Improved Henon Chaotic Map-based Progressive Block-based visual cryptography strategy for securing sensitive data in a cloud EHR system

Mohanasundaram, Aneruth; Aruna, S.K.

doi:10.1016/j.ijin.2022.08.004

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…The lack of cohesiveness signals in the text forces the reader to make inferences based on reasoning and past knowledge or to recall earlier material in order to fill in the gaps in understanding [49]. The most comprehensive image of language may be obtained by combining data from words, phrases, and relations [50]. Therefore, the multiple features of how those terms are connected undoubtedly have a part in reading the multidimensional nature of text and its subtext, regardless of their precise attributes, closeness, or cooccurrence (latent or nonlatent).…”

Section: Textual Meaningsmentioning

confidence: 99%

Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

Akshaya¹,

Bizotto

Sathiyanarayanan

2023

JMC

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Latent Semantic Analysis (LSA) is an approach used for expressing and extracting textual meanings using statistical evaluations or modeling applied to vast corpora of text, and its development has been a major motivation for this study to understand the design thinking approach. We introduced LSA and gave some instances of how it might be used to further our knowledge of cognition and to develop practical technology. Since LSA's inception, other alternative statistical models for meaning detection and analysis in text corpora have been created, tested, and refined. This study demonstrates the value that statistical models of semantics provide to the study of cognitive science and the development of cognition. These models are particularly useful because they enable researchers to study a wide range of problems pertaining to knowledge, discourse perception, text cognition, and language using expansive representations of human intelligence.

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Section: Textual Meaningsmentioning

confidence: 99%

Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

Akshaya¹,

Bizotto

Sathiyanarayanan

2023

JMC

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“…Nevertheless, the efficiency of healthcare data transfer was not improved. In [8], a technique called the Improved Henon Chaotic Map-based Progressive Block-based approach was proposed to enhance encryption security by preventing the disclosure of confidential data.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Stochastic Gradient Deep Multilayer Neural Network based Linear Congruential Generative Cryptosystem for Secured Data Communication in Cloud

Krishnaveni R

2023

IJRITCC

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Cloud computing is a kind of distributed computing that use a vast network of interconnected resources accessible over the internet. Security is a crucial concern in cloud computing due to the fact that users save their data on the cloud for convenient access from any location and at any time. Consequently, many users are worried about safeguarding their sensitive data in an unsafe location. Therefore, cloud computing architecture requires an innovative cryptographic method that ensures the secrecy, authenticity, integrity, and non-repudiation of data transfer in the cloud. A new technique called SEMcrypt, which stands for Stochastic Gradient DEep Multilayer Neural Network based Linear Congruential Generative Cryptography, has been developed to enhance secure data transmission. SEMcrypt ensures higher data confidentiality and reduces the time required for communication between the cloud user (i.e., patient) and the server. The SEMcrypt approach has two distinct processes: categorization and secure data transport. Initially, the data is gathered from the patients and is used as input for the stochastic gradient regularised deep multilayer neural network. The deep neural network consists of one input layer, two hidden layers, and one output layer. At first, the information is gathered from the patients and sent to the input layer. Next, the patient data that has been gathered is examined in hidden layer 1 using the generalised Tikhonov regularisation function. The patient data that has been analysed is sent to hidden layer 2. The hyperbolic tangent activation function is used at that layer to classify the patient data. Subsequently, the categorised data undergoes encryption via the use of Linear Congruential Generative Goldwasser-Micali encryption, ensuring safe transfer of the data. Subsequently, the encrypted data is sent to the cloud server. The patient data is encrypted on the server side using the Linear Congruential Generative Goldwasser-Micali decryption technique to prevent unauthorised access or assaults. Consequently, the authorised recipient receives the unaltered information, which is then kept in the database for further analysis. Secured data transmission is achieved by ensuring better levels of data confidentiality and reducing the time required for the process. The experimental assessment focuses on criteria such as the time it takes to generate keys, the level of data confidentiality and integrity, the computing time, and the accuracy of categorization. The empirical findings demonstrate that our suggested SEMcrypt approach delivers efficient performance outcomes by attaining superior levels of data confidentiality and integrity within a minimal timeframe.

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“…The epidemic has brought a turning point that will speed up the digital revolution such as artificial intelligence, data science, and machine learning (Jackins et al,2021). (Mohanasundaram, A et. al.,2022) During the course of the epidemic, artificial intelligence and machine learning knowledge have been used to develop different algorithms that seek to identify early-stage people who are likely to be infected.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Prediction of Covid-19 in Potential Patients

Adigun,

Rufai,

Okikiola

et al. 2023

FJS

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The coronavirus pandemic overwhelmed many countries and a shortage of testing kits and centers for exposed patients worsens the situation in most countries. These have prompted the need to quickly predict COVID-19 in patients and stop the spread of the virus. In this research, we present a method for predicting COVID-19 based on symptoms, and to make this system efficient, the dataset was obtained from Afriglobal Laboratory Nigeria, and preprocessing and feature extraction were done on the dataset. Three classifiers, logistic regression, support vector machines, and hybridization of the logistic regression and support vector machines were used to train the data. The test data were evaluated against the model, and the research found that the performance analysis values for accuracy, precision, recall, and F1score for logistic regression (LR) are 91%, 91%, 95%, and 93%, for Support Vector Machines (SVM), 94%, 93%, 100%, and 96% and for the Hybridized model (LR+SVM) are 95%, 94%, 98%, and 96%. To get the parameters needed for the performance evaluation of the classifiers, the confusion matrix method was employed. In comparison to existing methods and studies, the hybridized system performs better than LR and SVM models. As a result, the hybridized model can accurately predict Covid-19.

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Improved Henon Chaotic Map-based Progressive Block-based visual cryptography strategy for securing sensitive data in a cloud EHR system

Cited by 6 publications

References 16 publications

Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

Stochastic Gradient Deep Multilayer Neural Network based Linear Congruential Generative Cryptosystem for Secured Data Communication in Cloud

Machine Learning Techniques for Prediction of Covid-19 in Potential Patients

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