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Home > مستشرقین کے رد میں لکھی جانے والی کتب سیرت کا تحقیقی جائزہ برصغیر کے حوالے سے

مستشرقین کے رد میں لکھی جانے والی کتب سیرت کا تحقیقی جائزہ برصغیر کے حوالے سے

Thesis Info

Author

حافظ سیف الاسلام

Supervisor

عبد الرؤف ظفر

Program

Mphil

Institute

The Islamia University of Bahawalpur

Institute Type

Public

City

Bahawalpur

Province

Punjab

Country

Pakistan

Degree End Year

2011

Thesis Completion Status

Completed

Language

Urdu

Added

2021-02-17 19:49:13

Modified

2023-02-19 12:33:56

ARI ID

1676729569919

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خالد مسعود

آہ جناب خالد مسعود!!
یہ خبر علمی و دینی خصوصاً ترجمان القرآن مولانا حمید الدین فراہیؒ کے قدردانوں اور ان کے علوم و افکار کے شیدائیوں کے لیے بڑی غم ناک اور روح فرسا ہے کہ یکم اکتوبر ۲۰۰۳؁ء کو رسالہ تدبر لاہور کے مدیر جناب خالد مسعود صاحب کا انتقال ہوگیا، اناﷲ وانا الیہ راجعون۔
وہ مولانا امین احسن اصلاحی کے شاگرد رشید اور علمی وارث و جانشین تھے، انہوں نے اپنی زندگی فکر فراہی کی شرح و ترجمانی اور اس کی ترویج و اشاعت کے لیے وقف کردی تھی، ان کی وفات سے ہم قرآن و حدیث کے ایک بڑے خدمت گزار سے محروم ہوگئے۔
مرحوم کو جگر کی بیماری تھی، خون کی رگ بار بار پھٹ جاتی تھی اور خون کی قے ہونے لگتی تھی، آخر اس بیماری نے ان کا کام تمام کردیا۔
خالد مسعود صاحب ضلع جہلم کے ایک گاؤں ’’ﷲ‘‘ ۱۶؍ دسمبر ۱۹۳۵؁ء کو پیدا ہوئے، یہیں ابتدائی تعلیم حاصل کی اور ۱۹۵۱؁ء میں نوشہرہ سے فرسٹ ڈویژن میں میٹرک اور ۱۹۵۵؁ء میں اسلامیہ کالج لاہور سے فرسٹ ڈویژن میں بی۔ایس۔سی کیا، ۱۹۵۷؁ء میں پنجاب یونیورسٹی سے کیمسٹری میں ایم۔ایس۔سی کیا، ۱۹۵۸؁ء میں انڈسٹریل ریسرچ لیبارٹریز سے وابستہ ہوئے پھر مزید تعلیم کے لندن گئے اور ۱۹۵۹؁ء میں وہاں کے کنگز کالج سے کیمیکل انجینئرنگ میں ڈپلوما حاصل کیا، واپسی کے بعد ۱۹۷۴؁ء میں پنجاب یونیورسٹی سے علوم اسلامیہ میں ایم۔اے کیا، ۱۹۸۵؁ء میں قائداعظم لائبریری میں ملازمت کی، اس ے وابستگی کے زمانے میں بچوں کے لیے آسان زبان میں متعدد مفید اور معلوماتی کتابیں تحریر کیں، جو بہت مقبول ہوئیں اور بعض پر ان کو ایوارڈ بھی ملا، گو ان کتابوں میں فلکی طبعیات کی جدید ترین تحقیقات کے نتائج اور کائنات کے بارے میں نئے افکار و نظریات پیش کیے گئے ہیں تاہم اسلامی نقطہ نظر کو اوجھل...

طلب معاش کی اہمیت اور اس کی ترقی کے اسباب قرآن اور سنت کی روشنی میں

Trade has been considered as the most important mode of caring livelihood.  Trade is considered as sole biggest medium because all other modes carry secondary position, as they all derive from trade in one way or the other. That’s way crafts and services have gained much success in this era of machinery and technology. In spite of all this trade assumes the same highest positions in this era too. Islam has not left trade on its own but has framed principles. These principles are not only helpful on individual scale but they give benefits to whole community. It benefits in this world and hereafter of its users. Islam is an eternal religion, so it addresses all problems of this era and of upcoming in its teachings. We can gain both worldly and hereafter benefits by acting upon Islamic principles. We have glorious examples of past traders who visited the whole world in order to carry trade. They trade far and wide on the basis of Islamic principles. They not only maintained their businesses, but also preached Islam through their just dealings and humble behavior. Their customers or the people who come into their contact were so impressed that they not only became Muslims but also changed their traditions, Customs, culture and even languages. This article discusses the reasons that explicate the beauty of Trade.

Novel Disease Named Entity Recognition Dner & Hybrid Relation Extraction Hre Frameworks for Biomedical Text

Biomedical knowledge is usually presented in the form of unstructured segments; making the extraction of such information a complex task. Although, manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually, because its data size is rising exponentially. Thus, there is a need for automatic tools and techniques for information extraction and knowledge discovery in biomedical text mining. Named entity recognition and relation extraction are focused areas of research in biomedical information extraction systems. Relation Extraction hinders the known relationship between Named Entities and in some way these are dependent on each other yet research also takes both these steps in an independent manner also. A lot of work has been done on biomedical named entity recognition focusing mostly on supervised and semi supervised solutions but very less attention work is done on unsupervised methods. Due to limited availability of annotated corpora the researchers now directed their efforts towards achievement of unsupervised named entity recognition systems. Named Entity Recognition from annotated corpora has been matured and there is very less margin for performance optimization. The challenge is still alive for the named entity recognition from unannotated corpora in all domains generally and for biological and biomedical domain specifically. Biomedical text exhibits relationships between different entities which are important for practitioners and researchers. Relation extraction is a significant area in biomedical knowledge, which has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction and identification focusing on two major areas: 1) rule based technique and 2) machine learning technique. In the last decade, focus has changed to hybrid approaches which have shown better results. This research presents an unsupervised named entity recognition framework along with a hybrid feature set for classification of relations between biomedical entities. Our Named Entity Recognition uses UMLS concepts and creates signatures that automate signature vectors. The vectorization of UMLS concepts ensures application of the framework in a generic way. Our framework differs with previous un-supervised methods in a way that we rely on UMLS for vector space creation instead of corpus statistics. The Relation Extraction approach uses bag of word feature, along with Natural Language Processing (NLP) to identify the noun and verb phrases and semantic features based on UMLS concepts. This hybrid feature set is a better representation of the relation extraction task. The main contribution in this hybrid features is the addition of semantic feature xi | P a g e set where verb phrases are ranked using Unified Medical Language System (UMLS), and a ranking algorithm is designed to get the most suitable concepts as features for the classifier. For Named Entity Recognition, we used Arizona Disease Corpus (AZDC) a gold standard corpus for this task. Our framework achieved accuracy of 72.56% which is competitive with supervised techniques on the same corpus. Our Relation Extraction approach has been validated on standard biomedical text corpus obtained from MEDLINE 2001, an accuracy of 96.19%, 97.45%, 96.49% and F-measure of 98.05%, 93.55%, 88.89% has been achieved for the cure, prevent and side effect relations respectively.