In this work, output-feedback control problems for a class of discrete-time non-affine nonlinear systems with unknown control directions and input constraints are considered by using reinforcement learning (RL) method. Two neural networks (NNs) implement the control: 1) a critic NN that estimates a non-quadratic strategic utility function (SUF) and 2) an action NN that generates optimized control input

Image attribute editing is a challenging problem that has been recently studied by many researchers using generative networks. The challenge is in the manipulation of selected attributes of images while preserving the other details. The method to achieve this goal is to find an accurate latent vector representation of an image and a direction corresponding to the attribute. Almost all the works in

User-generated product reviews contain a lot of valuable information including users’ opinions on products and product features that is not fully exploited by the current recommendation models. Similarly, the metadata information related to the products, about the reviews and about the users who have written the reviews has rarely been exploited for recommender systems. These heterogeneous information

The amount of real-time communication between agents in an information system has increased rapidly since the beginning of the decade. This is because the use of these systems, e.g. social media, has become commonplace in today’s society. This requires analytical algorithms to learn and predict this stream of information in real-time. The nature of these systems is non-static and can be explained,

Abstract Dimensional accuracy in additive manufacturing (AM) is still an issue compared with the tolerances for injection molding. In order to make AM suitable for the medical, aerospace, and automotive industries, geometry variations should be controlled and managed with a tight tolerance range. In the previously published article, the authors used statistical analysis to develop linear models for

This paper presents an optimal communication relay positioning method to improve the communication performance of mobile multi-node networks in complex environments. The communication channel quality prediction between nodes is of primary concern to find the optimal relay node positions while considering the uncertain and dynamic nature of environments. To this end, the learning-based or the distance

The original support vector machine (SVM) uses the hinge loss function, which is non-differentiable and makes the problem difficult to solve in particular for regularized SVM, such as with ℓ1-regularized. On the other hand, the hinge loss is sensitive to noise. To circumvent these drawbacks, a huberized pinball loss function is proposed. It is less sensitive to noise, similar to the pinball loss which

Fault diagnosis plays a pivotal role in ensuring the safety of process industries. However, due to the diversity of process faults and the high coupling of fault data, it becomes very difficult to achieve high accuracy in the fault diagnosis of complex industrial processes. To address this concern, in this article, a novel AdaBoost-based discriminant locality preserving projection (DLPP) with resamples

Abstract Multi-view multi-label learning is widely used in multiple fields, and it aims to process data sets represented by multiple forms (views) and labeled by multiple classes. But most real-world data sets maybe loss some labels and views due to lack of manpower and equipment failure and this causes some difficulties in processing data sets. In this paper, we develop a global and local multi-view

Abstract The development of the computer-aided detection system placed an important role in the clinical analysis for making the decision about the human disease. Among the various disease examination processes, lung cancer needs more attention because it affects both men and women, which leads to increase the mortality rate. Traditional lung cancer prediction techniques failed to manage the accuracy

Abstract Systemic low-frequency oscillations (sLFOs) are non-neuronal oscillations at 0.01–0.15 Hz. These sLFOs travel through the entire body and the brain with symmetrical (across the midline of the body) and highly predictable delays, where they can be observed with functional near-infrared spectroscopy (fNIRS) and blood oxygen level-dependent functional magnetic resonance imaging. Their characteristics

Abstract With the development of science and technology and the need for multi-criteria decision making (MCDM), the optimization problem to be solved becomes extremely complex. The theoretically accurate and optimal solutions are often difficult to obtain. Therefore, meta-heuristic algorithms based on multi-point search have received extensive attention. The flower pollination algorithm (FPA) is a

Abstract The aim of this work is to develop efficient named entity recognition from the given text that in turn improves the performance of the systems that use natural language processing (NLP). The performance of IoT-based devices such as Alexa and Cortana significantly depends upon an efficient NLP model. To increase the capability of the smart IoT devices in comprehending the natural language,

Abstract This article presents a novel energy management strategy of multiple-stage evaporator (MSE). The maximum efficiency of MSE is achieved by optimum selection of unknown steady-state process parameters such as vapor temperatures and liquor flow rates. Various energy reduction schemes (ERSs) have been integrated to achieve a substantial enhancement in energy efficiency. For energy optimization

Abstract A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional

Abstract The metaheuristic approaches inspired by the nature are becoming powerful optimizing algorithms for solving NP-complete problems. This paper presents five nature-inspired metaheuristic optimization algorithms to find near-optimal Golomb ruler (OGR) sequences in a reasonable time. In order to improve the search space and further improve the convergence speed and optimization precision of the

Abstract Visual question answering (VQA) is a task that has received immense consideration from two major research communities: computer vision and natural language processing. Recently it has been widely accepted as an AI-complete task which can be used as an alternative to visual turing test. In its most common form, it is a multi-modal challenging task where a computer is required to provide the

This paper investigates the problem of control with prescribed performance tracking for a class of nonlinear systems in the presence of quantized input. A novel state feedback control scheme by self-scrambling gain is proposed, with the first merit that it is computationally inexpensive, since no linearly-parameterized approximators are used, and the second merit that it is self-adjustable with respect

Fuzzy approximation spaces (FA-spaces) can be seen as approximation spaces (A-spaces) in a fuzzy environment. This article studies relationships between FA-spaces and their uncertainty measures. Dependence, partial dependence and independence between FA-spaces, as characterized by the inclusion degree, are first proposed. Then, the distance between FA-spaces is considered in terms of the angle of difference

In this work, we reveal an essential problem rarely discussed in current semi-supervised learning literature: the learned feature distribution mismatch problem between labeled samples and unlabeled samples. It is common knowledge that learning from the limited labeled data easily leads to overfitting. However, the difference between the inferred labels of unlabeled data and the ground truths of labeled

Diabetes has become a disease that seriously endangers people’s health, then how to control the content of glycemic is an important issue. Since the treatment scheme of patient is usually a combination of multiple hypoglycemic drugs, multi-label learning is an effective method to solve this problem. By analyzing the type 2 diabetes data set including 2443 diabetics provided by the Chinese People’s

Traditional neural networks generally neglect the primary and secondary relationships of input information and process the information indiscriminately, which leads to their bad nonlinear approximation capacity and low generalization ability. As a result, traditional neural networks always show poor prediction accuracy in the performance degradation trend prediction of rotating machinery (RM). In view

Network embedding has recently garnered attention due to the ubiquity of the networked data in the real-world. A network is useful for representing the relationships among objects, and these network include social network, publication network, and protein-protein interaction network. Most existing network embedding methods assume that only a single type of relation exists between nodes. However, we

The worldwide outbreak of COVID-19 has led to great tragedy and poses unprecedented challenges for countries’ healthcare systems. Data has become an important instrument in the global fight against the unprecedented spread of the virus. But how will we ensure a return to previous forms of data privacy once the pandemic subsides?

In this paper, a method of Active Disturbance Rejection Controller (ADRC) is proposed based on Q-learning of Reinforcement Learning (RL) for multi-area interconnected power system. Excessive changes in load can cause instability to the system. Therefore, the ADRC controller is used to keep the load within rated range for its strong anti-interference performance and Q-learning algorithm to select the

Abnormal electricity consumption (AEC) caused huge economic losses to power supply enterprises in the past years, and also posed severe threats to the safety of peoples’ daily live. An accurate AEC detection is crucial to reducing the non-technical losses (NTLs) suffered by power supply enterprises and the State Grid. Comparing with the huge amount of electricity data flow, AEC data are relative few

This paper presents an adaptive neuro-fuzzy PID controller based on twin delayed deep deterministic policy gradient (TD3) algorithm for nonlinear systems. In this approach, the observation of the environment is embedded with information of a multiple input single output (MISO) fuzzy inference system (FIS) and have a specially defined fuzzy PID controller in neural network (NN) formation acting as the

In recent years, convolution neural networks (CNN) have achieved substantial advantages for single image super resolution (SISR). However, as the depth and width of the networks increase, the model parameters and computation become more complicated. To solve these problems, we propose a lightweight network structure for super-resolution which adopts the multi-view information fusion strategy. The proposed

In recent years, single image super-resolution (SR) methods based on deep convolutional neural networks (CNNs) have made significant progress. However, due to the non-adaptive nature of the convolution operation, they cannot adapt to various characteristics of images, which limits their representational capability and, consequently, results in unnecessarily large model sizes. To address this issue

Many-core neural network chip is widely developed and used for both the deep learning and neuromorphic computing applications. Many-core architecture brings high parallelism while makes the model-to-core mapping intractable. In order to decrease the routing time, transmission packets amount and energy consumption, along with deadlock-free performance for inter-core data movement, we formulate an optimization

In this paper, we propose an attention-guided dual spatial-temporal non-local network for video super-resolution (ADNLVSR). We integrate temporal and spatial non-local self-similar contexts from continuous video frames after motion compensation, and merge the features of different levels discriminatively with channel attention mechanism for target frame. During motion compensation, unlike previous

Face stands out as a preferable biometric trait for automatic human authentication as it is intuitive and non-intrusive. This paper investigates various feature-based automatic face recognition approaches in detail. High degree of freedom in head movement and human emotion leads a face recognition system to face critical challenges in terms of pose, illumination and expression. Human face also undergoes

Abstract Hospital cost analysis (HCA) becomes a key topic and forefront of politics, social welfare and medical discourse. HCA includes a wide range of expenses; yet the foremost attention relates to the money expense in which hospital managers would like to draw a figure of incomes in the past and future. Based on the HCA results, they can develop many plans for improving hospital’s service quality

Abstract An increase in the size of data repositories of spatiotemporal data has opened up new challenges in the fields of spatiotemporal data analysis and data mining. Foremost among them is “spatiotemporal clustering,” a subfield of data mining that is increasingly becoming popular because of its applications in wide-ranging areas such as engineering, surveillance, transportation, environmental and

Abstract A standard screening procedure involves video endoscopy of the Gastrointestinal tract. It is a less invasive method which is practiced for early diagnosis of gastric diseases. Manual inspection of a large number of gastric frames is an exhaustive, time-consuming task, and requires expertise. Conversely, several computer-aided diagnosis systems have been proposed by researchers to cope with

Abstract Covering-based rough set is an important extended type of classical rough set model. In this model, concepts are approximated through substitution of a partition in classical rough set theory with a covering in covering-based rough set theory. Various generalized covering-based rough sets have been investigated, however, little work has been done on extending four classical covering-based

Abstract Image analysis is a prolific field of research which has been broadly studied in the last decades, successfully applied to a great number of disciplines. Since the apparition of Big Data, the number of digital images is explosively growing, and a large amount of multimedia data is publicly available. Not only is it necessary to deal with this increasing number of images, but also to know which

Abstract Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is tedious, laborious and subjective. To overcome such disadvantages, numerous seizure detection techniques involving signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing

Abstract Computational modeling of empathy has recently become an increasingly popular way of studying human relations. It provides a way to increase our understanding of the link between affective and cognitive processes and enhance our interaction with artificial agents. However, the variety of fields contributing to empathy research has resulted in isolated approaches to modeling empathy, and this

Abstract Spoken keyword spotting refers to the detection of all occurrences of desired words in continuous speech utterances. This paper includes a comprehensive review on various spoken keyword spotting (especially discriminative spoken keyword spotting) approaches. The most common datasets and evaluation measures for training and evaluating the spoken keyword spotting systems are reviewed in this

Abstract Due to the high flow velocity over dam spillways and outlets, severe cavitation damage might occur to the structures. Aeration (introducing air into the passing flow) is a useful remedy for preventing or decreasing cavitation, however, proper estimation of aerators air demand is a complex problem. On that account, the standard GMDH model, integrated GMDH-HS (with the harmony search algorithm)

Abstract Dissimilarity representation is a very interesting alternative for the traditional feature space representation when addressing large multi-class problems or even problems with a small number of training samples. This paper describes the existing possibilities in terms of dissimilarity representation through some comprehensive examples. The justification for using such a problem representation

Abstract When multiple granulated knowledge in multigranulation spaces are involved in decision making, protocol principles are adopted to arrive at the final consensus. Multigranulation rough set theory utilizes a voting principle to combine the decision options derived from individual granulated knowledge. Note that those knowledge may provide different degrees of support to the final results, some

Abstract These days, many recommender systems (RS) are utilized for solving information overload problem in areas such as e-commerce, entertainment, and social media. Although classical methods of RS have achieved remarkable successes in providing item recommendations, they still suffer from many issues such as cold start and data sparsity. With the recent achievements of deep learning in various applications

Abstract Unsupervised feature selection is a powerful tool to process high-dimensional data, in which a subset of features is selected out for effective data representation. In this paper, we proposes a novel robust unsupervised features selection method based on graph-preserving feature selection embedding LLE. Specifically, we integrate the graph matrix learning and the low-dimensional space learning

Abstract This paper proposes a new version of the shuffled complex evolution (SCE) algorithm for solving parameter identification problems. The SCE divides a population into several parallel subsets called complex and then improves each sub-complex through an evolutionary process using a Nelder–Mead (NM) simplex search method. This algorithm applies its evolutionary process only on the worst member

Abstract In the age of big data, we often face the huge Volume, high Velocity, rich Variety, accuracy Veracity and high Value (5Vs) with complicated structures. Big data industrial decision making is crucial for a country or society to improve the effectiveness of leadership, which can remarkably promote scale development and industrialization. Under such circumstance of big data industrial decision

Abstract In automated screening of cervical cytology, the morphological features of cell play a determining role. To avoid false diagnosis, urgent need of precise extraction of these features led to emergence of new segmentation models. In this paper author aspire to present literature review of research done in the field of segmentation stage in automatic screening of cervical smear images. Total

Abstract Computer vision systems open a new challenge to recognize human faces under varied poses in similar capacity and capability as human-beings perform naturally. For surveillance applications, pose-invariant face recognition (PIFR) will become a major break-through by presenting the solution of this unique challenge. In recent decade, several techniques are presented to address this challenge

Abstract Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review

Abstract This work bespeaks an insight into a real imperfect multibody systems synthesis, wherein the flexible behaviour of its components is considered. Based on the end-effector mechanism velocity, acceleration and defined transversal deflection for a flexible component, the mechanism optimal design variables have been investigated. Subsequently, the three aforementioned responses have been involved

Abstract The stock market is a key pivot in every growing and thriving economy, and every investment in the market is aimed at maximising profit and minimising associated risk. As a result, numerous studies have been conducted on the stock-market prediction using technical or fundamental analysis through various soft-computing techniques and algorithms. This study attempted to undertake a systematic

Abstract An accurate mathematical model has a vital role in controlling and synchronization of different systems. But generally in real-world problems, parameters are mixed with mismatches and distortions. In this paper, an improved shuffled complex evolution (SCE) is proposed for parameter identification of engineering problems. The SCE by employing parallel search efficiently finds neighborhoods

Motivation Breast cancer is the leading cause of cancer deaths among women today. Survival rates in developing countries are around 50% to 60% due to late detection. A personalized, accurate risk scoring method can help in targeting the right population for follow-up tests and enables early detection of the breast abnormalities. Most of the available risk assessment tools use generic and weakly correlated

There has been considerable interest in transforming unstructured social tagging data into structured knowledge for semantic-based retrieval and recommendation. Research in this line mostly exploits data co-occurrence and often overlooks the complex and ambiguous meanings of tags. Furthermore, there have been few comprehensive evaluation studies regarding the quality of the discovered knowledge. We

In this paper, we propose a novel approach for Zero-Shot Learning (ZSL), where the test instances are from the novel categories that no visual data are available during training. The existing approaches typically address ZSL by embedding the visual features into a category-shared semantic space. However, these embedding-based approaches easily suffer from the “heterogeneity gap” issue since a single

Quality control in crowdsourcing is challenging due to the heterogeneous nature of the workers. The state-of-the-art solutions attempt to address the issue from the technical perspective, which may be costly because they function as an additional procedure in crowdsourcing. In this paper, an economics based idea is adopted to embed quality control into the crowdsourcing process, where the requestor

Neural nets have been used in an elusive number of scientific disciplines. Nevertheless, their parameterization is largely unexplored. Dense nets are the coordinate transformations of a manifold from which the data is sampled. After processing through a layer, the representation of the original manifold may change. This is crucial for the preservation of its topological structure and should therefore