Abstract This study examines the challenge of defining quality in peer-review reports, a crucial yet underexplored aspect of academic publishing. Reviewers are vital gatekeepers of scientific knowledge, but unclear skills and a lack of standardized guidelines have led to inconsistent and subjective practices, weakening the overall efficacy of the peer-review process. To address this issue, the primary objective of this paper is to answer the research question: How has literature addressed guidance for producing quality peer-review reports? A scoping review was conducted, utilizing Scopus, Web of Science, SpringerLink, ScienceDirect, PubMed, and SAGE databases to search for records using keywords related to guidelines for scientific peer reviewing. The review identified 111 primary studies offering recommendations on how to review scientific articles. Extracted data were analysed thematically, focusing on approaches to reviewing articles, manuscript evaluation criteria, and report-writing guidelines. The findings revealed six key categories of review criteria for evaluating scientific manuscripts: structural components, research approach, style, ethical conduct, scientific value, and overall suitability. Additionally, the review provides 70 actionable recommendations for writing peer-review reports and highlights eight essential quality features expected in review texts: constructive, specific, fair, thorough, courteous, consistent, objective, and readable feedback. This study contributes to developing a standardized guide for scientific reviewing, with a particular emphasis on supporting early-career reviewers. The findings encourage academic publishers, journal editors, and professional organizations to adopt the proposed guidelines to enhance consistency, reduce bias, and improve the peer-review process. They also provide a foundation for developing new tools to support the reviewing.

Abstract The purpose of this study is to create and validate a conceptual model that investigates the effects of management accounting’s intelligence activities on the business strategies and policies of Portuguese small and medium enterprises (SMEs) and analyses their impact on the company’s financial and non-financial performance. The research followed a quantitative approach. We used a survey design applied to SMEs, resulting in 310 complete and valid answers. The data were analysed using the statistical programmes SPSS 26 and AMOS 22. It uses structural equation modelling to examine the relationships between variables. The empirical study validated the conceptual model and revealed that the intelligence activities of management accounting have positive effects on the business strategies and policies of Portuguese small and medium enterprises (SMEs). Additionally, business strategies and policies have positive effects on the financial and non-financial performance of Portuguese SMEs.

Expert systems

Abstract The segmentation of dermoscopic‐based skin lesion images is considered to be challenging owing to various factors. Some of the most tangible reasons include poor contrast near the affected skin lesion, the fuzzy and unpredictable lesion limits, the presence of variations in noise, and capturing images under different conditions. This paper aims to develop an efficient segmentation model for dermoscopic images of different skin lesions based on deep learning. This paper proposes the 11‐layer convolutional deep neural network with two segmentation models trained from start to finish and do not depend on any previous information about the data. The viability, efficiency, and speculation ability of the models are evaluated on the ISIC2018 database. The proposed model achieves 0.903 accuracy and 0.820 Jaccard index in the segmentation of skin lesions. The model shows better performance compared to other image segmentation techniques from the leaderboards of ISIC2018 using deep learning.

The world, as we know it, is changing every day thanks to intensive research in fields such as expert systems, and the unexpected discoveries that influence our daily life and habits. Researchers develop more and more innovative concepts and novel paradigms, outstanding technologies that become mature and are launched on the market ‘in a blink of an eye’ with applications in a large number of fields of activities. Smart devices with these novel technologies embedded have become inseparable partners with humans. The knowledge incorporated in advanced programs assist humans in solving difficult problems and taking fast and smart decisions with applications in many areas, starting from industry, healthcare, agriculture, education and many more. In this special issue, we present a range of papers covering some of the subareas of expert systems such as intelligent and decision support systems, ethics, computers, linear regression and big data analytics. This special issue comprises six research papers. All manuscripts are extended versions of selected papers from WorldCIST'20—8th World Conference on Information Systems and Technologies, held in Budva, Montenegro between 7 and 10 April 2020. The WorldCIST conference is already a well-known global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences and concerns in several areas of Information Systems and Technologies, as well as computer science in general. The six selected papers in this special section include a study that identifies the main factors that can explain the number of patent filing requests made by residents in Brazil, the United States and Europe, detect if an information visualization can be potentially confusing and misunderstood based on the analytic task, a method to predict a model's performance metrics before it is trained, in order to decide whether it is worth to train it or not, a novel orca cultural algorithm, introduces the paradigm of machine culture as an extension to machine intelligence, and a decision model to take care of the water requirement of sensitive crops of agriculture industry. Sousa et al. (2020) present a study that identifies the main factors that can explain the number of patent filing requests made by residents in Brazil, the United States, Europe and triadic patent families. The methods used in the research are quantitative, using big data from private and public investments in Science and Technology, and about patent deposit numbers in Brazil from 2000 to 2017. A model of linear regression was performed and explains how these investments in Science and Technology influence patent deposit numbers. The results of this research study point towards the importance of universities, up and beyond the traditional training and education. The importance of public and private innovation investments is also shown to be important. This study shows that the patent registrations in the different regions under analysis are affected by different factors. There is thus no single formula towards the creation of innovation output and governments would do well to continue to invest in higher education while also investing in public research and development activities. Additionally, and not least important, private entities should be continually encouraged to make innovation investments and favourable government policies need to thus exist for this to happen. Finally, the low numbers regarding patent filings in Brazil may be linked to institutional deficiencies in the country. Patent breaches may be difficult to punish, and the judicial system may be slow and untrustworthy, compared with the United States and Europe—leading to diminished patent registrations in Brazil. Vázquez-Ingelmo et al. (2020), using the machine learning technique, search to find the possibility to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine-grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. Data visualizations encode data through different visual features, which have been captured and structured through a meta-modelling approach. The identified features were employed to automatically generate a set of parameterized visualizations that were subsequently discussed through a tagging process to obtain a training dataset of ‘helpful/not helpful’ information visualizations. Finally, the resulting dataset was employed to train ML algorithms that classify information visualizations as helpful or not helpful given their features and supported analytic task. The experiment shows promising results as the viability of the approach has been tested through a proof-of-concept in the domain of visualizations that display tri-variate datasets with the goal of identifying correlation among their variables. Although some limitations were identified, this experiment can set the foundations for subsequent research on this domain. Carneiro et al. (2020) present in this paper a method to predict a model's performance metrics before it is trained, in order to decide whether it is worth to train it or not. To see if the model proposed holds significantly better results than the current one, the authors propose the use of meta-learning. Therefore, two different meta-models are evaluated: one built for a specific machine learning problem, and another one built based on many different problems, meant to be a generic meta-model, applicable to virtually any problem. The focus is on the prediction of the root mean square error (RMSE). Results show that it is possible to accurately predict the RMSE of future models, event in streaming scenarios. Moreover, results also show that it is possible to reduce the need for re-training models between 60% and 98%, depending on the problem and on the threshold used. Drias et al. (2020) introduce in this article the paradigm of machine culture as an extension to machine intelligence. This new concept is modelled based on animal intelligence and culture. The example of orca intelligence and culture is considered as orcas possess in addition to skills allowing them to reach preys, the ability to transmit their culture from generation to generation. The orca intelligence is studied and then simulated to design an algorithm called Orca Algorithm (OA). OA consists in modelling the orca lifestyle and in particular the orca's social organization, echolocation behaviour and hunting techniques. In order to integrate the cultural dimension, OA was hybridized with the Cultural Algorithm (CA) to get an algorithm called Orca Cultural Algorithm (OCA). OCA was tested on 22 benchmark problems of the literature to evaluate its performance. Extensive experiments were first performed to set the algorithm parameters before measuring its effectiveness and efficiency. In the second stage, OCA was adapted to discrete problems and applied to the maze game with four levels of complexity. Additional experiments were held to compare the designed algorithm with recent state-of-the-art evolutionary algorithms. The overall obtained results are very promising. Nepomuceno et al. (2020) present in this work a time-series adaptation for the DEA directional model as an alternative for coping with this problem. The methodological approach has three stages for this benchmarking to occur: data, information and knowledge extraction. In the first stage, they compare the same unit in different moments to identify efficient periods instead of efficient competitors. As a result, successful performance strategies are investigated using the bibliometric coupling of employees' relevant statements in the second and third stages. The application in a branch of the Brazilian Federal Savings Bank allowed an internal benchmarking of efficient periods when specific performance incentives, innovative processes, competitive strategies, and human resource changes were adopted for improving the unit's performance. Thakur et al. (2020) introduce a decision model to take care of the water requirement of sensitive crops of agriculture industry. The proposed work presents a novel and proficient hybrid model for sensitive crop irrigation system (SCIS). For implementation of the model, brassica crop is taken. The duration and amount of water to be supplied are based upon the weather prediction and soil condition information. The decision model is developed using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) for brassica crops. In this model, if the input data values are available in range, then ANFIS model would be preferred and if the data sets are available for training, testing and validation then ANN model would be the best choice. The soil moisture, soil status in terms of temperature and leaf wetness are the input and flow control of sprinklers is the output for SCIS. The predicted outputs are analysed to assert the suitability of the proposed approach in the brassica crops. The proposed SCIS achieved an accuracy of 91% and 99% for ANFIS and ANN models, respectively. Simona Mirela Riurean is associate professor at the University of Petroşani, Faculty of Mechanical and Electrical Engineering, Department of Computers and Electrical Engineering, Romania. In 1991, she graduated from Technical University of Petroşani, Faculty of Mining Machines and Equipment, Petroșani Romania as Engineer in Specialization Mining Machines and Equipment. In 2000, she achieved Ph.D. degree at University of Petroşani, Faculty of Electromechanical Machines and Installations, awarded by Ministry of Education, Romania. In 2012 graduated the University ‘1 Decembrie 1918’ Alba Iulia, Romania earning diploma and Bachelor degree in Informatics. In 2016, she graduated the Master Program at University ‘1 Decembrie 1918’ Alba Iulia, Romania in Advanced Programming and Database and in 2019 achieved Ph.D degree in the field of Systems Control Engineering at University of Petroşani, Doctoral School, Romania. In 2018, she received Professor Bologna Grade with Diploma of Appreciation from National Alliance of the Students Organizations in Romania. In 2019, she received Diploma of Excellence for ‘Contributions regarding VLC applicable in Industry’ at EuroInvent 11th Edition, European exhibition of Creativity and Innovation in Iasi, Romania and Gold Medal at International Event of Inventions ‘Traian Vuia’, Timisoara, Romania for ‘Underground Personnel Monitoring System Based on VLC Technology’. Her main research areas are Computer Networks, Optical Wireless Communication (VLC, LiFi, OCC), ICT in Higher Education, e-Learning, e-Commerce and Network Security. As guest editors, the authors wish to appreciate the outstanding contributions of researchers/scholars to this special issue and be thankful to reviewers for their valuable and professional input. The authors would also take this opportunity to thank Jon Hall, Editor-in-Chief of the Wiley journal Expert Systems. The authors also wish to express their gratitude specifically to the WorldCIST'20 program committee members for their hard work and dedication, which is highly admirable.

Abstract Agriculture Industry is highly dependent on environmental and weather conditions. Many times, crops are spoiled because of sudden changes in weather. Therefore, we need a decision model to take care the water requirement of sensitive crops of agriculture industry. The proposed work presents a novel and proficient hybrid model for sensitive crop irrigation system (SCIS). For implementation of the model, brassica crop is taken. The duration and amount of water to be supplied is based upon the weather prediction and soil condition information. The decision model is developed using adaptive neuro‐fuzzy inference system (ANFIS) and artificial neural network (ANN) for brassica crops. In this model, if the input data values are available in range, then ANFIS model would be preferred and if the data sets are available for training, testing and validation then ANN model would be the best choice. The soil moisture, soil status in terms of temperature and leaf wetness are the input and flow control of sprinklers is the out for SCIS. The predicted outputs are analysed to assert the suitability of the proposed approach in the brassica crops. The proposed SCIS achieved an accuracy of 91% and 99% for ANFIS and ANN models respectively.

Abstract Interpreting high‐resolution satellite imagery could be an expensive and time‐consuming task for human eyes. Computer Vision and Deep Learning techniques can help to solve this major problem by applying detection algorithms, which can ease the task of analysing such images for the benefit of humans. It can help in changing the way we comprehend and anticipate the economic activity around the world. Such techniques help us to observe the urban development in high security areas such as national and international borders. Constant progressions in improving and making satellites deployment, a cost‐effective process to strengthen the networks of satellite orbiting the earth is one of the reasons such tasks can be easily solved with the help of high‐resolution images. Current computer vision research works have achieved significant milestones in accuracy and speed but, there are still room for improvements. In this paper, we addressed some of these methods to bring them to a combined pipeline and proposed a set of improvements to further improve the speed and the accuracy of the detections. We proposed a unified framework, which combines several object detection algorithms and the state‐of‐art architecture of YoloV4 along with the TensorFlow object detection API. This framework can detect small and well as large objects with improved speed and accuracy by using two detectors for different scales. Evaluation ran on these high‐resolution images yield mAP of 85.6% F1‐score of 0.84.

Abstract Programming is one of the basic skills that students must acquire. However, learning to program is not an easy task. Also teaching programming is an arduous but challenging task, requiring close follow‐up and constant and meaningful feedback. So the main question is: how can we help teachers and students to achieve these goals? We identified a tool that can be useful to this purpose. That is Virtual Programming Lab (VPL), a Moodle plugin that allows students to submit their code and get prompt feedback without the teacher's intervention. In order to test this concept, an experiment was performed with several classes of beginner programming students, in two editions of Algorithms and Programming course unit of the degree in Informatics Engineering lectured at the Informatics Engineering Department at the School of Engineering, Polytechnic Institute of Porto. The students were challenged to test their assignments in VPL with a set of test values previously defined by the teachers. After the experiments, we used surveys to gather the involved students' and teachers' opinion, and more than 70% of the students answered that they considered the VPL an added value for the teaching–learning process. The dynamics verified in the classes, the general opinion of the teachers, and the acceptance and participation of the students allow to classify the experience as positive.

In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. In this special issue, we present a range of articles covering some of the subareas of expert systems such as knowledge management, intelligent and decision support systems, ethics, computers, and security, health informatics, simulations, and big-data analytics. This special issue comprises six research papers. All manuscripts are extended versions of selected papers from WorldCIST'17-5th World Conference on Information Systems and Technologies, held in Porto Santo Island, Madeira, Portugal, in 2017. The WorldCIST conferences have become a global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences, and concerns in the several perspectives of Information Systems and Technologies, as well as computer science in general. The six selected articles in this special section include an application game simulator, empirical study to name a few, as well as studies that focus more on the design and implementation of knowledge management and decision-making tools. Tiago Oliveira, Gonçalves, Novais, Satoh, and Neves (2019) OWL-based acquisition and editing of computer-interpretable guidelines [CIG] with the CompGuide editor, present the characterization of the current landscape of CIG as medium for the delivery of clinical decision support acquisition tools based on the properties of guideline visualization, organization, simplicity, automation, manipulation of knowledge elements, and guideline storage and dissemination. Additionally, they described the CompGuide Editor, a tool for the acquisition of CIGs in their CompGuide model for Clinical Practice Guidelines that also allows the editing of previously encoded guidelines. Their editor guides the users throughout the process of guideline encoding and does not require proficiency in any programming language. The features of the CIG encoding process are revealed through a comparison with already established tools for CIG acquisition. João Carneiro, Martinho, Marreiros, and Novais (2019) how cognitive and affective aspects can influence the outcome of the group decision-making process, present mechanisms of automated negotiation, such as argumentation, that can be used in Ubiquitous Group Decision Support Systems to help decision makers find a solution based on their preferences. In this paper, they detailed a Ubiquitous Group Decision Support Systems architecture and explored two cognitive and affective methods that can be essential to the group decision-making process. They explained how agents can reason about self-expertise and other decision makers' credibility and how agents can verify and react to tendencies throughout the decision-making process. In their simulation environment that they tested for this work, agents that analysed credibility, expertise, and/or analysed tendencies always achieved a higher consensus compared with agents that used neither of the proposed methods. Likewise, agents that used neither of the proposed methods or only performed tendencies analysis obtained the worst average satisfaction levels for each simulation environment of decision-making. Habib and Marimuthu (2019) analysis of data trust through an intelligent-transparent-trust triangulation model, present a novel attempt to analyse the trustworthiness of computing sensor systems outcomes by modelling trust as an association triangle between intelligence, transparency, and trust in a sensor-based systems to establish the trustworthiness of data. They have proposed a triangulation model as a framework for assessing the trust and have selected two trust factors analogous to transparency and intelligence: the deviation of malfunctioning sensor data from its neighbours and the deviation from its own history. They have derived a set of relationships between the trust factors and their outcomes and have associated them with the proposed triangulation model. Furthermore, in the research, they formulated the generation of a trust subspace as an optimization problem with an objective function to maximize the trust. Tabu Search is then employed combined with Simulated Annealing to search for the best possible weighted combinations of trust factors. The authors have projected the experimental results onto a three-coordinate equilateral triangle to validate the decision analysis by displaying a definite trust or untrustworthiness of data. Faria, Ribeiro, Moreira, and Reis (2019) Boccia game simulator: Serious game adapted for people with disabilities, present their research about individuals with disabilities or motor disorders to feel more socially integrated, independent, and confident through integrating in the world of sports. This paper describes a realistic Boccia game simulator adapted for people with disabilities that integrates a set of features that includes real physics and social features. These features can be used to enhance the interest of nonpractitioners of the sport and to improve the training conditions. The official Boccia regulation was added to the design of the simulator. The usability and approximation to the reality of the simulator were tested and validated based on the tests performed and data collected via a survey of users with no motor or psychological disorders. Realism and usability rating was almost excellent, and good results were achieved at the assessment of the game experience. Gunel, Erdogdu, Polat, and Ozarslan (2019) an empirical study on evolutionary feature selection in intelligent tutors for learning concept detection, present concept map mining, which has emerged as a new research area with recent developments in computational intelligence in educational technology. The purpose of this study was to develop a mechanism using data-mining technique to determine the features that characterize a learning concept extracted automatically from a single educational text. The three major features that distinguish the real learning concepts from other sequences of strings are detected by using a hybrid system of a feed-forward neural network and some evolutionary algorithms. Ant colony optimization and genetic algorithm and particle swarm optimization are used as a binary feature selection method. In addition, the aforementioned methods are hybridized to get better accuracy and precision. The performance comparisons with two different state-of-the-art algorithms have been made from the viewpoint of a typical classification problem. This study enables researchers studying educational technology to gain time and space for learning concept detection for advancing concept map mining studies. It implies that there is no need to extract too many features for the problem, and to simplify the problem, it is enough to extract just a few features. Gonçalves, Rocha, Reis, and Barroso (2019) AppVox: An application to assist people with speech impairments in their speech therapy sessions, present in this study an application to assist people with speech impairments in their speech therapy sessions. AppVox simulates a vocalizer (audio stimulus feature) that can be used to train speech by repeating different words. In this paper, the authors presented the application as an assistive technology option and assessed it as a usable option for digital interaction for children with speech impairment. To assess the application, they have presented a case study in which the participants were asked to perform tasks using the AppVox application. The results showed that the group of participants attained a good performance when interacting with the application. The authors have further concluded that their designed application will help therapists, parents, and teachers of the special children in assessing which particular words the patients are having more difficulty in distinguishing. The application in the form of help provides patients to improve their pronunciation and helps specially the children to perform some exercises and also provide support to the parents for repetition of the exercises at home. Álvaro Rocha holds Habilitation in Information Science, PhD in Information Systems and Technologies, MSc in Information Management, and a BCs in Computer Science. He is Professor of Information Systems at the University of Coimbra and Honorary Professor at the Amity University, as well as a researcher at the Centre for Informatics and Systems of the University of Coimbra and a collaborative researcher at the Laboratory of Artificial Intelligence and Computer Science and at the Centre for Research in Health Technologies and Information Systems. His main research interests are Information Systems Planning and Management, Maturity Models, Information Systems Quality, Online Service Quality, Intelligent Information Systems, Software Engineering, e-Government, e-Health, and IT in Education. He is the President of the Iberian Association for Information Systems and Technologies and the Chair of the IEEE Portugal Section Systems, Man, and Cybernetics Society Chapter. He is also the Editor-in-Chief of both the Journal of Information Systems Engineering & Management and the Iberian Journal of Information Systems and Technologies. In addition, he has acted as Vice chair of Experts with the Horizon 2020 program of the European Commission, expert with the Italian Ministry of Education, Universities and Research, and expert with the Latvian Finances Ministry. Sajid Anwar is an Associate Professor in the Center of Excellence in Information Technology Institute of Management Sciences, Peshawar, Pakistan. He earned his BSc and MSc degree in computer science from University of Peshawar in 1997 and 1999, respectively. He completed MS degree (Computer Science, 2007) and PhD degree (Software Engineering, 2011) from NUCES-FAST, Islamabad. Currently, he is the Head of Undergraduate Program in Software Engineering at the Center of Excellence in Information Technology Institute of Management Sciences. Sajid Anwar is leading expert in Software Architecture Engineering and Software Maintenance Prediction. His research interests are cross-disciplinary and industry focused and includes search-based software engineering, prudent-based expert systems; customers churn prediction modelling, active learning and applying data mining and machine learning techniques to solve real-world problems. He has conducted and led collaborative research with Govt. organizations and academia. He has been a Guest Editor of numerous journals, such as Cluster Computing Journal Springer, Grid Computing Journal Springer, Expert Systems Journal Wiley, and Computational and Mathematical Organization Theory Journal Springer; Reviewer for IEEE Transactions on Evolutionary Computations, Neurocomputing Journal, IEEE Access, Expert Systems, Software: Practice and Experience, IEEE Transactions on Industrial Informatics, International Journal of Information Technology & Decision Making, and Telematics and Informatics Journal. He is also Member Board Committee Institute of Creative Advanced Technologies, Science and Engineering, Korea (iCatse.org) http://icatse.org/. As the guest editors, we are thankful to great researchers/scholars for their outstanding contributions to this special issue and reviewers for their timely and professional input. We would also take this opportunity to thank Jon Hall, Editor-in-Chief of the Wiley journal "Expert Systems." In the end, we would extend our special gratitude and thanks to the WorldCIST'17 programme committee members for their hard work and dedication, which is highly commendable.

The use of a methodology for the evaluation, comparison and quality improvement of Health Web Sites is justified by its widespread adoption and visibility to Internet users. Due to the sensitiveness of their content and impact on users, health related sites should be evaluated. This paper proposes three different dimensions for the development of quality evaluation methodologies of Health Web Sites: contents, services and technical. We consider that these dimensions should be addressed transversally, providing a better overall evaluation.