Using Major Pathway and Compound Analysis Methods to Identify Factors Affecting Diabetes

Zainab Sami Yaseen

doi:https://doi.org/10.54216/PMTCS.030105

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

Details open_in_new

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.

groups

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

International Journal of Neutrosophic Science | 2024

Robust Financial Market Share Prediction using Intuitionistic Possibility Fermatean Neutrosophic Soft Set

An addition of soft set theory, Neutrosophic soft set theory offers a versatile framework for handling indeterminacy and uncertainty in data. Using this theory for the prediction of market share includes representing market data in a neutrosophic soft-set format, where elements pose truth, indeterminacy, and false degrees. The predictive model is constructed to estimate future market shares with consideration for ambiguity and uncertainty by analyzing previous market factors and trends affecting market dynamics within these frameworks. The stock market prediction pattern is interpreted as a significant action and it is more beneficial. Therefore, stock prices will result in substantial profits from sound taking choices. Thus, stock market forecasting is a main task for investors to spend their money to create maximum profit due to the noisy and stagnant data. Stock market prediction uses learning tools and mathematical strategies. Therefore, this manuscript offers the design of Financial Market Share Prediction using the Intuitionistic Possibility Fermatean Neutrosophic Soft Set (FMSP-IPFNS) technique. In the FMSP-IPFNS technique, a three-stage approach is followed. Firstly, the data normalization process is executed using a min-max scalar approach. Secondly, the prediction process can be carried out using the IPFNS approach. Thirdly, the parameter adjustment of the IPFNS approach takes place using the grasshopper optimization algorithm (GOA). To validate the performance of the FMSP-IPFNS system, a sequence of experimentations were tested. The obtained values demonstrate the promising results of the FMSP-IPFNS system compared to other models

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Mohammed Basheri mail -

Mahmoud Ragab mail

link https://doi.org/10.54216/IJNS.240310

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

COPRAS Neutrosophic Approach with Big Data Analytics for Enhancing Multi-Dimensional Customer Churn Prediction on Corporate Performance Assessment

A neutrosophic set (NS) is a new computing technology that accesses ambiguous data through three memberships. A soft expert set (SES) is based on the concept of a “soft set” with an expert system. Now, this technique has been applied in different domains namely measurement theory, intelligent systems, game theory, probability theory, cybernetics, etc. Customer Churn prediction implies identifying which consumers are expected to cancel a subscription to a service or leave a service. It is a crucial forecast for several businesses because obtaining new users frequently costs more than holding existing ones. The Churn prediction modeling methods try to understand the accurate customer attributes and behaviors that signal the risk and timing of customers leaving. This manuscript offers the design of an AI-based Multi-Dimensional Customer Churn Prediction for Corporate Performance Assessment (AIMD-CCPCPA) technique. The AIMD-CCPCPA technique mainly aims to detect the presence of customer churns and non-churns. It involves a two-stage process. At the initial stage, the AIMD-CCPCPA technique exploits the COPRAS Neutrosophic Method for prediction purposes. Secondly, the AIMD-CCPCPA technique involves parameter selection using a butterfly optimization algorithm (BOA). The experimental analysis of the AIMD-CCPCPA model is examined using a benchmark dataset. The acquired outcomes stated the supremacy of the AIMD-CCPCPA technique equated to other models

groups

Adam Mohamed Omer mail

link https://doi.org/10.54216/IJNS.240311

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Trademark Empowerment using Optimal Neutrosophic Topological Vector Space for Maximizing Customer Attraction

Neutrosophic set is introduced as a generalization of intuitionistic fuzzy set, where any elements x ∈ X we have membership (T), non-membership (F), and indeterminacy (I)degrees. Neurosophic vague topological spaces are presented in various notations like neurosophic vague compactness and continuity. Trademarks are the essential components of intellectual property that allow owner to earn profit based on their name. In this industry, retailers typically use feedback channels like customer care service, website review complaints and suggestions boxes to gain user reviews on service satisfaction. But, there is a gap between these techniques. Customers are not fulfilled with them due to lack of trust in management, a lack of flexibility and slow responsiveness. This has prompted examination of the effect of customer feedback channels (CFCs) on client satisfaction and the necessity to develop a new CFC using artificial intelligence (AI). Thus, this study designs a Trademark Empowerment using Optimal Neutrosophic Topological Vector Space (TE-ONTVS) technique for Maximizing Customer Attraction. The intention of the TE-ONTVS technique lies in the prediction of customer behaviour and attraction. To accomplish this, the TE-ONTVS technique undergoes data scaling using Z-score normalization. In addition, the TE-ONTVS technique uses NTVS approach for the identification of customer behaviour and attraction. Lastly, whale optimization algorithm (WOA) is applied for optimal parameter tuning of the NTVS algorithm. A series of experiments were involved to demonstrate the enhanced outcomes of the TE-ONTVS algorithm. The obtained results stated that the TE-ONTVS technique reaches optimal performance over other models

groups

Alsadig Ahmed mail

link https://doi.org/10.54216/IJNS.240312

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Statistical Optimization of Industrial Processes for Sustainable Growth using Neutrosophic Maddala Distribution

The family of neutrosophic distributions has received considerable attention from the scientific community, due to the flexible parametric form of its probability density function, in modeling many physical phenomena with imprecise information. In this study, we consider a generalization of Singh Maddala distribution for handling fuzzy data sets. This study presents a new research endeavor: quantifying the lifespan of manufacturing enterprises using the Neutrosophic Singh Maddala Distribution (NSMD). This work significantly enhances the theoretical foundations by providing novel formulations for the moments and mode of the NSMD distribution. In addition, it expands the study beyond the traditional Maddala model by examining conventional statistical models. For estimating the unknown parameters, the maximum likelihood estimation has been used in neutrosophic framework. Characterizations are obtained in terms of neutrosophic measures. The assessment of model performance, carried out using the goodness of fit criterion, highlights the superiority of NSMD compared to other models. In the application section, a real data on carbon emission is provided for usefulness of the proposed model.

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Fuad S. Al-Duais mail

link https://doi.org/10.54216/IJNS.240313

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Anti homomorphism and homomorphism of bipolar valued multi Fuzzy HX-subgroups and it’s normal

In this article, homomorphism and anti-homomorphism bipolar valued multi normal fuzzy HX subgroup of a HX group is studied, discussed and their properties are introduced. The theorems presented in this article are considered a generalization of what has been publishedthrough the concept of fuzzy sets. As the concept of fuzzy sets was introduced by Zadeh in 1965, after that this concept, we expanded into more than one types, intuitionistic fuzzy set, bipolar fuzzy sets, tripolar fuzzy set, and bipolar valued multi fuzzy set are one of this.

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Mourad Oqla Massa’deh mail -

Ahlam omar Fallatah mail

link https://doi.org/10.54216/IJNS.240314

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2024

Using Major Pathway and Compound Analysis Methods to Identify Factors Affecting Diabetes

Legal analysis is one of the important methods to study the interrelationships between two types of variables. an important use of this analysis is to reduce the data. Many studies use this analysis as a way to study the interrelationships between two types of variables. There have been no empirical studies of the use of legal analysis as a method. From my point of view, this study aims to shed light on how to use legal analysis as a means of factor analysis, and to show how to apply it in this field by dealing with a practical problem in the active field. The applied problem includes the study of the factorial analysis, the method of the main compounds, the method of path analysis, and the compatibility between them on two types of data, represented by identifying the factors associated with diabetes, and then identifying the variables that affect the rise in the measurement of sugar two hours after eating. impact according to priority and importance.

groups

Zainab Sami Yaseen mail

link https://doi.org/10.54216/PMTCS.030105

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new