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

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

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

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

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

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

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

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

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

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

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

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

Insufficient study on error bound of cerebellar model articulation controller (CMAC) severely limits its application. To investigate the error prediction and structure determination of CMAC for multi‐dimensional and data‐generation objects, this paper builds a 10‐input 2‐output model for a desulfurization system to test 44,640 sets of operation data. Four test groups and one prediction group are designed

Nowadays, fierce competition enforces supply chain planners to develop market‐oriented production strategies. Customer order decoupling point (CODP) could increase the supply chain efficiency and responsiveness simultaneously. The right position of CODP in production industries will result in a pattern for trade‐off between responsiveness and operational efficiency. The purpose of this paper is to

Several sophisticated machine learning tools (e.g., ensembles or deep networks) have shown outstanding performance in different regression forecasting tasks. In many real world application domains the numeric predictions of the models drive important and costly decisions. Nevertheless, decision makers frequently require more than a black box model to be able to “trust” the predictions up to the point

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model

Evaluating the information quality of cQA (community question‐answering) websites helps users select a cQA website with high‐quality information and improves the information quality. In this paper, an approach to evaluating the information quality of cQA based on a novel hybrid multicriteria decision‐making (MCDM) model is proposed. First, the source, content, expression and usefulness criteria for

In this article, we present a novel neighbourhood based multi‐label classifier, Multi‐label Learning on principles of Reverse k‐Nearest Neighbourhood (ML‐RkNN) where we estimate the neighbourhood of the points on the basis of their reverse k ‐nearest neighbourhood (RkNN). Through RkNN, for the same value of k , we get different number of neighbours for different instances and this happens adaptively

The increase in retention of customers in gyms and health clubs is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused essentially on predictive analytics, neglecting the business domain. This work presents an actionable knowledge discovery system that uses the following pipeline (data collection, predictive model and retention interventions)

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context‐aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous

The demand for a food recommendation service for dogs has rapidly increased with the increasing number of pet owners, because it is generally difficult for dog owners to find food that is perfectly suitable for their dogs' health condition. The purpose of this study is to develop an algorithm for recommending dog food that contains appropriate nutrients based on the physical and health conditions of

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social

Given a connected, weighted, undirected graph G = (V , E ) and an integer D  ≥ 2, the bounded diameter minimum spanning tree (BDMST) problem seeks a spanning tree of minimum cost, whose diameter is no greater than D . The problem is known to be NP‐hard, and finds application in various domains such as information retrieval, wireless sensor networks and distributed mutual exclusion. This article presents

Some sexually motivated serial killers target victims on the basis of appearance. Therefore, multiple victims of a single serial killer are likely to have some facial features and geometries that are similar. The current research was undertaken to propose a technique, termed face similarity linkage, to evaluate whether victims of a serial killer have statistically more similar facial measurements than

Data envelopment analysis (DEA) is a data‐driven tool for performance evaluation, measuring decision‐making units (DMUs) and designating them with specific weightings. The standard DEA model typically sets up that decision‐makers (DMs) are wholly rational to select the most favourable weights to obtain the maximum performance score, but does not take into account their attitude toward risk during the

Manual testing of software requirements written in natural language for agile or any other methodology requires more time and human resources. This leaves the testing process error prone and time consuming. For satisfied end users with bug‐free software delivered on time, there is a need to automate the test oracle process for natural language or informal requirements. The automation of the test oracle

Parameter reduction is an important operation for improving the performance of decision‐making processes in various uncertainty theories. The theory of N ‐soft sets is emerging as a powerful mathematical tool for dealing with uncertainties beyond the standard formulation of the soft set theory. In this research article, we extend the notion of parameter reduction to N ‐soft set theory, and we also

This work analyses the decision‐making process underlying choice behaviour. First, neural and gaze activity were recorded experimentally from different subjects performing a choice task in a Web Interface. Second, choice models and ensembles were fitted using rational, emotional, and attentional features. The model's predictions were evaluated in terms of their accuracy and rankings were made for each

Triple‐negative breast cancer is the worst prognosis in breast cancer, accounting for 10.0–20.8% of all breast cancers. Considering that triple‐negative breast cancer has great heterogeneity and very poor prognosis, clinical medication guidance is in urgent need of a more detailed classification of breast cancer itself. Although many researchers have been dedicated to the clustering of triple‐negative

Automating chart pattern recognition is a relevant issue addressed by researchers and practitioners when designing a system that considers technical analysis for trading purposes. This article proposes the design of a trading system that takes into account any generic pattern that has been proven to be profitable in the past, without restricting the search to the specific technical patterns reported

Bone age estimation has been used in medicine to verify whether the bone structure development degree of a person corresponds to their chronological age. Such estimate is useful for prognosis about the development of children and adolescents, as well as for the diagnosis of endocrinological diseases. This work proposes a fully automated methodology for bone age estimation from carpal radiography images

Visual and multimedia analytics provides an emerging field of research combining strengths from information analytics, geospatial analytics, scientific analytics, statistical analytics, knowledge discovery, data management and knowledge representation, presentation, production and dissemination, cognition, perception, and interaction (Chen, Chiang and Storey, 2012). The aim is to gain insight into

In the multi‐criteria group decision‐making (MCGDM) problems with great uncertainty, making full use of participants' evaluation information could help improve the accuracy and reliability of decision results. Probabilistic linguistic term set (PLTS) is an effective tool to represent qualitative data and can fully express the hesitation and preference of decision makers. Therefore, this paper aims

In the paper, we propose two gene expression programming (GEP)‐based ensemble classifiers with different drift detection mechanisms. In the related work section, we briefly review GEP as a classification tool, incremental classifiers, and concept drift detectors. Next, the structure of our two‐level GEP ensemble with metagenes is described. Further on, two integrated classifiers with drift detection

This article proposes a heuristic model for sentiment analysis on luxury hotel reviews to analyse and explore marketing insights from attitudes and emotions expressed in reviews. We make several significant contributions to visual and multimedia analytics. This research will develop the practical application of visual and multimedia analytics as the research foundation is based on information analytics