EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain–Computer Interface Performance by Hamidreza Darvishi, Ahmadreza Mohammadi, Mohammad Hossein Maghami, Meysam Sadeghi, Mohamad Sawan

“…After preprocessing (resampling, normalization, bandpass filtering), FBCSP and multi-lag PLV features were fused, and the ReliefF algorithm selected the most informative subset. A feedforward neural network achieved average metrics of: Pearson correlation 0.829 ± 0.077, R-squared value 0.675 ± 0.126, and root mean square error (RMSE) 0.579 ± 0.098 in predicting EMG amplitudes indicative of arm movement angles. …”
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Identification of nitric oxide-mediated necroptosis as the predominant death route in Parkinson’s disease by Ting Zhang, Wenjing Rui, Yue Sun, Yunyun Tian, Qiaoyan Li, Qian Zhang, Yanchun Zhao, Zongzhi Liu, Tiepeng Wang

“…Using the Scaden deep learning algorithm, we predicted neurocyte subtypes and modelled dynamic interactions for five classic cell death pathways to identify the predominant routes of neuronal death during PD progression. …”
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Machine learning of automatic hierarchical multi-label classification method for identifying metal failure mechanisms by Ruitong Han, Chang-Bo Liu, Wanting Sun, Shuai Yu, Haoran Zheng, Lin Deng

“…To ensure that the model predictions are sufficiently reliable, a multi-level gradcam algorithm is also introduced for checking the regions of interest of the Hierarchical model at two levels and the comparisons are made with human experts. …”
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ATA ENCRYPTION USING THE CHAOTIC DYNAMICS IN WIRELESS SENSOR NETWORKS by A. V. Sidorenko, K. S. Mulyarchik

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A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles by Yuchang Zhu, Li Ke, Yijing Wei, Xiao Zheng

“…Compared with the opposition learning gray wolf optimizer and particle swarm optimization–gray wolf optimization, the proposed method achieves reductions of 52% and 68%, respectively. Additionally, under dual-frequency superimposed magnetic field excitation, a higher temperature inversion accuracy is achieved compared with that of the particle swarm optimization–gray wolf optimization algorithm, reducing the error from 0.237 K to 0.094 K.…”
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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…Chemical components were analyzed for statistical significance across leaf positions, and their influence on model predictions was interpreted using SHapley Additive exPlanations (SHAP). …”
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Adaptive DBP System with Long-Term Memory for Low-Complexity and High-Robustness Fiber Nonlinearity Mitigation by Mingqing Zuo, Huitong Yang, Yi Liu, Zhengyang Xie, Dong Wang, Shan Cao, Zheng Zheng, Han Li

“…Compared with conventional digital back-propagation and A-DBP based on a gradient-descent algorithm, our proposed method allows substantial complexity reductions of 31.35% and 58.47%, respectively. …”
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ELIMINATION OF ERRORS IN CODEWORDS ACCORDING TO THE RESULTS OF PARITY CHECKING by A. S. Poljakov, V. E. Samsonov

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Synthesis of the optimum two-channel direction finder with linear scanning of the antenna pattern by M. M. Bulash, S. V. Shaliapin

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Transfer Kernel Extreme Learning Machine Based on Bidirectional Cross Domain Approximation by Yuanxiao Zeng, Huimin Li, Yanbing Song

“…Finally, by combining the predictions from both transfer KELMs, our model significantly boosts its robustness. …”
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Consumer Behaviour: Analysing Marketing Campaigns through Recommender Systems and Statistical Techniques

“…This approach addresses the formidable challenges of accurately predicting consumer behaviour. We provide a detailed introduction to recommendation systems, emphasizing their vital role in the modern marketing landscape. …”
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Hybrid CNN-based Recommendation System by Muhammad Alrashidi, Roliana Ibrahim, Ali Selamat

“…In order to enhance the accuracy of predictions and address the challenges posed by sparsity, the proposed model incorporates both the extracted attributes and explicit interactions between items and users. …”
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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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Modeling Lane Changes at Freeway On-Ramps With a Novel Car-Following Model Based on Desired Time Headways by Moritz Berghaus, Markus Oeser

“…The model also includes components to predict the lane change start time based on surrogate safety measures and to describe the lateral behavior during the lane change. …”
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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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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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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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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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