Sana Shahab’s research while affiliated with Princess Nourah bint Abdulrahman University and other places

With the increasing complexity of hotel selection, traditional decision-making models often struggle to account for uncertainty and interrelated criteria. Multi-criteria decision-making (MCDM) techniques, particularly those based on fuzzy logic, provide a robust framework for handling such challenges. This paper presents a novel approach to MCDM within the framework of Circular Intuitionistic Fuzzy Sets (C-IFS) by combining three distinct methodologies: Weighted Aggregated Sum Product Assessment (WASPAS), an Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN), and the CRITIC method (Criteria Importance Through Intercriteria Correlation). To address the dynamic nature of traveler preferences in hotel selection, the study employs a comprehensive set of criteria encompassing aspects such as location proximity, amenities, pricing, customer reviews, environmental impact, safety, booking flexibility, and cultural experiences. The CRITIC method is used to determine the importance of each criterion by assessing intercriteria correlations. AROMAN is employed for the systematic evaluation of alternatives, considering their additive relationships and providing a weighted assessment. WASPAS further analyzes the results obtained from AROMAN, incorporating both positive and negative aspects for a comprehensive evaluation. The integration of C-IFS enhances the model’s ability to manage uncertainty and imprecision in the decision-making process. Through a case study, we demonstrate the effectiveness of this integrated approach, offering decision-makers valuable insights for selecting the most suitable hotel option in alignment with the diverse preferences of contemporary travelers. This research contributes to the evolving field of decision science by showcasing the practical applicability of these methodologies within a C-IFS framework for complex decision scenarios.

Dynamic situations and applications are supported by the diverse devices and communication technologies that constitute the Internet of Things concept. Despite this, communication backlogs are common due to rising network demand and insufficient resource allocation. This study provides a way to fix resource allocation problems using Mutable Resource Allocation and Distributed Federated Learning. Inadequacies and backlogs in resources are identified at the edge of the network. As part of this procedure, edge devices are assigned to link resources and users after independently determining which resources cannot be allocated and which shortcomings are linked with them. Adapting to demand and learning suggestions, this allocation is flexible. By classifying resources as sufficient or inadequate, the learning suggestions help avoid backlogs. This enables edge devices to choose between allocation and response, which improves network flexibility by prioritizing inadequate resource allocation. Accordingly, the recommendation factor periodically affects modifications to the edge connection and its interaction with the Internet of Things platform. The suggestion is particularly strong for situations with changing backlogs to ensure that subsequent resource allocations align with preference-based learning. Claiming to improve connection, services, and resource allocation while decreasing backlogs and allocation times, this approach is a hot commodity.

[This corrects the article DOI: 10.1371/journal.pone.0315251.].

Credit risk assessment uses finance-based behavioural patterns for micro-lending purposes and organizations. The repayment behaviour and credit stability patterns are analyzed across varying repayment tenures and financed amounts. Due to limited borrower data and fluctuating financial patterns, micro-lending platforms have substantial hurdles when it comes to effectively evaluating credit risk. This article introduces a Collaborative Filtering Method using Lending Pattern Analysis (CFM-LPA). The proposed method is enhanced through collaborative federated learning, enabling the analysis of these patterns. This approach evaluates the return rate, credit limit, and consumer response behaviours. Federated learning processes one or more of these factors to assess diverse lending patterns. Based on these evaluations, the behavioural factor is updated for each return period, influencing the credit risk for subsequent return periods and supporting the financial stability of micro-lending operations. The model is trained individually on the identified factors, allowing the behavioural factor to be filtered. New credit risks are identified using this filtered factor from the previous return period. These insights help define new behavioural patterns for the specified credit limit. The proposed method enhances risk detection accuracy by 14.03% and improves return rate analysis by 13.28% across financed amounts. The above abstract is also graphically presented.

In the context of Industry 4.0, improving energy efficiency in smart factories has emerged as a key priority to drive sustainable industrial growth. However, identifying optimal energy-saving solutions is challenging due to the inherent uncertainty and complexity in decision-making. This study addresses these challenges by proposing a multi-criteria decision-making (MCDM) framework that leverages intuitionistic fuzzy sets (IFSs) to manage ambiguity in the evaluation process. To advance this framework, we develop a suite of novel aggregation operators (AOs), including the intuitionistic fuzzy softmax Dubois-Prade (IFSDP), intuitionistic fuzzy softmax interactive Dubois-Prade weighted average (IFSIDPWA), intuitionistic fuzzy softmax interactive Dubois-Prade ordered weighted average (IFSIDPOWA), and intuitionistic fuzzy softmax interactive Dubois-Prade weighted geometric (IFSIDPWG), which effectively handle uncertainty and vagueness in the criteria assessments. The method based on the removal effects of criteria (MEREC) is utilized to objectively determine the criteria weights, ensuring a robust evaluation structure. For ranking, the alternatives are evaluated through the ranking of alternatives using functional mapping of criteria sub-intervals into a Single Interval (RAFSI) approach. A case study involving eight criteria and five energy-saving solutions demonstrates the framework’s feasibility, with results confirming the effectiveness of our AOs and RAFSI technique in guiding decision-makers toward sustainable energy solutions for smart factories. This framework is poised to support sustainable manufacturing practices in Industry 4.0, fostering greener and more efficient industrial operations.

As the global population grows, urbanization depletes water resources and significantly reduces cropland available for agriculture. This study proposes a Big Data Analytics-Integrated Agriculture Resource Management Framework (BDA-ARMF) to optimize resource utilization and enhance farm sustainability. The integration of BDA in agriculture offers substantial advantages, including improved management of consumer demand, enhanced farm operations, sustainable food production and better alignment of supply with demand. The framework combines BDA with the Internet of Things and cloud computing to improve accuracy, intelligence and sustainability in agriculture. Efficient data-driven farming requires actionable insights to minimize resource waste and environmental contamination. The proposed model outperforms previous approaches, delivering significant improvements in water management (97.8%), prediction accuracy (97.6%), production efficiency (96.4%), resource consumption reduction (11.5%) and risk assessment enhancement (94.7%). The proposed framework reduces resource waste and mitigates environmental impact, enabling sustainable agricultural systems and efficient, data-driven farming practices.

Aims In the dynamic landscape of healthcare, integrating Artificial Intelligence paradigms has become essential for sophisticated brain image analysis, especially in tumor detection. This research addresses the need for heightened learning precision in handling sensitive medical images by introducing the Fragmented Segment Detection Technique. Background The ever-evolving healthcare landscape demands advanced methods for brain image analysis, particularly in detecting tumors. This study responds to this need by introducing the Feature Segmentation and Detection Technique (FSDT), a novel approach designed to identify brain tumors precisely using MRI images. The focus is on enhancing detection accuracy, even for diminutive tumors. The primary objective of this study is to introduce and evaluate the efficacy of FSDT in identifying and sizing brain tumors through advanced medical image analysis. The proposed technique utilizes cross-section segmentation and pixel distribution analysis to improve detection accuracy, particularly in size-based tumor detection scenarios. Methods The proposed technique commences by fragmenting the input through cross-section segmentation, enabling meticulous separation of pixel distribution in various sections. A Convolutional Neural Network then independently operates sequentially on the minimum and maximum representations. The segmented cross-section feature, exhibiting maximum accuracy, is employed in the neural network training process. Finetuning of the neural network optimizes feature distribution and pixel arrangements, specifically in consecutive size-based tumor detection scenarios. Results The FSDT employs cross-sectional segmentation and pixel distribution analysis to enhance detection accuracy by leveraging a diverse dataset encompassing central nervous system CNS tumors. Comparative evaluations against existing methods, including ERV-Net, MRCNN, and ENet- B0, reveal FSDT's superiority in accuracy, training rate, analysis ratio, precision, recall, F1-score, and computational efficiency. The proposed technique demonstrates a remarkable 10.45% increase in accuracy, 14.12% in training rate, and a 10.78% reduction in analysis time. Conclusion The proposed FSDT emerges as a promising solution for advancing the accurate identification and sizing of brain tumors through cutting-edge medical image analysis. The demonstrated improvements in accuracy, training rate, and analysis time showcase its potential for effective realworld healthcare applications.

The healthcare industry, aided by technology, leverages the Internet of Things (IoT) paradigm to offer patient/user-related services that are ubiquitous and personalized. The authorized repository stores ubiquitous data for which access-level securities are granted. These security measures ensure that only authorized entities can access patient/user health information, preventing unauthorized entries and data downloads. However, recent sophisticated security and privacy attacks such as data breaches, data integrity issues, and data collusion have raised concerns in the healthcare industry. As healthcare data grows, conventional solutions often fail due to scalability concerns, causing inefficiencies and delays. This is especially true for multi-key authentication. Dependence on conventional access control systems leads to security flaws and authorization errors caused by static user behaviour models. This article introduces an Opportunistic Access Control Scheme (OACS) for leveraging access-level security. This approach is a defendable access control scheme in which the user permissions are based on their requirement and data. After accessing the healthcare record, a centralized IoT security augmentation and assessment is provided. The blockchain records determine and revoke the access grant based on previous access and delegation sequences. This scheme analyses the possible delegation methods for providing precise users with interrupt-free healthcare record access. The blockchain recommendations are analyzed using a trained learning paradigm to provide further access and denials. The proposed method reduces false rates by 11.74%, increases access rates by 13.1%, speeds up access and processing by 12.36% and 13.23%, respectively, and reduces failure rates by 9.94%. The OACS decreases false rates by 10.64%, processing time by 15.62%, and failure rates by 10.95%.