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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Enhancing depression recognition through a mixed expert model by integrating speaker-related and emotion-related features by Weitong Guo, Qian He, Ziyu Lin, Xiaolong Bu, Ziyang Wang, Dong Li, Hongwu Yang

“…Abstract The World Health Organization predicts that by 2030, depression will be the most common mental disorder, significantly affecting individuals, families, and society. …”
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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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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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Crowdsourced data leaking user's privacy while using anonymization technique by Naadiya Mirbahar Mirbahar, Kamlesh Kumar, Asif Ali Laghari, Mansoor Ahmed Khuhro

“…Five supervised standard learning algorithm classifiers have been utilized to predict the user identity i.e., Extra Tree, Bagging, Decision Tree, Nearest Neighbor (KNN), and Random Forest Tree classifiers. …”
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Machine Learning Advancements in Urban Traffic Simulation: A Comprehensive Survey by Harshit Maheshwari, Li Yang, Richard W. Pazzi

“…Urban traffic simulation is useful in many ways to understand, manage, and predict the growing complexities of traffic dynamics within a city. …”
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Rotor Dynamic Behaviour of Electro-Rheological Lubricant Operated Multi-Lobe Journal Bearings by Kuldeep Narwat, Vivek Kumar, Simran Jeet Singh, Abhishek Kumar

“…The equation of motion for the journal is solved utilizing the fourth-order Runge-Kutta method to predict journal center locus/trajectories. The utilization of ER lubricant in conjunction with a two-lobe journal bearing notably enhances the minimum film thickness by 63.5%, the direct stiffness parameter by 182.4%, and the damping parameter by 210.14%. …”
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Two-Phase Globally Coupled Low-Density Parity Check Decoding Aided with Early Termination and Forced Convergence by Kun Zhu, Hongwen Yang

“…The initial variant, ET-1, predicts whether local decoding can be successful according to data characteristics and stop the local decoding iteration that is not expected to be successful in time. …”
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Bandage Therapeutic-Optical Keratoplasty in Keratoconus Patients after Intrastromal Corneal Segments Implantation by G. A. Osipyan, V. M. Sheludchenko, N. Y. Youssef, Kh. Khraystin

“…The first group consisted of 7 patients (7 eyes) of 27 ± 3 years old with had past history of CRS implantation followed by the absence of predicted improvement of visual acuity during the observation period of up to 18 months. …”
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Sequence-to-Sequence Text Generation with Discrete Diffusion Models by JIANG Hang, CAI Guoyong, LI Sihui

“…To this end, this paper simplifies the training process of the discrete diffusion model by deriving an upper bound on the training objective, and subsequently introduces and modifies the mask-and-predict decoding strategy from the conditional mask language model as the inference algorithm of the diffusion model. …”
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Cutting guides in mandibular tumor ablation: Are we as accurate as we think? by Omar Suhaym, Loren Moles, Nicholas Callahan

“…Conclusion: The finding of this study suggests that virtual surgical planning is an unambiguous paradigm shift in the predictability of the surgical plan and achievement of the reconstruction goals. …”
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Forced motion control of a mobile robot by T. Yu. Kim, R. A. Prakapovich, A. A. Lobatiy

“…A side result of the developed control system is that when it is scaled to a group of robots, it becomes possible to predict the time and place of their potential collisions, in order to subsequently take into account a group of robots for effective control.Conclusion. …”
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Numerical modeling and experimental estimates of structural member fatigue characteristics by Yu. P. Man’shin, E. Yu. Man’shina

“…Introduction. In the algorithm for predicting the resource of machine parts, models of external actions, fracture resistance, and temporal development of a particular type of damage to these units interact. …”
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