Neutrosophic set with Adaptive Neuro-Fuzzy Inference System for Liver Tumor Segmentation and Classification Model

Mohammed I.

doi:https://doi.org/10.54216/IJNS.180202

Journal of Neutrosophic and Fuzzy Systems | 2022

Instance Segmentation and Labeling of Teeth from Dental X-Ray using Region Based Convolutional Neural Network

Radiological Examination of teeth is a primary step that a dentist usually takes to diagnose the problem before further treatment. The diagnosis involves searching for diseases ranging from cavities to tumors, So, correct diagnosis is vital for timely and precise treatment. This paper attempts to solve one of the elementary steps in diagnosis i,e, Labeling of Teeth, using Region-Based Convolutional Neural Networks that help reduce monotonous work for a dentist and provide segments of each tooth for further diagnosis of diseases with the use of Mask R-CNN. We used 200 panoramic X-Ray images of 4 categories to train, test and validate the model. Mask R-CNN with pre-trained weights of COCO Dataset is employed. We further tuned the weights of the dental X-ray dataset considered in the paper for better performance. On testing the learned model, the performance measures were encouraging.

groups

Sireesha Rodda mail -

Vaibhav Kovela mail -

Sanjay Dokula mail

link https://doi.org/10.54216/JNFS.020202

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2022

An Integrated Neutrosophic AHP and TOPSIS Methods for Assessment Renewable Energy Barriers for Sustainable Development

Technologies of renewable energy (RE) play a vital role in increasing economic growth in many countries and present a solution for many social, ecological, and political problems. Though, RE faces many barriers that prevent its development. So, these barriers are ranked and identified in this work, including five main barriers and fifteen sub barriers. In addition, five strategies are identified and ranked. The first step in this work, the Analytical Hierarchy Process (AHP) approach used to rank main and sub barriers under Single Valued Neutrosophic Sets (SVNSs). Then Neutrosophic Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach assessed the five strategies. The outcomes of this work show that Commercial barriers ranked as the highest barriers and social-ecological ranked as the lowest barriers by using the AHP approach. Outcomes by neutrosophic TOPSIS show that capital assistant ranked as the highest strategies and RE goals ranked as the lowest strategies. This work can help decision-makers, governments for building a RE by using these strategies to overcome barriers that faced them.

groups

Reem Atassi mail -

Kun Yang mail

link https://doi.org/10.54216/IJNS.180201

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2022

An Introduction To The Symbolic Turiyam R-Modules and Turiyam Modulo Integers

Recently, Turiyam set is introduced for dealing the fourth dimensional data sets. These types of data sets exists when an expert unable to categorize them in Euclidean, Non-Euclidean, Hybrid or NeutroGeometry. To deal with these types of data set Turiyam matrix and its algebra is required. Hence the current paper introduce the concept of Symbolic Turiyam R-module as a generalization of the corresponding neutrosophic one by using the algebra of symbolic Turiyam set. The paper also presents concept pf finite Turiyam modulo integer and illustrate many examples to show and clarify the validity of this work.

groups

Mikail Bal mail -

Prem Kumar Singh mail -

Katy D. Ahmad mail

link https://doi.org/10.54216/JNFS.020201

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science |

The Fuzziness, Similarity And The Symmetry Properties On The Neutrosophic Interval Probability

Abstract The neutrosophic interval statistical number (NISN) has been known to be very useful in expressing the interval values under indeterminate environments. One of the essential and so important useful as tools for measuring the degree of similarity between sets of given objects is the similarity measure . In this paper, neutrosophic numbers as well as the generalized Dice similarity measure for neutrosophic numbers for two sets are defined after which the axioms of fuzziness similarity and symmetry satisfying the NISN the properties were proved.

groups

link

Volume & Issue

Details open_in_new

International Journal of Neutrosophic Science |

The Fuzziness, Similarity And The Symmetry Properties On The Neutrosophic Interval Probability

Abstract The neutrosophic interval statistical number (NISN) has been known to be very useful in expressing the interval values under indeterminate environments. One of the essential and so important useful as tools for measuring the degree of similarity between sets of given objects is the similarity measure . In this paper, neutrosophic numbers as well as the generalized Dice similarity measure for neutrosophic numbers for two sets are defined after which the axioms of fuzziness similarity and symmetry satisfying the NISN the properties were proved.

groups

link

Volume & Issue

Details open_in_new

Journal of Cybersecurity and Information Management | 2022

A Framework for creating a Safety and Security Management System (SSMS)

Safety and security risks to critical infrastructure organizations are well known, and incidents in both fields have taken place. To help critical infrastructure organizations manage these areas, safety and security standards have been created.  The main aim of this paper is to present a framework that has been created to manage both safety and security by providing guidance on how to create a Safety and Security Management System (SSMS).   The framework identifies and remediates conflicts and issues between IT, OT, safety, and security. While also creating processes that can combine safety and security compliance to standards to reduce duplication of work and allow one process to manage both areas. A survey was carried out to understand if the framework would be of use to organizations and to better understand the issues users have with managing safety and security and how they manage conflicts that can occur.  The survey showed key areas of concern for organizations and how the framework can be of use to them.  It identified six themes from the research and identified improvements opportunities for the framework that can be implemented. 

groups

Robert Kemp mail -

Richard Smith mail

link https://doi.org/10.54216/JCIM.090201

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2022

Trust Aware Aquila Optimizer based Secure Data Transmission for Information Management in Wireless Sensor Networks

The province of wireless sensor network (WSN) is increasing continuously because of wide-ranging applications, namely, monitoring environmental conditions, military, and many other fields. But trust management in the WSN is the main objective as trust was utilized once cooperation among nodes becomes crucial to attaining reliable transmission. Thus, a new trust-based routing protocol is introduced to initiate secure routing. This study focuses on the design of Trust Aware Aquila Optimizer based Secure Data Transmission for Information Management (TAAO-SDTIM) in WSN. The presented TAAO-SDTIM model mainly intends to achieve maximum security and information management in WSN. The presented TAAO-SDTIM model determines optimum set of routes to base station (BS) utilizing a fitness function involving three parameters like residual energy (RE), distance to BS (DBS), and trust level (TL). The incorporation of the trust level of the nodes in the route selection process aids in appropriately selecting highly secure nodes in the data transmission procedure. For ensuring the enhanced performance of the TAAO-SDTIM model, a wide range of experiments are executed and the results pointed out the improved outcomes of the TAAO-SDTIM model over the other recent approaches. 

groups

Abedallah Zaid Abualkishik mail -

Ali A. Alwan mail

link https://doi.org/10.54216/JCIM.090104

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2022

Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification Model in IoT Enabled UAV Networks

Unmanned aerial vehicles (UAVs) can be placed effectively in offering high-quality services for Internet of Things (IoT) networks. It finds use in several applications such as smart city, smart healthcare, surveillance, environment monitoring, disaster management, etc. Classification of images captured by UAV networks, i.e., aerial image classification is a challenging task and can be solved by the design of artificial intelligence (AI) techniques. Therefore, this article presents an Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification (EWODTL-AIC) model in IoT enabled UAV networks. The major intention of the EWODTL-AIC technique is to effectually categorize different classes of aerial images captured by UAVs. The EWODTL-AIC technique initially employs AlexNet model as feature extractor for producing optimal feature vectors. Followed by, the hyperparameter values of the AlexNet model are decided by the utilization of earthworm optimization (EWO) algorithm. At last, the extreme gradient boosting (XGBoost) model is employed for the classification of aerial images. The experimental validation of the EWODTL-AIC model is performed using benchmark dataset. The extensive comparative analysis reported the better outcomes of the EWODTL-AIC technique over the other existing techniques. 

groups

Dr.R.PANDI SELVAM mail

link https://doi.org/10.54216/FPA.070104

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2022

Support System Based Computer-Aided Detection for Skin Cancer: A Review

According to the American Society of Clinical Oncology, Computer-Aided Diagnosis (CAD) techniques have the tremendous possibility for the screening and early identification of melanoma. They are evaluated in terms of their current state-of-the-art, as well as current practices, challenges, and prospects in the areas of image screening, pre-processing of an image, segmentation of Region of Interest (ROI), feature extraction, feature selection, and classification of dermoscopic images. It is stated in this study that statistical information and outcomes from the most major implementations that have been reported to date are presented. We investigated the evaluation performance of many classifiers that had been developed specifically for the diagnosis of skin cancer. The fundamental aim of this paper is to develop a framework that will serve as a complete guideline for choosing relevant techniques for various elements of an automatic detection technique.

groups

Nechirvan Asaad Zebari mail -

Mehmet Emin Tenekeci mail

link https://doi.org/10.54216/FPA.070103

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2022

Neutrosophic set with Adaptive Neuro-Fuzzy Inference System for Liver Tumor Segmentation and Classification Model

Lung cancer is the abnormal development of cells in the lung causes serious risk to the health since lung has an interconnected system of blood vessel and lymphatic channel exposed to metastasis. The survival rate of lung cancer depends greatly on the earlier diagnosis and staging of the lung cancer. Computed Tomography (CT) image is commonly employed for lung cancer diagnosis since they offer data regarding distinct portions of the lung. The exactness of finding tumor location, volume and shape acting a major role in positive treatment and diagnosis of tumor. This article designs a novel neutrosophic set with adaptive neuro-fuzzy inference system for liver tumor segmentation and classification (NSANFIS-LTSC) model. The presented NSANFIS-LTSC model aims to identify and classify the presence of liver tumor from medical images. The presented NSANFIS-LTSC model primarily undergoes pre-processing to eradicate the noise. Followed by, the neutrosophic set (NS) based segmentation is applied to identify the affected tumor regions in the CT images. Besides, DenseNet-169 model is utilized to create feature vectors and dragonfly algorithm (DFA) is applied to tune the hyper parameters of the DenseNet-169 model. Finally, ANFIS classifier is exploited for the occurrence and classification of liver tumor. The simulation analysis of the NSANFIS-LTSC model is experimented using benchmark dataset and the results are investigated under several aspects. The simulation outcome reported the betterment of the NSANFIS-LTSC model over the recent methodologies. 

groups

Mohammed I. Alghamdi mail

link https://doi.org/10.54216/IJNS.180202

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new