Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. Firstly, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations.

With the recent advancement in digital technologies, the size of data sets has become too large in which traditional data processing and machine learning techniques are not able to cope with effectively [1, 2]. However, analyzing complex, high dimensional, and noise-contaminated data sets is a huge challenge, and it is crucial to develop novel algorithms that are able to summarize, classify, extract important information and convert them into an understandable form [3-5]. To undertake these problems, deep learning (DL) models have shown outstanding performances in the recent decade. Deep learning (DL) has revolutionized the future of artificial intelligence (AI). It has solved many complex problems that existed in the AI community for many years. In fact, DL models are deeper variants of artificial neural networks (ANNs) with multiple layers, whether linear or non-linear. Each layer is connected to its lower and upper layers through different weights. The capability of DL models in learning hierarchical features from various types of data, e.g., numerical, image, text and audio, makes them powerful in solving recognition, regression, semi-supervised and unsupervised problems [6-8]. In recent years, various deep architectures with different learning paradigm are quickly introduced to develop machines that can perform similar to human or even better in different domains of application such as medical diagnosis, self-driving cars, natural language and image processing, and predictive forecasting [9]. To show some recent advances of deep learning to some extent, we select 14 papers from the articles accepted in this journal to organize this issue. Focusing on recent developments in DL architectures and their applications, we classify the articles in this issue into four categories: (1) deep architectures and conventional neural networks, (2) incremental learning, (3) recurrent neural networks, and (4) generative models and adversarial examples. In the following, we give a brief summary of each category and then individually introduce related articles.

Fuzzy twin support vector machine (FTSVM) is an effective machine learning technique which is able to overcome the negative impact of noise and outliers in tackling data classification problems. In FTSVM, the degree of membership function in the sample space just described the space between input data and class center, whilst ignored the position of input data in the feature space and simply miscalculated the ledge support vectors as noises. This paper presents an intuitionistic fuzzy twin support vector machine (IFTSVM) which combines the idea of intuitionistic fuzzy number with twin SVM (TSVM). An adequate fuzzy membership is employed to reduce the noise brought by the pollutant inputs. Two functions, i.e., linear and nonlinear, are used to formulate two non-parallel hyperplanes. IFTSVM not only reduces the influence of noises, it also distinguishes the noises from the support vectors. Further, this modification can minimize a newly formulated structural risk and improve the classification accuracy. Two artificial and eleven benchmark problems are employed to evaluate the effectiveness of the proposed IFTSVM model. To quantify the results statistically, the bootstrap technique with the 95% confidence intervals is used. The outcome shows that IFTSVM is able to produce promising results as compared with those from the original SVM, Fuzzy SVM (FSVM), FTSVM and other models reported in the literature.

With wide adoption of Internet of Things across the world, the IoT devices are facing more and more intensive computation task nowadays. However, the IoT devices are usually limited by their computing capability and battery lifetime. Mobile Edge Computing provides new opportunities for developments of IoT, since edge computing servers which are close to devices can provide more powerful computing resources. The IoT devices can offload the intensive computing tasks to edge computing servers, while saving their own computing resources and reducing energy consumption. However, the benefits come at the cost of higher latency, mainly due to additional transmission time, and it may be unacceptable for many IoT applications. In this paper, we try to find a trade-off between energy consumption and latency, in order to satisfy user demands of various IoT applications. We formalize the problem into a constrained multi-objective optimization problem and find the optimal solutions by a modified fast and elitist nondominated sorting genetic algorithm (NSGA-II). To improve performance of the algorithm, we propose a novel problem-specific encoding scheme and genetic operators in the proposed modified NSGA-II. We also conduct extensive simulation experiments to evaluate the proposed algorithm and its sensitivity under certain major parameters. The experimental results show that the proposed algorithm can find a large number of optimal solutions to adjust the corresponding offloading decision according to the real-world situation.

This paper investigates a relationship between the fuzziness of a classifier and the misclassification rate of the classifier on a group of samples. For a given trained classifier that outputs a membership vector, we demonstrate experimentally that s