Noise correlations and neuronal diversity may limit the utility of winner-take-all readout in a pop out visual search task. by Ori Hendler, Ronen Segev, Maoz Shamir

“…The analysis identifies specific response statistics that require further empirical characterization to accurately predict WTA performance in biologically plausible models of visual pop out detection.…”
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A Multidimensional Analysis Approach Toward Sea Cliff Erosion Forecasting by Maria Krivova, Michael J. Olsen, Ben A. Leshchinsky

“…This outcome is contingent on the condition that the time between the epochs used for prediction and the forecasted epochs does not increase.…”
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Modeling Lane Changes at Freeway On-Ramps With a Novel Car-Following Model Based on Desired Time Headways by Moritz Berghaus, Markus Oeser

“…The lane change model includes a leader selection algorithm, which enables merging vehicles to pass or be passed by vehicles in the main lane. …”
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A Deep Neural Network Framework for Dynamic Two-Handed Indian Sign Language Recognition in Hearing and Speech-Impaired Communities by Vaidhya Govindharajalu Kaliyaperumal, Paavai Anand Gopalan

“…In order to improve both model generalization and image quality, preprocessing is applied to images prior to prediction, and the proposed dataset is organized to handle multiple dynamic words. …”
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3D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine Learning by Anastasia Skonta, Myrto G. Bellou, Haralambos Stamatis

“…The Random Sample Consensus algorithm was used to train a simple linear regression model to predict glucose concentrations in unknown samples. …”
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Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning by Jos P. Kanning, Junfeng Wang, Shahab Abtahi, Mirjam I. Geerlings, Ynte M. Ruigrok

“…Using the UK Biobank, we identified aSAH cases via hospital-based ICD codes and analysed 618 baseline variables covering demographics, lifestyle, medical history, and physical measurements. The CatBoost ML algorithm and Shapley Additive Explanations (SHAP) identified the top 25 variables most influential in predicting aSAH. …”
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Learning Transferable Convolutional Proxy by SMI-Based Matching Technique by Wei Jin, Nan Jia

“…We design an iterative algorithm to update the parameters alternately and test it over benchmark data sets of abnormal behavior detection in video, Amazon product reviews sentiment analysis, etc.…”
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The nonlinear impact of cycling environment on bicycle distance: A perspective combining objective and perceptual dimensions by Yantang Zhang, Xiaowei Hu

“…This study uses 2019 cycling data from Shenzhen, China, employing the XGBoost algorithm to uncover the relative importance and thresholds of objective and perceived factors in the cycling environment. …”
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Counselling Career with Artificial Intelligence: A Systematic Review by Rifqi Muhammad, Patriana Patriana, Yusrain Yusrain, Astaman Astaman, Manja Manja

“…Objectives: career prediction, career matching with companies, career selection, career guidance, and career decision-making. …”
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Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloud by Yurui Li, Danhui Dan, Ruiyang Pan

“…Given the strong design prior information available during suspension bridge construction, Bayesian theory is applied to predict and adjust the global line shape of the main cable. …”
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Automatic detection of pupil reactions in cataract surgery videos. by Natalia Sokolova, Klaus Schoeffmann, Mario Taschwer, Stephanie Sarny, Doris Putzgruber-Adamitsch, Yosuf El-Shabrawi

“…We employ a Mask R-CNN architecture as a segmentation algorithm to segment the pupil and iris with pixel-based accuracy and then track their sizes across the entire video. …”
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Shear Capacity of Masonry Walls Externally Strengthened via Reinforced Khorasan Jacketing by Cagri Mollamahmutoglu, Mehdi Ozturk, Mehmet Ozan Yilmaz

“…The Horasan mortar was represented using an elastoplastic Mohr-Coulomb model with a custom softening law (parabolic-to-exponential), calibrated via inverse parameter fitting using the Nelder-Mead algorithm. The numerical predictions closely matched the experimental data. …”
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Modelling Red-Crowned Parrot (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) distributions in the Rio Grande Valley of Texas using elevation and vegetation indices and their... by Elise Varaela Voltura, James L Tracy, J Jill Heatley, Simon Kiacz, Donald J Brightsmith, Anthony M Filippi, Jesús G Franco, Robert Coulson

“…Our ensemble models accurately predicted the three distributions of interest ([Formula: see text] Area Under the Curve [AUC] = 0.904-0.969). …”
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Integration of smart insoles for gait assessment in exoskeleton assisted rehabilitation by Luigi D’Arco, Haiying Wang, Huiru Zheng

“…Gait phases were identified using a Finite State Machine, with transitions guided by predictions from a fuzzy c-means clustering algorithm. …”
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Geoinformatics-based prioritisation of natural resources rich regions at disaggregated levels for sustainable management by T. V. Ramachandra, Paras Negi

“…Landscape dynamics is assessed by classifying temporal remote sensing data using the supervised machine learning (ML) technique based on the Random Forest (RF) algorithm. Additionally, predicting likely land use changes in ecologically fragile areas would help formulate appropriate location-specific mitigation measures. …”
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Power Adaptation for Optimizing Secrecy Energy Efficiency in NOMA-Enabled Underlay Cognitive Radio Networks and DNN-Based Evaluation by P. P. Hema, A. V. Babu

“…Finally, a deep neural network (DNN) framework is developed as an innovative approach to accurately and quickly predict the JOTPA values that meet the desired objectives.…”
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Convolution neural network–based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonan... by Shaik Basheera, M Satya Sai Ram

“…Abstract In recent times, accurate and early diagnosis of Alzheimer's disease (AD) plays a vital role in patient care and further treatment. Predicting AD from mild cognitive impairment (MCI) and cognitive normal (CN) has become popular. …”
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Quasi-preview control: A novel method for active structural vibration control subject to seismic disturbances using adaptive filters and multiple remote seismic observations by Shinya FUJIMURA, Tomohiro WATANABE, Kazuhiko HIRAMOTO

“…The performance index involves the one-step-ahead prediction of the structural response obtained by the model of the structural system and the control input candidate to perform the switching action. …”
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An integrated bioinformatic investigation of succinic acid metabolism related genes in ccRCC followed by preliminary validation of SLC25A4 in tumorigenesis by Dong Yue, Fengyun Dong, Sen Wang, Miao Zheng

“…Our study reveals that succinate metabolism-related biomarkers can provide a basis for exploring the prediction of prognosis in ccRCC.…”
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Machine Learning Forecasting of Commercial Buildings’ Energy Consumption Using Euclidian Distance Matrices by Connor Scott, Alhussein Albarbar

“…A Euclidian distance matrix, akin to a K-nearest neighbour algorithm, determines the appropriate neural network(s) to utilise. …”
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