Artificial Intelligence Learning Techniques Research Articles

Graph embedding and geometric deep learning relevance to network biology and structural chemistry.

Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning.

On the K-Means Clustering Model for Performance Enhancement of Port State Control

Nowadays, the concept of port state control is viewed as a safety net to safeguard maritime security, protect the marine environment, and ensure decent working and living circumstances for seafarers on board to a large extent. The ship can be detained for further checking if significant deficiencies are discovered during a port state control inspection. There is much research on this topic, but there have been few studies on the relationship between ship deficiencies and ship detention decisions using unsupervised machine learning artificial intelligence techniques. Although the previous methods or models are feasible for ship detention decisions, they all have shortcomings to some extent, such as large training model errors caused by the imbalance of class labels in the dataset and the fact that the training model cannot comprehensively consider all factors influencing ship detention decision due to the complexity and diversity of the problem. Unsupervised algorithms do not need to label all data in advance, and we can incorporate some fields related to port state control inspection data that can be collected into the model to allow the computer to automatically classify the ships at different risk levels according to relative criteria, e.g., the Tokyo memorandum of understanding, which may result in more objective results, thus eliminating the influence of subjective domain knowledge. It may also have more comprehensive coverage and more information on port state control inspection and decision models. Therefore, this research explores and develops an unsupervised algorithm based on k-means to improve port state control inspection decision-making models using the six-years inspection data from the Tokyo memorandum of understanding. The results show that the accuracy rate is around 50%.

Data-Driven Railway Crosstie Support Condition Prediction Using Deep Residual Neural Network: Algorithm and Application

Ballasted track substructure is designed and constructed to provide uniform crosstie support and serve the functions of drainage and load distribution over trackbed. Poor and nonuniform support conditions can cause excessive crosstie vibration which will negatively affect the crosstie flexural bending behavior. Furthermore, ballast–tie gaps and large contact forces at the crosstie–ballast interface will result in accelerated ballast layer degradation and settlement accumulation. Inspection of crosstie support condition is therefore necessary while very challenging to implement using current methods and technologies. Based on deep learning artificial intelligence techniques and a developed residual neural network (ResNet), this paper introduces an innovative data-driven prediction approach for crosstie support conditions as demonstrated from a full-scale ballasted track laboratory experiment. The discrete element method (DEM) is leveraged to provide training and testing data sets for the proposed prediction model. K-means clustering is applied to establish ballast layer subsections with representative ballast particles and provide additional insights on layer zoning for dynamic behavior trends. When provided with DEM simulated particle vertical accelerations, the proposed deep learning ResNet could achieve 100% training and 95.8% testing accuracy. Fed with vertical acceleration measurements captured by advanced “SmartRock” sensors from a full-scale ballasted track laboratory experiment, the trained model could successfully reach a high accuracy of 92.0%. Based on the developed deep learning approach and the research findings presented in this paper, the innovative crosstie support condition prediction system is envisioned to provide railroaders accurate, timely, and repeatable inspection and monitoring opportunities without disrupting railway network operations.

Fault Detection using Unsupervised Feature Learning on Big Data Based on Improved Weighted SoftMax Regression with Jaya Optimization

Intelligent fault detection is promising to deal with big data due to its ability in rapidly and efficiently processing collected signals and providing accurate detection results. In traditional fault detection methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise, such processes take advantage of human ingenuity but are time-consuming. Inspired by the idea of unsupervised feature learning artificial intelligence techniques are used to learn features from the raw data.As the dimensionality increases, the accuracy of fault identification methods implemented on big data decreases significantly. For supervised learning, large volume of data is needed which leads to high cost and time consuming. In this paper, an unsupervised learning approach is proposed on the basis of weighted softmax regression for fault detection using the power signals. In the proposed approach, the features are extracted from the unlabelled data. The developed approach is based on squirrel search algorithm and voting based weighted softmax regression with jaya optimization algorithm (IWSRJO_SSA). The proposed approach is simple and easy to carry out, though it attains high accuracy when compared to that of more advanced techniques. The features from the signals are extracted normally and some of the best features are selected using the squirrel search algorithm (SSA). Because of selecting the best features and using the improved weighted softmax regression, the proposed method achieves high accuracy in fault detection. Experiment on the power signal dataset shows the applicability of the proposed approach in fault detection on big data. The proposed system will be applicable on medical field, industrial field, electrical field and so on. The experimental results prove that the proposed method attains high accuracy and is superior to the other advanced methods.