Intelligent Systems Research and Applications Journal
The Intelligent Systems Research and Applications Journal (ISRAJ) is a peer-reviewed, open-access publication dedicated to advancing the theory, design, and real-world deployment of intelligent computational systems. Bridging the gap between academic research and industrial application, ISRAJ covers a broad spectrum of sectors from smart cities and autonomous systems to healthcare and finance providing a rigorous platform for novel research that drives societal and technological change.
ISRAJ 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.
Efficient Outranking Framework Based on CoCoSo Method and T-Spherical Fuzzy Environments
College-level sports involve competitive athletic programs organized by universities and colleges, encompassing various disciplines. These programs require strategic decision-making at multiple levels, including athlete recruitment and financial management. Coaches, athletic directors, and sports analysts use data-driven approaches, performance metrics, and multi-criteria decision-making (MCDM) techniques to optimize team performance and resource allocation. To achieve the goal of this manuscript, we apply a broader framework of T-spherical fuzzy set (T-SFS) to manage the uncertainty of the expert’s opinion during the aggregation process. To overcome the impact of redundant and incomplete information on decision-makers, we established a decision algorithm of the CoCOSo method for resolving real-life applications and numerical examples using mathematical aggregation operators. To check the superiority of the discussed decision-making technique and fuzzy framework, we gave an experimental case study to evaluate a chosen expert for football ground using different criteria or key components. The sensitivity analysis is also conducted to showcase the consistency and reliability of diagnosed mathematical terminologies.
T-spherical fuzzy information, optimization technique, selections of coaching experts and decision-making problem.
Copyright (c) 2026 Abrar Hussain (Author) https://creativecommons.org/licenses/by/4.0
An Algebraic Framework for Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets
The extension of algebraic and analytical concepts to the theory of fuzzy sets appears to play a central role in the investigation of nondeterministic techniques. Since an exact description of any real physical situation is virtually impossible, it is necessary to develop schemes which deal analytically with decision processes in an imprecise environment. In this paper, we introduce the concept of Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert set and its operations and study some of their structural properties. We also discuss Algebras on Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert sets, fundamental laws, Algebras on relative complement and Algebra of inclusion with the new operations in a systematic way.
Fuzzy soft sets; Interval-valued fuzzy soft sets; Soft sets; Intuitionistic soft sets
Copyright (c) 2026 Noreen Mushtaq, Wajid Ali, Afshan Qayyum, Faiza Tufail, Amal Kumar Adak (Author) https://creativecommons.org/licenses/by/4.0
Explainable Machine Learning-Guided Virtual Screening and Molecular Docking for Identification of Novel FYN Kinase Inhibitors
FYN kinase is a non-receptor protein tyrosine kinase involved in various cancers and neurodegenerative diseases; however, no selective FYN inhibitor has been approved yet. Here we introduce the explainable Machine Learning (ML) coupled with virtual screening and Molecular Docking (MD) pipeline for fast prediction of new FYN kinase inhibitors. In this study, we constructed the training set of 906 molecules active against FYN kinase from the ChEMBL database. Molecules were encoded with Extended-Connectivity Fingerprints (ECFP4). The classification models Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were developed, and the latter showed the better performance in test (AUC=0.8118) and 5-fold cross-validation (AUC=0.8297). Based on the SHapley Additive exPlanations (SHAP) values obtained via TreeExplainer, nitrogen-containing heterocycles and hydrogen bond acceptors have been identified as the most important molecular substructures. Using the optimal XGBoost classifier, screening of 2,000 approved drugs has been performed, resulting in 470 hit molecules (23.5% hit rate). Five best molecules were further submitted to the MD procedure using AutoDock Vina to dock to FYN kinase domain (PDB RCSB: 2DQ7), showing binding energies in the interval of -9.57 to -6.32 kcal/mol. Dasatinib Anhydrous (CHEMBL1421) was the second strongest binder (-8.49 kcal/mol), effectively interacting with the ATP binding site. Although CHEMBL1171837 was the strongest binder (-9.57 kcal/mol), it was caught in the ADMET profiling. According to ADMET profiling, the top one inhibitor (CHEMBL1421) satisfies Lipinski’s rule of five and Veber rules. Analysis of hydrogen bond and hydrophobic interactions revealed hydrogen bonding with ASP148, LYS39, and ASN86 and hydrophobic interactions with ALA147, ILE80, and GLY88. Validation by self-docking procedure (self-docking or STS) showed low Root Mean Square Deviation (RMSD)<2.0 Å with a binding affinity of -11.53 kcal/mol. This work highlights how explainable ML can be used in combination with structure-based docking to expedite the drug discovery process against FYN kinase and can be applied to other kinase targets.
FYN kinase; Explainable machine learning; ECFP4; SHAP; Molecular docking: AutoDock Vina; Virtual screening; Drug discovery
Copyright (c) 2026 Ahmet Turan Demir (Author) https://creativecommons.org/licenses/by/4.0
Bridging Art and Logic: Evolving Artificial Intelligence for Emotionally Intelligent Art Communication Using Integrated PROMETHEE – Complex Circular Intuitionistic Fuzzy Dombi Framework
In modern communication systems, especially in art communication, emotional intelligence is essential in improving engagement and sympathy between audiences and creators. Existing decision-making models fail when faced with the complexity and subjectivity of human emotions, particularly in situations marked by vagueness and cultural diversity. This study aims to introduce the Dombi operational law model based on complex circular intuitionistic fuzzy (C-CrIF) information. The prioritized aggregation operators (AOs) involve assigning multiple levels of significance to the fuzzified criteria, the alternatives, and the expert opinions before combining them. We proposed new AOs, Dombi operational laws under the C-CrIF set (C-CrIFS), including complex circular intuitionistic fuzzy Dombi prioritized weighted averaging and complex circular intuitionistic fuzzy Dombi prioritized weighted geometric operators. These AOs are the improved forms of Dombi and prioritized AOs for fuzzy, complex fuzzy, intuitionistic fuzzy, complex intuitionistic fuzzy, and complex circular intuitionistic fuzzy data. Some essential properties of the proposed operators are also proposed. Also, we proposed the preference ranking organization method for enrichment evaluation (PROMETHEE) method based on the C-CrIF framework. Next, to demonstrate the applicability of established AOs, a case study of emotionally intelligent art communication is conducted in the presence of the proposed operators and using the PROMETHEE method. By combining these methods, the model can identify the most emotionally resonant communication alternatives, offering artists, curators, and artificial intelligence agents a powerful tool to improve audience connection. Lastly, by comparing the existing models, we provide clarifications on the established model to demonstrate the superiority and value of the developed model.
Emotionally intelligent art communication, multi-attribute decision-making, prioritized aggregation operator, complex circular intuitionistic fuzzy set, Dombi t-norm and t-conorm, and PROMETHEE approach
Copyright (c) 2026 Muhammad Rizwan Khan, Ali Raza, Kifayat Ullah, Mijanur Rahaman Seikh (Author) https://creativecommons.org/licenses/by/4.0
Block-Crypt Chat: Secure Messaging Reinvented With Blockchain and Real - Time Encryption
The increasing demand for secure and trustworthy digital communication necessitates advanced solutions beyond traditional centralized messaging systems. This paper proposes a blockchain-enabled secure messaging framework that integrates hybrid RSA-AES encryption with real-time communication mechanisms. The system ensures end-to-end confidentiality by performing client-side encryption, while message integrity is maintained through SHA-256 hash logging on a blockchain, enabling tamper-proof verification without revealing message content. Additionally, a PIN-based secure file-sharing mechanism enhances data protection. The proposed model is implemented using a modern web-based architecture and evaluated for performance and security. Experimental results indicate low latency, strong data protection, and improved transparency compared to existing systems. The framework demonstrates scalability and practical applicability in sensitive domains such as enterprise communication, education, and legal environments.
Blockchain; Cryptography; End-to-End Encryption; Real-Time Communication; Secure Messaging; SHA-256
Copyright (c) 2026 Alpa Patel, Sindhu S, Rakshitha S (Author) https://creativecommons.org/licenses/by/4.0
A Novel Decomposed Pythagorean Fuzzy CODAS Framework for Precision Customer Segmentation in Complex Market Environments
Segmentation of customers has become an essential aspect in market analysis as it allows the organizations to formulate strategies according to different customer behaviour. These processes are further enhanced with advanced data mining techniques such as those used to uncover hidden patterns and take into account the heterogeneity of customers, which result in better and more data-driven decisions. To evaluate the quality of the segmentation-in particular for accuracy and practical usefulness of the solution, we take into account the performance measures Silhouette Score and Davies–Bouldin Index for accuracy and cohesion of the solution and Market Response. The proposed framework integrates the strong data mining procedures with New Disaggregated Pythagorean Fuzzy Sets based CODAS (NDPFS–CODAS) technique to increase the flexibility and discriminative power as well as the clearness of resultant segmentation. On the basis of behavioral, demographic and psychographic criteria, the model incorporates a multidimensional uncertainty. Case study confirms that NDPFS-CODAS enhances the accuracy and robustness of conventional fuzzy technique. These findings confirm its usefulness in target marketing, new-product development and customer and product in section marketing strategy, and give guidance for strategic decisions to offer the product packaging with maximum optimization.
Copyright (c) 2026 Safiye Turgay, Bilal Torkul, Abdulkadir Aydin, Furkan Özyurt (Author) https://creativecommons.org/licenses/by/4.0
The Evolution of Intelligent Digital Profiling: A Multi-Sectoral Synthesis of Explainable Artificial Intelligence and Federated Learning Frameworks
Digital profiling has evolved into a cornerstone of computational intelligence, enabling the synthesis of complex user representations through the systematic analysis of multi-dimensional digital interactions. This study provides a comprehensive investigation into the conceptual and architectural frameworks of digital profiling, delineating its evolution across various strategic sectors. Utilizing a thematic synthesis methodology, we systematically analyzed 197 high-impact articles published between 2011 and 2025. Our findings categorize digital profiling into six fundamental computational stages: objective definition, multi-source data acquisition, feature selection, similarity modeling, representation synthesis, and iterative monitoring. While the analysis underscores the increasing deployment of profiling in finance, security, and healthcare, it reveals critical systemic risks, including algorithmic bias and privacy vulnerabilities. We propose a strategic transition toward "Privacy-by-Design" architectures, highlighting the integration of Federated Learning and Explainable Artificial Intelligence (XAI) as essential mechanisms for aligning profiling systems with global regulatory standards (e.g., GDPR). This research contributes a robust theoretical roadmap for developing transparent, accountable, and ethically-aligned intelligent systems, bridging the gap between technical efficiency and user-centric rights.
Digital Profiling Systems; Explainable Artificial Intelligence; Machine Learning; Federated Learning; Algorithmic Governance; User Representation Modeling
Copyright (c) 2026 Sefer Darıcı, Zekeriya Şahin, Ayla Darıcı (Author) https://creativecommons.org/licenses/by/4.0
Exponential Similarity Measures Under q-Rung Orthopair Spherical Fuzzy Environment Application To Multi-Criteria Decision-Making
Q-rung orthopair spherical fuzzy sets (q-RO-SFSs) extend the expressive capability of fuzzy models by incorporating satisfaction, non-satisfaction, neutrality, and hesitancy degrees under a flexible q-rung structure. In this paper, we develop a family of exponential similarity measures for q-RO-SFSs, designed to capture complex uncertainty with improved sensitivity to all four components. Weighted, average, and generalized forms of the measures are proposed, and their mathematical validity is established. The applicability of the method is illustrated through multi-criteria decision-making problems, including credit evaluation and pattern recognition. Comparative analysis with existing similarity-based approaches shows that the proposed measures provide more stable and reliable rankings, demonstrating their effectiveness for practical decision-making in uncertain environments.
Copyright (c) 2026 Antaksh Prashar, Preeti Devi, Anil Kumar, Neetu Dhiman, Mamta Devi, Arun Kumar, Abhishek Guleria (Author) https://creativecommons.org/licenses/by/4.0
Engineering Speed at Scale: A Comprehensive Study of Frontend Performance Optimization Using the PERFORM Framework
Frontend performance has become a critical factor influencing user experience, engagement, and business outcomes in modern web applications. As frontend architectures grow increasingly complex, performance bottlenecks related to resource loading, rendering behavior, and interaction latency become more prevalent. This paper presents a comprehensive study of frontend performance optimization conducted during a summer web development internship. A systematic optimization methodology, the PERFORM framework (Profile, Evaluate, Recommend, Fix, Observe, and Report), is used to analyze and improve performance across multiple production-grade web applications. The study focuses on optimizing the critical rendering path, improving Core Web Vitals, and applying practical optimization techniques, such as image optimization, lazy loading, and reducing render-blocking resources. Experimental results demonstrate significant improvements in page load performance, responsiveness, and visual stability. The findings highlight the effectiveness of structured performance engineering practices in real-world frontend development scenarios.
Frontend Performance Optimization; Core Web Vitals; Critical Rendering Path; Lighthouse; Web Engineering
Copyright (c) 2026 Izzath Fathima, Sukanya Vuppala (Author) https://creativecommons.org/licenses/by/4.0
Data-Driven Prediction of Wheat Production across Africa, the Middle East, and Oceania using Machine Learning Techniques
Wheat is a crop that is consumed and produced globally. It is used in many food items in our daily life. Therefore, its widespread use globally and its share in food grains makes its extremely important. Therefore, considering the importance of the wheat crop, it is very important to estimate its production level in advance so that equitable availability of this important grain can be ensured at the global level. This study addresses the limitations of traditional statistical models by incorporating advanced machine learning techniques capable of capturing nonlinear relationships in agricultural data. Experimental results demonstrate that XGBoost outperforms other models with the accuracy level 99.99%. and an RMSE of 18.37 and MAE of 13.96, indicating high predictive accuracy. The findings highlight the potential of machine learning-driven approaches for supporting data-informed agricultural planning and policy formulation in regions with high production variability. This framework will not only help predict wheat production in advance but also serve as a useful tool for agricultural policymaking.
Copyright (c) 2026 Naman, Anju, Paras Dahiya, Suraj Arya, Ayodeji Olalekan Salau (Author) https://creativecommons.org/licenses/by/4.0
Vertex Betweenness Centrality Of Some Graph Classes With Applications
Centrality is an excellent concept for measuring the important or critical vertices in graphs like social networks, biological networks, computer networks etc. Every vertex may be an important vertex from an angle depending on our assumption or how we define the importance. There are different centrality measurements to know the vertex characteristics in a network. Betweenness centrality is a crucial measurement for network analysis based on all shortest paths between each pair of vertices. The betweenness centrality of a node point $v$ is the sum of the ratios between the number of the shortest routes between each pair of other node points that pass through $v$ and the total number of the shortest routes between them. This study describes some new theoretical results related with the measurement of betweenness centrality, relative betweenness centrality and graph betweenness centrality of some special graph classes. In addition, we discuss real-life applications of the results studied in this article.
Centrality; betweenness centrality; graph
Copyright (c) 2026 Shaoli Nandi, Sukumar Mondal, Sambhu Charan Barman (Author) https://creativecommons.org/licenses/by/4.0
Distance-Based Similarity Measures in T-Spherical Fuzzy Hypersoft Environment for Cluster Analysis
The notion of T-spherical fuzzy hypersoft set (TSFHSS) provides a powerful generalization of the picture fuzzy hypersoft set, offering additional amount of flexibility in modeling uncertain and unreliable information where the sum of membership, non-membership, and neutrality degrees can exceed one. The concept of similarity/distance measures plays a crucial role in various pattern recognition, decision-making, and classification tasks. In the present study, we introduce a novel distance measure for T-spherical fuzzy hypersoft sets to effectively quantify the dissimilar aspects of two objects. The necessary basic axioms are satisfied by the proposed measures along with some key properties. Based on this, we define corresponding similarity measures that are particularly effective in distinguishing highly similar elements. Some important theorems demonstrating the mathematical properties of these measures have also been proved. For the sake of validation of the proposed measures, we provide a numerical illustration demonstrating their accuracy and discriminating behavior. Furthermore, we develop a clustering algorithm that well utilizes the introduced similarity measures under the TSFHSS framework, enabling robust classification of objects in uncertain environments.
Soft/Hypersoft Sets, T-spherical Fuzzy Hypersoft Sets, Similarity/Distance Measures, Clustering Technique
Copyright (c) 2026 Monika, Rakesh Kumar Bajaj, Gaurav Garg (Author) https://creativecommons.org/licenses/by/4.0
Thermo-Elastic Analysis of an Axisymmetric Layered Cylinder under Constant Thermal Loading with Artificial Intelligence-Based Validation
This study presents a numerical investigation of the thermo-elastic behavior of a two-layer disk composed of partially stabilized zirconia (PSZ) and aluminum subjected to uniform (constant) thermal loading. The analysis focuses on the evaluation of radial and circumferential stress distributions as well as radial displacement responses under different prescribed temperature levels. The governing thermo-elastic equations are formulated under plane stress assumptions and solved numerically by discretizing the disk geometry into finite radial segments.The material layers are assumed to be homogeneous, isotropic, and perfectly bonded, while the elastic properties are considered temperature-independent within the investigated temperature range of 12.5 °C to 100 °C. The numerical formulation ensures continuity of radial displacement and radial stress across the material interface. The results demonstrate that increasing temperature levels significantly influence the magnitude of thermo-elastic stresses and radial displacements. In particular, pronounced stress gradients are observed in the vicinity of the material interface, highlighting the effect of thermal expansion mismatch between PSZ and aluminum. The circumferential stress component is found to be more sensitive to temperature variations compared to the radial stress, while radial displacement increases monotonically with temperature. Beyond numerical analysis, the generated stress and displacement data are employed as a structured dataset for artificial intelligence–based validation. A supervised learning framework is developed to predict thermo-elastic responses based on temperature and radial position inputs. The AI model demonstrates strong agreement with numerical results, confirming its capability to accurately reproduce stress and displacement trends under constant thermal loading conditions. The combined numerical–AI approach provides a reliable and efficient tool for analyzing thermo-elastic behavior in layered disk structures.
Thermo-elastic analysis; Partially stabilized zirconia (PSZ); Aluminum; Artificial intelligence validation.
Copyright (c) 2026 Firat Kayiran (Author) https://creativecommons.org/licenses/by/4.0
A Reinforcement Learning-Based Framework for Multi-Criteria Decision-Making
Multi-Criteria Decision-Making (MCDM) methods have been applied to various ranking problems in finance, engineering, and management; however, many traditional methods rely on data normalization and fixed weights assigned to the criteria. Often, such data normalization has led to distortion of ordinal information and inconsistent results. In this study, we propose an RL-based framework for solving MCDM problems when the decision data are ranked. The ranking task was then reformulated as a learning-to-rank optimization problem, where the preference scores are assigned to alternatives and learned by maximizing agreement with a benchmark ranking. The proposed approach was first illustrated with a simple example and then applied to the real-world evaluation of banks using the CAMELS criteria. The results showed that the RL-based approach achieved greater consistency with benchmark rankings than normalization-based MCDM methods, thereby extending the applicability of rank-preserving approaches.
Copyright (c) 2026 Laxminarayan Sahoo, Sumanta Lal Ghosh (Author) https://creativecommons.org/licenses/by/4.0
Quality Assessment in Primary Healthcare Using Kano-Fuzzy-SERVQUAL: Young Women's Perspective
This paper aimed to evaluate the quality of services provided at Primary Healthcare Units (PHUs) in Salgueiro-PE from the perspective of young adult women users, through the integration of Kano Model, Fuzzy Logic and SERVQUAL. A structured questionnaire with 23 quality attributes was applied to 109 women between November 2020 and April 2021. The results revealed an overall quality mean of 2.96 (median=3.0), indicating a median satisfaction perception. The Kano-Fuzzy analysis identified six critical attributes that demand priority attention: physical environment (Attribute 2), reliable information transmission (Attribute 6), demonstration of commitment to improvement (Attribute 9), and aspects related to empathy and safety (Attributes 11, 13, 14). The predominantly unidimensional classification of attributes suggests that incremental improvements directly impact users' satisfaction. The study contributes methodologically by integrating innovative evaluation techniques, contextually by analyzing a medium-sized municipality in the northeastern semi-arid region, and managerially by providing an evidence-based prioritization matrix for primary care managers.
Copyright (c) 2026 Ericka Almeida Neto Santos, Fagner José Coutinho de Melo, Eryka Fernanda Miranda Sobral (Author) https://creativecommons.org/licenses/by/4.0
Archimedean T-norm Based q-Rung Orthopair Fuzzy Hamy Mean Operator With Ordinal Priority Weights
Archimedean t-norm provides an advantage to represent the formal structure of aggregation operators (AOs) in a simpler form i.e., from a multivariate to univariate function through an additive generator. This study focuses on q-rung orthopair fuzzy numbers, a field typically governed by t-norms. So in this paper, we introduced Hamy mean and weighted Hamy mean operators for q-rung orthopair fuzzy numbers under a generalized construction of continuous and Archimedean classes of t-norms. To further develop a comprehensive AO-based multi-criteria group decision-making model, an objective weight determination approach, the q-rung orthopair fuzzy Archimedean ordinal priority approach, is used to systematically evaluate attribute weights. Finally, the constructed approach is demonstrated in practice through a detailed illustration of a real-world decision-making problem.
Archimedean t-norm; Hamy mean; Multi-criteria group decision making; q-Rung orthopair fuzzy set
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
α-Domination in Fuzzy Graphs with Applications to Fake News Control
The uncontrolled spread of misinformation across online social networks poses serious societal risks, including political manipulation, public health misinformation and erosion of public trust. Due to uncertainty in user interactions and varying influence levels, classical crisp graph models are often inadequate for realistic modeling. In this paper, we introduce an α-domination framework in fuzzy graphs to model and control fake news propagation. We formally define fuzzy coverage via direct and two-step influence with decay and establish theoretical properties of the α-domination number, including upper bounds, monotonicity and results for complete fuzzy graphs. We demonstrate how strategically selected α-dominating sets can act as fact-checking nodes to effectively reduce misinformation spread. Our framework provides a mathematically rigorous and flexible tool for misinformation containment under uncertainty, bridging fuzzy graph theory and social network analysis.
Fuzzy Graph Theory; Domination; Social Networks; Information Containment.
LLM-Assisted Virtual Expert Weight Elicitation in Pharmaceutical Supply Chains: A Z-Number Multi-Agent Framework
The elicitation of criteria weights in spatial and logistical Multi-Criteria Decision Making (MCDM) typically relies on panels of human domain experts. However, in specialized high-stakes contexts such as pharmaceutical inventory management, expert availability is scarce, expensive, and subject to cognitive biases. This study proposes a novel methodological framework that offers a structured alternative to traditional human panels by employing a Multi-Agent System (MAS) of Large Language Models (LLMs) to generate subjective weights. We introduce a rigorous Z-number-based fuzzy AHP approach in which LLMs, acting as autonomous virtual experts, defined as Agents LLM1, LLM2, and LLM3, perform iterative pairwise comparisons. The methodology strictly separates internal logical consistency, verified via Consistency Ratios (CR) on crisp matrices, from confidence modeling, which is handled via Z-numbers. The LLM-derived weights were aggregated over k=3 iterations to mitigate stochasticity and hybridized with objective CRITIC weights to rank nine Vendor Managed Inventory (VMI) policies. Results indicate strong ranking invariance across all agents and hybridization ratios (ρ=1.0). Beyond numerical stability, the framework demonstrates "behavioral isomorphism" with human ethical standards, explicitly enforcing a "safety-first" constraint. This suggests that LLM-driven frameworks exhibit "dominance stability," positioning them as robust cognitive simulators that align optimization metrics with domain-specific priorities such as patient safety.
Multi-Criteria Decision Making (MCDM); Large Language Models (LLMs); Z-Numbers; Pharmaceutical Supply Chain; Virtual Expert Agents; Vendor Managed Inventory; Cognitive Simulation
Copyright (c) 2026 Jamal Musbah, Ibrahim Badi (Author) https://creativecommons.org/licenses/by/4.0
Optimal Path Selection for a Weighted Semidirected Network and its Application
Abstract This paper deals with optimal path selection within the context of weighted semidirected graphs, which combine both directed and undirected edges. In real life, it is seen that road networks are sometimes laid out in a unidirectional manner. Sometimes, it is also seen as a combination of unidirectional, bidirectional, or semidirected road structures. In a semidirected path, vehicles traverse both sides, resulting in congestion. Due to congestion, the travel time increased comparatively for semidirected paths relative to undirected paths. The study aims to develop efficient algorithms for identifying the most advantageous routes in scenarios where edges possess varying weights, reflecting diverse costs or distances between nodes. The chapter begins by introducing weighted semidirected graphs, establishing the shortest-path/optimal-path algorithm, and discussing its significance in real-world applications. The proposed algorithm is developed to evaluate the congestion of a semidirected graph, considering both the directionality and weights associated with each edge. The research emphasizes developing optimal path algorithms that enhance computational efficiency while identifying paths that minimize specific criteria, such as total weight, traversal time, or resource utilization. A real-world case study involving transport networks illustrates how the suggested algorithm can be applied in practice. This exploration contributes valuable knowledge to graph theory, operations research, and computer science, offering a sophisticated way to address complex routing challenges in dynamic and interconnected systems.
Semidirected graph; Weighted semidirected graph; Shortest path; Congestion
Copyright (c) 2025 Intelligent Systems Research and Applications Journal https://creativecommons.org/licenses/by/4.0
Machine Learning-Assisted Thermo-Elastic Analysis of Fiber-Reinforced Composite Rotating Disks
This study presents a numerical investigation of the thermo-mechanical behaviour of Glass Fiber Reinforced Polymer (GFRP) and Basalt/Epoxy composite rotating disks, focusing exclusively on the computation of radial displacements under coupled thermal and mechanical fields. The elastic modulus of the materials was assumed to remain constant with temperature, and the analysis was conducted over the temperature range 40 °C to 120 °C in 20 °C increments. Results indicate that temperature rise leads to noticeable changes in radial displacement distributions, exhibiting distinct deformation characteristics for each composite type. Basalt/Epoxy disks showed comparatively higher displacement responses than their GFRP counterparts, reflecting their different thermo-elastic sensitivities. In addition to the numerical analysis, the obtained displacement data were compared with machine learning predictions, demonstrating close agreement and indicating that the generated dataset is suitable for training data-driven models. These findings suggest that the numerical results can be effectively utilized within machine learning frameworks for predictive modelling and optimization tasks. Overall, the study provides theoretical guidance for safe design, performance evaluation, and material selection of composite rotating disks in aerospace, automotive, and energy applications.
Composite rotating disk; GFRP; Basalt/Epoxy; Thermo-mechanical behavior; Radial displacement; Thermal loading; Numerical analysis; Machine learning; Data-driven modeling; Predictive engineering design; SVR; ANN.