ﷺ
ہادیِ جنّ و بشرؐ کی صورت و سیرت کمال
شاہراہِ زندگی میں آپؐ کی سنّت کمال
آسماں پر اوج اُس کا تو برائے نام ہے
صاحبِ شقّ القمرؐ کی عظمت و رفعت کمال
آپؐ ہی اسریٰ کی شب ٹھہرے امامِ انبیاء
کثرتِ خاصاں میں بھی ہے خاصۂ وحدت کمال
عرش پر بلوا کے خود اللہ نے دل جوئی کی
آپؐ کے دلدار کی ہے آپؐ سے اُلفت کمال
باعثِ تسکینِ قلب و روح و جسم و جان ہے
تذکرۂ رحمتِ کونینؐ میں راحت کمال
دولتِ دیدارِ محبوبِ خداؐ معراجِ دید
حلقۂ اصحاب کو حاصل ہے یہ دولت کمال
رہبرِؐ کامل نے آ کر دین اکمل کر دیا
دولتِ عرفانِ رب کی مل گئی نعمت کمال
After reading the whole books and find out the interpretation, there were various sayings, the meanings and interpretations of the verses of Quran. The reader does not have the capability to select correct and incorrect. He does not know what to do about the various interpretations. At first the people of Mecca knew the status of Revelation; they do not need to explain that revelation, because it was their native language while the Prophet (S.A.W) explains it in detail. After the earlier periods, it was necessary to adapt some rules to know the correct sayings, that rules were already include the Quran itself, in the Sunnah, in the Quranic Sciences, in the books of fundamentals of Jurisprudence, and in the books of Quranic Sciences. Later on, however, wrote the books as contemporary independent science as such as book of Husain Al Ḥarbī named (قواعد الترجيح عند المفسرين) and book written by Khalid Al Sabbath named (قواعد التفسير جمعاً ودراسةً). These rules of preference are most important as with the help of these rules, the books of interpretation can be clarified from incorrect sayings. These rules are various, including, related to Quranic text, Sunnah, the views of Companions, the evidence, or related to the linguistics of Arabs. The preference proves the strength of a saying or strengthens an aspect than others through rules of preference. One of the objectives of this research is that the rules of preference can distinguish between correct and incorrect interpretation. The researcher recommended attention to these rules of preference and to study it as a separate subject to get full benefit from the books of interpretation.
Medical image analysis is very popular research area these days in which digital images are analyzed for the diagnosis and screening of different medical problems. Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated in time. Healthy retina contains blood vessels, optic disc and macula as main components but abnormal retina may contain other components and signs as well. An au- tomated system for early detection of DR can save patient’s vision and can also help the ophthalmologists in screening of DR. In this thesis, we develop algorithms for retinal image analysis based on image processing and pattern classification. Image processing techniques are used for retinal image enhancement and pattern recognition is used for classification of DR stages. The proposed system consists of different stages such as preprocessing, compo- nent extraction, candidate region detection, feature extraction and finally the classification. The first phase consists of input retinal image enhancement, noise removal, extraction of main retinal components and candidate lesions detection. We apply Gabor wavelets and Gabor filter banks for lesion detection. The system then extracts features from candidate lesions using four main properties, i.e. shape, color, gray level and statistical. Finally the classifier takes the feature vectors as inputs and grades the input retinal image into dif- ferent stages of DR. We present a hybrid classifier which combines the Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and an extension of multimodel mediod based modeling approach in an ensemble to improve the accuracy of classification. The im- plemented algorithms are tested and evaluated on publicly available retinal image databases using performance parameters such as sensitivity, specificity, positive predictive value and accuracy. The performance improvement of our proposed system is demonstrated by com- paring them with recently proposed and published methods.