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Keywords = fuzzy cognitive mapping

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18 pages, 1977 KiB  
Article
Exploring Critical Factors Influencing the Resilience of the Prefabricated Construction Supply Chain
by Tianyang Liu, Li Ma and Hongwei Fu
Buildings 2025, 15(2), 289; https://doi.org/10.3390/buildings15020289 - 19 Jan 2025
Viewed by 576
Abstract
In this volatile, uncertain, complex, and ambiguous (VUCA) era, resilient and sustainable construction methods, such as prefabricated construction, are essential for addressing the planet’s sustainability challenges. However, disruptions in the prefabricated construction supply chain (PCSC) frequently arise, seriously impeding the performance of prefabricated [...] Read more.
In this volatile, uncertain, complex, and ambiguous (VUCA) era, resilient and sustainable construction methods, such as prefabricated construction, are essential for addressing the planet’s sustainability challenges. However, disruptions in the prefabricated construction supply chain (PCSC) frequently arise, seriously impeding the performance of prefabricated building projects. Therefore, this study aims to identify the factors influencing the prefabricated construction supply chain (RPCSC) and analyze their intrinsic interconnections. Initially, an exhaustive literature review was conducted to identify the primary factors affecting the RPCSC. Subsequently, the Delphi technique was applied to validate and refine the list of factors, resulting in the identification of 11 key concepts. Finally, the impact of these concepts on the RPCSC, along with their interactions, was assessed using the fuzzy cognitive map (FCM) approach. The results indicate that these factors can be ranked by their degree of effect on the RPCSC: information exchange/sharing, research and development, the performance of prefabricated components, decision alignment, the construction of prefabricated buildings, relationship quality among members, professional management personnel/labor quality, supply–demand consistency, cost/profit sharing, policies and regulations, and transport risk. Furthermore, this study elucidates both the individual and synergistic effects of these factors on the RPCSC by constructing a pathway map. Full article
(This article belongs to the Special Issue Promoting Green, Sustainable, and Resilient Urban Construction)
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17 pages, 2400 KiB  
Article
Fuzzy Cognitive Maps for Decision Support in Post-COVID Syndrome with Speech-Language Pathology-Related Problems
by Manila Tola, Voula Chris Georgopoulos, Eleftheria Geronikou, Panagiotis Plotas and Chrysostomos Stylios
Appl. Sci. 2025, 15(1), 13; https://doi.org/10.3390/app15010013 - 24 Dec 2024
Viewed by 456
Abstract
Detecting and managing speech-language pathology symptoms in patients with post-COVID syndrome (long COVID) presents a significant challenge, as the condition is marked by persistent and varied symptoms such as cognitive deficits, communication difficulties, and voice disorders. To address these challenges, a Fuzzy Cognitive [...] Read more.
Detecting and managing speech-language pathology symptoms in patients with post-COVID syndrome (long COVID) presents a significant challenge, as the condition is marked by persistent and varied symptoms such as cognitive deficits, communication difficulties, and voice disorders. To address these challenges, a Fuzzy Cognitive Map Decision Support System (FCM-DSS) was developed to model causal relationships and integrate expert knowledge. A systematic review approach, though not comprehensive, was utilized to identify key symptoms and their prevalence from 19 studies, focusing on brain fog, attention deficits, memory problems, dyspnea, and swallowing difficulties. The weighted prevalence of these symptoms informed the development of the FCM-DSS model, designed to link symptoms to medical specialties and recommend specialist referrals. Preliminary testing on four case studies demonstrated the tool’s potential, but further validation through pilot studies is necessary. Full article
(This article belongs to the Special Issue Intelligent Diagnosis and Decision Support in Medical Applications)
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14 pages, 4499 KiB  
Article
Rural Road Assessment Method for Sustainable Territorial Development
by Leonardo Sierra-Varela, Álvaro Filun-Santana, Felipe Araya, Noé Villegas-Flores and Aner Martinez-Soto
Appl. Sci. 2024, 14(23), 11021; https://doi.org/10.3390/app142311021 - 27 Nov 2024
Viewed by 659
Abstract
In Latin America, initiatives have been advocated for developing rural roads that facilitate optimal conditions free from dust, mud, and noise. The criteria for assessing public investment do not align with the requirements of rural infrastructure. Indeed, in rural areas, the territorial conditions [...] Read more.
In Latin America, initiatives have been advocated for developing rural roads that facilitate optimal conditions free from dust, mud, and noise. The criteria for assessing public investment do not align with the requirements of rural infrastructure. Indeed, in rural areas, the territorial conditions such as openness to rural–urban markets, access to education and health, environmental protection, culture, and identity are more important than transportation times or traffic volume. Hence, a multicriteria evaluation method is proposed to prioritize the rural road improvements and maximize their contribution to sustainable territorial development. The roads with the highest sustainable contribution are optimized using a multi-objective decision-making analysis and prioritized based on a Manhattan distance. In addition, a fuzzy cognitive map analyzes the dynamic behavior of the optimal roads. Based on this proposal, a case study is applied where fifteen roads are selected from a sample of 101 in the Araucanía Region, Chile. For this, 16 evaluation criteria, 27 indicators, and sustainability’s social, environmental, technical, and economic dimensions are considered. The results detect reduced one-dimensional contributions despite identifying 15 optimal roads that collectively enhance sustainability. Two roads stand out for their long-term sustainability contribution, which are influenced by economic criteria of zonal productivity, tourism, and road maintenance. Thus, this method can help public agencies rank the roads that must be the subject of development projects. Full article
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15 pages, 969 KiB  
Article
Double Decomposition and Fuzzy Cognitive Graph-Based Prediction of Non-Stationary Time Series
by Junfeng Chen, Azhu Guan and Shi Cheng
Sensors 2024, 24(22), 7272; https://doi.org/10.3390/s24227272 - 14 Nov 2024
Viewed by 568
Abstract
Deep learning models, such as recurrent neural network (RNN) models, are suitable for modeling and forecasting non-stationary time series but are not interpretable. A prediction model with interpretability and high accuracy can improve decision makers’ trust in the model and provide a basis [...] Read more.
Deep learning models, such as recurrent neural network (RNN) models, are suitable for modeling and forecasting non-stationary time series but are not interpretable. A prediction model with interpretability and high accuracy can improve decision makers’ trust in the model and provide a basis for decision making. This paper proposes a double decomposition strategy based on wavelet decomposition (WD) and empirical mode decomposition (EMD). We construct a prediction model of high-order fuzzy cognitive maps (HFCM), called the WE-HFCM model, which considers interpretability and strong reasoning ability. Specifically, we use the WD and EDM algorithms to decompose the time sequence signal and realize the depth extraction of the signal’s high-frequency, low-frequency, time-domain, and frequency domain features. Then, the ridge regression algorithm is used to learn the HFCM weight vector to achieve modeling prediction. Finally, we apply the proposed WE-HFCM model to stationary and non-stationary datasets in simulation experiments. We compare the predicted results with the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models.For stationary time series, the prediction accuracy of the WE-HFCM model is about 45% higher than that of the ARIMA, about 35% higher than that of the SARIMA model, and about 16% higher than that of the LSTM model. For non-stationary time series, the prediction accuracy of the WE-HFCM model is 69% higher than that of the ARIMA and SARIMA models. Full article
(This article belongs to the Special Issue Emerging Machine Learning Techniques in Industrial Internet of Things)
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25 pages, 1411 KiB  
Article
Identifying Key Factors of Reputational Risk in Finance Sector Using a Linguistic Fuzzy Modeling Approach
by Uğur Hanay, Hüseyin İnce and Gürkan Işık
Systems 2024, 12(10), 440; https://doi.org/10.3390/systems12100440 - 17 Oct 2024
Viewed by 1426
Abstract
Management of reputational risk is crucial for financial institutions to establish a solid foundation for strategic decisions, gain customer trust, and enhance resilience against environmental adversities, as they largely operate on digital platforms. Since this becomes even more significant as online transactions and [...] Read more.
Management of reputational risk is crucial for financial institutions to establish a solid foundation for strategic decisions, gain customer trust, and enhance resilience against environmental adversities, as they largely operate on digital platforms. Since this becomes even more significant as online transactions and digital interactions amplify the visibility and potential impact of reputational issues in the context of electronic commerce, it is essential to thoroughly investigate environmental factors to achieve a comprehensive understanding of reputational risk. However, measuring and evaluating their influence on reputational risk is challenging due to their inherent connection to human perception. This study aims to explore the factors influencing reputational risk of financial organizations to mitigate potential reputational losses by addressing uncertainties associated with concepts such as vagueness. The employed methodology integrates the Decision-Making Trial and Evaluation Laboratory and Fuzzy Cognitive Map techniques using linguistic fuzzy terms. This approach focuses on both the direct effects of factors on reputational risk and the indirect effects arising from interdependencies between factors. Linguistic fuzzy variables enable us to consider the hesitation of the experts and the vagueness of human judgment. To validate the results, factors are also weighted using the fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) method. The most influential factors identified by both methods are market value, revenue, risk culture, shareholder value, firm performance, reputation awareness, and return on equity. Additionally, factors affecting other factors include firm performance, revenue, and growth opportunities. Full article
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19 pages, 2922 KiB  
Article
Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning
by Qingqing Wang, Zhengshan Luo and Pengfei Li
Sustainability 2024, 16(19), 8691; https://doi.org/10.3390/su16198691 - 9 Oct 2024
Viewed by 864
Abstract
Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is [...] Read more.
Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is proposed for natural gas consumption forecasting. Firstly, to obtain the potential data characteristics, a nonlinear concave and convex transformation-based data dimension enhancement method is designed. Then, with the aid of a stacking ensemble learning framework, the multiscale autoregressive integrated moving average (ARIMA) and high-order fuzzy cognitive map (HFCM) methods are chosen as the base learner models, while the meta learner model is constructed via a well-designed deep neural network with long short-term memory (LSTM) cells. Finally, with the natural gas energy consumption data of national and 30 provinces (where the data of Xizang are unavailable) of China from 2000 to 2019, the numerical results show the proposed algorithm has a better forecasting performance in accuracy, robustness to noise, and sensitivity to data variations than the seven compared traditional and ensemble methods, and the corresponding model applicability rate could achieve more than 90%. Full article
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26 pages, 3488 KiB  
Article
Interaction Mechanism between Inter-Organizational Relationship Cognition and Engineering Project Value Added from the Perspective of Dynamic Impact
by Mengyu Xu, Xun Liu, Zhen Bian and Yufan Wang
Systems 2024, 12(9), 362; https://doi.org/10.3390/systems12090362 - 12 Sep 2024
Viewed by 825
Abstract
Projects involve inter-organizational relationship cognition, which is central to collaborative engineering project value added. Interest in value added in the project lifecycle is mounting and gaining increasing attention in the research literature. However, little is known about how inter-organizational relationship cognition facilitates value [...] Read more.
Projects involve inter-organizational relationship cognition, which is central to collaborative engineering project value added. Interest in value added in the project lifecycle is mounting and gaining increasing attention in the research literature. However, little is known about how inter-organizational relationship cognition facilitates value added and how such cognition pushes a project toward higher end-states of value. The existing literature mainly analyzes and studies value added on functional analysis and cost control. There are predominantly static analyses of the factors that influence value added in studies. The guiding role of value added has not been adequately explored in the studies on the influencing factors of value added. Utilizing a combination of Structural Equation Modeling (SEM) and Fuzzy Cognitive Maps (FCMs), this study addresses how inter-organizational relationship cognition influences engineering project value added, identifying complex structures of interaction and cognition dynamics. Results indicate that: (1) A hybrid SEM–FCM method can be able to model dynamic interactions between inter-organizational relationship cognition and value added; (2) trust and shared vision have positive effects on in-role behavior and extra-role behavior. Shared vision has a negative effect on opportunistic behavior. In-role behavior and extra-role behavior have a positive impact on value added, while opportunistic behavior has a negative impact. Organizational behavior is an important mediating variable to explain the interaction between inter-organizational relationship cognitions and value added. This hybrid method explores the potential mechanisms of inter-organizational relationship cognition on project value added from novel perspectives on construction project management practices, proposing practical advice for further project management. Full article
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26 pages, 2355 KiB  
Article
Fuzzy Analytic Hierarchy Process–Technique for Order Preference by Similarity to Ideal Solution: A Hybrid Method for Assessing Vegetation Management Strategies under Electricity Distribution Lines to Prevent Deforestation Based on Ecosystem Service Criteria
by Ersin Güngör
Forests 2024, 15(9), 1503; https://doi.org/10.3390/f15091503 - 28 Aug 2024
Viewed by 1057
Abstract
This study evaluated vegetation management (VM) strategies under electricity distribution lines (EDLs) through ecosystem service (ES) criteria. Deforestation, worsened by insufficient VM practices, poses a threat to ecosystem stability. Using a hybrid FAHP (Fuzzy Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference [...] Read more.
This study evaluated vegetation management (VM) strategies under electricity distribution lines (EDLs) through ecosystem service (ES) criteria. Deforestation, worsened by insufficient VM practices, poses a threat to ecosystem stability. Using a hybrid FAHP (Fuzzy Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach, ten VM strategies were assessed based on 15 ES criteria. The FAHP results identified biodiversity, timber resources, and erosion control as the most crucial criteria due to their significant weights. The TOPSIS analysis determined that VM6 (creation and restoration of scrub edges) was the most effective strategy, achieving a value of 0.744 for reducing deforestation and enhancing energy security. VM6 helps preserve forest cover and protect infrastructure by creating a “V”-shaped structures within the EDLs corridor. This study underscores the importance of ES-oriented VM strategies for sustainable vegetation management and deforestation mitigation. It also highlights the need for incorporating scientific, ES-based decision support mechanisms into VM strategy development. Future research should expand stakeholder perspectives and conduct a comprehensive assessment of ESs to ensure that VM strategies align with ecological and socio-economic sustainability. This study provides a framework for improving VM practices and offers directions for future sustainable energy management research. This study focuses exclusively on ecological criteria for evaluating VM strategies, neglecting other dimensions. Future research should use methods like ANP and fuzzy cognitive maps to explore inter-dimension relationships and their strengths. Additionally, employing SWARA, PIPRECIA, ELECTRE, and PROMETHEE for ranking VM strategies is recommended. Full article
(This article belongs to the Special Issue Forest Restoration and Secondary Succession—Series II)
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25 pages, 2656 KiB  
Article
Digital Marketing Strategies and Profitability in the Agri-Food Industry: Resource Efficiency and Value Chains
by Nikos Kanellos, Panagiotis Karountzos, Nikolaos T. Giannakopoulos, Marina C. Terzi and Damianos P. Sakas
Sustainability 2024, 16(14), 5889; https://doi.org/10.3390/su16145889 - 10 Jul 2024
Cited by 3 | Viewed by 4756
Abstract
Agriculture is essential to any country’s economy. Agriculture is crucial not only for feeding a country’s population but also for its impact on other businesses. The paradox of agri-food companies generating substantial profits despite seemingly high product prices is explored in this article, [...] Read more.
Agriculture is essential to any country’s economy. Agriculture is crucial not only for feeding a country’s population but also for its impact on other businesses. The paradox of agri-food companies generating substantial profits despite seemingly high product prices is explored in this article, focusing on the role of digital marketing within the agri-food industry. Enhanced digital marketing performance leads to efficient advertising campaigns, through reduced advertising costs and increased resource efficiency. To do so, the authors collected web analytical data from five established agri-food firms with the highest market capitalization. Then, linear regression and correlation analyses were used, followed by the utilization of fuzzy cognitive mapping (FCM) modeling. The analysis revealed that increased traffic through search sources is associated with reduced advertising costs. Additionally, enhanced website engagement contributes to lower advertising expenses, emphasizing the optimization of the user experience. However, it has been discovered that allocating funds for social media advertising eventually results in higher expenses with higher website-abandoning rate. Ultimately, successful management of the balance between product costs and profitability in the agri-food sector lies on the increased use of search sources and greatly reducing the use of social media sources. Full article
(This article belongs to the Special Issue Digital Economy and Sustainable Development)
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28 pages, 3172 KiB  
Article
The Economic Dynamics of Desktop and Mobile Customer Analytics in Advancing Digital Branding Strategies: Insights from the Agri-Food Industry
by Nikos Kanellos, Marina C. Terzi, Nikolaos T. Giannakopoulos, Panagiotis Karountzos and Damianos P. Sakas
Sustainability 2024, 16(14), 5845; https://doi.org/10.3390/su16145845 - 9 Jul 2024
Cited by 3 | Viewed by 1578
Abstract
In the agri-food industry, strategic digital branding and digital marketing are essential for maintaining competitiveness. This study examines the economic dynamics and impact of desktop and mobile customer analytics on digital branding strategies within the sector. Through a comprehensive literature review, this research [...] Read more.
In the agri-food industry, strategic digital branding and digital marketing are essential for maintaining competitiveness. This study examines the economic dynamics and impact of desktop and mobile customer analytics on digital branding strategies within the sector. Through a comprehensive literature review, this research utilizes empirical evidence to validate hypotheses regarding the influence of desktop and mobile analytics metrics on key digital branding metrics and value creation. This study explores various branding indicators by utilizing descriptive statistics, correlation analyses, regression models, and fuzzy cognitive mapping (FCM). The findings reveal significant correlations between desktop and mobile analytics and digital branding outcomes, underscoring the critical role of digital analytics and Decision Support Systems (DSSs) in shaping modern branding strategies in the agri-food industry. This study highlights the economic implications of desktop and mobile customer analytics on digital branding, providing insights to enhance market performance and foster sustainable growth in the agri-food sector. Full article
(This article belongs to the Special Issue Digital Economy and Sustainable Development)
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15 pages, 3449 KiB  
Article
From Individual Motivation to Geospatial Epidemiology: A Novel Approach Using Fuzzy Cognitive Maps and Agent-Based Modeling for Large-Scale Disease Spread
by Zhenlei Song, Zhe Zhang, Fangzheng Lyu, Michael Bishop, Jikun Liu and Zhaohui Chi
Sustainability 2024, 16(12), 5036; https://doi.org/10.3390/su16125036 - 13 Jun 2024
Cited by 1 | Viewed by 1277
Abstract
In the past few years, there have been many studies addressing the simulation of COVID-19’s spatial transmission model of infectious disease in time. However, very few studies have focused on the effect of the epidemic environment variables in which an individual lives on [...] Read more.
In the past few years, there have been many studies addressing the simulation of COVID-19’s spatial transmission model of infectious disease in time. However, very few studies have focused on the effect of the epidemic environment variables in which an individual lives on the individual’s behavioral logic leading to changes in the overall epidemic transmission trend at larger scales. In this study, we applied Fuzzy Cognitive Maps (FCMs) to modeling individual behavioral logistics, combined with Agent-Based Modeling (ABM) to perform “Susceptible—Exposed—Infectious—Removed” (SEIR) simulation of the independent individual behavior affecting the overall trend change. Our objective was to simulate the spatiotemporal spread of diseases using the Bengaluru Urban District, India as a case study. The results show that the simulation results are highly consistent with the observed reality, in terms of trends, with a Root Mean Square Error (RMSE) value of 0.39. Notably, our approach reveals a subtle link between individual motivation and infection-recovery dynamics, highlighting how individual behavior can significantly impact broader patterns of transmission. These insights have potential implications for epidemiologic strategies and public health interventions, providing data-driven insights into behavioral impacts on epidemic spread. By integrating behavioral modeling with epidemic simulation, our study underscores the importance of considering individual and collective behavior in designing sustainable public health policies and interventions. Full article
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25 pages, 3058 KiB  
Article
Integrating Fuzzy Cognitive Maps and the Delphi Method in the Conservation of Transhumance Heritage: The Case of Andorra
by Lluís Segura, Rocío Ortiz, Javier Becerra and Pilar Ortiz
Heritage 2024, 7(6), 2730-2754; https://doi.org/10.3390/heritage7060130 - 28 May 2024
Viewed by 1254
Abstract
Transhumance and its associated heritage are extremely complex and dynamic systems, and their conservation requires the analysis of interdisciplinary factors. To this end, fuzzy cognitive maps (FCMs) and Delphi surveys were applied for the first time in the field of heritage conservation. The [...] Read more.
Transhumance and its associated heritage are extremely complex and dynamic systems, and their conservation requires the analysis of interdisciplinary factors. To this end, fuzzy cognitive maps (FCMs) and Delphi surveys were applied for the first time in the field of heritage conservation. The model was applied to the tangible and intangible transhumance heritage of Andorra to determine its current state of conservation and to evaluate strategies for its preservation. Two panels of experts worked on the development of the model. Five experts with profiles related to conservation and transhumance heritage formed the first panel, which designed the preliminary FCMs, while seven experts in Andorran cultural heritage (panel 2) adapted the preliminary FCM model to Andorran transhumance heritage using Delphi surveys. The FCM model allowed us to analyze the influence of different variables on the conservation of transhumance heritage and to assess policy decisions. Further studies will focus on the implementation of this model in other countries to establish common recommendations for the conservation of the cultural heritage of transhumance. Full article
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16 pages, 2552 KiB  
Article
Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications
by William Hoyos, Kenia Hoyos and Rander Ruíz
Diagnostics 2024, 14(5), 533; https://doi.org/10.3390/diagnostics14050533 - 2 Mar 2024
Viewed by 1321
Abstract
Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use [...] Read more.
Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Infectious Diseases)
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26 pages, 1901 KiB  
Review
Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study
by Ioannis D. Apostolopoulos, Nikolaos I. Papandrianos, Nikolaos D. Papathanasiou and Elpiniki I. Papageorgiou
Bioengineering 2024, 11(2), 139; https://doi.org/10.3390/bioengineering11020139 - 30 Jan 2024
Cited by 5 | Viewed by 3120
Abstract
Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their [...] Read more.
Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Diagnosis and Prognosis)
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28 pages, 2814 KiB  
Article
Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling
by Nikolaos T. Giannakopoulos, Marina C. Terzi, Damianos P. Sakas, Nikos Kanellos, Kanellos S. Toudas and Stavros P. Migkos
Information 2024, 15(2), 67; https://doi.org/10.3390/info15020067 - 23 Jan 2024
Cited by 2 | Viewed by 3230
Abstract
Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and [...] Read more.
Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and Machinery Equipment Price Index (MEPI) were selected as the basis of this study. This research aims to examine the connection between digital marketing analytics and the selected agroeconomic indexes while providing valuable insights into their decision-making process, with the utilization of AI (artificial intelligence) models. Thus, a dataset of website analytics was collected from five well-established agriculture firms, apart from the values of the referred indexes. By performing regression and correlation analyses, the index relationships with the agriculture firms’ digital marketing analytics were extracted and used for the deployment of the fuzzy cognitive mapping (FCM) and hybrid modeling (HM) processes, assisted by using artificial neural network (ANN) models. Through the above process, there is a strong connection between the agroeconomic indexes of AER, CPPI, FPPR, and MEPI and the metrics of branded traffic, social and search traffic sources, and paid and organic costs of agriculture firms. It is highlighted that agriculture firms, to better understand their sector’s employment rate and the volatility of farming, chemicals, and machine equipment prices for future investment strategies and better decision-making processes, should try to increase their investment in the preferred digital marketing analytics and AI applications. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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