آنکھوں کو بھلے لگتے ہیں آثار نبیؐ کے
ہر سمت ہیں بکھرے ہوئے انوارنبیؐ کے
در پیش ہے پھر مرحلہ خیبر و مرحب
پھر آئیں نظر حیدرِ کرارؓ نبیؐ کے
دونوں ہی دل و جان سے قربان تھے اُن پر
مکے کے مہاجر ؓ ہوں کہ انصارؓ نبیؐ کے
بو بکرؓ و عمرؓ ، حیدرؓ و عثمان ِ غنی ؓ تک
ہیں قابلِ اکرام سبھی یار نبیؐ کے
دل ہو کہ دماغ اِن سے منور ہیں ہمہ دم
آنکھوں کا اُجالا ہیں یہ افکار نبیؐ کے
اِن کے ہیں مکیں آیۂ تطہیر کے مالک
ہیں کتنے مقدس در و دیوار نبیؐ کے
سوچیں تو نہیں آتے ہیں گننے میں بھی عابدؔ
اُمت کے لیے اتنے ہیں ایثار نبی ؐ کے
Bukhari was an exemplary teacher. His teaching experiments, advices, and valuable analysis are beneficent even today. As he is famous for being expert in Ahaadeth (Muhadis), he is being ignored in the field of education. His thoughts, described in Kitabul Ilm of Sahi Bukhari can be used to reform an education system. According to his advices, a teacher should be vocal, should understand and consider psychology of students. He should make groups of students on the basis of their intelligence, and should assign assignments to students to make them more confident. About questions asked by students during teaching, Imam says this is not beneficial. Teachers also should not be upset with proper questions asked by students. No one can become expert with his own study without consultation with an expert of the field. Reading without the guidance of a teacher may be unhealthy. Imam stressed even beginner students to do hard work and travel for research, and academic work. To get knowledge and access to reliable teachers admission in an institution and fulfilling of all the requirements are necessary. Imam Also considered women education necessary. According to him women can get benefits only when their system is separate from men. Kitabul Ilm of Bukhari consists of advices for education system reforms. These advices can be used to reform the present education system. Purpose of this article is to analyze Imam Bukhari’s thoughts about education for understanding good and bad things in education.
The expansion of World Wide Web (WWW) in its size and exponential growth of its users has made the web most powerful and dynamic medium for information dissemination, storage, and retrieval. Moreover, the improvements in data storage technology have also made it possible to capture the huge amount of the user interactions (clickstreams) with the websites. The availability of such a huge amount of web user clickstreams has opened the new challenges for researchers to explore the weblog for the identification of hidden knowledge. For the last decade, web usage mining is playing a crucial role in the identification of trends from user clickstreams such as web personalization; user profiling; and user behavior analysis. These trends are beneficial in many ways such as information retrieval, website administration and improvement; customer relationship management; e-marketing; and recommender systems. The plenty of techniques are available in the literature, however, the accuracy, correctness and validity of the generated trends is totally relying on the proper selection of web mining process such as web sessionization, which is the benchmark for the later web usage mining stages. For the promising and optimized results, weblog sessionization is the eventual choice. Moreover, the extraction of proper, accurate and noise free sessionization is a demanding and challenging job in the presence of huge web clickstreams. The sessionization problem may fail to identify the focused and visualized groups from clickstreams records with high coverage and precision. Even though the well-known web session similarity measures such as Euclidean, Cosine, and Jaccard are prevalent in literature for mining process at the early learning stages. The web sessionization must take account of the validity of generated trends, which entirely depends upon the correctness and credibility of web sessions. To overcome the limitations of existing web sessionization techniques, we propose a Framework for Mining Trends (F MET) that empowers us to gauge the user activities on the website through evolutionary hierarchical Sessionization. Hierarchical Sessionization enhances the visualization of user click data to improve the business logic and mines the focused groups for scalable tracking of user activities. The foundation of the proposed framework is the swarm based optimized clustering technique along with a proposed web session similarity measure ST Index to address the Hierarchical Sessionization problem. The proposed web session similarity measure ST Index for hierarchical sessionization overcomes the limitations of Euclidean, Cosine and Jaccard measures, which may have failed to explicitly seek the proper and accurate trends. The Euclidean measures are of numerical in nature while the weblog data is of mixed nature. Moreover, existing measures are best for independent and isolated clustering groups. The proposed similarity measure ST Index computes the similarity among the user sessions through the common features (pages) shared among the sessions while assigning weight to uncommon features among the given sessions along with the minimum time shared by the given sessions time ratio. We validated and verify the proposed framework on three different datasets. The proposed ST Index measure produced the accurate and valid relationship among the sessions against common web session similarity measures. Furthermore, framework also produced the correct, accurate and valid trends. The performance of the proposed framework is validated against the well-known data analysis metrics such as VC (visitor coherence), accuracy, coverage and F1 Measures. The results show the significance improvements over the existing techniques of hierarchical Sessionization