Data Sheet 1_UrbanAgri: a transfer learning-based plant stress identification framework for sustainable smart urban growth.zip by Upinder Kaur (10805317)

“…Based on the capabilities of transfer learning, the model makes use of optimal feature extraction with small datasets, resolving the issue of data scarcity in cities. …”

DataSheet1_Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development and validation study.pdf by Yuchen Cui (5896310)

“…Introduction<p>Temporomandibular disorders (TMD) have a high prevalence and complex etiology. The purpose of this study was to apply a machine learning (ML) approach to identify risk factors for the occurrence of TMD in adults and to develop and validate an interpretable predictive model for the risk of TMD in adults.…”

Reinforcement Learning for Assessing Route Instruction Usability in Complex Indoor Spaces by Reza Arabsheibani (14244716)

“…Agents are trained via a curriculum learning approach in simulated text-based indoor environments of varying complexity and with different turn-based instruction grammars. …”

Data Sheet 1_Integrating groundwater pumping data with regression-enhanced random forest models to improve groundwater monitoring and management in a coastal region.pdf by Jamie Kim (14893220)

Comparison between FEEMSmix and Locator (a deep-learning based method from [39]) in predicting spatial locations of samples from the wolves data set in a leave-one-out based approa... by Vivaswat Shastry (18176036)

Data Sheet 1_A global model-agnostic rule-based XAI method based on Parameterized Event Primitives for time series classifiers.pdf by Ephrem Tibebe Mekonnen (19710493)

“…<p>Time series classification is a challenging research area where machine learning and deep learning techniques have shown remarkable performance. …”

Experimental protocol and behavioral evidence of perceptual learning. by Sarit F.A. Szpiro (21094755)

Data Sheet 1_Source attribution of human Campylobacter infection: a multi-country model in the European Union.docx by Cecilie Thystrup (19125203)

“…The proportion of cases that could be attributed with 70% probability to a source decreased when using the down-sampled data set (535 vs. 273 of 2627 cases). …”

Table 1_A novel α-conotoxin [D1G, ΔQ14] LvIC decreased mouse locomotor activity.xls by Wen Wang (6570)

“…</p>Results<p>The injection of [D1G, ΔQ14] LvIC led to a decrease in locomotor activity in mice. This treatment also resulted in reduced expression of neuronal calcium sensor 1 (NCS-1) and neuroligin 3 (NLGN-3) in the prefrontal cortex (PFC), hippocampus (Hip), and caudate putamen (CPu). …”

Table 2_A novel α-conotoxin [D1G, ΔQ14] LvIC decreased mouse locomotor activity.xls by Wen Wang (6570)

“…</p>Results<p>The injection of [D1G, ΔQ14] LvIC led to a decrease in locomotor activity in mice. This treatment also resulted in reduced expression of neuronal calcium sensor 1 (NCS-1) and neuroligin 3 (NLGN-3) in the prefrontal cortex (PFC), hippocampus (Hip), and caudate putamen (CPu). …”

Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset. by Seongil Han (20490232)

Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset. by Seongil Han (20490232)

Behavioral data. by Xiao Zhang (152326)

“…Pathological results revealed that, compared with the WKY group, the SHR group and Model group exhibited a decrease in the number of glial cells and neurons in the hippocampal tissue, with relatively loose arrangement of cell bodies and lighter Nissl staining. …”

The Karolinska Sleepiness Scale (KSS) is a 9-point Likert scale, ranging from “extremely 1 = alert” to “9 = extremely sleepy–fighting sleep” [44]. by Marcel F. Hinss (14675457)

Development of a machine learning method for predicting neutrophil-specific functional genes. by Guihua Wang (286390)

“…<p>(A) NeuRGI model training workflow involved: 1) extracting gene features from various databases. 2) using genes of neutrophil-related genes as positives and PU-learning as negatives. 3) balancing the training set with under-sampling and training the NeuRGI random forest model with 10-fold cross-validation, then employing a Gaussian Mixture Model (GMM) with NeuRGI scores to identify potential positives. 4) using OntoVAE for <i>in silico</i> knockout of GMM-classified genes to find key regulatory factors for guiding follow-up experiments. …”

Data Sheet 1_Convolutional neural networks decode finger movements in motor sequence learning from MEG data.docx by Aleksey Zabolotniy (22211695)

“…</p>Results<p>Sequence learning was reflected by a decrease in reaction times during SRTT performance. …”

Data Sheet 1_Prediction model of in-hospital mortality risk in intensive care unit patients with cardiac arrest: a multicenter retrospective cohort study based on an ensemble model... by Li Liu (75607)

“…Background<p>In-hospital cardiac arrest (IHCA) is a major adverse event with a high death risk. Machine learning (ML) models of prognosis in cardiac arrest (CA) patients have been established, but there are some interferences in their clinical application. …”

Data Sheet 1_Prognostic impact of dynamic changes of type I melanoma antigen gene proteins CT7 (MAGE-C1/CT7) transcripts in multiple myeloma.docx by Xuelin Dou (10938549)

“…Our data showed the predictive value of peri-ASCT frontline treatment. A 2-log decrease of MAGE-C1/CT7 post-induction cycle 2 compared to baseline correlated with a negative peri-ASCT MAGE-C1/CT7 status, providing an earlier prognostic marker of treatment response.…”

Dual-task learning across training visits for simple and complex reaction times. by Swati M. Surkar (8399427)

DataSheet1_A lightweight MHDI-DETR model for detecting grape leaf diseases.pdf by Zilong Fu (20392494)