WITHDRAWN: Word Embedding Generation for Urdu Language using Word2vec model

Kumhar, Sajadul Hassan; Kirmani, Mudasir M; Sheetlani, Jitendra; Hassan, Mudasir

doi:10.1016/j.matpr.2020.11.766

Cited by 18 publications

(15 citation statements)

References 7 publications

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“…The primary hyperparameters of the K-NN model (the number of neighbors' k and the similarity function or the distance metric) are tuned to get the optimal results [ 30 , 38 , 39 ].…”

Section: Results and Discussion Of Optimized Risk Modelsmentioning

confidence: 99%

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

Ansarullah¹,

Saif²,

Andrabi

et al. 2022

Journal of Healthcare Engineering

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Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparameter optimization of machine learning techniques. The multiple set of risk attributes is selected and ranked by the recursive feature elimination technique. The assigned rank and value to each attribute are validated and approved by the choice of medical domain experts. The enhancements of applying specific optimized techniques like decision tree, k-nearest neighbor, random forest, and support vector machine to the risk attributes are tested. Experimental results show that the optimized random forest risk model outperforms other models with the highest sensitivity, specificity, precision, accuracy, AUROC score, and minimum misclassification rate. We simulate the results with the prevailing research; they show that it can do better than the existing risk assessment models with exceptional predictive accuracy. The model is applicable in rural areas where people lack an adequate supply of primary healthcare services and encounter barriers to benefit from integrated elementary healthcare advances for initial prediction. Although this research develops a low-cost risk evaluation model, additional research is needed to understand newly identified discoveries about the disease.

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“…The primary hyperparameters of the K-NN model (the number of neighbors' k and the similarity function or the distance metric) are tuned to get the optimal results [ 30 , 38 , 39 ].…”

Section: Results and Discussion Of Optimized Risk Modelsmentioning

confidence: 99%

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

Ansarullah¹,

Saif²,

Andrabi

et al. 2022

Journal of Healthcare Engineering

Self Cite

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“…There are several techniques available also like word2vec, which uses local context-based learning and classical vector space model representation which uses matrix factorization techniques such as LSA (Latent Semantic Analysis). In this paper, we have used GloVe (Global Vectors for Word Representation) [ 28 , 29 ], which efficiently learns word vectors and combines the approaches like matrix factorization techniques like LSA and local context-based learning as in word2vec.…”

Section: Proposed Systemmentioning

confidence: 99%

Machine Translation System Using Deep Learning for English to Urdu

Andrabi

Wahid

2022

Computational Intelligence and Neuroscience

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Machine translation is an ongoing field of research from the last decades. The main aim of machine translation is to remove the language barrier. Earlier research in this field started with the direct word-to-word replacement of source language by the target language. Later on, with the advancement in computer and communication technology, there was a paradigm shift to data-driven models like statistical and neural machine translation approaches. In this paper, we have used a neural network-based deep learning technique for English to Urdu languages. Parallel corpus sizes of around 30923 sentences are used. The corpus contains sentences from English-Urdu parallel corpus, news, and sentences which are frequently used in day-to-day life. The corpus contains 542810 English tokens and 540924 Urdu tokens, and the proposed system is trained and tested using 70 : 30 criteria. In order to evaluate the efficiency of the proposed system, several automatic evaluation metrics are used, and the model output is also compared with the output from Google Translator. The proposed model has an average BLEU score of 45.83.

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“…Pedoman PICO sangat berguna dan memudahkan dalam merumuskan pertanyaan penelitian [20]. Tabel Dataset Peneliti Jumlah Twitter [33], [34], [35], [36], [23], [24], [37], [38], [39], [22], [40], [41], [42], [43], [44], [45], [46] 17 Berita berbahasa Arab [28], [47], [48], [49], [27], [25], [50], [51] 8…”

Section: Tahap Pembuatan Rencana Awalunclassified

“…Tabel 6. Analisis Metode Representasi Teks dalam Arabic Natural Language Processing Metode Untuk Representasi Teks Peneliti Jumlah TF-IDF [4], [9], [48], [30], [23], [24], [29], [53], [54], [7], [38], [55], [32], [56], [25], [31], [59], [1], [51], [60] 20 Word2Vec [34], [24], [49], [7], [57], [39], [41], [43], [45], [46] 10 AraVec [34], [35], [37], [40], [41], [42] 6 FastText [34], [47], [35], [57], [41], [42], [50], [46] 8 mBERT [27], [22], [41], [46] 4 AraBERT [36],…”

Section: Tahap Pembuatan Rencana Awalmentioning

confidence: 99%

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Ariyanto

fatichah²,

Arifin³

2021

RESTI

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Hadith is the second source of reference for Islamic law after the Qur'an, which explains the sentences in the Qur'an which are still global by referring to the provisions of the Prophet Muhammad SAW. Classification of text documents can also be used to overcome the problem of interrelation between the Qur'an and hadith. The problem of interrelation between books of hadith needs to be done because some hadiths in certain hadith books have the same meaning as other hadith books. This study aims to analyze the development of text representation and classification methods suitable to overcome similarity meaning problems in detecting interrelationships between hadith books. The research method used is Systematic Literature Review (SLR) sourced from Google Scholar, Science Direct, and IEEE. There are 42 pieces of literature that have been studied successfully. The results showed that contextual embedding as the newest text representation method considered word context and sentence meaning better than static embedding. As a classification method, the ensemble method has better performance in classifying text documents than using only a single classifier model. Thus, future research can consider using a combination of contextual embedding and ensemble methods to detect interrelationships between books of hadith.

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WITHDRAWN: Word Embedding Generation for Urdu Language using Word2vec model

Cited by 18 publications

References 7 publications

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

Machine Translation System Using Deep Learning for English to Urdu

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

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