- Volume 1
- Issue 1
- (8) Articles
Prem Kumar Singh
Recently, neutrosophic set is considered as one of the prominent tool to deal with human cognition in three-way fuzzy space. This set has given a way to characterize the human cognition in conscious, unconscious, or indeterminate state. The problem arises when a person realizes all of three states independently as silent mode. It is observed at voting time of Indian democratic system where some people vote in a favor of a party, do not vote in favor of a party, being absent and choose none of the above. The last consciousness is turiyam state which is independent from all. It also observed at time of feedback or rating given by an expert towards which used to based on internal communication rather than true, false and uncertain activity. The internal communication which prvodies the opinion towards rating about any organization or employee is called as Turiya or Fourth dimension cognition. This paper try to introduce this fourth dimension of human cognitions as a new set called as Turiyam set with its graphical visualization via an illustrative example.
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Doi: https://doi.org/10.54216/JNFS.010101
Vol. 1
Issue. 1
PP. 09-23, (2021)
Prem Kumar Singh
Recently, a problem is addressed while dealing with fourth dimensional or non-Euclidean data sets. These are the data sets does not follow one of the postulates established by Euclid specially the parallel postulates. In this case, the precise representation of these data sets is major issues for knowledge processing tasks. Hence, the current paper tried to introduce some non-Euclidean geometry or Anti-Geometry methods and its examples for various applications.
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Doi: https://doi.org/10.54216/JNFS.010102
Vol. 1
Issue. 1
PP. 24-33, (2021)
Florentin Smarandache
,
Said Broumi
In this short note we show that the newly introduced concept of Neutro-Intelligent Set (NIS) deserves attention in its applications to the human brain activity, and that NIS is a particular case of the Refined Neutrosophic Set.
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Doi: https://doi.org/10.54216/JNFS.010103
Vol. 1
Issue. 1
PP. 34-36, (2021)
Kranthi Kumar Singamaneni
,
S V Bharath Kumar Reddy
,
U Sreenivasulu
The use of IoT devices like sensors, actuators, smartphones etc. is the very rapid and useful source in order to cope smartly with the public and community growth requirements. Nevertheless, when you connect thousands of IoT devices to create a smart network as you communicate over the Internet, you produce a massive amount of data, known as Big Data. Integrating IoT services to receive city data in real time and then efficiently processing large amounts of data to create a smart city is a challenge. Therefore, in this paper the smart town framework based on IoT using Big Data Analytics was proposed and developed. We use sensors such as smart home sensors , network cars, water and weather sensors, smart parking sensors, tracking objects, etc. The entire design and implementation model is proposed and implemented in a specific world using Hadoop ecosystems. The system is implemented in various steps , starting from data collection , aggregation, filtering, classification, preprocessing, computing and decision-making. Spark over Hadoop achieves reliability in the production of big data. The program is realistic for building smart cities by using intelligent systems as the city data base.
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Doi: https://doi.org/10.54216/JNFS.010104
Vol. 1
Issue. 1
PP. 37-47, (2021)
Katy D. Ahmad
,
Mikail Bal
,
Malath Aswad
The aim of this paper is to define the concept of kernel subgroup of a fuzzy group and anti-fuzzy group respectively. Also, we prove that these kernels are groups in the ordinary algebraic meaning, as well as presenting many results about fuzzy groups and anti-fuzzy groups.
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Doi: https://doi.org/10.54216/JNFS.010105
Vol. 1
Issue. 1
PP. 48-54, (2021)
Nihal N. Mostafa
For the last several decades, detecting human brain tumors has evolved into one of the most difficult problems in the field of medical research. In the realm of medical image processing, the categorization of brain tumors is a difficult job to do. In this research, we offer a model for the detection of human brain tumors in magnetic resonance imaging (MRI) images that makes use of the template-depend neutrosophic c-means and is compared with the fuzzy C means method. This model is referred to as the NCM method. In this suggested method, well first of all, the pattern K-means method is used to initialize segmentation markedly through the ideal choice of a template, depending on the gray-level intensity of the image; besides which, the revised membership is calculated by the ranges from the closest centroid to cluster pieces of data by using neutrosophic C-means (NCM) method while it approaches its perfect outcomes; and at last, the NCM clustering method is used for sensing tumor positron emission tomography (PET) imaging The findings of the simulation reveal that the suggested method can produce improved identification of pathological and normal cells in the human brain despite a little separation in the intensity of the grey level.
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Doi: https://doi.org/10.54216/JNFS.010106
Vol. 1
Issue. 1
PP. 55-58, (2021)
Abdullah Ali Salamai
The industrial sector is among the most suited sectors that may considerably advantage from the implementation of the ideas and technology of the Industrial Internet of Things (IIoT), and it is one of the most competitive industries in the world. The increased use of automated processes in manufacturing sectors results in a wide variety of applications based on IIoT. These applications call for the efficient integration of a wide variety of different systems and the execution of smooth operations across all machines. The issue of integration and smooth operation presents IIoT as a new subject of study in smart manufacturing. This carries with it several problems, including those on security, accountability, confidence, and dependability. As part of the Industrial Internet of Things (IIoT), many devices will be linked to one another and interact with one another through wireless and internet infrastructure. When this kind of situation plays out, the reliability of the IIoT devices becomes a key component in the process of preventing injection by hostile machines. As a result, an intelligent computer model is required to effectively cluster and categorize the level of trustworthiness possessed by the IIoT devices. In this article, we describe a trust model for the Internet of Things (IIoT) that is based on the neutrosophic TOPSIS and is utilized by IIoT apps to determine the trust score of IIoT devices. The reliability of devices is evaluated by the model that was constructed using the historical knowledge, chronological knowledge, and network behavior information that is received from IIoT devices. In addition to that, the model suggests KNN, and a Decision tree to categorize the attributes that were collected.
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Doi: https://doi.org/10.54216/JNFS.010107
Vol. 1
Issue. 1
PP. 59-68, (2021)
Ahmed Abdelmonem
,
Mahmoud M.Ismail
Choice of materials is difficult since it requires considering several factors, assigning relative importance to those criteria, and ultimately choosing the most relevant criterion. Finally, it is important to set the criteria in a way that takes into account both the known material attributes and the needs of the application. Therefore, MCDM techniques may be used to the process of selecting materials. A possibility of incomplete dilemmas arises due to the decision maker's language inputs. Therefore, the inputs might be supplied as fuzzy numbers in order to circumvent the issue. Considering that a neutrosophic set is a metaphor to overcome uncertainty of human perceptions. To assess this recruitment process in a neutrosophic setting, this paper employs a neutrosophic-based version of the MCDM tool TOPSIS to determine which alternative materials should be used for the center console of an electric car.
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Doi: https://doi.org/10.54216/JNFS.010108
Vol. 1
Issue. 1
PP. 69-79, (2021)
