Journal of Operations Intelligence
type: channel
description: Journal of Operations Intelligence interact in many scientific fields of vital importance and publishes high-quality scientific papers. These include, intelligent systems, decision making, decision support systems, computational intelligence, expert systems, information science, knowledge based systems, soft computing, and artificial intelligence. Journal of Operations Intelligence is an international peer-reviewed journal. The focus is to publish the quality research in application and convergence of the field of these techniques and other similar techniques to address real world complexities. The journal will publish papers in operations intelligence and application in the areas of; engineering, business, transportation, logistic, finance, marketing, accounting, auditing, energy, economics, defense, law, procurement and contracting, project management, risk management, information management, waste management, information retrieval, crisis management, stock trading, strategic management, data science, network management, telecommunications, space, marine, business analytics, healthcare, chemistry, human resources management, production management, archaeology, digital technology, sustainable mining, and sustainable management. General Journal guidelines for authors Journal of Operations Intelligence publishes research articles, reviews, short communications, and case studies. Research articles must include: motivation for the work, an adequate overview of the representative work in the field including up-to-date references, a clear statement of the novelty in the presented research, suitable theoretical background, one or more examples to demonstrate and discuss the presented ideas and, finally, conclusions. Short communications are usually 4-7 pages long, research articles and case studies 8-14 pages, while reviews can be longer. Page number limits are not strict and, with appropriate reasoning, the submitted articles can also be longer or shorter. Authors are re…
publisher: Scientific Oasis
language: en
publication name: Journal of Operations Intelligence
issn: 3009-4267
items: Evaluation of Suitable Accounting and Auditing Firms for SMEs in the Textile Industry with Picture Fuzzy Set-based Entropy & MARCOS Approach
items: Measurement Method for Food Supply Chain Security Level from the Perspective of Resilience
items: Offshore Wind Power Site Selection in Türkiye Using q-Rung Orthopair Fuzzy Sets and the COPRAS Method
items: Analysis of the Stages of Military Cyber Operations using the COBRAC Method and p, q Quasirung Orthopair Fuzzy Decision Making Framework
items: A Data Evolvement Analysis for Sustainable Performance Evaluation of Educational Resource Use in Architecture Universities
items: A Hybrid Quadripartitioned Single-Valued Neutrosophic Method and its Application for the Selection of Emergency Logistics Outsourcing Suppliers
items: Prospects and Challenges of the Metaverse in Pursuing Sustainable Development
items: Decision Support System Using Centroidous and CoCoFISo Methods for Analyzing Resource Availability in High Schools
items: Predictive Stock Selection: A Hybrid RF-CNN XGBoost Model Integrated with Dynamic Adaptive Index and Stepwise Elimination Techniques
items: Transport Justice for Istanbul’s Urban Mobility: Equity, Accessibility, and Policy Challenges
items: Anti-swing Control for Underactuated Crane under Disturbances
items: The Effects of Public Transit in Vehicle Safety Measures for Women on Gender Equity
items: Enhancing Breast Cancer Diagnosis: A Comparative Evaluation of Machine Learning Algorithms Using the Wisconsin Dataset
items: Transforming Preventive Healthcare with Machine Learning Technologies
items: Selection of Biomass Resources with Fuzzy Multi-Criteria Decision-Making Methods: Alternatives for Sustainable Energy Production
items: Chinese Sentiment Analyses Research: A Systematic Review and Bibliometric Analysis
items: Cross-tabulation Analysis of the Impact of University Students on IEQ Factors
items: Development of Optimal Investment Strategies for Electric Vehicle Charging Stations with a Novel Decision-Making Technique
items: Ranking of Criteria Affecting the Implementation Readiness of Internet of Things in industries Using TISM and Fuzzy TOPSIS Analysis
items: Fermatean Fuzzy TOPSIS Method and Its Application in Ranking Business Intelligence-Based Strategies in Smart City Context
items: Connecting the Numerical Scale Model With Assessing Attitudes and its Application to Hesitant Fuzzy Linguistic Multi-attribute Decision Making
items: Literature Review on Multi-Criteria Analysis and Application in Education Environment
items: Stock Price Prediction Using Deep Learning Algorithms Based on Technical Indicators
items: Optimization of Stowage Strategy for Ship Loading Tasks
items: Evaluation of User Costs in Terms of Public Transportation Fare: A Literature Review
items: Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models
items: Assessing the Academic Performance of Turkish Universities in 2023: A MEREC-WEDBA Hybrid Methodology Approach
items: A Comparative Analysis of the Machine Learning Methods for Predicting Diabetes
items: Study on the Method of Selecting Sustainable Food Suppliers Considering Interactive Factors
items: Smart-Watches Assisted Sugar Level Monitoring with Different Activities and Nutrition based on Machine Learning Approaches

Evaluation of Suitable Accounting and Auditing Firms for SMEs in the Textile Industry with Picture Fuzzy Set-based Entropy & MARCOS Approach
type: item
description: In today's highly competitive and dynamic business environment, selecting appropriate accounting and auditing service providers is critical for the sustainable growth and regulatory compliance of small and medium-sized enterprises (SMEs), particularly in the textile industry. This study proposes an integrated multi-criteria decision-making (MCDM) framework based on Picture Fuzzy Sets (PFS), Entropy, and the Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) method to evaluate and rank suitable accounting and auditing firms for textile SMEs. Picture Fuzzy Sets are employed to model the inherent uncertainty and hesitancy in expert judgments more effectively, while the entropy method is utilized to determine objective weights for evaluation criteria. The MARCOS approach is then applied to assess and prioritize alternatives based on ideal and anti-ideal solutions. The proposed model is demonstrated through a case study involving expert evaluations from textile sector representatives and financial professionals. The results provide actionable insights for SMEs in selecting optimal accounting and auditing partners, and the robustness of the model is verified through sensitivity and comparative analyses. This study contributes to the literature by offering a novel and practical decision-support framework tailored to the specific needs of SMEs in a key industrial sector.
subject: Articles
  topic: section
subject: Picture Fuzzy Sets
  topic: keywords
subject: Entropy Method
  topic: keywords
subject: MARCOS Method
  topic: keywords
subject: SMEs
  topic: keywords
subject: Accounting and Auditing Firms
  topic: keywords
creator: Cem Niyazi Durmus
creator: Omer Faruk Gorcun
rights: Copyright (c) 2025 Cem Niyazi Durmus, Omer Faruk Gorcun (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-01
publication date: 2025-06-01
starting page: 1
ending page: 22
doi: 10.31181/jopi41202658

Measurement Method for Food Supply Chain Security Level from the Perspective of Resilience
type: item
description: The resilience of China's food supply chain is of significant importance for ensuring national food security and maintaining social stability, and there is a close connection between supply chain resilience and safety levels, which can be considered integrated to some extent. To quantitatively measure the safety level of the food supply chain, this study explores the measurement of food supply chain safety based on an improved MABAC model. First, content mining is used to collect and analyze literature, identifying resilience risk factors and relevant evaluation indicators in the food supply chain. Second, T-spherical fuzzy sets are employed to convert expert evaluation language, and expert weights are determined through score functions to establish a weighted evaluation matrix. Finally, the entropy method is used to calculate indicator weights, combined with the MABAC method for risk ranking to derive the final results. The findings indicate that emergencies, the economy, and the market are the primary risk factors affecting the resilience of the food supply chain. The conclusion emphasizes the need to focus on controlling emergencies, guiding economic and market development directions, preparing risk prevention plans in advance, reducing the probability of unexpected events and their severity, and enhancing supply chain resilience and safety levels.
subject: Articles
  topic: section
subject: Food Supply Chain Resilience
  topic: keywords
subject: MCDM
  topic: keywords
subject: Fuzzy set
  topic: keywords
subject: MABAC
  topic: keywords
creator: Yan Shi
creator: Xinyue Yu
creator: Chuansen Shen
rights: Copyright (c) 2025 Yan Shi, Xinyue Yu, Chuansen Shen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-05-28
publication date: 2025-05-28
starting page: 324
ending page: 342
doi: 10.31181/jopi31202557

Offshore Wind Power Site Selection in Türkiye Using q-Rung Orthopair Fuzzy Sets and the COPRAS Method
type: item
description: Wind energy has significant potential for electricity and energy generation. At the same time, Türkiye is among the important markets in wind energy production worldwide. With the increasing energy demand and sustainability goals, energy production is gradually shifting towards offshore wind farms (OWFs). In this study, three alternative locations in the Aegean Sea region of Türkiye were evaluated to determine the most suitable site for OWF installation based on two main and twelve sub-criteria. The methodology applied in the study generally consists of four stages. In the first stage, a normalized weighted decision matrix was created using the steps of the q-rung Orthopair fuzzy set. In the second stage, the COPRAS method, one of the multi-criteria decision-making (MCDM) methods, was applied to rank the alternatives, and the best alternative was determined to be Bozcaada offshore. In the third stage, a comparison analysis was performed using q-ROF TOPSIS and q-ROF WASPAS methods. As a result of the comparison analysis, it was determined that all three methods gave the same results. In the last stage, the sensitivity of all three MCDM methods was checked by changing the q levels. The sensitivity analysis revealed that the q-ROF COPRAS method was insensitive to changes in q levels, while the q-ROF TOPSIS and q-ROF WASPAS methods were sensitive, as the alternative rankings changed as a result of these changes.
subject: Articles
  topic: section
subject: Offshore wind farm
  topic: keywords
subject: Wind energy
  topic: keywords
subject: Site selection
  topic: keywords
subject: q-Rung Orthopair fuzzy sets
  topic: keywords
subject: COPRAS method
  topic: keywords
subject: Renewable energy
  topic: keywords
creator: Javanshir Guliyev
creator: Bartu Güneri
creator: Mine Konur
creator: Şeyma Duymaz
creator: Ali Türk
rights: Copyright (c) 2025 Javanshir Guliyev, Bartu Güneri, Mine Konur, Şeyma Duymaz, Ali Türk (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-05-26
publication date: 2025-05-26
starting page: 283
ending page: 307
doi: 10.31181/jopi31202551

Analysis of the Stages of Military Cyber Operations using the COBRAC Method and p, q Quasirung Orthopair Fuzzy Decision Making Framework
type: item
description: This study aims to analyze the phased structure of military cyber operations and reveal the relative importance of each stage based on expert evaluations. The analysis, which covers seven fundamental phases—reconnaissance, weaponization, delivery, exploitation, installation, command and control (C2), and actions on objectives—was conducted using the Comparisons between Ranked Criteria (COBRAC) method. The results demonstrate that operational success in military cyber activities is not solely determined by the direct impact on the target, but also by the preparatory processes that lead to such effects. The findings indicate that reconnaissance and weaponization are particularly critical in terms of strategic planning and technical capability development. The delivery and installation phases are also prominent, as they ensure the sustainability of the operation and persistence within the target system. In contrast, exploitation and C2 stages, while functionally important, appear to play a more flexible and instrumental role. The lowest score was assigned to the actions on objectives phase, suggesting that in military cyber operations, long-term access, intelligence gathering, and maintaining digital superiority are often prioritized over immediate destructive outcomes. To verify the reliability of the ranking results, a sensitivity analysis was performed using the p, q Quasirung Orthopair Fuzzy Decision-Making Framework, and the consistency of the outcomes was confirmed. Overall, the study offers a comprehensive perspective on the nature of military cyber operations and recommends that future research focus on modeling dynamic decision-making processes, conducting interdisciplinary analyses, and developing advanced decision support systems.
subject: Articles
  topic: section
subject: Military Operations
  topic: keywords
subject: Cyber Operations
  topic: keywords
subject: Cyber Attacks
  topic: keywords
subject: Cyber Space
  topic: keywords
subject: Cyber Security
  topic: keywords
subject: MCDM
  topic: keywords
subject: COBRAC
  topic: keywords
subject: p, q Quasirung Orthopair Fuzzy Numbers
  topic: keywords
creator: Hakan Ayhan Dağıstanlı
rights: Copyright (c) 2025 Hakan Ayhan Dağıstanlı (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-01
publication date: 2025-06-01
starting page: 343
ending page: 353
doi: 10.31181/jopi31202550

A Data Evolvement Analysis for Sustainable Performance Evaluation of Educational Resource Use in Architecture Universities
type: item
description: In the context of China’s dual-carbon strategy and the ongoing promotion of green campus initiatives, efficient resource allocation in universities, particularly architecture-focused institutions, has garnered growing attention. This study evaluates 28 architecture universities in mainland China using Data Envelopment Analysis (DEA), applying standardized input–output indicators. The analysis examines three key dimensions: technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE). The results indicate that while many universities exhibit strong managerial efficiency, significant disparities in scale efficiency persist, suggesting widespread misalignments between institutional size and resource utilization. Further classification based on returns to scale (RTS) reveals distinct inefficiency types, providing a foundation for targeted improvement strategies. The study concludes with policy recommendations aimed at differentiated resource optimization and sustainable campus development, offering practical insights to enhance the green performance and long-term sustainability of architecture universities.
subject: Articles
  topic: section
subject: Architecture Universities
  topic: keywords
subject: Data Envelopment Analysis
  topic: keywords
subject: Sustainable Development
  topic: keywords
subject: Scale Efficiency
  topic: keywords
creator: Youzhi Pan
creator: Min Kong
creator: Weijian Zhang
creator: Amir M. Fathollahi-Fard
rights: Copyright (c) 2025 Youzhi Pan, Min Kong, Weijian Zhang, Amir M. Fathollahi-Fard (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-05-27
publication date: 2025-05-27
starting page: 308
ending page: 323
doi: 10.31181/jopi31202549

A Hybrid Quadripartitioned Single-Valued Neutrosophic Method and its Application for the Selection of Emergency Logistics Outsourcing Suppliers
type: item
description: The selection of an emergency logistics outsourcing supplier (ELOS) is critical for enhancing disaster response efficiency, reducing operational costs, and strengthening supply chain resilience. A scientifically robust supplier evaluation framework optimizes resource allocation, mitigates risks, and ensures timely material distribution, serving as a cornerstone of effective emergency management systems. This study introduces a novel hybrid quadripartitioned single-valued neutrosophic (QSVN) multi-criteria decision-making (MCDM) approach, integrating the coefficient of variation method (CVM) and complex proportional assessment (COPRAS) to address ambiguity and uncertainty in decision-making. Key contributions include (1) establishing QSVN Aczel-Alsina operational rules based on Aczel-Alsina norms, (2) developing weighted averaging and geometric Aczel-Alsina aggregation operators for QSVN numbers (QSVNNs), and (3) proposing a hybrid CVM-COPRAS framework for criteria weighting and alternative ranking. A case study on ELOS selection validates the method’s efficacy, supported by sensitivity and comparative analyses demonstrating its robustness and superiority over existing techniques. The results confirm the model’s stability and improved performance in complex decision-making under uncertainty.
subject: Articles
  topic: section
subject: ELOSs
  topic: keywords
subject: QSVN set
  topic: keywords
subject: Multiple criteria decision analysis
  topic: keywords
subject: Aczel–Alsina
  topic: keywords
creator: Dongmei Li
creator: Yuan Rong
rights: Copyright (c) 2025 Dongmei Li, Yuan Rong (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-23
publication date: 2025-04-23
starting page: 130
ending page: 148
doi: 10.31181/jopi31202548

Prospects and Challenges of the Metaverse in Pursuing Sustainable Development
type: item
description: The metaverse, with its immersive technologies, is rapidly expanding across various industries, offering promising features that are transforming how businesses operate. While the initial hype surrounding the metaverse in the gaming sector has subsided, its industrial applications are growing at an unprecedented pace. Even traditionally risk-averse business leaders—initially hesitant to adopt new technologies—are now testing and integrating metaverse innovations into their operations. However, despite its vast potential, the metaverse presents significant challenges, particularly concerning its impacts on economies, societies, and the environment. This study explores the diverse opportunities and challenges associated with the metaverse, especially in the context of sustainable development, by providing a comprehensive literature review. It examines the metaverse’s potential across multiple dimensions—including economic, social, and environmental sustainability—and highlights policy implications based on data collected from a broad range of academic sources. The findings suggest that while the metaverse offers substantial opportunities for enhancing environmental and industrial sustainability, social sustainability remains more complex and context-dependent. Achieving social sustainability through the metaverse requires complementary policies focused on education, digital literacy, and social psychology to address concerns such as social isolation, digital dependence, and unequal access. Furthermore, the study emphasizes that the global legal framework is currently ill-equipped to address the challenges posed by the metaverse, with legal systems in most countries lagging behind rapid technological advancements. Establishing a robust and adaptable legal infrastructure is therefore critical to realizing the metaverse’s full potential in supporting sustainable development goals. The study concludes that although the metaverse presents a transformative pathway toward su…
subject: Articles
  topic: section
subject: Metaverse
  topic: keywords
subject: Sustainable development
  topic: keywords
subject: Digital transformation
  topic: keywords
creator: Mehtap Isik
rights: Copyright (c) 2025 Mehtap Isik (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-05-19
publication date: 2025-05-19
starting page: 256
ending page: 266
doi: 10.31181/jopi31202547

Decision Support System Using Centroidous and CoCoFISo Methods for Analyzing Resource Availability in High Schools
type: item
description: The allocation of educational resources significantly impacts student enrollment and success across different levels of study. This study examines the distribution of human and material resources in Madagascar’s high schools, administered by the Regional Directorates of National Education (DRENs). To address inequities in resource allocation, we propose a systematic evaluation of the 23 DRENs using three contextual approaches: rural, urban, and a hybrid model. We developed a multi-criteria decision support system (MADREN) to assess DRENs annually, incorporating nine criteria: four measuring material resources, three evaluating human resources, and two tracking student success. The Centroidous method was employed to determine criterion weights, while the Combined Compromise for Ideal Solution (CoCoFISo) method ranked the DRENs. Results revealed consistent top performance by the Analamanga DREN across all contexts, while the Ihorombe (urban), Melaky (rural), and hybrid models consistently ranked lowest. The rankings highlight disparities in resource availability, with materially advantaged DRENs outperforming others. Crucially, the study identifies significant inequities in current resource distribution. This research provides policymakers with a data-driven tool to optimize resource allocation, ensuring equitable opportunities for student success across Madagascar’s high schools. The MADREN system offers a replicable framework for evidence-based educational planning in resource-constrained settings.
subject: Articles
  topic: section
subject: Resources availability to schools
  topic: keywords
subject: Decision Support System (DSS)
  topic: keywords
subject: Multi-criteria decision-making (MCDM)
  topic: keywords
subject: Centroidous
  topic: keywords
subject: CoCoFISo
  topic: keywords
creator: Rôlin Gabriel Rasoanaivo
rights: Copyright (c) 2025 Rôlin Gabriel Rasoanaivo (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-05-06
publication date: 2025-05-06
starting page: 202
ending page: 231
doi: 10.31181/jopi31202546

Predictive Stock Selection: A Hybrid RF-CNN XGBoost Model Integrated with Dynamic Adaptive Index and Stepwise Elimination Techniques
type: item
description: This paper presents a predictive model for stock selection using a hybrid approach. The proposed model, which combines advanced machine learning algorithms with a dynamic adaptive index (DAI), aims to enhance portfolio performance and risk management. In this context, Random Forest (RF) and XGBoost algorithms are utilized to select optimal features and forecast stock returns. In the subsequent step, the DAI method assesses and selects stocks. By integrating various metrics such as correlation, volatility, relative strength index (RSI), and cumulative returns, the DAI method enables a comprehensive assessment of available stocks that dynamically adapts to changing market conditions. In addition, the stepwise elimination technique optimizes the portfolio by eliminating stocks with high volatility and low returns, ensuring that only stocks with strong performance and acceptable risk are retained. The two-stage structure of the proposed model, encompassing stock return prediction and stock selection, enhances the model's overall efficiency. The presented hybrid model excels in stock selection and risk management, helping investors achieve higher returns while reducing portfolio risk.
subject: Articles
  topic: section
subject: Stock Selection
  topic: keywords
subject: Stock prediction
  topic: keywords
subject: Random Forest
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Dynamic Adaptive Index
  topic: keywords
creator: Farshid Abdi
creator: Shaghayegh Abolmakarem
creator: Amir Karbassi Yazdi
rights: Copyright (c) 2025 Farshid Abdi, Shaghayegh Abolmakarem, Amir Karbassi Yazdi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-05-24
publication date: 2025-05-24
starting page: 267
ending page: 282
doi: 10.31181/jopi31202545

Transport Justice for Istanbul’s Urban Mobility: Equity, Accessibility, and Policy Challenges
type: item
description: Transport justice is a critical dimension of urban mobility, ensuring that access to urban mobility is equitable across different socioeconomic groups. The concept goes beyond mere infrastructure development to consider fairness in accessibility, affordability, and participation in transport planning. In Istanbul, a megacity of over 16 million people, mobility is shaped by a complex interplay of geographic, economic, and social factors. While the city boasts an extensive public transport network, including buses, ferries, metro lines, and tramways, many residents face barriers to mobility due to income disparities, gender dynamics, and infrastructural inefficiencies. Istanbul’s rapid urbanization and complex transport landscape have created significant disparities in mobility access. This paper explores Istanbul’s urban mobility through the lens of transport justice, focusing on equity, accessibility, and policy challenges, and proposes policies that contribute to more equitable and sustainable urban mobility. Drawing on theoretical frameworks such as distributive justice, mobility justice, and Rawlsian equity, this study highlights how socioeconomic status, gender, disability, and geography influence mobility experiences. The paper presents an evaluation of Istanbul’s public transport policies in promoting sustainable and inclusive mobility. The findings suggest that while recent transport infrastructure developments aim to improve accessibility, existing inequalities persist, particularly for marginalized groups.
subject: Articles
  topic: section
subject: Transport justice
  topic: keywords
subject: Istanbul urban mobility
  topic: keywords
subject: Equity
  topic: keywords
subject: Accessibility
  topic: keywords
subject: Transport policies
  topic: keywords
creator: Fatih Canitez
rights: Copyright (c) 2025 Fatih Canitez (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-05-09
publication date: 2025-05-09
starting page: 232
ending page: 244
doi: 10.31181/jopi31202544

Anti-swing Control for Underactuated Crane under Disturbances
type: item
description: In practical applications, a crane must achieve both precise positioning and effective suppression of swing. However, these dual requirements often present a contradiction: the acceleration and deceleration of the trolley may induce oscillations in the swing angle. This phenomenon stems from the inherent complexity of crane dynamics. On the one hand, cranes exhibit underactuated characteristics, as only the trolley is equipped with an actuator, while no direct actuator controls the swing angle. On the other hand, cranes possess highly coupled nonlinear dynamics, where actuated and unactuated variables interact, increasing the complexity of control design. To address these challenges, this paper investigates anti-swing control for an overhead crane under disturbances. The proposed approach suppresses swing by coupling its integral information with the trolley position and introducing negative damping into the unactuated subsystem. To handle disturbances, both matched and mismatched disturbances are incorporated into the control channel and compensated through feedforward mechanisms. Theoretical analysis demonstrates that the designed control system is stable. Simulation results validate the effectiveness of the proposed approach.
subject: Articles
  topic: section
subject: Anti-swing Control
  topic: keywords
subject: Underactuated Crane
  topic: keywords
subject: Disturbances
  topic: keywords
subject: Stability
  topic: keywords
creator: Tong Song
creator: Juan Guo
rights: Copyright (c) 2025 Tong Song, Juan Guo (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-05-13
publication date: 2025-05-13
starting page: 245
ending page: 255
doi: 10.31181/jopi31202543

The Effects of Public Transit in Vehicle Safety Measures for Women on Gender Equity
type: item
description: Security-related risk perception significantly influences passengers' travel decisions, yet current security infrastructure in public transportation fails to equally address the safety needs of men and women. Research indicates that women experience criminal and violent acts more frequently in public transit despite existing security measures, leading to behavioral adaptations such as trip avoidance, mode shifts, or destination changes. These adjustments create barriers to accessing jobs, education, healthcare, and social services, ultimately undermining transportation equity. This study examines the relationship between in-vehicle security applications and gender equity through a literature review of global and Turkish implementations, assessing their effectiveness in promoting equitable transit access. Additionally, the potential future impacts of autonomous vehicles on in-vehicle security are explored. The paper concludes with policy recommendations to enhance security infrastructure while advancing gender equity in public transportation.
subject: Articles
  topic: section
subject: Public Transit
  topic: keywords
subject: In-vehicle Security
  topic: keywords
subject: Autonomous Vehicles
  topic: keywords
subject: Gender Equity
  topic: keywords
creator: Ilgin Yasar
creator: Gulin Goksu Basaran
rights: Copyright (c) 2025 Ilgin Yasar, Gulin Goksu Basaran (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-23
publication date: 2025-04-23
starting page: 149
ending page: 160
doi: 10.31181/jopi31202540

Enhancing Breast Cancer Diagnosis: A Comparative Evaluation of Machine Learning Algorithms Using the Wisconsin Dataset
type: item
description: Breast cancer remains a leading cause of morbidity, particularly among women, underscoring the critical importance of early detection. In recent years, highly accurate machine learning algorithms have revolutionized breast cancer identification, significantly improving early diagnosis by analyzing tumor attributes to aid in detection and treatment decisions. This study evaluates seven machine learning algorithms using the Wisconsin breast cancer dataset, revealing that the Support Vector Machines (SVM) algorithm outperforms all others with an exceptional accuracy of 97.66%. These findings highlight the transformative potential of machine learning in clinical practice, offering healthcare professionals a powerful tool to enhance diagnostic precision, improve patient outcomes, and advance progress in oncology.
subject: Articles
  topic: section
subject: Breast Cancer Detection
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Support Vector Machines
  topic: keywords
subject: CatBoost
  topic: keywords
subject: Gradian Boost
  topic: keywords
subject: KNN
  topic: keywords
subject: Wisconsin Dataset
  topic: keywords
creator: Yigitcan Cakmak
creator: Ishak Pacal
rights: Copyright (c) 2025 Yigitcan Cakmak, Ishak Pacal (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-05-06
publication date: 2025-05-06
starting page: 180
ending page: 201
doi: 10.31181/jopi31202539

Transforming Preventive Healthcare with Machine Learning Technologies
type: item
description: In this paper, we explore the transformative role of machine learning (ML) in preventive healthcare (PHC), a proactive approach to health management that aims to prevent diseases and promote overall well-being. We begin by providing an overview of PHC, its importance, and its applications across various healthcare settings. The manuscript then presents a brief review of ML techniques in this field, examining their potential to revolutionize early disease detection, personalized risk assessment, and targeted interventions. We review key studies that demonstrate the capabilities of ML in areas such as cancer screening, cardiovascular risk prediction, population health management, and traditional medicine. By synthesizing current research and identifying future directions, this work aims to enhance the understanding of how ML is reshaping the PHC domain, potentially improving health outcomes and reducing healthcare costs.
subject: Articles
  topic: section
subject: Machine learning
  topic: keywords
subject: Preventive healthcare
  topic: keywords
subject: Early disease detection
  topic: keywords
subject: Risk prediction
  topic: keywords
subject: Health analytics
  topic: keywords
creator: Mumtaz Karatas
creator: Zahra Zare
creator: Yu-Jun Zheng
rights: Copyright (c) 2025 Mumtaz Karatas, Zahra Zare, Yu-Jun Zheng (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-07
publication date: 2025-04-07
starting page: 113
ending page: 129
doi: 10.31181/jopi31202538

Selection of Biomass Resources with Fuzzy Multi-Criteria Decision-Making Methods: Alternatives for Sustainable Energy Production
type: item
description: Selecting the most sustainable and efficient biomass resource for energy production requires a strategic multi-criteria decision-making approach. This study evaluates four biomass alternatives—plant-based, forest/wood-based, animal-based, and urban/industrial waste biomass—across economic, environmental, technical, and social dimensions, using twelve sub-criteria. The Fuzzy Bonferroni Aggregation Operator consolidated data, while the Fuzzy Weight by Envelope and Slope (F-WENSLO) method prioritized main and sub-criteria, and the Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) method ranked alternatives. Results identified animal-based biomass (Qᵢ = 1.1228) as the optimal choice due to its superior energy efficiency and economic benefits, followed by plant-based (Qᵢ = 1.0989), forest/wood-based (Qᵢ = 1.0919), and urban/industrial waste biomass (Qᵢ = 1.0848). The latter’s low ranking reflects environmental and compatibility challenges. The study underscores the need for holistic biomass selection, balancing economic, technical, environmental, and social factors to inform sustainable energy policies.
subject: Articles
  topic: section
subject: Biomass resources
  topic: keywords
subject: Fuzzy set
  topic: keywords
subject: WENSLO
  topic: keywords
subject: RAWEC
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Renewable energy
  topic: keywords
subject: Decision-making
  topic: keywords
subject: Aggregation operator
  topic: keywords
creator: Gülay Demir
rights: Copyright (c) 2025 Gülay Demir (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-23
publication date: 2025-04-23
starting page: 161
ending page: 179
doi: 10.31181/jopi31202537

Chinese Sentiment Analyses Research: A Systematic Review and Bibliometric Analysis
type: item
description: This study aims to shed light on topics related to sentiment analysis in the Chinese language, due to the scarcity of studies in this area and its growing importance. We used a systematic, bibliometric approach for its effectiveness in analyzing the current state of sentiment analysis research in Chinese. The study identifies three main stages based on the analysis of previous research in this field. In the first stage, we examine the challenges faced by researchers in analyzing sentiment in Chinese, including the lack of access to a comprehensive lexicon. In the second stage, we discuss the motives underlying the growing importance and potential impact of sentiment analysis in Chinese. Finally, we make recommendations to guide future research in this field to improve the accuracy and reliability of sentiment analysis tools for Chinese.
subject: Articles
  topic: section
subject: Opinion mining
  topic: keywords
subject: Opinion analysis
  topic: keywords
subject: Chinese language
  topic: keywords
subject: Taiwanese language
  topic: keywords
creator: Shahd Al-Majali
creator: Ibrahim Alshakhatreh
rights: Copyright (c) 2025 Shahd Al-Majali, Ibrahim Alshakhatreh (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-03-31
publication date: 2025-03-31
starting page: 96
ending page: 112
doi: 10.31181/jopi31202536

Cross-tabulation Analysis of the Impact of University Students on IEQ Factors
type: item
description: This study investigates the impact of indoor environmental quality (IEQ) factors—thermal environment, indoor air quality, lighting, and acoustics—on university students' satisfaction. Conducted at University College London (UCL), the research employs a mixed-method approach, combining subjective assessments through questionnaires with objective measurements of environmental conditions. The findings reveal that students exhibit higher satisfaction with cooler indoor environments during spring, with thermal comfort significantly influencing their overall satisfaction. Indoor air quality, particularly air movement and relative humidity, also plays a crucial role, with students preferring neutral conditions. Acoustic and lighting environments further impact satisfaction, with a notable preference for brighter lighting despite overall contentment with current levels. The study underscores the complex interplay between various IEQ factors and their collective influence on students' well-being, highlighting the need for tailored environmental designs in educational settings to enhance student satisfaction.
subject: Articles
  topic: section
subject: Thermal comfort
  topic: keywords
subject: Indoor environmental quality
  topic: keywords
subject: University students
  topic: keywords
creator: Junpeng Lyu
creator: Michael Pitt
rights: Copyright (c) 2025 Junpeng Lyu, Michael Pitt (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-02-24
publication date: 2025-02-24
starting page: 76
ending page: 95
doi: 10.31181/jopi31202535

Development of Optimal Investment Strategies for Electric Vehicle Charging Stations with a Novel Decision-Making Technique
type: item
description: Electric vehicles (EVs) have become widespread, especially in recent years. However, the infrastructure required for charging these vehicles is inadequate, and the necessary investment strategies need to be determined for the effective establishment of this infrastructure. Electric vehicle charging stations are a fundamental component of sustainable transportation systems. In parallel with the increasing EV demand, determining appropriate investment strategies to support the installation of these stations is a critical need. This study aims to identify the optimal investment strategies for the effective establishment of EV charging stations, addressing the growing demand for sustainable and environmentally friendly transportation solutions. With the increasing adoption of electric vehicles, the development of an efficient EV charging infrastructure becomes critical. Thus, it is intended to determine the most suitable strategies for building efficient charging station infrastructures. The motivation for this research arises from the necessity to strengthen the existing charging infrastructure in response to the rising number of electric vehicles and the need for sustainable transport solutions. Electric vehicle charging stations are vital components of sustainable mobility, making it essential to evaluate the investment strategies that will enable their successful establishment and operation. To achieve the study’s goal, a detailed analysis has been conducted using the decision-making trial and evaluation laboratory (DEMATEL) technique. The findings from this analysis emphasize that technological improvement is the most crucial factor in enhancing the performance of EV charging infrastructure projects. Technological advancements such as faster charging technologies, greater energy efficiency, and better user interfaces are paramount to ensuring the success of these projects. In addition to technological improvements, financial performance and legal effectiveness also…
subject: Articles
  topic: section
subject: Electric vehicle
  topic: keywords
subject: Charging
  topic: keywords
subject: Investment
  topic: keywords
subject: Decision-making
  topic: keywords
creator: Mustafa Feyzi Yardım
creator: Serhat Yüksel
creator: Hasan Dinçer
rights: Copyright (c) 2025 Mustafa Feyzi Yardım, Serhat Yüksel, Hasan Dinçer (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-02-11
publication date: 2025-02-11
starting page: 68
ending page: 75
doi: 10.31181/jopi31202534

Ranking of Criteria Affecting the Implementation Readiness of Internet of Things in industries Using TISM and Fuzzy TOPSIS Analysis
type: item
description: The Internet of Things (IoT) technology has emerged as a vital driver across various fields, engaging businesses, platforms, and industries. IoT involves a holistic ecosystem and a value chain that necessitates the evaluation of impactful dimensions for successful implementation. This research employs the TISM method to identify driver and dependent criteria regarding IoT implementation readiness and uses the fuzzy TOPSIS method to rank these criteria. In the initial step, 15 criteria were identified through a review of previous studies. The TISM results reveal five levels reflecting the driver power and dependence of the criteria. Based on these results, “Implementation Knowledge and Expertise (C2),” “Technical and Infrastructural Readiness (C9),” and “Financial and Investment Readiness (C12)” were placed at level 5, marking them as the most driving criteria. Additionally, “Implementation Roadmap (C8)” was identified as the most dependent criterion at level one. According to the fuzzy TOPSIS results, “Senior Management Support (C6),” “IoT Usage Culture (C1),” “Business Model Development Capability (C15),” “Financial and Investment Readiness (C12),” and “Technical and Infrastructural Readiness (C9)” ranked first to fifth, respectively. The combined results provide valuable insights for decision-makers and stakeholders involved in IoT implementation. By determining driver and dependent levels and ranking the criteria, industries can effectively prepare for the successful implementation of IoT.
subject: Articles
  topic: section
subject: IoT readiness
  topic: keywords
subject: Fuzzy TOPSIS
  topic: keywords
subject: Decision Making
  topic: keywords
subject: TISM
  topic: keywords
subject: Industry
  topic: keywords
creator: Neda Seifi
creator: Mitra Keshavarz
creator: Hassan Kalhor
creator: Sara Shahrakipour
creator: Ali Adibifar
rights: Copyright (c) 2025 Neda Seifi, Mitra Keshavarz, Hassan Kalhor, Sara Shahrakipour, Ali Adibifar (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-01-01
publication date: 2025-01-01
starting page: 47
ending page: 67
doi: 10.31181/jopi31202533

Fermatean Fuzzy TOPSIS Method and Its Application in Ranking Business Intelligence-Based Strategies in Smart City Context
type: item
description: With the expansion of smart cities, the use of business intelligence (BI) has emerged as a crucial tool for resource optimization, increasing efficiency, and improving citizens’ quality of life. BI enables companies to make better strategic decisions by analyzing vast amounts of urban data, helping them remain competitive in the dynamic smart city environment. This study utilizes content analysis and the Fermatean Fuzzy TOPSIS (FF-TOPSIS) method to rank strategies based on business intelligence in the context of smart cities. Initially, relevant criteria were identified through content analysis, and subsequently, five strategies were developed and ranked based on these criteria. The results revealed that the "Development of IoT-enabled smart networks (S2)" ranked highest, as it plays a significant role in optimizing resource management and enhancing urban service performance, thereby contributing greatly to the advancement of smart cities. "Process automation and the deployment of robotic systems (S5)" ranked second, as it enhances efficiency and reduces human errors. "Cloud platform integration for seamless access to data and services (S3)" also proved to be of considerable importance, ranking third, as it provides seamless access to data and services. "Artificial intelligence deployment for predictive analytics and process optimization (S4)" ranked fourth and was vital for predictive analytics and process optimization, while "Big data analytics for smart decision-making (S1)"—despite its importance—ranked fifth. Urban managers should prioritize the development of IoT networks to fully leverage their potential for resource management and efficiency gains. Following this, attention to process automation and AI integration can significantly enhance citizens’ quality of life and reduce urban costs.
subject: Articles
  topic: section
subject: Smart City
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Fermatean Fuzzy Set
  topic: keywords
subject: TOPSIS
  topic: keywords
subject: Decision Making
  topic: keywords
creator: Seyed Shabahang Majd
creator: Alireza Maleki
creator: Sepideh Basirat
creator: Amin Golkarfard
rights: Copyright (c) 2025 Seyed Shabahang Majd, Alireza Maleki, Sepideh Basirat, Amin Golkarfard (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-01-01
publication date: 2025-01-01
starting page: 1
ending page: 16
doi: 10.31181/jopi31202532

Connecting the Numerical Scale Model With Assessing Attitudes and its Application to Hesitant Fuzzy Linguistic Multi-attribute Decision Making
type: item
description: The aim of this paper is to provide a specific numerical scale model for making transformations between linguistic terms and numerical values. The proposed method represents a wide range of existing numerical scale models and can quantitatively reflect the linguistic behaviors of decision makers (DMs). The pessimistic-optimistic principle-based supplementary regulation for hesitant fuzzy linguistic term sets (HFLTS) may lead to initial information distortion and loss. Based on the lowest common multiple principle in number theory, an improved supplementary regulation is proposed to preserve the fidelity of the original information; this improved regulation also brings a new concept for information measures of HFLTS. Then, based on traditional generalized distance and Hausdorff distance measures, some new distance measures for HFLTS are presented within the numerical scale framework. Furthermore, an extended TOPSIS method for hesitant fuzzy linguistic multi-attribute decision making (MADM) is developed. Finally, a numerical example concerning movie preferences is elaborated to demonstrate the performance of our approach. Sensitivity and comparative analyses are also provided and discussed to show the effectiveness and advantages of the proposed method.
subject: Articles
  topic: section
subject: Multiple attribute decision making
  topic: keywords
subject: Numerical scale
  topic: keywords
subject: Hesitant fuzzy linguistic term set
  topic: keywords
subject: Assessing attitudes
  topic: keywords
subject: Weights
  topic: keywords
creator: Qun Wu
creator: Yimeng Wang
creator: Yuhan Wangzhu
rights: Copyright (c) 2025 Qun Wu, Yimeng Wang, Yuhan Wangzhu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-01-01
publication date: 2025-01-01
starting page: 17
ending page: 46
doi: 10.31181/jopi31202531

Literature Review on Multi-Criteria Analysis and Application in Education Environment
type: item
description: Due to the increasing complexity of educational data, the use of decision analysis techniques such as Multi-Criteria Decision Making (MCDM) models has become more popular in the education system in recent years. Multi-criteria decision-making methods provide faster and more efficient data analysis techniques for revealing hidden patterns and other meaningful information from vast educational data that conventional analytics are unable to discover in a reasonable amount of time. Particularly, MCDM techniques have been demonstrated to be effective methods for pattern recognition in educational systems. Motivated by this consideration, the purpose of this paper is to investigate the MCDM approaches applied to education systems through a review of new architectures, applications, and educational trends. The primary objective of this paper is to provide extensive insight into the application of MCDM models to education solutions in order to bridge the gap between MCDM techniques and human-based education interpretability. Then, the application of MCDM to various aspects of education is categorized. Finally, we present the current open challenges and future directions.
subject: Articles
  topic: section
subject: MCDM
  topic: keywords
subject: Education
  topic: keywords
subject: Decision Analysis
  topic: keywords
subject: E-learning
  topic: keywords
subject: Evaluation
  topic: keywords
creator: Ibrahim Alshakhatreh
creator: Dramane Thiombiano
creator: Shahd Al-Majali
rights: Copyright (c) 2024 Ibrahim Alshakhatreh, Dramane Thiombiano, Shahd Al-Majali (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-10-13
publication date: 2024-10-13
starting page: 336
ending page: 367
doi: 10.31181/jopi21202428

Stock Price Prediction Using Deep Learning Algorithms Based on Technical Indicators
type: item
description: Accurately forecasting stock prices helps investors decide when and where to invest. However, the dynamic, non-linear, complex, and chaotic nature of the stock market makes price forecasting difficult. Market movements are influenced by many macroeconomic factors such as political events, corporate policies, economic conditions, commodity prices, and bank interest rates. In addition, advances in technology and communication systems allow these events to be processed quickly, causing stock prices to fluctuate rapidly. Banks, financial institutions, and large investors are therefore forced to act quickly, and the complexity of the market makes accurate forecasting even more difficult. Therefore, new and effective methods must be developed for accurate stock price predictions. In this study, technical indicators were utilized to reduce noise in raw data, obtain meaningful results, and enhance prediction accuracy. Artificial Neural Network (ANN) models demonstrate significant efficiency in analyzing financial time series data. The most appropriate parameters for predictions made with ANN were selected using the correlation coefficient method. The objective was to predict stock prices using technical indicators obtained from websites such as Yahoo Finance. Following data preprocessing, various methods were applied, including Single Layer Long Short-Term Memory (LSTM), 3-Layer LSTM, 3-Layer Bidirectional Long Short-Term Memory (BiLSTM), and Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). This model aimed not only to find realistic price estimates but also to reduce the features that influence stock price estimates through technical indicators. The results indicate that the Single Layer LSTM method provides more realistic estimates than other deep learning (DL) techniques.
subject: Articles
  topic: section
subject: Artificial neural networks
  topic: keywords
subject: Stock price prediction
  topic: keywords
subject: Technical Indicators
  topic: keywords
creator: Mine Konur
creator: Mustafa Göçken
creator: Ayşe Tuğba Dosdoğru
rights: Copyright (c) 2024 Mine Konur, Mustafa Göçken, Ayşe Tuğba Dosdoğru (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-08-13
publication date: 2024-08-13
starting page: 300
ending page: 320
doi: 10.31181/jopi21202427

Optimization of Stowage Strategy for Ship Loading Tasks
type: item
description: In recent years, the logistics industry has witnessed rapid growth, drawing considerable attention to pre-transport cargo loading as a focal point of research for scholars globally. However, the escalating complexity of actual transportation processes and the diversification of application scenarios have rendered traditional loading methodologies inadequate to meet the evolving demands of enterprises. To address this issue, this paper presents a solution to the loading problem of rectangular pulp bales in non-rectangular complex cargo holds, effectively meeting the demand for optimization algorithms and thereby conserving manpower and resources. The paper introduces a packing algorithm tailored to custom regions, which integrates the optimized Skyline algorithm with domain-search iterative strategies to achieve optimal loading outcomes. Furthermore, the efficacy of the algorithm is validated using real maritime data, demonstrating its robust performance and loading efficiency.
subject: Articles
  topic: section
subject: Ship Stowage
  topic: keywords
subject: Bin Packing Problem
  topic: keywords
subject: Optimization Algorithm
  topic: keywords
creator: Zhuangle Yao
creator: Lubin Wu
creator: Meng Yuan
creator: Zehui Wu
creator: Shancheng Jiang
creator: Zhen-Song Chen
rights: Copyright (c) 2024 Zhuangle Yao, Lubin Wu, Meng Yuan, Zehui Wu, Shancheng Jiang, Zhen-Song Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-08-04
publication date: 2024-08-04
starting page: 273
ending page: 284
doi: 10.31181/jopi21202425

Evaluation of User Costs in Terms of Public Transportation Fare: A Literature Review
type: item
description: The welfare level differs in every geography depending on the economy. This also differentiates the variables taken into account when determining transportation fares. While underdeveloped or developing countries determine prices by focusing only on operating and investment costs, developed countries take user and environmental costs into consideration. However, the failure to describe the parameters used in determining these costs leads to the emergence of ineffective pricing policies. Especially, the concept of user cost has become the focus of human-oriented policies in recent years. To this end, the paper provides a literature-based analysis of user costs used in the calculation of public transportation fares. The fact that studies in the literature did not address user costs within a clear framework has created an important gap in this regard. The aim of this study is to fill this gap in the literature by systematically examining the user cost factor affecting transportation fares and to provide a source for other research.
subject: Articles
  topic: section
subject: Public Transportation Fare
  topic: keywords
subject: User Costs
  topic: keywords
subject: In-vehicle Time
  topic: keywords
subject: Waiting Time
  topic: keywords
subject: Access-egress Time
  topic: keywords
subject: Value of Time
  topic: keywords
creator: Ahmet Karakurt
creator: Ilgin Gokasar
rights: Copyright (c) 2024 Ahmet Karakurt, Ilgin Gokasar (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-08-05
publication date: 2024-08-05
starting page: 285
ending page: 299
doi: 10.31181/jopi21202426

Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models
type: item
description: Sugarcane is a crucial agricultural crop, providing 75% of the world's sugar production. Like all plant species, any disease that affects sugarcane can significantly impact yield and planning. Traditional manual methods for diagnosing diseases in sugarcane leaves are slow, inefficient, and often lack accuracy. In this study, we present a deep learning-based approach for the robust detection of diseases in sugarcane leaves. Specifically, we trained and evaluated models from the EfficientNetv1 and EfficientNetv2 architectures, which are among the most notable convolutional neural network (CNN) architectures, using the publicly available Sugarcane Leaf Dataset. This dataset includes 11 disease classes and a total of 6,748 images. Additionally, we compared these models with other popular CNN models. Our findings reveal that there is no direct correlation between model complexity, depth, and accuracy for the 11-class sugarcane dataset. Among the 13 models tested, EfficientNet-b6 and InceptionV4 achieved the highest accuracy rates of 93.39% and 93.10%, respectively. These results have a significant impact on how managers can manage diseases and the agricultural processes of sugarcane production. A deep learning-based disease detection system facilitating the diagnostic process can, in turn, result in more accurate and faster identification of diseases. This may enable farmers and agricultural managers to make timely and informed decisions, reducing crop loss and enhancing overall yield. These findings highlight the potential of deep learning in developing fast, accurate, and automatic disease diagnosis systems, which can significantly improve disease management and increase sugarcane yield.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Deep-Learning
  topic: keywords
subject: CNN
  topic: keywords
subject: Plant diseases
  topic: keywords
creator: İsmail Kunduracıoğlu
creator: İshak Paçal
rights: Copyright (c) 2024 İsmail Kunduracıoğlu, İshak Paçal (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-08-25
publication date: 2024-08-25
starting page: 321
ending page: 335
doi: 10.31181/jopi21202423

Assessing the Academic Performance of Turkish Universities in 2023: A MEREC-WEDBA Hybrid Methodology Approach
type: item
description: Research and reporting on university rankings serve as valuable tools for students in evaluating universities and understanding their current performance status. Within academic literature, university rankings are established using diverse criteria across various domains, each carrying varying degrees of importance. This study adopts a multi-criteria decision-making (MCDM) perspective to analyze the academic performance ranking of Turkish universities in 2023. Data sourced from the 2023 reports of sixty-one universities from Times Higher Education (THE) serve as the basis for this research, with THE indicators—teaching, research, citations, industry income, and international outlook—considered as primary research criteria. The Method based on the Removal Effects of Criteria (MEREC) is employed to ascertain criterion weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) is utilized for university ranking. The study identifies "citations" as the criterion of highest significance. Notably, the top-performing universities in the ranking include Çankaya University, Fırat University, and Bahçeşehir University. Furthermore, by comparing the rankings from this study with THE university rankings, the research offers tailored suggestions for universities. This study underscores the importance of deriving criterion weights from university performance datasets rather than relying on fixed weights, facilitating a more nuanced approach to university rankings. Moreover, it presents THE performance rankings for sixty-one Turkish universities, offering valuable insights for strategic planning within the university sector.
subject: Articles
  topic: section
subject: The Ranking of University Performance
  topic: keywords
subject: Multi-Criteria Decision Making
  topic: keywords
subject: The Method based on the Removal Effects of Criteria Method
  topic: keywords
subject: The Weighted Euclidean Distance-Based Approach Method
  topic: keywords
subject: MEREC
  topic: keywords
subject: WEDBA
  topic: keywords
creator: Karahan Kara
creator: Galip Cihan Yalçın
creator: Esra Gökçen Kaygısız
creator: Sercan Edinsel
rights: Copyright (c) 2024 Karahan Kara, Galip Cihan Yalçın, Esra Gökçen Kaygısız, Sercan Edinsel (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-05-19
publication date: 2024-05-19
starting page: 252
ending page: 272
doi: 10.31181/jopi21202422

A Comparative Analysis of the Machine Learning Methods for Predicting Diabetes
type: item
description: Diabetes can lead to various health problems and complications, such as cardiovascular disease, kidney damage (nephropathy), eye issues, neuropathy, and foot ailments. Therefore, early diagnosis of diabetes can be immensely beneficial in preventing the development of these conditions. Utilizing machine learning is one method to detect diabetes in individuals at an early stage. In this study, we compare the performance of nine machine learning classification models in predicting diabetes. These models include XGBoost, gradient boosting, AdaBoost, logistic regression, decision tree, KNN, perceptron, random forest, and naïve Bayes. We utilize several evaluation metrics, focusing on the F1-score, area under the curve (AUC), and computational runtime. Our comparison reveals that complex tree-based models exhibit the highest F1-score and AUC, albeit with longer execution times.
subject: Articles
  topic: section
subject: Diabetes Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Classification Models
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Comparative Analysis
  topic: keywords
creator: Mohammad Maydanchi
creator: Mehrbod Ziaei
creator: Mehrdad Mohammadi
creator: Armin Ziaei
creator: Mina Basiri
creator: Fatemeh Haji
creator: Kazhal Gharibi
rights: Copyright (c) 2024 Mohammad Maydanchi, Mehrbod Ziaei, Mehrdad Mohammadi, Armin Ziaei, Mina Basiri, Fatemeh Haji, Kazhal Gharibi (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-05-18
publication date: 2024-05-18
starting page: 230
ending page: 251
doi: 10.31181/jopi21202421

Study on the Method of Selecting Sustainable Food Suppliers Considering Interactive Factors
type: item
description: The existing sustainable supplier selection methods are not sufficient to deal with the problem of sustainable food supplier selection with the interaction of criteria under uncertainty. Therefore, this paper proposes a method of sustainable food supplier selection based on an extended decision model. First, a processing method for supplier evaluation information is constructed using the Pythagorean fuzzy set, which has the function of processing complex uncertain information. Second, to obtain the objective weights of decision experts, a Pythagorean fuzzy weighted distance measure model is constructed, and an expert information fusion method based on a weighted power mean operator is proposed to construct the group decision matrix. Then, the decision experiment and evaluation experiment methods are integrated with the traditional MARCOS method to construct a sustainable food supplier selection method considering the interaction of factors. This method can effectively deal with the complicated and uncertain problem of sustainable food supplier selection with interactive factors. Finally, the feasibility of the proposed method is verified by an example of sustainable food supplier selection. In addition, parameter sensitivity analysis and multi-method comparative analysis verify the rationality of the proposed selection method for sustainable food supplier selection.
subject: Articles
  topic: section
subject: Food supplier
  topic: keywords
subject: MARCOS method
  topic: keywords
subject: DEMATEL
  topic: keywords
subject: Pythagorean fuzzy
  topic: keywords
subject: Sustainable
  topic: keywords
creator: Yi Wang
creator: Huizhi Yang
creator: Xiao Han
rights: Copyright (c) 2024 Yi Wang, Huizhi Yang, Xiao Han (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-04-21
publication date: 2024-04-21
starting page: 202
ending page: 218
doi: 10.31181/jopi21202420

Smart-Watches Assisted Sugar Level Monitoring with Different Activities and Nutrition based on Machine Learning Approaches
type: item
description: These days, sugar glucose monitoring is very important for both diabetic and non-diabetic patients while they are eating and engaging in different activities. There are different ways to monitor body glucose levels, such as blood-based glucose monitoring and smartwatch-based glucose monitoring. However, continuous glucose monitoring (CGM) is an emerging non-invasive method for various subjects (e.g., patients and consumers). Although smartwatches have limitations, in this paper, we present a new smartwatch framework that monitors the body's glucose level with new features such as nutrition and activities. We present a modified dataset with an additional feature: sugar glucose level during different activities (e.g., running, sitting, sleeping, and walking) while consuming different nutrition at different time intervals. We also present an empirical machine learning approach called the activity glucose monitoring algorithm (ASA), which processes all datasets with more optimal results. Simulation results show that our proposed framework is more optimal, monitoring glucose with different activities and more features compared to existing smartwatches, achieving an accuracy of 78%, which surpasses existing machine learning methods.
subject: Articles
  topic: section
subject: CGM
  topic: keywords
subject: Glucose
  topic: keywords
subject: Diabetes
  topic: keywords
subject: Sugar
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Activities and Nutrition
  topic: keywords
creator: Sajida Memon
rights: Copyright (c) 2024 Sajida Memon (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-04-21
publication date: 2024-04-21
starting page: 219
ending page: 229
doi: 10.31181/jopi21202419