Search from the Journals, Articles, and Headings
Advanced Search (Beta)
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...

سِک ماہی دی

سِک ماہی دی
جہلم دے دریا دا ٹھنڈا ٹھنڈا پانی اے
لَے اللہ دا ناں جے کشتی پار لنگھانی اے
دلبر ساتھوں دور ہے وسدا
ناز ، ادا تھیں دلڑی کھسدا
رستہ رب رسولؐ دا دسدا
آجا در تے چھیتی جے قسمت ازمانی اے
لَے اللہ دا ناں جے کشتی پار لنگھانی اے
دلبر دی میں دید دی پیاسی
دلبر باہجھوں رہے اداسی
کدوں اوہ سوہنا مکھ وکھاسی
ہک دن ماہی اِن شاء اللہ دید کرانی اے
لَے اللہ دا ناں جے کشتی پار لنگھانی اے
دلبر یار دا شہر سنگوئی
جس دی جگ وچ ریس ناں کوئی
پیر اصغر دی دیو دھروئی
جس نے اُس دا ورد پکایا اوہو مرد گیانی اے
لَے اللہ دا ناں جے کشتی پار لنگھانی اے

جہلم شہر ہے بڑا رنگیلا
اوگنہاراں دا اے وسیلہ
اوتھے جان دا کر کجھ حیلہ
عیباں والڑیاں دی تے ہو بخشش جانی اے
لَے اللہ دا ناں جے کشتی پار لنگھانی اے
ٹلے جوگی ریت بنائی
رانجھا آیا تلک لگائی
چنڑی رنگدیاں دیر نہ لائی
سجناں ریت قلندری ایہا بہت پرانی اے
لَے اللہ دا ناں جے کشتی پار لنگھانی اے
’’ڈھوک رجو‘‘ ہے پنڈ نرالا
جتھے وسدا اللہ والا
میلے دلاں نوں کرے اُجالا
کامل اکمل سوہنا مرشد جس دا فیض روحانی اے
لَے اللہ دا ناں جے کشتی پار لنگھانی اے

Socio-Economic Conditions of Home-Based Working Women: A Qualitative Study in Hyderabad, Sindh

This research paper focuses on socio-economic conditions of home-based working women in Hyderabad Division, of Sindh Pakistan. Main objectives of this research are (i) to analyze the Socio-economic condition of home-based working women (ii) to assess the poverty and home-based work (iii) to find out the illiteracy and home-based work (iv) to investigate the role of handicrafts and home-based work in cultural and economic development (v) to unearth the Sindhi culture of handicrafts in Hyderabad Division. To achieve research objectives qualitative research approach is adopted and data is collected by four case studies in Hyderabad division. All cases are selected randomly and analyzed by using thematic analysis method. Present study concluded that researched area is rich in handicrafts business. Women engaged themselves in home-based work due to poverty, unemployment and poor financial conditions of their families. This business has very low profit but female preferred this work due less skills and education required to carry handicrafts business. Home-based workers felt empowered due to having their own income and took part in decision making. In last it is recommended for policy makers and government agencies to give priority to this business because it has potential. It is necessary for economic development of families, culture and country.

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.
Asian Research Index Whatsapp Chanel
Asian Research Index Whatsapp Chanel

Join our Whatsapp Channel to get regular updates.