غلام آیا ہے آقاؐ مگر ہے شرمندہ
جھکی ہوئی ہیں نگاہیں ، نظر ہے شرمندہ
لبوں پہ کوئی بھی اک لفظ آ نہیں پاتا
دُعا کو ہاتھ اُٹھیں کیا اثر ہے شرمندہ
ہمارے ہاتھ بھی خالی ہیں اور دامن بھی
مدینے جاتے ہیں لیکن سفر ہے شرمندہ
بڑھا سکا نہ یہ ایمان کی حرارت کو
سو میرے دل میں دہکتا شرر ہے شرمندہ
اب ایک در پہ جُھکے بھی تو کیسے جُھک پائے
کہاں کہاں اِسے رکھا کہ سر ہے شرمندہ
مجھے یقین ہے اپنے کریم آقاؐ پر
صدا کرم کی سنوں گا، ’’کدھر ہے شرمندہ‘‘
مجھے سلیقۂ توصیف ہی نہیں عابدؔ
میں نعت لکھتا ہوں لیکن ہنر ہے شرمندہ
Penelitian ini bertujuan untuk mengetahui strategi digital marketing industri halal pada Business Center (BC) Halal Network International Herba Penawar Alwahida Indonesia (HNI-HPAI) Pekanbaru 4 dalam menghadapi Volatility, Uncertainty, Complexity dan Ambiguity (VUCA) di era society 5.0 dan untuk mengetahui tantangan yang ada di era si society 5.0 pada BC HNI-HPAI Pekanbaru 4. Penelitian ini merupakan penelitian lapangan dengan menggunakan pendekatan kualitatif. Informan penelitian berjumlah 3 orang. Sumber penelitian yang digunakan adalah sumber primer dan sumber sekunder. Hasil penelitian menemukan bahwa strategi pemasaran digital yang dilakukan adalah menggunakan media whatsapp dan juga memanfaatkan beberapa media website dan aplikasi digital yang telah disediakan oleh HNI pusat dan unofficial, dan juga BC HNI Pekanbaru 4 mengandalkan para mitra-mitranya dalam pemasaran di media digital. Pada kondisi uncertainty, dalam mengumpulkan informasi BC menggunakan sosial media, dan melakukan edukasi. Pada kondisi kompleksitas, kesulitan yang di alami yaitu masih kekurangan pengelola khusus dibagian sosial media seperti facebook (fb), dan Instagram (ig). Sedangkan pada kondisi ambiguity, sasaran HNI adalah para generasi milenial yang sudah mahir menggunakan media digital. Adapun tantangan yang ada di era society 5.0 ini adalah harus siap dengan kehadiran teknologi, aktif dan mampu mengelola sosial media, inovasi produk, dan terus mengupgrade skill di era society 5.0.
Biomedical data is growing up rapidly and a better retrieval system is the need for its utilization. A basic problem while retrieving data from a system related to the queries is mismatch of words, which indicates the use of dissimilar words for expressing the identical concepts in given queries and in the stored documents. Two techniques are commonly used to solve this problem i.e. query paraphrasing as well as query expansion. Query paraphrasing refers that the query is paraphrased by using synonyms of terms in the query. Query expansion techniques are further categorized as local and global. Local query expansion technique focuses on the analysis of the documents having top ranks retrieved for a query. Different ranking models have been introduced to rank documents in collections based on terms and features. A collection of candidate terms is obtained for expanding the given query from these documents. On feature selection from term pool, final selected candidate expansion terms contain a few terms which cause query drift problem. To overcome this problem the semantic filtering technique was used. Semantic similarity measures are the basic techniques for successful semantic filtering. However, global query expansion relies on the analysis of the whole collection to find out word relationships. Synonyms of query words are extracted from a dictionary or thesaurus. In this research, we evaluated the famous probability-based ranking models such as LM-Dirichlet, LM Jelinek Mercer and BM25 for biomedical data retrieval process. We performed experimental analysis using diverse preprocessing techniques iteratively on 36 biomedical related queries for the evaluation. State of the art biomedical data set Trec Genomic was used as a core for whole experimentation. It was observed that BM25 was the best information retrieval model for biomedical data. We used different terms scoring techniques such as Baseline, BNS, Chi-Square, Codice, BIM, KLD, LRF, PRF and RSV to score the terms related to the query. The average of MAP scores of all the queries was compared that exhibited BNS term scoring technique is the best for biomedical data. Different semantic similarity measures such as Path-based, Wu and palmer, Leacock and Chodorow were applied on terms extracted from BNS to get most appropriate terms for query expansion. Finally, queries expanded with the most similar terms each time and documents retrieved through the expanded queries and the MAP results were evaluated for the purpose of final declarations of this research. The results of biomedical data retrieval through query expansion were improved and the LCH semantic similarity measuring technique found best for query expansion in biomedical data retrieval system.