Reduce the Spread Risk of COVID-19 based on Clinical Fusion Data and Monitoring System in Wireless Sensor Network

Majed Hamed; Ahmed Noori

doi:https://doi.org/10.54216/FPA.110102

International Journal of Neutrosophic Science | 2023

A novel extension of hesitant fuzzy sets on UP (BCC)-algebras: neutrosophic hesitant fuzzy sets

In this paper, we introduce the concepts of neutrosophic hesitant fuzzy UP (BCC)-subalgebras, UP (BCC)-ideals, and strong UP (BCC)-ideals of UP (BCC)-algebras. The characteristic neutrosophic hesitant fuzzy UP (BCC)-subalgebras, UP (BCC)-ideals, and strong UP (BCC)-ideals have also been studied. The relationshipbetween neutrosophic hesitant fuzzy UP (BCC)-subalgebras, UP (BCC)-ideals, and strong UP (BCC)-ideals and their level subsets is provided. The Cartesian product of neutrosophic hesitant fuzzy UP (BCC)-subalgebras, UP (BCC)-ideals, and strong UP (BCC)-ideals is also supplied. Finally, we also find the property of the homomorphic pre-image of neutrosophic hesitant fuzzy UP (BCC)-subalgebras, UP (BCC)-ideals, and strong UP (BCC)-ideals.

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Aiyared Iampan mail -

S. Yamunadevi mail -

P. Maragatha Meenakshi mail -

N. Rajesh mail

link https://doi.org/10.54216/IJNS.200406

Volume & Issue

Vol. Volume 20 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2023

δ-open sets in Neutrosophic Hypersoft Topological Spaces

In this paper, neutrosphic hypersoft δ-open sets are introduced by defining the neutrosophic hypersoft regular open sets, pre-open sets, δ-interior and δ-closure. Under the guidance of these definitions, neutrosophic hyper- soft δ semi-open sets and δ pre-open sets are also introduced. Further, we have deduced inuitionistic hypersoft topology and fuzzy hypersoft topology from the neutrosophic hypersoft topology. Moreover, we discuss about the relations between neutrosphic hypersoft δ-open sets, δ semi-open sets, δ pre-open sets, semi-open sets and pre-open sets and their properties with examples.

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P. Surendra mail -

K. Chitirakala mail -

A. Vadivel mail

link https://doi.org/10.54216/IJNS.200407

Volume & Issue

Vol. Volume 20 / Iss. Issue 4

Details open_in_new

American Journal of Business and Operations Research | 2023

Intelligent Decision Support System for Optimizing Inventory Management

Inventory management (InvM) is a critical aspect of supply chain management (SCM) and optimizing inventory levels can lead to significant cost savings and improved customer satisfaction. Hence, business organizations have recently worked to increase the value of their operations by utilizing modern and digital technologies as industry 4.0 (Ind 4.0). In the era of Ind 4.0, the technologies as Internet of Every Things (IoET), AI, BDA…etc. Recognizing the significance of InvM in a supply chain (SC), motivated us to volunteer technologies of Ind 4.0 as machine learning (ML) techniques to boosting Decision Making (DM) process to optimize InvM. Subsequently, this study is constructed for providing an intelligent Decision Support ML framework for automating the process of optimizing inventory management. Our constructed framework ensembles powerful ML prediction algorithms for inventory management, such as Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Machine (SVM) for building robust sales regressors.  The extensive experimentations on a case study of Walmart suggested that the proposed system has the potential to transform inventory management and improve supply chain performance, but further research is needed to address the challenges of data availability and quality.

groups

Mona Mohamed mail -

Nissreen El Saber mail

link https://doi.org/10.54216/AJBOR.090205

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2023

Evaluation and Selection of Cloud Service: A neutrosophic model

The variety of cloud-based services that are now accessible is expanding at a fast pace, and as a result, it has become much more difficult for regular customers to choose the appropriate cloud services. When selecting an appropriate service to use in a setting where there is a high degree of unpredictability, it is in the user's best interest to be able to deal with ambiguous information. This is because the Cloud service environment contains a great number of unknowns, which may prevent the user from making wise choices. In this paper, the authors propose a framework that they call the Optimal Service Choice and Priority of Cloud Computing (CC) Service. This methodology gives cloud customers the ability to evaluate the various service options available to them relying on QoS (Quality of Requirements) standards. The model employs a mixed approach to decision making that is based on many factors. The PROMETHEE is used for the purpose of ranking and prioritizing the QoS criteria, and to get the final rank of cloud services. The suggested methodology is a Multi-criteria decision making approach due to this problem contain many conflicting criteria. The PROMETHEE is integrated with neutrosophic environment to overcome uncertainty information. The application of methodology is provided.

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Abdullah Ali Salamai mail

link https://doi.org/10.54216/NIF.010202

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2023

ChatGPT: Virtual Creative Model

We offer ChatGPT, an autoregressive language model that employs deep learning to generate sentences that resemble those written by humans, and assess them using the preceding distinction. We discuss the nature of ChatGPT based on a review of its output. Also, we reviewed some details about the different versions of ChatGPT. We show, based on the resulting ChatGPT outputs, which the recently released AI-enabled ChatGPT can be of great help to daily life. We showed the full benefit behind ChatGPT.

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Abduallah Gamal mail

link https://doi.org/10.54216/NIF.010203

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2023

A Neutrosophic Model for Blockchain Platform Selection based on SWARA and WSM

The blockchain as a distributed ledger with flourishing blocks are secured and linked with cryptographic hashes. The blockchain is a type of distributed database that is used in many vital business transactions of replication, sharing, tracking, synchronization data among various sites. Recently, the global technological and industrial revolution is accelerating, the bitcoin extends the industrial revolution to become a lot of interest from both the business world and academic circles. This paper aims to take the advantages of blockchain concepts to be applied in Enterprise Banking Systems (EBS). The EBS depend on smart contract and blockchain technologies for trust only the installation of a blockchain platform with a solid design and a proven user base. Unfortunately, only a few blockchain platforms (BP) have achieved stable design and confident implementation. The selection of appropriate BP is leading step for decision makers that pretended to be a real challenge. Therefore, any digital transformation project that makes use of blockchain must contend with the difficulty of selecting a BP that is suited to the requirements of EBS. In this study, a hybrid approach of a neutrosophic theory for uncertainty conditions in a multi-criteria decision-making problem with the use wise weight assessment ratio analysis (SWARA) and Weighted Sum Method (WSM) to select the appropriate and efficient BP. A case study is applied on EBS, as an uncertain environment, to show the efficiency for the proposed model in aiding decision makers to achieve to ideal BP according to challenges to achieve sustainability.

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Nada A. Nabeeh mail -

Alshaimaa A. Tantawy mail

link https://doi.org/10.54216/NIF.010204

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2023

Prioritization Thermochemical Materials based on Neutrosophic sets Hybrid MULTIMOORA Ranker Method

Present era, several technologies are combining in various industries to strengthen sustainable ecological, economic, and societal. For example, in storage energy industrial where a sophisticated technique for storing thermal energy called thermal energy storage (TES) can lessen the effects on the environment and enable cleaner and more effective energy systems. Particularly, thermochemical energy storage (TES) which is characterized by substantial density of energy. So, selecting suitable material among the set of materials is crucial process. This study emphasized employing durable techniques to elucidate complex interrelationships between criteria and several materials. Thus, this study employs Multi-criteria Decision Making (MCDM) methods. Also, we are supporting these methods with robust theory represents in neutrosohic theory to fortify MCDM methods in uncertainty and non-aligned situations.  Moreover, we are utilizing Multi-objective Optimization by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA) assists with Single Value Neutrosophic sets (SVNs). Finally, we applied our constructed framework to a real case study to guarantee that our framework is accurate and valid. 

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Mona Mohamed mail -

Nissreen El Saber mail

link https://doi.org/10.54216/NIF.020101

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2023

Sentiment Analysis for Fake News Detection in Online Media Networks: A survey, fusion techniques and quality metrics

The development of Online media sites in recent years has led to the spread of content sharing like commercial advertisements, political news, celebrity news, and so on. Various social media applications, such as Facebook, Instagram, and Twitter, have been impacted by fake news. Due to the easier access and rapid expansion of data through online media platforms, distinguishing between fake and real data has become difficult. The massive volume of news transmitted over online media portals makes manual verification impractical, which has prompted the development and deployment of automated methods for detecting fake news. Given the clear dangers of misleading and deception, fake news study has seen an increase in efforts that employ machine learning approaches, and sentiment analysis. In this study, we review the many implementations of sentiment analysis and machine learning methodologies in the fake news detection, as well as the most pressing difficulties and future research prospects.

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Mahmoud Ibrahim mail

link https://doi.org/10.54216/NIF.010205

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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Fusion: Practice and Applications | 2023

A Review on Artificial Intelligence and Quantum Machine Learning for Heart Disease Diagnosis: Current Techniques, Challenges and Issues, Recent Developments, and Future Directions

This study presents a comprehensive analysis of the existing techniques and applications of artificial intelligence (AI) to cardiovascular disease diagnosis. The application of AI to the diagnosis of cardiac diseases can enhance diagnostic precision, diagnostic throughput, and patient outcomes. This literature survey analyzes state-of-the-art AI-based methods, rates their efficiency, examines potential future research and development avenues, and finds challenges and limitations, providing a foundational overview of main developments in AI, machine learning, deep learning, and quantum computing in relation to heart disease prevention. This study seeks to guide the use of AI-based techniques for heart disease detection, having an ultimate objective of enhancing patient outcomes through research and development. This review mainly emphasizes the significance of further studying and advancing AI for its ability to revolutionize the diagnosis and management of heart diseases.

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Huda Ghazi Enad mail -

Mazin Abed Mohammed mail

link https://doi.org/10.54216/FPA.110101

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

Reduce the Spread Risk of COVID-19 based on Clinical Fusion Data and Monitoring System in Wireless Sensor Network

The expression “COVID-19” has been the fiercest but most trending Google search since it first appeared in November 2019. Due to advances in mobile technology and sensors, Healthcare systems based on the Internet of Things are conceivable. Instead of the traditional reactive healthcare systems, these new healthcare systems can be proactive and preventive. This paper suggested a framework for real-time suspect detection based on the Internet of Things. In the early phases of predicting COVID-19, the framework evaluates the existence of the virus by extracting health variables obtained in real-time from sensors and other IoT devices, in order to better understand the behavior of the virus by collecting symptom data of COVID-19, In this paper, four machine learning models (Random Forest, Decision Tree, K-Nearest Neural Network, and Artificial Neural Network) are proposed, these data and applied as a machine learning model to obtain high diagnostic accuracy, however, it is noted that there is a problem when collecting clinical fusion data that is scarce and unbalanced, so a dataset augmented by Generative Adversarial Network (GAN) was used. Several algorithms achieved high levels of accuracy (ACC), including Random Forest (99%), and Decision Tree (99%), K-Nearest Neighbour (98%), and Artificial Neural Network (99%). These results show the ability of GANs to generate data and their ability to provide relevant data to efficiently manage Covid-19 and reduce the risk of its spread through accurate diagnosis of patients and informing health authorities of suspected cases.

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Majed Hamed Fahad mail -

Ahmed Noori Rashid mail

link https://doi.org/10.54216/FPA.110102

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new