English-Vietnamese machine translation model based on sequence to sequence algorithm

Jiang, Hao; He, Yue; Liao, Mengfan; Jing, Yanmei; Zhang, Chao

doi:10.1109/itoec49072.2020.9141548

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…The advancement of MLPs into CNNs marks a significant evolution, enabling sequential connections of multiple MLPs to further enhance their analytical capabilities. [6,167,168] In the context of predictive modeling within ANNs, MLPs serve as a powerful tool for data-driven analysis. An MLP typically comprises three principal layers: the input, hidden, and output layers.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

“…This ability has found practical applications in various domains of natural language processing, including speech recognition, handwriting recognition, and machine translation. The advancement of MLPs into CNNs marks a significant evolution, enabling sequential connections of multiple MLPs to further enhance their analytical capabilities [6,167,168] …”

Section: Artificial Neural Networkmentioning

confidence: 99%

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The Role of AI in Drug Discovery

Abbas,

Rassam,

Karamshahi

et al. 2024

ChemBioChem

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The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

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Section: Multilayer Perceptronmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

The Role of AI in Drug Discovery

Abbas,

Rassam,

Karamshahi

et al. 2024

ChemBioChem

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“…Penterjemahan termasuk pada kategori Natural Language Processing (NLP) atau pemrosesan bahasa alami [3], dalam bidangnya yang disebut dengan Machine Translation (MT) atau mesin penterjemah. MT melakukan sebuah konversi dari bahasa sumber atau bahasa yang digunakan ke bahasa target organisasi atau bahasa tujuan [4], banyak pendekatan yang dilakukan dalam MT yaitu Statistical Machine Translation (SMT), Rule-based Machine Translation (RMT), Phrase-based Machine Translation (PMT) dan Neural Machine Translation (NMT) [5]. Penelitian terdahulu mengenai penterjemahan Bahasa Sunda ke Bahasa Indonesia dilakukan dengan menggunakan pendekatan Phrase-based Statistical Machine Translation (PSMT).…”

Section: Pendahuluanunclassified

“…Setelah proses input gate selesai terdapat pembaharuan cell state untuk melakukan update terhadap informasi yang dimiliki pada cell dengan mengalikan cell state sebelumnya sengan nilai vektor forget gate lalu mengambil nilai dari input gate dan kandidat hidden state. Proses dilakukan dengan Persamaan (4).…”

Section: Recurrent Neural Networkunclassified

Mesin Penterjemah Bahasa Indonesia-Bahasa Sunda Menggunakan Recurrent Neural Networks

Fauziyah¹,

Ilyas²,

Kasyidi³

2022

JTI

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Translator is a process where one language is changed into another language. Translator in the last research was carried out using a Phrase-based Statistical Machine Translation (PSMT) approach. This research builds an Indonesian to Sundanese translator. The stages used start from pre-processing using text preprocessing and word embedding Word2Vec and the approach used is Neural Machine Translation (NMT) with Encoder-Decoder architecture in which there is a Recurrent Neural Network (RNN). Tests in the study resulted in the optimal value by the GRU of 99.17%. Models using Attention got 99.94%. The use of optimization model got optimal results by Adam 99.35% and BLEU Score results with optimal bleu 92.63% and brievity penalty 0.929. The results of the machine translator produce training predictions from Indonesian to Sundanese if the input sentences are in accordance with the corpus and the translation results are not suitable when the input sentences are different from the corpus.

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