Prognostic and molecular multi-platform analysis of CALGB 40603 (Alliance) and public triple-negative breast cancer datasets by Brooke M. Felsheim, Aranzazu Fernandez-Martinez, Cheng Fan, Adam D. Pfefferle, Michele C. Hayward, Katherine A. Hoadley, Naim U. Rashid, Sara M. Tolaney, George Somlo, Lisa A. Carey, William M. Sikov, Charles M. Perou

“…We train exploratory multi-modal elastic net models of TNBC patient overall survival to determine the added impact of DNA-based features to RNA and clinical features. …”
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Self-Supervised Multi-Task Learning for the Detection and Classification of RHD-Induced Valvular Pathology by Lorna Mugambi, Ciira wa Maina, Liesl Zühlke

“…We compared two prominent self-supervised learning (SSL) methods: DINOv2, a vision-transformer-based approach known for capturing implicit features, and simple contrastive learning representation (SimCLR), a ResNet-based contrastive learning method recognised for its simplicity and effectiveness. …”
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D-SPDH: Improving 3D Robot Pose Estimation in Sim2Real Scenario via Depth Data by Alessandro Simoni, Guido Borghi, Lorenzo Garattoni, Gianpiero Francesca, Roberto Vezzani

“…Therefore, in this work, we propose a vision-based system, referred to as D-SPDH, able to estimate the 3D robot pose. …”
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Toward Automated Arabic Synonyms Extraction Using Arabic Lexical Substitution by Eman Naser-Karajah, Nabil Arman

“…The generated substitutions are filtered and then ranked based on three high-quality features to compare thoroughly: word similarity, word frequency, and the BERT score. …”
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A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data by Shenyi Ding, Zhijie Wang, Jue Zhang, Fang Han, Xiaochun Gu, Guangxiao Song

“…Temporal convolutional networks are trained to extract features and make predictions on each subset. …”
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A UNet Model for Accelerated Preprocessing of CRISM Hyperspectral Data for Mineral Identification on Mars by P. Kumari, S. Soor, A. Shetty, A. M. Nair

“…The proposed model automates key preprocessing steps, such as smoothing and continuum removal, while preserving essential mineral absorption features. Trained on augmented spectra from the MICA spectral library, the model introduces realistic variability to simulate MTRDR data conditions. …”
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Blockchain-Enabled Federated Learning in Healthcare: Survey and State-of-the-Art by Nasim Nezhadsistani, Naghmeh S. Moayedian, Burkhard Stiller

“…Centralization of health data to train ML models does pose privacy, ownership, and regulatory problems. …”
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Improved Segmentation of Infant Retinal Images and Quantitative Vascular Analysis by Ying Wang, Xiaoyu Zheng, Chunlei He, Jianfeng Zhang, Shoujun Huang

“…In this study, we proposed a computer-based image analysis method aimed at segmenting blood vessels and the optic disc in retinal images and providing quantitative features of the vessels to assist doctors in diagnosing plus disease. …”
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Clinical state tracking in serious mental illness through computational analysis of speech. by Armen C Arevian, Daniel Bone, Nikolaos Malandrakis, Victor R Martinez, Kenneth B Wells, David J Miklowitz, Shrikanth Narayanan

“…Clinic providers (n = 13) reviewed responses and provided global assessment ratings. We computed features of speech and used machine learning to create models of outcome measures trained using either population data or an individual's own data over time. …”
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Prediction of Rheological Parameters of Polymers by Machine Learning Methods by T. N. Kondratieva, A. S. Chepurnenko

“…This paper used k-nearest neighbor method and the support vector machine to determine the rheological parameters of polymers based on stress relaxation curves. The models were trained on synthetic data generated from theoretical relaxation curves constructed using the nonlinear Maxwell-Gurevich equation. …”
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Marked point process variational autoencoder with applications to unsorted spiking activities. by Ryohei Shibue, Tomoharu Iwata

“…The marked point process is a stochastic process that generates a sequence of events with marks. Spike train models based on such processes use the waveform features of spikes as marks and express the generative structure of the unsorted spikes without applying spike sorting. …”
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DPN-GAN: Inducing Periodic Activations in Generative Adversarial Networks for High-Fidelity Audio Synthesis by Zeeshan Ahmad, Shudi Bao, Meng Chen

“…To address this issue, we propose Deformable Periodic Network based GAN (DPN-GAN), a novel GAN architecture that incorporates a kernel-based periodic ReLU activation function to induce periodic bias in audio generation. …”
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Market state transitions and crash early warning in the Chinese stock market by Wu-Yue Pang, Wu-Yue Pang, Li Lin, Li Lin

“…Building on this insight, we construct an early warning model using decision trees trained on temporal features derived from market state transitions—including medium-term state distributions, directional change ratios, and short-term evolutionary paths. …”
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XFP-recognizer: detecting cross-file browser fingerprinting by Xiaoxi Wang, Zhenxu Liu, Chunyang Zheng, Xinyu Liu, Wei Liu, Yuling Liu, Qixu Liu

“…It extracts dynamic and static features to train random forest models for detecting and classifying the aggregated files, and then backtracks based on FCRgraphs to mark original scripts. …”
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Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction by Yixuan Wang, Chuang Huang, Dawei Li

“…To address the issues in existing 3D point cloud facet generation networks, specifically, the tendency to produce a large number of empty facets and the uncertainty in facet count, this paper proposes a novel deep learning framework for robust facet segmentation. Based on the generated facet set, two exploratory applications are further developed. …”
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From Narratives to Diagnosis: A Machine Learning Framework for Classifying Sleep Disorders in Aging Populations: The <i>sleepCare</i> Platform by Christos A. Frantzidis

“…First, a baseline model used a Multinomial Naïve Bayes classifier with n-gram features from a Bag-of-Words representation. Next, a Support Vector Machine (SVM) was trained on GloVe-based word embeddings to capture semantic context. …”
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Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms by Sandra D’Souza, Niranjan Reddy S, Saikonda Krishna Tarun, Sohan P, aneesha acharya k

“…In the research, machine learning-based prediction methods work on the audio recordings of heartbeats known as phonocardiograms (PCG) to develop an algorithm that differentiates a normal healthy heart from an abnormal heart based on the heart sounds. …”
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Steganalysis Using KL Transform and Radial Basis Neural Network by Safwan Hasoon, Farhad Khalifa

“…Then radial basis neural network (RBNN) trained using these features to discriminate  whether the image contains hidden information or not. …”
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Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network by Fanar E. K. Al-Khuzaie, Oguz Bayat, Adil D. Duru

“…This study is aimed at recognizing the presence of Alzheimer’s disease based on the magnetic resonance imaging of the brain. …”
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Dual-Streams Edge Driven Encoder-Decoder Network for Image Super-Resolution by Feng Li, Huihui Bai, Lijun Zhao, Yao Zhao

“…Instead of utilizing two sub-networks to learn edge information and color image contents respectively, a multitask learning framework is developed to jointly train edge-EDN and color-EDN. Therefore, as the structure prior, the reconstructed HR edge maps are fused with learned features of color stream to refine the HR color images. …”
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