Comparative analysis of clinical characteristics between ovarian cancer and ovarian cyst patients by Li (20568581)

“…This study aims to integrate serum biomarkers with clinical features to construct efficient diagnostic prediction models and staging prediction algorithms for ovarian cancer. This multidimensional prediction model has the potential to improve early diagnosis rates of ovarian cancer, optimize treatment decision-making processes, reduce unnecessary surgical interventions, and provide scientific basis for individualized treatment plans, ultimately improving patient prognosis and quality of life. …”

Generalized Additive Spatial Smoothing (GASS): A Multiscale Regression Framework for Modeling Neighborhood Effects Across Spatial Supports by Taylor M. Oshan (19825257)

“…Through multiscale data-driven spatial smoothing, GASS conducts a form of change of support and therefore also facilitates the incorporation of data from diverse sources. …”

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

“…<b>G)</b> Deep feature extraction using VGG16. <b>H)</b> Training data comprising 80% of the dataset. <b>I)</b> Testing data consisting of 20% of the entire dataset. …”

Supplementary file 1_Almond yield prediction at orchard scale using satellite-derived biophysical traits and crop evapotranspiration combined with machine learning.pdf by Manuel Quintanilla-Albornoz (22653386)

“…In this study, remote sensing-based evapotranspiration estimates were evaluated for predicting almond yield at the orchard scale using machine learning (ML) algorithms. The almond prediction models were calibrated and validated using data provided by commercial growers, along with meteorological reanalysis and remote sensing products. …”

Comparison of accuracies with other authors. by Kazi Arman Ahmed (21567972)

“…Additionally, the study demonstrated the potential of synthetic data generation methods to improve prediction performance, aiding decision-making in the diagnosis and treatment of breast cancer.…”

Best models of Tables 2–4. by Kazi Arman Ahmed (21567972)

“…Additionally, the study demonstrated the potential of synthetic data generation methods to improve prediction performance, aiding decision-making in the diagnosis and treatment of breast cancer.…”

Table 1_Advances in the application of human-machine collaboration in healthcare: insights from China.docx by Wuzhen Wang (20675405)

“…“Human–machine collaboration” is based on an intelligent algorithmic system that utilizes the complementary strengths of humans and machines for data exchange, task allocation, decision making and collaborative work to provide more decision support. …”

Literature review summary. by Kazi Arman Ahmed (21567972)

“…Additionally, the study demonstrated the potential of synthetic data generation methods to improve prediction performance, aiding decision-making in the diagnosis and treatment of breast cancer.…”

The steps in Stage 2. by Kazi Arman Ahmed (21567972)

“…Additionally, the study demonstrated the potential of synthetic data generation methods to improve prediction performance, aiding decision-making in the diagnosis and treatment of breast cancer.…”

Framework of the methodology. by Kazi Arman Ahmed (21567972)

“…Additionally, the study demonstrated the potential of synthetic data generation methods to improve prediction performance, aiding decision-making in the diagnosis and treatment of breast cancer.…”

AUC ROC curves of all models for TVAE dataset. by Kazi Arman Ahmed (21567972)

“…Additionally, the study demonstrated the potential of synthetic data generation methods to improve prediction performance, aiding decision-making in the diagnosis and treatment of breast cancer.…”

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

“…A dropout layer is applied for regularization, and the output is flattened for the fully connected (dense) layer. The processed data is then fed into the PQC, where classical data is encoded into quantum states, followed by ansatz layers with learnable parameters updated using the gradient descent algorithm defined in Eq <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0331870#pone.0331870.e057" target="_blank">8</a>, and finally measured to produce classification probabilities, resulting in the output vector <i>γ</i>.…”

Planarity measurement of a low-cost mount for attaching a laser tracker's SMR to a robot flange by Florian Stöckl (19824654)

“…<p dir="ltr">This data is part of the paper: "Design and Evaluation of a Low-Cost Mount for Attaching a Laser Tracker’s SMR to a Robot Flange". …”

Table 2_Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment—implications for clinical practice.docx by Geerthy Thambiraj (22407274)

“…</p>Conclusion<p>ML algorithms have shown promising results in predicting in-hospital cardiac and respiratory arrest using readily available clinical data. …”

Table 1_Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment—implications for clinical practice.docx by Geerthy Thambiraj (22407274)

“…</p>Conclusion<p>ML algorithms have shown promising results in predicting in-hospital cardiac and respiratory arrest using readily available clinical data. …”

Enhancing Healthcare Transparency: Leveraging Machine Learning, GIS Mapping and Power BI for Private Hospital Insurance Claims Analysis by Maryam Binti Haji Abdul Halim (20249544)

“…</p><p dir="ltr">Key Features and Tools:</p><ul><li><b>Machine Learning Algorithms:</b> Leveraging <b>Python (pandas, numpy, scikit-learn)</b> for predictive modeling to assess claim validity and treatment outcomes.…”

<b>SAFE: </b><b>s</b><b>ensitive </b><b>a</b><b>nnotation </b><b>f</b><b>inding and </b><b>e</b><b>xtraction from multi-type Chinese maps via hybrid intelligence and knowledge grap... by jiaxin ren (20482655)

“…<p dir="ltr">Sensitive annotations typically contain key geographic elements or sensitive information vital for geographic information security. …”

Unveiling optimal SDG pathways: an innovative automated recommendation approach integrating graph pruning, intent graph, and attention mechanism by Qiang Wang (32383)

“…In conclusion, RGB-ER provides a robust, explainable framework for data-driven decision-making in sustainable development.…”

Table 4_Navigating ethical minefields: a multi-stakeholder approach to assessing interconnected risks in generative AI using grey DEMATEL.xlsx by Sridhar Jonnala (21635318)

“…Through a comprehensive literature review and expert validation across three key stakeholder groups (AI developers, end users, and policymakers), we identified and analyzed 14 critical ethical challenges across the input, training, and output modules, including both traditional and emerging risks, such as deepfakes, intellectual property rights, data transparency, and algorithmic bias. This study analyzed the perspectives of key stakeholders to understand how ethical risks are perceived, prioritized, and interconnected in practice. …”

Table 3_Navigating ethical minefields: a multi-stakeholder approach to assessing interconnected risks in generative AI using grey DEMATEL.xlsx by Sridhar Jonnala (21635318)

“…Through a comprehensive literature review and expert validation across three key stakeholder groups (AI developers, end users, and policymakers), we identified and analyzed 14 critical ethical challenges across the input, training, and output modules, including both traditional and emerging risks, such as deepfakes, intellectual property rights, data transparency, and algorithmic bias. This study analyzed the perspectives of key stakeholders to understand how ethical risks are perceived, prioritized, and interconnected in practice. …”