High-Performance English–Chinese Machine Translation Based on GPU-Enabled Deep Neural Networks with Domain Corpus

The ability to automate machine translation has various applications in international commerce, medicine, travel, education, and text digitization. Due to the different grammar and lack of clear word boundaries in Chinese, it is challenging to conduct translation from word-based languages (e.g., English) to Chinese. This article has implemented a GPU-enabled deep learning machine translation system based on a domain-specific corpus. Our system takes an English text as input and uses an encoder-decoder model with an attention mechanism based on Google’s Transformer to translate the text to Chinese output. The model was trained using a simple self-designed entropy loss function and an Adam optimizer on English–Chinese bilingual text sentences from the News area of the UM-Corpus. The parallel training process of our model can be performed on common laptops, desktops, and servers with one or more GPUs. At training time, we not only track loss over training epochs but also measure the quality of our model’s translations with the BLEU score. We also provide an easy-to-use web interface for users so as to manage corpus, training projects, and trained models. The experimental results show that we can achieve a maximum BLEU score of 29.2. We can further improve this score by tuning other hyperparameters. The GPU-enabled model training runs over 15x faster than on a multi-core CPU, which facilitates us having a shorter turn-around time. As a case study, we compare the performance of our model to that of Baidu’s, which shows that our model can compete with the industry-level translation system. We argue that our deep-learning-based translation system is particularly suitable for teaching purposes and small/medium-sized enterprises.

Prediction of Heart Disease Using Machine Learning Algorithms

As big data becomes more prevalent in the healthcare and medical sectors, accurate medical data collection benefits early diagnosis of heart disease, hospital treatment, and government resources. However, where medical data quality is lacking, understanding accuracy suffers. Consequently, some field diseases have unique features in different regions, which can make illness more difficult. It is now more hard to predict outbreaks. We automate machine learning algorithms for efficient epidemic detection in bacterial infection population in this paper. We put the modified forecasts to the test using securely and efficiently datasets. areas of the region to improve the situation of lost data, we use a predictive modeling approach to restore inaccurate value. Focused upon its patient's signs, a heart attack is suspected. Models were built using machine learning techniques. As a consequence, the accuracy is pinpoint accurate. The Flask web interface is used to build the Application. In this research, we shall conduct experiments using machine learning methods.

Promoting Modularity in Evolutionary Design

Evolutionary design systems apply principles inspired from biological evolution to automate machine design. These systems have been shown to generate simple designs for simple tasks — but their practical ability to scale up to higher complexities remains questioned. One of the keys to accomplishing higher-level evolutionary design is the ability of the process to identify and reuse knowledge discovered at lower levels, thus scaling its search capacity. One way to capture this knowledge is in the form of reusable building blocks — modules. In this paper we define modularity and discuss several approaches to promoting modularity in evolutionary design systems. In particular, we propose a new mechanism that can enhance modularization. This mechanism is based on the observation that designs that exhibit modularity have higher adaptability and consequently have better survival rates under changing requirements. Contrary to other techniques, this is a weak (indirect) formulation that docs not require representation of partial solutions or definition of a genotype from which a design is developed. We demonstrate this principle on an abstract general design problem on which modularity can be statistically quantified.