سیرت طیبہ کے تناظر میں منصبی ذمے داریاں اور تقاضا ہائے حقوق مصطفی

 Rights of Holy Prophet Muḥammad (P.B.U.H) have been studied from various perspectives. This paper reviewing the extant research on the subject; identifies the duties of government officials from the referred side. It concludes that Prophet Muḥammad (P.B.U.H) is the most benefactor and humanitarian to mankind in the word. In this context only those Govt. Officials can be considered true in their claim of love for Prophet Muḥammad (P.B.U.H) who adhere to his teachings, concerning ability of one’s position, piety, liability, morality and uprightness and those who refrain from being footloose and profligate, and free themselves from the hunger of wealth and status, censoriously evaluate their deeds, keep an eye on the life hereafter and accountability. Moreover, those who hold justice and avoid dishonesty and bias are true according to the teachings of Islam. Without such qualities and characteristics claim of love is just deceit and forgery.

Classified Image Indexing for Content Based Retrieval

Increased availability of devices with camera and rapid growth and development of the internet has enlarged image databases for individuals and organizations. It has raised a need for an effective and efficient approach for searching, browsing and retrieving images from large image databases of different fields of life e.g. medicine, economics, education, architecture, etc. This problem is being addressed using Content-Based Image Retrieval (CBIR) by developing the number of algorithms that use different kind of features that includes both low and high level features. These features are used to measure the similarity between images. But these features extraction and similarity measure approaches have many limitations that resulted in excessive response time and retrieval accuracy. CBIR mainly depends on extracted features from image contents and if possible, with semantic concepts. So, the semantic gap needs to be narrowed down while extracting features that can help in increasing retrieval accuracy. To minimize the retrieval time, instead of comparing features, we can transform features into deep hash codes. Comparing numbers takes less time than text or features. So, we can reduce the retrieval time by comparing the deep hash codes of query and database images. We proposed changes in the objective function of Convolutional Auto encoder to extract features while triplet loss function in the last layer to convert features to binary hash code with semantic preservation and similar code for same types of extracted features. This approach reduces the dimensions of the features to binary hash code length that make retrieval easier and faster. Proposed image retrieval system has been tested with different data sets and comparisons have been made with different loss functions and hashing approaches. Experimental results indicate the effectiveness of the proposed approach and show improved retrieval accuracy.