Hyperparameters of self-supervised learning algorithms used in Experiments 1–5. by Lalit Pandey (13195488)

Deep learning model parameters of LSTM. by Abdelrahman Hamdy (22146220)

Data Sheet 1_Algorithms on the rise: a machine learning–driven survey of prostate cancer literature.docx by Simin Gu (16475498)

“…</p>Discussion<p>ML applications in PCa research have experienced exponential growth, with methodological innovations driving interest in deep learning and radiomics. However, a persistent translational gap exists between algorithmic development and clinical implementation. …”

TreeMap 2016 Stand Size Code Algorithm (Image Service) by U.S. Forest Service (17476914)

“…format=iso19139 "> ISO-19139 metadata</a></li><li> <a href="https://data-usfs.hub.arcgis.com/datasets/usfs::treemap-2016-stand-size-code-algorithm-image-service "> ArcGIS Hub Dataset</a></li><li> <a href="https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_ForestEcology/TreeMap_2016_StandSizeCode_Algorithm/ImageServer "> ArcGIS GeoService</a></li></ul><div> For complete information, please visit <a href="https://data.gov">https://data.gov</a>.…”

QQESPM-GS - Codes and Data by Carlos Pontes (20835533)

“…<p dir="ltr">Codes and Data for the algorithms, methods and experiments presented in the paper titled "QQESPM-GS: an Efficient Solution for Geo-textual Search Parameterized by Graph-based Spatial Patterns".…”

Data Sheet 1_Enhanced vehicle routing for medical waste management via hybrid deep reinforcement learning and optimization algorithms.pdf by Norhan Khallaf (20710673)

“…<p>Modern technologies, particularly artificial intelligence, play a crucial role in improving medical waste management by developing intelligent systems that optimize the shortest routes for waste transport, from its generation to final disposal. Algorithms such as Q-learning and Deep Q Network enhance the efficiency of transport and disposal while reducing environmental pollution risks. …”

Speed efficiency of the proposed technique in comparison with the baseline deep learning models. by Amin Ullah (12015113)

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Distritbuions of AUC statistic for PathoRM, DeepConv-DTI, DeepDTA, DeepDCA and AMVLM under LODOCV scheme. by Hui Liu (33624)

DT Algorithm. by Nashmia Khalid (20496403)

Three types of models are compared using the Accuracy metric for multiclassification in an imbalanced CTG test dataset: basic deep learning models, advanced deep learning models, a... by Amin Ullah (12015113)

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Main code. by Jingguo Bi (21439766)

Initial parameter settings for the optimization algorithms. by Doaa Sami Khafaga (21463870)

Comparison of RC signals for the three algorithms. by Yucai Wang (513301)

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Code and data. by Crispin H. V. Cooper (21484906)

“…We suggest that the metaphor of discrete echo chambers may be inappropriate for meta-opinions: while measures of meta-opinion accuracy and its influences can reveal echo chamber characteristics where other metrics confirm their presence, the presence or absence of meta-opinion clusters is not itself sufficient to define an echo chamber. We publish both data and analysis code as supplementary material.…”

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Enabling Weather-Independent Gas Detection through Deep Learning on Light-Activated Sensors by Kichul Lee (11961609)

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Enabling Weather-Independent Gas Detection through Deep Learning on Light-Activated Sensors by Kichul Lee (11961609)

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Image 2_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.jpeg by Haochen Liu (487914)

Table 2_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx by Haochen Liu (487914)

Table 6_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx by Haochen Liu (487914)