Multi-layer Perceptrons

Kruse, Rudolf; Mostaghim, Sanaz; Borgelt, Christian; Braune, Christian; Steinbrecher, Matthias

doi:10.1007/978-3-030-42227-1_5

Cited by 61 publications

(24 citation statements)

References 18 publications

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“… is the UMAP mapping including the parameters n_neighbors set to 199, and min_dist set to 0. The ResMLP is based on the residual module and multi-layer perceptron 43,44 . The residual block is constructed by multi-layer perceptron with two layers.…”

Section: Methodsmentioning

confidence: 99%

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Social Behavior Atlas: A computational framework for tracking and mapping 3D close interactions of free-moving animals

Han

Chen

Wang

et al. 2023

Preprint

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The study of social behaviors in animals is essential for understanding their survival and reproductive strategies. However, accurately tracking and analyzing the social interactions of free-moving animals has remained a challenge. Existing multi-animal pose estimation techniques suffer from drawbacks such as the need for extensive manual annotation and difficulty in discriminating between similar-looking animals in close social interactions. In this paper, we present the Social Behavior Atlas (SBeA), a novel computational framework that solves these challenges by employing a deep learning-based video instance segmentation model, 3D pose reconstruction, and unsupervised dynamic behavioral clustering. SBeA framework also involves a multi-camera setup to prevent occlusion, and a novel approach to identify individual animals in close social interactions. We demonstrate the effectiveness of SBeA in tracking and mapping the 3D close interactions of free-moving animals using the example of genetic mutant mice, birds, and dogs. Our results show that SBeA is capable of identifying subtle social interaction abnormalities, and the models and frameworks developed can be applied to a wide range of animal species. SBeA is a powerful tool for researchers in the fields of neuroscience and ecology to study animal social behaviors with a high degree of accuracy and reliability.

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Section: Methodsmentioning

confidence: 99%

“…The ResMLP is based on the residual module and multi-layer perceptron43,44 . The residual block is constructed by multi-layer perceptron with two layers.…”

mentioning

confidence: 99%

Social Behavior Atlas: A computational framework for tracking and mapping 3D close interactions of free-moving animals

Han

Chen

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This type of neural network consists of at least three layers of neurons: input, hidden, and output layers, and each neuron has an activation function. The algorithm is trained using backpropagation, which means that the output data is sent back to check the prediction error, which improves the accuracy of the algorithm the more times the process is repeated [ 52 ]. Stochastic gradient descent (SGD) minimizes a loss function by approximating the gradient descent technique.…”

Section: Methodsmentioning

confidence: 99%

Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers

et al. 2023

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The processing of liquisolid systems (LSS), which are considered a promising approach to improving the oral bioavailability of poorly soluble drugs, has proven challenging due to the relatively high amount of liquid phase incorporated within them. The objective of this study was to apply machine-learning tools to better understand the effects of formulation factors and/or tableting process parameters on the flowability and compaction properties of LSS with silica-based mesoporous excipients as carriers. In addition, the results of the flowability testing and dynamic compaction analysis of liquisolid admixtures were used to build data sets and develop predictive multivariate models. In the regression analysis, six different algorithms were used to model the relationship between tensile strength (TS), the target variable, and eight other input variables. The AdaBoost algorithm provided the best-fit model for predicting TS (coefficient of determination = 0.94), with ejection stress (ES), compaction pressure, and carrier type being the parameters that influenced its performance the most. The same algorithm was best for classification (precision = 0.90), depending on the type of carrier used, with detachment stress, ES, and TS as variables affecting the performance of the model. Furthermore, the formulations with Neusilin® US2 were able to maintain good flowability and satisfactory values of TS despite having a higher liquid load compared to the other two carriers.

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“…Multi-Layer Perceptron (MLP) is a type of Artificial Neural Network (ANN), which attempts to mimic how the human brain is organized and functions [39]. Namely, an ANN is a network of neurons (i.e., nodes) with the strength of their connections defined by their weight.…”

Section: Materials and Proceduresmentioning

confidence: 99%

Is Initial Performance in a Course Informative? Machine Learning Algorithms as Aids for the Early Detection of At-Risk Students

et al. 2022

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The extent to which grades in the first few weeks of a course can predict overall performance can be quite valuable in identifying at-risk students, informing interventions for such students, and offering valuable feedback to educators on the impact of instruction on learning. Yet, research on the validity of such predictions that are made by machine learning algorithms is scarce at best. The present research examined two interrelated questions: To what extent can educators rely on early performance to predict students’ poor course grades at the end of the semester? Are predictions sensitive to the mode of instruction adopted (online versus face-to-face) and the course taught by the educator? In our research, we selected a sample of courses that were representative of the general education curriculum to ensure the inclusion of students from a variety of academic majors. The grades on the first test and assignment (early formative assessment measures) were used to identify students whose course performance at the end of the semester would be considered poor. Overall, the predictive validity of the early assessment measures was found to be meager, particularly so for online courses. However, exceptions were uncovered, each reflecting a particular combination of instructional mode and course. These findings suggest that changes to some of the currently used formative assessment measures are warranted to enhance their sensitivity to course demands and thus their usefulness to both students and instructors as feedback tools. The feasibility of a grade prediction application in general education courses, which critically depends on the accuracy of such tools, is discussed, including the challenges and potential benefits.

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Multi-layer Perceptrons

Cited by 61 publications

References 18 publications

Social Behavior Atlas: A computational framework for tracking and mapping 3D close interactions of free-moving animals

Social Behavior Atlas: A computational framework for tracking and mapping 3D close interactions of free-moving animals

Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers

Is Initial Performance in a Course Informative? Machine Learning Algorithms as Aids for the Early Detection of At-Risk Students

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