Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption

Manal; Ahmed N.

doi:https://doi.org/10.54216/FPA.050101

Journal of Intelligent Systems and Internet of Things | 2021

Optimized Resource Allocation Algorithm for Crowd-Creation Space Computing Based on Cloud Computing Environment

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

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Mustafa El-Taie mail -

Aaras Y.Kraidi mail

link https://doi.org/10.54216/JISIoT.040101

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things |

A short study on SDN-Based IOT, Security challenges and its solutions

The Internet of Things has grown exponentially with many applications from industrial systems to smart homes. Heterogeneous networks with different needs that traditional networks are not able to meet. Software defined networks have come to help meet the needs and challenges of this network, and the main challenges in this area, security and reliability, are solved with the help of new ideas. Different methods, such as using blockchain, have all been proposed to detect, prevent, and eliminate IoT attacks. In this paper, we review some of these methods.

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Mohammad SaeedAnsari mail -

SomayehJafarali Jassbi mail

link

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Journal of Intelligent Systems and Internet of Things | 2021

Intelligent Fault Diagnosis of Gears Based on Deep Learning Feature Extraction and Particle Swarm Support Vector Machine State Recognition

Gear faults have always been a problem encountered in mechanical processing. For gear fault diagnosis, using mathematical-statistical feature extraction methods, deep learning neural networks (DLNN), particle swarm algorithm (PSA), and support vector machines (SVM), etc. According to the feature extraction of deep learning and particle swarm SVM state recognition, the intelligent diagnosis model is established, and the reliability of the model is verified by experiments. The model uses the combination of spectral features extracted by deep learning adaptively and the time domain features extracted by mathematical statistics methods to form a joint feature vector and then uses particle swarm SVM to diagnose the joint feature vector. After research, this paper draws a classification fitness curve combining the fault spectrum features extracted by DLNN and traditional time-domain statistical features. The classification result obtained by using this method is 95.3%. The reliability of the model is verified, and satisfactory diagnosis results are obtained. In addition, the application results also verify the effectiveness of adaptively extracting spectral features based on deep learning.

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Ahmed N. Al-Masri mail -

Hamam Mokayed mail

link https://doi.org/10.54216/JISIoT.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2021

Intelligent system for IoT botnet detection using SVM and PSO optimization

Botnet attacks involving Internet-of-Things (IoT) devices have skyrocketed in recent years due to the proliferation of internet IoT devices that can be readily infiltrated. The botnet is a common threat, exploiting the absence of basic IoT security technologies and can perform several DDoS attacks. Existing IoT botnet detection methods still have issues, such as relying on labeled data, not being validated with newer botnets, and using very complex machine learning algorithms, making the development of new methods to detect compromised IoT devices urgent to reduce the negative implications of these IoT botnets. Due to the vast amount of normal data accessible, anomaly detection algorithms seem to promise for identifying botnet attacks on the Internet of Things (IoT). For anomaly detection, the One-Class Support vector machine is a strong method (ONE-SVM). Many aspects influence the classification outcomes of the ONE-SVM technique, like that of the subset of features utilized for training the ONE-SVM model, hyperparameters of the kernel. An evolutionary IoT botnet detection algorithm is described in this paper. Particle Swarm Optimization technique (PSO) is used to tune the hyperparameters of the ONE-SVM to detect IoT botnet assaults launched from hacked IoT devices. A new version of a real benchmark dataset is used to evaluate the proposed method's performance using traditional anomaly detection evaluation measures. This technique exceeds all existing algorithms in terms of false positive, true positive and rates, and G-mean for all IoT device categories, according to testing results. It also achieves the shortest detection time despite lowering the number of picked features by a significant amount.   

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Mahmoud A. Salam mail

link https://doi.org/10.54216/JISIoT.030203

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2021

A Proposed AI-based Algorithm for Safety Detection and Reinforcement of Photovoltaic Steel

 In the era of fossil energy depletion and increasing environmental pollution, clean and renewable new energy represented by photovoltaic power generation has become an increasingly important part of multinational companies’ energy structure. With the advent of the era of photovoltaic parity, the use of photovoltaic tracking systems has become the best choice for many new large-capacity power stations. The cost of the support occupies a very large proportion in the investment of the entire power station construction. Therefore, the rationality of the design of the support, cost control and service life have become important ways for competition in the photovoltaic support industry. Based on the above background, the research content of this article is the application of artificial intelligence algorithms in the safety detection and reinforcement of photovoltaic steel supports. To be able to pass the monitoring data, this paper applies intelligent algorithms to perform faster and more accurate safety inspections on photovoltaic steel supports while minimizing labor costs, and to strengthen the photovoltaic steel supports, this paper chooses neural networks as the basic algorithm A structural model of a photovoltaic steel support was proposed. Finally, experimental simulations showed that the wavelet neural network reached 93.87%. Compared with traditional neural networks, wavelet neural networks perform better in fault prediction accuracy, but the speed needs to be improved. The method proposed in this paper has successfully completed the diagnosis of each component of the photovoltaic bracket in the safety inspection of the photovoltaic steel bracket, and meets the immediateness and accuracy required for the safety inspection of the photovoltaic bracket.

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Ali A. Alwan mail -

Abedallah Zaid Abualkishik mail

link https://doi.org/10.54216/JISIoT.040103

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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American Journal of Business and Operations Research | 2021

Taxation issues for sharing economic business models

In recent years, the sharing economy has risen rapidly, infiltrating into many fields such as travel, accommodation, medical care, and finance, and has injected new development momentum into these industries. In the shared economy, the sharing of resources not only promotes the flow of social wealth, but also enables idle items to continue to use value and improve resource utilization. The rapid development of the sharing economy brings many benefits, but at the same time, some traditional business models are dying. In China, there is no relevant policy to integrate the economic business model into the scope of tax collection and management. This phenomenon has caused China's tax source. Loss. In response to this problem, this paper studies the taxation of the shared economic business model. Using big data analysis technology as a basic tool, through the analysis of domestic shared economic data, the research on the shared economic business model was completed. Based on the analysis of the status quo of the taxation system of the shared economic business model, the suggestions for perfecting the taxation system of the shared economic business model in China are given, so that the tax collection and management system such as the tax collection concept and the collection and management system can be better adapted. Sharing new requirements for economic development.

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Vitalina Babenko mail -

Maryna Nehrey mail -

Berislava Staresinic mail -

Shoujin Wang mail

link https://doi.org/10.54216/AJBOR.040101

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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International Journal of Artificial Intelligence and Education Technology | 2022

Securing Management Information Systems Using Blockchain Technology

This study aims to present the basic principles of blockchain technology that has received the attention of various sectors, including the higher education sector, which studies the application of these technologies to improve information traceability, accountability, and integrity, while ensuring privacy, transparency, durability, trustworthiness, and authenticity. Various interesting proposals and projects launched and being developed, including verification of digital certificates. Through this study, we are building a digital certificate validation system that overcomes the limitations of paper-based digital certificates and non-blockchain-based digital certificates. It explains how to verify a certificate and gives a new idea to create a certificate in the most secure and tamper-resistant way using blockchain technology.

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S. A. Elsayed abou Elwafa mail -

S. Aboul Fotouh Saleh mail -

E. E. Mohamed Abd El-razk mail -

Safaa M. Elatawy mail

link https://doi.org/10.54216/IJAIET.010202

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2021

A New Chaos-based Approach for Robust Image Encryption

Chaotic encryptions offered various advantages over traditional encryption methods, like high security, speed, reasonable computational overheads. This paper introduces novel perturbation techniques for data encryption based on double chaotic systems. A new technique for image encryption utilizing mixed the proposed chaotic maps is presented. The proposed hybrid system parallels and combines two chaotic maps as part of a new chaotification method. It based on permutation, diffusion and system parameters, which are then involved in pixel shuffling and substitution operations, respectively. Many statistical test and security analysis indicate the validity of the results, e.g., the average values for NPCR and UACI are 99.67145% and 33.63288%, respectively. The proposed technique can achieve low residual intelligibility, high sensitivity and quality of recovered data, high security performance, and it show that the encrypted image has good resistance against attacks. 

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Ibrahim Yasser mail -

Abeer T. Khalil mail -

Mohamed A. Mohamed mail -

Fahmi Khalifa mail

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

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

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American Journal of Business and Operations Research | 2021

The Impact of Free Cash Flows to the Financial Flexibility of the Banks listed in the Colombo Stock Exchange

There are considerable arguments in favour of and against the positive relationship between free cash flows (FCF) and financial flexibility. The aim of the study is to determine the impact of free cash flows on the financial flexibility of the banks listed in the Colombo Stock Exchange (CSE). The free cash flow will measure according to the model in Journal of Finance: Agency costs and ownership structure in 2000 and financial flexibility will determine using the financial leverage based on the model captured according to the Accounting Horizons Journal: Financial flexibility and investment decisions in 2007 . The population of the study is the banks listed in the CSE. The sample consists of 60 observations covering 12 banks for a period of over 05 years from 2015 to 2019. The panel regression model has been used to test hypotheses. The results indicate that there is a positive significant relationship between free cash flows and the financial flexibility of the banks listed in CSE.

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Nadhira Mahath mail -

Maha Saad Metawea mail

link https://doi.org/10.54216/AJBOR.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2021

Multi-source Heterogeneous Ecological Big Data Adaptive Fusion Method Based on Symmetric Encryption

In recent years, with the rapid development of the domestic economy, the concept of sustainable development has been paid more and more attention. Ecological environment protection is more and more important, and the ecological environment is closely related to economic development. How to measure the relationship between the two is very important. Whether it is to build ecological environment protection or to ensure sustainable development of the economy, we should take the green development concept as a guiding concept, promote ecological economic development, and study the integration of ecological data is of great significance for solving these problems. The research of this thesis studies the multi-source heterogeneous (MSH) ecological big data (BD)adaptive fusion based (FM) based on symmetric encryption. This paper sets up a comparative experiment, multi-sensor (MS) data fusion based (DFM) based on Rough set theory, MSH data fusion based on data information conversion. The method is compared with the symmetric fusion MSH BD adaptive FM proposed in this paper. The results show that the MSH DFM based on Rough set theory has the highest confidence of 0.812; the MSH DFM based on data information conversion has the highest confidence of 0.68; based on symmetric encryption MSH BD The fusion confidence of the adaptive FM is up to 0.965, and the MSH ecological BD adaptive FM based on symmetric encryption is superior.

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Manal Nasir mail -

Ahmed N. Al-Masri mail

link https://doi.org/10.54216/FPA.050101

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new