Research on liveable cities has received increased attention in recent years because of the complexity and diversity of liveability standards. Evaluating the liveable environment is a multiple criteria decision-making (MCDM) problem, and the results can be used to control environmental pollution and protect human health. However, different evaluation methods can lead to different results; hence, determining how to effectively to obtain the consistent results is a main consideration of this study. The objective of this work is to design an optimal method based on the difference ratio concept. A hopfield neural network is selected to validate the experimental results. Referring to the liveable city rankings established by the Economist Intelligence Unit, thirteen large cities in China are used to illustrate the application of the model, evaluate the liveable urban environment, and demonstrate the effectiveness and feasibility of the proposed model. The results show that Hangzhou is the most liveable city and Beijing has the worst liveable urban environment. Therefore, a common policy should be strengthening environmental governance, with a special focus on the development of low-carbon cities, for which both the local and global environmental impacts could be mitigated.

Water pollution is a worldwide problem that needs to be solved urgently and has a significant impact on the efficiency of sustainable cities. The evaluation of water pollution is a Multiple Criteria Decision-Making (MCDM) problem and using a MCDM model can help control water pollution and protect human health. However, different evaluation methods may obtain different results. How to effectively coordinate them to obtain a consensus result is the main aim of this work. The purpose of this article is to develop an ensemble learning evaluation method based on the concept of water quality to help policy-makers better evaluate surface water quality. A valid application is conducted to illustrate the use of the model for the surface water quality evaluation problem, thus demonstrating the effectiveness and feasibility of the proposed model.

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In recent years, tumor classification based on the gene expression omnibus has become a continuous attention field in the area of bioinformatics . Integration machine learning techniques are an efficient methods to solve these problems. Generally, in order to obtain good performance in the supervised learning tasks, a large number of labelled samples will be required. However, in many cases, only a few labelled samples and abundant unlabelled samples exist in the training database. The process of labelling these unlabelled samples manually is difficult and expensive. Therefore, semi-supervised learning approaches have been proposed to utilize unlabelled samples to improve the performance of a model. However, noisy samples decrease the robustness of model in semi-supervised learning. We wish training style that samples can be implemented to train by from high-to low-confidence, self-training can meet this requirement, and the deep forest approach with the hyper-parameter settings used in this paper can obtain excellent accuracy. Therefore, in this paper, we present a novel semi-supervised learning approach with a deep forest model to increase the performance of tumor classification, which employs unlabelled samples and minimizes the cost; that is, a updated unlabelled sample mechanism is investigated to expand the number of high-confidence pseudo-labelled samples. Multiple real-world experiments indicate that our proposed approach can obtain results up 0.96 accuracy and F1-Score, and 0.9798 AUCs.