International Journal of Neutrosophic Science

In this work, fractional partial equations' and neutrosophic fractional partial equations analytical series solutions are presented, we consider the fractional derivative in the meaning of Caputo in these formulas. We offer a novel objective method the LRPS which is a strong instrument for precise analytically and numerical solutions to these problems by setting an excellent example, we stress precision, effectiveness, and application style, also we can find exact answers when there is a pattern between the series' parts; alternatively, we can only offer approximations. The Mathematica application is used to assess the numerical and graphical findings to make sure the solutions generated are accurate and that the approach can be modified to solve this kind of this problem. The findings obtained demonstrated that our current procedure is appropriate and efficient for resolving PDEs.

The COVID-19 epidemic has greatly expedited the utilization of technology in the realm of education, resulting in the extensive implementation of totally online teaching approaches. These approaches have undergone thorough analysis in various scholarly articles in recent years. This study applies theories of technology acceptance and use in the educational process, employing Neutrosophic analysis to assess criteria for technology utilization in education. The study commenced by formulating an equation to investigate the patterns of technology uptake and use between 2010 and 2024. Additionally, a comprehensive evaluation of the latest literature since 2000 was conducted to identify prevailing trends. The findings suggest that usage plays a vital role in the Technology Acceptance Model (TAM), and structural equations are used as a method to measure it. Neutrosophic analysis provides a thorough and sophisticated viewpoint on the integration of technology in education, emphasizing both the accomplishments made and the obstacles that still exist in this developing area.

Neutrosophic multicriteria analysis of the competitiveness and sustainability of companies in the agri-food sector, with suggestions for improvement strategies. Competitiveness is measured using a tool developed by the IDB (Inter-American Development Bank) that includes 103 indicators and 9 operational areas (strategic planning, value chain, quality assurance, accounting and finance, environmental management, sales, and human resources). Talents and information systems). Sustainability is assessed using the tool proposed by the " InnovaRSE " methodology (from Navarra), which includes 30 indicators divided into three aspects: social, economic, and environmental. The study population was 100 catering establishments officially established according to the Tourism Registration Body. To obtain the sample size, the finite population formula was applied, and the results were obtained for the 20 companies studied. Sampling was done using the "simple random probability" method. In the Spearman correlation test, the P value is "0.01" (there is a connection between the company's competitiveness and sustainability). 16 improvement strategies were developed using diagnostic tools.

Internships play a crucial role in the comprehensive education of university students as they provide practical experience and promote the development of technical and soft skills. These practices not only promote personal development but also ease the transition into the world of work. The study aims to use a Neutrosophic Delphi method to analyze the extent to which work practices influence the comprehensive education of university students in Ecuador in 2023. A descriptive study was conducted with a sample of 410 students from academies and universities in Ecuador. Country Ecuador. Center of the country This method uses structured surveys to collect qualitative and quantitative data about the experiences, advantages, and skills acquired during internships. The results are presented in the form of data tables and statistical graphics that illustrate the close connection between professional experience and the overall educational level of students. Emphasis was placed on acquiring skills such as teamwork, leadership, and problem-solving. In summary, internships are a valuable learning tool for university students as they provide the opportunity to apply knowledge, develop skills, and improve their employability.

Intrusion Detection is crucial in contemporary cybersecurity landscapes to proactively thwart and identify possible threats. The risk of data breaches, malicious activities, and unauthorized access escalates as organizations increasingly rely on interconnected systems. Intrusion Detection Systems (IDS) are imperative for the continuous monitoring of system and network activities, quickly identifying patterns or anomalies indicative of cyber threats. IDS acts as a frontline defense mechanism with the ability to identify abnormal behaviors and known attack signatures. Prompt recognition allows for safeguarding sensitive data, timely response, fortifying the overall resilience of IT infrastructures, and reducing the effect of security incidents. The implementation of robust IDS is vital in an era marked by evolving cyber threats to ensure the confidentiality, availability, and integrity of digital assets. This study develops an improved Arithmetic Optimization Algorithm with an Extended Fuzzy Neutrosophic Classifier technique (AOA-EFNSC) for Accurate Intrusion Detection and Classification. The main goal of proposing this model is to recognize the presence of intrusions effectually. A min-max scalar is applied to normalize the input data before using the improved AOA as a feature selection method. For intrusion detection, the proposed model uses the FNSC technique for the recognition and classification of the intrusions. A sequence of experimentations was involved to validate the superior performance of the proposed model. The experimental value pointed out that our proposed approach outperforms the previous models and enhances the intrusion detection results.

Neutrosophic have found their place in neutrosophic studies due to the prevalence of indeterminacy in the world. We present the novel notion of neutrosophic truncated distribution, which is highly significant in analyzing events that involve the exclusion of certain data from the original dataset, particularly where there is a presence of indeterminacy in data. Unsure or ambiguous information, which is disregarded in classical logic, is incorporated within neutrosophic logic due to its focus on both certain and uncertain data. In this paper, the approach of neutrosophic truncation, and truncated distribution of neutrosophic random variable have been introduced, in addition to deriving some of its properties. And other cases discussed neutrosophic truncation depends on the neutrosophic probability function, a classical probability function, and studies neutrosophic probability and neutrosophic interval together. It studies the neutrosophic left truncated and neutrosophic right truncated. Some illustrative examples and statistical properties such as the cumulative function, the moment generating function, the order statistic, and the rth moment are presented.             

The objective of this research is to examine the decision-making processes of teachers and explore their self-assessments of teaching competency levels based on the competency indicators proposed by the Ministry of National Education (MoNE) in Turkey. The study adopts a constructivist perspective, offering a fresh look at the cognitive levels of teachers and their decision-making mechanisms. Additionally, it integrates neutrosophic science principles to address the uncertainties and indeterminacies present in teachers' self-evaluation and decision-making processes. Data were gathered using the "General Competencies for Teaching Profession (GCTP)" scale, which was developed according to the competencies defined by the MoNE. This new scale, featuring 15 Likert-type items, was validated and tested for reliability before being administered to a sample of 320 volunteering teachers from various disciplines in Turkey. The scale measures data within the "Professional skills" domain and captures teachers' self-perceived competency beliefs related to their professional skills, considering factors such as years of teaching experience, gender, subjects taught, and the type of school (primary or secondary) where they are employed. SPSS 16.0 was used for data analysis and to obtain descriptive statistics for the item results. The analysis revealed that primary school teachers scored higher on the GCTP scale compared to high school teachers. By incorporating neutrosophic science, the study effectively navigates the uncertainties in assessing teaching competencies, offering a more nuanced understanding of the factors that influence teachers' decision-making processes.

This study focuses on improving the predicting accuracy of the daily ASE's weighted price index of the insurance sector (ICI) using a nonlinear spectral model called maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (d4), least square (la8), best localization (bl14), and Coiflet (c6). Using a nonlinear spectral model called maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions—Haar, Daubechies (d4), least square (la8), best localization (bl14), and Coiflet (c6)—this study aims to increase the daily ASE's weighted price index of the insurance sector's (ICI) prediction accuracy. The model utilizes a genetic fuzzy system based on Thrift's methodology (GFS.Thrift). The Amman Stock Exchange (ASE) supplied a dataset with 4,478 observations for the purpose of the study. The dataset represented daily data from January 2, 2006, to March 24, 2024.  The adaptive GFS.THRIFT model was trained with 90% of the dataset, while the remaining 10% was used to test its prediction performance. Multiple egressions and multicollinearity tests were used to select input variables such as standardized foreign direct investment (FDI), standardized value traded (VT) and consumer price index (CPI). Insights from this study indicate that all input variables are positively related to the output variable. Secondly, the proposed model (MODWT-Haar-GFS. Thrift) significantly outperforms other existing models including the GFS. Thrift model.

We describe certain operations and present the theory of the Type-I extension Diophantine neutrosophic interval valued soft set. Additionally, we go over an algorithm that uses the Type-I soft set model to address the decision-making problem. We present a similarity measure between two Type-I extension Diophantine neutrosophic interval valued soft sets and talk about how it might be used in practical applications. A few exemplary cases are provided to demonstrate their practical application in solving uncertain problems.

In our previous paper we discussed about the concept of SOBPFS, SOBPFT and its mathematical modelling in medical diagnosis. In this paper, the detailed study about SOBPFT accordance with FOBPFT and crisp topological spaces are analysed and also some natural examples of SOBPFT are provided. In third section, the connections between FOBPFT and SOBPFT under five different cases are discussed. And last section tells that, from a crisp topology τ on X there exists three different SOBPFT denoted by (ω(τ)) ̂, (ω_* (τ)) ̂ and (ω_ε (τ)) ̂ and from a SOBPFT on X there exists three crisp topologies denoted by i(τ ̂_B ), i^* (τ ̂_B ) and i_ε (τ ̂_B ).  

In this research, we construct new type of convergence of bornological vector spaces called convergence of filters through using conception bounded sets. As well, we have considered several characteristics of these concepts like Fréchet filter associated with sequence, filter that has a unique limit and ultra-filter which is very useful in the study of neutrosophic topological spaces and neutrosophic filters.

In this paper, we introduce the concept of Possibility Fermatean Neutrosophic Soft Set and define some related concepts such as Possibility Fermatean Neutrosophic Soft subset, Possibility Fermatean Neutrosophic Soft null set, and Possibility Fermatean Neutrosophic Soft universal set. Then, we define set-theoretical operations of Possibility Fermatean Neutrosophic Soft Sets such as union, intersection, and complement, and investigate some properties of these operations. We also introduce AND-product and OR-product operations between two Possibility Fermatean Neutrosophic Soft Sets. We propose a decision-making method called the Possibility Fermatean Neutrosophic Soft decision-making method (PFNS-decision-making method) which can be applied to decision-making problems involving uncertainty based on AND-product operation. We finally give a numerical example to display the application of the method that can be successfully applied to the problems.

The study emphasizes the need of implementing several ways to promote guinea pig farming in small family units. It highlights the relevance of enhanced nutrition, effective health management, genetic enhancement, and acceptable habitat conditions as essential factors for enhancing productivity and profitability. Suggestions encompass the adoption of advanced breeding methods, offering training and technical support, and expanding the range of goods and markets to ensure the long-term economic viability of guinea pig farming. The utilization of neutrosophic sets provided a strong framework for assessing these techniques, enabling a thorough study that considers the inherent uncertainties in decision-making processes. To enhance future study, it is recommended to improve and broaden neutrosophic approaches to comprehend the intricacies of guinea pig farming systems more effectively. It will be beneficial to create more advanced models that include a broader set of factors and extensive data, as well as to undertake longitudinal studies to evaluate the long-term effects. It is essential to work together with local communities to customize tactics that are suitable for specific geographical conditions and socioeconomic contexts. This is necessary to ensure that these interventions are practical and successful.

Avocado oil is defined by the composition of the fruit and its nutritional value, which according to previous studies suggests that it provides health benefits, reduces cardiovascular disease, and provides anti-inflammatory and antioxidant effects. However, the nutritional value is determined by the amount of acid. Monounsaturated and polyunsaturated fatty acids make this product useful in cooking. The quality of the oil is affected by the method and conditions of extraction, as these processes affect the preservation of nutrients and beneficial properties of avocado oil. This study aimed to conduct a Neutrosophic analysis of avocado oil extraction conditions depending on the cultivar, dehydration and cold pressing conditions. As a result, the physicochemical properties of the reaction variables were determined and the values of acidity, moisture, density, and impurities were obtained for the oil obtained from the Hass variety by dehydration and pressing.

Neutrosophic soft sets (NSS) are highly effective in representing neutral uncertain data. NSS model attracts several authors because it has huge range of applications in several areas such as decision-making, data analysis, smoothness of functions, probability theory, measurement theory, predicting, and operations research. Oral squamous cell carcinoma (OSCC) is the most general tumor around the world and its occurrence is on the increase in several populations. Early diagnosis plays vital role in improving diagnosis, treatment outcomes and survival rates. Although the new developments in understanding molecular mechanisms, late analysis and the implementation of precision medicine for OSCC patients continue to present problems. Early diagnosis and detection can support doctors in offering optimum patient care and effectual treatment. In recent years, the execution of several machine-learning (ML) approaches in cancer analysis has provided valuable insights, facilitating more effective and precise treatment decision-making. Oral Cancer screening can progress with the execution of artificial intelligence (AI) approaches. AI offers support to the oncology region by correctly examining a huge database in many imaging modalities. This article develops a Bat Algorithm with Rough Neutrosophic Soft Set for Oral Cancer Diagnosis (BARNSS-OCD) technique. The main intention of the BARNSS-OCD technique is to exploit deep learning (DL) model for enhanced identification of OC. In the BARNSS-OCD technique, median filtering (MF) is used for image pre-processing and the feature extraction takes place using deep convolutional neural network (DCNN) model. In addition, bat algorithm (BA) is used for the hyperparameter selection of the DCNN model. For OC detection process, the BARNSS-OCD technique applies RNSS model. To exhibit the improved performance of the BARNSS-OCD technique, a sequence of experiments is involved. The simulation outcomes indicate that the BARNSS-OCD technique gains better performance compared to other DL models