Artificial neural network-driven modeling of Ebola transmission dynamics with delays and disability outcomes.

This study develops an Artificial Neural Network (ANN)-based framework to model the transmission dynamics and long-term disability outcomes of Ebola Virus Disease (EVD). Building on existing deterministic SEIR models, we extend the framework by introducing a disability compartment, capturing the progression of Ebola survivors to chronic health complications, such as post-Ebola syndrome. The proposed model stratifies the population into various epidemiological states, incorporating delays to better reflect the natural progression and intervention strategies associated with EVD.

Corrigendum to "A multiple attribute group decision making model based on 2-tuple linguistic Pythagorean fuzzy Dombi aggregation operators for optimal selection of potential global suppliers" [Heliyon 10 (14) (July 2024) e34570].

[This corrects the article DOI: 10.1016/j.heliyon.

MCGDM approach based on (p, q, r)-spherical fuzzy Frank aggregation operators: applications in the categorization of renewable energy sources.

The growing demand for energy, driven by population growth and technological advancements, has made ensuring a sufficient and sustainable energy supply a critical challenge for humanity. Renewable energy sources, such as biomass, solar, wind, and hydro, are inexhaustible and environmentally friendly, offering a viable solution to both the energy crisis and the fight against global warming. However, selecting the optimal renewable energy source remains a complex decision-making problem due to the varying characteristics and impacts of these sources.

A novel algorithmic multi-attribute decision-making framework for solar panel selection using modified aggregations of cubic intuitionistic fuzzy hypersoft set.

To address the shortcomings of the cubic intuitionistic fuzzy sets (CIFSs) for the entitlement of multi-argument approximate function, the cubic intuitionistic fuzzy hypersoft set (Ω-set) is an emerging study area. This type of setting associates the sub-parametric tuples with the collection of CIFSs. Categorizing the evaluation of parameters into their corresponding sub-parametric values based on non-overlapping sets has significance in decision making and optimization related situations.

Selecting the foremost big data tool to optimize YouTube data in dynamic Fermatean fuzzy knowledge.

Big data pertains to extensive and intricate compilations of information that necessitate the implementation of proficient and cost-effective evaluation and analysis tools to derive insights and support decision making. The Fermatean fuzzy set theory possesses remarkable capability in capturing imprecision due to its capacity to accommodate complex and ambiguous problem descriptions. This paper presents the study of the concepts of dynamic ordered weighted aggregation operators in the context of Fermatean fuzzy environment.

A multiple attribute group decision making model based on 2-tuple linguistic pythagorean fuzzy dombi aggregation operators for optimal selection of potential global suppliers.

Multiple-Attribute Group Decision-Making (MAGDM) is a significant area of research in decision-making, and its principles and methodologies are widely implemented. A Pythagorean Fuzzy Set (PFS) is an extension of an Intuitionistic Fuzzy Set (IFS) that is highly valuable for representing uncertain information in real-world scenarios. The 2-Tuple Linguistic Pythagorean Fuzzy Number (2TLPFN) is a specific type of Pythagorean Fuzzy Number (PFN) that can be used to represent uncertainty in real-world decision making through the use of 2-Tuple Linguistic Terms (2TLTs).

Selecting optimal celestial object for space observation in the realm of complex spherical fuzzy systems.

The sensible selection of celestial objects for observation by the James Web Space Telescope (JWST) is pivotal for the precise decision-making (DM) process, aligning with scientific priorities and instrument capabilities to maximize valuable data acquisition to address key astronomical questions within the constraints of limited observation time. Aggregation operators are valuable models for condensing and summarizing a finite set of data of imprecise nature. Utilization of these operators is critical when addressing multi-attribute decision-making (MCDM) challenges.

Optimizing decision-making with aggregation operators for generalized intuitionistic fuzzy sets and their applications in the tech industry.

Intuitionistic fuzzy sets (IFSs) represent a significant advancement in classical fuzzy set (FS) theory. This study advances IFS theory to generalized intuitionistic fuzzy sets (GIFSs) and introduces novel operators GIFWAA, GIFWGA, GIFOWAA, and GIFOWGA, tailored for GIFSs. The primary aim is to enhance decision-making capabilities by introducing aggregation operators within the GIFS framework that align with preferences for optimal outcomes.

Neutrosophic aggregation operators and their applications in the software site selection.

In this paper, we expended the concept of neutrosophic sets (NS) by introducing the idea neutrosophic set ( NS). The existing models under conventional NSs, fail to adequately address the management of membership degree influence during the aggregation process. While the proposed framework manages the influence of membership degree (MD), indeterminacy membership degree (IMD), and non-membership degree (NMD) by incorporating parameters , , and .

A novel perspective on the selection of an effective approach to reduce road traffic accidents under Fermatean fuzzy settings.

Road traffic accidents (RTAs) pose a significant hazard to the security of the general public, especially in developing nations. A daily average of more than three thousand fatalities is recorded worldwide, rating it as the second most prevalent cause of death among people aged 5-29. Precise and reliable decisionmaking techniques are essential for identifying the most effective approach to mitigate road traffic incidents.

Selecting an optimal approach to reduce energy crises under interval-valued intuitionistic fuzzy environment.

The concept of interval-valued intuitionistic fuzzy sets is intellectually stimulating and holds significant utility in the representation and analysis of real-world problems. The development of similarity measures within the class of interval-valued intuitionistic fuzzy sets possesses significant importance across various academic disciplines, particularly in the fields of decision-making and pattern recognition. The utilization of similarity measures is of utmost importance in the decision-making process when implementing interval-valued intuitionistic fuzzy sets.

Development of quasirung orthopair fuzzy hamacher aggregation operators and its application in decision-making problems.

The concept of quasirung orthopair fuzzy ( QOF) sets is an advanced extension of rung orthopair fuzzy sets ( ROFSs). This paper introduces the adaptation of Hamacher t-norm and t-conorm to the QOF environment. A series of Hamacher aggregation operators (AOs) and their associated properties are presented.

A comprehensive study for selecting optimal treatment modalities for blood cancer in a Fermatean fuzzy dynamic environment.

Cancer is characterized by uncontrolled cell proliferation, leading to cellular damage or death. Acute lymphoblastic leukemia (ALL), a kind of blood cancer, that affects lymphoid cells and is a challenging malignancy to treat. The Fermatean fuzzy set (FFS) theory is highly effective at capturing imprecision due to its capacity to incorporate extensive problem descriptions that are unclear and periodic.

Stability of magnetohydrodynamics free convective micropolar thermal liquid movement over an exponentially extended curved surface.

Micro polar fluids have a wide variety of applications in biomedical, manufacturing, and technical activities, such as nuclear structures, biosensors, electronic heating and cooling, etc. The aim of this study is to investigate the properties of heat transfer on a magnetohydrodynamic free convection movement of micro polar fluid over an exponentially stretchable curved surface. The flow is non-turbulent and steady.

A Novel Generalization of Q-Rung Orthopair Fuzzy Aczel Alsina Aggregation Operators and Their Application in Wireless Sensor Networks.

Q-rung orthopair fuzzy sets have been proven to be highly effective at handling uncertain data and have gained importance in decision-making processes. Torra's hesitant fuzzy model, on the other hand, offers a more generalized approach to fuzzy sets. Both of these frameworks have demonstrated their efficiency in decision algorithms, with numerous scholars contributing established theories to this research domain.

Investigation of the Three-Dimensional Hybrid Casson Nanofluid Flow: A Cattaneo-Christov Theory.

We consider the Casson hybrid nanofluid (HN) (ZnO + Ag/Casson fluid) that flows steadily along a two-directional stretchable sheet under the influence of an applied changing magnetic flux and is electrically conducting. The basic Casson and Cattaneo-Christov double diffusion (CCDD) formulations are used for the simulation of the problem. This is the first study on the analysis of the Casson hybrid nanofluid by using the CCDD model.

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application.

Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing.

Corrigendum to "A Decision-Making Approach Based on Score Matrix for Pythagorean Fuzzy Soft Set".

[This corrects the article DOI: 10.1155/2021/5447422.].

Significance of nanoparticles aggregation on the dynamics of rotating nanofluid subject to gyrotactic microorganisms, and Lorentz force.

The significance of nanoparticle aggregation, Lorentz and Coriolis forces on the dynamics of spinning silver nanofluid flow past a continuously stretched surface is prime significance in modern technology, material sciences, electronics, and heat exchangers. To improve nanoparticles stability, the gyrotactic microorganisms is consider to maintain the stability and avoid possible sedimentation. The goal of this report is to propose a model of nanoparticles aggregation characteristics, which is responsible to effectively state the nanofluid viscosity and thermal conductivity.

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning.

The purpose of this study is to achieve a novel and efficient method for treating the interval coefficient linear programming (ICLP) problems. The problem is used for modeling an uncertain environment that represents most real-life problems. Moreover, the optimal solution of the model represents a decision under uncertainty that has a risk of selecting the correct optimal solution that satisfies the optimality and the feasibility conditions.

Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning.

In this study, we propose a general method for tackling the Pickup and Drop-off Problem (PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our aim is to reduce the overall tour length traveled by an agent while remaining within the truck's capacity restrictions and adhering to the node-to-node relationship. In such instances, the agent does not allow any drop-off points to be serviced if the truck is empty; conversely, if the vehicle is full, the agent does not allow any products to be picked up from pickup points.

On Stability of Continuous Cooperative Static Games with Possibilistic Parameters in the Objective Functions.

The objective of this paper is to present a novel idea about the continuous possibilistic cooperative static game (Poss-CCSTG). The proposed Poss-CCSTG is a continuous cooperative static game (CCSTG) in which parameter associated with the cost functions of the players involves the possibility measures. The considered Poss-CCSTG is converted into the crisp -CCSTG problem by using the -cuts and hence into the multiple objective nonlinear programming problem.

A Novel Approach for Characterizing Solutions of Rough Optimization Problems Based on Boundary Region.

Rough set theory, presented by Pawlak in 1981, is one of the most well-known methods for communicating ambiguity by estimating an item based on some knowledge rather than membership. The concept of a rough function and its convexity and differentiability in regard to its boundary region are discussed in this work. The boundary notion is also used to present a new form of rough programming issue and its solutions.

An Intelligent Multiattribute Decision-Support Framework Based on Parameterization of Neutrosophic Hypersoft Set.

Hypersoft set is a novel area of interest which is able to tackle the real-world scenarios where classification of parameters into their respective sub-parametric values in the form of overlapping sets is mandatory. It employs a new approximate mapping which considers such sets in the form of sub-parametric tuples as its domain. The existing soft set-like structures are insufficient to tackle such kind of situations.