Journal of Intelligent Decision Making and Information Science
The Journal of Intelligent Decision Making and Information Science is an interdisciplinary, peer-reviewed journal that publishes cutting-edge research on data-driven decision-making, artificial intelligence, and information management. The journal provides a platform for innovative approaches that enhance decision processes and optimize information systems, serving researchers, industry professionals, and decision-makers across diverse fields such as healthcare, finance, environmental science, and technology.
JIDMIS is published Open Access under a Creative Commons CC-BY 4.0 license. Authors retain full copyright, with the first publication right granted to the journal.
AI Pair Programming and Knowledge Sharing in Developer Communities
With the increasing availability of artificial intelligence-supported pair programming tools in professional software development, past research has largely concentrated on their impact on individual developers. Much less is known about their potential impact on developer communities. Specifically, we still lack empirical insight into whether the proliferation of artificial intelligence coding assistants is associated with observable changes in community participation, collaborative communication, and externalized knowledge creation across large-scale developer platforms. In this paper, we introduce the Loneliness Framework, a socio-technical process model comprising six mechanisms that link AI pair programming to community-level behavioral change, and examine whether statistically significant longitudinal shifts in platform-trace measures of developer community behavior are temporally correlated with the adoption of artificial intelligence-supported pair programming. We employ an observational, non-causal longitudinal design and analyze two real-world data sets: a Stack Overflow data set from the Stack Exchange Data Dump and a public GitHub events data set from GH Archive, spanning January 2018 to December 2024. We analyze nine monthly behavioral metrics through trend, period-based comparison, correlation, and effect-size analyses, using false discovery rate correction for the primary hypothesis tests. We find that all nine metrics exhibit statistically significant longitudinal changes. The largest changes include decreases in question volume, pull request discussion density, and documentation-related behaviors, together with increases in median time to first answer. Many of these changes became most pronounced after late 2022, when large language model-based coding assistants became widely available. We also find positive relationships among selected documentation-related metrics, indicating coordinated shifts in externalized knowledge behaviors. Overall, the results indicate statistically detectable changes in developer community behavior during the era of artificial intelligence-supported programming. However, because this is an observational study, the findings should be interpreted in a descriptive, non-causal manner.
Copyright (c) 2026 Abdullah Önden (Author) https://creativecommons.org/licenses/by/4.0
Assessing the Accuracy and Reliability of Artificial Intelligence in First Permanent Molar Extraction Decisions
The first permanent molar (M1) plays a critical role in maintaining functional occlusion during mixed dentition. When this tooth is severely compromised by deep caries, structural weakness, or developmental enamel defects, extraction may be required. However, the decision to extract M1 is influenced by multiple prognostic factors, including the child’s age, dental development stage, second molar (M2) angulation, and the presence of third molars. In this context, artificial intelligence (AI)–based conversational systems can support clinical judgment and patient education by offering quicker access to relevant information. This descriptive-analytical study evaluated the performance of two AI-powered language models (ChatGPT-3.5 and ChatGPT-4) in providing clinical guidance on M1 extraction. Ten standardized questions were formulated based on established prognostic parameters associated with spontaneous space closure. Both AI systems produced coherent and clinically relevant content. However, ChatGPT-4 tended to produce longer responses with greater sentence complexity, resulting in lower readability and higher academic demand. By comparison, ChatGPT-3.5 produced shorter, more straightforward responses that may be more appropriate for communication with parents or patients. Future research incorporating varied question structures, such as case-based scenarios, radiographic-based questions, and real-user feedback, may help define how conversational AI can be safely and effectively integrated into pediatric dental workflows. Until then, AI-based tools should be used as supportive resources rather than as independent clinical decision-makers, and their outputs should be reviewed by qualified professionals to ensure patient safety.
Artificial intelligence; Pediatric dentistry; First permanent molar; Extraction decisions; Decision support
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
A Comparative Analysis of Transformer Architectures for Automated Lung Cancer Detection in CT Images
The imperative for early-stage lung cancer detection is widely recognized as a critical determinant of therapeutic efficacy and patient survival. Conventional diagnostic workflows, however, are frequently constrained by their labour-intensive nature and susceptibility to interpretive inaccuracies, positioning artificial intelligence (AI) as a transformative technology in medical imaging. This research conducts a rigorous comparative analysis of four prominent vision transformer (ViT) architectures; Swin-Base, ViT-Base, DeiT-Base, and BEiT-Base evaluating their performance in the automated classification of lung cancer from computed tomography (CT) scans. The empirical validation was performed on the open-access IQ-OTH/NCCD dataset, a corpus of 1,097 images distributed across benign (n=120), malignant (n=561), and normal (n=416) classes. Model proficiency was quantified using established metrics of accuracy, precision, recall, and F1-score. The findings unequivocally establish the superiority of the Swin-Base model, which, by utilizing its innovative hierarchical design and shifted-window mechanism, attained a benchmark accuracy of 98.80% and an F1-score of 97.52%. While its counterparts achieved commendable accuracies ViT-Base (95.18%), DeiT-Base (96.39%), and BEiT-Base (95.78%) they did not match the performance of Swin-Base. Notably, this leading performance was achieved with greater computational efficiency, requiring a lower GFLOPS count than competing models.
Lung Cancer Classificaiton; Computer-Aided Diagnosis (CAD; Deep Learning, Transformer Architectures; Medical Image Analysis.
Transformer-based Mobile Traffic Prediction in Internet of Vehicular Networks
This paper presents a Transformer-based mobile traffic prediction model for the Internet of Vehicular Things (IoVT), addressing the challenges of handling long-term dependencies and improving computational efficiency in mobile traffic forecasting. The proposed model, which integrates a gated residual attention unit (GRAU) and channel embedding (CE) technique, leverages the strengths of the Transformer architecture to enhance predictive accuracy and efficiency while maintaining recurrent dynamics. Through experiments on a real-world dataset, the model demonstrated superior performance over existing baselines in terms of root mean square error (RMSE) and mean absolute error (MAE), showcasing its effectiveness in capturing complex temporal patterns in mobile traffic data. The study contributes to the IoVT by providing a robust prediction tool that can optimize network resource allocation and support the development of intelligent transport systems within smart city frameworks.
Internet of Vehicular Things, Mobile Traffic Prediction, Deep Learning, Transformer
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
The Protective Effects of Ramadan Fasting against Cancer: Exploring Metabolic, Cellular, and Epigenetic Mechanisms
Fasting, particularly the fasting during Ramadan, is becoming very common due to the potential health benefits of which it is inclusive of its anti-cancer properties as well as therapy. It is mentioned in a recent study that intermittent fasting, as during Ramadan, induces some alterations in the metabolism, physiology, and epigenetics, which could suppress cancer growth. The problem that this study seeks to address is the deficiency of scientific honesty and transparency in explaining the mechanisms by which Ramadan fasting affects cancer development and treatment response. The research seeks to systematically update the literature to evaluate the protective mechanisms of Ramadan fasting against cancer progression based on scientific transparency. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for executing sound and transparent reporting. The key findings are that fasting controls metabolism towards increased production of ketone bodies, reduced availability of glucose, and reduced levels of insulin and insulin-like growth factor-1 (IGF-1)—vital modulators of cancer cell growth. Also, fasting increases the DNA damage response (DDR), stimulates cellular homeostasis through autophagy, and decreases oxidative stress, thereby inhibiting malignant transformation. Epigenetically, fasting regulates non-coding RNAs, DNA methylation, and histone modification, which act to activate tumor suppressor genes and silence oncogenes. The most significant finding is that Ramadan fasting is of immense potential as an adjuvant intervention in cancer therapy. These findings have crucial implications for integrative oncology practice, dietary intervention, and public health policy in the integrative oncology setting.
Ramadan Fasting , Cancer Prevention , Metabolic Epigenetic Modification , Autophagy, Oxidative
Evaluation of Strategic Management and Growth Alternatives in Independent Auditing Firms Using Interval Type-2 Fuzzy ARAS Method
Independent audit firms (IAFs) need to base their strategic management and growth decisions on more logical and rational foundations due to the technological and digital transformation, which has been increasing pressure in the recent period, the intensification of competition in the industry, and the rapid and frequently changing structure of legal regulations. In this context, this study aims to evaluate and prioritize strategic management and growth decisions systematically and analytically by identifying the strategic decision problems faced by IAFs. In this direction, six strategic alternatives determined by a comprehensive field study were analyzed with the help of the Interval Type-2 Fuzzy ARAS (IT2F–ARAS) method, considering thirteen criteria determined based on expert opinions. IT2F–ARAS technique has been preferred as it can effectively include differences in interpretation in specialists' judgments and ambiguities in the model. This procedure enables the integrated and flexible evaluation of quantitative and qualitative data in decision-making processes. When the results obtained by applying the proposed model are examined, it is seen that benefit-oriented criteria such as expanding the customer base, providing a competitive advantage, and increasing brand value are more decisive. It has been observed that cost-side criteria, such as initial investment cost and technological incompatibility, limit the applicability of some strategies. In addition, "digital transformation," "expanding the range of services," and "market diversification" have emerged as priority strategies for IAFs in the context of strategic management and growth decisions. The study aims to contribute to developing more consistent and sustainable growth policies by providing independent audit firms with a multi-criteria and fuzz-based decision support perspective in their strategic orientations.
Independent auditing, strategic management, growth strategies, fuzzy MCDM, IT2F-ARAS
Comparative Analysis of Neural Network Architectures for Image Classification
An important challenge in the growing domains of computer vision and machine learning is the precise categorisation of images, which drives the growth of innovative methodologies and methods. The proposed study will examine three distinct picture classification frameworks. The structures include a Single Layer Network (SLN), a Multi-Layer Perceptron (MLP), and a Convolutional Neural Network (CNN) based on LeCun's technique. The study will employ the MNIST digit identification dataset, the Fashion-MNIST dataset, and the CIFAR-10 dataset. Precision, recall, and F1-score are the measures used for model evaluation. Compared to the SLN and CNN models, the MLP model exhibits superior precision and recall.
Neural Networks, MLP , CNN, Image Classification
The Synergy of Fuzzy Logic and Multi-Criteria Decision-Making: Application Areas and Global Trends
This study conducts a bibliometric analysis to understand the evolution and application of Multi-Criteria Decision Making (MCDM)/Fuzzy MCDM methods in scientific literature. The literature review process, utilizing the Scopus database, analyzed 13278 articles out of 30144 published between 2014-2024. The analysis reveals substantial academic output in MCDM/Fuzzy MCDM fields with an annual growth rate of 13.38%. However, the declining citation-per-paper ratio suggests that increasing publication volume may negatively impact individual studies' visibility. Journals like Sustainability, PLOS One, and IEEE Access emerge as top publishers, highlighting the rise of open-access platforms while cautioning that rapid acceptance processes may introduce methodological weaknesses. Leading authors such as Zeshui Xu and Dragan Pamučar have made significant theoretical and practical contributions. Xu's operators for intuitionistic fuzzy sets and Pamučar's novel MCDM methods for healthcare supplier selection represent key milestones. The University of California System demonstrates strong research infrastructure as the most prolific institution. China dominates as the most productive country (14545 articles, 122430 citations), followed by India, Pakistan, and Turkey, whose collaborations enhance scientific interaction. Yager's (2013) IEEE Transactions on Fuzzy Systems paper, "Pythagorean Membership Grades in Multicriteria Decision Making," remains the most cited (2433 citations). Science mapping identifies key trends: fuzzy logic's role in decision model evolution and integration with machine learning. Applications in energy efficiency, maritime safety, and sustainability are growing. Future integration with AI/ML is projected to enhance decision-making efficiency.
multi-criteria decision making, fuzzy logic, bibliometric analysis, VOSviewer, Biblioshiny, Python
Investigation of Factors Driving Manufacturing Companies to Green Manufacturing Using Direct Rating Technique
Environmental practices have been incorporated into business models and procedures in today's competitive climate, and they are now significant challenges that require attention. Businesses have undergone several changes as a result of these practices, including concerns about preserving a habitable environment for future generations and guaranteeing energy efficiency with sustainable production. To the extent that green manufacturing practices have been included into business strategies and used to inform future planning. In this context, green production practices have emerged as a crucial factor that requires careful attention from businesses. Nowadays, green production is a crucial tool for manufacturing businesses. The study investigated what motivates manufacturing companies to engage in green production practices. The goal was to weight the characteristics that were identified as driving businesses to engage in green manufacturing operations. To do this, the identified criteria were weighted using one of the multi-criteria decision analysis techniques, direct rating. The results of the application showed that "support from top management," "increasing demand for green products," and "increasing public pressure" were the main drivers of manufacturing companies' transition to green production practices.
Green manufacturing, green manufacturing activities, multi-criteria decision analysis, direct rating technique
Examing the impact of digital transformation on the resilience of food supply chains: a comprehensive analysis
This study analyzes the relationship between digital transformation and food supply chain resilience(FSCR). First, it identifies 10 factors that affect structural changes in FSCR. These factors are selected from organizational, social, and technological aspects. The Analytic Hierarchy Process (AHP) is used to hierarchically classify and analyze the factors influencing FSCR and their mechanisms of action. Then, Interpretive Structural Modeling (ISM) is established to study the interactions among the factors of FSCR, with digital technology as an external manifestation of digital transformation. Through those methods, the study gets the identification of the interrelationship and intrinsic connections between digital transformation and FSCR, and helps us to have a better understanding of how digital transformation affects FSCR. The study provides valuable insights for decision-makers in improving FSCR, emphasizing the importance of digital transformation. Digital technology plays an intermediate role in the factor system of FSCR. It acts like a bridge linking the causal factors, such as government macro-control, and result factors, such as environmental protection. While the study offers some guidance for the improvement of FSCR, the study has some limitations in methods and regions. Future research should focus on integrating advanced techniques and looking for more specific solutions.
Food supply chain resilience, Digital transformation, Interaction relationship, AHP, ISM
Analysis of Factors Affecting the Vulnerability of the Hazardous Materials Transportation System
A method for identifying key vulnerability factors in hazardous materials transportation systems is proposed, combining qualitative and quantitative analytical techniques. First, we establish a conceptual framework that Integrates definitions of hazardous materials, their transportation systems, and vulnerability (including supply chain vulnerability). Through qualitative analysis, we identify critical factors that contribute to systemic vulnerability. Subsequently, the interrelationships among these factors are quantified using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. By applying DEMATEL, we model causal relationships and prioritize key factors based on centrality and causality indices. Finally, we proposed targeted mitigation measures aligned with these prioritized factors to enhance the resilience of hazardous materials transportation systems.
hazardous materials, transportation system, vulnerability, supply chain, DEMATEL
Evaluation of Banking Performance of the Balkan Countries in Type-2 Neutrosophic Fuzzy Environment
This paper proposes the PSI & CODAS integrated model based on the Type-2 Neutrosophic Fuzzy Numbers (T2fNNs) to solve the decision-making problems encountered in banking and make performance analyses of banking institutions. The proposed model is also an integrated MCDM approach that can overcome many complex situations and many misevaluations by decision-makers. Besides, it provides an applicable and stable assessment, as it is not sensitive to the number of criteria and decision alternatives. The T2NN MCDM approach was applied to evaluate the banking performances of banks in Balkan countries after the COVID-19 global pandemic and demonstrate the implementation of the suggested model. Next, a comprehensive robustness check consisting of two stages was performed to test the validation of the suggested MCDM model and its results. According to the study results, A13 Türkiye İş Bankası was the best option for applying the proposed model and has remained the best option for all scenarios. Besides, minor alterations have been surveyed that did not alter the overall conclusions in the ranking performance of a few options. The sensitivity analysis results prove that the proposed PSI & CODAS approach is a powerful and applicable computational tool, and its results are reasonable, realistic, and substantially accurate.
Bank performance analysis, Balkan banks, Preference Selection Index, Power-Heronian Function, CODAS
Novel Hamy Mean Aggregation Operators Based on Advanced Operations for T-Spherical Fuzzy Group Decision-Making
Some advanced operation laws (AdOLs) are put forward with the T-spherical fuzzy (TSF) information. A series of novel Hamy mean aggregation operators are developed, specifically TSF advanced Hamy mean (TSFAdHM), TSF advanced dual Hamy mean (TSFAdDHM), TSF advanced weighted Hamy mean (TSFAdWHM) and TSF advanced weighted dual Hamy mean (TSFAdWDHM) operators. These aggregation operators integrate the strengths of Hamy mean and AdOLs in that they not only reveal correlations among multi-input variables but also eliminate counter-intuitive problems, as well as reflect decision-maker risk preferences. Several reasonable natures and peculiar types of these aggregation operators are investigated. Moreover, a novel group decision-making framework for the type of TSF aggregation operator is constructed. The tractability and usefulness of the decision-making model are examined by some numerical examples. The sensitivity and method comparison analyses are used to demonstrate that the proposed model can remedy the drawbacks of existing methods, and this article offers a very useful method for the complicated group decision-making issues.
Advanced operational laws, Hamy mean, Aggregation operator, multi-attribute group decision-making, T-spherical fuzzy sets
Experience of Artificial Intelligence in Architectural Space Production within the Scope of Creating "Brand Identity" for a Local Brand
Branding and brand identity emerged from the desire to stand out in line with consumption needs. Branding and corporate identity design are processes that must be carried out simultaneously. In this context, the design of the brand's structure, interior spaces, and stores intersects with the discipline of architecture in the creation of a "brand identity." Spatial design, through the use of architectural elements and spatial configurations, differentiates the brand and aims to capture customer attention. Today, the role of artificial intelligence (AI) in the design process is being questioned. Text-to-image AI algorithms redefine architectural formation by generating unique and diverse design outputs. In this context, the question, "Can artificial intelligence produce architectural spaces for brand identity?" forms the basis of this research. The study aims to use text-to-image technology to rapidly generate diverse design alternatives for a local brand. The relationship between text to-image technology and decision-making is shaped by its ability to guide and optimize users' creative processes. This technology allows users to provide text-based inputs to generate visual outputs, playing a critical role in visualizing both design options and possibilities in the decision-making process. By examining the contributions of the generated visuals to brand identity and architectural design, this method is proposed as a new approach to architectural design. While designers assume the role of decision-makers in this process, AI emerges as a significant design parameter.
Brand Identity, Store Design, Artificial Intelligence, Text-to-Image Generation, Architectural Design
Enhancing Cybersecurity Risk Assessment Techniques Based on Bipolar Complex Intuitionistic Fuzzy Soft Robust Aggregation Operators: A Comprehensive Approaches
This article describes the cybersecurity risk assessment model’s accuracy, timely response, and actions against potential threats by providing the key areas in which the cybersecurity risk assessment model can be improved and enhanced. For this, we propose the model of robust aggregation operators based on bipolar complex intuitionistic fuzzy soft information, called the bipolar complex intuitionistic fuzzy soft weighted averaging operator, bipolar complex intuitionistic fuzzy soft order weighted averaging operator, bipolar complex intuitionistic fuzzy soft weighted geometric operators, and bipolar complex intuitionistic fuzzy soft order weighted geometric operators, with some practical properties, called idempotency, monotonicity, and boundedness. Further, we discuss the multi-attribute decision-making model based on proposed operators for evaluating the enhancement of the cybersecurity risk assessment techniques with a comprehensive approach. Finally, we illustrate numerical examples based on decision-making techniques for assessing the comparative analysis among the proposed ranking values with existing ranking values to show the worth and verity of the designed models.
Bipolar complex intuitionistic fuzzy sets, Cybersecurity risk assessments, Decision-making techniques, ; Robust aggregation operators
Designing and Analyzing Fermatean Fuzzy Decision Models: A Comprehensive Decision Making Approach
In the framework of Multi-Criteria Decision Making (MCDM), this study explores the incorporation of fuzzy set theory with decision-making techniques. It starts by giving a summary of fuzzy set theory and the basic ideas that are necessary to comprehend how decisions are made. The paper presents the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Logarithmic Percentage Change Driven Objective Weighting (LOPCOW), which are implemented using R programming. The paper illustrates the use of these approaches for alternative ranking and objective weight evaluation with a thorough numerical example. The outcomes demonstrate how well these strategies work together to handle challenging decision-making situations. The paper offers a precise and effective approach for resolving MCDM issues by utilising the computational power of R programming.
Decision making, Fermatean fuzzy set, R-programming , LOPCOW, TOP- SIS
Artificial Intelligence and Medicine 2014-2024: Bibliometric Analysis and Global Impacts
Artificial intelligence (AI) has radically transformed the field of medicine in the last decade, with a significant increase in academic publications. Based on 1783 English-language articles analyzed with Biblioshiny and VOSviewer tools, the findings highlight an annual growth rate of 30.38% and a significant increase from 2018 onwards. Each article received an average of 17.54 citations. The studies had contributions from 11678 authors and an international collaboration rate of 29%. There were single-author (118) and single-country (129) articles. Prominent contributing authors include Forestiero A, Mazzuca D and Zinno F. Harvard Medical School (104 papers) and the University of Toronto (83 papers) have played important roles in the advancement of AI applications in medicine. The USA stands out with both publication volume (3241 articles) and number of citations (9176). Journals such as Journal of Medical Internet Research (26 articles) and Frontiers in Medicine (25 articles) stand out as the leading publication venues in the field. The most cited articles were published in journals such as Jama, Radiology and Nature. This study highlights the wide-ranging applications of AI in areas such as machine learning, deep learning, natural language processing and computer vision, demonstrating its potential in medical imaging, genetic analysis and clinical decision support systems. Future research needs to focus on maintaining collaboration, increasing methodological rigor and finding solutions to emerging challenges.
Medicine, Artificial Intelligence, Bibliometric Analysis, Biblioshiny, VOSviewer
Sustainable Urban Mobility Plan for Istanbul: A Critical Review
The concept of a sustainable urban mobility plan (SUMP) for cities was first developed to address the shortcomings of conventional transport planning with its focus on accessibility, social equity, environmental quality, and an integrated development of all transport modes as well as an involvement of stakeholders and citizens through a transparent and participatory approach. Since its development in early 2010s, many European cities have adopted this concept for their mobility planning. The sustainable mobility literature already includes many examples and reviews of SUMPs which have been implemented at various cities across Europe. Having implemented the SUMP concept in 2022, the city of Istanbul, having a population of around 16 million people, offers a novel case in terms of the complexity of urban issues addressed. This paper reviews Istanbul’s sustainable urban mobility plan, discussing the benefits as well as shortcomings for achieving a sustainable mobility transition in Istanbul. The review provides many highlights that would be helpful for other cities going through a similar transition.
sustainable urban mobility plan, transport planning, Istanbul, sustainability, mobility transitions
Using Bayesian BWM to Analyze Elevator Performance Requirements for Collaborative Product Innovation Design
This study addresses the critical need to evaluate elevator performance requirements within collaborative product innovation systematically. Leveraging insights from sales, design, and maintenance experts, the research identifies and prioritizes critical performance dimensions, including safety, design, service, and technological innovation. By employing the Bayesian Best-Worst Method (Bayesian BWM), the study overcomes the limitations of traditional decision-making approaches, offering robust and consistent results through probabilistic analysis. The findings highlight "mechanical and structural safety" as the paramount criterion, emphasizing its pivotal role in ensuring elevator reliability. Key factors, such as operational efficiency, economic performance, and maintenance, further inform actionable strategies to optimize elevator design and manufacturing processes. This research contributes a structured framework for performance evaluation and fosters collaboration among industry stakeholders, enhancing innovation and sustainability in the elevator industry.
Product Innovation, Safety and Reliability Prioritization , Bayesian BWM, Multiple Criteria Decision-Making
Model Proposal for Ammunition Demand Forecasting and Ammunition Distribution Network Design: Military Unit Application
During the war, the safe provision of the required ammunition at the desired place and time can affect the fate of the war and even the country. Determining the amount of ammunition to be needed in the war is as important as planning its distribution. Two problems are important research topics in the field of military logistics. A two-stage methodology is presented in the study to solve these problems. In the first stage, an analytical approach based on suitability tests is presented to estimate the amount of ammunition that military artillery units will need based on past general data, and ammunition requirements for possible battles are estimated. In the second stage, a distribution network design is carried out for the distribution of ammunition from the ammunition depot to the deployed artillery units. This distribution network also includes reverse logistics activities, as it is necessary to collect the waste left by the ammunition used by the artillery units. In order to achieve this in the study, a Multi-Period Multi Depot Simultaneous Pick Up&Delivery Vehicle Routing Problem Model has been developed. The results obtained as a result of the solution of the model have been examined in detail and analyzed by comparing with the current situation.
Ammunition Demand Forecast, Ammunition Pick Up&Delivery Network Design, Simultaneously Pick Up&Delivery Vehicle Routing, Reverse Logistics
A multi-criteria decision-making framework for addressing youth unemployment in developing countries: Pathways to sustainable solutions
Youth unemployment is among Africa’s most urgent challenges, with over 60% of its population under 25, the largest youth demographic globally. This youthful population presents a significant opportunity to drive economic growth, innovation, and development. However, the lack of sufficient employment opportunities risks perpetuating poverty, inequality, and social instability. To address this issue and unlock Africa’s potential, this study evaluates and prioritizes six strategies identified through a comprehensive literature review and expert input. Using the Stepwise Weight Assessment Ratio Analysis (SWARA) method within an interval-valued spherical fuzzy (IVSF) framework, the research facilitates collective decision-making. It highlights three key strategies as the most appropriates: reforming education systems to align skills with labor market demands, fostering growth in sectors such as agriculture, manufacturing, and tourism, and promoting entrepreneurship to empower Africa’s young innovators. The study offers actionable insights for policymakers to effectively combat youth unemployment and maximize the continent’s demographic advantage.
Youth unemployment, Strategy, IVSF, SWARA, Decision-making analysis, Africa.
Failure Mode and Effects Analysis (FMEA) using Neutrosophic Best Worst Method (BWM) : A Case Study of Helicopter Assembly Line for Military Aviation Area
One of the most popular structured approaches in risk assessment is Failure Mode and Effects Analysis (FMEA) that helps in discovering potential failures existing within the design of a product or process. Determining the risk weights of failure types correctly is a critical part of the decision-making process of the analysis. In this study, the Best Worst Method (BWM) was integrated with the Neutrosophic Fuzzy Set and applied to the FMEA problem. The aim of the study is to determine the failure types encountered in the helicopter assembly line in the military aviation field and to offer solution suggestions. A case study was conducted for AH-64E Apache type helicopters for the effectiveness of the proposed method. The results show that gap and mismatch for structural elements, molds should not be used appropriately, unnecessary materials should be brought to the assembly line and attention should be paid to cabling problems.
FMEA, BWM, MCDM, Neutrosophic Fuzzy Set, Helicopter
Construction of New Similarity Measures for Complex Pythagorean Fuzzy Sets and Their Applications in Decision-Making Problems
Complex Pythagorean fuzzy sets (CPFSs) extend Pythagorean fuzzy sets by expressing membership, non-membership, and hesitancy degrees using complex numbers. An urgent issue in CPFSs is determining how to accurately measure the similarity between sets. Similarity measures are crucial for assessing the closeness between two objects and are extensively applied in decision-making. In this paper, we propose new similarity measures based on trigonometric functions and their weighted representations. Additionally, we investigate the properties these measures satisfy and demonstrate their effectiveness through several numerical examples. Lastly, we apply these similarity measures to decision-making problems, including pattern recognition and medical diagnosis.
Complex Pythagorean fuzzy sets, similarity measures, trigonometric function, decision-making
PERAM: An Efficient Readiness Assessment Model for the Banking Industry to Implement IoT – A Systematic Review and Fuzzy SWARA Methods
Since the emergence of the IoT and its significant benefits, various economic, social, and political groups have been deploying and implementing IoT in different sectors. The large amounts of data generated from connected devices and products can be analysed, transforming the goals and attitudes of businesses and industries, including the banking industry. However, implementing IoT on a large scale in an industry as complex as banking is challenging. While the advantages and role of IoT in banking are clear and increasing with various studies, inadequate implementation of IoT can lead to potential risks and failures. This research aims to assess the banking industry's readiness for implementing IoT, specifically focusing on their IoT implementation readiness. The study identified the factors and aspects affecting the research topic using a systematic review method and classified its enablers into dimensions, components, indicators, and sub-indicators. After validating the initial model with experts, the final model revealed that 7 dimensions, 8 components, and 63 indicators influence the readiness of the banking industry to implement IoT. To rank the main aspects of the study, the Fuzzy SWARA method was used, and the results showed the following ranking: the dimensions of hard infrastructures, soft infrastructures, supply chain infrastructures, organizational factors, environmental factors, education and users, and security and privacy ranked first to seventh, respectively. The identified dimensions, components, and indicators provide a robust model for assessing the readiness of the banking industry for IoT implementation. The findings highlight the complexity involved in implementing IoT on a large scale within the banking sector.
Internet of Things, Readiness, Banking, Systematic Review, Fuzzy SWARA, Decision Making
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
Utilizing ResNet Architectures for Identification of Tomato Diseases
In this study, the effectiveness of various deep learning models in detecting diseases in tomato leaves has been thoroughly evaluated, with a particular emphasis on the ResNet architecture family. The primary focus was on assessing the capabilities of advanced models like Res2Next50 and Res2Net50d, which have shown exceptional performance in identifying and classifying various tomato leaf diseases. The results of the study reveal that the Res2Next50 model achieved the highest accuracy rate of 99.85%, accompanied by an impressive F1 score of 99.82%. This indicates a high level of precision and robustness in distinguishing between healthy and diseased leaves. Additionally, the Res2Net50d model has also proven to be highly effective, attaining an accuracy rate of 99.78%, precision of 99.73%, and recall of 99.72%. These metrics suggest that the model is not only capable of correctly identifying diseased leaves but also has a low false positive rate, making it a reliable tool for practical applications in agriculture. When compared to other popular convolutional neural network (CNN) architectures, such as VGG16 and DenseNet121, the ResNet models, particularly Res2Next50 and Res2Net50d, have demonstrated superior performance. This highlights the strength of the ResNet family in handling complex classification tasks, especially those involving fine-grained distinctions between different types of tomato leaf diseases. The findings of this research suggest that CNN models like Res2Next50 and Res2Net50d can significantly enhance the accuracy and reliability of automated disease detection systems in agricultural settings. Such systems can provide valuable support to farmers and agricultural professionals, enabling early and accurate identification of diseases and, consequently, better crop management and productivity.
Artificial intelligence, Deep-Learning, CNN, Plant diseases, ResNet
The factors influencing online shopping decisions of customers
The outbreak of the COVID-19 pandemic has changed consumers’ purchase behaviour from offline to online, so many Chinese consumers depend on online platforms to purchase commodities. There are two basic online shopping platforms available to Chinese consumers: traditional online shopping platforms and social media platforms. While previous studies have focused on a single platform and analysed the factors affecting online shopping, this study combines traditional and social media and conducts analyses of the factors affecting online shopping and the future development of online shopping, including convenience, wide selection, affordability, economy promotion, and celebrity benefits. The study found that the factors affecting online shopping can be divided into three categories. The most important point is the epidemic, as government control has forced people to purchase goods online, which has had a mandatory impact on the development of online shopping. Secondly, online shopping also has significant advantages, allowing it to constantly follow the trend of the times and be favoured by consumers. Finally, the digitalization trend of the overall environment, national policy support for e-commerce, and the development of e-commerce are also affecting the development of online shopping.
Online shopping; Influencing factors; COVID-19; Consumer behaviour.
Sustainable Energy Solutions: Evaluation of Solar Panel Installation Using Fuzzy Multi-Criteria Decision-Making Methods
This study evaluates solar panel installation decisions by applying a hybrid fuzzy multi-criteria decision-making (FMCDM) methodology, integrating the Fuzzy Logarithm Methodology of Additive Weights (F-LMAW) for criterion weighting and the Fuzzy Alternative Ranking Technique based on Adaptive Standardized Intervals (F-ARTASI) for ranking alternatives. The primary aim is to identify and prioritize critical criteria influencing solar panel installation and optimize the decision-making process to propose sustainable and strategic solutions. Eighteen comprehensive criteria-ranging from installation costs and energy efficiency to environmental impact and public acceptance-were analyzed across five potential locations in Turkey: Gürün, Kangal, Divriği, Altınyayla, and Imranlı. The findings highlight that the ranking of alternatives remains consistent under varying F-Bonferroni mean aggregation operator parameters ( and ), demonstrating the robustness and reliability of the adopted approach. Sensitivity analysis confirmed that the established criteria play a decisive role in the ranking, with Gürün consistently ranked first across all scenarios. Validation of the F-ARTASI results against established FMCDM methods, including F-TOPSIS, F-CoCoSo, F-MARCOS, F-WASPAS, and F-RAWEC, showed high consistency, with Spearman Correlation Coefficients (SCC) averaging 0.90. This reinforces the methodological reliability of the proposed model and underscores its applicability in real-world energy management scenarios.
Sustainable energy, solar panel installation, fuzzy multi-criteria decision-making, LMAW, ARTASI
A Dual-Stage Decision-Making Model for Overcoming Investment Barriers in Nuclear Energy Projects in Turkey
Nuclear energy projects have many advantages. High energy efficiency is one of the most important advantages of this type of energy. With nuclear reactors, a very large amount of energy can be produced with a small amount of fuel. Low carbon emissions are another advantage of nuclear energy. Compared to fossil fuels, the carbon emissions that occur in obtaining energy from nuclear reactors are almost zero. Nuclear energy is also of vital importance for Turkey. The most fundamental reason for this is that Turkey is a country that is dependent on foreign energy. This situation negatively affects the country's current account deficit. The increasing current account deficit also makes the country's economy much more fragile. Nuclear energy increases energy supply security as a domestic energy source and reduces external dependency. The development of Turkey's nuclear energy investments is essential. For this process to be implemented successfully, measures must be taken to eliminate these difficulties. In this study, it is aimed to identify the most appropriate strategies for the development of Turkey's nuclear energy investments. Within this framework, a two-stage decision-making model is established. In the first part of the study, the importance weights of the selected criteria are calculated. In this process, the DEMATEL technique is taken into consideration. In the second part of the model, the aim is to determine the most appropriate investment strategies. To achieve this goal, a Rankin analysis is carried out using the TOPSIS technique. It is concluded that technological improvement is the most critical indicator to overcome the problems of nuclear energy investments. Financial issues and customer expectations are other essential items in this regard. However, organizational effectiveness has the lower weight in comparison with the other determinants. On the other side, it is identified that technological and operational development is the most critical strategy to minimize the negative impacts of the nuclear energy investments. Similarly, overcoming financing and economic challenges and improving regulation and policies are other critic issues for this situation.
Nuclear energy, strategy development, hybrid decision-making model, DEMATEL, TOPSIS
Sustainable Urban Mobility in Istanbul: Transition and Planning
Sustainable urban mobility reduces environmental impacts, promotes social equity, and increases economic efficiency. This paradigm aims to improve the long-term quality of life of individuals, communities, and cities. Urban mobility in Istanbul is on the way to becoming more sustainable with plans and projects undertaken; however, there are still issues such as poor air quality, worsening traffic density, noise, and inefficient public transport. The inadequacy of existing public transport systems in the city causes many people to use private vehicles, which increases problems such as traffic congestion and environmental pollution. This paper presents an overview of the state of sustainable urban mobility in Istanbul regarding its transition management and planning. An overview of key planning changes and transport projects and key challenges must be addressed to achieve sustainable urban mobility.
mobility transitions, planning, Istanbul, Sustainable urban mobility
Predicting Duration of Residential Units’ Sale Using Machine Learning Techniques
Real estate is vital, meeting basic needs and offering solid investments. Informed investment decisions require understanding home sale factors. Estimating residential property sales time can boost rewards and decrease risks. Two steps are involved in this paradigm. Significant characteristics are identified using filter weighting and regression techniques. K-nearest Neighbour, Naïve Bayes, and Decision Trees use characteristics identified in the first stage. Determining the most efficient model requires comparing their precision. The study provides brokers with insights for improved sales forecasting and transaction management. This technique allows stakeholders to adjust their plans based on market fluctuations, potentially leading to more profitable real estate investments. This research clarifies real estate investment strategies for greater returns.