Complex cubic neutrosophic set applied to subbisemiring and its extension of bisemiring

Brikena; Nasreen; Murugan; Ervin

doi:https://doi.org/10.54216/IJNS.250231

International Journal of Neutrosophic Science | 2025

Characterization of various (b,l) neutrosophic ideals of an ordered Gamma semigroups

In this paper, we introduce the notion of $\flat,\ell$-neutrosophic subsemigroup (NSS), neutrosophic left ideal(NLI), neutrosophic right ideal(NRI), neutrosophic ideal (NI), neutrosophic bi-ideal(NBI), $(\epsilon, \epsilon \vee q)$-neutrosophic ideal, neutrosophic bi-ideal of an ordered $\Gamma$-semigroups and discuss some of their properties. The concept of $\flat,\ell$-neutrosophic ideal is a new extension of neutrosophic ideal over ordered $\Gamma$-semigroups $\mathcal{Z}$. A non-empty subset $\xi_{\flat}$ is a $(\flat, \ell)$-NSS (NLI, NRI, NBI, (1,2)-ideal) of $\mathcal{Z}$. Then the lower level set $\Delta_{\flat}$ is an subsemigroup $(LI, RI, BI, (1,2)-ideal)$ of $\mathcal{Z}$, where $\Delta_{\flat}=\{\varrho\in \mathcal{Z}|\Delta(\varrho)> \flat\}$, $\Psi_{\flat}=\{\varrho\in \mathcal{Z} |\Delta(\varrho)> \flat\}$ and $\mho_{\flat}=\{\varrho\in \mathcal{Z}|\Delta(\varrho)< \flat\}$. A subset $\xi=[\Delta,\Psi,\mho]$ is a $(\flat, \ell)- NSS[NLI,NRI,NBI,(1, 2)-ideal]$ of $\mathcal{Z}$ if and only if each non-empty level subset $\xi_{t}$ is a subsemigroup $[LI,RI,BI,(1,2)-ideal]$ of $\mathcal{Z}$ for all $t\in(\flat, \ell]$. Every $(\epsilon, \epsilon \vee q)$NBI of $\mathcal{Z}$ is a $(\flat,\ell)$NBI of $\mathcal{Z}$, but converse need not be true and examples are provided to illustrate our results.

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A. Rajalakshmi mail -

Nasreen Kausar mail -

Brikena Vrioni mail -

K. Lenin Muthu Kumaran mail -

Nezir Aydin mail -

Murugan Palanikumar mail

link https://doi.org/10.54216/IJNS.250228

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Adaptive FPGA-Based Intrusion Detection System for Real-Time Internet of Things Security

The rapidly evolving landscape of cyber threats demands robust and adaptive Intrusion Detection Systems (IDS) capable of real-time operation. This paper presents a novel approach to augmenting Field-Programmable Gate Arrays (FPGA) for the development of a high-performance IDS designed to enhance communication security by rapidly and accurately identifying threats. The proposed system integrates advanced techniques, including Meta Ensemble Learning (MEL), Extreme Gradient Boosting (XGBoost), and a Hybrid Deep Learning (HDL) model that combines Long Short-Term Memory (LSTM) networks for temporal analysis and Convolutional Neural Networks (CNN) for feature extraction. This synergistic approach significantly reduces detection latency and improves the accuracy of threat identification. The effectiveness of the FPGA-based IDS is evaluated using four widely recognized datasets—NSL-KDD, IoTID20, CICIDS2017, and UNSW NB15—all of which focus on communication attacks, making them ideal for testing IDS performance in diverse IoT environments. The results demonstrate that the proposed IDS not only achieves a high detection rate with a low false positive rate but also operates efficiently in real-time settings, underscoring its viability as a critical security solution in data communication networks. Moreover, the system's exceptional performance in securing IoT devices, which are frequently targeted due to their ubiquity and vulnerabilities, highlights its potential as a reliable and scalable security measure. The FPGA-based IDS offers a significant contribution to the field by providing a rapid, accurate, and real-time security solution that addresses the pressing need for effective threat detection and prevention in modern communication networks.

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Israa Ali Al-Neami mail -

Zaynab Saeed Hameed mail -

Zahraa Abbas Al-zubaydi mail

link https://doi.org/10.54216/JISIoT.140122

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

Arabic Music Classification Using Machine Learning Algorithms with Combined Robust Features

Machine learning (ML) is the most up-to-date approach for classifying music genres. Due to technological ML advancements, its technologies can help in music genre recognition best. In machine learning, effective fusion of different features could improve recognition performance. Hence, this paper presents a new robust method for Arabic music classification based on the fusion of different sets of features. Frequency-domain, time-domain, and cepstral domain features have been combined and compared with other state-of-the-art approaches. Four machine-learning models that categorize music into its appropriate genre have been created: support vector machines (SVM), K-nearest neighbors (KNN), naïve Bayes (NB), and random forest (RF) classifiers were utilized in a comparative analysis of other ML algorithms, and the accuracy of these models has been assessed and derives the appropriate conclusions. To assess the performance of our method, two various datasets are used: the collected dataset, namely Zekrayati, which was collected by authors in favor of this paper, and the global GTZAN dataset, which was used to compare with previous studies. The experimental findings indicated that the SVM exhibited a higher optimal accuracy of 99.2% and has proven that the fusion proposed features will help to classify music in different fields.

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M. E. ElAlami mail -

S. M. K. Tobar mail -

S. M. Khater mail -

Eman A. Esmaeil mail

link https://doi.org/10.54216/FPA.170119

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

Quadripartitioned Neutrosophic Probability Distributions

Quadripartitioned neutrosophic set is an extension of neutrosophic set and n-valued neutrosophic logic for solving real-world issues. In order to demonstrate the validity of the suggested idea, this paper's major goal is to provide several quadripentapartition neutrosophic probability distributions with numerical examples.  Neutosophic probability has up till now been obtained from traditional statistical distributions, with less contributions to the statistical distribution's creation. With the help of numerical examples, we introduced the quadripartition neutrosophic binomial distribution, the quadripartitioned Poisson distribution, and the quadripartitioned Poisson distribution as a limiting case of the neutrosophic binomial distribution. We also proposed the quadripartitioned exponential distribution and the quadripartitioned uniform distribution.  This paper paves the door for addressing problems that adhere to the classical distributions while still include inaccurately stated data.

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S. Sudha mail -

B. Felcia Merlin mail -

B. Shoba mail -

A. Rajkumar mail

link https://doi.org/10.54216/IJNS.250229

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Single valued Neutrosophic soft set for the segregation to elect progressive mode of student in the bias of etiquette in Neutrosophic environment

This article proposed a novel method to categorize the best student in all progressive studies by using the single valued Neutrosophic soft set-in variable sense. An ambivalence set of multi-observer data which is related to analyse the students, taken as input for categorizing the best student identification. Neutrosophic soft set is an immense application to find out the choice-making problem in the Neutrosophic area. The creation of an analogous table has shaped the classification investigation. It helps to put up things, people into groups according to their quality, ability, performance etc., in Neutrosophic environment.

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S. Gomathy mail -

A. Rajkumar mail -

N. Jose Parvin Praveena mail -

broumi said mail

link https://doi.org/10.54216/IJNS.250230

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Smart E-commerce Recommendations with Semantic AI

In e-commerce, web mining for page recommendations is widely used but often fails to meet user needs. To address this, we propose a novel solution combining semantic web mining with BP neural networks. We process user search logs to extract five key features: content priority, time spent, user feedback (both explicit and implicit), recommendation semantics, and input deviation. These features are then fed into a BP neural network to classify and prioritize web pages. The prioritized pages are recommended to users. Using book sales pages for testing, our results demonstrate that this solution can quickly and accurately identify the pages users need. Our approach ensures that recommendations are more relevant and tailored to individual preferences, enhancing the online shopping experience. By leveraging advanced semantic analysis and neural network techniques, we bridge the gap between user expectations and actual recommendations. This innovative method not only improves accuracy but also speeds up the recommendation process, making it a valuable tool for e-commerce platforms aiming to boost user satisfaction and engagement. Additionally, our system’s ability to handle large datasets and provide real-time recommendations makes it a scalable and efficient solution for modern e-commerce challenges.

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Mohamed Badouch mail -

Mehdi Boutaounte mail

link https://doi.org/10.54216/FPA.170120

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Deep Learning Driven LSTM with Spider Wasp Optimizer Algorithm for Frictional Force Based Landslides Prediction Model

Landslides establish a main geologic threat of strong concern in many parts of the world. The vigor of soil, rocks, or other rubbish moving down a slope can destroy whatever in its track. Landslides happen in an extensive variety of geological and structural settings, geomechanical contexts, and as a response to numerous triggering and loading procedures. They are frequently related to other main natural disasters like floods, earthquakes, and volcanic waves. Landslides occasionally attack without noticeable warning. While only some cases have been examined the earlier, modern monitoring models are certain to deliver a wealth of novel quantitative observations based on SAR (synthetic aperture radar) and GPS technology for mapping the surface velocity area. This study emphasizes the latent of incorporating advanced machine learning (ML) models with geophysical data to improve prediction of landslides and risk management strategies. This study develops a Predicting Landslides with frictional-based Deep Learning using Spider Wasp Optimizer (PLFFDL-SWO) Method. The major intention of the PLFFDL-SWO technique lies in the robust frictional force based on predicting landslides. In the presented PLFFDL-SWO model, Z-score normalization is performed to transform the raw data into compatible format. Then, the long short-term memory (LSTM) model is utilized for the prediction of landslides. LSTM is a recurrent neural network (RNN) type, for predicting landslides based on frictional force data. Traditional landslide prediction methods often struggle with temporal dynamics and nonlinear relationships inherent in geophysical data. Finally, the spider wasp optimizer (SWO) algorithm is exploited for the optimal hyper parameter adjustment of the LSTM model to improve prediction accuracy. The experimentation result investigation of the PLFFDL-SWO technique can be examined by employing a benchmark dataset. The simulation outcomes reported the supremacy of the PLFFDL-SWO technique under different measures  

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Domi Evangeline . S mail -

G. Usha mail

link https://doi.org/10.54216/JISIoT.140123

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

Proposing Triple fuzzy distribution based on the Quantile Function for monotonically decreasing failure data

In this paper, we propose a novel generalization for one parameter inverse Lindley distribution to fitting monotonically descending data named the T-ILD{Y} distribution class ,   T is one parameter inverse exponential distribution ,  R has an one parameter inverse Lindley distribution , and the variable Y is one parameter exponential distribution, the resulting distribution is inverse exponential- inverse Lindley- exponential (IEILDE). The theory of fuzzy sets  are used by converting the distribution to fuzzy by using  a fuzzy triangular distribution based on the quantile function (FIEILE), the maximum likelihood , and the maximum likelihood, and  the maximum product spacing method were used estimate the parameters of the distribution. We conclude that at cutoff α=0.1, ML is better than the MPS, and at cutoff coefficients α=0.3, 0.5, 0.7, MPS was better than the ML, The higher the cutoff, the better the maximum likelihood method.

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Shams Najy Elaiwey mail -

Mahdi Wahab Neama mail

link https://doi.org/10.54216/GJMSA.0110201

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

A Study on the Wiener Polynomials for the Paraffin Polynomial-Rings

In this paper, we find the Wiener polynomial of multi-circles of Paraffin structural. We prove that this obtained formula is better than the formulas, which are previously presented. Also, we evaluate the coefficients for any limited power of  without depending on the number of circles, and we find the Wiener index and average distance for this structural. On the other hand, we build a MATLAB program to evaluate the Wiener polynomial coefficient, Wiener index, and average distance.

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Sandra Terazic mail

link https://doi.org/10.54216/GJMSA.0110202

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025