Literature Review and Novel Trends of Mobile Edge Computing for 5G and Beyond

Germien G.; Alshimaa H.; Basma M.

doi:https://doi.org/10.54216/JAIM.020202

International Journal of Neutrosophic Science | 2023

NEP-2020's Implementation & Execution: A Study Conducted Using Neutrosophic PESTEL Analysis

It is evident how crucial education is to a person's overall development. The knowledge of the economy and society is still in its infancy. In terms of social and economic elements, education has emerged as the most significant factor for individual and national growth. Given this context, it would be worthwhile to examine the New Education Policy 2020 for the benefits and impacts it has on the various stakeholders. Such analysis is important to fulfill the needs and objectives of NEP-2020. Despite having many universities and schools, Indian education still needs some improvements.  Many Indian children still do not have access to education, and more importantly, the education system in India has not undergone significant reform in the last few decades, so changes must be made to keep up with the changing needs of society. The purpose of this study is to use the neutrosophic PESTEL analysis technique to mathematically identify and rank the major factors required to be identified for the successful implementation of NEP. Numerous factors that are grouped into six primary categories—political, economic, social, technological, legal, and environmental. These are presented by a thorough literature review of the subject. The present work employs neutrosophic PESTEL analysis, to identify the main obstacles to the implementation and execution of NEP-2020 in India. The study shows that social and economic factors, with 84% and 60% respectively play a significant role while political and technical factors are also important and come in second place since they each represent 25% and 34% of the barriers to the implementation of the NEP-2020. The last two factors are legal and environmental, contributing only 13% and 3%, respectively. The primary goal of the study is to identify and statistically rank the biggest obstacles to NEP-2020 implementation in India. In many aspects, this research will help government organizations and policymakers prioritize the main obstacles early in the implementation process as well as during execution, ensuring that the results are as anticipated and that the project is finished within the allotted time limit.

groups

Mohd Yasir mail -

Aasim Zafar mail -

M. Anas Wajid mail

link https://doi.org/10.54216/IJNS.200207

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2022

An Enhanced Hybrid Chaotic Technique for Protecting Medical Images

Medical data has attracted much interest; a quick, lossless, and secure cryptosystem is required for saving and transferring images over open networks while maintaining the image's details. This paper shows how to protect medical images with an encryption method based on hybrid chaotic maps. The proposed hybrid method is constructed to deal with problems like confusion and diffusion with a large key space. The technique uses a mix of different chaos maps for a specific set of control settings. There is a complete explanation of how encryption and decryption operations work. The security analysis results showed that the suggested cryptosystem is safe from statistical, brute force, and differential attacks. Compared to already known methods, the estimated times for encryption and decryption make it likely that the proposed scheme can be applied in real-time applications.

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Marwa M. Eid mail -

Shaimaa A. Hussien mail

link https://doi.org/10.54216/JCIM.100104

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

A new type of neutrosophic set in Pythagorean Fuzzy environment and Applications to multi-criteria decision making

In this paper, we introduce the concepts of Pythagorean fuzzy valued neutrosophic set (PFVNS) and Pythagorean fuzzy valued neutrosophic (PFVNV) constructed by considering Pythagorean fuzzy values (PFVs) instead of numbers for the degrees of the truth, the indeterminacy and the falsity, which is a new extension of intuitionistic fuzzy valued neutrosophic set (IFVNS). By means of PFVNSs, the degrees of the truth, the indeterminacy and the falsity can be given in Pythagorean fuzzy environment and more sensitive evaluations are made by a decision maker in decision making problems compared to IFVNSs. In other words, such sets enable a decision maker to evaluate the degrees of the truth, the indeterminacy and the falsity as PFVs to model the uncertainty in the evaluations. First of all, we propose the concepts of Pythagorean fuzzy t-norm and t-conorm and show that some Pythagorean fuzzy t-norms and t-conorms are expressed via ordinary continuous Archimedean tnorms and t-conorms. Then we define the concepts of PFVNS and PFVNV and provide a tool to construct a PFVNV from an ordinary neutrosophic fuzzy value. We also define some set theoretic operations between PFVNSs and some algebraic operations between PFVNVs via t-norms and t-conorms. With the help of these algebraic operations we propose some weighted aggregation operators. To measure discrimination information of PFVNVs, we define a simplified neutrosophic valued modified fuzzy cross-entropy measure. Moreover, we introduce a multi-criteria decision making method in Pythagorean fuzzy valued neutrosophic environment and practice the proposed theory to a real life multi-criteria decision making problem. Finally, we study the comparison analysis and the time complexity of the proposed method.

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Mahmut Can Boziygit mail -

Murat Olgun mail -

Florentin Smarandache mail -

Mehmet Unver mail

link https://doi.org/10.54216/IJNS.200208

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Intelligent Model for Customer Churn Prediction using Deep Learning Optimization Algorithms

Business intelligence (BI) mentions to the technical and procedural structure which gathers, supplies, and examines the data formed by company action. BI is a wide term that includes descriptive analytics, procedure analysis, data mining, and performance benchmarking. Customer churn is a general problem across businesses from several sectors. Companies are working always for improving their supposed quality by way of providing timely and quality service to its customer. Customer churn is developed most initial challenges which several firms were facing currently. Many churn prediction techniques and methods were presented before in literature for predicting customer churn from the domains like telecom, finance, banking, and so on. Researchers are also working on customer churn prediction (CCP) from e-commerce utilizing data mining and machine learning (ML) approaches. This manuscript focuses on the development of Stacked Deep Learning with Wind Driven Optimization based Business Intelligence for Customer Churn Prediction model. The proposed model is considered an intelligent system that applies golden sine algorithm (GSA) based feature selection approach to derive a set of features. In addition, the stacked gated recurrent unit (SGRU) model is applied for the prediction of customer churns.

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Abedallah Zaid Abualkishik mail -

Rasha Almajed mail -

William Thompson mail

link https://doi.org/10.54216/JISIoT.080104

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2022

Chaos Based Stego Color Image Encryption

Intensive studies have been done to get robust encryption algorithms. Due to the importance of image information, image encryption has become played a vital rule in information security. Many image encryption schemes have been proposed but most of them suffer from poor robustness against severe types of attacks. In this paper two proposed techniques will be presented for color image encryption to be robust to severe attacks: composite attack. One of these approaches is represented by hybrid use of both steganography and Discrete Wavelet Transform (DWT) based encryption and the other one in which Fractional Fast Fourier Transform (FRFFT) has been used with DWT. Not only new techniques will be presented but also a new chaotic map has been used as random keys for both algorithms. After extensive comparative study with some traditional techniques, it has been found that the proposed algorithms have achieved better performance.

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M. I. Fath Allah mail

link https://doi.org/10.54216/JCIM.100201

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2022

Coded DCO-OFDM Techniques in Intensity Modulation/Direct Detection (IM/DD) Systems

Optical wireless communications (OWC) are among the best alternative techniques for transmitting information-laden optical radiation across a free-space channel from one place to another. DC-biased optical OFDM (DCO-OFDM) is a technique that sacrifices the power efficiency to transmit unipolar OFDM signals. The primary drawback with DCO-OFDM is its clipping noise, which causes distortion and lowers the bit error rate (BER). Thus, in this paper, we show the performance of DCO with different coded techniques to improve the BER in additive white Gaussian noise (AWGN) for IM/DD systems. The experimental results show that the coded DCO-OFDM has the best performance. Furthermore, turbo coding has the best coding technique added to the DCO-OFDM system.

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Mohamed Abdelaziz mail -

Essam Abdellatef mail

link https://doi.org/10.54216/JCIM.100202

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2022

An Enhanced Deep Learning Technique to Measure the Impact of Cryptocurrency on the World Payment system using Random Forest

Cryptocurrency is a technology that uses an encrypted peer-to-peer network to facilitate digital barter. Bitcoin, the first and most popular cryptocurrency, is paving the way as a disruptive technology to long-standing and unchanging financial payment systems. While cryptocurrencies are unlikely to replace traditional fiat currency, they have the potential to alter how Internet-connected global markets interact with one another, removing the restrictions that exist around traditional national currencies and exchange rates. Technology advances at a breakneck pace, and a technology's success is almost entirely determined by the market it tries to improve. Cryptocurrencies have the potential to change digital trade marketplaces by enabling a fee-free trading mechanism. A SWOT analysis of Bitcoin is offered, which highlights some of the recent events and movements that may have an impact on whether Bitcoin contributes to a paradigm change in economics. Cryptocurrency is a relatively new payment option, and users are naturally drawn to it because it offers privacy. To measure the impact of cryptocurrency on the world payment system, we use a Cryptocurrency extra data – Bitcoin. The proposed algorithm uses Random Forest Algorithm for prediction. The RFPA has achieved a 0.073 MSE. The RFPA has achieved the best results as it can handle huge datasets with a lot of dimensionality. It improves the model's accuracy and eliminates the problem of overfitting. When compared to other algorithms, it takes less time to train.

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Fatma M. Talaat mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2022

Bank Marketing Data Classification Using Optimized Voting Ensemble, Sine Cosine, and Genetic Algorithms

Nowadays, the banking industry is no exception to the general trend of massive data production in all spheres of modern life. In this research, we analyze the categorization of marketing data from banks using a variety of machine learning techniques. The term "banking" refers to the supply of services by a bank to an individual consumer. The data was first compiled from the UCI Machine Learning repository and the Kaggle website. Phone-based banking marketing statistics are the focus of this data set. Python is utilized as the language of implementation, and the Machine Learning concept is employed for statistical learning and data analysis in this work. An improved prediction is the primary goal of machine learning's model-building phase. In order to classify the results, a supervised Naive Bayes algorithm is used to the data. The primary goal of the modeling effort is to characterize whether or not the consumer has chosen a term deposit. The bank should devote substantial time to returning phone calls from prospective customers. Accuracy, precision, recall, and F1 score were all evaluated as a consequence of this study in the direction of term deposit forecasting.

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Marwa M. Eid mail -

El-Sayed M. El-Kenawy mail -

Abdelhameed Ibrahim mail -

Abdelaziz A. Abdelhamid mail -

Mohamed Saber mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2022

Metaheuristic Optimized Voting Ensemble for Recognizing Daily and Sports Activities

This research analyzes the effectiveness of several methods for categorizing human actions captured by inertial and magnetic sensor units worn on the chest, arms, and legs. Each device has tri-axial sensors, including a gyroscope, accelerometer, and magnetometer. Voting ensemble classification models, where votes are weighted and optimized with a new optimization technique, are offered as a means to actualize this classification problem. The optimization technique is a combination of the sine cosine and particle swarm optimization algorithms, and the ensemble model is made up of three classifiers: support vector machines, decision trees, and multilayer perceptron. The classifiers are checked for accuracy using three distinct cross-validation strategies. Classifiers' proper differentiation rates and computational costs are compared to help you choose the best one for your needs. When it comes to body location, sensor devices worn on the legs provide the most valuable data. From a comparison of the various sensor modalities, we can deduce that magnetometers, followed by accelerometers and gyroscopes, provide the best classification results when only a single sensor type is employed. Furthermore, the study contrasts three machine learning models—support vector machines, decision trees, and multilayer perceptron —with respect to their usability, controllability, and classifier performance. Results reveal that the suggested method performs well in categorizing both typical daily activities and athletic endeavors.

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El-Sayed M. El-Kenawy mail -

Abdelhameed Ibrahim mail -

Abdelaziz A. Abdelhamid mail -

Mohamed Saber mail -

Marwa M. Eid mail

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

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2022

Literature Review and Novel Trends of Mobile Edge Computing for 5G and Beyond

Because of the rapid evolution of communications technologies, such as the Internet of Things (IoT) and fifth generation (5G) systems and beyond, the latest developments have seen a fundamental change in mobile computing. Mobile computing is moved from central mobile cloud computing to mobile edge computing (MEC). Therefore, MEC is considered an essential technology for 5G technology and beyond. The MEC technology permits user equipment (UEs) to execute numerous high-computational operations by creating computing capabilities at the edge networks and inside access networks. Consequently, in this paper, we extensively address the role of MEC in 5G networks and beyond. Accordingly, we first investigate the MEC architecture, the characteristics of edge computing, and the MEC challenges. Then, the paper discusses the MEC use cases and service scenarios. Further, computations offloading is explored. Lastly, we propose upcoming research difficulties in incorporating MEC with the 5G system and beyond.

groups

Germien G. Sedhom mail -

Alshimaa H. Ismail mail -

Basma M. Yousef mail

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

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new