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کیوں اس طرح کی صورتِ حالات ہو گئی

کیوں اس طرح کی صورتِ حالات ہو گئی
مشکل ہی دوستوں سے ملاقات ہو گئی

زلفیں ہٹیں جو رُخ سے تو روشن ہوا تھا دن
واپس ہوئیں تو دیکھیے پھر رات ہو گئی

اک بے وفا کی یاد بھلانے کو زندگی
افسوس یہ کہ نذرِ خرابات ہو گئی

سارے جہاں نشاط کے جب اس کے ہو گئے
پھر رنج و غم کی دنیا مرے ساتھ ہو گئی

تائبؔ کچھ اس طرح سے میں رویا ہوں رات بھر
لگتا ہے جیسے شہر میں برسات ہو گئی

The Lung Mass and Nodule: A Case Series The Lung Mass and Nodule

Lung mass is an abnormal region of 3 cm or more in size present in the lungs mainly due to underlying pulmonary caner. It is usually round, opaque and poorly differentiated on X-ray. Common etiological key players are smoking, exposure to asbestos, radon, however, familial history may also play a role. We presented retrospectively7 cases of lung mass and nodule encountered during our clinical practice. We have discussed their clinical presentation, manifestation, medical history, radiological findings and differential diagnosis. In this case series, most of the patients were young, only 2 cases were older patients. There was one infant one month old, one female child 12 years old, one female 25 years, 2 males, 22 and 21 years, one male of 50 years and another male of 60 years age. Correct diagnosis on the basis of clinical profile, radiological findings and histology may help in proper management and hence, timely treatment of the patient

A Frequent Graph Pattern Mining Approach for Evaluation of Trends in Social Media

Graph mining is a well-established research field and lately it has drawn considerable attention of research communities. It allows to process, analyze, and discover significant knowledge from graph data. Graph mining has been highly motivated by the enormous number of applications. Such applications include Chemoinformatics, Bioinformatics, and societal networks. In graph mining, one of the most challenging tasks is Frequent Subgraph Mining (FSM). FSM has been applied to many domains, such as graphical data management and knowledge discovery, social network analysis, Bioinformatics, and security. In this context, a large number of techniques have been suggested to deal with the graph data. However, FSM approaches are facing some challenges, including enormous numbers of Frequent Subgraph Patterns (FSPs); no suitable mechanism for applying ranking at the appropriate level during the discovery process of the FSPs; extraction of repetitive and duplicate FSPs; user involvement in supplying the support threshold value; large number of subgraph candidate generation; and there exists no specialized scheme to decide the discovered FSPs are optimized patterns as well. Thus, the aim of this research is to make cope with the challenges of enormous FSPs, avoid duplicate discovery of FSPs, use the ranking for the discovered FSPs, and to suggest an optimization strategy to illustrate an association between the frequent and the optimized subgraph patterns. The exploration of this association will further help to decide on the FSPs as optimized FSPs. Therefore, to address the aforementioned challenges a new FSM framework A RAnked Frequent pattern-growth Framework (A-RAFF) is developed. The proposed FSM framework, A-RAFF, provides an efficient answer to these challenges through the initiation of a new ranking measure called FSP-Rank. The proposed ranking measure FSP-Rank, based on the characteristics of the FSPs, effectively reduced the duplicate and enormous FSPs. Moreover, in this study, we have investigated the association between FSPs and optimized subgraph using a Particle Swarm Optimization technique. The effectiveness of the techniques proposed in the dissertation is validated by extensive experimental analysis using different benchmarks, both real and synthetic graph datasets. Finally, our experiments have consistently demonstrated promising empirical results, thus confirming the superiority and practical feasibility of the proposed FSM framework.
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