The study of propositional logic—fundamental to the theory of computing—is a cornerstone of the undergraduate computerscience curriculum. Learning to solve logical proofs requires repeated guided practice, but unde...
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The task of structural identification the interval models of static objects is considered. It is shown that this task at each iteration is a task the forming and solving interval systems of nonlinear algebraic equatio...
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Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks....
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Biometric authentication scheme has been widely adopted for authentication purpose due to its uniqueness, universality, and distinctiveness. However, research has shown that these schemes are not necessarily more secu...
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Medical images often occupy large storage space and contain patient privacy or sensitive information, which makes them difficult and unsafe to be transmitted through the network. This paper proposed an adaptive compre...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Medical images often occupy large storage space and contain patient privacy or sensitive information, which makes them difficult and unsafe to be transmitted through the network. This paper proposed an adaptive compression-encryption scheme based on improved compressive sensing (CS) and deoxyribonucleic acid (DNA) coding-compression, which helps to solve the above problem. In the proposed scheme, first the original medical image was compressed to a floating point CS matrix using the discrete wavelet transform and the partial Hadamard matrix. The CS matrix was then normalized and rounded for matrix quantification. The result of quantification was fed to DNA fixed encoding and DNA run length coding for encryption and secondary compression. Finally the compressed-encrypted image was obtained after DNA dynamic encoding-decoding and regroups operation. The proposed scheme was tested against 9 images and proved to be effective in reducing the quantization errors and enhancing the compression performance. For instance, when the benchmark compression ratio (CR) is 0.5, the CR can be reduced by 5%(CR = 0.4453) ∼ 23%(CR = 0.2636), with the corresponding peak signal-to-noise ratio values consistently surpassing the benchmark. Furthermore, the execution of DNA compression and DNA dynamic encoding-decoding provided double guarantee for the algorithm’s security.
Navigation safety of both sea-going crewed ships and sea-going autonomous unmanned ships is possible to improve by enhancing navigation engineering facilities, including software and soft hardware (firmware), providin...
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In Nigeria, prose format is used to present and perform analysis on chest x-ray reports and this often results in delayed response from the clinicians. Therefore, with a view to developing a system for analyzing chest...
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With the explosive increase in mobile data volume, traditional cloud platforms can no longer meet the real-time requirements of edge devices. In this context, some scholars have proposed deploying a middle cloud, also...
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ISBN:
(数字)9798350376777
ISBN:
(纸本)9798350376784
With the explosive increase in mobile data volume, traditional cloud platforms can no longer meet the real-time requirements of edge devices. In this context, some scholars have proposed deploying a middle cloud, also known as an edge cloud. The data storage and processing capabilities of the edge cloud are far inferior to those of the center cloud, thus it cannot efficiently handle large-scale edge data samples. Moreover, traditional wireless communication pursues high data rates or reliable transmission, with data bits being transmitted indiscriminately to the network edge, resulting in high redundancy. Therefore, reducing the scale of edge data samples and eliminating high redundant data samples has become an urgent issue to be addressed. This paper mainly focuses on the edge service side and proposes a method for reducing data sample redundancy based on data-importance analysis, which includes three steps: firstly, clustering the original edge data samples to obtain important classes; Secondly, calculating the similarity of samples within each important class; And thirdly, screening the samples. We trained SVM, KNN, and DT classification models for the Iris dataset using the proposed method. The average accuracy of the DT and SVM models has increased by 1.192% and 3.432% respectively, while it has almost no impact on the accuracy of KNN. The experiment shows that removing highly redundant samples can still preserve the features of model training, and even improve the accuracy of the model.
Aiming at low detection rate problem of existing paper defects detection algorithms for low contrast paper defects, a low contrast paper defect detection algorithm based on Gabor Filter and Laplasian is proposed. To s...
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With rising dropout rates and extended degree completion times in South African institutions, there's a pressing need to better understand and address the hurdles faced by students during their academic journey. T...
With rising dropout rates and extended degree completion times in South African institutions, there's a pressing need to better understand and address the hurdles faced by students during their academic journey. This research harnesses the power of explainable AI to predict and classify students based on their likelihood of not completing their degrees on time. Utilizing synthetic data generated via a Bayesian network, we used predictive models that categorize students into four distinct risk profiles. This clarity in prediction not only illuminates the underlying causes of academic delays but also empowers faculty, advisors, and student support services with actionable insights. The goal of this research is to facilitate timely interventions, tailored support, and seamless transitions for students transferring between universities, ensuring more students can realize their academic aspirations within expected time frames.
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