Securing 5G-IoT Device Connectivity and Coverage Using Boltzmann Machine Keys Generation

Rajawat, Anand Singh; Bedi, Pradeep; Goyal, S. B.; Shukla, Piyush Kumar; Jamal, Sajjad Shaukat; Alharbi, Adel R.; Aljaedi, Amer

doi:10.1155/2021/2330049

Cited by 21 publications

(15 citation statements)

References 41 publications

(38 reference statements)

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“…Cloud security is one of the hottest area of research, and a large number of approaches have been proposed in the past few decades [7][8][9][10][11]. Identity-based cryptosystem instigated by Shamir [2] where user's identities are used as public key and private key generator (PKG) generates private keys corresponding to their identities.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Secure Key Generation Using Enhanced Identity-Based Encryption for Cloud Computing in Large-Scale 5G

Gupta

Almuzaini

Pateriya

et al. 2022

Wireless Communications and Mobile Computing

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The demand of cloud computing and 5G networks has increased in the current scenario due to their attractive features and also the security related to the data over the cloud. In the context of cloud security, there is a number of computationally hard methods available. One of the most popular methods used to secure data over the cloud is the identity-based encryption (IBE). It is an access policy that allows only authorized users to access legible data in order to avoid a malicious attack. IBE comprises of four stages, namely, setup, key generation or extract, encryption, and decryption. Key generation is one of the important and time-consuming phases in which a security key is generated. It is a computational and decisional hard problem for generating unbreakable and nonderivable secure keys. This paper proposes an enhanced identity-based encryption approach where a secure key is generated using part of an identity bit string in order to avoid leakage of users’ identity even if an adversary or attacker decodes the key or encrypted data. Experiment results show that the prosed algorithm takes less time in the encryption and decryption as compared to the competitive approach named efficient selective-ID secure identity-based encryption approach. One of the most important features of the proposed approach is that it hides the user’s identity by using the Lagrange coefficient, which consists of a polynomial interpolation function. The security of the system depends on the hardness of computing the bilinear Diffie-Hellman problem.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved Secure Key Generation Using Enhanced Identity-Based Encryption for Cloud Computing in Large-Scale 5G

Gupta

Almuzaini

Pateriya

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

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“…In the previous decade, digital pathology has evolved from the practice of using microscopes equipped with cameras to the digital scanning of complete tissue samples, which is now the standard procedure. In recent years, factors like a significant increase in available processing power, lower-cost storage devices [ 11 ], and significant advancements in image analysis techniques have helped to mainstream computer-aided design (CAD) systems into the everyday routine of pathology labs. For illness detection, diagnosis, and prognosis [ 10 ], these technologies have been developed to complement the judgement of the human expert, that is, the pathologist.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques

Reshma

Arya

Ahmad

et al. 2022

BioMed Research International

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Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. For people all around the world, it has now become a huge issue and a threat to their safety and wellbeing. Breast cancer is one of the major causes of death among females all over the globe, and it is particularly prevalent in the United States. It is possible to diagnose breast cancer using a variety of imaging modalities including mammography, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound, and biopsies, among others. To analyze the picture, a histopathology study (biopsy) is often performed, which assists in the diagnosis of breast cancer. The goal of this study is to develop improved strategies for various CAD phases that will play a critical role in minimizing the variability gap between and among observers. It created an automatic segmentation approach that is then followed by self-driven post-processing activities to successfully identify the Fourier Transform based Segmentation in the CAD system to improve its performance. When compared to existing techniques, the proposed segmentation technique has several advantages: spatial information is incorporated, there is no need to set any initial parameters beforehand, it is independent of magnification, it automatically determines the inputs for morphological operations to enhance segmented images so that pathologists can analyze the image with greater clarity, and it is fast. Extensive tests were conducted to determine the most effective feature extraction techniques and to investigate how textural, morphological, and graph characteristics impact the accuracy of categorization classification. In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with a Convolutional Neural Network Classifier. The practical application of the suggested improved segmentation and classification algorithms for the CAD framework may reduce the number of incorrect diagnoses and increase the accuracy of classification. So, it may serve as a second opinion tool for pathologists and aid in the early detection of diseases.

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“…It is a technique for optimizing the performance of computer programmes. This approach is based on the number of fragments used in exploring the inner region of a multidimensional space, and it is very fast [ 35 ]. The primary goal of this iterative approach is to improve the performance of the PI controller's parameters (K p and K i).…”

Section: Proposed Methodologymentioning

confidence: 99%

Particle Swarm Optimization and Modular Multilevel Converter Communication in Electrical Applications with Machine Learning Algorithm

Kamal

Sayeed²,

Ahanger

et al. 2022

Computational Intelligence and Neuroscience

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As a result of their natural capacity to recover harmonic current and reactive power from alternating current sources, power electronic devices utilized in conjunction with nonlinear loads have the potential to generate significant harmonic problems within the power system when employed in this way. When this occurs, voltage instability occurs, which must be avoided in order to maintain the consistency and dependability of the power system’s power flow. With this approach, the series controller has been replaced by a multilevel modular controller in order to improve power handling capability and achieve higher modular levels with minimal distortions. The shunt compensator is the most effective way to achieve an extremely protected energy system as well as righteous steadiness in electric potential difference under a variety of load constraints. The DQ thesis is employed in this proposed converter to separate the harmonic components by establishing reference frame current, which is accomplished by machine learning techniques. As part of the constant mode operation, the PI controller contributes to maintaining the direct current-potential difference, which is given to the PWM generator. Optimization of the values of K p and K i is accomplished by the use of particle swarm optimization (PSO). The construction of this power system simulation model has been made feasible by the use of time-fluctuating characteristics modeling and the MATLAB programming environment. The new (unified power flow controller) UPFC research that has been made available is persuasive in its capacity to reduce distortions and watt-less power components while simultaneously enhancing efficiency and reducing costs.

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Securing 5G-IoT Device Connectivity and Coverage Using Boltzmann Machine Keys Generation

Cited by 21 publications

References 41 publications

An Improved Secure Key Generation Using Enhanced Identity-Based Encryption for Cloud Computing in Large-Scale 5G

An Improved Secure Key Generation Using Enhanced Identity-Based Encryption for Cloud Computing in Large-Scale 5G

[Retracted] Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques

Particle Swarm Optimization and Modular Multilevel Converter Communication in Electrical Applications with Machine Learning Algorithm

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