Presentation 1_Nonlinear kernel-based fMRI activation detection.pdf by Chendi Han (22221919)

“…<p>Kernel Canonical Correlation Analysis (KCCA) is an effective method for globally detecting brain activation with reduced computational complexity. …”

Presentation 2_Nonlinear kernel-based fMRI activation detection.zip by Chendi Han (22221919)

“…<p>Kernel Canonical Correlation Analysis (KCCA) is an effective method for globally detecting brain activation with reduced computational complexity. …”

PSO-Optimized Electronic Load Controller with Intelligent Energy Recovery for Self-Excited Induction Generator Based Micro-Hydro Systems by MRINAL KANTI RAJAK (21838169)

“…The dataset includes: (1) <b>PSO configuration parameters</b> - complete algorithm setup with population size (N=20), adaptive inertia weights (0.9→0.4), time-varying cognitive/social coefficients (c1: 2.5→0.5, c2: 0.5→2.5), search space boundaries for all 10 optimization variables, and convergence criteria specifications; (2) <b>Multi-objective fitness function data</b> - detailed weight adaptation formulas, individual objective convergence statistics (voltage: 15.3 iter, frequency: 19.2 iter, THD: 12.8 iter, energy: 23.0 iter), and composite fitness evolution from 0.537 to 0.903 over 50 iterations; (3) <b>Particle dynamics tracking</b> - complete position and velocity trajectories for all 20 particles across optimization dimensions [Kpv, Kiv, Kdv, Kpf, Kif, Kdf, ma, θphase, fc, Ppump,ref], diversity evolution (100%→8%), and exploration/exploitation transition patterns; (4) <b>Real-time implementation metrics</b> - computational requirements (2.6 kB memory, 67% CPU utilization), execution timing (0.83 ms average, 1.2 ms worst-case), and synchronization protocols for 100 Hz optimization loops; and (5) <b>Validation datasets</b> - performance verification across six different load conditions, convergence statistics, and algorithm robustness testing results demonstrating consistent ±1.8% voltage regulation and ±0.9% frequency stability achievements, all provided in structured CSV/JSON formats with comprehensive documentation under CC-BY license.…”

High-Dimensional Covariance Regression with Application to Co-Expression QTL Detection by Rakheon Kim (21656910)

“…In this article, we present a new sparse covariance regression framework that models the covariance matrix as a function of subject-level covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can be used to determine if and how gene co-expressions vary with genetic variations. …”

MHSC: A meta-heuristic method to optimize throughput and energy using sensitivity rate computing by Arash GhorbanniaDelavar (22563696)

“…<h2>Summary</h2><p dir="ltr">This study presents <b>MHSC</b> (Meta-Heuristic Scheduling with Sensitivity Computing), a novel hybrid algorithm that optimizes energy consumption, execution time, and throughput simultaneously for cloud datacenter workflows. …”

Fig 4 - by Michael S. Bradshaw (11772750)

“…Models were then used to make predictions about clusters from the most up-to-date version of the network. <b>B.</b> AUC model performance as a function of p-value threshold. …”

Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation by Ryo Tamura (1957942)

“…We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. …”

database of normalized water temperature by Yiming Li (20581391)

“…</p><p dir="ltr">Note that: the normalized water temperature under (H coils,b / H tank) is not transferred to function of Fourier Number. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…The analysis was conducted in a Jupyter Notebook environment, using Python and libraries such as Scikit-learn and Pandas. …”

Examples of model selection methods for the PHMM. by Tianshu Li (1527088)

“…<i>Left panel:</i> LL on validation sets as a function of <i>m</i>. The black curves show the average log-likelihoods (across initial guesses) of all models whose algorithm converged to a solution during training (vertical bars display one standard deviation across 100 different initial guesses for the parameter values). …”

PEG neurons encoded more complex features than A1 neurons. by Shoutik Mukherjee (18626028)

“…The magnitudes of STRFs were computed (second column) and approximated by a probability distribution function for a Gaussian mixture model (GMM) fit with a boosting algorithm with large- and small-covariance Gaussian weak learners (third and fourth columns, respectively) and by <i>k</i> components of its singular value decomposition (fifth column). …”

Models and Dataset by M RN (9866504)

“…<p dir="ltr"><b>P3DE (Parameter-less Population Pyramid with Deep Ensemble):</b><br>P3DE is a hybrid feature selection framework that combines the Parameter-less Population Pyramid (P3) metaheuristic optimization algorithm with a deep ensemble of autoencoders. …”

Parameter estimates of mixed generalized Gaussian distribution for modelling the increments of electroencephalogram data by Zoe Salinger (19090613)

“…<p>Electroencephalogram (EEG) is used to monitor child's brain during coma by recording data on electrical neural activity of the brain. …”

spine_quantification_all_images.xlsx by Ana M Jiménez-García (20396996)

“…It includes a number of neuropsychiatric disturbances including impaired motor activity and coordination, intellectual and cognitive function.</p><p dir="ltr">Results: In the present study, we used a rat early-stage HE model by triple portal vein ligation for 50 days To gain a better understanding of the effect of HE on the brain, artificial intelligence algorithms based on convolutional neuronal networks were implemented for the unbiased quantification of the brain images which were stained by Golgi-Cox immunohistochemistry. …”

Perturbations and their effects within networks. by Matthew Aguirre (9558032)

“…<b>(D)</b> KO effects as a function of network distance between two genes, and <b>(E)</b> within and across modules given by the generating algorithm. …”

Simulated and Field-Based Error Characterisation of Animal Geolocalisation and Relative Positioning via Commercial Drones by Kilian Meier (22303138)

“…<p dir="ltr"><b><i>Related paper currently in press!</i></b></p><p dir="ltr">Drones can be used for wildlife monitoring, specifically, to monitor the absolute location and relative position of individuals, which can be achieved with commercial drones using a monoplotting algorithm. …”

Table1_Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning.pdf by Lijuan Liang (4277053)

“…We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. …”

Table 1_Decoding ferroptosis in ischemic stroke: key genes and the therapeutic potential of acupuncture.docx by Chunxiao Wu (48606)

“…Hub genes were identified using the random forest algorithm, and their RNA expression levels were validated via RT-qPCR in sham-operated, MCAO model, and acupuncture groups.…”

Data Sheet 1_No reliable gray matter alterations in idiopathic dystonia.docx by Zhen-Yu Wang (6672833)

“…Background<p>The structural brain abnormalities associated with idiopathic dystonia (ID) remain inadequately understood. …”

Artificial Neural Network Model for Predicting the Viscosity of Crosslinked Polyacrylamide and Polyethylenimine Polymer Gel for Oilfield Water Control by S M Safayet Aziz (20832236)

“…The model was trained using the Levenberg-Marquardt algorithm. The hidden layer uses the tangent sigmoid (Tansig) activation function, and the output layer employs a linear (Purelin) activation function. …”