International Journal of Neutrosophic Science

This study highlights the importance of urban agriculture in ensuring food security and promoting sustainability in urban areas, using a neutrosophic multi-criteria approach to evaluate the impact of biostimulants and organic additives on soil quality, plant growth, and crop yields. The research demonstrates that biofertilizers such as Chromococcus and Azotobacter significantly improve nutrient availability and plant health, resulting in robust and high-quality harvests, while mineral additives like zeolites enhance soil fertility and moisture retention. Three scenarios were analyzed using neutrosophic logic to handle the inherent uncertainty in urban agricultural systems: the first scenario shows exceptional plant growth and yield with high sustainability (valued as "Very Very High" according to neutrosophic logic), the second scenario highlights challenges in vegetative growth and sustainability (valued as "Low"), and the third scenario combines good plant growth with high sustainability and significant contributions to climate change mitigation (valued as "Medium High"). In summary, integrating organic amendments and biofertilizers in urban agriculture, evaluated through neutrosophic methods, is essential for creating resilient and productive agricultural systems, benefiting soil health, biodiversity, resource conservation, and local economies.

This study explores the rich cultural heritage of indigenous peoples and communities, whose traditions and adaptability have intrigued scholars interested in understanding their relevance in modern times. The Neutrosophic Delphi Method emerges as a vital tool in this research, offering a dynamic and versatile approach to address the inherent complexity of indigenous activities. By investigating the uncertainty and ambiguity in decision-making, this method enables a thorough examination of cultural practices. The interdisciplinary methodology employed focuses on the interaction between traditional and modern aspects, examining the main activities that define the daily lives of indigenous communities. The use of the Neutrosophic Delphi Method is highlighted for its ability to handle diverse perspectives and complex data, and the deneutrosophication process to improve precision and clarity in the findings. This technique ensures an accurate and harmonized representation of indigenous knowledge with modern scientific research. This effort seeks not only to enhance the academic legacy but also to foster international dialogue, promoting the recognition and appreciation of cultural diversity. By empowering indigenous populations to contribute to the generation of knowledge about their experiences, the study advocates for a more inclusive and equitable approach in scientific inquiry, acknowledging the invaluable contributions of indigenous communities to the cultural richness of our world.

In the current context of accelerated urbanization and the urgent need for sustainability, urban agriculture has become a vital alternative to guarantee food security and ecological management of cities. This study addresses possible soil organic transformations in these systems using Treesoft Set and neutrosophic sets. Treesoft Set, an advanced tool for complex data analysis, is complemented by neutrosophic set theory, which allows you to manage the uncertainty inherent in natural and human systems. Together, these methodologies provide a more complete and detailed view of how urban land can adapt and improve under sustainable agricultural practices, highlighting the importance of integrating technology and ecology in the design of green cities. The analysis carried out not only unravels the dynamics of soil organic transformations, but also highlights the variability and complex interactions that occur in urban environments. Research shows that, through the application of Treesoft Set and neutrosophic sets, it is possible to identify patterns and trends that would otherwise go unnoticed. Additionally, it highlights how these tools can influence decision-making to optimize land use and encourage agricultural practices that improve the health of the urban ecosystem. This innovative approach opens new avenues for research and development of urban agriculture, promoting more resilient and efficient systems in the management of natural resources in an increasingly urbanized world.  

The extraction of dehydrated proteins from whey is not only a technological innovation in the field of biotechnology, but also a complex intersection of socioeconomic and environmental factors that deserve detailed evaluation. This article delves into the analysis using the neutrosophic PEST-SWOT approach, revealing how the political, economic, social, and technological dimensions interact with the strengths, opportunities, weaknesses, and threats of this emerging practice. The neutrosophic methodology allows us to unravel nuances that other approaches might overlook, highlighting both the potential benefits and the possible negative repercussions that may arise in different contexts. Whey, traditionally considered waste, is revalued by being transformed into a source of protein, which has profound implications for sustainability and the circular economy. However, neutrosophic analysis also exposes the complexities and ambiguities inherent to this activity. From an environmental perspective, whey extraction and processing pose significant challenges, such as energy consumption and waste generated, that must be carefully managed. In the socioeconomic sphere, the creation of new value chains can generate employment and foster innovation, but it can also destabilize existing markets and generate inequalities. Adopting a neutrosophic approach allows for a more holistic evaluation, recognizing the coexistence of multiple truths and the need for a balance between the various interests involved. Thus, this article invites deep reflection on the implications of technology, proposing an informed and multifaceted debate on its future development and application.

The kidneys have an important role in keeping blood pressure, electrolyte sense, and acid-base sense of body balance to remove toxins from our body. Malfunction is responsible for irrelevant to life-threatening diseases, along with malfunction in the other functional organs. As a result, scholars worldwide have committed to finding methods for effectively diagnosing and accurately treating chronic kidney disease. As machine learning (ML) classifier is widely deployed in the healthcare field for diagnoses, also CKD is now involved in the collection of disorders that could be predicted through the ML classifier. Neutrosophic logic (NL) can be employed as a form of logic that expands classical, fuzzy, and intuitionistic fuzzy logic (IF) by integrating a third constituent: indeterminacy. It enables data handling and representation with three dissimilar membership functions: truth (T), indeterminacy (I), and false (F). The complete set is independent and may differ in the interval [0, 1], providing a convoluted strategy to handle, data incompleteness, vagueness and uncertainty. This makes NL especially relevant in complicated systems where data might be partially unknown, ambiguous, or inconsistent. This article employs a Rough Neutrosophic Attribute Reduction with Deep Learning based Enhanced Kidney Disease Diagnosis (RNSAR-DLKDD) technique. Initially, the RNSAR-DLKDD technique reduces the attributes via the RNSAR technique. Followed by, the detection and classification of kidney disease take place using long short-term memory (LSTM) model. Finally, the hyperparameter selection process is carried out via crow search algorithm (CSA). To highlight the performance of the RNSAR-DLKDD technique, a series of experiments were involved. The extensive results inferred the betterment of the RNSAR-DLKDD technique over other models

Chronic kidney disease (CKD) is a non-communicable disease that has made a significant contribution to admission, morbidity, and mortality rates of patients globally. CKD is a common kidney disease that happens when both kidneys fail, and the CKD patient suffers from these conditions for a long time. Machine learning (ML) is becoming more crucial in medical diagnoses as it allows detailed examination, thus reducing human error and optimizing prediction accuracy. Now, ML classifiers and algorithms are highly dependable techniques for the diagnoses of diverse diseases such as diabetes, heart disease, liver disease, and tumor disease predictions. A neutrosophic set (NS) is especially suitable in applications where information is vague, incomplete, or inconsistent, which provides an effective means for analyzing and modeling intricate mechanisms. A NS is a mathematical approach to handle indeterminacy, uncertainty, and imprecision. It expands IF sets, classical sets, and fuzzy sets by introducing three degrees: truth (T), indeterminacy (I), and false (F). This manuscript offers a Double-Valued Neutrosophic Set for Chronic Kidney Disease Detection and Classification (DVNS-CKDDC) technique. In the DVNS-CKDDC technique, three major processes are involved. At the primary phase, the DVNS-CKDDC technique performs a linear scaling normalization (LSN) model. Next, the DVNS-CKDDC technique makes use of the DVNS model for the identification of CKD. Finally, the beluga whale optimization (BWO) algorithm is employed for the parameter tuning of the DVNS method. To ensure the supremacy of the DVNS-CKDDC technique, a widespread simulation analysis is involved. The experimental values stated that the DVNS-CKDDC approach attains improved performance over other models  

Variables that have revived new interest through computational developments and extensive data analysis. This article contributes to the subject by generalizing the bivariate copula introduced recently in8 and based on the concept of the counter-monotonic shock method. The proposed copula has the feature of covering the full range of negative dependence induced by two dependence parameters, which is not so common in the specialized literature. We examine the main characteristics of this copula. In particular, the absolutely continuous and singular copula components are derived. Analytical expressions of important concordance measures, such as Spearman’s rho and Kendall’s tau, are established, along with expressions of the product moments. A real neutrosophic data set, based on the daily quality of air in the New York Metropolitan Area, is used to illustrate the applicability of the proposed copula, with quite convincing results.  

This review of the literature delves into the complex interplay between energy measures and decision-making processes in the domains of fuzzy graphs, intuitionistic fuzzy graphs, and neutrosophic graphs. In graph theory, energy is a key quantity that is used to measure structural properties and evaluate decision model dynamics. The research methodically examines the theoretical underpinnings, computational techniques, and practical applications of energy measures in contexts involving decision-making, considering the special features brought forth by fuzzy, intuitionistic fuzzy, and neutrosophic graph models. This review attempts to provide a thorough understanding for researchers and practitioners looking to use energy measures for efficient decision support in the setting of uncertainty contained within these specific graph topologies by synthesizing prior research.  

The Topp-Leone Extended Exponential distribution is used to simulate human lifetime data patterns in the field of survival analysis. To characterize a variety of uncertain survival data, the neutrosophic Topp-Leone extended exponential distribution (NTLEED) is used. The specified distribution is a great tool for modeling unknown data that is somewhat positively biased. This study covers the primary statistical properties of the constructed NTLEED, including the survival function, hazard rate, and neutrosophic moments. In addition, the neutrosophic parameters are estimated using the popular maximum likelihood estimation technique. To determine whether the predicted neutrosophic parameters were obtained, a simulation study is carried out. Not to mention that actual data has been used to discuss potential real-world applications of NTLEED. Real data were utilized to demonstrate how well the proposed model performed in contrast to the current distributions.  

The aim of this study is to present novel collections of bi-univalent functions, which are characterized using the Bell Distribution. These collections are delineated through the application of Jacobi polynomials. We have established bounds for the Taylor-Maclaurin coefficients, particularly |a2| and |a3|. Additionally, we have investigated the Fekete-Szeg¨o functional issues pertinent to functions within these subclasses. By concentrating on particular parameters in our principal findings, we have identified numerous new insights.  

 For the treatment of contamination produced by the variant presence of heavy metals such as lead (Pb), copper (Cu), zinc (Zn) and arsenic (As) in the waters of the Irrigation Canal of the Left Bank of the Mantaro River (CIMIRM is Spanish), a purification procedure was carried out using different doses of cerium oxide nanoparticles (CeO2) and evaluating their effectiveness in the elimination of these metals in the aforementioned mass of water. As a first step, the water from the CIMIRM canal was characterized using Modular Ultraviolet-Visible Spectrophotometry techniques with high NIR sensitivity and Inductively Coupled Plasma Mass Spectrometry (ICP-MS), to measure the concentrations of heavy metals. Additionally, an analysis of the CeO2 nanoparticles was carried out using techniques to confirm their size and structure. The efficacy of the treatment was determined statistically using a four-stage four-factor factorial design, comparing the differences in the control groups and target groups. The classic statistical test used is the Wilcoxon rank sum test. One of the problems of the simulation of the study carried out in the laboratory is the lack of accuracy because the concentration of heavy metals in the Mantaro River varies during the year. This is why a single crisp value is not enough to study the effectiveness of treatments. One solution to this problem is to use Neutrosophic Statistics, where the data is replaced by Neutrosophic Numbers or intervals instead of crisp values.

This research is carried out at the Educational Institution No. 35005 Reverend Father Bardo Bayerle of the Province of Oxapampa, Peru. We demonstrate that when there is a strong cultural identity, this means that the intercultural attitude of students is also strengthened. Cultural identity is a value that is currently being lost. This is a negative phenomenon, since with the reaffirmation of what one is culturally then one can consolidate the relationship with other groups. In this paper this phenomenon is studied from a statistical perspective on a survey carried out on students of this institution, some of them belonging to the target group and others belonging to the control group. To obtain more reliable results we apply Plithogenic Statistics, which is a generalization of Multivariate Statistics, where more than one random variable is studied simultaneously. Specifically, plithogenic statistics incorporates new components within the statistical study such as falsity or indeterminacy.  

The Mantaro Valley in Peru is an inter-Andean River valley, through which the Mantaro River passes. Approximately a population of one million inhabitants live here. Currently, the damage caused by the use of non-renewable energy sources is very evident, both to the environment and to the local economy, which will become unsustainable in the future. That is why we want to critically study the implementation of renewable energy projects that support the generation of electricity and other types of energy in this area. However, this has some positive and negative elements. In this paper, we apply a PEST-SWOT analysis to evaluate the balance of each of these aspects. Furthermore, we use an evaluation in the form of single-valued neutrosophic numbers, which allow us to capture the uncertainty and indeterminacy in this decision-making problem.  

Sustainable agriculture is of utmost importance in Saudi Arabia to resolve problems like environmental degradation and water scarcity. The country has made considerable investments in modern agricultural systems such as vertical farming and hydroponics to maximize crop yields and water efficiency. The most direct manifestation of earlier crop growth problems is Pepper leaf disease. Rapid and accurate detection of pepper leaf disease is crucial to immediately detect growth issues and enable accurate control and preventive measures. The traditional method based on human experience and visual inspection to recognize pepper leaves is costly, subjective and laborious. Hence, it is essential to develop fast, convenient, and precise techniques for identifying pepper leaf disease. The Q-neutrosophic soft relation is a generalization that integrates the concepts of soft set and neutrosophic set, enabling for truth, indeterminacy, and false degree in the membership of element with respect to a relation in a soft computing framework. Therefore, this study introduces a new Q Neutrosophic Soft Relation with Deep Learning based Pepper Leaf Disease Recognition (QNSRDL-PLDR) technique for Sustainable Agriculture in KSA. The proposed QNSRDL-PLDR method leverages DenseNet for feature extraction, the model uses the Adam optimizer for effective parameter optimization. Unique to this framework is the combination of a Q-neutrosophic soft relation classifier, allowing nuanced classification considering truth, indeterminacy, and falsity degrees in disease presence assessment. A comprehensive set of simulations is conducted to demonstrate the better efficiency of the QNSRDL-PLDR technique. This technique aims to improve reliability and accuracy in detecting Pepper Leaf Diseases, critical for crop management and sustainable agricultural practices

For smart cities to succeed, substantial developments to take place in roads, city streets, public transportation, houses, businesses, and other aspects of city life must be drawn up. In today’s world, there is a crucial necessity for effective management of cities to reduce the effect of COVID19 disease with increasing population in cities. Multiple metrics had already been taken to lower the infection rate of COVID19, from the beginning of the outbreaks, such as maintaining distance from another person and wearing face masks. Ensuring security in public places of smart cities needs state-of-the-art technology, including computer vision, deep learning and deep transfer learning for automated detection of face masks and monitoring of whether people wear masks accurately.  The achievement of machine learning (ML and) artificial intelligence (AI) techniques in face recognition and object detection makes it fit for the development of FMD methods. The fundamental concept behind the generalized intuitionistic fuzzy soft set is highly productive in making decisions because it considers ways to manipulate an additional intuitionistic fuzzy input from the director to balance any disturbance in the data delivered by the assessment analyst. This manuscript offers the design of Weighted Soft Discernibility Matrix with Deep Learning Assisted Face Mask Detection (WSDMDL-FMD) technique for Smart City Environment. The WSDMDL-FMD technique proficiently discriminates the facial images with the presence or absence of masks. The WSDMDL-FMD technique comprises two stages: Mask RCNN-based face detection and WSDM-based face mask classification. Primarily, the WSDMDL-FMD technique uses Mask RCNN-based face detection. Next, the convolutional neural network (CNN) model derives features from the detected faces and its hyperparameters can be chosen by cuckoo optimization algorithm (COA). For face mask classification, the WSDMDL-FMD technique applies WSDM model. To evaluate the results of the WSDMDL-FMD technique, a series of experiments were involved. The obtained outcomes stated that the WSDMDL-FMD method reaches superior performance than other models