Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Code and data for the paper "Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms" by Noelle Madyson Daigle (22120114)

“…<p dir="ltr">Code and data for analysis detailed in the paper "Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms." …”

The comparison of results from the proposed architecture and state-of-the-art algorithms on the UNSW-B15, NSL-KDD and KDDCUP’99 dataset. by Abdullah Asım Yılmaz (20715311)

The method for determining the chemical constituents of unknown materials in the algorithm. by Dong Hyeok Choi (12562048)

<b>Python codes for machine learning algorithms for subclinical mastitis prediction</b> by Hamza Yalçin (21182759)

<b>Prediction of Heart Attack Using Machine Learning Classification Algorithms</b> by Khaled Mohamad Almustafa (20937680)

Data and code resources. by Sam Hall-McMaster (10343795)

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Source Code and Dataset by Swathi Ganesan (14394924)

Scatter diagram of different principal elements. by Jizhong Wang (7441697)

<b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> by BRISC Dataset (22559540)

“…It provides high-quality, physician-validated pixel-level masks and a balanced multi-class classification split, suitable for benchmarking segmentation and classification algorithms as well as multi-task learning research.</p><p dir="ltr">Highlights<br>- 6,000 T1-weighted MRI slices (5,000 train / 1,000 test)<br>- Four classes: Glioma, Meningioma, Pituitary Tumor, No Tumor<br>- Pixel-wise segmentation masks reviewed by radiologists<br>- Slices from three anatomical planes: Axial, Coronal, Sagittal<br>- Clean, stratified train/test splits and aligned image–mask filenames</p><h2> Dataset structure</h2><p dir="ltr"><b>brisc2025/</b><br>├─ classification_task/<br>│ ├─ train/<br>│ │ ├─ glioma/<br>│ │ │ ├─ brisc2025_train_00001_gl_ax_t1.jpg<br>│ │ │ └─ ...…”

<b>Ensemble Optimized Deep Learning-Based Framework</b> by D. Mahesh (19730962)

<b>Forecasting Organizational Culture with the Denison Model: Evaluating Five Distinct Machine Learning Algorithms</b> by Ashulekha Gupta (14598836)

“…<p dir="ltr"><b>Forecasting Organizational Culture with the Denison Model: Evaluating Five Distinct Machine Learning Algorithms</b></p>…”

<b>Research on Differential Genes in Rheumatoid Arthritis Synovium Based on Multiple Machine Learning Algorithms</b> by changsheng liao (18193081)

“…<p dir="ltr"><b>Research on Differential Genes in Rheumatoid Arthritis Synovium Based on Multiple Machine Learning Algorithms</b></p>…”

The Appendix 3 provides additional figures for "<b>Global</b><b>to</b><b>l</b><b>ocal</b><b>burden </b><b>and inequality</b><b>of </b><b>chronic kidney disease with frontier analys... by XIAN SHAO (11693422)

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

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The genome coding scheme. by Wenbing Shi (5806160)

Subjects: