Image_3_Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition.png by G. Ramesh (602758)

“…Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.…”

Image_8_Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition.png by G. Ramesh (602758)

“…Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.…”

Image_7_Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition.png by G. Ramesh (602758)

“…Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.…”

Image_5_Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition.png by G. Ramesh (602758)

“…Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.…”

Image_6_Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition.png by G. Ramesh (602758)

“…Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.…”

Image_11_Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition.png by G. Ramesh (602758)

“…Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.…”

Image_9_Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition.png by G. Ramesh (602758)

“…Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.…”

Image_1_Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition.png by G. Ramesh (602758)

“…Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.…”

Image_10_Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition.png by G. Ramesh (602758)

“…Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes.…”

Data_Sheet_1_FOntCell: Fusion of Ontologies of Cells.ZIP by Javier Cabau-Laporta (10128161)

“…Additionally, we adapted the slower Blondel method to work for structure matching. We implemented our algorithms into FOntCell, a software module in Python for efficient automatic parallel-computed merging/fusion of ontologies in the same or similar knowledge domains. …”

Table_2_A New Method for CTC Images Recognition Based on Machine Learning.DOCX by Binsheng He (5689220)

“…We will further revise our models, hoping to achieve a higher sensitivity and specificity.…”

Table_1_A New Method for CTC Images Recognition Based on Machine Learning.XLSX by Binsheng He (5689220)

“…We will further revise our models, hoping to achieve a higher sensitivity and specificity.…”

Global 1-km present and future hourly anthropogenic heat flux by Alvin Christopher Varquez (8701623)

“…GeoTIFFs can be further analyzed or process using any GIS software or programming tools, such as R or Python.</p><p><br></p><p>Files prefixed "AHE_2010_" and "AHE_2050_" correspond to anthropogenic heat emissions for 2010s and 2050s, respectively. …”

EEG dataset for the analysis of age-related changes in motor-related cortical activity during a series of fine motor tasks performance by Nikita Frolov (8746413)

“…<br><br>Acquired EEG signals were then processed via preprocessing tools implemented in <b>MNE Python</b> package. Specifically, raw EEG signals were filtered by a Butterworth 5th order filter in the range 1-100 Hz, and by 50Hz Notch filter. …”

Microscopic Detection and Quantification of Microplastic Particles in Environmental Water Samples by Derek Lam (11944213)

“…Image processing algorithms, implemented in Python using adaptive thresholding techniques, were applied to segment particles from the background. …”

Research Bazaar 2018 – Highlights, insights, and what can we learn from each other? by Dharani Sontam (3798478)

“…As an example, instead of fullfledged software carpentry lessons in core digital tools (such as R programming language, Python and Git) we offered close to 40 “taster sessions” where audience had the chance to learn about the digital tools they were interested in. …”

Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF by Minhua Guo (18392646)

“…Machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR), were implemented using Python 3.10.2.</p>Results<p>Population demographic analysis revealed a significantly lower average BI score of 65.044 in the “Diagnosed with Emotional Problems by a Doctor” group compared to 85.128 in the “Not diagnosed with Emotional Problems by a Doctor” group. …”

Gladier - A programmable data capture, storage, and analysis architecture for experimental facilities by Kyle Chard (5131859)

“…Each service can be accessed via REST APIs, and/or from Python via a simple client library (which calls the REST APIs). …”

Decoding fairness motivations - repository by Sebastian Speer (6489207)

“…</div><div>Practice trials were implemented to in order to familiarize the participants with the task. …”