Digital Commons Network^™

Anti-Fuzzy Hypergraphs And Superhypergraphs: Max-Oriented Uncertainty Models For Higher-Order And Hierarchical Networks, Takaaki Fujita, Ajoy Kanti Das, Suman Das, Sankar Prasad Mondal

Sustainable Machine Intelligence Journal

Hypergraphs extend classical graphs by allowing hyperedges to connect more than two vertices, while superhypergraphs further generalize this framework through iterated powerset constructions that capture hierarchical and nested incidence structures. Within fuzzy graph theory, anti-fuzzy graphs provide max-oriented uncertainty models in which each edge grade is at least the maximum grade of its incident vertices. However, corresponding max-oriented frameworks for hypergraphs and superhypergraphs have not been systematically developed. To address this gap, we introduce anti-fuzzy hypergraphs and anti-fuzzy superhypergraphs, formulate their mathematical definitions, and establish basic well-definedness and canonical representation results. These models provide a unified foundation for max-based …

Type-2 And Type-N Rough Sets: A Hierarchical Generalization Of Pawlak Approximations With Some Applications", Takaaki Fujita, Ajoy Kanti Das, Suman Das, Sankar Prasad Mondal

Sustainable Machine Intelligence Journal

Uncertainty is ubiquitous in real-world systems and has motivated the development of various generalized set-theoretic frameworks, including rough sets, fuzzy sets, intuitionistic fuzzy sets, and neutrosophic sets. In particular, rough set theory represents uncertainty by approximating a target subset of a universe through two crisp sets, namely the lower and upper approximations, which are induced by an equivalence, or indiscernibility, relation.
Motivated by the well-established hierarchy of Type-2 and, more generally, Type-(n) fuzzy sets, this paper proposes and studies analogous higher-order extensions of rough sets. First, we introduce Type-2 rough sets as a two-level parameterized framework in which each primary …

A Neutrosophic Homological-Gröbner Framework For Ai-Driven Media Education Evaluation, Mona Gharib, Hafiz Muhammad Athar Farid, José M. Merigó, Muhammad Riaz

Sustainable Machine Intelligence Journal

The integration of artificial intelligence (AI) with media technologies has transformed teaching in Journalism and Communication, introducing new challenges for quality evaluation. Traditional assessment methods fail to capture the uncertainty, inconsistency, and incompleteness inherent in modern educational data. This paper proposes a Neutrosophic Homological–Gröbner Framework, which unifies three perspectives: neutrosophic sets to encode uncertain evidence, homological algebra to analyze structural coherence, and Gröbner bases to systematically simplify interdependent evaluation rules.

Within this framework, diverse evidence is encoded into neutrosophic triplets, daggregate into course-level indicators, and organized into neutrosophic chain complexes, where Betti indices reveal coherence and fragmentation. Educational policies are …

Barriers To Digital Payment Adoption In Saudi Arabia: A Plithogenic Indetermhypersoft Complex Refined Modelling Approach, Mohanned H. Alharbi

Sustainable Machine Intelligence Journal

Digital payments are central to Saudi Arabia's financial modernization under Vision 2030. While infrastructure and regulations have advanced rapidly, adoption is still hindered by barriers such as security concerns, unclear costs, usability issues, and religious ambiguity. Standard analytical models often struggle to evaluate these barriers because user feedback is frequently incomplete, conflicting, or vague. To address this uncertainty, this paper proposes a new decision-making framework: the Plithogenic IndetermHyperSoft Complex Refined Neutrosophic model (PIHCRN-DPA). This model integrates advanced mathematical concepts to handle complexity. It uses refined neutrosophic sets to process degrees of truth and indeterminacy, while complex sets account for contextual …

Sustainable Framework For Performance-Based Learning Assessment Under Uncertainty: Computational Modeling, Mona Gharib, Muhammad Imran, Zaka Ur Rehman

Sustainable Machine Intelligence Journal

Evaluating performance-based learning, such as dance or performing arts education, poses significant computational challenges due to uncertainty, subjectivity, and heterogeneous evaluation data. Conventional scoring models fail to capture the coexistence of quantitative indicators (timing, attendance) and qualitative judgments (artistry, expressiveness).

This study introduces a computational neutrosophic functional framework that models ambiguity using set-valued, indeterminate representations within a rigorous analytic structure. We construct a neutrosophic function space CN(X) and a Hilbert C*-module HN for integrating evaluator signals through algebraic fusion. A strongly continuous neutrosophic operator semigroup (T(t)) is developed to model temporal …

Framework For Assessing Sustainability In Industry 4.0: Supply Chain Optimization, Manufacturing Processes, And Energy Consumption Reduction, Mohamed Abouhawwash, Nebojsa Bacanin

Sustainable Machine Intelligence Journal

Decision making has uncertainty problems in evaluation process, so to overcome uncertainty existing fuzzy decision-making approaches, the neutrosophic set is used to solve incomplete data. Interval valued neutrosophic numbers (IVNNs) are used in this study to solve uncertainty problems. The IVNNs are used with the multi-criteria decision making (MCDM) methods. MCDM method is used to deal with different criteria and alternatives. This study proposes an MCDM methodology for assessment sustainability in industry 4.0. Twenty cases and ten alternatives are used in this study. The criteria weights are computed using the average method. The alternatives are ranked using the ARAS method. …

Evaluating Large Language Models In Smart Mobility Based On Uncertainty Mathematical Methodologies, Mona Mohamed, Khalid Mohamed, Ahmed M. Abdelmouty

Sustainable Machine Intelligence Journal

The swift development of digital technologies, especially Large Language Models (LLMs), offers a revolutionary opportunity to improve urban mobility. LLMs are used widely in various areas, like traffic prediction, autonomous vehicle (AV) communication, and customized trip assistance. General speaking, users now consider the integration of LLMs in mobility to be intelligent friends. The study’s primary objective is to demonstrate how an integrated LLM in mobility services can significantly increase network efficiency, lessen congestion, and improve each traveler’s experience. Practically speaking, deploying a suitable LLM for providing various services to users is imperative. Additionally, this process is an endeavor due to …

Evaluation Of Challenges In The Open Data Services Industry Under Uncertainty: A Methodology Based On Artificial Intelligence And Digital Transformation, Eman Sayed, Gawaher Soliman Hussein

Sustainable Machine Intelligence Journal

This paper proposes a novel multi-criteria decision-making (MCDM) framework based on Triangular Linguistic Neutrosophic Cubic Fuzzy Sets (TLNCFSs) integrated with the COBRA method to evaluate strategic alternatives addressing key challenges in the Open Data Services (ODS) industry. In the era of digital transformation and artificial intelligence (AI), open data initiatives face multifaceted obstacles, including regulatory compliance, data interoperability, public engagement, and sustainable governance. To assess effective strategies, this study considers five potential alternatives aligned with AI and digital transformation, evaluated against fourteen critical criteria spanning policy, technical, governance, strategic, economic, and human-centric dimensions. Expert assessments are collected using TLNCFSs to …

Sustainable Model For Analyzing The Impact Of Social Media On Customer Buying Behavior: A Case Study, Bshair Alharthi, Mohammed A. Farsi

Sustainable Machine Intelligence Journal

In today's digital landscape, chatbots have become widely used across various applications, particularly in systems that provide intelligent assistance to users. To enhance user experience, these systems are often equipped with chatbots capable of processing user inquiries and delivering accurate, timely responses. This study proposes a structured framework for evaluating the impact of social media on customer buying behavior through a case study focused on selecting the most suitable Online Chatbot Platform (OCP). The selection of an optimal OCP is a complex multi-criteria decision-making (MCDM) problem, involving multiple interdependent and often conflicting factors. One of the key challenges in this …

Deep Learning For Precise Mri Segmentation Of Lower-Grade Gliomas, Wusat Ullah, Hamza Naveed, Saalam Ali

Sustainable Machine Intelligence Journal

Brain tumors represent a significant public health issue worldwide, affecting individuals across all age groups and leading to severe neurological and cognitive deficits. Lower-grade gliomas (LGGs), classified by the World Health Organization (WHO) as grade II or III, are characterized by more diffuse infiltration into brain tissue compared to high-grade gliomas but exhibit a slower growth rate. Precise evaluation of tumor resection and detection of residual tumor cells are critical, as incomplete resection is associated with an increased risk of disease recurrence. This study reviews an automated, deep learning-based approach for brain tumor segmentation in Magnetic Resonance Imaging (MRI) using …

Enhancing Software Effort Estimation In Healthcare Informatics: A Comparative Analysis Of Machine Learning Models With Correlation-Based Feature Selection, Muhammad Abid, Sama Bukhari, Muhammad Saqlain

Sustainable Machine Intelligence Journal

Software effort estimation is one of the most crucial processes in the management of software projects predominantly related to the healthcare industry. It involves the prediction of efforts needed to develop and endorse different software applications. To render clinical projects on time within the budget range, flawless projection with efficient planning is incumbent. This paper discloses the techniques that utilize machine learning models for ameliorating software effort estimation by using biomedical datasets, including Breast Cancer Wisconsin, COVID-19, Sleepy Drivers EEG Brainwave, Heart Disease Prediction and Food Nutrition. All of these datasets are cleaned and prepared by handling missing values, converting …

Innovative Soft Cryptosystem For Encrypting And Decrypting Messages With Idea, Muhammad Saeed, Hafiz Inamul Haq, Mubashir Ali, Mudassira Mustafa

Sustainable Machine Intelligence Journal

This paper introduces an advanced cryptosystem for message encryption and decryption, integrating the International Data Encryption Algorithm (IDEA) with soft set and soft matrix principles. Soft sets, conceptualized by Molodtsov, are adept at managing uncertainty, making them ideal for cryptographic frameworks. Our approach utilizes inverse and characteristic products of soft sets and matrices, combined with the robust IDEA algorithm, to significantly enhance security. The IDEA algorithm operates on 64 -bit data blocks with a 128 -bit key, ensuring strong encryption. Additionally, we incorporate the symmetric to introduce complex permutations, further strengthening the cryptographic process by ensuring unique encryptions for identical …

Im4.0ef: Tele-Medical Realization Via Integrating Vague T2nss With Owcm-Ram Toward Intelligent Medical 4.0 Evaluator Framework, Alaa Salem, Mona Mohamed, Florentin Smarandache

Sustainable Machine Intelligence Journal

In the modern world, when nearly everyone considers the Internet to be indispensable, there is a constant desire to discover new applications for the technologies that are already in place. As well, recent research has found several synonyms for the adoption of information and communication technology. Industry 4.0, Industry 5.0, and modern technologies are some of these synonyms. Harnessing these technologies in important domains such as medical services and healthcare is vital. Wherein, the medical field has undergone changes, going from version 1.0 to version 4.0 currently. Medical 4.0 represents a significant advancement in medical practices and systems, leveraging cutting-edge …

Partnership Of Lean Six Sigma And Digital Twin Under Type 2 Neutrosophic Mystery Toward Virtual Manufacturing Environment: Real Scenario Application, Mona Mohamed, Ahmed A. Metwaly, Mahmoud Ibrahim, Florentin Smarandache, Michael Voskoglou

Sustainable Machine Intelligence Journal

The correlation between Lean Six Sigma performance strategy (LSS) and contemporary innovations, including Digital Twin (DT) technology (DLSS) has been scrutinized in this study. The merger's mission is to overcome some of the organizational tensions. Environmentally speaking, it's critical to cut waste and save resources. On a social level, demonstrated boosts to labor and machine efficiency through scrutinizing analysis, simulation, and virtual-physical replication. Economically speaking, improved quality, lower prices and lead times, continuous improvement, and performance enhancement techniques are all part of the worldwide manufacturing scene. Actually, harnessing the suitable DT application amongst available applications is a noteworthy procedure. Hence, …

A Comparative Study On X-Ray Image Enhancement Based On Neutrosophic Set, Nihal N.Mostafa, Amit Krishan Kumar, Yasir Ali

Sustainable Machine Intelligence Journal

Medical image noise, ambiguity, and fuzziness pose challenges to the medical image analysis process. To lessen these problems, fuzzy sets are utilized; however, they frequently ignore the pixel's spatial context. The neutrosophic set (NS) is employed in picture denoising to get over these restrictions. Neutronosophic theory, in particular the NS Bilateral filter, NS Wiener filter, NS Median filter, NS gaussian filter, and NS rank-ordered filter, is covered in this paper. The Lung-cancer dataset of X-Ray images is used to assess the performance of different denoising techniques. The effectiveness of NS-based denoising techniques over conventional techniques is demonstrated through comparisons with …

Unveiling Quantum Communication: From Bell's Inequality To Neutrosophic Logic, Victor Christianto, Florentin Smarandache

Sustainable Machine Intelligence Journal

In this mini-review, we delve into the intriguing realm of quantum communication, spurred by China's recent development of the Tiantong communication satellite. This satellite, shrouded in secrecy, hints at a revolutionary approach to communication that transcends the limitations of conventional signal transmission. We explore the foundational concepts of Bell's inequality and neutrosophic logic, elucidating their roles in understanding the non-local phenomena of quantum entanglement and its potential for secure and rapid communication. Additionally, we examine the connection between Bell's inequality and Shannon information entropy, unveiling the intricate relationship between quantum mechanics and information theory. Through this exploration, we glimpse a …

Collaboration Of Vague Theory And Mathematical Techniques For Optimizing Healthcare By Recommending Optimal Blockchain Supplier, Mona Mohamed, Asmaa Elsayed, Michael Voskoglou

Sustainable Machine Intelligence Journal

The Internet of Medical Things (IoMTs) has the potential to revolutionize healthcare delivery by connecting medical devices and applications to healthcare IT systems via the internet. This connectivity enables the collection, transmission, and analysis of patient data, facilitating remote healthcare delivery and enhancing patient outcomes. However, the security and privacy of the data transmitted and stored within IoMT systems remain critical concerns. Blockchain technology offers a promising solution to address these challenges in IoMTs applications. Blockchain provides a decentralized and immutable ledger where transactions, in this case, patient data, are securely recorded and cannot be altered retroactively. This ensures the …

A Neutrosophic Multi-Criteria Model For Evaluating Sustainable Soil Enhancement Methods And Their Cost Implications In Construction, Ahmed El-Douh, Songfeng Lu, Ahmed Abdelhafeez, Alber Aziz

Sustainable Machine Intelligence Journal

This paper provides a comprehensive overview of the application of life cycle assessment (LCA) to promote sustainable soil practices in construction foundation projects. The aim is to evaluate the environmental impact and long-term sustainability of soil-related activities in construction through the lens of LCA. This assessment encompasses a wide array of factors, including soil quality, erosion control, contaminant remediation, soil stability, conservation, drainage management, and compliance with regulatory standards. To address this multifaceted evaluation, we employ a multi-criteria decision-making (MCDM) model, specifically introducing the Multi-Attributive Border Approximation Area Comparison (MABAC) method. This MCDM technique is utilized to appraise the sustainability …

Neutrosophic Multi-Criteria Decision-Making Framework For Sustainable Evaluation Of Power Production Systems In Renewable Energy Sources, Ahmed M.Ali, Myvizhi Muthuswamy

Sustainable Machine Intelligence Journal

To make the change to a cleaner, more sustainable energy future, it is essential that power production systems be evaluated sustainably. To estimate the overall sustainability performance of various power production systems, this evaluation takes into account a wide range of parameters. Impact on the environment, availability of renewable resources, resource efficiency, social and economic implications, economic feasibility, grid integration and dependability, technical maturity, and scalability are all taken into account. Stakeholders may make better judgments and give higher priority to power production systems that take into account environmental impacts, resource efficiency, social impacts, and economic viability by evaluating these …

Assessment The Health Sustainability Using Neutrosophic Mcdm Methodology: Case Study Covid-19, Ahmed A. El-Douh, Songfeng Lu, Ahmed Abdelhafeez, Alber S. Aziz

Sustainable Machine Intelligence Journal

As a result of the severe difficulties presented by the COVID-19 pandemic, a holistic response is required, one that takes into account both the urgent needs of patients and the long-term viability of healthcare institutions. This study aims to give a complete knowledge of the tactics and techniques necessary to maintain the continuing well-being of people and communities by examining the idea of health sustainability in the context of the COVID-19 pandemic. This study conducts a literature review to investigate the many facets of health sustainability, such as emergency preparedness, mental health care, health workforce support, health education and communication, …

Empowering Deep Learning Based Organizational Decision Making: A Survey, Mona Mohamed

Sustainable Machine Intelligence Journal

The advent of deep learning has revolutionized the landscape of organizational decision-making by offering powerful tools for data analysis and prediction. In this comprehensive survey, we explore the intersection of deep learning and organizational decision-making, elucidating the theoretical underpinnings, empirical evidence, and practical implications of this synergy. Theoretical foundations and research hypotheses are rigorously examined, providing a solid framework for understanding the role of deep learning models in enhancing decision-making processes. We delve into the systematic survey, which encompasses a wide spectrum of applications across various industries and domains, showcasing how deep learning empowers decision support systems, augments data-driven decision-making, …

Sustainable Supplier Selection Using Neutrosophic Multi-Criteria Decision Making Methodology, Zenat Mohamed, Mahmoud M. Ismail, Amal Abd El-Gawad

Sustainable Machine Intelligence Journal

Sustainable supplier selection is an important part of supply chain management since it encourages ethical and eco-friendly procedures. This study examines the most important criteria and factors for judging the sustainability performance of suppliers and presents a thorough overview of sustainable supplier selection. A decision-making framework for sustainable supplier selection is created via a review of relevant research, case studies, and best practices. Key factors for assessing suppliers include environmental performance, social responsibility, and economic viability, all of which are included in the framework. The results stress the need to take into account suppliers' energy efficiency, waste management, and social …

Evaluation Factors Of Solar Power Plants To Reduce Cost Under Neutrosophic Multi-Criteria Decision Making Model, Mohamed Abouhawwash, Mohammed Jameel

Sustainable Machine Intelligence Journal

Solar power facilities must be efficient, reliable, and sustainable to meet energy demands. This study conducts a comprehensive analysis of criteria for evaluating solar power installations, focusing on factors impacting performance, economic viability, and environmental sustainability. Drawing from extensive literature, industry practices, and case studies, we identify key considerations such as technological feasibility, economic viability, environmental impact, legal frameworks, and social acceptance. To address the complexity and uncertainty associated with these criteria, we employ a multi-criteria decision-making approach. Specifically, the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is utilized to determine the weights of criteria, while the neutrosophic set theory …

Assessment And Contrast The Sustainable Growth Of Various Road Transport Systems Using Intelligent Neutrosophic Multi-Criteria Decision-Making Model, Nada Nabeeh

Sustainable Machine Intelligence Journal

This study analyses the elements and approaches to creating sustainable transport systems, with a focus on road travel. The study examines the environmental and economic aspects of sustainable road transport and stresses the need to curb carbon emissions, boost energy efficiency, clean the air, ensure everyone has easy access to transport, and think about societal goals as a whole. Important considerations including environmental effect, energy efficiency, legislative frameworks, and economic impact are highlighted in the study. The MCDM model is used as a complexity instrument to strike a balance between competing objectives and criteria. This research may help stakeholders use …