Magnetic field focusing in longitudinal direction has been a missing link for three-dimensional synthesized magnetic focusing (3-D SMF). Deep magnetic focusing (DMF) by multiple coaxial coils, a sort of 3-D SMF, is fi...
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We define and investigate the Fréchet edit distance problem. Given two polygonal curves π and σ and a threshhold value δ > 0, we seek the minimum number of edits to σ such that the Fréchet distance be...
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Low density parity check (LDPC) codes allow a communications link to operate reliably at signal to noise ratios that are very close to the Shannon limit. Because of this, in the early 2000s they were studied in connec...
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This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration ...
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This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the *** designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing *** adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam *** are shown for the relationships among the applied bias voltage,the duration of shock impact,and the tunable *** fabricated prototypes are tested using a shock-table *** analytical results agree with the experimental *** designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.
Electroencephalogram (EEG) has been widely used in studies using rodent models to understand brain functions and neurological disorders. However, conventional EEG setups have limits as recording devices are bulky and ...
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Low density parity check (LDPC) codes allow a communications link to operate reliably at signal to noise ratios that are very close to the Shannon limit. Because of this, in the early 2000s they were studied in connec...
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the emergence of the Internet of Things (IoT) gadgets and cloud-enabled networks has brought about a dramatic growth in the quantity and kinds of programs that require real-time facet computing offerings. Aspect compu...
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The proliferation of fake news on social media has intensified the spread of misinformation, promoting societal biases, hate, and violence. While recent advancements in Generative AI (GenAI), particularly large langua...
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Heart failure is now widely spread throughout the *** disease affects approximately 48%of the *** is too expensive and also difficult to cure the *** research paper represents machine learning models to predict heart ...
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Heart failure is now widely spread throughout the *** disease affects approximately 48%of the *** is too expensive and also difficult to cure the *** research paper represents machine learning models to predict heart *** fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for *** supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best *** boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data *** and Scikit-learns are used for *** Flow and Keras,along with Python,are used for ANN model *** DT and RF algorithms achieved the highest accuracy of 95%among the ***,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.
Multiarmed bandits (MAB) is a sequential decision-making model in which the learner controls the trade-off between exploration and exploitation to maximize its cumulative reward. Federated multiarmed bandits (FMAB) is...
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