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  • A semantic‐enabled and context‐aware monitoring system for the internet of medical things
    Expert Syst. (IF 1.546) Pub Date : 2020-09-21
    Ahlem Rhayem; Mohamed Ben Ahmed Mhiri; Khalil Drira; Said Tazi; Faiez Gargouri

    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

  • A smartly designed automated map based clustering algorithm for the enhanced diagnosis of pathologies in brain MR images
    Expert Syst. (IF 1.546) Pub Date : 2020-09-21
    Vigneshwaran Senthilvel; Vishnuvarthanan Govindaraj; Yu‐Dong Zhang; Pallikonda Rajasekaran Murugan; Arun Prasath Thiyagarajan

    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

  • Special issue on new trends and challenges of bio‐inspired computational intelligence algorithms in massively complex systems
    Expert Syst. (IF 1.546) Pub Date : 2020-09-20
    Antonio Gonzalez‐Pardo; Antonio J. Tallón‐Ballesteros; Hujun Yin

    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

  • Optimizing a bi‐objective vehicle routing problem that appears in industrial enterprises
    Expert Syst. (IF 1.546) Pub Date : 2020-09-15
    Ana D. López‐Sánchez; Julián Molina; Manuel Laguna; Alfredo G. Hernández‐Díaz

    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

  • Recommendation of users in social networks: A semantic and social based classification approach
    Expert Syst. (IF 1.546) Pub Date : 2020-09-13
    Lamia Berkani; Sami Belkacem; Mounira Ouafi; Ahmed Guessoum

    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

  • NetHALOC: A learned global image descriptor for loop closing in underwater visual SLAM
    Expert Syst. (IF 1.546) Pub Date : 2020-09-10
    Francisco Bonin‐Font; Antoni Burguera Burguera

    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

  • Sibilant consonants classification comparison with multi‐ and single‐class neural networks
    Expert Syst. (IF 1.546) Pub Date : 2020-09-09
    Ivo Anjos; Nuno Cavalheiro Marques; Margarida Grilo; Isabel Guimarães; João Magalhães; Sofia Cavaco

    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

  • Adaptive dialogue management using intent clustering and fuzzy rules
    Expert Syst. (IF 1.546) Pub Date : 2020-09-09
    David Griol; Zoraida Callejas; Jose Manuel Molina; Araceli Sanchis

    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

  • Complex Pythagorean Dombi fuzzy operators using aggregation operators and their decision‐making
    Expert Syst. (IF 1.546) Pub Date : 2020-09-09
    Muhammad Akram; Ayesha Khan; Arsham Borumand Saeid

    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

  • Integrity verification and behavioral classification of a large dataset applications pertaining smart OS via blockchain and generative models
    Expert Syst. (IF 1.546) Pub Date : 2020-09-09
    Salman Jan; Shahrulniza Musa; Toqeer Ali; Mohammad Nauman; Sajid Anwar; Tamleek Ali Tanveer; Babar Shah

    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

  • Detection of anomalous episodes in urban Ozone maps
    Expert Syst. (IF 1.546) Pub Date : 2020-09-08
    Miguel Cárdenas‐Montes

    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

  • An integrated information systems architecture for the agri‐food industry
    Expert Syst. (IF 1.546) Pub Date : 2020-09-08
    Frederico Branco; Ramiro Gonçalves; Fernando Moreira; Manuel Au‐Yong‐Oliveira; José Martins

    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

  • Semantic segmentation and colorization of grayscale aerial imagery with W‐Net models
    Expert Syst. (IF 1.546) Pub Date : 2020-09-08
    Maria Dias; João Monteiro; Jacinto Estima; Joel Silva; Bruno Martins

    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

  • Fifth special issue on knowledge discovery and business intelligence
    Expert Syst. (IF 1.546) Pub Date : 2020-09-04
    Paulo Cortez; Albert Bifet

    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

  • A hybrid model for financial time‐series forecasting based on mixed methodologies
    Expert Syst. (IF 1.546) Pub Date : 2020-09-02
    Zhidan Luo; Wei Guo; Qingfu Liu; Zhengjun Zhang

    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

  • From mobility data to habits and common pathways
    Expert Syst. (IF 1.546) Pub Date : 2020-09-02
    Thiago Andrade; Brais Cancela; João Gama

    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

  • Knowledge based approach to ground refuelling optimization of commercial airplanes
    Expert Syst. (IF 1.546) Pub Date : 2020-09-01
    Elías Plaza; Matilde Santos

    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

  • Algorithms for complex interval‐valued q‐rung orthopair fuzzy sets in decision making based on aggregation operators, AHP, and TOPSIS
    Expert Syst. (IF 1.546) Pub Date : 2020-08-27
    Harish Garg; Zeeshan Ali; Tahir Mahmood

    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

  • A co‐training‐based approach for the hierarchical multi‐label classification of research papers
    Expert Syst. (IF 1.546) Pub Date : 2020-08-24
    Abir Masmoudi; Hatem Bellaaj; Khalil Drira; Mohamed Jmaiel

    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

  • Building an expert system for printer forensics: A new printer identification model based on niching genetic algorithm
    Expert Syst. (IF 1.546) Pub Date : 2020-08-19
    Saad M. Darwish; Hany M. ELgohary

    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

  • A deep learning approach for specular highlight removal from transmissive materials
    Expert Syst. (IF 1.546) Pub Date : 2020-08-19
    Amanuel Hirpa Madessa; Junyu Dong; Yanhai Gan; Feng Gao

    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

  • Improving answer selection with global features
    Expert Syst. (IF 1.546) Pub Date : 2020-08-18
    Shengwei Gu; Xiangfeng Luo; Hao Wang; Jing Huang; Qin Wei; Subin Huang

    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

  • Error prediction and structure determination for CMAC neural network based on the uniform design method
    Expert Syst. (IF 1.546) Pub Date : 2020-08-18
    Zhiwei Kong; Yong Zhang; Xudong Wang; Shuanzhu Sun; Chunlei Zhou; Dou Li; Baosheng Jin

    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

  • Positioning push–pull boundary in a hesitant fuzzy environment
    Expert Syst. (IF 1.546) Pub Date : 2020-08-16
    Seyedeh Roya Pournamazi; R. Ghasemy Yaghin; Fariborz Jolai

    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

  • Visual interpretation of regression error
    Expert Syst. (IF 1.546) Pub Date : 2020-08-13
    Inês Areosa; Luís Torgo

    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

  • CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs
    Expert Syst. (IF 1.546) Pub Date : 2020-08-11
    Ningning Jia; Xiang Cheng; Sen Su; Liyuan Ding

    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

  • A hybrid MCDM model combining DANP with TODIM to evaluate the information quality of community question answering in a two‐dimensional linguistic environment
    Expert Syst. (IF 1.546) Pub Date : 2020-08-11
    Ming Li; Ying Li; Qijin Peng; Jun Wang

    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

  • Multi‐label learning on principles of reverse k‐nearest neighbourhood
    Expert Syst. (IF 1.546) Pub Date : 2020-08-11
    Payel Sadhukhan; Sarbani Palit

    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

  • A bi‐objective procedure to deliver actionable knowledge in sport services
    Expert Syst. (IF 1.546) Pub Date : 2020-08-10
    Paulo Pinheiro; Luís Cavique

    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 context‐aware recommender method based on text and opinion mining
    Expert Syst. (IF 1.546) Pub Date : 2020-08-10
    Camila Vaccari Sundermann; Renan de Padua; Vítor Rodrigues Tonon; Ricardo Marcondes Marcacini; Marcos Aurélio Domingues; Solange Oliveira Rezende

    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

  • A dog food recommendation system based on nutrient suitability
    Expert Syst. (IF 1.546) Pub Date : 2020-08-07
    Hee Seok Song; Young Ae Kim

    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

  • Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks
    Expert Syst. (IF 1.546) Pub Date : 2020-08-01
    Ruili Shi; Chunxiang Guo; Xin Gu

    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

  • Serial and parallel memetic algorithms for the bounded diameter minimum spanning tree problem
    Expert Syst. (IF 1.546) Pub Date : 2020-08-01
    Prem Prakash Vuppuluri; Patvardhan Chellapilla

    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

  • Face similarity linkage: A novel biometric approach to sexually motivated serial killer victims
    Expert Syst. (IF 1.546) Pub Date : 2020-07-23
    Sarah Bernadette Hackett; David Keatley; Brendan Chapman

    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

  • Common set of weights in data envelopment analysis under prospect theory
    Expert Syst. (IF 1.546) Pub Date : 2020-07-23
    Yu Yu; Weiwei Zhu; Qinfen Shi; Shangwen Zhuang

    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

  • Automating test oracles from restricted natural language agile requirements
    Expert Syst. (IF 1.546) Pub Date : 2020-07-22
    Maryam Imtiaz Malik; Muddassar Azam Sindhu; Akmal Saeed Khattak; Rabeeh Ayaz Abbasi; Khalid Saleem

    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 reductions in N‐soft sets and their applications in decision‐making
    Expert Syst. (IF 1.546) Pub Date : 2020-07-20
    Muhammad Akram; Ghous Ali; José C. R. Alcantud; Fatia Fatimah

    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

  • Rational, emotional, and attentional models for recommender systems
    Expert Syst. (IF 1.546) Pub Date : 2020-07-13
    Ameed Almomani; Cristina Monreal; Jorge Sieira; Juan Graña; Eduardo Sánchez

    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

  • Retyping of triple‐negative breast cancer based on clustering method
    Expert Syst. (IF 1.546) Pub Date : 2020-07-10
    Bo Liu; Xingrui Li; Huina Wang; Shuangtao Zhao; Jianqiang Li; Guangzhi Qu; Fei Wang

    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

  • What makes trading strategies based on chart pattern recognition profitable?
    Expert Syst. (IF 1.546) Pub Date : 2020-07-09
    Prodromos Tsinaslanidis; Francisco Guijarro

    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 from carpal radiography images using deep learning
    Expert Syst. (IF 1.546) Pub Date : 2020-07-03
    Yih An Ding; Filipe Mutz; Klaus F. Côco; Luiz A. Pinto; Karin S. Komati

    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

  • Special issue on “advances in visual analytics and mining visual data”
    Expert Syst. (IF 1.546) Pub Date : 2020-06-29
    Victor Chang; Shadi A. Aljawarneh; Chung‐Sheng Li

    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

  • An integrated probabilistic linguistic projection method for MCGDM based on ELECTRE III and the weighted convex median voting rule
    Expert Syst. (IF 1.546) Pub Date : 2020-06-27
    Zi‐yu Chen; Xiao‐kang Wang; Juan‐juan Peng; Hong‐yu Zhang; Jian‐qiang Wang

    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

  • GEP‐based classifiers with drift‐detection
    Expert Syst. (IF 1.546) Pub Date : 2020-06-18
    Joanna Jedrzejowicz; Piotr Jedrzejowicz

    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

  • An improved model for sentiment analysis on luxury hotel review
    Expert Syst. (IF 1.546) Pub Date : 2020-06-14
    Victor Chang; Lian Liu; Qianwen Xu; Taiyu Li; Ching‐Hsien Hsu

    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

  • Hybrid genetic‐discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries
    Expert Syst. (IF 1.546) Pub Date : 2020-06-14
    Roohallah Alizadehsani; Mohamad Roshanzamir; Moloud Abdar; Adham Beykikhoshk; Abbas Khosravi; Saeid Nahavandi; Pawel Plawiak; Ru San Tan; U Rajendra Acharya

    Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus

  • Are you sure? Prediction revision in automated decision‐making
    Expert Syst. (IF 1.546) Pub Date : 2020-06-12
    Nadia Burkart; Sebastian Robert; Marco F. Huber

    With the rapid improvements in machine learning and deep learning, decisions made by automated decision support systems (DSS) will increase. Besides the accuracy of predictions, their explainability becomes more important. The algorithms can construct complex mathematical prediction models. This causes insecurity to the predictions. The insecurity rises the need for equipping the algorithms with explanations

  • Secure third‐party data clustering using SecureCL, Φ‐data and multi‐user order preserving encryption
    Expert Syst. (IF 1.546) Pub Date : 2020-06-09
    Nawal Almutairi; Frans Coenen; Keith Dures

    Secure collaborative data clustering using SecureCL is presented. SecureCL is founded on the concept of Φ‐data implemented using Super Secure Chain Distance Matrices and encrypted using Multi‐User Order Preserving Encryption. The advantage offered, unlike comparable systems, is that SecureCL does not require any user participation once the Φ‐data proxy has been encrypted; it does not require recourse

  • Domain problem‐solving expert identification in community question answering
    Expert Syst. (IF 1.546) Pub Date : 2020-06-08
    Weizhao Tang; Tun Lu; Hansu Gu; Peng Zhang; Ning Gu

    Question‐Answering (Q&A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&A sites, or Community Question Answering (CQA), mainly depends on the high‐quality content continuously contributed by users with high‐level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&A communities to encourage commitment

  • Towards an automatic coding of observational studies: Coding neurofeedback therapies of children with autism
    Expert Syst. (IF 1.546) Pub Date : 2020-05-30
    Víctor R. López‐López; Lizbeth Escobedo; Leonardo Trujillo

    The coding of observational data is commonly used to analyse and evaluate human behaviours. The technique can help researchers inform the design and impact of, for example, an Ubicomp system by studying specific behaviours of interest. There are some tools that can alleviate the burden of observational coding, like those that help to collect and organise data, but can still be error‐prone and time‐consuming

  • Decision support system on credit operation using linear and logistic regression
    Expert Syst. (IF 1.546) Pub Date : 2020-05-30
    Germanno Teles; Joel J. P. C. Rodrigues; Sergei A. Kozlov; Ricardo A. L. Rabêlo; Victor Hugo C. Albuquerque

    The act of lending is based on trust in the borrower to honour the obligation of paying back the lender. Greater spreads on credit operations may help predict the expected recovery of the credit, based on the sufficiency and liquidity of the guarantee. This study aims to understand how predictive models can provide different estimations of expected recovery based on the same data sets. It classifies

  • Developing a novel inverse data envelopment analysis (DEA) model for evaluating after‐sales units
    Expert Syst. (IF 1.546) Pub Date : 2020-05-26
    Seyed S. S. Hosseininia; Reza F. Saen

    This paper proposes a novel model of inverse data envelopment analysis (IDEA) based on the slack‐based measure (SBM) approach. The developed inverse SBM model can maintain relative efficiency of decision making units (DMUs) with new input and output. This model can also measure the input and output volumes when a decision maker (DM) increases efficiency score. The inverse SBM model is a kind of multi‐objective

  • A neural approach for detecting inline mathematical expressions from scientific documents
    Expert Syst. (IF 1.546) Pub Date : 2020-05-19
    Sreekanth Madisetty; Kaushal Kumar Maurya; Akiko Aizawa; Maunendra Sankar Desarkar

    Scientific documents generally contain multiple mathematical expressions in them. Detecting inline mathematical expressions are one of the most important and challenging tasks in scientific text mining. Recent works that detect inline mathematical expressions in scientific documents have looked at the problem from an image processing perspective. There is little work that has targeted the problem from

  • On the identification and analysis of citation pattern irregularities among journals
    Expert Syst. (IF 1.546) Pub Date : 2020-05-13
    Joyita Chakraborty; Dinesh K. Pradhan; Subrata Nandi

    Recent studies report that few journals are adopting unethical citation practices to inflate Impact Factor (IF) artificially. “Clarivate Analytics” has started to blacklist such journals since 2006. As reported in the literature, evaluation of journals individually, to detect anomalies from vast and dynamically changing citation network is not efficient. The primary purpose of this work is to define

  • Entropy‐controlled deep features selection framework for grape leaf diseases recognition
    Expert Syst. (IF 1.546) Pub Date : 2020-05-13
    Alishba Adeel; Muhammad Attique Khan; Tallha Akram; Abida Sharif; Mussarat Yasmin; Tanzila Saba; Kashif Javed

    Several countries are most reliant on agriculture either in terms of employment opportunities, national income, availability of a raw material, food production, to name but a few. However, it faces a big challenge such as climate changes, diseases, pets, weeds etc. Therefore, last decade has provided a machine learning‐based solution to the agricultural community, which helped farmers to identify the

  • A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging
    Expert Syst. (IF 1.546) Pub Date : 2020-05-10
    Riyaj Uddin Khan; Mohammad Tanveer; Ram Bilas Pachori;

    Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD

  • Deep OCR for Arabic script‐based language like Pastho
    Expert Syst. (IF 1.546) Pub Date : 2020-05-07
    Saeeda Naz; Naila H. Khan; Shizza Zahoor; Muhammad I. Razzak

    Developing cursive script recognition systems have always been a challenging task for researchers. This article proposes a ligature‐based recognition system for the cursive Pashto script using four pre‐trained CNN models using a fine‐tuned approach. The SqueezeNet, ResNet, MobileNet and DenseNet models have been observed for the classification and the recognition of Pashto sub‐word (ligature). Overall

  • Modelling the vibration response of a gas turbine using machine learning
    Expert Syst. (IF 1.546) Pub Date : 2020-05-06
    Josué Zárate; Perla Juárez‐Smith; Javier Carmona; Leonardo Trujillo; Salvador de Lara

    This work deals with modelling the vibration response of a gas turbine obtained during the start‐up process until reaching the nominal speed for power generation. Analysing the vibrations of a complex systems like a gas turbine is useful for the diagnostic of faults or damages in the internal mechanical components of the different stages that integrate a turbine. This work focuses on the study of the

  • COVID‐19 special issue: Intelligent solutions for computer communication‐assisted infectious disease diagnosis
    Expert Syst. (IF 1.546) Pub Date : 2020-05-05
    Fadi Al‐Turjman

    COVID‐19 (Corona Virus Disease 19) is an infectious disease which is having a significant health and economic impact across the world. The primary source for the transmission of the disease, its detection and treatment methods are still unknown. Hence, a scientific response to this new corona virus is being hampered by a lack of knowledge on how it spreads, possible prevention measures and vaccinations

  • Contracting in Brazilian public administration: A machine learning approach
    Expert Syst. (IF 1.546) Pub Date : 2020-05-04
    Bruno M. Henrique; Vinicius A. Sobreiro; Herbert Kimura

    The risk of non‐fulfilment of a contract can harm public administration or even interrupt public services. Therefore, models that assist manager decision making in the audit and control of contracts with a higher disqualification risk may be important tools, with economic and even social repercussions. In this article, public contracts are classified with respect to the risk of non‐compliance with

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