Translating cuneiform symbols using artificial neural network

Hamdany, Arwa Hamed Salih; Omar-Nima, Raid Rafi; Albak, Lubab H.

doi:10.12928/telkomnika.v19i2.16134

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…Недоліками нейронних мереж є [12][13][14]:  труднощі визначення структури мережі, оскільки відсутні методи розрахунку кількості шарів та нейронів у кожному шарі для конкретних додатків;  складність формування представницької вибірки;…”

Section: метод динамічного управління буфером запасів на основі м'яки...unclassified

Метод Динамічного Управління Буфером Запасів На Основі М’яких Обчислень

Fedorov¹,

Nechyporenko²

2023

Вісник ЧДТУ

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У роботі пропонується метод динамічного управління буфером запасів на основі м’яких обчислень. Новизна дослідження полягає в тому, що для динамічного управління буфером запасів було створено метод на основі нечіткої логіки та штучної нейронної мережі, дві моделі штучної нейро-нечіткої мережі динамічного управління буфером запасів, вибрано три критерії оцінки ефективності запропонованих моделей, ідентифіковано параметри запропонованих моделей на основі методу зворотного поширення в пакетному режимі, орієнтовані на технологію паралельної обробки інформації. Запропоновані моделі та процедури їх параметричної ідентифікації дозволяють підвищити швидкодію, точність і надійність прийняття рішення. Запропонований метод динамічного управління буфером запасів на основі м’яких обчислень може використовуватись у різних інтелектуальних системах.

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Section: метод динамічного управління буфером запасів на основі м'яки...unclassified

Метод Динамічного Управління Буфером Запасів На Основі М’яких Обчислень

Fedorov¹,

Nechyporenko²

2023

Вісник ЧДТУ

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show abstract

“…where X is the data distribution and Z(x) is the loss function over the sample x. Substituting equation ( 6) into equation (7) e proposed method further enhances the corpus based on the reverse translation by using word substitution methods to enhance the training data containing low-frequency words through language model substitution for lowfrequency words, which constructs a more diverse pseudoparallel corpus and makes the pseudoparallel corpus covers a more realistic data distribution, thus improving the translation performance in resource-rich scenarios. In lowresource scenarios, the word substitution method additionally adds a linguistic error correction module to eliminate errors such as syntactic semantics generated by substitution.…”

Section: Deep Learning Combined With Prior Knowledge English Translat...mentioning

confidence: 99%

“…In this paper, we investigate the implicit learning capability of the neural machine translation model to stimulate its potential and enhance its performance accordingly in several translation subdomains [6]. Specifically, we first exploit its implicitly acquired multiheaded attention mechanism to achieve improvement on the diversity translation task, then improve the quality of low-resource translation by masking the attention heads within the model, and finally exploit its own potential long-range text modeling capability to achieve a breakthrough on the document translation task [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Classification Model for English Translation Styles Introducing Attention Mechanism

Zhang

2022

Mathematical Problems in Engineering

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Both short-distance association knowledge and long-distance interaction knowledge in the knowledge base contain rich semantics. When learning entity and relation representation in a knowledge base, if we can learn short-distance association knowledge and long-distance interaction knowledge at the same time, we can learn the representation method with rich semantic information and keep the original structure of the knowledge base. Among the knowledge contained in a large number of records, some knowledge reflects individual characteristics and can be called local knowledge; others reflect group characteristics and can be called global knowledge. Using different ways to learn local and global knowledge in the deep learning model will better reflect the difference between the two kinds of knowledge at the model level, and make the model have the ability to understand both individual characteristics and overall characteristics. Through layer-by-layer forward propagation and error back propagation algorithms, the entire network is gradually optimized in an “end-to-end” manner. This “end-to-end” approach leaves some means of introducing prior knowledge flexibly into the model. Although it can reduce the burden on researchers, this “data-driven” approach brings the shortcomings of poor interpretability of learning results and weak generalization ability. Combining the specific prior knowledge implicit in the data with the deep learning algorithm can optimize the algorithm in a targeted manner and avoid blind searching in the solution space, so as to obtain a model with better performance and wider use. To this end, this paper investigates combining prior knowledge with deep learning to design efficient algorithms to address the classification of English translation styles. This paper combines local knowledge with global knowledge and deep learning methods and proposes a memory neural network method combining local knowledge and global knowledge. By recording the local knowledge in the local memory module and simultaneously recording the global knowledge in the global memory module, the method effectively learns the latent information in a large number of records. This paper combines short-distance association knowledge with long-distance interaction knowledge and a distributed representation learning method based on deep learning and proposes a deep learning method combining short-distance association knowledge and long-distance interaction knowledge. On the IWSLT English translation task, experiments show that the method significantly improves translation quality, confirming that grammatical dependencies enhance attention by supplementing dependent grammatical information, resulting in more effective and richer context vectors that more accurately represent contextual situations. Additional experimental analysis showed that the model underwent careful parameter selection and analysis. By mining valuable long-distance interactive knowledge in the knowledge base and using it in the distributed representation learning of the knowledge base, while constraining the short-distance related knowledge and constraining the long-distance interactive knowledge, the learned knowledge can be used to effectively complete the knowledge base distributed representation for discovering new relations.

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“…Adopting the paradigm of handwritten text recognition, preliminary results published in the project's website suggest an 83% success 5 and, furthermore, their transliteration to English letters [35].…”

Section: Re Shaping Interpretation and Restoration With Aimentioning

confidence: 99%

AI trends in digital humanities research

Pavlidis¹

2022

Trends Comput Sci Inf Technol

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Recent advances in specialised equipment and computational methods had a significant impact in the Humanities and, particularly, cultural heritage and archaeology research. Nowadays, digital technology applications contribute in a daily basis to the recording, preservation, research and dissemination of cultural heritage. Digitisation is the defining practice that bridges science and technology with the Humanities, either in the tangible or in the intangible forms. The digital replicas support a wide range of studies and the opening of new horizons in the Humanities research. Furthermore, advances in artificial intelligence methods and their successful application in core technical domains opened up new possibilities to support Humanities research in particularly demanding and challenging tasks. This paper focuses on the forthcoming future of intelligent applications in archaeology and cultural heritage, by reviewing recent developments ranging from deep and reinforcement learning approaches to recommendation technologies in the extended reality domain.

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Translating cuneiform symbols using artificial neural network

Cited by 13 publications

References 20 publications

Метод Динамічного Управління Буфером Запасів На Основі М’яких Обчислень

Метод Динамічного Управління Буфером Запасів На Основі М’яких Обчислень

Deep Learning Classification Model for English Translation Styles Introducing Attention Mechanism

AI trends in digital humanities research

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