In Neuroscience, steady‐state visually evoked potentials (SSVEPs) have been generally used for the characterization of dynamic visual processes along the afferent pathway. In Neuroengineering, SSVEP‐based brain‐computer interfaces (SSVEP‐BCI) have been used in applications such as communications or entertainment for the detection of the overt attention to static‐flickering stimuli or other structured

This article deals with energy saving in the hybrid flow shop scheduling problem with batch production at last stage, which has important application in energy‐intensive steelmaking‐continuous casting (SCC) process. We first establish a mixed integer programming model to reduce extra energy consumption, and then adopt genetic algorithm to solving the scheduling problem. Based on traditional genetic

The recent outbreak of a novel coronavirus, named COVID‐19 by the World Health Organization (WHO) has pushed the global economy and humanity into a disaster. In their attempt to control this pandemic, the governments of all the countries have imposed a nationwide lockdown. Although the lockdown may have assisted in limiting the spread of the disease, it has brutally affected the country, unsettling

As a vital modelling technique, fuzzy Petri nets (FPNs) have been widely used in various areas for knowledge representation and reasoning. However, the conventional FPNs have many deficiencies in representing inaccurate knowledge, acquiring knowledge parameters and conducting approximate reasoning when used in the real world. In this article, a new version of FPNs, called R‐numbers Petri nets (RPNs)

Influence maximization in a social network focuses on the task of extracting a small set of nodes from a network which can maximize the propagation in a cascade model. Though greedy methods produce good solutions to the aforementioned problem, their high computational complexity is a major drawback. Centrality‐based heuristic methods often fail to overcome local optima, thereby producing sub‐optimal

Rao algorithms are recently proposed optimization algorithms used to solve optimization problems. These algorithms are based on the best and the worst solutions, which are computed during the optimization process. However, these algorithms apply to continuous problems only. In this article, the binary versions of Rao algorithms are proposed, which can be used for solving feature selection problems

The objective of this article is to introduce several new methods or techniques for solving simultaneous linear and nonlinear system of equations with the help of a new hybrid algorithm based on advanced quantum behaved particle swarm optimization and the concept of binary tournamenting process. Depending on different options of binary tournamenting, six different variants of hybrid algorithms are

The main purpose of this work is the development of a performance prediction model of projects, considering the influence of the leadership style and organizational factors on the agility and flexibility of the organization. The motivation of this work is the absence in the literature of a model to establish the relationship among leadership and agility factors, by means of the integration of prediction

This study seeks to answer the following research question: “What factors can explain the number of patent filing requests made by residents in Brazil at patent offices in Brazil, the United States, Europe, and triadic patent families?”. The methods used in this research are quantitative, using big data from private and public investments in Science and Technology, and about patent deposit numbers

Structural design and optimization are important topics for the control and design of industrial robots. The motivation behind this research is to design a robot gripper mechanism. To explore robust design of the robot gripper mechanism, a new optimization approach based on a grasshopper optimization algorithm and Nelder–Mead algorithm is developed for requiring a fast and accurate solution. Additionally

Multi‐objective optimization problems frequently appear in many diverse research areas and application domains. Metaheuristics, as efficient techniques to solve them, need to be easily accessible to users with different expertise and programming skills. In this context, metaheuristic optimization frameworks are helpful, as they provide popular algorithms, customizable components and additional facilities

This paper explores a novel technique for learning the fitness function for search algorithms such as evolutionary strategies and hillclimbing. The aim of the new technique is to learn a fitness function (called a Learned Guidance Function) from a set of sample solutions to the problem. These functions are learned using a supervised learning approach based on deep neural network learning, that is,

Data preservation is the mechanism of protecting and safeguarding the confidentiality and integrity of data. Data stored in huge databases may contain metadata, elements that may be imprecise and unstable, It may include sensitive data, personal profiles and so on, which is vulnerable to third parties such as hackers or attackers. They may misuse the data and as a consequence of this the confidentiality

Multi‐label classification algorithms deal with classification problems where a single datapoint can be classified (or labelled) with more than one class (or label) at the same time. Early multi‐label approaches like binary relevance consider each label individually and train individual binary classifier models for each label. State‐of‐the‐art algorithms like RAkEL, classifier chains, calibrated label

Foreign language anxiety (FLA) has been a perennial concern in language learning, as foreign language (FL) learners often communicate feelings of anxiety, stress, or nervousness. This study explored the role of artificial intelligence (AI) applications in speaking practice for FLA management of 48 undergraduate participants in an EFL class in Egypt. An eight‐week, quasi‐experimental pretest–posttest

Recently, linear prediction analysis (LP) related features have been successfully used for replay attack detection due to the imperfection in the LP‐based signal produced by recording and playback devices. In this paper, we propose a weighted linear combination of classifier scores for replay attack detection where our classifiers, including Gaussian mixture models (GMMs) and support vector machines

Codebook‐based writer characterization is an effective technique that has been investigated in a number of recent studies on identification and verification of writers. These methods divide a set of writing samples into small units (fragments or graphemes) and cluster these patterns to produce a codebook. Writer of a handwritten sample is then characterized by the probability (distribution) of producing

Heart diseases are a major cause of human casualties each year. An accurate and efficient diagnosis is essential to minimize their risk. This paper presents a system for the classification of multiple cardiac disorders based on pulse plethysmographic (PuPG) signal analysis. In particular, the work focuses on the detection and classification of ischemic and rheumatic heart diseases using proposed 1D

Tremendous growth in the availability of judicial documents has demanded the rise of information extraction (IE) techniques that support the automatic extraction of relevant concepts or data from judicial texts. Among various approaches available for IE, ontology‐based IE has proven to be the most appropriate for extracting domain‐specific information from natural language text. Through this article

Mining data streams for predictive analysis is one of the most interesting topics in machine learning. With the drifting data distributions, it becomes important to build adaptive systems which are dynamic and accurate. Although ensembles are powerful in improving accuracy of incremental learning, it is crucial to maintain a set of best suitable learners in the ensemble while considering the diversity

Social network analysis is an efficient tool to investigate the relationships of decision‐makers in large‐scale group decision making (LSGDM). Existing social network‐based LSGDM studies generally assumed that each decision‐maker has a single role or belongs to only one subgroup. The assumption that a decision‐maker has multiple roles or belongs to multiple subgroups is rarely taken into consideration

Compared to patients readmitted to general wards, readmitted patients in the intensive care unit (ICU) are exposed to higher mortality rates and prolonged hospital stays. Moreover, the readmission of ICU patients brings pressing challenges for ICU management. Most models are devoted to identifying the risk factors and developing classification models that can predict whether ICU patients will be readmitted

Data clustering is an important technique of data mining in which the objective is to partition N data objects into K clusters that minimize the sum of intra‐cluster distances between each data object to its nearest centroid. This is an optimization problem, and various optimization algorithms have been suggested for capturing the position vectors of optimal centroids. However, in these approaches

Artifice is the human prerogative. Expert Systems are, perhaps, the greatest in a long line of clever technologies—artifices—that augment human abilities. And, whereas Moore's law severely constrained early Expert Systems research, the growth in available computing power now gives current generation Expert Systems the potential for massive real‐world impact—a happy situation increasingly celebrated

In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well‐known CF recommenders suffer from data sparsity

This article presents an advanced visualization and analytics approach for financial research. Statistical arbitrage, particularly pairs trading strategy, has gained ground in the financial market and machine learning techniques are applied to the finance field. The cointegration approach and long short‐term memory (LSTM) were utilized to achieve stock pairs identification and price prediction purposes

Content‐based medical image retrieval (CBMIR) is a mechanism to handle a huge quantity of image data generated in various medical imaging modalities. In recent years, due to the evolution of computer vision and digital imaging modalities, a large number of medical images are generated. Consequently, the task of retrieving medical images from a large image database becomes more tedious due to variation

World Health Organization recognized COVID‐19 as a pandemic on March 11, 2020. A total of 213 countries and territories around the world have reported a total of 27,948,441 confirmed cases as on September 9, 2020. This article adopted two non‐linear growth models (Gompertz, Verhulst) and exponential model (SIR) to analyse the coronavirus pandemic across the world. All the models have been used for

Chronic Myelogenous Leukaemia (CML) is a haematopoietic stem cells disease with complex dynamical behaviour. One of the effective factors in treating patients is to determine the appropriate drug dosage. A physician should test the different drug dosages through trial and error in order to find its optimal value. This procedure is normally a time‐consuming and error‐prone task that can even be harmful

This paper proposes a unique disturbance rejection factor (DRF) based design of direct stable adaptive fuzzy logic controllers (AFLCs) for a class of non‐linear systems with large and fast disturbances. The proposed AFLCs are realized by employing hybrid combinations of Lyapunov theory based local adaptation and harmony search algorithm based global optimization technique. These hybrid AFLCs are designed

We have developed an expert system comprising a self‐aware framework for resource‐efficient and accurate data transmission within a low‐power lossy sensor network (LLN) deployed for indoor monitoring. We derived both individual and group awareness, which could ensure the awareness of each sensor regarding its resources, neighbours and network environment. The proposed expert system facilitates decision‐making

Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item

Stock investment is an important mode of asset allocation and a crucial means of financial management. How to grasp the movement of stock price and predict its trend have been the focus of investors and investment companies. Since expert stock comments contain abundant essential information for investment decisions, how to identify the topic of expert stock comments with high precision and efficiency

Most of the information on the Internet is represented in the form of microtexts, which are short text snippets such as news headlines or tweets. These sources of information are abundant, and mining these data could uncover meaningful insights. Topic modelling is one of the popular methods to extract knowledge from a collection of documents; however, conventional topic models such as latent Dirichlet

A modified adaptive butterfly optimization algorithm is established with the aim of addressing the “early search blindness” and the relatively poor adaptability of the sensory modality. A normal‐distribution‐based model and a Weibull‐distribution‐based adaptive model of sensory modalities are respectively proposed for the global search process and iteration process. Among them, the Weibull‐distribution‐based

Knowledge Engineering has become essential in the fields of Medical and Health Care with emphasis for helping citizens to improve their health and quality of life. This includes individual methods and techniques in health‐related knowledge acquisition and representation and their application in the construction of intelligent systems capable of using the acquired information to improve the patients'

The emergence of the Internet of Things (IoT) in the medical field has led to the massive deployment of a myriad of medical connected objects (MCOs). These MCOs are being developed and implemented for remote healthcare monitoring purposes including elderly patients with chronic diseases, pregnant women, and patients with disabilities. Accordingly, different associated challenges are emerging and include

The competitive segmentation of fuzzy clustering is utilized in a greater manner to deal with the local spatial information of input medical images. Fuzzy clustering favours lesions and tumour identification through the segmentation process where less accuracy attainment and time complexity might be instigated for the identification of oddities. To rectify the above‐said problems, a novel methodology

Massively complex systems, such as social networks (Camacho, Panizo‐LLedot, Bello‐Orgaz, Gonzalez‐Pardo, & Cambria, 2020; Lara‐Cabrera et al., 2017), renewable energy problems (Twidell & Weir, 2015), or Internet‐of‐Things problems (Lin et al., 2017), generate massive amounts of data. These massively complex systems have attracted the attention of both industrial and research communities, because the

In this paper, a new solution method is implemented to solve a bi‐objective variant of the vehicle routing problem that appears in industry and environmental enterprises. The solution involves designing a set of routes for each day in a period, in which the service frequency is a decision variable. The proposed algorithm, a muti‐start multi‐objective local search algorithm (MSMLS), minimizes total

Recently, the study of social network‐based recommender systems has become an active research topic. The integration of the social relationships that exist between users can improve the accuracy of recommendation results since the users' preferences are similar or influenced by their connected friends. We focus in this article on the recommendation of users in social networks. Our approach is based

Malware analysis and detection over the Android have been the focus of considerable research, during recent years, as customer adoption of Android attracted a corresponding number of malware writers. Antivirus companies commonly rely on signatures and are error‐prone. Traditional machine learning techniques are based on static, dynamic, and hybrid analysis; however, for large scale Android malware

This article presents the experimental assessment of a hash‐based loop closure detection methodology for visual simultaneous localization and mapping (SLAM), addressed to underwater autonomous vehicles. This methodology uses a new global image descriptor called net hash‐based loop closure (NetHALOC), which is learned with a simple and fast convolutional neural network. The results using NetHALOC have

Many children with speech sound disorders cannot pronounce the sibilant consonants correctly. We have developed a serious game, which is controlled by the children's voices in real time, with the purpose of helping children on practicing the production of European Portuguese (EP) sibilant consonants. For this, the game uses a sibilant consonant classifier. Since the game does not require any type of

Conversational systems have become an element of everyday life for billions of users who use speech‐based interfaces to services, engage with personal digital assistants on smartphones, social media chatbots, or smart speakers. One of the most complex tasks in the development of these systems is to design the dialogue model, the logic that provided a user input selects the next answer. The dialogue

A complex Pythagorean fuzzy set, an extension of Pythagorean fuzzy set, is a powerful tool to handle two dimension phenomenon. Dombi operators with operational parameters have outstanding flexibility. This article presents certain aggregation operators under complex Pythagorean fuzzy environment, including complex Pythagorean Dombi fuzzy weighted arithmetic averaging (CPDFWAA) operator, complex Pythagorean

As information systems and technologies grow in usage in the agri‐food industry, the same has happened to the relevance of Information Systems (IS) that allow for a parallel control, monitoring and management of the organizations' activities and business processes. As the literature proves, the benefits of implementing adequate and interoperable IS are very numerous and tend to represent a significant

In addition to classification and regression, outlier detection has emerged as a relevant activity in deep learning. In comparison with previous approaches where the original features of the examples were used for separating the examples with high dissimilarity from the rest of the examples, deep learning can automatically extract useful features from raw data, thus removing the need for most of the

The semantic segmentation of remotely sensed aerial imagery is nowadays an extensively explored task, concerned with determining, for each pixel in an input image, the most likely class label from a finite set of possible labels. Most previous work in the area has addressed the analysis of high‐resolution modern images, although the semantic segmentation of historical grayscale aerial photos can also

Artificial Intelligence (AI) is impacting our world. In the 1970s and 1980s, Expert Systems (ES) consisted of AI systems that included explicit knowledge, often represented in a symbolic form (e.g., by using the Prologue language), that was extracted from human experts. Since then, there has been an AI shift, due to three main phenomena (Darwiche, 2018): data explosion, with availability of several

This paper proposes a hybrid model that combines ensemble empirical mode decomposition (EEMD), autoregressive integrated moving average (ARIMA), and Taylor expansion using a tracking differentiator to forecast financial time series. Specifically, the financial time series is decomposed by EEMD into some subseries. Then, the linear portion of each subseries is forecasted by the linear ARIMA model, while

Many aspects of our lives are associated with places and the activities we perform on a daily basis. Most of them are recurrent and demand displacement of the individual between regular places like going to work, school or other important personal locations. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics, especially

This work aims to establish a general and optimized procedure for the initial refuelling of commercial airplanes, as this loading process is strongly related to safety and energy saving issues. The on‐ground refuelling is addressed as an optimization problem whose cost function involves expert knowledge about constraints and factors that influence the aircraft stability and performance. Several heterogeneous

The interval‐valued q‐rung orthopair fuzzy set (IVq‐ROFS) and complex fuzzy set (CFS) are two generalizations of the fuzzy set (FS) to cope with uncertain information in real decision making problems. The aim of the present work is to develop the concept of complex interval‐valued q‐rung orthopair fuzzy set (CIVq‐ROFS) as a generalization of interval‐valued complex fuzzy set (IVCFS) and q‐rung orthopair

This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised

The appearance of specular highlights in images is one main factor affecting accurate material or object recognition tasks. Such an appearance has a misleading effect on the true gradient information found in transmissive material images. Certain methods use specular highlights as an intrinsic feature of transparency to detect transparent objects. However, this process reduces the robustness of methods

Inside digital forensic science, expert systems are utilized to clarify suspicions where normally one or more human experts would need to be consulted. Expert systems‐based printer identification is provided with the objective of distinguishing the printer that produced a suspicious or questioned document. The arising problem is that the extraction of many features of the printed document for printer

Given a question and its answer candidates (named QA corpus), answer selection is the task of identifying the most relevant answers to the question. Answer selection is widely used in question answering, web search, and so on. Current deep neural network models primarily utilize local features extracted from input question‐answer pairs (QA pairs). However, the global features contained in QA corpora