The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is commonly used in examining and illustrating the relationship between different factors in a complex system. This paper proposes a novel approach that integrates the Bipolar neutrosophic Dombi-based IGWHM operator into the DEMATEL method, in which the criteria are analyzed by means of the cause-and-effect relationship diagram. The current studies on the classical DEMATEL approach have some limitations on the aggregation process, particularly in capturing the interrelationship of individual arguments by assessing their impact on each other within a complex system. To enhance the aggregation of complex information in the decision-making framework, the Bipolar neutrosophic Dombi-based Improved Generalized Weighted Heronian mean (IGWHM) operators are employed. The applicability and effectiveness of the proposed approach are demonstrated when solving a selection of transport service providers. The ability of the method to highlight the intricate interdependencies and ranking criteria based on their importance. The sensitivity of the developed approach is observed with variations in the involved parameter. Moreover, a comparative analysis is made with other methods to demonstrate its validity.
Read MoreDoi: https://doi.org/10.54216/IJNS.250101
Vol. 25 Issue. 1 PP. 08-22, (2025)
In this paper, we present the topological space of intervals based symbolic m-plithogenic real numbers of orders between 2 and 5, where we clarify how m-plithogenic real intervals can be expressed according to the symbolic plithogenic partial order relation, and we use these intervals to build a topological space. On the other hand, many illustrated and related examples on open and closed sets will be provided to explain the validity of our approach.
Read MoreDoi: https://doi.org/10.54216/IJNS.250102
Vol. 25 Issue. 1 PP. 23-37, (2025)
Network security is any endeavor intended to defend the integrity and usability of the data and network. Fast development in network technology and the scope and amount of information transported on a network is gradually growing. Based on these situations, the complexity and density of cyber-attacks and threats are also increasing. The constantly expanding connectivity makes it more difficult for cyber-security specialists to monitor all the movements on the network. More complex and frequent cyber-attack makes anomaly identification and detection in network events challenging. Machine learning (ML) provides different techniques and tools to automate cyber-attack detection and for prompt prognosis and analysis of attack types. The model of a neutrosophic hypersoft set (NHSS) is a combination of a neutrosophic set with a hypersoft set. It is a useful structure to handle multi-objective problems and multi-attributes with disjoint attributable values. This study derives the Possibility Neutrosophic Hypersoft Set for Cyberattack Detection (pNHSS-CAD) technique to improve network security. The pNHSS-CAD method has its formation in feature selection with the Whale Optimization Algorithm (WOA), which successfully recognizes the important features from the data, thus improving processing speed and reducing dimensionality. Following feature selection, the pNHs-set classifier is employed for the robust detection and identification of cyber-attacks, which leverages the power of the neutrosophic set to deal with ambiguity and uncertainty in the information. The Firefly (FF) technique is applied for hyperparameter fine-tuning, which ensures the model operates at maximum effectiveness to enhance the performance of the classification. This wide-ranging method leads to a very efficient cyberattack recognition method, which can able to accurately mitigate and identify risks in the real world
Read MoreDoi: https://doi.org/10.54216/IJNS.250103
Vol. 25 Issue. 1 PP. 38-50, (2025)
The swift development in social media through the internet produces vast data in a real-time scenario that has startling effects on large datasets. It generated the high-level use of sentiments and emotions in social networking media. Sentiment analysis (SA) using a neutrosophic set presents a new technique to handle the integral ambiguity and uncertainty in text datasets. Different from classical approaches, which categorize sentiment as positive, negative, or neutral, the neutrosophic set allows for the comparison analysis of truth-, indeterminacy-, and falsie-membership functions for all the sentiments. This allows a more flexible and nuanced representation of sentiments, which accommodates the contradictions and complexities commonly depicted in natural language. SA can accomplish high performance and depth in interpreting and understanding the emotions expressed in uncertain and diverse text datasets by leveraging a neutrosophic set. This manuscript presents a Neutrosophic Vague N-Soft set with a Chimp Optimization Algorithm for Sentiment Analysis (NVNSS-COASA) technique on Social Media. The NVNSS-COASA technique is initiated by the comprehensive preprocessing stage to normalize and clean the text dataset, which ensures superior input for the succeeding stage. Then, the Term Frequency-Inverse Document Frequency (TF-IDF) mechanism is employed to convert the preprocessed text into mathematical features, which capture the word importance in terms of datasets. Subsequently, a strong NVNSS classifier is employed for accurately categorizing the sentiment. We integrate COA for the parameter tuning to further improve the performance of the method. The simulation outcomes emphasized that the NVNSS-COASA method shows superior outcomes over other techniques. The outcomes indicated that the NVNSS-COASA can able to deliver reliable and precise insights from the text dataset.
Read MoreDoi: https://doi.org/10.54216/IJNS.250104
Vol. 25 Issue. 1 PP. 51-63, (2025)
COVID19 otherwise called Severe Acute Respiratory Syndrome Corona virus-2 is an infectious illness. Another transmittable infection called Pneumonia is mainly attributable to infection because of bacteria in the alveoli of the lungs. Once a diseased lung tissue has infection, it elevates excretion in it. Specialists conduct health examinations and identify the patient through ultrasound, biopsy, or Chest X-ray of lungs to identify whether the patient has these diseases. Incorrect treatment, misdiagnosis, and if the disease was disregarded will result in the fatality. The development of Deep Learning and neutrosophic set (NS) supports the decision-making procedure of professionals to identify patients with this disease. NS is a prolongation of the fuzzy set and classical theories. The NS determines three memberships such as T, I and F. T, I, and F display the degree of truth, the false, and the indeterminacy membership, correspondingly. This enables a more nuanced representation of contradiction, uncertainty, and ambiguity within the dataset, allowing superior handling of imprecise and complex data. This study develops a new Deep learning with Neutrosophic Set-Based k-Nearest Neighbors Classifier for disease detection (DLNSKNN-DD) technique. The major purpose of the DLNSKNN-DD method is to identify the existence of virus pneumonia and COVID-19. In the DLNSKNN-DD technique, the feature extraction from the medical images is carried out by residual network (ResNet50v2). Moreover, the parameter tuning of the ResNetv2 model is done using Adadelta optimizer. The DLNSKNN-DD technique exploits NSKNN model for classification purposes. The performance evaluation of the DLNSKNN-DD algorithm can be assessed on medicinal image dataset. The experimental outcomes underlined the effectual recognition results of the DLNSKNN-DD technique on the identification of diseases
Read MoreDoi: https://doi.org/10.54216/IJNS.250105
Vol. 25 Issue. 1 PP. 64-74, (2025)
In the field of survival analysis, the inverse Gompertz distribution is used to mimic human lifetime data patterns. The goal of the neutrosophic inverse Gompertz distribution (NIGD) is to describe a range of indeterminate survival data. The defined distribution is very helpful for modeling somewhat positively skewed unknown data. The main statistical characteristics of the created NIGD, such as the neutrosophic moments, hazard rate, and survival function, are covered in this paper. Additionally, the well-known maximum likelihood estimation method is used to estimate the neutrosophic parameters. A simulation study is conducted to see whether the projected neutrosophic parameters were reached. Not to mention that possible real-world uses of NIGD have been discussed using actual data. To show how well the suggested model performed in comparison to the present distributions, real data were used.
Read MoreDoi: https://doi.org/10.54216/IJNS.250106
Vol. 25 Issue. 1 PP. 75-80, (2025)
In the domain of optimization, linear programming (LP) is recognized as an exceptionally effective method for ensuring the most favorable outcomes. Within the context of LP, the minimum cost flow (MCF) problem is fundamental, with its primary objective being to reduce the transportation costs for a single item moving through a network, under the constraints related to capacity. This network is made up of supply nodes, directed arcs, and demand nodes and each arc has an associated cost and capacity constraint, these factors are certain. However, in practical scenarios, these factors are susceptible to variation due to causal uncertainty. The neutrosophic set theory has surfaced as a challenging approach to tackle the uncertainty that is often encountered in optimization processes. In this manuscript, our primary objective is to address the minimal cost flow (MCF) problem while accounting for the uncertainty inherent in the neutrosophic set. We specifically focus on the cost aspect as SVTN numbers and introduce a new approach based on a customized ranking function handmade for the MCF problem a pioneering endeavor within the field of neutrosophic sets. Additionally, we present numerical example to validate the effectiveness and robustness of our model.
Read MoreDoi: https://doi.org/10.54216/IJNS.250107
Vol. 25 Issue. 1 PP. 81-92, (2025)
In the financial industry, financial fraud is an ever-evolving risk with extreme consequences. Data mining has been instrumental in the recognition of credit card fraud (CCF) during online transactions. CCF recognition, which is a data mining problem, become a challenge owing to its two main reasons - firstly, the profiles of fraudulent and normal behaviors modify continually and then, CCF dataset is extremely lopsided. The implementation of fraud recognition in credit card transactions is tremendously influenced by the sampling methodology on data, detection approach and variable selection utilized. The conception of the neutrosophic hypersoft set (NHSS) is a parameterized family that handles the sub-attributes of the parameter and is an appropriate extension of the NHSS to correctly evaluate the uncertainty, deficiencies, and anxiety in decision-making. In comparison to previous research, NHSS can accommodate additional uncertainty, which is the crucial approach to describe fuzzy datasets in the decision-making algorithm. This study introduces an Automated Credit Card Risk Assessment using Fuzzy Parameterized Neutrosophic Hypersoft Expert Set (ACCRA-FPNHES) technique. In the ACCRA-FPNHES technique, a three-step process is involved. As a primary step, the ACCRA-FPNHES technique designs sparrow search algorithm (SSA) for choosing features. In the second step, the detection of CCF takes place using FPNHES technique. Finally, in the third step, the parameters related to the FPNHES technique can be adjusted by arithmetic optimization algorithm (AOA). The simulation validation of the ACCRA-FPNHES technique can be studied on credit card dataset. The obtained values indicate that the ACCRA-FPNHES technique showcases better performance
Read MoreDoi: https://doi.org/10.54216/IJNS.250108
Vol. 25 Issue. 1 PP. 93-103, (2025)
Neutrosophic fuzzy logic (NFL) is a prolongation of classical FL that integrates the neutrosophic conception that handles the indeterminacy concept. This method offers a more comprehensive and flexible architecture to handle inconsistent, uncertain, and indeterminate data, which makes it especially helpful in complicated reasoning and decision-making scenarios where classical FL might be defeated. A learning scheme, which is made from the internet and computer as the main components, is called as an e-learning platform. Although the training might happen on or off campuses, utilizing the internet is an integral part of online learning. In the meantime, to significantly augment the education standard, it is essential to forecast the learning style of the user through supervision and feedback. Nonetheless, it averts the intrinsic relationship amongst e-learning behaviors. There might be technological difficulty ranging from network connectivity issue to users memorizing their username and password while executing and developing an educational program. The learning style prediction in e-learning network is complex one and therefore we recommend a new methodology which employs web mining method for the feature extraction and log files of students from the e-learning network. This study develops an Automated Learning Style Prediction using Weighted Neutrosophic Fuzzy Soft Rough Sets (ALST-WNSFSRS) technique in E-learning Platform. The ALST-WNSFSRS technique mainly aims for the prediction of automated learning styles. Initially, the information is gathered from the Kaggle websites and utilizing a web mining method the feature from the web and log files are pre-processed. The preprocessed information is scrutinized to discover the pattern of approach to learning and later investigated the pattern. Then, the feature patterns are clustered by the fuzzy c-means (FCM) clustering technique and later utilizing the WNSFSRS method, the approach to students learning is anticipated. To improve the performance of the WNSFSRS technique, glowworm swarm optimization (GSO) algorithm is used. The performance of the ALST-WNSFSRS technique is compared with existing models and the results reported the supremacy of the ALST-WNSFSRS technique interms of different measures
Read MoreDoi: https://doi.org/10.54216/IJNS.250109
Vol. 25 Issue. 1 PP. 104-116, (2025)
Diabetic retinopathy (DR) is an eye disorder triggered by diabetes that might result in loss of sight. Earlier diagnosis of DR is critical since it might cause loss of sight. Manual diagnoses of DR severity by ophthalmologists are time-consuming and challenging. As a result, there has been considerable attention on designing an automatic technique for DR detection using fundus photographs. In medical science, prognosis and diagnosis are the most challenging tasks due to the presence of fuzziness in medical images and the restricted subjectivity of the experts. Neutrosophic Set (NS) in medical image analysis provides an understanding of the NS concepts, together with knowledge of how to collect, handle, interpret, and analyze clinical images using NS techniques. The neutrosophic set (NS), which is a generality of fuzzy set, provides the overcoming prospect of the restriction of fuzzy-based models for the analysis of medical images. This manuscript develops a Robust Diabetic Retinopathy Detection and Grading using Neutrosophic Topological Vector Space (DRDG-NSTVS) technique on fundus images. The DRDG-NSTVS technique begins with Median Filter (MF) noise removal to optimize the clarity of fundus photographs by successfully eliminating noises. Later, the InceptionV3 is used to perform feature extraction for identifying complicated features and patterns related to DR. The parameter tuning is performed by the moth flame optimization (MFO) technique to ensure superior performance of the model. The final diagnoses and classification of DR are accomplished utilizing the NSTVS classifiers that easily perform the uncertainties inherent in medicinal statistics. The simulation was conducted on a benchmark dataset to examine the proposed model performance. This combined method gives a greatly reliable and accurate solution for the earlier diagnosis and detection of DR
Read MoreDoi: https://doi.org/10.54216/IJNS.250110
Vol. 25 Issue. 1 PP. 117-129, (2025)
In this paper, we present the topological space of intervals based neutrosophic real numbers , where we clarify how neutrosophic real intervals can be expressed according to the neutrosophic partial order relation, and we use these intervals to build a topological space. On the other hand, we use a similar argument to build a topological space over the intervals of refined neutrosophic numbers, with many illustrated and related examples on open and closed sets.
Read MoreDoi: https://doi.org/10.54216/IJNS.250111
Vol. 25 Issue. 1 PP. 130-136, (2025)
Beurling (or generalized) prime system has been defined by Arne Beurling in 1937, and several couthers have been working on this during the last century. This work focuses on addressing some concrete examples of an outer generalized prime system involving Beurling zeta function. The core of this work is to create a discrete generalized prime system under a fixed condition to give a new upper bound for Beurling zeta function.
Read MoreDoi: https://doi.org/10.54216/IJNS.250112
Vol. 25 Issue. 1 PP. 137-147, (2025)
Due to the rapid increase in population density, medical sciences now face a major challenge in the automated detection of diseases. Intelligent system assists health personnel in earlier disease diagnosis and provides reliable treatment to reduce the fatality rates. Skin cancer is one of the most severe and deadliest kinds of cancer. A health professional uses dermoscopic images to manually diagnose skin tumors. This technique can be time-consuming and labor-intensive and needs a considerable level of expertise. The automatic recognition method is essential for the earlier diagnosis of skin tumors. In recent times, N-soft Set model has become widespread, which is a generalization of fuzzy set where all the elements have a membership value in the complement (0 to 1) and in the set (0 or 1). This study presents a Skin Cancer Diagnosis using Cubic Pythagorean Fuzzy Hypersoft Set (SCD-CPFHSS) technique. The presented SCD-CPFHSS technique performs identification of skin cancer using the application of NSs and metaheuristic algorithms. In the SCD-CPFHSS technique, neural architectural search network (NASNet) model derives feature extractors from the dermoscopic image. In addition, the efficacy of the NASNet model can be boosted by the design of salp swarm algorithm (SSA). For skin cancer recognition, the SCD-CPFHSS technique applies CPFHSS model. The experimental outcome of the SCD-CPFHSS methodology was validated using medical dataset. The extensive results pointed out that the SCD-CPFHSS technique reaches better results on skin cancer diagnosis
Read MoreDoi: https://doi.org/10.54216/IJNS.250113
Vol. 25 Issue. 1 PP. 148-159, (2025)
The breakthrough technologies of the Internet of Things (IoT) have modernized classical Consumer Electronics (CE) into next-generation CE with high intelligence and connectivity. This connectivity amongst appliances, actuators, sensors, etc., offers automated control in CE and enables better data availability. However, the data traffic has been exponentially increased owing to its decentralization, diversity, and increasing number of CE devices. Furthermore, the static network-based approaches need exclusive management and manual configuration of CE devices. The generalization of a Neutrosophic Hypersoft Set (NHSS) is a concept of a soft set. This architecture is a mixture of neutrosophic sets with hypersoft sets. Therefore, the study introduce a Blockchain with Single-Valued Neutrosophic Hypersoft Sets Assisted Threat Detection (BCSVNHS-TD) technique for Secure IoT Assisted CE. The presented BCSVNHS-TD technique applies BC technology for secure communication among CEs. For threat detection, the BCSVNHS-TD method introduces the SVNHS model. Also, the parameter selection of the SVNHS method takes place using the chicken swarm optimization (CSO) technique. An extensive set of tests was involved for exhibiting the better effiency of the BCSVNHS-TD method. The experimental results emphasized that the BCSVNHS-TD method reaches optimal results over other techniques
Read MoreDoi: https://doi.org/10.54216/IJNS.250114
Vol. 25 Issue. 1 PP. 160-171, (2025)
In this paper, we study the two-fold algebra based on the n-standard fuzzy number theoretical system as a special type of two-fold fuzzy algebras, where we study the elementary properties of the algebraic operations defined over this system. Also, we prove many results that describe the relations between two-fold substructures and sub-algebras defined by fuzzy number theoretical systems. On the other hand, we provide many different examples to explain our results.
Read MoreDoi: https://doi.org/10.54216/IJNS.250115
Vol. 25 Issue. 1 PP. 172-178, (2025)
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
Read MoreDoi: https://doi.org/10.54216/IJNS.250116
Vol. 25 Issue. 1 PP. 179-189, (2025)
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
Read MoreDoi: https://doi.org/10.54216/IJNS.250117
Vol. 25 Issue. 1 PP. 190-202, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/IJNS.250119
Vol. 25 Issue. 1 PP. 211-218, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/IJNS.250120
Vol. 25 Issue. 1 PP. 219-227, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/IJNS.250121
Vol. 25 Issue. 1 PP. 228-238, (2024)
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.
Read MoreDoi: https://doi.org/10.54216/IJNS.250122
Vol. 25 Issue. 1 PP. 239-245, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/IJNS.250123
Vol. 25 Issue. 1 PP. 247-257, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/IJNS.250124
Vol. 25 Issue. 1 PP. 258-278, (2025)
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
Read MoreDoi: https://doi.org/10.54216/IJNS.250125
Vol. 25 Issue. 1 PP. 279-290, (2025)
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
Read MoreDoi: https://doi.org/10.54216/IJNS.250126
Vol. 25 Issue. 1 PP. 291-302, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/IJNS.250127
Vol. 25 Issue. 1 PP. 303-314, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/IJNS.250128
Vol. 25 Issue. 1 PP. 315-328, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/IJNS.250129
Vol. 25 Issue. 1 PP. 329-337, (2025)
The article examines the neutrosophic approach as an innovative tool to optimize production in small guinea pig farming systems. Through the exploration of bipolar sets and interval values, the application of this methodology in improving breeding processes is investigated, thus identifying areas of improvement and opportunities for economic and sustainable growth in the sector. The research highlights the importance of considering the uncertainty and imprecision inherent in these systems, proposing a flexible and adaptive framework that allows informed and strategic decision making to increase productivity and profitability. Likewise, the study highlights the need for a holistic and multidisciplinary understanding of the challenges and opportunities in guinea pig farming, recognizing the complexity of the social, economic, and environmental factors involved. Through an interdisciplinary approach, we seek to integrate traditional knowledge and practices with innovative approaches, thus promoting sustainability and the well-being of both producers and animals. Ultimately, this article offers a comprehensive and dynamic perspective on how the neutrosophic approach can significantly contribute to the development and optimization of guinea pig farming systems, thereby driving progress and prosperity in the agricultural sector.
Read MoreDoi: https://doi.org/10.54216/IJNS.250132
Vol. 25 Issue. 1 PP. 358-369, (2025)
The financial constraints of companies listed jeopardize the interests of employees and internal managers but also carries significant threats to outer investor and other stakeholders. Thus, there is need to create an effective financial distress predictive system. The two most pressing issues in finance are assessing credit risk and predicting bankruptcies. Thus, credit scoring and financial distress prediction remain crucial areas of research in the financial industry. Previous research has aimed at the design of ML and statistical approaches to predict the financial distress of the company. Neutrosophic set may be utilized, which is a generality of classical, fuzzy, and intuitionistic fuzzy sets (IFS). They establish a foundation for addressing inconsistency, indeterminacy, and uncertainty associated with real-world challenges. This study presents an Innovative Approach to Financial Distress Prediction using Relative Weighted Neutrosophic Valued Distances (IAFDP-RWNVD) technique. The IAFDP-RWNVD technique intends to estimate the occurrence of financial distress in any firm or organization. In the IAFDP-RWNVD technique, two major processes are comprised. At the primary stage, the IAFDP-RWNVD technique applies RWNVD technique for the identification of financial distress. In the second stage, the IAFDP-RWNVD technique designs fish swarm algorithm (FSA) for finetuning the RWNVD model. The experimental outcomes of the IAFDP-RWNVD method is investigated using distinct aspects. The experimentation outcome shows the improvements of the IAFDP-RWNVD technique.
Read MoreDoi: https://doi.org/10.54216/IJNS.250133
Vol. 25 Issue. 1 PP. 370-381, (2025)
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.
Read MoreDoi: https://doi.org/10.54216/IJNS.250130
Vol. 25 Issue. 1 PP. 338-346, (2025)
In this study, a thorough evaluation of the impact of whey use in the production of flavored beverages was carried out, using the neutrosophic analysis of variance method as the central research tool· The research focused on analyzing how whey, a byproduct of cheese production, could be used effectively in the production of flavored beverages, exploring its possible benefits and challenges from a comprehensive and multidisciplinary perspective· Through a series of experiments and exhaustive analyses, different variants of flavored beverages were examined, evaluating both their sensory quality and physical-chemical stability, and compared with beverages made without whey, revealing valuable insights about their viability and potential in the beverage industry· food and drinks· The findings of this study not only offer a deeper understanding of the role of whey in the production of flavored beverages, but also highlight the importance of the variance neutrosophic approach in evaluating this complex relationship· By integrating sensory analyzes with physicochemical measurements and stability considerations, a holistic and accurate picture of the effects of whey on the quality and characteristics of beverages could be obtained· These results not only have practical implications for the food industry, but also contribute to the advancement of research in multidimensional analysis methods and their application in the evaluation of innovative and sustainable food products.
Read MoreDoi: https://doi.org/10.54216/IJNS.250131
Vol. 25 Issue. 1 PP. 347-357, (2025)
In decision-making, neutrosophic set allows for the information representation with three membership functions: truth (T), indeterminacy (I), and false (F). Each component in a neutrosophic set has membership, non-membership, and indeterminacy degrees that are independent and range from 0 to 1. This makes neutrosophic set especially suitable in complex decision-making scenarios where information is contradictory, incomplete, or ambiguous, which enables robust and more nuanced analysis and solutions. A large portion of finance companies experience problems handling vast amounts of data. These data are often left unstructured and unorganized. Therefore, it is necessary to classify them to exploit it. Data classification also simplifies to use, locating, and retrieval of information. It becomes vital while handling risk management, legal discovery, data security, and compliance. Therefore, this manuscript presents an Intuitionistic Fuzzy-Valued Neutrosophic Multiset based Financial Data Classification (IFVNMS-FDC) technique in Complex Systems. The main aim of the IFVNMS-FDC technique is to recognize and categorize the financial data into respective classes. To do so, the IFVNMS-FDC technique initially uses min-max scalar as a pre-processing step. Besides, the high-dimensional financial data can be handled by the design of whale optimization algorithm (WOA) based feature selection. Finally, the IFVNMS-FDC technique derives IFVNMS technique for the identification of various classes related to the financial data. A wide-ranging experiments were involved in exhibiting the performance of the IFVNMS-FDC technique. The experimental values depicted that the IFVNMS-FDC method obtains reasonable performance on financial data recognition.
Read MoreDoi: https://doi.org/10.54216/IJNS.250134
Vol. 25 Issue. 1 PP. 382-392, (2025)
Neutrosophic logic (NL) goes further by introducing a third component: indeterminacy. Each logical proposition in NL belongs to three degrees: truth (T), indeterminacy (I), and false (F), each taking value within the range of zero and one. This allows the processing and representation of uncertain, incomplete, and inconsistent data in a superior way. NL finds it beneficial in partially contradictory, partially known, and partially unknown scenarios, it becomes an effective instrument for applications in fields such as information fusion, artificial intelligence, and data analysis, where logical framework might be unsuccessful in handling the nuances and complexities of real-time data. Recently, Arabic sentiment analysis has become a hot research topic, which mainly intends to recognize sentiments that exist in Arabic social media. Therefore, this study introduces a Single-Valued Linguistic Complex Neutrosophic Set based Arabic Sentiment Classification (SVLCNS-ASC) technique on NLP applications. The presented SVLCNS-ASC technique undergoes Arabic data pre-processing and Glove word embedding process. For sentiment recognition, the SVLCNS-ASC technique applies the SVLCNS model, which enables to identification of various kinds of sentiments. At last, the performance of the SVLCNS model can be boosted by the use of artificial bee colony (ABC) based parameter-tuning approach. The results of the SVLCNS-ASC system has been studied on Arabic database. The experimental values indicate the supremacy of the SVLCNS-ASC approach compared to recent models.
Read MoreDoi: https://doi.org/10.54216/IJNS.250135
Vol. 25 Issue. 1 PP. 393-404, (2025)
Neutrosophic logic extends conventional and fuzzy logic (FL) by integrating the concepts of indeterminacy, truth, and falsity, enabling for a further extensive management of uncertainty. In classical binary logic, a statement can be either true or false. FL extends this by adding degree of truth, where a statement is partially true or false. The smart city technology shown to be an effective solution to the problems regarding improved urbanization. The practical applications of a smart city technology to video surveillance relies on the ability of processing and gathering large quantities of live urban data. Violence detection is considered as a major challenge in smart city monitoring. The required computational power is substantial due to the large volume of video data gathered from the extensive camera network. As a result, the algorithm based on handcrafted features utilizing video and image processing fails to provide a promising solution. Deep Learning (DL) and Deep Neural Networks (DNNs) models are more reliable to handle these data. In this study, we introduce a Transfer Learning with Neutrosophic Weighted Extreme Learning Machine for Violence Detection (TL-NWELMVD) technique in smart cities. The TL-NWELMVD technique aims to recognize the presence of the violence in the smart city environment. In the TL-NWELMVD technique, the features can be extracted using SE-RegNet model. To enhance the performance of the TL-NWELMVD technique, a hyperparameter optimizer using monarch butterfly optimization (MBO) is involved. Finally, the NWELM classifier is applied for the identification of violence in the smart city environment. To investigate the accomplishment of the TL-NWELMVD technique, a widespread investigational outcome is involved. The simulation results portrayed that the TL-NWELMVD technique gains better performance compared to other models.
Read MoreDoi: https://doi.org/10.54216/IJNS.250136
Vol. 25 Issue. 1 PP. 405-417, (2025)
The objective of this paper is to introduce for the first time the concept of two-fold neutrosophic and fuzzy topological space defined over real numbers, where we combine the two-fold neutrosophic sets with real numbers to get a novel topological space based on them. Also, we present many of its elementary properties and special subsets such as two-fold neutrosophic open sets, two-fold neutrosophic closed sets, and two-fold neutrosophic closure. Many examples and theorems will be provided to clarify the validity of our approach.
Read MoreDoi: https://doi.org/10.54216/IJNS.250137
Vol. 25 Issue. 1 PP. 418-427, (2025)
Big Data Analytics are said to help in transforming huge amounts of raw data towards valuable information that can be used, but there are formidable challenges in feature selection and classification due to the complexity and high dimensionality of the data. Traditional methods are usually too weak to handle the built-in uncertainty, imprecision, and inconsistency within big data and they often fail to perform well. This paper aims to induce the new methodology on these problems using the sets of neutrosophic in dealing with more flexible and nuanced data analysis. The key contributions to the current approach proposed are threefold. First, generalization of the classical set through extension of the notions of truth, indeterminacy, and falsity by allowing representations of uncertainty in data. The second combines a powerful process for selecting features based upon neutrosophic set theory that is optimal by genetic algorithms and advances a step further by applying these features in training and validating the classification models across a set of different domains. Therefore, the major aim from this study is to increase accuracy and reliability in feature selection and classification in big data analytics. This methodology has been implemented and tested over datasets of the following types: healthcare, finance, social media, and more. Results have proved great improvement against conventional performance metrics, for example, the classification accuracy with an SVM classifier over the Cleveland Heart Disease dataset increases from 83.5% to 87.2%, and of a Random Forest classifier over a financial dataset from 76.4% to 81.9%. For instance, the accuracy of social media sentiment analysis changed to 82.7% from 78.3%. All these findings establish that the neutrosophic set-based method holds good advantages in addressing the limitations of classical alternatives. The proposed approach of neutrosophism, through an explicit model, enhances performances in classifications and, at the same time, augments overall robustness and reliability in big data analytic. The importance of this study lies in establishing the groundwork for further research and practical applications, thus indicating possible further development in this field.
Read MoreDoi: https://doi.org/10.54216/IJNS.250138
Vol. 25 Issue. 1 PP. 428-438, (2025)
To address the complex challenges of cyberviolence and gender-based violence among young students, it is crucial to employ analytical approaches that consider the multifaceted nature of these phenomena. The Neutrosophic Analytical Hierarchy (NAHP) method is presented as an innovative tool that allows us to unravel the different layers of influences and factors involved in these behaviors. This approach not only recognizes the diversity of perspectives and experiences that contribute to online and gender-based violence, but also offers a structured framework to assess and prioritize these factors holistically. By applying the NAHP, not only the visible and direct aspects of cyberviolence and gender violence are explored, but also the more subtle and underlying aspects that may go unnoticed in conventional analyses. This method allows us to capture the dynamic complexity of how individual perceptions, social norms, and power dynamics interact to perpetuate these problems in student environments. Thus, a deeper and more nuanced understanding of the triggering and contributing factors is fostered, facilitating the formulation of more effective interventions and policies that are sensitive to the specific needs of affected young people.
Read MoreDoi: https://doi.org/10.54216/IJNS.250139
Vol. 25 Issue. 1 PP. 439-452, (2025)
This article explores the innovative application of multi-criteria methods and neutrosophic logic in the analysis of productive factors, highlighting how these approaches can offer a more nuanced and comprehensive view of industrial and business dynamics ̣ Multicriteria methods allow different aspects to be evaluated simultaneously, considering complex variables that affect productivity and efficiency in various sectors ̣ On the other hand, neutrosophic logic introduces a theoretical framework that manages the uncertainty and imprecision inherent in many business decisions, offering tools to better interpret and manage the variabilities and ambiguities that influence productive results ̣ This integrative approach not only seeks to improve accuracy in the evaluation of critical factors such as cost, quality and time, but also to promote more informed and strategic decision making in competitive and changing environments ̣ By combining rigorous analysis with interpretive flexibility, the door is opened to new methodologies that can effectively adapt to the complexities of the globalized market and the dynamic demands of consumers ̣ This article examines case studies and practical examples to illustrate how these methods can be successfully applied in the optimization of production processes and in the formulation of business strategies that seek not only to remain competitive, but also to anticipate and proactively respond to emerging challenges ̣
Read MoreDoi: https://doi.org/10.54216/IJNS.250140
Vol. 25 Issue. 1 PP. 453-462, (2025)
In this article, an innovative approach is presented that combines analysis of variance (ANOVA) with the Neutrosophic 2-Tuple linguistic method to explore and analyze the complex interactions between elements in various contexts. ANOVA, known for its ability to decompose variance and detect significant differences between groups, is here merged with the Neutrosophic method, which provides tools to handle the uncertainty and linguistic ambiguity present in many real data sets. This methodological synergy not only expands analytical possibilities, but also allows for a more nuanced and profound interpretation of the relationships between variables, overcoming the limitations of traditional approaches that assume absolute certainty in the data. Through detailed case studies and practical examples, it is demonstrated how this hybrid model can be effectively applied in fields as diverse as scientific research, business management, and public policy evaluation. The results obtained illustrate how the combination of ANOVA and 2-Tuple Neutrosophic not only improves the precision of statistical analysis, but also enriches the understanding of complex phenomena by considering and modeling uncertainty in a more realistic and adaptable way to different contexts and scenarios.
Read MoreDoi: https://doi.org/10.54216/IJNS.250141
Vol. 25 Issue. 1 PP. 463-474, (2025)
In the fast-paced world of business decision-making, where clarity and precision are vital, an integrated approach that combines the indeterminate Likert scale with the neutrosophic Analytical Hierarchy Process (AHP) offers a fresh and enriching perspective for SWOT analysis. This innovative methodology not only allows us to capture the ambiguity inherent in human evaluations, but also enhances analytical depth by incorporating neutrosophic thinking, which considers elements of truth, falsehood and indeterminacy. Instead of traditional methods that often oversimplify complexities, this integrated approach facilitates a more nuanced and holistic assessment of strengths, weaknesses, opportunities and threats, thus providing a more robust and reliable basis for formulating business strategies. Additionally, the adoption of the indeterminate Likert scale, fused with the neutrosophic AHP, introduces conceptual flexibility that is particularly useful in contexts of uncertainty and changing market dynamics. This approach not only allows decision makers to better capture the subjective and often contradictory perceptions of experts, but also facilitates the weighing of multiple criteria in a coherent and logical manner. Doing so ensures that the strategies developed are not only thoughtful and detailed, but also adaptable to the fluctuating realities of the modern business environment. In short, this integrated approach is presented as a powerful and versatile tool for strategic planning, capable of transforming complex challenges into tangible opportunities through a deep and balanced understanding of organizational reality.
Read MoreDoi: https://doi.org/10.54216/IJNS.250142
Vol. 25 Issue. 1 PP. 475-484, (2025)
The development of Neutrosophic Cognitive Maps (NCM) for the evaluation and ranking of the main causes of the appearance of fruit fly pests represents a significant advance in the field of agriculture and entomology ̣This innovative approach allows for a holistic and integrated view of the complex and often interdependent factors that contribute to the proliferation of these destructive pests ̣Using neutrosophic theory, which incorporates degrees of truth, falsehood, and indeterminacy, NCMs offer a powerful tool for identifying and prioritizing critical variables ̣In this way, a more nuanced and precise understanding of the phenomenon is facilitated, enabling the design of more effective and sustainable management strategies ̣The methodology applied in the construction of the NCM is characterized by its ability to manage the uncertainty and ambiguity inherent to ecological and agricultural systems ̣Through the participation of experts and the analysis of empirical data, maps can be outlined that reflect the real complexity of the problem ̣These maps not only highlight direct causes, such as weather conditions and poor agricultural practices, but also address underlying and systemic factors ̣Thus, the use of NCM provides a robust conceptual framework for informed decision making, improving the efficiency of interventions and contributing significantly to crop protection and global food security.
Read MoreDoi: https://doi.org/10.54216/IJNS.250143
Vol. 25 Issue. 1 PP. 485-494, (2025)
In a world where efficiency and sustainability in poultry production are crucial, the need arises to find natural additives that enhance the growth of broiler chickens ̣Recent research has put ginger powder under the microscope, evaluating its impact as a growth promoter through a detailed analysis of plithogenic statistics ̣This study not only focuses on the quantitative aspects of weight gain and improved feed conversion, but also on the qualitative effects that this additive may have on the general health and well-being of the birds ̣ The methodology used involves a rigorous and multifaceted approach, integrating biological and nutritional variables, which allows a deep and holistic understanding of the benefits of ginger powder in poultry farming ̣Preliminary results suggest that ginger powder could be a viable alternative to synthetic growth promoters, showing significant improvement in growth parameters of broilers ̣ However, plithogenic analysis reveals complex nuances that require careful interpretation, as variations in bird response indicate that factors such as dosage and administration time are crucial to maximizing benefits ̣ This finding opens a range of possibilities for future research and practical applications, pointing towards more natural and sustainable poultry production ̣ Additionally, it raises important questions about the integration of herbal supplements into animal diets, inviting a broader debate about science and ethics in the food industry.
Read MoreDoi: https://doi.org/10.54216/IJNS.250144
Vol. 25 Issue. 1 PP. 495-504, (2025)
The Type-II Diophantine neutrosophic interval valued soft set (Type-II DioNSIVSS) and related similarity measure are presented in this study. An extension of the neutrosophic interval valued soft set (NSIVSS) and the Diophantine fuzzy soft set is the Type-II DioNSIVSS. The suggested measure for Type-II DioNSIVSS assessment. We support a method of solving the problem using the Type-II soft set model. To demonstrate how they can be applied to successfully handle uncertainty-related challenges, illustrative examples are given.
Read MoreDoi: https://doi.org/10.54216/IJNS.250145
Vol. 25 Issue. 1 PP. 505-517, (2025)