An effective model for Selection of the best IoT platform: A critical review of challenges and solutions

Mahmoud A.; Nabil M.

doi:https://doi.org/10.54216/JISIoT.070204

International Journal of Neutrosophic Science | 2023

A Review on Metaheuristic Algorithms with Neutrosophic Sets for Image Enhancement

Breast cancer has emerged as a major killer in recent years. With a yearly rate of about one million new cases, it is the most prevalent among women in the world's poorest countries. Grading of cellular images has emerged as a key prognostic factor during the past decade. Neutrosophic sets used to enhance medical images in the last decade. Neutrosophic sets can overcome the uncertainty and indeterminacy of information. In recent years, metaheuristics have integrated with neutrosophic sets. Because of their adaptability, simplicity, and task independence, metaheuristics have been extensively employed to tackle many difficult non-linear optimization problems. The purpose of this research is to investigate several approaches to image classification for breast cancer pictures. This includes the use of metaheuristics and neutrosophic sets for optimization and image enhancement. This research was undertaken to better understand the current state of the art in breast cancer identification from medical pictures and to provide insight into the difficulties that lie ahead. We hope that this will encourage academics to investigate hitherto understudied facets of breast cancer identification.

groups

M. A. El-Shorbagy mail -

Hossam A. Nabwey mail -

Mustafa Inc mail -

Mostafa M. A. Khater mail

link https://doi.org/10.54216/IJNS.200113

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

Metaheuristics and Neutrosophic Sets for COVID-19 Detection: A review study

The fast spread of COVID-19 has been a problem for several nations since February 2020. Computer-aided diagnostic technologies that are both effective and affordable are urgently needed to help ease the burden on healthcare systems. Researchers are delving further into the feasibility of using image analysis to detect COVID-19 in X-ray and CT-scan pictures of patients. In the past ten years, deep learning has surpassed every other method for classifying images. However, deep learning-based approaches' effectiveness is very sensitive to the design of the underlying deep neural network. In recent years, metaheuristics and neutrosophic sets have become more popular as a means of fine-tuning the structure of deep networks. Because of their adaptability, simplicity, and task dependence, metaheuristics have been extensively employed to tackle many difficult non-linear optimization problems. To correctly identify COVID-19 patients from their chest X-rays, the authors of this research made a review of a neurotrophic model and metaheuristics methods.

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M. A. El-Shorbagy mail -

Hossam A. Nabwey mail -

Mustafa Inc mail -

Mostafa M. A. Khater mail

link https://doi.org/10.54216/IJNS.200114

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

Impact analysis of Macroeconomic Variables on Stock Market using Neutrosophic Interval Valued Dependent Matrix Model

Macroeconomic factors in general are crucial for developing a country's economy. This analysis takes into account the chosen macroeconomic factors for the years 2015 to 2019 including inflation, interest rate, GDP, and GDP per capita. The present study considered the new method of Neutrosophic environment in terms of the Fuzzy CETD matrix to determine the impact of the stock market for a particular year. This article describes a technique for examining how macroeconomic factors affect the stock market in a specific year. The proposed method reveals that the impact of the stock market is higher in 2015 than in 2016.

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G. Kavitha mail -

S. Bhuvaneswari mail

link https://doi.org/10.54216/IJNS.200115

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2023

Aspects of Language Monadic Predicate Logic System plus Identity (LMPL+I)

In this paper, we devoted to study the language monadic predicate logic system plus identity (LMPLS+I) as extension of the language of propositional logic system (LPLS). I.e., ( (LMPLS+I), which it contains all the hereditary traits (or features) of   , furthermore that, we will add some new data information between relationship of object, subject and predicate. This is the task of monadic predicate logic system addressed.  As mentioned in pervious papers, the main task of system of logic is classifying between valid and invalid arguments, moreover, the central role the system of logic how distinguishes between the conclusions which follow from their premises of the arguments and   those do not follow from their premises. As a matter of fact, when we encounter some proofs that seem perceptually (or intuitively) sound, but we are -unable to prove their validity due to the inability language of propositional logic system (LPLS). Hence, it was necessary to uses the monadic predicate logic system (LMPLS+I) to overcome this problem. In this article, we study syntax, semantics and inference on language monadic predicate logic system plus identity (LMPLS+I) and investigation characteristics of arguments such valid \ invalid and types of formulas and relations between formulas like consistency and inconsistency sets.

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Adel Mohammed Al-Odhari mail

link https://doi.org/10.54216/PAMDA.010103

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

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International Journal of Wireless and Ad Hoc Communication | 2022

Semi-supervised Transformer Network for Anomaly Detection in Cellular Internet of Things

Because of the lightning-fast expansion of the Internet of Things (IoT) technologies, an enormous amount of data has been produced. This traffic can be mined for information that can be used to identify and avoid intrusions into IoT networks. Despite the significant efforts that have been put into labeling Internet of Things traffic records, the total number of labeled records is still quite low, which makes it more difficult to detect intrusions. This study introduces a semi-supervised deep learning approach for intrusion detection (S2T-Net), in which we propose a temporal transformer module to empower the model to learn valuable interactions in cellular data. An improved spatial transformer is presented to capture local representation in the cellular traffic flow. At the same time, a multilevel semi-supervised training technique is used to account for the consecutive structure of the IoT traffic information. In order to provide effective real-time threat intelligence, the suggested S2T-Net can be tightly coupled into a cellular IoT network. Last but not least, empirical assessments on two current databases (CIC-IDS2017 and CIC-IDS2018) show that S2T-Net boosts intrusion detection accuracy and resilience while retaining resource-efficient computing.

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Waleed Abd Elkhalik mail -

Ibrahim Elhenawy mail

link https://doi.org/10.54216/IJWAC.040106

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2022

Intelligent and Secure Detection of Cyber-attacks in Industrial Internet of Things: A Federated Learning Framework

The increasing integration of traditional industrial systems with smart networking and communications technology (such as fifth-generation networks, software-defined networking, and digital twin), has drastically widened the security vulnerabilities of the industrial internet of things (IIoT). Nevertheless, owing to the lack of sufficient instances of high-quality attacks, it has been incredibly difficult to resist the cyberattacks that directed at such a substantial, complicated, and dynamic IIoT. This work introduces an intelligent federated deep learning framework, termed FED-SEC, for automatic and early identification of cyber-attacks against IIoT infrastructure. In particular, a new convolutional recurrent network designed to detect cyberattacks within IIoT data. Then, a secure federated learning scheme  presented to promote making use of mobile edge computing to enable the distributed IIoT entities to cooperate together to train a unified model for cyberattack detection in a privacy-preserved manner. More, a safe communication channel constructed via an improved Homomorphic Encryption scheme aiming to keep the model parameters secure against any leakage of inferential attacks, especially throughout the training procedure. Massive experimentations on multiple public datasets of IIoT cyberattacks proved the high-level efficacy of the FED-SEC in discovering different categories of cyber-attacks against IIoT and the superiorities over cutting-edge approaches.

groups

Ahmed Sleem mail

link https://doi.org/10.54216/JISIoT.070105

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2022

Collaborative Segmentation of COVID-19 From non-IID Topographies in the Internet of Medical Things (IoMT)

The Internet of Medical Things (IoMT) offers numerous advantages in the diagnosis, monitoring, and treatment of a wide variety of illnesses for both patients. COVID-19 has caused a global pandemic and turned out to be the utmost crucial danger threatening the whole world. Thus, scholars’ attention moved toward Deep learning (DL) and IoMT for developing automated systems for COVID-19 diagnosis and/or prognosis based on chest computed tomography (CT) scans, and it has shown great success in several tasks, including classification and segmentation. Nevertheless, developing and training a superior DL approach necessitates accumulating a substantial amount of patients’ CT scans together with their labels. This is an expensive and time-consuming task that restricts attaining large enough data from a single site/institution, However, owing to the necessity for protecting data privacy, it is difficult to accumulate the data from several sites and store them at a centralized server. Federated learning (FL) alleviates the need for centralized data by spreading the public segmentation model to different institutional models, training the segmentation model at the institution, and followingly calculating the mean of the parameters in the public model. Nevertheless, researchers advocated that private information could be restored using the parameters of the model. This study presents a privacy-protection technique for the challenge of multi-site COVID-19 segmentation. To tackle the challenge, we introduce the FL technique, in which a distributed optimization procedure is developed, and randomization techniques are proposed to change the joint parameters of private institutional segmentation models. Bearing in mind the complete heterogeneity of COVID-19 distributions from diverse institutions, we develop two domain adaptation (DA) techniques in the proposed FL design. We explore several applied characteristics of optimizing the FL approach and analyze the FL approach in comparison with alternate training approaches. Finally, the results validate that it is auspicious to employ multi-site non-shared CT scans to improve the COVID-19 infection segmentation.

groups

Ahmed Sleem mail -

Ibrahim Elhenawy mail

link https://doi.org/10.54216/JISIoT.070201

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2022

An advanced optimization technique for integrating IoT and cloud computing on manufacturing performance for supply chain management

The discipline of Supply Chain Management (SCM) is getting more difficult to master. It is necessary to address information silos on the demand and production frontiers of goods in order to execute the de-coupling factor in the preferences of customers who are engaged in a supply chain to optimize business performance, which in today's world has become a difficulty. The so-called "Amazon Effect" has, once again, compelled competitors to rethink their approaches to achieving maximum efficiency. The Analytic Hierarchy Process (AHP), which is part of the Multi-Criteria Decision Making (MCDM) Approaches, has been used to offer the preferences of clients of various criteria versus various features (products). AHP is used to compute the weights of criteria, then rank the various alternatives. The AHP method is used to build the pairwise comparison between criteria to check the importance of these criteria. The AHP method checks the consistency of the experts to ensure all data is consistent.

groups

Ahmed M. AbdelMouty mail

link https://doi.org/10.54216/JISIoT.070203

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

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International Journal of Advances in Applied Computational Intelligence | 2022

Interval Valued Neutrosophic VIKOR Method for Assessment Green Suppliers in Supply Chain

In order to remain competitive, businesses must now invest in developing environmentally responsible green suppliers. The purpose of this article is to determine which vendors should be incorporated into green supplier growth programs in order to enhance their ecological sustainability, as well as the suppliers' current green/environmental efficiency. Factor evaluation was used to examine the reliability of the parameters used to assess green suppliers' efficiency and overall quality. To determine which provider offers the greatest ecological performance, the suggested technique uses a hybrid interval-valued neutrosophic set (IVNS) and VIKOR structure to assign relative importance to each criterion. To manage ambiguity while choosing choices, we combine the neutrosophic method with the VIKOR technique. We used 10 criteria and ten vendors in this research to demonstrate the usefulness and effectiveness of the suggested framework. The suggested methodology is applied in the application.

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Shereen Zaki mail -

Mahmoud M. Ibrahim mail -

Mahmoud M. Ismail mail

link https://doi.org/10.54216/IJAACI.020102

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2022

An effective model for Selection of the best IoT platform: A critical review of challenges and solutions

The process of making an informed decision on which Internet of Things (IoT) platform to choose is an extremely important one in the modern world. The choice procedure is made more difficult as a result of (a) the vast number of IoT platforms that are offered on the market for IoT applications and (b) the wide diversity of functions and solutions that are provided by these platforms. In this article, the multi-criteria decision-making (MCDM) methodologies for selecting the specific Internet of Things platform are taken into consideration. The TOPSIS method is used in this paper to select the best IoT platform. TOPSIS method is a common MCDM method. TOPSIS method used the idea of the best and cost criteria to compute the distance from it. During the IoT platform choice procedures, relevant aspects, such as the stability, consistency, protection, and privacy of IoT platforms, are regarded to be the most significant ones for making decisions.

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Mahmoud A. Zaher mail -

Nabil M. Eldakhly mail

link https://doi.org/10.54216/JISIoT.070204

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new