distilled water to see whether it can be used for daily chores and ablution rituals or not.

Architecture Recovery of Legacy Software Systems Using Unsupervised Machine Learning Techniques

Perhaps the most important aspect in maintaining software legacy systems is un- derstanding their architecture. Architectural documentation is often unavailable. Thus efforts need to be made to recover the architectural design from the source code. This thesis addresses the problem of recovering the architecture of software systems for greater understanding, and modularizing them for greater maintainability, using machine learning techniques. We use clustering to obtain a high-level view of a software’s architecture, by identify- ing major sub-systems within it. For this purpose, we analyze the behaviour of existing similarity and distance measures when applied to software artifacts and keeping in view software characteristics, yielding explanations to some previously unanswered questions. We develop two new hierarchical clustering algorithms that address the problem of ar- bitrary decisions taken by existing hierarchical algorithms. We also propose a similarity measure suitable for software clustering. The performance of the proposed algorithms and similarity measure is evaluated using internal and external assessment. Instead of using only one expert decomposition for external assessment, as is commonly done, we use decompositions prepared by 4-5 experts for each test system. Such an approach allows us to validate the idea of multiple views of a software system. Experiments carried out on five open source legacy software systems show that the performance of our proposed algorithm is better than previously used algorithms. Interpreting the results of clustering algorithms is often difficult. To make clusters easier to understand, we propose a labeling scheme for clusters and compare two alter- native ranking schemes that can be utilized for this purpose. We demonstrate how the labels assigned by our scheme aid understanding of the clustering process of clustering algorithms. We also provide a comparison between cluster analysis and concept analysis as modularization techniques, and give examples of their application to different software structures, thus indicating the strengths and limitations of the two techniques. Finally, we use association rule mining to gain insight into the low-level structure of software systems by examining relationships between architectural quarks i.e. functions, global variables and user defined types. Metarule-guided association rule mining is used to ividentify problems within structured legacy systems. Re-engineering patterns that present solutions to these problems are proposed. Results for the test systems reveal interesting characteristics which allow us to understand legacy systems and their evolution.