Smart Sensor Networks for Industrial IoT Applications

Nihal N.; Esmeralda

doi:https://doi.org/10.54216/JISIoT.080204

Neutrosophic and Information Fusion | 2023

Ranking Renewable Energy Alternatives by using Triangular Neutrosophic Sets Integrated with MCDM

In this age of ecological sustainability, energy planning has grown more complicated as a result of the inclusion of numerous standards, including technological, political, financial, and environmental considerations. As a result, this places significant limitations on the ability of policymakers to independently and covertly optimize energy sources, which is particularly problematic for rural populations. In contrast, the constraints imposed by the topography of the land on renewable energy (REEN) systems, which are for the most part dispersed across the natural environment, make energy planning more difficult. In these kinds of situations, decision analysis plays a crucial part in the process of creating these kinds of systems by taking into account a wide range of requirements and goals, even at fragmented levels of digitization. Many criterion decision making, often known as MCDM, is a subfield of operational research that focuses on finding optimum outcomes in complicated situations that include various measures, competing goals, and multiple criteria. Because it enables decision-makers to make choices while simultaneously taking into account all of the standards and goals, this tool is gaining traction in the area of energy planning, which is one of the reasons why it is becoming more famous. In this paper, the TOPSIS MCDM methodology is integrated with the triangular neutrosophic sets to rank and select best source of REEN in Egypt. The neutrosophic sets used due to incomplete and uncertainty in this ranking.

groups

Ahmed M. Ali mail

link https://doi.org/10.54216/NIF.010102

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2023

Sustainable Supplier Selection using Neutrosophic Sets and MCDM Framework

Because of stricter rules from the government and growing awareness among the general public, sustainable supply chain management (SSCM) performs a significant role in the management of firm manufacturing operations. Companies that want to promote sustainable supply chain management (SSCM) must first choose the most suitable sustainable supplier, which is a MCDM dilemma, as highlighted in a number of research studies. In addition, because of their limited expertise, those who make decisions have a propensity to convey their opinions via the use of language phrases. The purpose of this work is to report on a unique MCDM model for the choice of sustainable suppliers. This approach integrates the MCDM MABAC method inside an uncertain language situation. With the assistance of uncertain linguistic sets, the neutrosophic sets used to overcome these uncertainty. When it comes to generating the ranking of possible suppliers, the MABAC is dependable and easy to understand. In conclusion, an iron maker is used as an example to illustrate the practicability and efficacy of the suggested strategy for the selection of sustainable suppliers.

groups

Abduallah Gamal mail -

Nehal Nabil Mostafa mail

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

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2023

An Interval Valued Neutrosophic Sets Integrated with the AHP MCDM Methodology to Assess the Station of 5G Network

In latest days, 5G technology has undergone fast development and has since found widespread use in a variety of industries including medicine, travel, agriculture, and others. The 5G network's fundamental equipment, known as 5G ground stations, are responsible for achieving wireless signal transfer among wired communications systems and wireless endpoints. Additionally, 5G stations give communication range. Nevertheless, as the size of 5G ground stations continues to progressively develop, difficulties such as inadequate coverage area and subpar user experiences commonly arise. As a result, it is essential to conduct an all-encompassing performance evaluation of 5G ground stations in order to better understand the challenges that now exist in the development of ground stations. To begin, the components of the performance assessment index system, which include operating efficiency, economic condition, ecological effects, and social pressure, are assembled from their respective vantage points. In the next step, a unique hybrid multi-criteria decision-making (MCDM) approach that is built on the AHP methodology is used. In conclusion, ten 5G base stations are selected as samples for further investigation. The AHP is integrated with the Interval Valued Neutrosophic Sets (IVNSs). The IVNSs used to overcome incomplete and vague information.  The AHP method used to compute the weights of criteria.

groups

Ahmed Sleem mail -

Ibrahim Elhenawy mail

link https://doi.org/10.54216/NIF.010104

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2023

Intelligent Traffic Management using IoT and Machine Learning

The continuous improvements in the Internet of Things (IoTs) and machine learning (ML) make them the key enabling technologies for intelligent traffic management (ITM).The ability to accurately predict network traffic has been demonstrated as crucial for effective network management and strategic planning. Proactive management of future congestion incidents requires access to reliable long-term forecasting models. Conventional prediction methods often fail to completely capture the spatiotemporal features of the traffic flows because of the complexity of the interdependence between the flows. To this end, we proposed to improve the management of traffic with a novel framework for the predictive modeling of traffic flows. The proposed formwork introduces an improved graph network to capture the positional information in traffic follows. It is also capable of precisely capturing temporal dynamics using an improved bidirectional learning module. An attention mechanism is presented to capture the interactions among spatial and temporal patterns to further empower the predictive power of the model. Proof-of-concept experimentations are conducted on the PeMSD7 dataset, and the results (MAE: 0.197, MSE: 0.13, RMSE: 0.36, ) demonstrate the efficiency of our model over the state-of-the-art.

groups

Reem Atassi mail -

Aditi Sharma mail

link https://doi.org/10.54216/JISIoT.080201

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2021

Intelligent Video Moving Target Detection Based on Multi-Attribute Single Value Medium Neutrosophic Method

Competition in social sports has many benefits for athlete training due to this competition gives researchers a chance to making and developing new methods and ways that support them. The competition in sport growth rapidly these days. During the last several years, there has been a significant increase in the volume of traffic using multimedia. In addition, some of the most recent paradigm shifts suggested, such as IoT, bring about the introduction of new kinds of traffic and applications. Software-defined networks, often known as SDNs, are beneficial to network management since they enhance its capabilities. When used with SDN, artificial intelligence (AI) has the potential to solve network issues using categorization and estimate strategies. So, in this paper discuss and develop a new method for sports video moving target detection. This method is based on multi-criteria decision making (MCDM) because targeting detection has many criteria and sub-criteria. This paper collected five main criteria and twenty sub-criteria impacts in target detection of sports video. We use the Analytical hierarchy Process (AHP) to determine the importance of these criteria and their weights. These criteria were evaluated under a neutrosophic environment. An application is provided to measure the outcome of the proposed method.

groups

Gopal Chaudhary mail -

Manju Khari mail -

Amena Mahmoud mail

link https://doi.org/10.54216/JISIoT.050105

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2023

Neutrosophic Sets and Metaheuristic Optimization: A Survey

Smarandache presents neutrosophic sets and provides a domain area that is made up of three separate subsets to reflect the various kinds of uncertainty. Neutrosophic sets are defined as the sets where every other element of the universe possesses a degree of truthiness, indeterminacy, and falsity, which range from 0 to 1, and where these degrees are subsets of the neutrosophic sets that are independent of each other. Neutrosophic sets are also known as neutrosophical subsets. In the neutrosophic sets, impreciseness is represented as truth and falsity functions, but the indeterminacy function represents degrees of belongingness and non-belongingness and differentiates between absoluteness and relativeness. Neutrosophic sets can deal with the unpredictability of the system and cut down on the paralysis brought on by conflicting information thanks to this notation. As a result, one might argue that this capacity is the single most significant benefit offered by neutrosophic sets in comparison to the many other forms of fuzzy extensions. By making use of these three functions, neutrosophic sets are able to create a domain area. This area makes it possible for various kinds of mathematical operations to be carried out separately despite the presence of uncertainty. Due to the fact that the behavior of these methodologies is inspired by Nature and its capacity for adapting to issues, in addition to the potential for combining more than one method to reach the best alternatives, metaheuristic algorithms are employed to initiate the finest or the best possible alternatives to a lot of optimization techniques. This is possible because metaheuristic algorithms have the ability to adapt to problems. The fact that numerous academics have utilized these techniques with neutrosophic science to offer several systems in recent years was the impetus for writing this overview study in the first place, which was based on the above rationale.

groups

Ahmed Abdelhafeez mail -

Ahmed E Fakhry mail -

Nariman A. Khalil mail

link https://doi.org/10.54216/NIF.010105

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms

The COVID-19 epidemic has in fact placed the whole community in a dire predicament that has led to numerous tragedies, including an economic downturn, political unrest, and job losses. Forecasting and identifying COVID-19 infection cases is crucial for the government at all levels because the pandemic grows exponentially and results in fatalities. Hence, by giving information about the spread of the epidemic, the government can move quickly at multiple levels to establish new policies and modalities in order to minimize the trajectory of the COVID-19 pandemic's effects on both public health and the economic sectors. Forecasting models for COVID-19 infection cases in the Ural region in Russia were developed using two deep Long Short-Term Memory (LSTM) learning-based approaches namely Encoder–Decoder LSTM and Attention LSTM algorithms. The models were evaluated based on five standard performance evaluation metrics which include Mean Square Error (MSE), Mean Absolute Error (MAE), Root MSE (RMSE), Relative RMSE (RRMSE), and coefficient of determination (R2). However, the Encoder–Decoder LSTM deep learning-based forecasting model achieved the best performance results (MSE=32794.09, MAE=168.56, RMSE=181.09, RRMSE=13.46, and R2=0.87) compared to the model developed with Attention LSTM models.

groups

Khder Alakkari mail -

Alhumaima Ali Subhi mail -

Hussein Alkattan mail -

Ammar Kadi mail -

Artem Malinin mail -

Irina Potoroko mail -

Mostafa Abotaleb mail -

El-Sayed M El-kenawy mail

link https://doi.org/10.54216/JISIoT.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Smart Security Area (SSA) for Radar system technology

Our ability to align with the trend of innovations in science and technology will not only emancipate ignorance but also unfold our ability to evaluate, understand and predict possibilities in our society, environment, and the world at large. Radar system technology gives us the privilege to achieve the above-mentioned fact. The word Radar is an acronym for Radio Detection and Ranging. It is a mean of getting information about a distant target, by sending electromagnetic waves to them and analyzing the echoes from the target to generate relevant reports about the target. In this paper, we will focus on some metrics and the effect of changes in them on the performance of the radar system using the MATLAB Radar Designer.

groups

Shimaa A. Hussein mail -

Eslam Hesham mail

link https://doi.org/10.54216/JISIoT.080203

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

Approximate Solution of Boundary Value Problem for Heat Equation after Represented by Volterra Integral Equation of the First Kind

In this work, we study the regularization method for solving the Boundary Value Problem (BVP) for heat equation. The discretization method applied with two variables on Volterra integral equation in order to covert the problem into a linear operator equation after applied the separation of variables method to solve the partial differential equation. The regularization way used to obtain the estimate solution by using the Lavrentiev regularization method.

groups

H.K. Al-Mahdawi mail -

Mostafa Abotaleb mail -

Hussein Alkattan mail -

El-Sayed M El-kenawy mail

link https://doi.org/10.54216/GJMSA.030205

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Smart Sensor Networks for Industrial IoT Applications

Smart Sensor Networks (SSNs) are an indispensable part of the Industrial Internet of Things (IIoT), which seeks to improve efficiency, productivity, and safety in different industrial applications. SSNs consist of a large number of sensors, regularly deployed in a wireless ad-hoc network, which communicates with each other and with other devices, such as gateways and servers. Nevertheless, the building of SSNs in IIoT environments encounters many challenges, such as trust management, data reliability, privacy, and security. These challenges necessitate proposing novel solutions and protocols, to provide a reliable, secure, and efficient SSN. To this end, this study presents a novel DL system that can effectively discriminate between normal traffics and malicious traffic in SSNs. A convolutional feature extractor is developed to learn important discriminative features necessary for the early detection of security threats in SSNs. Then, an improved LSTM (ILSTM) is presented to model the temporal dynamics of the SSNs flows, which helps model long interdependency between traffic samples. A focal loss function is applied to deal with the imbalance between class samples. Experimental analysis is performed on an open-source SSN security dataset, named WSN-DS, the findings demonstrated the competitive advantages of our system over the prevailing solutions.

groups

Nihal N. Mostafa mail -

Esmeralda Kazia mail

link https://doi.org/10.54216/JISIoT.080204

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new