عبدالمجیب سہالوی
افسوس ہے کہ ۲؍ نومبر ۲۰۰۱ء کو مشہور مزاحیہ نگار اور صحافی عبدالمجیب سہالوی کا انتقال ہوگیا ان کا وطن ضلع بارہ بنکی کا قصبہ سہالی تھا جو درس نظامی کے بانی ملا نظام الدین کا وطن ہونے کی بنا پر عالم گیر شہرت رکھتا ہے۔ مرحوم عبدالمجیب سہالوی کی تعلیم دار العلوم ندوۃ العلماء لکھنؤ میں ہوئی پھر انہوں نے لکھنؤ یونیورسٹی سے وکالت کی ڈگری لی مگر صحافت کے پیشہ سے وابستہ رہے۔
ان کو ادب کی مخصوص صنف طنز و مزاح سے دلچسپی تھی، ایک زمانے میں لکھنو سے نکلنے والا مشہور روزنامہ قومی آواز پورے اترپردیش میں چھایا ہوا تھا، مگر پچھلے کئی برسوں سے وہ یہاں سے تو غائب ہوگیا مگر اس کو اور اس کے فکاہی کالم ’’گلوریاں‘‘ کو ابھی تک لوگ بھولے نہیں ہیں۔ یہ کالم سہالوی صاحب ہی لکھتے تھے اور اس کی وجہ سے ان کو بڑی شہرت ملی۔ لکھنؤ کی شستہ و شیریں زبان اور طنز و مزاح کا کالم سونے پر سہاگا ہوتا تھا۔
ان کے دلچسپ فکاہی مضامین کا ایک مجموعہ ’’مفلسی میں آٹا گیلا‘‘ کے نام سے عرصہ ہوا شائع ہوا تھا جو بہت پسند کیا گیا۔ انہوں نے طویل عمر پائی لیکن عرصے سے ان کا نام سننے میں نہیں آرہا تھا گویا موتواقبل ان تموتوا کی تفسیر ہوگئے تھے، اﷲ تعالیٰ ادب و صحافت کے اس خادم کی مغفرت فرمائے اور پس ماندگان کو تسلی عطا کرے، آمین۔ (ضیاء الدین اصلاحی، جنوری ۲۰۰۲ء)
Loans or credits offered by Kopdit credit unions are a potential source of funds that need to be developed, to help accelerate the home industry and the micro and small economies. Therefore, we want to see the impact of several conditions such as the loan interest rate, GDP per capita growth, inflation rate and economic growth. Quite a number of studies have looked at the impact of interest rates, GDP growth, inflation rates and economic growth on loans or credits to banks or banking institutions. We do not look at credit or loans from banks, but on Kopdit credit unions (CU). The results of our research show that simultaneously the loan interest rate, GDP growth, inflation rate and economic growth have a strong enough influence on loans at Credit Union Credit Unions, namely 79.2454%. Partially the variable of loan interest rate, GDP growth per capita, inflation rate affects outstanding loans, while economic growth partially has no effect on outstanding loans.
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.