In other instances, the cloud transfers payment information directly to the merchant’s server without first doing any fraud checks. Block authentication between the cloud and a healthcare merchant server is the goal ...
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In other instances, the cloud transfers payment information directly to the merchant’s server without first doing any fraud checks. Block authentication between the cloud and a healthcare merchant server is the goal of this study, which is designed to prevent the deployment of fraudulent servers. Smart contract verification on the blockchain will be used to this end. The cloud assault must be prevented from accessing personal payment and card information. It is via the employment of cryptography methods that this concept of privacy reservation is made possible. The Boolean crossover depth first search (DFS) technique is used to encrypt data in the payment gateway protocol at its inception. When the hospital administrator solicits card data from patients or users, our innovative approach swiftly initiates data encryption using the DFS technique. This method meticulously traverses the data structure in a depth-first manner, employing Boolean operations to systematically encrypt sensitive information. Through this process, the patient's payment and card data undergo secure transformation, rendering it unreadable to unauthorized entities. Additionally, an authentication key is generated concurrently with the encryption process, enabling verification between the system and the user before any data transmission occurs. The integration of the DFS technique serves as a fundamental layer of defense against potential cloud-based attacks. Its application ensures the utmost protection of personal payment and card details throughout the transaction process, bolstering the security infrastructure of our proposed system. A comprehensive analysis comparing our approach with existing methods showcases the efficiency and reliability of our proposed system in terms of Execution Time (ms), Encryption Time (ms), Decryption Time (ms), and Memory (bits). This study aims to bridge the gap in transactional security, ensuring robust protection against unauthorized access and data breaches in
Various scholars have investigated Fibonacci arrays to uncover its combinatorial features and applications. As an extension of Involutive Fibonacci words, Involutive Fibonacci arrays were introduced. We will look at s...
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Creating infrastructures, virtual servers, computing resources, along with devices is termed virtualisation. In this methodology, to augment resource usage along with to mitigate the total power consumption, mapping o...
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Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model...
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Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.
This study introduces a deep learning method for automatically detecting bone fractures with the YOLOv8 object detection model, developed to aid radiologists in accurately and efficiently interpreting X-ray images. Th...
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An intrusion detection system(IDS)becomes an important tool for ensuring security in the *** recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the ne...
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An intrusion detection system(IDS)becomes an important tool for ensuring security in the *** recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network *** resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique,called BBOFS-DRL for intrusion *** proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the *** attain this,the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature ***,DRL model is employed for the proper identification and classification of intrusions that exist in the ***,beetle antenna search(BAS)technique is applied to tune the DRL parameters for enhanced intrusion detection *** ensuring the superior intrusion detection outcomes of the BBOFS-DRL model,a wide-ranging experimental analysis is performed against benchmark *** simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.
Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acq...
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Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral *** in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and *** HSI restoration method for such a mixed noise model is ***,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise ***,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image ***,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is *** the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)*** the best of authors'knowledge,this joint optimisation framework is the first attempt in this *** handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is ***,an efficient split-Bregman approach is used to solve the resulting optimisation *** results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.
Latent Lip groove application is been a notable topic in forensic applications like crime and other investigations. The detection of lip movement is been a challenging task since it is a smaller integral part of the h...
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In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high c...
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In an infrastructure cloud environment, task scheduling should focus on optimizing execution time and saving energy. The data center consumes a large amount of energy during the execution of the task. Energy-saving te...
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