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1,782 results for “Algorithm”
The Research of Multi-Model Cascade Algorithm in Quantifying Renal Interstitial Indicators
<p>After the relevant research is published, relevant data can be obtained through application.</p>
Ad Delivery Algorithm Datasets & Repro Code
Open the record for dataset details and reuse information.
Dataset related to the article "Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm"
<p>This record contains raw data related to the article “Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm”</p> <p>Abstract</p> <p><strong>Background: </strong>Despite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative patients instead of standard 2D single segmented inversion recovery gradient echo late gadolinium enhancement sequences (2D-SSLGE). The aim of the study is to assess the feasibility and comparison of 2D-MSLGE reconstructed with artificial intelligence reconstruction deep learning noise reduction (NR) algorithm compared to standard 2D-SSLGE in consecutive patients with ischemic cardiomyopathy (ICM).</p> <p><strong>Methods: </strong>Fifty-seven patients with known ICM referred for a clinically indicated CMR were enrolled in this study. 2D-MSLGE were reconstructed using a growing level of NR (0%,25%,50%,75%and 100%). Subjective image quality, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were evaluated in each dataset and compared to standard 2D-SSLGE. Moreover, diagnostic accuracy, LGE mass and scan time were compared between 2D-MSLGE with NR and 2D-SSLGE.</p> <p><strong>Results: </strong>The application of NR reconstruction ≥50% to 2D-MSLGE provided better subjective image quality, CNR and SNR compared to 2D-SSLGE (p < 0.01). The best compromise in terms of subjective and objective image quality was observed for values of 2D-MSLGE 75%, while no differences were found in terms of LGE quantification between 2D-MSLGE versus 2D-SSLGE, regardless the NR applied. The sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 2D-MSLGE NR 75% were 87.77%,96.27%,96.13%,88.16% and 94.22%, respectively. Time of acquisition of 2D-MSLGE was significantly shorter compared to 2D-SSLGE (p < 0.01).</p> <p><strong>Conclusion: </strong>When compared to standard 2D-SSLGE, the application of NR reconstruction to 2D-MSLGE provides superior image quality with similar diagnostic accuracy.</p>
Effects of Algorithmic Music on the Cardiovascular Neural Control.
<p>Raglio A, De Maria B, Perego F, Galizia G, Gallotta M, Imbriani C, Porta A, Dalla Vecchia LA. Effects of Algorithmic Music on the Cardiovascular Neural Control. J Pers Med. 2021 Oct 25;11(11):1084. doi: 10.3390/jpm11111084. PMID: 34834436; PMCID: PMC8618683.</p> <p>Abstract</p> <p>Music influences many physiological parameters, including some cardiovascular (CV) control indices. The complexity and heterogeneity of musical stimuli, the integrated response within the brain and the limited availability of quantitative methods for non-invasive assessment of the autonomic function are the main reasons for the scarcity of studies about the impact of music on CV control. This study aims to investigate the effects of listening to algorithmic music on the CV regulation of healthy subjects by means of the spectral analysis of heart period, approximated as the time distance between two consecutive R-wave peaks (RR), and systolic arterial pressure (SAP) variability. We studied 10 healthy volunteers (age 39 ± 6 years, 5 females) both while supine (REST) and during passive orthostatism (TILT). Activating and relaxing algorithmic music tracks were used to produce possible contrasting effects. At baseline, the group featured normal indices of CV sympathovagal modulation both at REST and during TILT. Compared to baseline, at REST, listening to both musical stimuli did not affect time and frequency domain markers of both SAP and RR, except for a significant increase in mean RR. A physiological TILT response was maintained while listening to both musical tracks in terms of time and frequency domain markers, compared to baseline, an increase in mean RR was again observed. In healthy subjects featuring a normal CV neural profile at baseline, algorithmic music reduced the heart rate, a potentially favorable effect. The innovative music approach of this study encourages further research, as in the presence of several diseases, such as ischemic heart disease, hypertension, and heart failure, a standardized musical stimulation could play a therapeutic role.</p>
data set from the article De Marco F, Casenghi M, Spagnolo P, Popolo Rubbio A, Brambilla N, Testa L, Bedogni F. A patient-specific algorithm to achieve commissural alignment with Acurate Neo: The sextant technique. Catheter Cardiovasc Interv. 2021 Nov 15;98(6):E847-E854. doi: 10.1002/ccd.29737. Epub 2021 May 7. PMID: 33960624.
<p>data set from the article De Marco F, Casenghi M, Spagnolo P, Popolo Rubbio A, Brambilla N, Testa L, Bedogni F. A patient-specific algorithm to achieve commissural alignment with Acurate Neo: The sextant technique. Catheter Cardiovasc Interv. 2021 Nov 15;98(6):E847-E854. doi: 10.1002/ccd.29737. Epub 2021 May 7. PMID: 33960624</p> <p>abstract:</p> <p><strong>Aims: </strong>The aim of this proof-of-concept study was to investigate safety and efficacy of a CT-scan based patient-specific algorithm to maximize coronary clearance and secondarily to achieve anatomically correct commissural alignment with the Acurate Neo device.</p> <p><strong>Method and results: </strong>A total of 45 consecutive patients undergoing TAVR with the Acurate Neo THV were prospectively enrolled in the study. Mean age was 81.6 ± 5.5 years, mean STS score was 6.1 ± 3.7. Device success rate was 100%. Aim of the technique was to rotationally deploy the TAVR device with a commissure lying on the bisector between the coronary ostia as calculated on the pre-procedural CT-scan. At post-TAVR CT-scan, coronary clearance was achieved in 98% of patients with no cases of severe coronary artery overlap. In 42 out of 45 patients, THV was aligned or, at most, mildly misaligned; there were 2 cases of moderate misalignment without any case of severe misalignment. Post-TAVR selective coronary artery engagement was attempted and succeeded in all patients (100%).</p> <p><strong>Conclusion: </strong>Our CT-scan based patient-specific algorithm is safe and proven to be effective in avoiding coronary artery overlap and providing commissural alignment with Acurate Neo in all treated patients.</p>
INTERLEAVE : An Empirically Faster Symbolic Algorithm for Maximal End Component Decomposition of MDPs -- Evaluation
<p>Please refer to the updated artifact for our CAV 2025 paper - https://zenodo.org/records/15220239.</p>
Supplementary Data and Models of Melt-based Thermo-barometer for paper "'No Free Lunch' in Tabular Geochemical Data: An example of Shallow versus Deep Machine Learning Algorithms for Geothermobarometry"
Open the record for dataset details and reuse information.
What Influences Algorithmic Decision-Making? A Systematic Literature Review on Algorithm Aversion
<p>Abstract</p> <p> </p> <p>With the continuing application of artificial intelligence (AI) technologies into decision-making, algorithmic decision-making is becoming more efficient, even often outperforming human counterpart. Despite this superior performance, people often consciously or unconsciously display reluctance to rely on algorithms, a phenomenon known as algorithm aversion. Viewed as a behavioral anomaly, algorithm aversion has recently attracted much scholarly attention. With a view to synthesize the findings of this literature, we systematically review 80 empirical studies identified through searching in seven academic databases and performing citation chaining. We map the emergent themes following grounded theory and categorize the influencing factors of algorithm aversion under four main themes: algorithm, individual, task, and high-level. Our analysis reveals that although algorithm and individual factors have been investigated extensively, very little effort has been given to explore the task and high-level factors. We contribute to algorithm aversion literature by proposing a comprehensive framework, highlighting open issues in existing studies, and outlining several research avenues that could be handled in future research. Implications for research and practitioners about the findings of the study are discussed.</p>
SLR on Algorithmic Decision Making
<p>Abstract</p> <p> </p> <p>With the continuing application of artificial intelligence (AI) technologies into decision-making, algorithmic decision-making is becoming more efficient, even often outperforming human counterpart. Despite this superior performance, people often consciously or unconsciously display reluctance to rely on algorithms, a phenomenon known as algorithm aversion. Viewed as a behavioral anomaly, algorithm aversion has recently attracted much scholarly attention. With a view to synthesize the findings of this literature, we systematically review 80 empirical studies identified through searching in seven academic databases and performing citation chaining. We map the emergent themes following grounded theory and categorize the influencing factors of algorithm aversion under four main themes: algorithm, individual, task, and high-level. Our analysis reveals that although algorithm and individual factors have been investigated extensively, very little effort has been given to explore the task and high-level factors. We contribute to algorithm aversion literature by proposing a comprehensive framework, highlighting open issues in existing studies, and outlining several research avenues that could be handled in future research. Implications for research and practitioners about the findings of the study are discussed.</p> <p> </p> <p> </p>
Dataset related to the article "Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion"
<p>This record contains raw data related to the article “Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion"</p> <p><strong>Purpose: </strong>To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation.</p> <p><strong>Methods: </strong>One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DL<sub>rest</sub>) and stress dataset (CTP-DL<sub>stress</sub>) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DL<sub>rest</sub>, and CCTA + CTP-DL<sub>stress</sub> was measured and compared. The time of analysis for CTP stress, CTP-DL<sub>rest</sub>, and CTP-DL<sub>Stress</sub> was recorded.</p> <p><strong>Results: </strong>Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTP<sub>Stress</sub> were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DL<sub>rest</sub> and CCTA + DL<sub>stress</sub> were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DL<sub>Rest</sub>, and CCTA + CTP-DL<sub>Stress</sub> significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001).</p> <p><strong>Conclusion: </strong>Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress<sub>.</sub>.</p>
Detection and Quantification of Cotton Trichomes by Deep Learning Algorithm
<p>This file includes three image datasets for singled and clustered trichomes: the original image dataset, the black-and-white image dataset, and the enhanced image dataset. In addition, the codes of the four models are included. The details are in the article "Detection and Quantification of Cotton Trichomes by Deep Learning Algorithm".</p>
Advanced physiological maturation of iPSC-derived human cardiomyocytes using an algorithm-directed optimization of defined media components
GEO Series GSE214617. Homo sapiens. 29 samples. Type: Expression profiling by high throughput sequencing.
The significance of different algorithms in transcriptome analysis of leukemic cells with rearranged MLL (gene expression)
GEO Series GSE54604. Homo sapiens. 4 samples. Type: Expression profiling by array.
Raw data for "Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm": unpublished figure and source data.
<p>Classification Performances for each stroke participant according to the GUIDER and the MANUAL procedure. </p>
Chapter 5 Identifying Adaptive Algorithms for Increasing Comparative Judgment Efficiency: A Systematic Literature Review
<p>This is a taxonomy of adaptive algorithms as a supplementary file with the article "Identifying Adaptive Algorithms for Increasing Comparative Judgment Efficiency: A Systematic Literature Review" and Chapter 5, with the same title, of the dissertation "Beyond a Mere Rank Order: The Method, the Reliability and the Efficiency of Comparative Judgment" by San Verhavert (University of Antwerp).</p>
Algorithm of the major and minor diagnostic criteria for active myopic choroidal neovascularization
<p>Key Messages:<br> Leakage on fluorescein angiography and hyperreflectivity on SD-OCT are the gold standard diagnostic biomarkers<br> To date, the diagnostic accuracy of several biomarkers has been investigated singularly. However, the totality of biomarkers deriving from clinical and multimodal imaging has not been investigate thoroughly<br> This study proposes an algorithm with a combination of major and minor clinical and imaging parameters<br> identifying a classification tree based on rigorous statistical analysis.for myopic CNV.</p>
A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem
<p>The dataset for our paper 'A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem'. It consists of 5 real KGs (<code>Mondial</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, <code>DBpedia</code>) and 5 synthetic KGs (<code>LUBM-10U</code>, <code>LUBM-50U</code>, <code>LUBM-250U</code>, <code>LUBM-2U</code>, <code>DBP-50K</code>). Each KG is compressed in one file, which including (for example, in <code>LUBM-2U</code>):</p> <ul> <li> <p><code>lubm_2u_nodes.sql</code>: the id, the name and the weight of a node,</p> </li> <li> <p><code>lubm_2u_edges.sql</code>: the ids of two nodes an edge connects,</p> </li> <li> <p><code>lubm_2u_queries.sql</code>: a query consists of some keywords,</p> </li> <li> <p><code>lubm_2u_keymap.sql</code>: a keyword maps to a set of nodes,</p> </li> <li> <p><code>lubm_2u_nodevec.sql</code>: the vector of a node, used to compute quadratic function qw,</p> </li> <li> <p><code>lubm_2u_hub_hop.sql</code>: the hub labeling index to compute in Section 4.1,</p> </li> <li> <p><code>lubm_2u_hub_mix_1.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.1,</p> </li> <li> <p><code>lubm_2u_hub_mix_5.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.5,</p> </li> <li> <p><code>lubm_2u_hub_mix_9.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.9.</p> </li> </ul> <p>You can dump the data into MySQL database. For example,</p> <pre><code>create database lubm_2u; use lubm_2u; source lubm_2u_nodes.sql; …</code></pre> <p>Unfortunately, due to the limit of space, for large KGs (<code>DBpedia</code> and <code>LUBM-250U</code>), we don't directly provide the data of hub labeling, i.e., these two compressed files only contains the first 5 sql files. You should generate hub labeling by yourself where the process is detailed in <a href="https://github.com/nju-websoft/QGSTP-HB/blob/main/README.md">README</a> of our project.</p> <p> </p>
algorithm
not available
Multiple Kernel Learning based Heterogeneous Algorithm
Paper on this topic has been submitted to KDD 2010.
Drivers of bat activity at wind turbines advocate for mitigating bat exposure using multicriteria algorithm-based curtailment
<p>data used for the paper</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.