Extraction of signal features in Voice Signals to train Machine Learning-based Classifier algorithms for Emotion Detection

Simran; Bhagyashree; Bhisaji C.

doi:https://doi.org/10.54216/JCHCI.070202

International Journal of Neutrosophic Science | 2024

Application of the Neutrosophic COPRAS Information Fusion Method to Assess the Impact of Retirement on Well-being and Family Cohesion

This study leverages the Neutrosophic COPRAS method to assess strategies for easing the transition into retirement, focusing specifically on the critical role of emotional and psychological support. By incorporating neutrosophic sets, the research captures the complexity and ambiguity inherent in human perceptions of retirement and its impact on family dynamics, especially within the socio-economic and cultural context of Ecuador. The Neutrosophic COPRAS method facilitates a nuanced analysis, enabling the evaluation of strategies under conditions of uncertainty and indeterminacy. Key findings highlight the necessity of implementing targeted support programs that directly address the emotional and psychological challenges associated with retirement. The study’s innovative approach not only contributes to the understanding of retirement's effect on family dynamics but also showcases the Neutrosophic COPRAS method as a valuable tool for decision-making in complex scenarios. It calls for the development of policies and programs that are specifically designed to meet the unique needs of the Ecuadorian population, emphasizing the importance of cultural and socio-economic considerations in crafting interventions to promote healthy retirement adaptation and family cohesion.

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Mesías Elías M. Maliza mail -

Ned Vito Q. Arnaiz mail -

Diego Xavier C. Valencia mail -

Nasser El-Kanj mail

link https://doi.org/10.54216/IJNS.240111

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Algorithms for Computing M-Plithogenic Eigen Values and Vectors for Some Special Values of M

Plithogenic matrices are considered as advanced mathematical generalizations of classical square matrices. One of the most research problems that is related to them is finding the eigen-values and vectors. This paper aims to present an easy algorithm to find all eigen-values and eigen-vectors for 10 different symbolic square n-plithogenic matrices with plithogenic real entries, where n is between 20 and 29. All algorithms are explained through clear theorems and proofs.

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Isra Al-Shbeil mail -

Wael Mahmoud M. Salameh mail -

Hossam Almahasneh mail -

Khalid Kaabneh mail -

Mutaz Shatnawi mail -

Khaled Al-Husban mail

link https://doi.org/10.54216/IJNS.240112

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Neutrosophic MOOSRA with Whale Optimization Algorithm for Unraveling Financial Futures through Inverse Problem Solving

Resolving financial futures through inverse problem-solving delves into the complicated process of deciphering the difficulties subjective in the financial market to forecast behaviours and future trends. Inverse problem-solving involves working backwards from observed outcomes to uncover the underlying conditions or parameters, unlike prediction models, which often rely on past information to predict future outcomes. This method in the finance sector includes untangling the numberless factors influencing the market dynamics, like technological advancements, economic indicators, investor sentiment, and geopolitical events. Analysts can tease out hidden patterns and relationships within financial data using statistical techniques and complex mathematical algorithms, enabling them to generate accurate predictions of market volatility, asset prices, and other crucial metrics. The financial future becomes less opaque through the lens of inverse problem solving, providing policymakers and investors great foresight and insight into navigating the uncertainties of global markets. Hence, this study introduces a Neutrosophic MOOSRA with Whale Optimization Algorithm (NMOOSRA-WOA) for Unraveling Financial Futures through Inverse Problem Solving. The NMOOSRA-WOA incorporates linear scaling normalization, NMOOSRA-based prediction, and WOA-based parameter tuning to boost the robustness and accuracy of financial predictions. The NMOOSRA technique generates predictions based on past financial time series data. Moreover, the framework integrates the Whale Optimization Algorithm (WOA) for parameter tuning, leveraging whale pods' search abilities to optimize predictive performance and finetune model parameters. The NMOOSRA-WOA provides a comprehensive algorithm for financial prediction by synergistically combining these methodologies, which facilitates more accurate forecasts of market trends, asset prices, and other critical indicators. Experimental results on real-time financial datasets demonstrate the superiority and efficacy of the proposed framework over other classical prediction techniques, highlighting its potential for risk management within dynamic financial markets and real-time applications in investment decision-making.

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Abdelgalal O. I. Abaker mail

link https://doi.org/10.54216/IJNS.240113

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

On Neutrosophic Topological Spaces Generated by Single Value Neutrosophic Graph

The concept of a single-valued neutrosophic graph SVNS-G is recently studied, the bond between neutrosophic graph N-G and neutrosophic topological graph NT-G was my goal in this research, I try to find a relation on the vertices of the SVN-G  to structure  NT-G add some theorems and corollaries.

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Ekram Abd Ali mail -

Ahmed Salam Razzaq mail -

Banin Shaker Jubeir mail -

Qays Hatem Imran mail

link https://doi.org/10.54216/IJNS.240114

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Applied Statistics with Single-Valued Neutrosophic Fuzzy Soft Expert Sets for Market Trend Forecasting Model

Applied statistics has been instrumental in predicting behaviours and future market trends. In the field of financial time series analysis, the incorporation of deep learning (DL) methods and applied statistics has made a significant contribution to the prediction model. Practitioners and researchers can extract complex features and dependencies from past financial data by leveraging neural network structures like long short-term memory (LSTM) and recurrent neural networks (RNNs). These DL approaches advance the development of predictive models prone to forecasting different financial metrics, such as asset returns, stock prices, and market volatility, with outstanding accuracy. With the combination of statistical approaches with DL techniques, researchers can leverage the power of both worlds to make more informed investment decisions and improve forecasting capabilities in volatile and dynamic financial markets. This study develops a new Applied Statistics with Single Valued Neutrosophic Fuzzy Soft Expert Sets (AS-SVNFSES) technique for Financial Time Series Forecasting. The presented AS-SVNFSES technique aims to forecast the input financial time series data. The AS-SVNFSES technique primarily applies data preprocessing using a Z-score normalization approach. For the forecasting of financial data, the AS-SVNFSES technique makes use of the SVNFSES technique. Finally, the parameter tuning of the SVNFSES technique is performed using the chimp optimization algorithm's (ChOA) design. A series of experimentations have illustrated the amended performance of the AS-SVNFSES model. The experimental value inferred that the AS-SVNFSES technique gains improved performance over other models.

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Mohanned H. Alharbi mail -

Khalil A. Alruwaitee mail -

Sulima M. Awad Yousif mail -

Ashraf A. Awad Alotaibi mail -

Abdelgalal O. I. Abaker mail -

Azhari A. Elhag mail

link https://doi.org/10.54216/IJNS.240115

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Computer purchasing using new type neutrosophic sets and its extension based on aggregation operators

This article discusses a new approach to multiple attribute decision-making (MADM) based on (l1, l2, l3) neutrosophic sets (NS). This is an extension of the NS. Neuosophic weighted averaging (NWA), neutrosophic weighted geometrics (NWG), generalized neutrosophic weighted averaging (GNWA), and generalized neutrosophic weighted geometrics (GNWG) are the topics of this article. The flowchart we presented during our discussion showed an algorithm that used these operators. Numerical examples are provided for the extended Euclidean and Hamming distance measures. As part of this communication, we will also elaborate on the properties of neutrosophic sets, such as their idempotency, their boundness, their commutativity, and their monotonicity. They make it quicker, easier, and more convenient to find the best option. Thus, there is a stronger connection between (l1, l2, l3) and more precise conclusions. Some of the current models are compared with those that have been proposed in order to demonstrate their dependability and utility. The study also revealed fascinating and intriguing findings.

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Ibraheem Abu Falahah mail -

T. T. Raman mail -

Abdallah Al-Husban mail -

Ayman Alahmade mail -

S. Azhaguvelavan mail -

Murugan Palanikumar mail

link https://doi.org/10.54216/IJNS.240116

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

An neutrosophic topological operator and its application in the building of new neutrosophic sets

In this article, we use the notion of neutrosophic local function to introduce a new neutrosophic operator in the context of a neutrosophic topological space equipped with a neutrosophic ideal. Also, we introduce and study some new classes of neutrosophic sets defined in terms of the neutrosophic local function and the new notion of neutrosophic operator given.

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Jos´e Sanabria mail -

Carlos Granados mail -

Carlos Carpintero mail

link https://doi.org/10.54216/IJNS.240117

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

A Fabrication Repertoire Replica Amidst Partisan Commerce Layaway Strategem And Infalllibity Cannibalizing Neutrosophic Fuzzy Number

Infallibility is an important factor both in fabrication repertoire replica and in the great demand of products. During a fabrication process, more exemplary products with high reliableness aim for increase in product demand although credit rating too is a prominent business strategy. Integrating the above duo concepts, we explain and explore mathematically a fabricating repertoire replica with partisan layaway stratagem and infallibility effect on the fabrication system wherein the demand of the customers is reliant on the product cost and rate of decay is regarded as constant. In this propounded model, commerce layaway stratagem on the fabricator and the customer is acquainted by considering all the achievable sitch due to permitted credit (layaway) duration. As a consequence, considering all the achievable instances for the fabricator and the customer’s layaway duration, seven non-linear optimization issues for the proposed replica are required.

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R. Saarumathi mail -

W. Ritha mail

link https://doi.org/10.54216/IJNS.240118

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

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Journal of Cognitive Human-Computer Interaction | 2024

Application of SAFARI in Prediction of Heart Disease

Cardiovascular disease has been the major cause of mortality worldwide for last several decades. Diagnosis of heart disease through traditional approaches is a complex, time consuming and error prone process. To address this issue, several techniques have been proposed to automate the process of diagnosing the heart disease accurately in timely manner. However these techniques report limited accuracy of diagnosing the disease. In this paper SAFARI algorithm is used to diagnose the heart disease. Safari uses rule based approach i.e. it extracts rules from a dataset and uses the extracted rules for diagnosis. The various attribute values are first discretised into specific ranges, each range corresponds to a symbol. This results in a symbol table. Safari extracts rules from this symbol table. The approach has been thoroughly tested on the heart disease dataset publicly available on UCI machine learning repository. The results obtained using this approach are compared with the results of various techniques reported by other authors, a significant improvement was observed.

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Irfan Rajab Bhat mail -

M. Arif Wani mail

link https://doi.org/10.54216/JCHCI.070201

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2024

Extraction of signal features in Voice Signals to train Machine Learning-based Classifier algorithms for Emotion Detection

This research aims to detect human emotions using speech signals through the development and implementation of methodologies, namely the frequency domain synthesis. To achieve improved results, various machine learning and deep learning models were applied for implementation and their resulting model performance was analyzed. The research findings revealed that each model exhibited different accuracy rates for different emotions but weighted accuracy is best for deep learning based model. This study provides valuable insights into the feasibility and effectiveness of utilizing different methodologies and models for emotion detection through voice signals synthesis. The audio signals are synthesized for Mel-Frequency Cestrum Coefficients (MFCC), Chroma, and MEL characteristics, which are then used as features to train the various machine learning-based classifiers. Python libraries like Librosa, Sklearn, Pyaudio, Numpy, and sound files are used to analyze voice modulations and identify emotions.

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Simran Somani mail -

Bhagyashree Shah mail -

Bhisaji C. Surve mail

link https://doi.org/10.54216/JCHCI.070202

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new