Emotion detection and recognition from text is a recent essential research area in Natural Language Processing (NLP) which may reveal some valuable input to a variety of purposes. Nowadays, writings take many forms of social media posts, micro-blogs, news articles, customer review, etc., and the content of these short-texts can be a useful resource for text mining to discover an unhide various aspects, including emotions. The previously presented models mainly adopted word embedding vectors that represent rich semantic/syntactic information and those models cannot capture the emotional relationship between words. Recently, some emotional word embeddings are proposed but it requires semantic and syntactic information vice versa. To address this issue, we proposed a novel neural network architecture, called SENN (Semantic-Emotion Neural Network) which can utilize both semantic/syntactic and emotional information by adopting pre-trained word representations. SENN model has mainly two sub-networks, the first sub-network uses bidirectional Long-Short Term Memory (BiLSTM) to capture contextual information and focuses on semantic relationship, the second sub-network uses the convolutional neural network (CNN) to extract emotional features and focuses on the emotional relationship between words from the text. We conducted a comprehensive performance evaluation for the proposed model using standard real-world datasets. We adopted the notion of Ekman’s six basic emotions. The experimental results show that the proposed model achieves a significantly superior quality of emotion recognition with various state-of-the-art approaches and further can be improved by other emotional word embeddings.

Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments.

Defective die on a wafer map tend to cluster in distinguishable patterns, and such defect patterns can provide crucial information to identify equipment problems or process failures in the semiconductor manufacturing. Therefore, it is important to accurately and efficiently classify the defect patterns. In this research, we propose a novel clustering-based defect pattern detection and classification framework for wafer bin map. The proposed framework has many advantages. Outlier detection and defect cluster pattern extraction can be done at the same time; arbitrarily shaped cluster patterns can be detected; and there is no need to specify the number of clusters in advance. Based on wafer bin map property, the parameters used in the clustering algorithm are fixed so that parameter sensitivity can be avoided. Since the defect patterns are classified based on extracted features, no labeled data and no supervised classification training are needed and single-type patterns as well as mixed-type patterns can be found. Extensive experiments conducted on a real-world WM-811K dataset has shown the superiority of proposed framework. The proposed framework can also be used in a big data environment to accelerate performance.

Machine learning and artificial intelligence have achieved a human-level performance in many application domains, including image classification, speech recognition and machine translation. However, in the financial domain expert-based credit risk models have still been dominating. Establishing meaningful benchmark and comparisons on machine-learning approaches and human expert-based models is a prerequisite in further introducing novel methods. Therefore, our main goal in this study is to establish a new benchmark using real consumer data and to provide machine-learning approaches that can serve as a baseline on this benchmark. We performed an extensive comparison between the machine-learning approaches and a human expert-based model—FICO credit scoring system—by using a Survey of Consumer Finances (SCF) data. As the SCF data is non-synthetic and consists of a large number of real variables, we applied two variable-selection methods: the first method used hypothesis tests, correlation and random forest-based feature importance measures and the second method was only a random forest-based new approach (NAP), to select the best representative features for effective modelling and to compare them. We then built regression models based on various machine-learning algorithms ranging from logistic regression and support vector machines to an ensemble of gradient boosted trees and deep neural networks. Our results demonstrated that if lending institutions in the 2001s had used their own credit scoring model constructed by machine-learning methods explored in this study, their expected credit losses would have been lower, and they would be more sustainable. In addition, the deep neural networks and XGBoost algorithms trained on the subset selected by NAP achieve the highest area under the curve (AUC) and accuracy, respectively.

Novelty detection is a classification problem to identify abnormal patterns; therefore, it is an important task for applications such as fraud detection, fault diagnosis and disease detection. However, when there is no label that indicates normal and abnormal data, it will need expensive domain and professional knowledge, so an unsupervised novelty detection approach will be used. On the other hand, nowadays, using novelty detection on high dimensional data is a big challenge and previous research suggests approaches based on principal component analysis (PCA) and an autoencoder in order to reduce dimensionality. In this paper, we propose deep autoencoders with density based clustering (DAE-DBC); this approach calculates compressed data and error threshold from deep autoencoder model, sending the results to a density based cluster. Points that are not involved in any groups are not considered a novelty; the grouping points will be defined as a novelty group depending on the ratio of the points exceeding the error threshold. We have conducted the experiment by substituting components to show that the components of the proposed method together are more effective. As a result of the experiment, the DAE-DBC approach is more efficient; its area under the curve (AUC) is shown to be 13.5 percent higher than state-of-the-art algorithms and other versions of the proposed method that we have demonstrated.

An accurate exchange rate forecasting and its decision-making to buy or sell are critical issues in the Forex market. Short-term currency rate forecasting is a challenging task due to its inherent characteristics, which include high volatility, trend, noise, and market shocks. We propose a novel deep learning architecture consisting of an adaptive activation function selection mechanism to achieve higher predictive accuracy. The proposed architecture is composed of seven neural networks that have different activation functions as well as softmax layer and multiplication layer with a skip connection, which are used to generate the dynamic importance weights that decide which activation function is preferred. In addition, we introduce an extended Min-Max smoothing technique to further normalize financial time series that have non-stationary properties. In our experimental evaluation, the results showed that our proposed model not only outperforms deep neural network baselines but also other classic machine learning approaches. The extended Min-Max smoothing technique is step towards forecasting non-stationary financial time series with deep neural networks.