Learning efficient haptic shape exploration with a rigid tactile sensor array. by Sascha Fleer, Alexandra Moringen, Roberta L Klatzky, Helge Ritter

“…Its active nature is evident in humans who from early on reliably acquire sophisticated sensory-motor capabilities for active exploratory touch and directed manual exploration that associates surfaces and object properties with their spatial locations. …”
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Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study by Yanqi Kou, Shicai Ye, Yuan Tian, Ke Yang, Ling Qin, Zhe Huang, Botao Luo, Yanping Ha, Liping Zhan, Ruyin Ye, Yujie Huang, Qing Zhang, Kun He, Mouji Liang, Jieming Zheng, Haoyuan Huang, Chunyi Wu, Lei Ge, Yuping Yang

“…Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. …”
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A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or Violent Environment by Zhihong Li, Yang Dong, Yanjie Wen, Han Xu, Jiahao Wu

“…There are still many deficiencies in the research of multiobject trajectory prediction, which mostly employ object detection and data association. Compared with the tremendous progress in object detection, data association still relied on hand-crafted constraints such as group, motion, and spatial proximity. …”
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Deep learning-based energy efficient LSFD weights prediction for user centric cell free massive MIMO system by Moustafa Mohamed, Salwa El-Ramly, Bassant Abdelhamid

“…Accordingly, this model could be implemented in a more distributed fashion at each AP. These models are trained using dataset generated from heuristic sparse LSFD optimization algorithm, this allows the models to learn the sparsity nature of the system and apply AP-UE association based on the values of the predicted LSFD weights at the receiver side while using the large scale fading coefficients as the models’ input. …”
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Comparative effects of metformin and varying intensities of exercise on miR-133a expression in diabetic rats: Insights from machine learning analysis by Elahe Alivaisi, Sabrieh Amini, Karimeh Haghani, Hori Ghaneialvar, Fatemeh Keshavarzi

“…This study investigated the effects of metformin, high-intensity interval training (HIIT), and moderate-intensity continuous training (MCT) on miR-133a expression in a diabetic rat model. miR-133a, a microRNA associated with skeletal muscle insulin resistance, served as a key indicator of treatment efficacy. …”
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Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model by Jiacan Wu, Guanghong Tao, Siyuan Xie, Han Yang, Fenglin Qi, Naiyue Bao, Zhuo Li, Guanglei Chang, Hua Xiao

“…The cohort was randomly divided into training (70%) and test (30%) sets. Feature selection utilized the Boruta algorithm and least absolute shrinkage and selection operator regression. …”
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Modular-based psychotherapy (MoBa) versus cognitive–behavioural therapy (CBT) for patients with depression, comorbidities and a history of childhood maltreatment: study protocol fo... by Elisabeth Schramm, Martin Hautzinger, Carolin Jenkner, Moritz Elsaesser, Sabine Herpertz, Hannah Piosczyk

“…Crucial feasibility aspects include targeted psychopathological mechanisms, selection (treatment algorithm), sequence and application of modules, as well as training and supervision of the study therapists.Ethics and dissemination This study obtained approval from the independent Ethics Committees of the University of Freiburg and the University of Heidelberg. …”
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Sustainable energy: Advancing wind power forecasting with grey wolf optimization and GRU models by Zainab Al-Ibraheemi, Samaher Al-Janabi

“…The GRU model demonstrated the highest accuracy (99.20 %) with the BDG-GWO dataset, with precision (0.9965), recall (0.9978), and F1 scores (0.9897) indicating superior performance. Training and testing times varied significantly, highlighting the computational challenges associated with deep learning techniques. …”
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Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes. by Helene Bei Thomsen, Livie Yumeng Li, Anders Aasted Isaksen, Benjamin Lebiecka-Johansen, Charline Bour, Guy Fagherazzi, William P T M van Doorn, Tibor V Varga, Adam Hulman

“…Potential healthcare disparities can happen when machine learning models, used in diabetes technologies, are trained on data from primarily White patients. We aimed to evaluate algorithmic fairness in glucose predictions. …”
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Recorded poor insight as a predictor of service use outcomes: cohort study of patients with first-episode psychosis in a large mental healthcare database by Robert Stewart, Neha Ramu, Anna Kolliakou, Jyoti Sanyal, Rashmi Patel

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Video Abnormal Action Recognition Based on Multimodal Heterogeneous Transfer Learning by Hong-Bo Huang, Yao-Lin Zheng, Zhi-Ying Hu

“…The proposed method reduces the reliance on labeled video data, improves the performance of the abnormal human action recognition algorithm, and outperforms the popular video-based models, particularly in scenarios with sparse data. …”
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Prediction of Human Papillomavirus-Host Oncoprotein Interactions Using Deep Learning by Sheila Santa, Samuel Kojo Kwofie, Kwasi Agyenkwa-Mawuli, Osbourne Quaye, Charles A Brown, Emmanuel A Tagoe

“…Method: To achieve this, available HPV and host protein interaction data was retrieved from the protocol of Eckhardt et al and used to train a Recurrent Neural Network algorithm. Training of the model was performed on the SPYDER (scientific python development environment) platform using python libraries; Scikit-learn, Pandas, NumPy, and TensorFlow. …”
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Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome by Satya P. Singh, Sukrit Gupta, Jagath C. Rajapakse

“…For decoding, we propose recursive feature elimination (RFE) algorithm based on DeepLIFT, layer-wise relevance propagation (LRP), and Integrated Gradients (IG) algorithms to remove irrelevant features and thereby identify key biomarkers associated with AD and PD. …”
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Semisupervised Learning for Detecting Inverse Compton Emission in Galaxy Clusters by Sheng-Chieh Lin, Yuanyuan Su, Fabio Gastaldello, Nathan Jacobs

“…The algorithm is trained and tested using synthetic NuSTAR X-ray spectra with instrumental and astrophysical backgrounds included. …”
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Exploring nanoparticle dynamics in binary chemical reactions within magnetized porous media: a computational analysis by Saleem Nasir, Abdallah Berrouk, Asim Aamir

“…Considering benchmark datasets set aside for training (70%), testing (15%), and validation (15%), the Levenberg-Marquardt algorithm, which employs back-propagation in artificial neural networks, is implemented. …”
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View-invariant object representation in anterior and posterior inferotemporal cortex: A machine learning approach by Jun-ya Okamura, Daisuke Fukano, Keisuke Murakami, Gang Wang

“…In the present study population activities were compared between cell populations in area TE and those in area TEO using machine learning algorithm. An object set consisted of four similar objects created by deforming a prototype object, and four views each separated by 30°. …”
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Towards ML Models’ Recommendations by Lara Kallab, Elio Mansour, Richard Chbeir

“…Today, a multitude of ML models exist having diverse characteristics, including the algorithm type, training dataset, and resultant performance. …”
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Development of a Diagnostic Model for Focal Segmental Glomerulosclerosis: Integrating Machine Learning on Activated Pathways and Clinical Validation by Ge Y, Liu X, Shu J, Jiang X, Wu Y

“…We then developed a highly accurate diagnostic model by integrating nine machine learning algorithms into 101 combinations, achieving near-perfect AUC values across training, validation, and external cohorts. …”
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Factores clave en la práctica de la Ciencia Abierta. Un análisis multivariado en el contexto universitario = Key Factors in Open Science Practice: A Multivariate Analysis within th... by Sebastián Araya-Pizarro, Héctor García-Leal

“…Data were analyzed using descriptive statistical techniques, association tests, and multivariate analysis (binary logistic regression and K-means algorithm). …”
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Modeling the Relationship Between Radon Anomalies and Seismic Activity Using Artificial Neural Networks and Statistical Methods by Kostadin Yotov, Emil Hadzhikolev, Stanka Hadzhikoleva

“…Significant deviations from the predicted values are interpreted as radon anomalies potentially associated with upcoming seismic events. The methodology includes wavelet transformation for noise removal, a multilayer ANN trained using the Levenberg–Marquardt algorithm, and a segmentation approach based on radial zones (annuli) for localized predictions. …”
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