Besemah Language Translation Machine Model Based on Machine Learning with Recurrent Neural Network (RNN) Model Algorithm

A recurrent neural network (RNN) model is developed for simulating and predicting shear behavior of both a fine-grained residual soil and a dune sand. The RNN model with one hidden layer of 20 nodes appears very effective in modeling complex soil behavior, due to its feedback connections from a hidden layer to an input layer. A dynamic gradient descent learning algorithm is used to train the network. By training part of the experimental data, which include strain-controlled undrained tests and stress-controlled drained tests performed on a residual Hawaiian volcanic soil, the network is able to capture significant variability of shear behavior existing in the residual soil. The unusual characteristics that the denser soil samples dilate under a higher stress level and the looser soil samples contract under a lower stress level are well represented by the RNN model. The RNN model also shows encouraging results in simulation and prediction of behavior of a dune sand which experienced loading-unloading-reloading conditions. Excellent agreements between the measured data and the modeling results are observed in both stress-strain behavior and volumetric-change characteristics. As compared with a traditional model, the RNN model shows more effectiveness and less effort.Key words: neural network, modeling, soil behavior, shear tests, simulation, prediction.

Accurate production forecasting is crucial for effective reservoir management and field development planning. Traditional methods; such as numerical simulation, material balance equations coupled with well models, and advanced decline curve analysis (DCA) require extensive data and involve tradeoffs between accuracy and complexity. Recently, deep learning techniques, specifically artificial neural network (ANN) and recurrent neural network (RNN) models have demonstrated promising performance in this domain. This study applies ANN and RNN for production forecasting and compares their effectiveness with traditional DCA models.An actual production dataset for a gas field containing 13 wells is used to compare ANN and RNN models to traditional decline curve analysis models. After thorough data cleaning and preparation, the dataset was divided into training (historical) and test data sets. The training data was used to build ANN and RNN models in addition to estimating the model parameters for traditional DCA models. Traditional DCA models that were investigated include Arps (exponential, hyperbolic, and harmonic), Duong, Power Law Exponential Decline (PLE), and Stretched Exponential Decline (SEPD). Finally, the prediction of different models was evaluated and compared using the testing dataset. The comparison also included long, medium, and short-term prediction.Based on the comparison of daily gas production rate forecast, the ANN model demonstrated notable improvements over Arps models for the majority of wells, with a mean absolute percent error (MAPE) of 3.9% compared to 9.1%. However, across all wells, the average MAPE was slightly lower at 6.3% versus 8.1%. Conversely, the RNN model exhibited varied performance compared to Arps models. It showed poorer long-term performance (13% versus 4.5%), similar performance in the medium term (4.4% versus 4.2%), and superior performance in the short term (2.7% versus 4.2%). Furthermore, among DCA models specifically developed for unconventional wells, SEPD exhibited nearly identical performance to Arps (8.3% compared to 8.1%). In contrast, Duong model demonstrated the poorest performance with MAPE of 21.9%.This study provides insights into model analysis and introduces guidelines in using artificial and recurrent neural networks for predicting production rates. The ANN and RNN models were developed through careful testing with different designs and techniques. All the models in this research were created using Python scripts and powerful open-source libraries like SciPy and TensorFlow.

At present, complete machine translation (MT) cannot meet the needs of information communication and cultural exchange, and the speed of complete human translation is too slow. Therefore, if MT is used to assist in the process of English-Chinese translation, it can not only prove that machine learning (ML) can translate English to Chinese but also improve the translation efficiency and accuracy of translators through human-machine cooperation. The research on the mutual cooperation between ML and human translation has an important research significance for translation systems. An English-Chinese computer-aided translation (CAT) system is designed and proofread based on a neural network (NN) model. First, it gives a brief overview of CAT. Second, the related theory of the NN model is discussed. An English-Chinese CAT and proofreading system based on the recurrent neural network (RNN) is constructed. Finally, the translation accuracy and proofreading recognition rate of the translation files of 17 different projects under different models are studied and analyzed. The research results reveal that according to the different translation properties of different texts, the average accuracy rate of text translation under the RNN model is 93.96%, and the mean accuracy of text translation under the transformer model is 90.60%. The translation accuracy of the RNN model in the CAT system is 3.36% higher than that of the transformer model. The English-Chinese CAT system based on the RNN model has different proofreading results for sentence processing, sentence alignment, and inconsistency detection of translation files of different projects. Among them, the recognition rate for sentence alignment and the inconsistency detection of English-Chinese translation is high, and the expected effect is achieved. The design of the English-Chinese CAT and proofreading system based on the RNN can make the translation and proofreading be carried out simultaneously, which greatly improves the efficiency of translation work. Meanwhile, the above research methods can improve the problems encountered in the current English-Chinese translation, provide a path for the bilingual translation process, and have certain promotion prospects.

Deep learning models, including recurrent neural network (RNN) and gated recurrent unit (GRU), were used to construct the clinical prognostic prediction models for peritoneal dialysis (PD) patients based on routine clinical data. The performance of the RNN and GRU were compared with logistic regression (LR), which is commonly used in medical researches. The possible underlining clinical implications based on the result from the GRU model were also investigated. We used the clinical data from the PD center of Peking University Third Hospital as the data source. Both the baseline data at the beginning of dialysis, and the follow-up and prognostic data of the patients were used by the RNN and GRU prediction models. The hyper-parameters were tuned based on the 10-fold cross-validation. The risk prediction performance of each model was evaluated via area under the receiver operation characteristic curve (AUROC), recall rate and F1-score on the testset. A total of 656 patients with the 261 occurrences of death were included in the experiment. The total number of all diagnostic records were 13 091. The results on the testset showed that the AUROC of the LR model, RNN model, and GRU model was 0.701 4, 0.786 0, and 0.814 7, respectively. The predictive performances of the GRU and RNN models were significantly better than that of the LR model. The performances of the GRU and RNN models assessed by recall rate and F1-score were also significantly better than that of the LR model, in which the GRU model reached the best performance. In addition, the recall rates were different among different causes of death or by different prediction time windows. The recurrent neural network model, especially the GRU model, is more effective in predicting PD patients' prognosis as compared with the LR model. This new model may be helpful for clinicians to provide timely intervention, thus improving the quality of care of PD.

Accurate temperature and rainfall (T&R) forecasting is vital for the climate-sensitive regions of Northern India, particularly Jammu, Kashmir, and Ladakh, where volatile weather patterns significantly affect livelihoods, socio-economic development, and disaster management efforts. Despite their importance, traditional forecasting methods often fall short due to their high computational demands and inability to provide localized, real-time predictions, leaving a critical research gap in addressing these challenges. This study addresses the need for precise and efficient T&R forecasting using deep learning-based framework tailored to the unique climatic conditions of these regions. The major research focus is to develop and evaluate a model capable of capturing complex temporal dependencies in localized time-series weather data. Utilizing data from the Indian Meteorological Department (IMD) for Jammu, Srinagar, and Ladakh stations covering the period from January 1, 2000, to December 31, 2023, the proposed framework employs recurrent neural networks (RNN) and long short-term memory (LSTM) architectures, both optimized for time-series forecasting. Key findings reveal that while both RNN and LSTM models exhibit robust performance in single input single output (SISO) setups, RNN model consistently outperforms the LSTM in capturing intricate temporal relationships. The RNN model in MIMO configuration achieved significantly lower mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) for Jammu, Srinagar, and Ladakh, with respective values of [0.0636, 0.1011, 0.0401] for Jammu, [0.1048, 0.1555, 0.0455] for Srinagar, and [0.0854, 0.1344, 0.0411] for Ladakh. These results underscore the RNN model’s precision, making it a practical tool for real-time weather forecasting. By enhancing the accuracy of T&R predictions in regions with challenging meteorological conditions, this study contributes to improved climate adaptation strategies, disaster preparedness, and sustainable development. Its findings hold broader implications for advancing localized forecasting technologies in other regions with similar climatic complexities.

Recurrent Neural Network (RNN) functions can be used to model highly nonlinear driver circuits. In this paper, RNN modeling technique is extended to multiple ports to model both the power supply noise and the ground noise accurately. RNN driver models can be extended to multiple ports to capture sensitive effects like Simultaneous Switching Noise (SSN) accurately when multiple driver circuits are switching. A comparison study is performed on test cases between RNN models and transistor level driver circuits for accuracy and computational speed-up is performed on few test cases. Results show that RNN driver models have huge computational speed-up over transistor level driver circuits.

Network security field had gained research community attention in the last decade due to its growing importance. This paper addresses directly one vital problem in that field is “Intrusion Detection System” (IDS). As much as many researchers tackle this problem, many challenges arise while converting this research to reliable automatic system. The biggest challenge is to make the system works with low false alarm with new unseen threats. In this paper, we address this challenge by building a descriptive model using different models of deep Recurrent Neural Network (RNNs). (RNN) models has the ability to generalize the knowledge that can be used to identify seen and unseen threats. This generalization comes from RNN capabilities to define in its terms the normal behavior and the deviation accepted to be normal. Four different models of RNN were tested on a benchmark dataset, NSL-KDD, which is a standard test dataset for network intrusion. The proposed system showed superiority over other previously developed systems according to the standard measurements: accuracy, recall, precision and f-measure.

Prediction of rail-wheel contact parameters for a metro coach using machine learning

Predictive control of reactor network model using machine learning for hydrogen-rich gas and biochar poly-generation by biomass waste gasification in supercritical water

Artificial intelligence is a promising futuristic concept in the field of science and technology, and is widely used in new industries. The deep-learning technology leads to performance enhancement and generalization of artificial intelligence technology. The global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems such as climate change, but few environmental applications have so far been developed. This study uses deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network (RNN) model. To verify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial neural network models. The RNN model predicts the pro-environmental consumption index better than any other model. We expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly sophisticated as the volume of data grows. Moreover, the framework of this study could be useful in environmental forecasting to prevent damage caused by climate change.

One of the most investigated issues in health informatics is breast cancer diagnostics. Cancer treatment has also been widely researched, showing the importance of early cancer detection. Health records are integrated into a Deep Learning Model to provide an enhanced prognosis. The goal of the work is to create a system that automated those leverages with deep learning and recurrent neural network (RNN) models. The proposed work used the technique for cancer prediction as RNN and a Gated Recurrent Unit (GRU). In the simulation, Python and the cancer dataset have been used. The performance of the system represents that cancer predictions using GRU and RNN models gives a significant role.

The predictive power of environmental factors such as CO2 levels and Brent oil price was evaluated on the stock market (S&P 500) and foreign exchange (FX) rates for market currency pairs (EUR/USD) and emerging market currency pairs (MXN/USD) with recurrent neural networks (RNN). The S&P 500 is a collection of well-established companies in the United States representing the macroeconomic health of the nation and the global economy. The FX market is a global decentralized over-the-counter (OTC) market used to determine the spot price of currency pairs. A highly leveraged market usually trades within a specific price range. Since stocks and FX pairs are highly correlated to macroeconomic factors, it was hypothesized that environmental factors such as CO2 levels and oil prices also have predictive power due to their close causal relationship with anthropological economic activities. To verify the predictive power, an RNN model was built, and a bi-directional neural network with an internal state was used to process data sequences. The performance of RNN was quantified by measuring the residual prediction from the true value. Although the study at its current state might need further statistical rigor, it concluded that environmental factors increased the predictive power for the S&P 500 while decreasing it for the DM FX pair and the EM FX pair showed mixed results.

The redundant manipulator as a ubiquitous and essential component of robots, acts as a momentous role in mechanized production. The motion control of a redundant manipulator is an essential problem that must be handled. Consequently, for the repetitive motion control of redundant manipulators, a recurrent neural network (RNN) model for the double-index (DI) scheme is researched in this paper. The DI scheme sets the minimum kinetic energy (MKE) and minimum joint-angle offset (MJAO) as optimization indexes, for which the time-dependent weights are designed, and takes the dynamic equations of redundant manipulators and joint limit constraints into consideration as well. Then, the DI scheme is reformulated as a quadratic programming (QP) problem. Besides, the RNN model based on the iteration of related parameters is derived to solve the QP problem, through which joint data (i.e., joint angles, joint velocities, and joint accelerations) are obtained to drive the motion of the redundant manipulator. To verify the effectiveness of the DI scheme for motion control of redundant manipulator, simulation results of the DI scheme solved by the RNN model, the minimum acceleration norm (MAN) scheme and MKE scheme are compared.

Recurrent neural networks (RNNs) have been widely used in text similarity modeling for text semantic representation learning. However, referring to the classical topic models, a text contains many different latent topics, and the complete semantic information of the text is described by all the latent topics. Previous RNN based models usually learn the text representation with the separated words in the text instead of topics, which will bring noises and loss hierarchical structure information for text representation. In this paper, we proposed a novel fractional latent topic based RNN (FraLT-RNN) model, which focuses on the text representation in topic-level and largely preserve the whole semantic information of a text. To be specific, we first adopt the fractional calculus to generate latent topics for a text with the hidden states learned by a RNN model. Then, we propose a topic-wise attention gating mechanism and embed it into our model to generate the topic-level attentive vector for each topic. Finally, we reward the topic perspective with the topic-level attention for text representation. Experiments on four benchmark datasets, namely TREC-QA and WikiQA for answer selection, MSRP for paraphrase identification, and MultiNLI for textual entailment, show the great advantages of our proposed model.

The object of the study is methods based on neural networks for analyzing the tonality of a corpus of texts. To achieve the goal set in the work, it is necessary to solve the following tasks: study the theoretical material for learning deep neural networks and their features in relation to natural language processing; study the documentation of the Tensorflow library; develop models of convolutional and recurrent neural networks; to develop the implementation of linear and non-linear classification methods on bag of words and Word2Vec models; to compare the accuracy and other quality indicators of implemented neural network models with classical methods. Tensorboard is used for learning visualization. The work shows the superiority of classifiers based on deep neural networks over classical classification methods, even if the Word2Vec model is used for vector representations of words. The model of recurrent neural network with LSTM blocks has the highest accuracy for this corpus of texts.