Muhammad Kamran

The aim of this study is to determine the main risk factors affecting adult entertainmenteconomics and to rank them using the CPF-OCRA methodology withrelative closeness weighting. The proposed framework addresses limitations ofconventional fuzzy ranking models by integrating relative closeness coefficientswith circular Pythagorean fuzzy numbers. We propose three main contributions,namely, (1) a new distance measurement for the CPFS, (2) a criteria weightingmethod with the relative closeness coefficients, and (3) an operational competitivenessrating method for the CPFS. The model is validated on the data providedby experts on the adult entertainment risk assessment. Comparative analysiswith seven MCDM methods (TOPSIS, VIKOR, COPRAS, ELECTRE II, PROMETHEE,MABAC, WASPAS) shows strong correlation (avg. 0.96), confirming consistency.Sensitivity analysis demonstrates ranking stability up to ±20% weight perturbation.Results indicate that regulated harmony is the optimal strategy, followed byurban prohibition.

Topology awareness and scalable, adaptive network control have become critical with the development of 5G/6G, the Internet-of-Things, vehicular networks, and edge computing. Traditional rule-based and centralized networking models are unable to support dynamic topologies, heterogeneous traffic models, and demands with strict quality-of-service requirements. Structural and topological dependencies are encoded using graph neural networks (GNNs) and combined with deep reinforcement learning (DRL) to make decisions sequentially. Exploiting rewards is an avenue toward intelligent end-to-end network optimization. This review is a systematic examination of modern GNN–DRL models implemented in routing, congestion control, chaining of service functions, vehicular communication, and the optimization of optical networks. It also highlights their performance strengths, including topology awareness, cross-topology generalization, high sample efficiency, and high scalability, as well as their weaknesses, such as inference overhead, inconsistent benchmarking practices, low real-time deployability, and sensitivity to noisy or partial state observations. The main findings of this review are: (i) a coherent taxonomy of GNN-based, DRL-based, and hybrid GNN–DRL effective designs; (ii) comparative analysis of algorithms, architecture components, and learning pipelines; (iii) generalized performance trends in major areas of intelligent networking; and (iv) a collection of grounded research directions to be followed in the future, lightweight architecture, transfer learning pipeline, fault tolerant learning, and unified evaluation frameworks. Finally, this review focuses on enabling resilient infrastructure through intelligent, scalable, and autonomous end-to-end networking solutions.

Colorectal cancer is challenging to treat because many anticancer drugs do not achieve optimal therapeutic effects. Moreover, these drugs can cause systemic side effects, and patients often respond differently to treatment. This study provides a computational framework that merges quantitative structure property relationship analysis with a computational methodology combining structural descriptors and ranking analysis to systematically analyze and rank 10 United States Food and Drug Administration approved medicines for colorectal cancer. A suite of degree-based and neighborhood degree-based topological indices were generated and examined for their association with essential physicochemical properties, specially molecular weight and molecular complexity. Correlation research revealed that specific degree-based indices, and neighborhood degree-based indices, exhibited strong predictive power for these properties. By employing ratio weighting alongside with the VIekriterijumsko Kompromisno Rangiranje and Technique for Order Preference by Similarity to Ideal Solution decision techniques, the medicines were ranked based on their predicted physicochemical performance. The results from both decision methods consistently showed fluorouracil as the highest ranked therapeutic agent, followed by tipiracil hydrochloride and bevacizumab, underlining their favorable structural and pharmacological properties. This comprehensive modelling technique provides a consistent and systematic strategy to aid in early-phase drug screening and inform decision-making in colorectal cancer therapy.

In this paper, we introduce a novel hybrid Multi-Criteria Decision-Making (MCDM) model that integrates Pythagorean fuzzy sets and rough numbers to more effectively manage various uncertainties inherent in complex decision problems. To overcome the limitations of traditional weight determination methods, we propose a new model called Ordinal Preference Analysis under Fuzziness (OrdPA-F). This method calculates robust criteria weights using only simple ordinal rankings from experts, offering a practical advantage over data-intensive methods like entropy and cognitively demanding techniques like Analytic Hierarchy Process (AHP). The OrdPA-F framework uniquely integrates subjective expert preferences with an objective quantitative measure of overall ranks. These derived weights are subsequently utilized within a Pythagorean Fuzzy Rough Number (PFRN)-based Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model, enhanced by Dombi aggregation operators to accurately capture nonlinear relationships between criteria. A comprehensive case study on hypertension risk management demonstrates the practical efficacy and specific results of the proposed model. Our findings systematically rank elevated systolic blood pressure and high cholesterol level as the two most critical risk factors, with lifestyle modification identified as the most effective mitigation strategy. A comparative analysis with established methods (Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenjee (VIKOR)) quantitatively confirms the superiority of our framework, showing a 12% improvement in ranking stability and an 8% higher insensitivity to weight perturbations. These results demonstrate that the model provides a theoretically sound approach and delivers a reliable, robust, and practical decision support tool for healthcare diagnostics, enabling medical professionals to prioritize interventions with greater confidence.

Material science, drug design and toxicology studies, which relate a molecule’s structure to its numerous properties and activities, are studied with the use of the topological index. Graphs with finite algebraic structure find extensive applications in fields such as mathematics, elliptic curve cryptography, physics, robotics and information theory. Quasigroup is the generalization of a group due to the absence of the associative law. In this paper, we calculate some degree‐based topological indices and M‐polynomials of quadratic graphs associated with finite commutative and noncommutative weak inverse property quasigroups (WIPQs) broadly.

Cryptographic hash functions are indispensable for today’s information security because they secure data integrity, authentication and encrypted storage. Recently, graph‐theoretic constructions have acquired interest in the design of hash functions because of their noteworthy sensitivity to structural alterations and rich combinatorial structure. Chromatic polynomials of graphs, in particular, are intriguing possibilities for cryptographic hashing processes because they exhibit strong combinatorial invariants whose values alter substantially under minor changes to the underlying graph. In this paper, we give a hashing framework based on chromatic polynomials generated by graphs related to weak inverse property quasigroups. Graph models can be utilized naturally represent the algebraic subloops of weak inverse property quasigroups, an important type of nonassociative algebraic structures. The chromatic polynomials , and have been calculated and investigated along with the polynomial of the complete bipartite graph K _{3 n ,3 n} . These polynomials are the fundamental components of the recommended hashing algorithm and encompass significant combinatorial characteristics found in the underlying algebraic structures.