Score difference across severities when CNN confidence (also referred to as DL confidence) is used vs. not used. by Sulav Baral (22477755)

A data processing scheme for spatially explicit soil spectral calibration. by José L. Safanelli (20552654)

Flowchart of the GAN–BWGNN HAD algorithm. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

The algorithm for the Transformer-DN model. by Liang Na (22226855)

Learning of the transformer layer. by Xueliang Guo (4797057)

Subjects:

Precise spatial delineation of brain cell types in adult mouse brain. by Nuray Sogunmez Erdogan (21533872)

Using TIPC to discern spatial patterns of immune activity in an HCC cohort and their correlation with patient treatment response. by Mai Chan Lau (3135303)

Subjects:

Characterization of CD3⁺ T-cell spatial distribution in the CRC tumor microenvironment using TIPC. by Mai Chan Lau (3135303)

Subjects:

A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms by Xingan Chen (11334186)

PyNoetic’s feature extraction module that supports time domain features, frequency domain features, time-frequency domain features, spatial features as well as Brain Connectivity m... by Gursimran Singh (575288)

Credit Card Fraud Classification Using Applied Machine Learning – A Comparative Study of 24 ML Algorithms by Kelechi Amamba (21022064)

“…</p><p dir="ltr">The study aims to contribute to the enhancement of the accuracy and stability of fraud detection models while noting the minimum features that would result in the fastest detection. It also had an objective to contribute to industry-standard fraud detection systems by its use of an innovative machine learning algorithms comparator to derive the best model for detecting fraud in real-time, minimizing financial risk and ensuring security while reducing false positives, ensuring legitimate transactions are not flagged as fraud, preserving customer trust.…”

Demonstration of the limitations of existing spatial analysis methods, namely nearest neighbor distance (NND), G-cross function and Morisita-Horn index. by Mai Chan Lau (3135303)

Subjects:

Data Sheet 1_Spatial distribution prediction of pore pressure based on Mamba model.zip by Xingye Liu (14643367)

“…Advanced seismic inversion techniques are then employed to obtain three-dimensional elastic properties like subsurface velocity and density, which serve as input features for the trained deep learning model.</p>Results<p>Through complex nonlinear mappings, the model effectively captures the intrinsic relationship between input attributes and formation pressure, enabling accurate spatial distribution prediction of formation pore pressure. …”

Validation of TIPC spatial subtypes which were first determined in NHS/ HPFS cohort and later recapitulated in TCGA cohort, using eosinophils identified morphologically in H&E imag... by Mai Chan Lau (3135303)

Subjects:

Background-anomaly separation in Cri dataset. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

Comparison of running time (seconds). by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

Comparison of AUC values. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

Influence of C on the AUC on the seven datasets. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

AUC for ablation study. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”

Influence of K on the AUC on the seven datasets. by Ruhan A (12403982)

“…The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision. …”