Search

Abstract The role of venture capital in promoting entrepreneurship and funding innovation cannot be underestimated. However, it is important to acknowledge noteworthy concerns associated with collusive behavior in the venture capital process. “Guanxi” is a relatively intimate and closed social relationship, which can impact venture capital through trust and social obligations. Drawing on the theories of unethical pro-relational behavior and social networks, using data from GEM-listed companies supported by venture capital from 2009 to 2021, this research investigates the phenomenon of collusion behavior between venture capitalists and entrepreneurs, with a particular focus on the influence of “guanxi”. The research finds that the presence of “guanxi” between venture capitalists and entrepreneurs tends to facilitate collusive behavior. Specifically, “guanxi” tends to increase entrepreneurial self-interest behavior, and venture capitalists’ returns, infringe upon minority shareholders’ interests, and increase corporate operational risk. Moreover, the study further explores the moderator effect of equity incentives on such collusion behavior. This research not only enriches the theoretical framework of the relationship between “guanxi” and venture capital activities but also provides insight into the ethical implications associated with “guanxi”, contributing to the broader literature on social relationships and unethical behavior.

Phishing has become an increasing concern and captured the attention of end-users as well as security experts. Existing phishing detection techniques still suffer from the deficiency in performance accuracy and inability to detect unknown attacks despite decades of development and improvement. Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention to phishing detection that capitalizes on machine learning techniques. Deep learning has emerged as a branch of machine learning that becomes a promising solution for phishing detection in recent years. As a result, this study proposes a taxonomy of deep learning algorithm for phishing detection by examining 81 selected papers using a systematic literature review approach. The paper first introduces the concept of phishing and deep learning in the context of cybersecurity. Then, taxonomies of phishing detection and deep learning algorithm are provided to classify the existing literature into various categories. Next, taking the proposed taxonomy as a baseline, this study comprehensively reviews the state-of-the-art deep learning techniques and analyzes their advantages as well as disadvantages. Subsequently, the paper discusses various issues that deep learning faces in phishing detection and proposes future research directions to overcome these challenges. Finally, an empirical analysis is conducted to evaluate the performance of various deep learning techniques in a practical context, and to highlight the related issues that motivate researchers in their future works. The results obtained from the empirical experiment showed that the common issues among most of the state-of-the-art deep learning algorithms are manual parameter-tuning, long training time, and deficient detection accuracy.

The size of data that we generate every day across the globe is undoubtedly astonishing due to the growth of the Internet of Things. So, it is a common practice to unravel important hidden facts and understand the massive data using clustering techniques. However, non- linear relations, which are essentially unexplored when compared to linear correlations, are more widespread within data that is high throughput. Often, nonlinear links can model a large amount of data in a more precise fashion and highlight critical trends and patterns. Moreover, selecting an appropriate measure of similarity is a well-known issue since many years when it comes to data clustering. In this work, a non-Euclidean similarity measure is proposed, which relies on non-linear Jeffreys-divergence (JS). We subsequently develop c- means using the proposed JS (J-c-means). The various properties of the JS and J-c-means are discussed. All the analyses were carried out on a few real-life and synthetic databases. The obtained outcomes show that J-c-means outperforms some cutting-edge c-means algorithms empirically.

The design of the packages of products/services to be offered by a telecom company to its clients is a complex decision-making process that must consider different criteria to achieve both customer satisfaction and optimization of the company’s resources. In this process, Intuitionistic Fuzzy Sets (IFSs) can be used to manage uncertainty and better represent both preferences and non-preferences expressed by people who value each proposed alternative. We present a novel approach to design/develop new products/services that combines the Lean Six Sigma methodology with IFSs. Its main contribution comes from considering both preferences and nonpreferences expressed by real clients, whereas existing proposals only consider their preferences. By also considering their non-preferences, it provides an additional capacity to manage the high uncertainty in the selection of the commercial plan that best suits each client’s needs. Thus, client satisfaction is increased while improving the company’s corporate image, which will lead to customer loyalty and increased revenue. To validate the presented proposal, it has been applied to a real case study of the telecom sector, in which 2135 users have participated. The results obtained have been analysed and compared with those obtained with a model that does not consider the non-preferences expressed by users. First published online 27 October 2022

Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel pre-processing mechanism for non-precipitation data by using principal component analysis (PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data. The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SCFFNN), feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing meteorological data into an understandable format for the missing data imputation.

Green supply chain has developed rapidly due to the advocacy of ecological civilization, and choosing a proper green supplier is a crucial issue. Considering the fuzziness of evaluation information and the psychological states of decision makers (DMs) in selecting process, a novel TODIM based on prospect theory with q-rung orthopair fuzzy set (q-ROFS) is proposed. The novel TODIM concerns both the perceived transformed probability weighting function and the differences in risk attitudes. A new distance, which concerns the herd mentality, is carried out to measure the perceived difference of the q-ROFS. Besides, a new systematic evaluation index system, named as PCEM (Product, Cooperation ability, Environment, Market), has been established. A case related to pork supplier companies is presented and fully demonstrates the effectiveness of the novel TODIM when compared with the extended one, the intuitionistic fuzzy TODIM, the Pythagorean fuzzy TODIM as well as the TOPSIS with q-ROFS. Finally, a series of comparative analyses illustrate the advantages of the proposed TODIM. First published online 4 September 2020

Advances in FUZZY techniques and applications: in occasion of Lotfi Zadeh 100 birth anniversary. Technological and Economic Development of Economy, 27(2), pp. 280-283.

A botnet is a malware program that a hacker remotely controls called a botmaster. Botnet can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and identity stealing. The botnet also can avoid being detected by a security system. The traditional method of detecting botnets commonly used signature-based analysis unable to detect unseen botnets. The behavior-based analysis seems like a promising solution to the current trends of botnets that keep evolving. This paper proposes a multilayer framework for botnet detection using machine learning algorithms that consist of a filtering module and classification module to detect the botnet’s command and control server. We highlighted several criteria for our framework, such as it must be structure-independent, protocol-independent, and able to detect botnet in encapsulated technique. We used behavior-based analysis through flow-based features that analyzed the packet header by aggregating it to a 1-s time. This type of analysis enables detection if the packet is encapsulated, such as using a VPN tunnel. We also extend the experiment using different time intervals, but a 1-s time interval shows the most impressive results. The result shows that our botnet detection method can detect up to 92% of the f-score, and the lowest false-negative rate was 1.5%.

Today, predictive analytics applications became an urgent desire in higher educational institutions. Predictive analytics used advanced analytics that encompasses machine learning implementation to derive high-quality performance and meaningful information for all education levels. Mostly know that student grade is one of the key performance indicators that can help educators monitor their academic performance. During the past decade, researchers have proposed many variants of machine learning techniques in education domains. However, there are severe challenges in handling imbalanced datasets for enhancing the performance of predicting student grades. Therefore, this paper presents a comprehensive analysis of machine learning techniques to predict the final student grades in the first semester courses by improving the performance of predictive accuracy. Two modules will be highlighted in this paper. First, we compare the accuracy performance of six well-known machine learning techniques namely Decision Tree (J48), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (kNN), Logistic Regression (LR) and Random Forest (RF) using 1282 real student’s course grade dataset. Second, we proposed a multiclass prediction model to reduce the overfitting and misclassification results caused by imbalanced multi-classification based on oversampling Synthetic Minority Oversampling Technique (SMOTE) with two features selection methods. The obtained results show that the proposed model integrates with RF give significant improvement with the highest f-measure of 99.5%. This proposed model indicates the comparable and promising results that can enhance the prediction performance model for imbalanced multi-classification for student grade prediction.

Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting model for the highest possible accuracy. In this paper, a methodology that embraces these three virtues of data mining from a small dataset is proposed. An experiment that is based on the recent coronavirus outbreak originated from Wuhan is conducted by applying this methodology. It is shown that an optimized forecasting model that is constructed from a new algorithm, namely polynomial neural network with corrective feedback (PNN+cf) is able to make a forecast that has relatively the lowest prediction error. The results showcase that the newly proposed methodology and PNN+cf are useful in generating acceptable forecast upon the critical time of disease outbreak when the samples are far from abundant.

Abstract Background In biomedicine, infrared thermography is the most promising technique among other conventional methods for revealing the differences in skin temperature, resulting from the irregular temperature dispersion, which is the significant signaling of diseases and disorders in human body. Given the process of detecting emitted thermal radiation of human body temperature by infrared imaging, we, in this study, present the current utility of thermal camera models namely FLIR and SEEK in biomedical applications as an extension of our previous article. Results The most significant result is the differences between image qualities of the thermograms captured by thermal camera models. In other words, the image quality of the thermal images in FLIR One is higher than SEEK Compact PRO. However, the thermal images of FLIR One are noisier than SEEK Compact PRO since the thermal resolution of FLIR One is 160 × 120 while it is 320 × 240 in SEEK Compact PRO. Conclusion Detecting and revealing the inhomogeneous temperature distribution on the injured toe of the subject, we, in this paper, analyzed the imaging results of two different smartphone-based thermal camera models by making comparison among various thermograms. Utilizing the feasibility of the proposed method for faster and comparative diagnosis in biomedical problems is the main contribution of this study.

Knowledge-base is a fundamental platform in the architecture of an intelligent system. Relations and operators are popular knowledge in practice knowledge domains. In this paper, we propose a method to represent the model by combining these kinds of knowledge, called the Rela-Ops model. This model includes foundation components consisting of concepts, relations, operators, and inference rules. It is built based on ontology and object-oriented approaches. Besides the structure, each concept of the Rela-Ops model is a class of objects which also have behaviors to solve problems on their own. The processing of algorithms for solving problems on the Rela-Ops model combines the knowledge of relations and operators in the reasoning. Furthermore, we also propose a knowledge model for multiple knowledge domains, in which each sub-domain has the form as the Rela-Ops model. These representation methods have been applied to build knowledge bases of Intelligent Problems Solver (IPS) in mathematics. The knowledge base of 2D-Analytical Geometry in a high-school is built by using the Rela-Ops model, and the knowledge base of Linear Algebra in university is designed by using the model for multiple knowledge domains. The IPS system can automatically solve basic and advanced exercises in respective courses. The reasoning of their solutions is done in a step-by-step approach. It is similar to the solving method by humans. The solutions are also pedagogical and suitable for the learner's level and easy to be used by students studying 2D-Analytical Geometry in high-school and Linear Algebra in university.

The conventional histogram equalisation (CHE), though being simple and widely used technique for contrast enhancement, but fails to preserve the mean brightness and natural appearance of images. Most of the improved histogram equalisation (HE) methods give better performance in terms of one or two metrics and sacrifice their performance in terms of other metrics. In this paper, a novel fuzzy based bi-HE method is proposed which equalises low contrast images optimally in terms of all considered metrics. The novelty of the proposed method lies in selection of fuzzy threshold value using level-snip technique which is then used to partition the histogram into segments. The segmented sub-histograms, like other bi-HE methods, are equalised independently and are combined together. Simulation results show that for wide-range of test images, the proposed method improves the contrast while preserving other characteristics and provides good trade-off among all the considered performance metrics.

In the last decade, the advertising industry has experienced a quantum leap, powered by recent advances in neuroscience, a large investment in artificial intelligence, and a high degree of consumer expertise. Within this context, opinion mining, sentiment analysis, and emotion understanding bring us closer to one of the most sought-after objectives of advertising: to offer relevant ads at scale. The importance of studies about opinion mining, sentiment analysis, and emotion understanding in advertising has been rising exponentially over the last years. The peak of this new situation has been the interest of the research community in studying the relationship between such innovations and the spread of smart and contextual advertising. This article analyzes those works that address the relationship between sentiment analysis, opinion mining, and emotion understanding in advertising. The main objective is to clarify the current state of these studies, explore issues, methods, findings, themes, and gaps as well as to define their significance within the current convergence advertising research scenario. To reach such objectives, a bibliometric analysis was conducted, retrieving and analyzing 919 research works published between 2010 and 2019 based on results from Web of Science (WoS).

In a very open competitive context where pure online players are consistently gaining market share, the use of digital devices is a steady trend which is penetrating physical retail stores as a tool for retailers to improve customer experience and increase engagement. This need has increased with the COVID-19 pandemic as electronic devices in physical stores reduce the contact between people providing a greater sense of health safety, hence improving the customer experience. This work develops a multiple-criteria decision-making model for retailers who want to digitize their physical stores, providing a systematic approach to manage investment priorities in the organization. Important decisions should involve all different areas of the organization: Finance, Clients, Internal Processes and Learning & Growth departments. This strategic decision can be made hierarchically to obtain consistent decisions, also the use of the Order Weighted Average operator allows for alternative scenarios to be presented and agreed among the different areas of the business. The authors develop a use case for a Spanish fashion retailer. In the most widely agreed scenario the preferred devices were more technologically complex and expensive, while in the scenarios where the head of Finance is more predominant, cheaper and simpler devices were selected.

In the consensus reaching process (CRP) permitting negotiation, the efficiency of negotiation is affected by the order of negotiation with decision makers (DMs), the time, and the number of moderators. In this paper, the sorted negotiation against DMs considering efficiency and time is initiated into consensus decision making, which can improve the speed and effectiveness of consensus. Based on the opinion dynamics (opinion evolution), uniform and normal distributions are used to describe the uncertainty of DMs’ opinions and negotiation time, the opinion order efficiency and cost coefficient are coined, and the cost-constrained optimal efficiency sorted negotiation model and the optimal efficiency sorted negotiation model involving multiple moderators and time constraints are respectively constructed. The optimal solution of the chance-constrained model is obtained in the context of China’s urban demolition negotiation using an improved genetic algorithm, and an optimum set of influential individuals based on opinion similarity is introduced so that assessment criteria for validating the reasonableness of the sorting sequence are determined. Sorted consensus negotiation combined with complex scenarios such as different representation formats of opinions, characteristics of DMs, other solving algorithms, Bayesian dynamics, etc. can be included in future works.

How an autonomous underwater vehicle (AUV) performs fully automated task allocation and achieves satisfactory mission planning effects during the search for potential threats deployed in an underwater space is the focus of the paper. First, the task assignment problem is defined as a traveling salesman problem (TSP) with specific and distinct starting and ending points. Two competitive and non-commensurable optimization goals, the total sailing distance and the turning angle generated by an AUV to completely traverse threat points in the planned order, are taken into account. The maneuverability limitations of an AUV, namely, minimum radius of a turn and speed, are also introduced as constraints. Then, an improved ant colony optimization (ACO) algorithm based on fuzzy logic and a dynamic pheromone volatilization rule is developed to solve the TSP. With the help of the fuzzy set, the ants that have moved along better paths are screened and the pheromone update is performed only on preferred paths so as to enhance pathfinding guidance in the early stage of the ACO algorithm. By using the dynamic pheromone volatilization rule, more volatile pheromones on preferred paths are produced as the number of iterations of the ACO algorithm increases, thus providing an effective way for the algorithm to escape from a local minimum in the later stage. Finally, comparative simulations are presented to illustrate the effectiveness and advantages of the proposed algorithm and the influence of critical parameters is also analyzed and demonstrated.

Recommender systems are tools that help users in the decision-making process of choosing items that may be relevant for them among a vast amount of other items. One of the main problems of recommender systems is the cold start problem, which occurs when either new items or new users are added to the system and, therefore, there is no previous information about them. This article presents a multi-source dataset optimized for the study and the alleviation of the cold start problem. This dataset contains info about the users, the items (movies), and ratings with some contextual information. The article also presents an example user behavior-driven algorithm using the introduced dataset for creating recommendations under the cold start situation. In order to create these recommendations, a mixed method using collaborative filtering and user-item classification has been proposed. The results show recommendations with high accuracy and prove the dataset to be a very good asset for future research in the field of recommender systems in general and with the cold start problem in particular.

In the literature there are some proposals for integrated schemes for campaign management based on segmentation from the results of the RFM model. RFM is a technique used to analyze customer behavior by means of three variables: Recency, Frequency and Monetary value. It is s very much in use in the business world due to its simplicity of use, implementation and interpretability of its results. However, RFM applications to campaign management present known limitations like the lack of precision because the scores of these variables are expressed by an ordinal scale. In this paper, we propose to link customer segmentation methods with campaign activities in a more effective way incorporating the 2–tuple model both to the RFM calculation process and to its subsequent exploitation by means of segmentation algorithms, specifically, k-means. This yields a greater interpretability of these results and also allows computing these values without loss of information. Therefore, marketers can effectively develop more effective marketing strategy.

Under the cloud trend of enterprises, how do traditional businesses get on the cloud becomes a worth pondering question. To help those traditional businesses that have no experience to dispel the clouds and see the sun as soon as possible, we are planning to choose one corporation with rich experience to take them into the cloud market. The quintessence of dual probabilistic linguistic term sets (DPLTSs) is that it uses the combination of several linguistic terms and their proportions to reveal decision information by opposite angles. This paper proposes the dual probabilistic multiplicative linguistic preference relations (DPMLPRs) based upon the dual probabilistic multiplicative linguistic term sets (DPMLTSs). Then, it defines the comparable degree between the DPMLPRs and studies the consensus of the group DPMLPR. Moreover, it probes the expanding grey relational analysis (EGRA) under the proposed comparable degree between the DPMLTSs. After that, one example of choosing the experienced cloud cooperative partner is simulated under the dual probabilistic linguistic circumstance. Besides, the comparative analysis is performed by considering the similarity among the EGRA, TODIM, and VIKOR.

There are dozens of definitions of smart furniture with meanings that vary greatly. Thus, the aim of this paper is to provide an exact definition of the phrase “smart furniture” based on literature and patent analysis. Why a definition? Because by providing a good definition, we have a statement that captures the meaning, the use, the function, and the essence of a term or a concept and allows the impacts on stakeholders to be described. A literature search was undertaken between 20 July 2018 and 31 August 2018, and the databases searched included SCOPUS, Web of Science, and IEEE Xplore (1998-2017), which were searched by keywords that included the phrase “smart furniture.” Patent searching was performed in the ESPACENET database, where 226 articles from scientific databases and 737 patent applications were examined. After the application of strict criteria, we obtained 23 articles and six patents containing meaningful definitions of smart furniture. Based on the results, smart furniture should be defined as designed, networked furniture that is equipped with an intelligent system or is controller operated with the user's data and energy sources. Smart furniture needs to have the ability to communicate and anticipate a user's needs using a plurality of sensors and actuators inside the user's environment, resulting in user-adapted furniture. The research results and discussion presented in this paper are based on the recognition that the smart furniture research has great policymaking, technological, and economy potential while contributing to the user's wellbeing and Quality of Life (QoL). This paper indicates that the collaboration between the ICT and social-economic research has to be initiated and consolidated in a sustainable way or in an environment that satisfies the needs expressed by the user.

Deciding which film is the best or which portfolio is the best for investment are examples of decisions made by people every day. Decision-making systems aim to help people make such choices. In general, a decision-making system processes and analyses the available information to arrive at the best alternative solution of the problem of interest. In the preference modelling framework, decision-making systems select the best alternative(s) by maximising a score or choice function defined by the decision makers’ expressed preferences on the set of feasible alternatives. Nevertheless, decision-making systems may have logical errors that cannot be appreciated by developers. The main contribution of this paper is the provision of a verification theorem of the score function based on the quantifier-guided dominance degree (QGDD) with the mean operator in the context of additive preference relations. The provided theorem has several benefits because it can be applied to verify that the result obtained is correct and that there are no problems in the programming of the corresponding decision-making systems, thus improving their reliability. Moreover, this theorem acts on different parts of such systems, since not only does the theorem verify that the order of alternatives is correct, but it also verifies that the creation of the global preference relation is correct.

Los objetivos de este artículo son cuatro: analizar las principales diferencias en torno a las dimensiones mejor valoradas de tres estudios sobre satisfacción de personas usuarias basados en LibQUAL+® llevados a cabo en bibliotecas universitarias de España, Argentina y Chile; analizar las diferencias atendiendo a la variable de género (hombres y muje­res); determinar si las diferencias en cuanto a género son estadísticamente significativas; y discutir y reflexionar sobre los resultados hallados en el ámbito concreto de las bibliotecas como reflejo de la realidad social en general. Los resultados per­miten concluir que en España las personas usuarias están menos satisfechas con la dimensión de la biblioteca como espacio, mientras que en Latinoamericana se percibe menor grado de satisfacción también con el control de la información. El mayor grado de satisfacción en todos los estudios se da en el valor afectivo del servicio. Las mujeres en España muestran menor satisfacción y mayor exigencia que los hombres en el valor afectivo del servicio. Además, se hallaron diferencias estadísti­camente significativas en el análisis de las medias por género. En la discusión se ponen en relación los resultados hallados con la literatura existente, definiendo los paradigmas posibles de la biblioteca informativa frente al de la social-creadora y la percepción de la satisfacción por géneros según el contexto social en que se hallan las bibliotecas. La limitación principal del artículo es que se basa en tres estudios concretos; y su principal valor y originalidad, que puede considerarse como punto de partida para futuras investigaciones sobre la cuestión de género en lo que a bibliotecas y los servicios que ofrecen se refiere.

This study provides an overview of science from the Wikipedia perspective. A methodology has been established for the analysis of how Wikipedia editors regard science through their references to scientific papers. The method of co-citation has been adapted to this context in order to generate Pathfinder networks (PFNET) that highlight the most relevant scientific journals and categories, and their interactions in order to find out how scientific literature is consumed through this open encyclopaedia. In addition to this, their obsolescence has been studied through Price index. A total of 1 433 457 references available at Altmetric.com have been initially taken into account. After pre-processing and linking them to the data from Elsevier's CiteScore Metrics the sample was reduced to 847 512 references made by 193 802 Wikipedia articles to 598 746 scientific articles belonging to 14 149 journals indexed in Scopus. As highlighted results we found a significative presence of "Medicine" and "Biochemistry, Genetics and Molecular Biology" papers and that the most important journals are multidisciplinary in nature, suggesting also that high-impact factor journals were more likely to be cited. Furthermore, only 13.44% of Wikipedia citations are to Open Access journals.

The judgments of decision-makers are frequently the best way to process the information on complex alternatives. However, the performances of the alternatives are often not observable in their entirety, which prevents researchers from conducting controlled empirical studies. This paper justifies a functional representation that, due to its good predictive results, has been widely used ad hoc in studies in different branches of knowledge; it formalizes aspects of the human mental structure that influence the ability of people to decide and the intentional bounded rationality, and it subsequently analyzes how the reliability of decision-makers is affected by the difficulty of the problem and the expertise and beliefs of the decision-maker. The main research objective of this paper is to derive explicitly a general functional form that represents the behavior of a decision-maker linked to their way of thinking. This functional form allows a laboratory to be created to study a priori the performance of human decisions, i.e., the probability of choosing each of the alternatives, once the returns of the alternatives, the level of expertise, and the initial beliefs of the decision-maker are known exogenously. This laboratory will allow (1) the evaluation of decision support techniques; (2) the creation of agent-based models that anticipate group performances due to individual interactions; and (3) the development of other investigations based on statistical simulations.

One of the most favorable renewable energy sources, solar photovoltaic (PV) can meet the electricity demand considerably. Sunlight is converted into electricity by the solar PV systems using cells containing semiconductor materials. A PV system is designed to meet the energy needs of King Abdulaziz University Hospital. A new method has been introduced to find optimal working capacity, and determine the self-consumption and sufficiency rates of the PV system. Response surface methodology (RSM) is used for determining the optimal working conditions of PV panels. Similarly, an adaptive neural network based fuzzy inference system (ANFIS) was employed to analyze the performance of solar PV panels. The outcomes of methods were compared to the actual outcomes available for testing the performance of models. Hence, for a 40 MW target PV system capacity, the RSM determined that approximately 33.96 MW electricity can be produced, when the radiation rate is 896.3 W/m2, the module surface temperature is 41.4 °C, the outdoor temperature is 36.2 °C, the wind direction and speed are 305.6 and 6.7 m/s, respectively. The ANFIS model (with nine rules) gave the highest performance with lowest residual for the same design parameters. Hence, it was determined that the hourly electrical energy requirement of the hospital can be met by the PV system during the year.

En la literatura científica hay numerosos ejemplos de la utilización de los términos documentación histórica y documentación histórica de archivo; sin embargo, no hemos encontrado ninguna investigación que trate de esclarecer a qué nos referimos exactamente con estos términos. El presente artículo quiere rellenar ese espacio realizando la revisión y análisis de la legislación, de la normativa y de la opinión de los especialistas en la materia, tanto de ambos términos como de la base de estos, es decir, el término documento, al igual que se presentan las leyes y normas establecidas para el tratamiento y eliminación de este tipo de documentos. La estructura de esta investigación, tras una breve introducción, irá paso a paso despejando las siguientes incógnitas: ¿Qué se entiende por documento? ¿Qué se entiende por documentación histórica? ¿Qué se entiende por documentación histórica de archivo? Por último, en las conclusiones se expondrán las opiniones de los autores.

Modelling and Simulation in Business, Economics and Management. Technological and Economic Development of Economy, 25(4), pp. 571-575.

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work. First published online 6 May 2019

Background: The prevalence of cancer has increased over time worldwide. Nevertheless, the number of deaths has been reduced during the past 2 decades. Thus, one-third of the cancer patients are users of complementary and alternative therapies, looking for other types of interventions. The main aim of the present study is to understand the current status of the research in integrative and complementary oncology. Three different aspects were analyzed: production trends, country collaboration, and leading research topics. Methods: The dataset was obtained from the documents indexed under the Integrative and Complementary Medicine category of the Web of Science database from 1976 to 2017. VOSviewer and SciMAT software were employed to perform the bibliometric analysis. Results: The Journal of Ethnopharmacology , China Medical University and the People’s Republic of China are the leading producers in the field. Regarding the collaboration, the United States and China present a close connection. The scientific community is focused on the following topics: apoptosis, breast cancer, oxidative stress, chemotherapy, and nuclear factor-Kappa-B (NF-Kappa-B). Conclusions: The present article shows potentially important information that allows understanding of the past, present, and future of research in integrative and complementary oncology. It is a useful evidence-based framework on which to base future research actions and academic directions.

The reuse of business processes (BPs) requires similarities between them to be suitably identified. Various approaches have been introduced to address this problem, but many of them feature a high computational cost and a low level of automation. This paper presents a clustering algorithm that groups business processes retrieved from a multimodal search system (based on textual and structural information). The algorithm is based on Incremental Covering Arrays (ICAs) with different alphabets to determine the possible number of groups to be created for each row of the ICA. The proposed algorithm also incorporates Balanced Bayesian Information Criterion to determine the optimal number of groups and the best solution for each query. Experimental evaluation shows that the use of ICAs with strength four (4) and different alphabets reduces the number of solutions needed to be evaluated and optimizes the number of clusters. The proposed algorithm outperforms other algorithms in various measures (precision, recall, and F-measure) by between 12% and 88%. Friedman and Wilcoxon non-parametric tests gave a 90-95% significance level to the obtained results. Better options of repository search for BPs help companies to reuse them. By thus reusing BPs, managers and analysts can more easily get to know the evolution and trajectory of the company processes, a situation that could be expected to lead to improved managerial and commercial decision making.

Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.

Neuromarketing, consumer neuroscience and neuroaesthetics are a broad research area of neuroscience with an extensive background in scientific publications. Thus, the present study aims to identify the highly cited papers (HCPs) in this research field, to deliver a summary of the academic work produced during the last decade in this area, and to show patterns, features, and trends that define the past, present, and future of this specific area of knowledge. The HCPs show a perspective of those documents that, historically, have attracted great interest from a research community and that could be considered as the basis of the research field. In this study, we retrieved 907 documents and analyzed, through H-Classics methodology, 50 HCPs identified in the Web of Science (WoS) during the period 2010–2019. The H-Classic approach offers an objective method to identify core knowledge in neuroscience disciplines such as neuromarketing, consumer neuroscience, and neuroaesthetics. To accomplish this study, we used Bibliometrix R Package and SciMAT software. This analysis provides results that give us a useful insight into the development of this field of research, revealing those scientific actors who have made the greatest contribution to its development: authors, institutions, sources, countries as well as documents and references.

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

Nowadays, wine has become a very popular item to purchase. There are a lot of brands and a lot of different types of wines that have different prices and characteristics. Since there is a lot of options, it is easy for buyers to feel lost among the high number of possibilities. Therefore, there is a need for computational tools that help buyers to decide which is the wine that better fits their necessities. In this article, a decision support system built over a fuzzy ontology has been designed for helping people to select a wine. Two different possible architecture implementation designs are presented. Furthermore, imprecise information is used to design a comfortable way of providing information to the system. Users can use this comfortable communication system to express their preferences and provide their opinion about the selected products. Moreover, mechanisms to carry out a constant update of the fuzzy ontology are exposed.

The development of innovative solutions that allow the aging population to remain healthier and independent longer is essential to alleviate the burden that this increasing segment of the population supposes for the long term sustainability of the public health systems. It has been claimed that promoting physical activity could prevent functional decline. However, given the vulnerability of this population, the activity prescription requires to be tailored to the individual’s physical condition. We propose mobile Senior Fitness Test (m-SFT), a novel m-health system, that allows the health practitioner to determine the elderly physical condition by implementing a smartphone-based version of the senior fitness test (SFT). The technical reliability of m-SFT has been tested by carrying out a comparative study in seven volunteers (53–61 years) between the original SFT and the proposed m-health system obtaining high agreement (intra-class correlation coefficient (ICC) between 0.93 and 0.99). The system usability has been evaluated by 34 independent health experts (mean = 36.64 years; standard deviation = 6.26 years) by means of the System Usability Scale (SUS) obtaining an average SUS score of 84.4 out of 100. Both results point out that m-SFT is a reliable and easy to use m-health system for the evaluation of the elderly physical condition, also useful in intervention programs to follow-up the patient’s evolution.

The relationship between customer churn and their complaints is sufficiently contrasted in the telecom sector. Therefore, a key part of a company’s strategy is the measurement of this dissatisfaction. It is important to conduct periodic surveys on complaints in a standard form like the SERVPERF scale because it enables the organization to benchmark. Many of these complaints are stored in the company’s CRM. Our first aim is to define a model to transform CRM customer complaints, expressed in natural language, into SERVPERF scales. In the proposed model, we use the 2-tuple model, which allows computing this linguistic data without losing information. Our second purpose is to implement a prototype to apply the model in a 4G Company. As a practical conclusion, most complaints in this emerging technology (which still has some deficiencies) are related to technical aspects of the services rather than to staff.

The market launch of new products and services is a basic pillar for large and medium-sized companies in the ICT (Information and Communications Technology) sector. Choosing the right moment for it is usually a differentiating factor in terms of competition, since it is a source of competitive advantage. There are several mechanisms and strategies to address this problem from the market perspective. However, the criteria of the different actors involved – managers, sales representatives, experts, etc. – coexist in the corporate sphere and they often differ, causing difficulties in priority setting processes in the launch of a product or service. The assessment of the prioritization of these criteria is usually expressed in natural language, thus adding a great deal of uncertainty. Fuzzy linguistic models have proved to be an efficient tool for managing the intrinsic uncertainty of this type of information. This paper presents a linguistic multi-criteria decision-making model, able to reconcile the different requirements and viewpoints existing in the corporate sector when planning the launch of new products and services. The proposed model is based on the fuzzy 2-tuple linguistic model, aimed at managing linguistic data expressing different corporate criteria, without compromising accuracy in the calculation of said data. In order to illustrate this, a practical case study is presented, in which the model is applied for scheduling the launch prioritization of several new products and services by a telecommunications company, within the deadlines set in its strategic planning.

Physical Therapy is a profession which development and conceptual framework have been performed in the twentieth century. One of the major challenges of Physical Therapy in the twenty-first century is to identify the intellectual basis of its scientific domain. The aim of the present paper is to analyze the evolution of Physical Therapy research throughout scientific literature published in Web of Science (WoS) and Scopus databases. In order to perform the analysis, WoS and Scopus bibliographic databases were used to download all records. A set of journals with overall topics in Physical Therapy discipline with the largest history in the Journal Citation Report were selected: Physiotherapy, Physical Therapy, and Australian Journal of Physiotherapy/Journal of Physiotherapy. The bibliometric tool SciMAT was employed to perform a science mapping analysis in order to detect and visualize the research themes and their evolution across three periods: 1951–1984, 1985–2000, and 2001–2013. The analysis carried out shows that Physical Therapy research field is mainly divided into nine thematic areas: Middle-Aged People, Muscle Function Alterations and Recovery, Health Education, Gait and Biomechanics, Central Nervous System, Heart Rate and Exercise, Diagnosis, Low Back Pain, and Animals. In conclusion, Physical Therapy citations and production are primarily focused on two large thematic areas, diseases that involve middle-aged population and muscle function and its alterations. Results can be used to provide a historical perspective on research conducted in this emerging field and to provide a scientific evidence-based practice model on which future studies can be built.

Low back pain is the most prevalent musculoskeletal condition. This disorder constitutes one of the most common causes of disability worldwide, and as a result, it has a severe socioeconomic impact. Endurance tests are normally considered in low back pain rehabilitation practice to assess the muscle status. However, traditional procedures to evaluate these tests suffer from practical limitations, which potentially lead to inaccurate diagnoses. The use of digital technologies is considered here to facilitate the task of the expert and to increase the reliability and interpretability of the endurance tests. This work presents mDurance, a novel mobile health system aimed at supporting specialists in the functional assessment of trunk endurance by using wearable and mobile devices. The system employs a wearable inertial sensor to track the patient trunk posture, while portable electromyography sensors are used to seamlessly measure the electrical activity produced by the trunk muscles. The information registered by the sensors is processed and managed by a mobile application that facilitates the expert’s normal routine, while reducing the impact of human errors and expediting the analysis of the test results. In order to show the potential of the mDurance system, a case study has been conducted. The results of this study prove the reliability of mDurance and further demonstrate that practitioners are certainly interested in the regular use of a system of this nature.

Market-Oriented companies are committed to understanding both the needs of their customers, and the capabilities and plans of their competitors through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most companies in the last years have defined that one of their main strategic objectives for the next years is to become a truly data-driven organisation in the current Big Data context. They are willing to invest heavily in Data and Artificial Intelligence Strategy and build enterprise data platforms that will enable this Market-Oriented vision. In this paper, it is presented an Artificial Intelligence Cloud Architecture capable to help global companies to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches).

El objetivo de este trabajo es analizar la investigación que se ha desarrollado en el área de Trabajo Social de acuerdo a los trabajos publicados en revistas del área de Trabajo Social según las bases de datos bibliográficas de referencia internacional propiedad de Thomson Reuter, Web of Science y Journal Citation Reports. Para ello, recuperaremos desde el Web of Science todos los trabajos publicados en las revistas internacionales indexadas en la categoría de trabajo social del Journal Citation Report entre los años 2010-2015. Entonces, analizamos mediante técnicas de mapas de ciencia el contenido de los trabajos e identificamos los temas de investigación más relevantes, los temas emergentes, los temas en decadencia y temas periféricos

The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) published its first issue ten years ago. Currently, IJIMAI is indexed in the important database Emerging Sources Citation Index. This paper aims to identify, through a mapping of science, those most relevant aspects of the structure of publications made during the first 10 years of IJIMAI. Using VOSviewer software, the structural maps of the IJIMAI publications are analysed according to techniques such as bibliographic coupling, co-citations and cooccurrence of keywords. In addition, the evolution of the publications, citations and an analysis of the most cited papers of the journal are presented. The results show that IJIMAI has experienced a remarkable growth of both publications and citations in the last five years. We also observe that IJIMAI does not only capture the attention of the Spanish scientific community, but also of emerging countries such as India and Iran and emerging Latin American countries such as Colombia. With a such increasing behaviour, it is expected in the coming years that IJIMAI will position itself among the best journals with similar scientific scope.

Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

El proyecto web semántica se perfila cada vez más como una nueva alternativa que viene a resolver algunos de los problemas que presenta el actual modelo de Web. Este trabajo pretende, mediante el planteamiento de ejemplos sencillos y desde el punto de vista del "profesional de la información", hacer una aproximación a las tecnologías básicas que permiten definir una infraestructura semántica sobre la cual desarrollar los sistemas de información del futuro. Además, se analiza el impacto que puede suponer la implantación de estas tecnologías en las metodologías y técnicas de trabajo de una disciplina como la documentación. Por último se discute brevemente la viabilidad de este nuevo modelo.The Semantic Web is arising as a new alternative that could solve some of the problems of the actual Web model. By means of simple examples from an information professional perspective, this paper attempts to approach the basic technologies that will define the semantic infrastructure for developing the information systems of the future. Furthermore, the impact that the implementation of these technologies may have on work methodologies and techniques of a research area such as Information Science, is analyzed. Finally, the viability of this new model is briefly discussed.

„Fuzzy sets and fuzzy logic in multi-criteria decision making. The 50th anniversary of Prof. Lotfi Zadeh’s theory: introduction" Technological and Economic Development of Economy, 21(5), pp. 677–683