In industrial production, rolling element bearings are key rotating components in a wide range of applications. By predicting rolling element bearings, the failure of bearings as well as problems can be detected in ti...
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Extended Reality (XR) encompasses a spectrum of technologies that go beyond the conventional realms of Virtual Reality (VR) and Augmented Reality (AR). Recently gaining momentum, XR has begun influencing various secto...
Extended Reality (XR) encompasses a spectrum of technologies that go beyond the conventional realms of Virtual Reality (VR) and Augmented Reality (AR). Recently gaining momentum, XR has begun influencing various sectors, notably education. It allows students to immerse themselves in virtual environments, engage with interactive simulations, and cooperate with peers in real time. Such hands-on experiences can enrich learning, potentially enhancing memory retention and understanding of the subject matter. The following study investigates the potential effectiveness of Extended Reality (XR) in Higher Education Society.
Data mining and machine learning are gaining popularity for fraud detection due to their effective results to cater the exponentially growing card transactions that comes with the fast-growing frauds. The aim of this ...
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Real-time monitoring is one of the Emerging duties in IoMT, and numerous systems have been developed to ensure the patient health monitor with efficient sensors capable of sensing, processing, and wireless communicati...
Real-time monitoring is one of the Emerging duties in IoMT, and numerous systems have been developed to ensure the patient health monitor with efficient sensors capable of sensing, processing, and wireless communication may be used to collect data for environmental and smart health monitoring. These sensors are connected via wireless sensor networks. They transfer data to the cloud via IoT protocols and technology for storage and processing. This allows for the prediction of possible equipment breakdowns using past data. At times, the volume of data transmitted to the cloud or the time it takes to transfer data to the cloud and back to the sensors/actuators might be excessive. Moving some of the processing closer to the sensors can help minimize the amount of network and cloud resources consumed in these instances. In order to give cloud access, fog installations need to determine the architecture for joining sensors and gateways. Sensors often create data streams that may be pre-processed, aggregated, or filtered before they reach the cloud. Gateways can also be utilized to undertake data analytics. As a result, fog organization is critical for balancing computational load and network resource utilization on public clouds in order to reduce latency and save money. Service irregularity detection is a type of predictive maintenance that may be carried out even if no data from prior equipment failures is available. When available, machine-learning algorithms based on binary classification are used to predict breakdowns in the near future, allowing for repairs or replacements to be scheduled. The prediction models are trained and assessed using historical data, which includes information on prior equipment failures. Because historical data might be massive, real-time cloud storage is a possibility, leading in cloud-based predictive maintenance. This paper proposes a fog-based smart-health monitoring system with a single feed-forward neural network learning method to re
This manuscript studies the optical dromions with beta derivative(BD)applied to the Complex Ginzburg Landau equation(CGLE)with Kerr law,parabolic law,cubic quintic septic law and quadratic cubic *** obtain bright drom...
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This manuscript studies the optical dromions with beta derivative(BD)applied to the Complex Ginzburg Landau equation(CGLE)with Kerr law,parabolic law,cubic quintic septic law and quadratic cubic *** obtain bright dromians by using the sine-cosine method(SCM).We will also obtain domain walls with the assistance of Bernoulli equation approach(BEA).Constraint conditions are also listed.
Automation software need to be continuously updated by addressing software bugs contained in their ***,bugs have different levels of importance;hence,it is essential to prioritize bug reports based on their sever-ity ...
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Automation software need to be continuously updated by addressing software bugs contained in their ***,bugs have different levels of importance;hence,it is essential to prioritize bug reports based on their sever-ity and *** managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolu-tion of critical ***,bug report prioritization is *** study pro-poses a new model for bug prioritization based on average one dependence estimator;it prioritizes bug reports based on severity,which is determined by the number of *** more the number of attributes,the more the *** proposed model is evaluated using precision,recall,F1-Score,accuracy,G-Measure,and Matthew’s correlation *** of the proposed model are compared with those of the support vector machine(SVM)and Naive Bayes(NB)*** and Mozilla datasetswere used as the sources of bug *** proposed model improved the bug repository management and out-performed the SVM and NB ***,the proposed model used a weaker attribute independence supposition than the former models,thereby improving prediction accuracy with minimal computational cost.
We introduce a forward-backward-forward (FBF) algorithm for solving bilevel equilibrium problem associated with bifunctions on a real Hilbert space. This modifies the forward-backward algorithm by relaxing cocoercivit...
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This paper introduces XNL-CNN, an extended version of the previously introduced, compact NL-CNN model. While the NL-CNN was designed with typical Tiny-ML constraints in order to make possible integration of various AI...
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Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,*** key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for thi...
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Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,*** key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)***,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or *** alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced *** a high effort is required for image edge *** a support vector regression(SVR)model is proposed to reduce this computational load and processing *** dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable *** show that the implementation in hardware is 68 times faster than that in *** architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without *** is also the lowest power consumption in current machine learning hardware ***,the circuitry area is 41 times *** with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning *** system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.
In this article, we present a modified variant of the Dai-Liao spectral conjugate gradient method, developed through an analysis of eigenvalues and inspired by a modified secant condition. We show that the proposed me...
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