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عبد القیوم انصاری

عبدالقیوم انصاری
عبدالقیوم صاحب انصاری بہار کے مشہور نیشنلسٹ اورقومی کارکن تھے۔ انھوں نے قید وبند کے مصائب بھی برداشت کیے اورتحریک پاکستان کے زمانہ میں اپنوں کے ہدف ملامت بھی بنے، لیکن ان کے پائے استقلال میں کوئی لغزش نہیں آئی۔وہ بہار گورنمنٹ میں وزیر تھے لیکن ان کاطورطریق بالکل عوامی تھا۔ ان کی زندگی ارباب حاجت کی مدد کے لیے وقف تھی۔چنانچہ ان کی وفات بھی خدمت کرتے ہی واقع ہوئی۔ سیاسیات میں غایت درجہ انہماک کے باوجود نماز، روزہ کے پابند اوربڑے خلیق اور متواضع تھے۔ [فروری۱۹۷۳ء]

Performance of Banking Industry After Privatization in Pakistan: A Case Study of Mcb Bank Limited

This research work aims to investigate the impact of privatization on the performing efficiency of MCB Bank Limited Privatization and the phenomenon of denationalization after the failure of socialism and communism globally. As the direction of enteritis was predetermined by state which in long term affected the performance of state-owned entities on many fronts even they reached at the verge of collapse and state was compelled to inject capital for their survival. Ultimately the state took drastic steps and initiated the process of denationalization and privatization to keep the industry intact in the changed scenario. In 1974, during Z.A. Bhutto regime Pakistan’s banking industry was nationalized with prime objective to address the issues of backward segments of economy but unfortunately after privatization industry was used for political motives and witnessed poor performance and financial indiscipline due to frequent interference in the affairs of banks particularly in lending activities and hiring of inefficient human resources. Resultantly banks failed to deliver as per expectation of masses and could not deliver quality customer services on one hand and accumulation of infected portfolio on the other which in turn swallowed the profitability and the capital of banks. It is revealed that bank has tremendously performed in all Key Performing Indicators, it has improved its profitability manifold, deposit base is significantly enhanced and became more liquid and solvent.

A New Framework to Bridge Semantic Gap for Semantics Based Image Retrieval

The performance of Content Based Image Retrieval (CBIR) is limited because of the Semantic Gap (SG). This motivates to extend the image retrieval process beyond low level descriptions to image semantics. Therefore, researchers proposed Semantic Based Image Retrieval (SBIR) to bridge SG. In the literature, various approaches for SBIR such as image annotation, relevance feedback, and object ontology have been proposed to bridge SG. It has been observed that not very promising results with these methods are reported in the literature. Annotation based approaches are constrained by low precision because real life images usually have a diverse set of image concepts. Relevance feedback approach is also constrained by low precision and recall because it forces to alter the query vector that causes to modify image semantics. Object ontology is a considerably good approach, but its metadata architecture appears to be very complex and suggested to use only for semantic web instead of SBIR. Moreover, this approach is reported to have low precision for conceptually diverse images. Therefore, the first contribution of our work is the performance evaluation of CBIR methods using soft computing techniques for image comparison and retrieval. In this regard, we have reported performance improvement in terms of retrieving visually similar images by employing proposed modifications in soft computing methods. However, these modifications do not translate into semantically correct retrieval of images. This leads us towards the logical conclusion of planning and development of a new SBIR framework using image concepts. By the term “image concept” we means that a set of noticeable objects and regions in an image e.g. Sky, group of person, land etc. In our first contribution we have also explored existing CBIR approaches in order to find a suitable 11 candidate (or its modified version) to be used for SBIR. The second contribution of our work is therefore to bridge the SG with a maximum tradeoff between precision and recall. The proposed framework is extensively examined by evaluating precision and recall for large segmented datasets. For the rigorous testing of our major contributions, three datasets have been opted, namely, Wang’s, COIL, and IAPR TC-12.
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