- Dr. ELPINIKI I. PAPAGEORGIOU is an Assistant Professor in the Department of Computer Engineering at Technical Educati... moreDr. ELPINIKI I. PAPAGEORGIOU is an Assistant Professor in the Department of Computer Engineering at Technical Educational Institute, University of Applied Sciences of Central Greece, Lamia, Hellas. She holds a PhD in Computer Engineering and Informatics from The University of Patras (Sept. 2004). She has been working for over fourteen years as researcher in several research projects related with the development of novel computational intelligence methodologies for decision support systems, intelligent algorithms for decision making, data analysis and mining, knowledge-based systems and expert systems. She has been actively involved in several research projects, European and National R&D, related with the development of new methodologies and learning algorithms based on fuzzy and artificial intelligent techniques for decision support systems in engineering, medicine, agriculture and environment. Recently she has worked in four European Research Programs FP6 and FP7, which are: FP7-ICT-2013-11 ISS-EWATUS, FP7-ICT-2011 USEFIL, FP7-ICT-2007-1 DEBUGIT, FP6-SESAME, focused on the development of decision support systems using intelligent/cognitive methodologies, fuzzy cognitive maps and data mining algorithms. Also, her strong relationship with external funding bodies and especially EU and national funding environments, gave her an experience in managing research teams in different application domain projects. Asst. Prof. Dr. Elpiniki Papageorgiou was the main organizer of the previously accomplished Special Sessions on Fuzzy Cognitive maps entitled as “Methods and Applications of Fuzzy Cognitive Maps” in previous FUZZ-IEEE conferences at 2011, 2012, 2013, WCCI2014 (IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014) and FUZZ-IEEE2015 and has chaired four of them; moreover she served as a PC member for the same conferences. She was also Chair and main organizer of the 1st Workshop on FCMs held at Paphos, Cyprus in AIAI 2013. She is also a reviewer in many international journals, mainly IEEE and Elsevier. She is author and co-author of more than 154 publications in journals, conference papers and book chapters. 60 of them are in journals with high Impact factors, such as IEEE Transactions, Expert Systems with Applications, and has more than 1486 citations from independent researchers (h-index=20 in scopus and h-index=27 in googlescholar). Dr. Elpiniki Papageorgiou is the Editor of the Springer book “Fuzzy Cognitive Maps for Applied Sciences and Engineering - from fundamentals to extensions and learning algorithms”, Intelligent Systems Reference Library 54, Springer 2014. Her research interests include expert systems, fuzzy cognitive maps, soft computing methods, decision support systems, computational intelligent algorithms, data mining and machine learningedit
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Abstract Fuzzy cognitive maps is a hybrid method based on fuzzy systems and neural networks and belonging in soft computing. The methodology of developing fuzzy cognitive maps (FCMs) is easily adaptable and relies on human expert... more
Abstract Fuzzy cognitive maps is a hybrid method based on fuzzy systems and neural networks and belonging in soft computing. The methodology of developing fuzzy cognitive maps (FCMs) is easily adaptable and relies on human expert experience and knowledge, but it exhibits weaknesses in utilization of learning methods. The external intervention (typically from experts) for the determination of FCM parameters and the convergence to undesired steady states are significant FCM deficiencies. Thus, it is necessary to ...
Research Interests: Computer Science, Artificial Intelligence, Machine Learning, Fuzzy Logic, Computer Networks, and 15 moreSoft Computing, Neural Networks, Convergence, Fuzzy Systems, Neural Network, Fuzzy Cognitive Maps, Unsupervised Learning, Robustness (evolution), Steady state, Robustness, Fuzzy System, Learning Process, Fuzzy Cognitive Map, Learning Methods, and Hybrid Method
Abstract Fuzzy Cognitive Maps have been introduced as a combination of Fuzzy logic and Neural Networks. In this paper a new learning rule based on unsupervised Hebbian learning and a new training algorithm based on Hopfield nets are... more
Abstract Fuzzy Cognitive Maps have been introduced as a combination of Fuzzy logic and Neural Networks. In this paper a new learning rule based on unsupervised Hebbian learning and a new training algorithm based on Hopfield nets are introduced and are compared for the training of Fuzzy Cognitive Maps. Keywords: Fuzzy Cognitive Maps, Learning algorithms, Hopfield Networks, Unsupervised Hebbian learning law.
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Research Interests: Computer Science, Artificial Intelligence, Fuzzy Logic, Cognition, Fuzzy set theory, and 13 moreSoft Computing, Fuzzy Systems, Medical Decision Making, Infectious Disease, Patient Monitoring, Knowledge base, Intensive Care Unit, Medical Diagnosis, Knowledge Representation and Reasoning, Fuzzy Cognitive Map, Physical examination, Graphical Representation, and Representation Politics
Research Interests: Computer Science, Health Promotion, Depression, Decision Support Systems, Market Research, and 15 moreInference, Telemedicine, Medicine, Independent Living, Humans, Female, Male, Model Based Clinical Decision Support Systems, Aged, Reproducibility of Results, Ambulatory BP monitoring, Geriatric Assessment, Clinical Decision Support Systems, Sensitivity and Specificity, and Classification Algorithms
This work investigates the yield modeling and prediction process in apples (cv. Red Chief) using the dynamic influence graph of Fuzzy Cognitive Maps (FCMs). FCMs are ideal causal cognition tools for modeling and simulating dynamic... more
This work investigates the yield modeling and prediction process in apples (cv. Red Chief) using the dynamic influence graph of Fuzzy Cognitive Maps (FCMs). FCMs are ideal causal cognition tools for modeling and simulating dynamic systems. They gained momentum due to their simplicity, flexibility to model design, adaptability to different situations, and easiness of use. In general, they model the behavior of a complex system, have inference capabilities and can be used to predict new knowledge. In this work, a data driven non-linear FCM ...
Research Interests: Management, Engineering, Computer Science, Artificial Intelligence, Soil, and 15 moreDecision Making, Machine Learning, Modeling, Fuzzy Sets, Knowledge Representation, Algorithm, Applications, Cognitive Map, Artificial Neural Networks, Fuzzy Cognitive Maps, Corn, System, Fuzzy Cognitive Map, Crop Yield, and Malus domestica
Fuzzy Cognitive Map (FCM) is a soft computing technique for modeling systems. It combines synergistically the theories of neural networks and fuzzy logic. The methodology of developing FCMs is easily adaptable but relies on human... more
Fuzzy Cognitive Map (FCM) is a soft computing technique for modeling systems. It combines synergistically the theories of neural networks and fuzzy logic. The methodology of developing FCMs is easily adaptable but relies on human experience and knowledge, and thus FCMs exhibit weaknesses and dependence on human experts. The critical dependence on the expert's opinion and knowledge, and the potential convergence to undesired steady states are deficiencies of FCMs. In order to overcome these deficiencies ...
Research Interests: Computer Science, Artificial Intelligence, Machine Learning, AI Planning (Artificial Intelligence), Fuzzy Logic, and 10 moreSoft Computing, Neural Network, Hebbian learning, Unsupervised Learning, Robustness (evolution), Steady state, Fuzzy Cognitive Map, Model System, Philosophy of Mind and Artificial Intelligence & AI, and Learning Methods
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Abstract The presented research aims at the design and implementation of a medical decision support system for forecasting the state of pulmonary infection. As a part of such system, we propose a new method for modeling a decision making... more
Abstract The presented research aims at the design and implementation of a medical decision support system for forecasting the state of pulmonary infection. As a part of such system, we propose a new method for modeling a decision making process. The model is learned using a population based algorithm in order to discover relationships among medical variables. We show how this model can be used to forecast the consequent state of the disease supporting this way the doctors' decisions on medical therapy. For the ...
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Research Interests: Environmental Science, Computer Science, Artificial Intelligence, Decision Making, Fuzzy Logic, and 15 morePrecision Agriculture, Modeling, Decision Support Systems, Fuzzy Sets, Fuzzy Systems, Models, Fuzzy Cognitive Maps, Cotton, Crops, Cultivation, Fuzzy Cognitive Map, Modeling and control, Fuzzy Rules, Cotton Crop, and Cause and Effect Relationships
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This research study proposes a new method for automatic design of Fuzzy Cognitive Maps (FCM) using ordinal data based on the efficient capabilities of mixed graphical models. The approach is able to model all variables on the proper... more
This research study proposes a new method for automatic design of Fuzzy Cognitive Maps (FCM) using ordinal data based on the efficient capabilities of mixed graphical models. The approach is able to model all variables on the proper domain of ordinal data by combining a new class of Mixed Graphical Models (MGMs) with a structure estimation approach based on generalized covariance matrices. It can work with a large amount of categorical data. It represents its structure as a sparser graph, while maintaining a high likelihood, by producing an adjacent weight matrix, where relationships are expressed by conditional independences. By maximizing the likelihood indicates that the model fits better to the data under the assumption that the observed data are the most likely data. The whole approach was implemented in a business intelligence problem of evaluating the attractiveness of Belgian companies. Through the analysis of results and conducted scenarios, the usefulness of the proposed MGM method for designing FCM capable to make decisions, is demonstrated. Comparisons with the previous known methodology for automatic construction of FCMs based on distance-based algorithm, showed that the proposed approach provides more understandable/useful relationships among nodes, through a less complex structure for making decisions.
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Abstract A two-stage learning algorithm based on Hebbian learning rule and evolutionary computation technique is presented in this paper for training Fuzzy Cognitive Maps. Fuzzy Cognitive Maps is a soft computing technique for modeling... more
Abstract A two-stage learning algorithm based on Hebbian learning rule and evolutionary computation technique is presented in this paper for training Fuzzy Cognitive Maps. Fuzzy Cognitive Maps is a soft computing technique for modeling complex systems, which combines the synergistic theories of neural networks and fuzzy logic. The methodology of developing Fuzzy Cognitive Maps (FCMs) relies on human expert experience and knowledge, but still exhibits weaknesses in utilization of learning methods. We investigate ...
Research Interests: Information Systems, Computer Science, Artificial Intelligence, Evolutionary Computation, Fuzzy Logic, and 15 moreComputer Networks, Neural Networks, Differential Evolution, Fuzzy Systems, Neural Network, Hebbian learning, Fuzzy Cognitive Maps, Learning Strategies, Evolutionary Computing, Fuzzy Cognitive Map, Neuro Fuzzy, Model Complexity, Learning Methods, Human Knowledge, and learning algorithm
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This research study is devoted to the investigation of deep neural networks (DNN) for classification of the complex problem of knee osteoarthritis diagnosis. Osteoarthritis (OA) is the most common chronic condition of the joints revealing... more
This research study is devoted to the investigation of deep neural networks (DNN) for classification of the complex problem of knee osteoarthritis diagnosis. Osteoarthritis (OA) is the most common chronic condition of the joints revealing a variation in symptoms' intensity, frequency and pattern. A large number of features/factors need to be assessed for knee OA, mainly related with medical risks factors including advanced age, gender, hormonal status, body weight or size, family history of disease etc. The main goal of this research study is to implement deep neural networks as a new efficient machine learning approach for this classification task taking into account the large number of medical factors affecting OA. The potential of the proposed methodology was demonstrated by classifying different subgroups of control participants from self-reported clinical data and providing a category of knee OA diagnosis. The investigated subgroups were defined by gender, age and obesity. Furthermore, to validate the proposed deep learning methodology, a comparison analysis between the proposed DNN and some benchmark machine learning techniques recommended for classification was conducted and the results showed the effectiveness of deep learning in the diagnosis of knee OA.
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Research Interests: Engineering, Decision Making, Machine Learning, Fuzzy Logic, Precision Agriculture, and 15 moreSoft Computing, Modeling, Fuzzy Sets, Knowledge Representation, Cognitive Map, Production economics, Fuzzy Cognitive Maps, Cotton, Crop Production, Crop management, Fuzzy Cognitive Map, Expert knowledge, Fuzzy Set, Support System, and Springer Ebooks
Research Interests: Computer Science, Artificial Intelligence, Expert Systems, Data Mining, Fuzzy Logic, and 15 moreData Analysis, Decision Support Systems, Hybrid Learning, Decision Support, Knowledge Based Systems, Conference Proceedings, Knowledge Extraction, data analysis methods in fMRI, Fuzzy Cognitive Map, Clinical Data, Clinical Decision Support Systems, Fuzzy Rules, Data Type, Decision Support Techniques, and Fuzzy Rule Base
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Research Interests: Computer Science, Algorithms, Decision Making, Fuzzy Logic, Biomedical informatics, and 15 moreKnowledge Representation, Biological Sciences, Humans, expert System, Decision Support, Cognitive Process, Knowledge Integration, Knowledge base, Evaluation Studies, Fuzzy Cognitive Map, Fuzzy Rules, Front end, Internet, Antibiotic treatment, and Medical and Health Sciences
The task of prediction in the medical domain is a very complex one, considering the level of vagueness and uncertainty management. The main objective of the presented research is the multi-step prediction of state of pulmonary infection... more
The task of prediction in the medical domain is a very complex one, considering the level of vagueness and uncertainty management. The main objective of the presented research is the multi-step prediction of state of pulmonary infection with the use of a predictive model learnt on the basis of changing with time data. The contribution of this paper is twofold. In the application domain, in order to predict the state of pneumonia, the approach of fuzzy cognitive maps (FCMs) is proposed as an easy of use, interpretable, and flexible ...
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Research Interests: Computer Science, Artificial Intelligence, Statistics, Machine Learning, Fuzzy Logic, and 15 moreSoft Computing, Neural Network, Hebbian learning, Artificial Neural Networks, Complex System, Fuzzy Cognitive Maps, Unsupervised Learning, Steady state, Fuzzy Cognitive Map, Model System, Approximate Reasoning, Numerical Analysis and Computational Mathematics, Learning Methods, Artificial Neural Network, and learning algorithm
Background: Recent advances in Artificial Intelligence (AI) algorithms, and specifically Deep Learning (DL) methods, demonstrate substantial performance in detecting and classifying medical images. Recent clinical studies have reported... more
Background: Recent advances in Artificial Intelligence (AI) algorithms, and specifically Deep Learning (DL) methods, demonstrate substantial performance in detecting and classifying medical images. Recent clinical studies have reported novel optical technologies which enhance the localization or assess the viability of Parathyroid Glands (PG) during surgery, or preoperatively. These technologies could become complementary to the surgeon’s eyes and may improve surgical outcomes in thyroidectomy and parathyroidectomy. Methods: The study explores and reports the use of AI methods for identifying and localizing PGs, Primary Hyperparathyroidism (PHPT), Parathyroid Adenoma (PTA), and Multiglandular Disease (MGD). Results: The review identified 13 publications that employ Machine Learning and DL methods for preoperative and operative implementations. Conclusions: AI can aid in PG, PHPT, PTA, and MGD detection, as well as PG abnormality discrimination, both during surgery and non-invasively.
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This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series... more
This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemb...
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The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. This paper implements a literature review... more
The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. This paper implements a literature review protocol and reports the latest advances in Root Cause Analysis (RCA) toward Zero-Defect Manufacturing (ZDM). The most recent works are reported to demonstrate the use of machine learning methodologies for root cause analysis in the manufacturing domain. The popularity of these technologies is then summarized and presented in the form of visualizing graphs. This enables us to identify the most popular and prominent methods used in modern industry. Although artificial intelligence gains more and more attraction in smart manufacturing, machine learning methods for root cause analysis seem to be under-explored. The literature survey revealed that only limited reviews are available in the field of RCA towards zero-defect manufacturing using AI and machine...
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The features of a dataset play an important role in the construction of a machine learning model. Because big datasets often have a large number of features, they may contain features that are less relevant to the machine learning task,... more
The features of a dataset play an important role in the construction of a machine learning model. Because big datasets often have a large number of features, they may contain features that are less relevant to the machine learning task, which makes the process more time-consuming and complex. In order to facilitate learning, it is always recommended to remove the less significant features. The process of eliminating the irrelevant features and finding an optimal feature set involves comprehensively searching the dataset and considering every subset in the data. In this research, we present a distributed fuzzy cognitive map based learning-based wrapper method for feature selection that is able to extract those features from a dataset that play the most significant role in decision making. Fuzzy cognitive maps (FCMs) represent a hybrid computing technique combining elements of both fuzzy logic and cognitive maps. Using Spark’s resilient distributed datasets (RDDs), the proposed model ...
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Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due... more
Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due to the model’s complex non-linear structure, and thus, it is considered a «black box», making it hard to gain a comprehensive understanding of its internal processes and explain its behavior. Existing explainable artificial intelligence tools can provide insights into the internal functionality of deep learning and especially of convolutional neural networks, allowing transparency and interpretation. Methods: This study seeks to address the identification of patients’ CAD status (infarction, ischemia or normal) by developing an explainable deep learning pipeline in the form of a handcrafted convolutional neural network. The proposed RGB-CNN model utilizes various pre- and post-processing tools and deploys a state-of-the-art explainability tool to pro...
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Nowadays, fuzzy cognitive maps (FCMs) are one of the most efficient artificial intelligence techniques for modeling large and complex systems. However, traditional FCMs have the limitation of not representing the indeterminacy situations... more
Nowadays, fuzzy cognitive maps (FCMs) are one of the most efficient artificial intelligence techniques for modeling large and complex systems. However, traditional FCMs have the limitation of not representing the indeterminacy situations presented in many decision-making problems. To overcome this limitation, neutrosophic cognitive maps (NCMs) were proposed as a new extension of traditional FCMs. Nevertheless, the way that NCMs reported in the bibliography handle the indeterminacy is still insufficient since they cannot quantify the degree of indeterminacy. Moreover, there are decision-making problems in which decisions should be considered as a sequence of decisions hardly interconnected in sequential order. This situation is presented in scenarios such as projects evaluation characterized by the existence of multiple interconnected processes (diagnosis, decision, and prediction). The lack of a suitable FCMs topology for modeling this kind of decision-making problems constitutes another challenging issue of FCMs. This paper presents a new neutrosophic cognitive map based on triangular neutrosophic numbers for multistage sequential decision-making problems (MS-TrNCM). In the proposed model, all the map’s connections are represented by triangular neutrosophic numbers, making it possible for decision makers to express their preferences considering the truth, indeterminacy, and falsity degrees. Furthermore, a new topology for representing multistage sequential processes is introduced. The suggested MS-TrNCM is applied to make diagnoses, decisions, and predictions during the evaluation of 1011 projects records from project evaluation database ”uci-gp-eval-201903051137” provided by the University of Informatics Sciences. In validation process, the superiority of the proposed MS-TrNCM over the NCMs and traditional FCMs has been demonstrated.
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CrossFit has gained recognition and interest among physically active populations being one of the most popular and rapidly growing exercise regimens worldwide. Due to the intense and repetitive nature of CrossFit, concerns have been... more
CrossFit has gained recognition and interest among physically active populations being one of the most popular and rapidly growing exercise regimens worldwide. Due to the intense and repetitive nature of CrossFit, concerns have been raised over the potential injury risks that are associated with its training including rhabdomyolysis and musculoskeletal injuries. However, identification of risk factors for predicting injuries in CrossFit athletes has been limited by the absence of relevant big epidemiological studies. The main purpose of this paper is the identification of risk factors and the development of machine learning-based models using ensemble learning that can predict CrossFit injuries. To accomplish the aforementioned targets, a survey-based epidemiological study was conducted in Greece to collect data on musculoskeletal injuries in CrossFit practitioners. A Machine Learning (ML) pipeline was then implemented that involved data pre-processing, feature selection and well-kn...
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Backorders occur when a product is out of stock, but the costumer is willing to place an order for this product and wait until it will be available for shipment instead of purchasing another. It is an important part of the inventory... more
Backorders occur when a product is out of stock, but the costumer is willing to place an order for this product and wait until it will be available for shipment instead of purchasing another. It is an important part of the inventory system contributing to the total costs of the production. Hence, it is important for companies to be able to predict when a product will be backordered to develop mitigation strategies and reorganize their production. Limited studies have focused on the prediction of backorders, a high imbalanced binary classification problem that needs special treatment. However, no previous study has aimed to explain and interpret the main features that contribute to the prediction task. To this end, in this study a machine learning pipeline is developed supported by an explainability analysis in order to identify the most important features that contribute to the prediction of backorders. The results showed that the inventory level of a product combined with the forecast demands and transit time play are the main factors that lead to products’ backordering.
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Processing information in a city is simultaneously a primary task and a pivotal challenge. Urban data are usually expressed in natural language and thus imprecise but can contain relevant information that should be processed to advance... more
Processing information in a city is simultaneously a primary task and a pivotal challenge. Urban data are usually expressed in natural language and thus imprecise but can contain relevant information that should be processed to advance the city. Fuzzy cognitive maps (FCMs) can be used to model interconnected and imprecise urban data and are therefore suitable to both address this challenge and to fulfil the primary task. Cognitive cities are based on connectivism, which assumes that knowledge is built through the experiences and perceptions of different people. Hence, the design of a cognitive learning process in a city is crucial. In this article, the current state-of-the-art research in the field of FCMs and FCMs combined with learning algorithms is presented based on an extensive literature review and grounded theory. In total, 59 research papers were gathered and analyzed. The results show that the application of FCMs already facilitates the acquisition and representation of urban data and, thus, helps to make a city smarter. However, using FCMs combined with learning algorithms optimizes this smartness and helps to foster the development of cognitive cities.
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Enterprise resource planning (ERP) systems are very costly and difficult to be implemented. Organizations need to be ready for implementing them. They require assessing their current readiness and then, they can perform a range of... more
Enterprise resource planning (ERP) systems are very costly and difficult to be implemented. Organizations need to be ready for implementing them. They require assessing their current readiness and then, they can perform a range of activities to improve their readiness. The ERP readiness is influenced by many factors which are interrelated and any improvement in one of them has direct and indirect influences on the others. This paper develops a new approach for assessing the ERP readiness in organization by considering casual relationships between influential factors. The approach enables an organization to evaluate its ERP implementation readiness by considering two issues: (1) how the factors influence each other and (2) how they contribute on overall readiness. To address the first issue we use fuzzy cognitive maps (FCMs) and for the second issue we use the fuzzy analytic hierarchy process (FAHP). An empirical study is conducted to demonstrate the assessment.
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This research study proposes a new method for automatic design of Fuzzy Cognitive Maps (FCM) using ordinal data based on the efficient capabilities of mixed graphical models. The approach is able to model all variables on the proper... more
This research study proposes a new method for automatic design of Fuzzy Cognitive Maps (FCM) using ordinal data based on the efficient capabilities of mixed graphical models. The approach is able to model all variables on the proper domain of ordinal data by combining a new class of Mixed Graphical Models (MGMs) with a structure estimation approach based on generalized covariance matrices. It can work with a large amount of categorical data. It represents its structure as a sparser graph, while maintaining a high likelihood, by producing an adjacent weight matrix, where relationships are expressed by conditional independences. By maximizing the likelihood indicates that the model fits better to the data under the assumption that the observed data are the most likely data. The whole approach was implemented in a business intelligence problem of evaluating the attractiveness of Belgian companies. Through the analysis of results and conducted scenarios, the usefulness of the proposed MGM method for designing FCM capable to make decisions, is demonstrated. Comparisons with the previous known methodology for automatic construction of FCMs based on distance-based algorithm, showed that the proposed approach provides more understandable/useful relationships among nodes, through a less complex structure for making decisions.
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Knee Osteoarthritis (KOA) is a multifactorial disease-causing joint pain, deformity and dysfunction. The aim of this paper is to provide a data mining approach that could identify important risk factors which contribute to the diagnosis... more
Knee Osteoarthritis (KOA) is a multifactorial disease-causing joint pain, deformity and dysfunction. The aim of this paper is to provide a data mining approach that could identify important risk factors which contribute to the diagnosis of KOA and their impact on model output, with a focus on posthoc explainability. Data were obtained from the osteoarthritis initiative (OAI) database enrolling people, with nonsymptomatic KOA and symptomatic KOA or being at high risk of developing KOA. The current study considered multidisciplinary data from heterogeneous sources such as questionnaire data, physical activity indexes, self-reported data about joint symptoms, disability and function as well as general health and physical exams’ data from individuals with or without KOA from the baseline visit. For the data mining part, a robust feature selection methodology was employed consisting of filter, wrapper and embedded techniques whereas feature ranking was decided on the basis of a majority vote scheme. The validation of the extracted factors was performed in subgroups employing seven well-known classifiers. A 77.88 % classification accuracy was achieved by Logistic Regression on the group of the first forty selected (40) risk factors. We investigated the behavior of the best model, with respect to classification errors and the impact of used features, to confirm their clinical relevance. The interpretation of the model output was performed by SHAP. The results are the basis for the development of easy-to-use diagnostic tools for clinicians for the early detection of KOA.
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Anthracnose is a fungal disease that infects a large number of trees worldwide, damages intensively the canopy, and spreads with ease to neighboring trees, resulting in the potential destruction of whole crops. Even though it can be... more
Anthracnose is a fungal disease that infects a large number of trees worldwide, damages intensively the canopy, and spreads with ease to neighboring trees, resulting in the potential destruction of whole crops. Even though it can be treated relatively easily with good sanitation, proper pruning and copper spraying, the main issue is the early detection for the prevention of spreading. Machine learning algorithms can offer the tools for the on-site classification of healthy and affected leaves, as an initial step towards managing such diseases. The purpose of this study was to build a robust convolutional neural network (CNN) model that is able to classify images of leaves, depending on whether or not these are infected by anthracnose, and therefore determine whether a tree is infected. A set of images were used both in grayscale and RGB mode, a fast Fourier transform was implemented for feature extraction, and a CNN architecture was selected based on its performance. Finally, the be...