A new generalized topology coarser than the old generalized topology

Jos´e; Alexandra; Jorge

doi:https://doi.org/10.54216/IJNS.240307

Fusion: Practice and Applications | 2024

Knowledge Navigator: Revolutionizing Education through LLMs in Generative AI

The education landscape is shifting towards automation and digitalization to cater to the increasing demand for personalized learning experiences and more efficient teaching methods. In response to this trend, we propose the development of an integrated educational automation fusion platform that aims to overhaul educating and learning practices across various educational sectors. The integration of cutting-edge language models like GPT-3.5 and Gemini Pro in information retrieval and conversational AI has opened fresher opportunities, even within the realm of education. With its advanced features, LangChain, a powerful framework for large language models, enables seamless integration of AI-driven functionalities, including document analysis, question generation, and chatbot interaction, revolutionizing the educational landscape. Also, by harnessing the vast resources of the OpenAI API, our platform empowers educators and learners to engage in dynamic conversations with educational materials, generate personalized assessments, and gain deeper insights from complex datasets within a single forum. This single platform disseminates information on all facets of research and development in educational domain on the grounds of fusion practices and applications. This system is successful in combining multiple models for intelligent systems. On evaluation, our system was successful in generating decent performance compared to existing systems, even though they are singular modules. Overall, our platform aims to empower educators, students, and institutions to embrace the digital era of learning and unlock new avenues for fusion-based knowledge acquisition and innovation.

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Malathi S. mail -

Hemamalini S. mail -

Ashwin M. mail -

Rijo Benny mail

link https://doi.org/10.54216/FPA.160114

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Applied Linguistics Driven Deceptive Content Recognition using Single Valued Trapezoidal Neutrosophic Number with Natural Language Processing

Single valued neutrosophic number is a special case of single valued neutrosophic set and are of importance for neutrosophic multi-attribute decision making problem. A single valued neutrosophic number seems to define an ill-known quantity as a generalization of intuitionistic number. Applied linguistics in the context of Natural Language Processing (NLP) comprises the practical applications of linguistic approaches for addressing real time language processing issues. Social media become indispensable components in many people’s lives and have been growing rapidly. In the meantime, social networking media have become a widespread source of identity deception. Several social media identity deception cases have appeared presently. The research was performed to detect and prevent deception. Identifying deceptive content in natural language is significant to combat misrepresentation. Leveraging forward-thinking NLP methods, our model contextual cues analyze linguistic patterns, and semantic inconsistencies to flag possibly deceptive contents. By assimilating complex procedures for parameter optimization, feature extraction, and classification, the NLP focused on precisely recognizing deceptive content through different digital platforms, which contributes to the preservation of data integrity and the promotion of digital literacy. This study presents a Single Valued Trapezoidal Neutrosophic Number with Natural Language Processing for Deceptive Content Recognition (STVNNLP-DCR) technique on Social Media. The presented technique includes four important elements: preprocessing, GloVe word embedding, STVN classification, and Chicken Swarm Optimization (CSO) for parameter tuning. The preprocessing stage includes tokenization and text normalization, preparing text information for succeeding analysis. Then, GloVe word embedding represents the word in a continuous vector space, which captures contextual relationships and semantic similarities. The STVN classifier deploys the embedding to discern deceptive patterns within the text, leveraging its capability to effectively manage high-dimensional and sparse datasets. Moreover, the CSO technique enhances the hyperparameter of the STVN classifier, improving its generalization capabilities and performance. Empirical analysis implemented on varied datasets validates the efficacy of the presented technique in precisely recognizing deceptive content. Comparative studies with advanced approaches demonstrate high efficiency. The presented technique shows robustness against different forms of deceptive content, such as clickbait, misinformation, and propaganda

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Abdulkhaleq Q. A. Hassan mail

link https://doi.org/10.54216/IJNS.240227

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

Design of Single Valued Neutrosophic Hypersoft Set VIKOR Method for Hedge Fund Return Prediction

The theory of neutrosophic hypersoft set (NHSS) is an appropriate extension of the neutrosophic soft set to precisely measure the uncertainty, anxiety, and deficiencies in decision-making and is a parameterized family that handles sub-attributes of the parameters. In contrast to recent studies, NHSS could accommodate more uncertainty, which is the essential procedures to describe fuzzy data in the decision-making method. Hedge funds are financial funds, finance institutions that increase funds from stockholders and accomplish them. Usually, they try to make certain predictions and work with the time sequence dataset. A hedge fund is heterogeneous in its investment strategies and invests in a different resource class with various return features. Furthermore, hedge fund strategy is idiosyncratic and proprietary to the hedge fund manager, and the correct skills of fund managers are not visible to the stockholders. These reasons, united, make hedge fund selection a complex task for the stockholders. Different techniques have been analyzed to select the portfolio of hedge funds for investment. Machine-learning (ML) models employed used for performing individual hedge fund selection within hedge fund style classifications and forecasting hedge fund returns. Therefore, this study designs a new Single Valued Neutrosophic Hypersoft Set VIKOR Model for Hedge Fund Return Prediction (SVNHSS-HFRP) technique. The presented SVNHSS-HFRP technique aims to forecast the hedge fund returns proficiently. In the SVNHSS-HFRP technique, two stages of operations are involved. At the initial stage, the SVNHSS-HFRP technique, the SVNHSS is used for forecasting the hedge funds. Next, in the second stage, the moth flame optimization (MFO) system is applied to optimally choose the parameter values of the SVNHSS model. The performance validation of the SVNHSS-HFRP model is verified on a benchmark dataset. The experimental values highlighted that the SVNHSS-HFRP technique reaches better performance than existing techniques

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Fadoua Kouki mail

link https://doi.org/10.54216/IJNS.240228

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

Applied Linguistics driven Artificial Intelligence for Automated Sentiment Detection and Classification

The widespread dissemination of World Wide Web has paved the way to express individual sentiments. Also, it is a medium with a massive quantity of data where the user can view the opinions of other users that are categorized into dissimilar sentimental classes and are growing increasingly as a major aspect in decision making. Sentiment analysis (SA) is a method utilized in natural language processing (NLP) that defines the emotion or sentiment formulated in the text portion. SA method is often performed on text datasets to assist in accepting client requirements, businesses monitoring brands, and product sentiment in customer feedback. SA is the challenging and most common complication in artificial intelligence (AI). It applies automated mechanisms to identify physiological information namely feelings, thoughts, and attitudes shown in text and indicated through blogs, social networks, and news. This manuscript develops Applied Linguistics driven Artificial Intelligence for Automated Sentiment Detection and Classification (ALAI-ASDC) technique. The preprocessing stage includes tokenizing and cleaning textual information, followed by encoder words into vector representation using pretrained GloVe embeddings. This embedding captures semantic similarities between words, which provides an abundant depiction of textual information for SA. Integrating single-valued neutrosophic fuzzy soft expert set (SVNFSES) improves the SA method by addressing imprecision, uncertainty, and ambiguity inherent in text sentiment expression. FNS enables the representation of linguistic variables with degrees of truth, falsity, and indeterminacy, allowing a nuanced understanding of sentiment polarity. Moreover, the Hybrid Jelly Particle Swarm Optimization (HJPSO) is applied for the parameter tuning of the SA technique. Enhancing the performance of the SA model. Empirical analysis illustrates the efficiency of the presented technique in precisely categorizing sentiment polarity in different textual datasets

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Abdulkhaleq Q. A. Hassan mail

link https://doi.org/10.54216/IJNS.240301

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

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International Journal of Neutrosophic Science | 2024

Bankruptcy Prediction using Diophantine Neutrosophic Number for Enterprise Resource Planning on Value of Accounting Information

Enterprise Resource Planning (ERP) is paramount in modern business, integrating many fundamental processes such as human resources, economics, customer relationship management, and supply chain management into a comprehensive infrastructure. Leveraging the wide-ranging data apprehended by ERP techniques, an organization could improve its financial analysis abilities, involving bankruptcy prediction. By using analytics methods like predictive modeling and machine learning, the ERP system could examine market trends, historical financial information, key performance indicators, and other related factors to evaluate the financial stability and health of the company. This prediction insight empowers businesses to vigorously detect advanced indicators of financial distress, alleviate risks, and make informed strategic decisions to avoid bankruptcy. Integrating bankruptcy prediction techniques within the ERP system allows organizations to reinforce contingency strategies, financial planning, and risk management, protecting long-term competitiveness and sustainability in a dynamic business environment. This study introduces a Bankruptcy Prediction using the Diophantine Neutrosophic Number for Enterprise Resource Planning (BPDNN-ERP) technique on the value of accounting information. The BPDNN-ERP technique begins with a harmony search algorithm (HSA) for electing feature subsets. In addition, the BPDNN-ERP technique applies the DNN model for the prediction of bankruptcies. To increase the performance of the DNN model, the manta ray foraging optimization (MRFO) model can be used. The experimental study demonstrated the enhanced performance of the BPDNN-ERP algorithm equated to existing forecasting methods

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Adeeb Alhebri mail -

Gubarah Farah Gubarah mail -

Abdulkarim Alsayegh mail -

Radwan Hussien Alkebssi mail -

Mohammed Al-Matari mail

link https://doi.org/10.54216/IJNS.240302

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Integrating N‐person Intuitionistic Neutrosophic Soft Games with Neutrosophic Cognitive Maps for Cloud Storage with Accounting Information Systems

Neutrosophic logic is founded on non-standard evaluation. In neutrosophic set (NS), the computation of indefiniteness is explicit, while the membership of truth, falsity, and indeterminacy are non-reliable. Recently, various attractive game theory application is extended by entrenching the fuzzy set logic. The accounting data analysis has to contribute the innovativeness. This includes various characteristics. It applies billions of individuals in the business to develop and design novel products until the sale managing of the enormous sales staff. While cloud storage provides flexibility and scalability, it also comes with related costs, involving data transfer fees, subscription fees, and storage costs. Optimizing and managing cloud storage costs associated with business needs and budgetary constraints is crucial. Cloud-based AIS decreases the necessity for localized infrastructure and hardware, oscillating rate from capital to operational expenditure. This enables organizations to pay only for the used resource, resulting in economic efficiencies. Incorporating cloud technology into accounting information systems (AIS) provides several advantages. To increase the capability of accounting data statistics and analysis, this article introduces an N‐person intuitionistic neutrosophic soft game with Neutrosophic Cognitive Maps (NINSG-NCM) for cloud storage with AIS. The NINSG-NCM technique considered a contemporary accounting data analysis is built based on the block bit sequence evaluation technique and extracts the association rule representative amount of accounting data. Integrated with cloud computing (CC) framework, the NINSG-NCM method is proposed, and the NCM clustering technique is used for realizing the modern accounting data clustering, and the NINSG method could enhance the capability of statistical analysis and parallel computing of accounting data. Lastly, salp swarm algorithm (SSA) is applied for hyperparameter tuning method. The experimental outcomes illustrate that the designed intelligent data evaluation technique makes the parallel computing efficacy high and the statistical evaluation capability of accounting data better.

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Adam Mohamed Omer mail -

Abdulkarim Alsayegh mail

link https://doi.org/10.54216/IJNS.240303

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Interval-valued Fermatean Neutrosophic Graph with Grey Wolf Optimization for Sarcasm Recognition on Microblogging Data

Game theory is more popular in competitive situations due to its importance in decision making. Several kinds of fuzzy sets can manage uncertainty in matrix games. Neutrosophic set theory has been instrumental in investigating ambiguity, complexity, inconsistency, and incompleteness in real-time issues. Nowadays, sarcastic comments on social media have become a general tendency. Sarcasm is frequently used by individuals to pester or taunt others. It is often conveyed via inflection, tonal stress in speech, or lexical, hyperbolic, and pragmatic features existing in the text. Sentiment Analysis (SA) is regarded as the data mining targets of sentiment organization of the client's criticisms obtainable in textual form. Sarcasm is a form of speech that states an individual's downside feeling through a positive term. Labeling sarcasm in characters is a dynamic task for Natural Language Processing to evade the misconception of sarcastic speeches as a verbatim declaration. The outcome of these kinds of sarcastic speeches is hard for the people and machines. Sarcasm has a considerable influence on the efficacy of SA techniques that are impacted by mendacious sentiments that frequently belong to sarcastic classes. This study introduces an Interval-valued Fermatean Neutrosophic Graph with Grey Wolf Optimization for Sentiment Analysis (IFeNG-GWOSA) on Microblogging Data. The IFeNG-GWOSA technique includes a sarcasm detection technique that categorizes words in sarcastic or non-sarcastic form. The initial phase is preprocessing, where the tokenization and stop word removal are implemented. Then, the preprocessed data is subjected to feature extraction, where the BERT word embedding is applied. The IFeNG model is used for sarcasm detection, and the grey wolf optimizer (GWO) generates its parameter selection technique. Lastly, the efficiency of the presented technique is compared with existing approaches under different measures

groups

Abdulkhaleq Q. A. Hassan mail

link https://doi.org/10.54216/IJNS.240304

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Optimization of Neutrosophic EOQ Model for Effective Demand Management in Uncertain Environment Using Genetic Optimization

Inventory management is characterized by a continuous struggle to lower goods levels and related costs while also providing customers with the goods they need. However, reducing costs while simultaneously striving for ideal inventory levels is difficult, notably in the current situation of high unpredictability of goods demand and lead time. Traditional inventory models are not strong enough to endure changes like goods demand and lead-time demand. As a result, it must be adjusted to achieve results. The oeuvre below presents a new kind of inventory model that deals with uncertainty in the demand for goods and lead time. In this regard, the presented work, the novel Neutrosophic Economic Order Quantity approach is a mechanism to account for the likely imprecision in the model. Specifically, the Neutrosophic set theory is integrated into the EOQ model so that it can handle variations in the demand and lead-time pattern successfully. An objective function is established for obtaining economical order quantities that include demand, lead-time, and other necessary components’ irregularities. The process variables in the model are given the final values using genetic algorithms and simulated annealing. To highlight the impact of the proposed Neutrosophic approach, it is then applied to several realistic examples. This will provide the audience a sense of how effective inventory management may be in high-uncertainty situations. The rapid evolution of organizations necessitates innovative inventory control tactics to meet growing demands

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Manjula G. J. mail -

N. Anitha mail -

A. P. Pushpalatha mail -

K. Vinaya Laxmi mail -

M. Premalatha mail -

Mekala Selvaraj mail

link https://doi.org/10.54216/IJNS.240305

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Development of a novel uncertainty model for interval-valued Q-fuzzy soft sets: Application in design-making

In actual life, dealing with uncertain information has become a challenge for researchers who strive day after day to develop more accurate mathematical tools for better dealing with this information. The Q-Fuzzy soft model can process uncertain information in two dimensions by dealing with the subjective judgments of users effectively. Therefore, this article aims to increase the effectiveness of the Q-fuzzy soft model and address the challenges of design-making under uncertain information by proposing a new model called the interval-valued Q-fuzzy soft (IV-Q-FSS) model. Under the IV-Q-FSSs, we discuss strongly set-theory operations such as subset, union of two IV-Q-FSSs, intersection of two IV-Q-FSSs, complement of IV-Q-FSS, AND operation, and OR operation for IV-Q-FSSs, and some operations like the possibility and necessity operations of an IV-Q-FSS. In addition, we hand over numerous properties held up by numerical examples that describe how they toil. Finally, this recently developed model has been successfully trying out in dealing with one of the design-making problems based on hypothetical data for a respiratory disease. This algorithm is built based on the aggregation operator for IV-Q-FSS data to break this issue (i.e., selecting the optimal alternative).

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Mohanad H. Jameel mail -

Sinan O. Al-Salihi mail -

Faisal Al-Sharqi mail

link https://doi.org/10.54216/IJNS.240306

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024