This paper investigates the application of Deep Reinforcement (DRL) Learning to address motion control challenges in drones for additive manufacturing (AM). Drone-based promises flexible and autonomous material deposition large-scale or hazardous environments. However, achieving robust real-time a multi-rotor aerial robot under varying payloads potential disturbances remains challenging. Traditional controllers like PID often require frequent parameter re-tuning, limiting their applicability...

This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments considers its application to dynamic magnetic-resonance imaging (dMRI). Each temporal-domain MR image is viewed as point that lies onto or close smooth manifold, landmark points are identified describe the cloud concisely. To facilitate computations, dimensionality reduction module generates low-dimensional/compressed renditions of points. Recovery...

This paper introduces a non-parametric approximation framework for imputation-by-regression on data with missing entries. The framework, coined kernel regression imputation in manifolds (KRIM), is built the hypothesis that features, generated by measured data, lie close to an unknown-to-the-user smooth manifold. A reproducing Hilbert space (RKHS) forms feature where manifold embedded in. Aiming at concise representations, KRIM identifies small number of "landmark points" define approximating...

Type 2 diabetes also known as mellitus is a condition that affects the way body processes blood sugar. Researchers are working at improving detection accuracy of machine learning based model for type diabetes. Dataset available to train classifier imposes severe challenges in this research due observed missing values, unbalanced data. This paper examines effectiveness Decision Tree Ensemble classifiers by tuning them using assorted combinations parameter values. The results early studies and...

The amount of people using social media is very large and increasing day by day. impact public figures in quite big. Fake accounts are created platforms used for various purposes like inflating the follower list a particular account. These also called spam usually post messages which marketing certain products or spreading agendas. Such can be dangerous as they may alter normal user's perspective on topics. to modify help creating fake sense popularity influence political situations. In this...

This paper establishes a kernel-based framework for reconstructing data on manifolds, tailored to fit the dynamic-(d)MRI-data recovery problem. The proposed methodology exploits simple tangent-space geometries of manifolds in reproducing kernel Hilbert spaces, and follows classical kernel-approximation arguments form data-recovery task as bilinear inverse Departing from mainstream approaches, uses no training data, employs graph Laplacian matrix penalize optimization task, costly (kernel)...

This paper establishes a modeling framework for data located onto or close to (unknown) smooth manifolds, embedded in Euclidean spaces, and considers its application dynamic magnetic resonance imaging (dMRI). The comprises several modules: First, set of landmark points is identified describe concisely cloud formed by highly under-sampled dMRI data, second, low-dimensional renditions the are computed. Searching linear operator that decompresses high-dimensional ones, those combinations which...

This paper establishes a kernel-based framework for reconstructing data on manifolds, tailored to fit the dynamic-(d)MRI-data recovery problem. The proposed methodology exploits simple tangent-space geometries of manifolds in reproducing kernel Hilbert spaces and follows classical kernel-approximation arguments form data-recovery task as bi-linear inverse Departing from mainstream approaches, uses no training data, employs graph Laplacian matrix penalize optimization task, costly (kernel)...

This paper introduces a non-parametric kernel-based modeling framework for imputation by regression on data that are assumed to lie close an unknown-to-the-user smooth manifold in Euclidean space. The proposed framework, coined kernel manifolds (KRIM), needs no training operate. Aiming at computationally efficient solutions, KRIM utilizes small number of ``landmark'' data-points extract geometric information from the measured via parsimonious affine combinations (``linear patches''), which...

Gliders are unmanned aerial vehicles that have no propulsion system integrated in them. They used a variety of applications ranging from spacecraft to military applications. Designing glider and optimizing it's design using computational analysis has helped retrieve relevant data for creating responsive model testing environment. Developing an autonomous navigation these gliders by can come handy as the respond rapidly fluctuations environmental factors than humans. Although traditional...