On The 4-Cyclic Refined Neutrosophic Solutions of The Diophantine Equation  X^n=1 and m-Cyclic Refined Neutrosophic Modulo Integers

Lee; Maretta; Ammar; Ahmad

doi:https://doi.org/10.54216/JNFS.080205

International Journal of Neutrosophic Science | 2024

New approach to bisemiring via the q-neutrosophic cubic vague subbisemiring

We introduce the notion of q-neutrosophic cubic vague subbisemiring (q-NSCVSBS) and level set of q- NSCVSBS of a bisemiring. The q-NSCVSBS is a new concept of subbisemirings of bisemirings.  Let X be a neutrosophic vague subset of L. Then W = ([T-, T+ ], [I-, I+ ],[F-,F+ ]) is a q-NSCVSBS of L if and only if all non-empty level set is also a SBS of L. Let X be the q-NSCVSBS of L and ¡ be the strongest cubic q-neutrosophic vague relation of L*L. Then X is a q-NSCVSBS of L* L. Let X be the q-NSCVSBS of L, show that pseudo cubic q-neutrosophic vague coset is also a q-NSCVSBS of L. Let X1, X2,….. Xn be the any family of q-NSCV SBSs of L1, L2,…., Ln respectively, then X1* X2 *….. * Xn is also a q-NSCVSBS of L1 * L2 *…. *Ln .The homomorphic image of every q-NSCVSBS is also a q-NSCVSBS. The homomorphic pre-image of every q-NSCVSBS is also a q-NSCVSBS.

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S. Selvaraj mail -

M. Palanikumar mail -

Faisal Al-Sharqi mail -

Ashraf Al-Quran mail -

Ali M. A. Bany Awad mail -

K. Lenin Muthu Kumaran mail -

M. Geethalakshmi mail

link https://doi.org/10.54216/IJNS.240308

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

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Fusion: Practice and Applications | 2024

Deep Neural Network Discipline and Consequence to be Achieved through Internet Implementation Dropdown

The difficulty of automatically modifying and updating operations within Deep Learning (DL) frameworks can slow down the performance of Deep Neural Network processing (DNNs). This research presents a novel approach to software optimization by leveraging dynamically collected profile data. A unique online auto-tuning system for DNNs was developed to enhance both the training and inference phases. Python Distributed Training of Neural Networks (PyDTNN) is a lightweight toolkit designed for distributed DNN training and estimation. It is utilized to evaluate the VGG19 model on two distinct multi-core architecture options. In testing, our auto-tuning system performs comparably, if not better, than a static selection strategy. The performance of each variation of PyDTNN that employs static selection remains consistently high throughout execution. Conversely, the auto-tuned version initially performs at a set level and progressively improves as more feasible choices become available. While both variations yield similar results in training, the selection strategy outperforms all other inference options by autonomously determining the best strategy for each layer in VGG19. The new online implementation selection tool assists in choosing the best performance option from numerous alternatives while the program is running. Its key features include constructing layered judgments and thoroughly examining 35 possibilities. Our advanced systems represent the optimal choice for monitoring sustainable environmental systems with maximum effectiveness, efficiency, and timeliness.

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Haitham S. Hasan mail

link https://doi.org/10.54216/FPA.160115

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2024

The Mathematical Formulas for Inverting Plithogenic Matrices of Special Orders Between 20 and 24

This paper is dedicated to study the Invertibility properties of all plithogenic square matrices of high orders between 20 and 24, where we present the mathematical conditions and formulas for computing the inverses of all plithogenic square matrices with real plithogenic entries. This goal will be completed by proving many theorems that describe the Invertibility of all classic matrix parts of the corresponding symbolic m-plithogenic matrix.

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Ahmad A.Abubaker mail -

Wael M. Mohammad Salameh mail -

Heba Alrawashdeh mail -

Amani Shatarah mail -

Norah Mousa Alrayes mail -

Abdallah Al-Husban mail

link https://doi.org/10.54216/IJNS.240309

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

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Journal of Neutrosophic and Fuzzy Systems | 2024

On the Non-Commutative Logical Rings As Novel Extensions of Neutrosophic Rings

This paper uses some logical algebraic elements to extend any ring into a non-commutative ring containing the original ring with many generalized substructures and special elements. On the other hand, we study the substructures of non-commutative logical rings such as AH-homomorphisms and AH-ideals with many examples that explain their algebraic validity. Also, we discuss the possibility of solving a linear Diophantine equation with two variables in the non-commutative logical ring of integers, where we present an easy algorithm to solve this kind of generalized Diophantine equation.

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Murat Ozcek mail

link https://doi.org/10.54216/JNFS.080203

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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Journal of Neutrosophic and Fuzzy Systems | 2024

On the Usage of Orthogonal Polynomials with Picard Iteration Method to Find Numerical Solutions of Neutrosophic Non-Linear Ordinary and Partial Differential Equations

This research aims to modify the Picard iteration method by hybridizing it with some orthogonal polynomials and then applying the hybrid method in solving neutrosophic nonlinear elementary value problems. This method is based on modifying the Picard iteration method by approximating the right-hand side of the neutrosophic differential equation of the studied problem either by Legendre polynomials or by Chebyshev polynomials of the first kind to obtain two different hybrids of the Picard iteration method. Also, we apply this modification to neutrosophic elementary value problems represented by neutrosophic nonlinear and right-handed nonlinear differential equations to demonstrate the reliability and efficiency of the proposed modified method. For this goal, we prove how effective this method is, we calculate the neutrosophic absolute error of approximate solutions resulting from the application of the proposed modification of the Picard iteration method and with the exact solution.

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Arwa Hajjari mail

link https://doi.org/10.54216/JNFS.080204

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Modeling of Dung Beetle Optimization-based Sink Node Localization Approach for Wireless Sensor Networks

Wireless sensor network (WSN) performs monitoring of each aspect of the area of interest by detecting the surrounding physical phenomena with sensor nodes and transferring the information to the gateway through the corresponding system. Several researcher workers have introduced localization methods to accomplish high accuracy of localization. An intelligent optimization technique has attracted various researcher workers due to its advantages such as strong optimization capability and few parameters to optimize the localization performance of the DV-Hop method. Sink node localization (NL) using metaheuristics in WSN includes applying optimization techniques inspired by human behavior or natural phenomena to define the geographical coordinates of the sink nodes within the network coverage region. WSNs can accomplish better localization performance, especially in dynamic or complex environments, improving the efficiency and reliability of network management and data transmission by leveraging metaheuristics. In this view, this manuscript develops a Dung Beetle Optimization based Sink Node Localization Approach (DBO-SNLA) for WSN. In the DBO-SNLA technique, the DBO algorithm involved is based on the social behavior of dung beetle populations and is developed with five updated rules to assist in finding high-quality solutions. In addition, the DBO-SNLA technique addresses the issues of defining the sink node location with lowest localization error once the data between the nodes is transferred wirelessly. Finally, the localization errors are calculated and the location of the different unknown nodes is computed. A detailed set of simulation takes place to examine the performance of the DBO-SNLA technique. The empirical analysis stated the betterment of the DBO-SNLA method than other techniques

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R. Padmaraj mail -

K. Selvakumar mail

link https://doi.org/10.54216/JISIoT.130101

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

A DEMATEL Analysis of the Complex Barriers Hindering Digitalization Technology Adoption in the Malaysia Agriculture Sector

This study investigates the challenges to the digitalization technology adoption in Malaysia agriculture sector by using the DEMATEL (Decision-Making Trial and Evaluation Laboratory) approach, which will give a complete knowledge of the interdependencies among the barriers. The research objectives are to determine the cause and effect of digital agriculture using DEMATEL and to recommend the best way to overcome the obstacles in using digital technology.  The findings from this study reveals the cause and effect from the barriers which is lack of skills, lack of technology, high cost, infrastructure and connectivity, and resistance to change are in the cause group while limited locality, data privacy and security concerns, low level of education, market access and regulatory and policy are in the effect group.  The research findings are utilized to give policymakers and stakeholders with practical recommendations aimed at addressing the identified barriers and promoting the adoption of digital technologies in Malaysian agriculture.  Thus, this study offers recommendations for the most important obstacles found, which are an improvement in infrastructure and the implementation of financial assistance mechanisms.  All things considered, this research makes a significant contribution to the subject of agriculture and sheds light on the difficulties associated with implementing new technologies in Malaysia's agriculture industry.

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Zahari Md Rodzi mail -

Nur A. Mat Rosly mail -

Nurul A. Mohd Zaik mail -

Muhammad Hakimi Rusli mail -

Ghafur Ahmad mail -

Faisal Al-Sharqi mail -

Ashraf Al-Quran mail -

Ali M. A. Bany Awad mail

link https://doi.org/10.54216/JISIoT.130102

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

Revolutionizing Healthcare: A Comprehensive Framework for Personalized IoT and Cloud Computing-Driven Healthcare Services with Smart Biometric Identity Management

Medical care conveyance has been transformed by the Internet of Things (IoT's) combination into wellbeing systems, which provides doctors and patients with continuous on-request services. However, this coordination poses questions with respect to the precision of the information and possible security risks. This research expects to present a sharp character the executives structure planned for IoT and distributed computing based personalized medical care frameworks. The purpose is to upgrade confirmation processes while restricting security threats through the double-dealing of multimodal encoded biometric features. The suggested approach incorporates biometric-based continuous authentication together with combined and concentrated personality access strategies. To safeguard patient information in the cloud, it combines electrocardiogram (ECG) and photoplethysmogram (PPG) signals for authentication, which is further bolstered by homomorphic encryption (HE). An AI (ML) model was used to assess the system's reasonability including a dataset of 20 clients in various seating configurations. The merged based biometric structure defeated standalone ECG or PPG signal-based procedures in perceiving and authenticating every client with 100% exactness. The proposed framework makes significant improvements to the privacy and security of personalized healthcare frameworks. It fulfills the essential security necessities and is by the by viable enough to run on low-end processors. It guarantees trustworthy authentication and protects against conventional security threats by utilizing multimodal biometric features and cutting-edge encryption techniques.

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S. Phani Praveen mail -

Chandra Shikhi Kodete mail -

Saibaba velidi mail -

Srikanth Bhyrapuneni mail -

Suresh Babu Satukumati mail -

Vahiduddin Shariff mail

link https://doi.org/10.54216/JISIoT.130103

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Enhancing Air Pollution Monitoring and Prediction using African Vulture Optimization Algorithm with Machine Learning Model on Internet of Things Environment

An optimal solution for monitoring air pollution, the Internet of Things (IoT)-enabled system delivers real-time data and insights on the air quality within a specific location. Air pollution poses a substantial risk to human health worldwide, with pollutants like nitrogen dioxide, particulate matter, ozone, and sulfur dioxide contributing to a range of cardiovascular and respiratory ailments. Monitoring air pollution levels is critical to understand the effect on public health and the environment. Air Pollution Monitoring includes the systematic analysis and measurement of pollutant concentration in the air, through a network of monitoring stations equipped with instruments and sensors. This station provides real-time data on air quality, allowing authorities to evaluate issue warnings, and pollution levels, and implement strategies to alleviate its negative impact. Machine learning (ML) approaches are becoming more integrated into air pollution monitoring systems for enhancing efficiency and accuracy. By analyzing vast quantities of information gathered from satellite imagery, monitoring stations, and other sources, ML approaches could detect patterns, forecast pollution levels, and pinpoint sources of pollution. This study introduces Air Pollution Monitoring and Prediction using African Vulture Optimization Algorithm with Machine Learning (APMP-AVOAML) model in IoT environment. The drive of the APMP-AVOAML methodology is to recognize and classify the air quality levels in the IoT environment. In the APMP-AVOAML technique, a four stage process is encompassed. Firstly, min-max normalization is applied for scaling the input data. Secondly, a harmony search algorithm (HSA) based feature selection process is executed. Thirdly, the extreme gradient boosting (XGBoost) model is utilized for air pollution prediction. Finally, AVOA based parameter selection process is exploited for the XGBoost model. To illustrate the performance of the APMP-AVOAML algorithm, a brief experimental study is made. The resultant outcomes inferred that the APMP-AVOAML methodology has resulted in effectual outcome.

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Naresh Sharma mail -

Rohit Sharma mail

link https://doi.org/10.54216/JISIoT.130104

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2024

On The 4-Cyclic Refined Neutrosophic Solutions of The Diophantine Equation X^n=1 and m-Cyclic Refined Neutrosophic Modulo Integers

The ring of n-cyclic refined neutrosophic integers is a logical extension of the integer ring Z based on a special multiplication operation defined between the indeterminacy algebraic elements. In this paper, we provide a full description of the 4-cyclic refined neutrosophic integer roots of unity, where we prove that for odd values of n we get exactly two different solutions. For even values of n, we get exactly 15 different solutions. On the other hand, we characterize the m-cyclic refined neutrosophic modulo integers rings and present many of their algebraic properties based on neutrosophic homomorphisms and substructures.

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Lee Xu mail -

Maretta Sarkis mail -

Ammar Rawashdeh mail -

Ahmad Khaldi mail

link https://doi.org/10.54216/JNFS.080205

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new