Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf by Sai Sakunthala Guddanti (17739363)

“…A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. …”

MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

“…Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. …”

Table 1_Non-obtrusive monitoring of obstructive sleep apnea syndrome based on ballistocardiography: a preliminary study.docx by Biyong Zhang (20906192)

“…Applying fast change-point detection to first isolate apnea-suspected episodes would allow for processing only those suspected episodes for further feature extraction and OSAS severity classification. This can reduce both the data to be stored or transmitted and the computational load. …”

iNCog-EEG (ideal vs. Noisy Cognitive EEG for Workload Assessment) Dataset by Fariya Bintay Shafi (21692408)

“…Inside each folder, four <b>.EDF</b> files represent the workload conditions:</p><pre><pre>subxx_nw.EDF → No Workload (resting state) <br>subxx_lw.EDF → Low Workload (easy multitasking) <br>subxx_mw.EDF → Moderate Workload (medium multitasking) <br>subxx_hw.EDF → High Workload (hard multitasking) <br></pre></pre><ul><li><b>Subjects 01–30:</b> Clean EEG recordings</li><li><b>Subjects 31–40:</b> Noisy EEG recordings with real-world artifacts</li></ul><p dir="ltr">This structure ensures straightforward differentiation between clean vs. noisy data and across workload levels.</p><h3>Applications</h3><p dir="ltr">This dataset can be applied to a wide range of research areas, including:</p><ul><li>EEG signal denoising and artifact rejection</li><li>Binary and hierarchical <b>cognitive workload classification</b></li><li>Development of <b>robust Brain–Computer Interfaces (BCIs)</b></li><li>Benchmarking algorithms under <b>ideal and noisy conditions</b></li><li>Multitasking and mental workload assessment in <b>real-world scenarios</b></li></ul><p dir="ltr">By combining controlled multitasking protocols with deliberately introduced environmental noise, <b>iNCog-EEG provides a comprehensive benchmark</b> for advancing EEG-based workload recognition systems in both clean and challenging conditions.…”