One of the most important methods of predicting the future is through past events and data repeated over time, as time series are those data indexed using time sequentially on data points distributed according to time...
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To eliminate the negative effects of existing methods such as social distance rule violation, slow test time and to create a pre-diagnosis method, deep learning and sound analysis work has been carried out for the Cov...
To eliminate the negative effects of existing methods such as social distance rule violation, slow test time and to create a pre-diagnosis method, deep learning and sound analysis work has been carried out for the Covid-19 disease, which has turned into a pandemic. For this purpose, experiments were performed on the crowdsourced Coswara dataset for Covid-19 Detection from Cough, Breath and Speech Sounds with Short-Time Fourier Transform and a CNN Model. On Coswara dataset, Covid-19 tested samples were selected and the CNN model was trained on different type of sounds. The best result was achieved with cough-heavy sound type as 0.980 precision, 0.998 AUC, 0.990 F1-score on test set.
Context: Smart contracts are prone to numerous security threats due to undisclosed vulnerabilities and code weaknesses. In Ethereum smart contracts, the challenges of timely addressing these code weaknesses highlight ...
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Super-resolution is a widely used technology in many applications, such as video repair. Aiming at the insufficiency of the method Fast Super-Resolution Convolutional Neural Networks (FSRCNN), an image super-resolutio...
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Previous work introduced a relation-algebraic framework for reasoning about weighted-graph algorithms. We use this framework to prove partial correctness of a sequential version of Borůvka’s minimum spanning tree alg...
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Third-party libraries (TPLs) are frequently reused in software to reduce development cost and the time to market. However, external library dependencies may introduce vulnerabilities into host applications. The issue ...
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Unstructured road segmentation is a key task in self-driving technology and it's still a challenging problem. Mostly available point cloud datasets focus on data collected from urban areas, and approaches are eval...
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Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D *** accurate residue-residue contact map is one of the essential elemen...
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Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D *** accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure ***,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant ***,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party *** this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial *** have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical *** proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.
Assurance of evolving large cyber-physical systems (CPS) is time-consuming, and usually a bottleneck for deploying them with confidence. Several factors contribute to this problem, including the lack of effective reus...
ISBN:
(纸本)9798400716072
Assurance of evolving large cyber-physical systems (CPS) is time-consuming, and usually a bottleneck for deploying them with confidence. Several factors contribute to this problem, including the lack of effective reuse of assurance results, the difficulty to integrate multiple analyses for multiple subsystems, and the lack of explicit consideration of the different levels of trust that different analyses provide. In this paper, we present an approach to assure large CPS that aims to overcome these barriers.
Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. This study offers a unique intrusion detection model for IoT deep learning meth...
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ISBN:
(数字)9798331529635
ISBN:
(纸本)9798331529642
Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. This study offers a unique intrusion detection model for IoT deep learning methods used in networks include Convolutional Neural Networks (CNN), TabNet, and the Transient Search Optimization (TSO) method. The project starts by developing and evaluating distinct intrusion detection components. CNN, specializing in feature extraction, analyzes network traffic for potential intrusions. TabNet, known for interpretability and effectiveness with structured data, captures intricate relationships. The novel TSO algorithm improves the system's ability to detect temporary anomalies. Each component's performance is assessed individually, highlighting unique strengths. TabNet excels in structured IoT data pattern identification, surpassing CNN. The TSO algorithm strengthens temporary anomaly recognition, enhancing IoT network security. An ensemble technique combines CNN and TabNet predictions, reducing false positives and negatives to elevate overall detection accuracy. The ensemble, using both CNN and TabNet, achieves an impressive accuracy rate exceeding 99%, demonstrating significant gains in intrusion detection performance.
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