The reduced visibility during the winter season in an outdoor setting can be attributed primarily to the presence of haze or fog. Despite adjusting the lens of an optical sensor system for various purposes, such as au...
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The printed circuit boards (PCBs) should be inspected during the manufacturing process to minimize defects such as printing errors, incorrect component selections, and incorrect soldering. Convolutional neural network...
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This study is presented to investigate the influence of the neutrosophic (NS) domain on the performance of the most common machine learning (ML) models. Specifically, it evaluates the effectiveness of Random Forest (R...
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The implementation of enterprise architecture (EA) is no longer exclusive to large corporations;its principles have been adapted for various organizations, including small and medium-sized enterprises (SMEs) and indus...
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Liver cirrhosis often occurs as a result of the lengthy and persistent progression of chronic liver disorders. It is a key crucial cause of death on a global scale. Early diagnosis and identification of cirrhosis are ...
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During recent decades, using credit cards represents a pivotal part of the financial lifeline. Credit cards and online payment gateways are vital elements in the world of world-wide-web. Given the fact that credit car...
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Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce ***,this trend introduces security challenges,such as unauthorized *** access control systems,such as Attribute-Base...
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Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce ***,this trend introduces security challenges,such as unauthorized *** access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and *** paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN *** technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access *** proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy ***,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern ***,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.
Diabetes disease is prevalent worldwide, and predicting its progression is crucial. Several model have been proposed to predict such disease. Those models only determine the disease label, leaving the likelihood of de...
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Portable document formats (PDFs) are widely used for document exchange due to their widespread usage and versatility. However, PDFs are highly vulnerable to malware attacks, which pose significant security risks. Exis...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but th...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.
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