THE CLINICAL CONFERENCE AS GUIDANCE TO FORMING INTERNS’ PROFESSIONAL COMPETENCIES DURING FORMATION IN THE UMSA’S DEPARTMENT OF POSTGRADUATED EDUCATION OF DENTISTS by T.P. Skripnikova, M.V. Khrebor, Ju. I. Sylenko, O.A. Pisarenko

“…The clinical conferences are effective method of theoretical training and acquiring practical skills by interns and way to be involved in communication in the medical society. …”
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Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study by Mengting Gu, Wenjie Zou, Huilin Chen, Ruilin He, Xingyu Zhao, Ningyang Jia, Wanmin Liu, Peijun Wang

“…Univariate and multivariate logistic regression analyses were used to determine independent clinicoradiological predictors significantly associated with VETC, which then constituted the clinicoradiological model. …”
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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…Image features are extracted using the pre-trained convolutional neural network model Inception ResNetV2 and image data is clustered by K-means clustering algorithm. …”
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Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics by Guoda Han, Xu Liu, Tian Gao, Lei Zhang, Xiaoling Zhang, Xiaonan Wei, Yecheng Lin, Bohong Yin

“…Features selected via minimum Redundancy - Maximum Relevance (mRMR)- recursive feature elimination (RFE) screening were used to train a model using the Gradient Boosting Machine (GBM) algorithm. …”
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An echo state network based on enhanced intersecting cortical model for discrete chaotic system prediction by Xubin Wang, Pei Ma, Jing Lian, Jizhao Liu, Yide Ma

“…A Bayesian Optimization algorithm was employed for the selection of hyperparameters. …”
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Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma by Xiangyun Li MD, MS, Xiaoqun Yang MD, PhD, Xianwei Yang BSc, Xin Xie MD, PhD, Wenbin Rui MD, PhD, Hongchao He MD, PhD

“…A total of 368 pathomics features were extracted from H&E-stained slides of ccRCC patients, and a pathomics model comprising two subtypes (Cluster 1 and Cluster 2) was successfully constructed using the NMF algorithm. KM survival curves and Cox regression analysis revealed that Cluster 2 was associated with worse OS. …”
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High Accuracy of Epileptic Seizure Detection Using Tiny Machine Learning Technology for Implantable Closed-Loop Neurostimulation Systems by Evangelia Tsakanika, Vasileios Tsoukas, Athanasios Kakarountas, Vasileios Kokkinos

“…<b>Methods:</b> A dataset containing iEEG signal values from both non-epileptic and epileptic individuals was utilized for the implementation of the proposed algorithm. Appropriate data preprocessing was performed, and two training datasets with 1000 records of non-epileptic and epileptic iEEG signals were created. …”
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Developing an alternative data-driven model to resemble geomorphologic rainfall-runoff models by Pin-Chun Huang, Kwan Tun Lee

“…The proposed artificial intelligence (AI) model, which incorporates a classification algorithm for preprocessing input features prior to training a model based on the recurrent neural network, exhibits outstanding performance in runoff discharge prediction. …”
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Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network by Wenlong Zou, Haipeng Zhao, Ming Ren, Chaoxiong Cui, Guobin Yuan, Boyi Yuan, Zeyu Ji, Chao Wu, Bin Cai, Tingting Yang, Jinjun Zou, Guangzhi Liu

“…Patients were allocated to the training and test sets in an 8:2 ratio according to the principle of randomness. …”
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Factors affecting refractoriness or recurrence in diffuse large B-cell lymphoma: development and validation of a novel predictive nomogram by Yiwei Guo, Jie Lian, Yao Chen, Lina Quan, Xiuchen Guo, Jingbo Zhang, Zhiqiang Liu, Aichun Liu

“…These variables were also prioritized using a random forest algorithm. The developed nomogram was evaluated with the receiver-operator characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) for its clinical utility.Results Univariable analysis pinpointed several factors significantly associated with refractoriness/recurrence, including pathological subtype, lactate dehydrogenase (LDH), International Prognostic Index (IPI), treatment, absolute lymphocyte count (ALC), lymphocyte/monocyte ratio (LMR), and prognostic nutritional index (PNI). …”
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Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning by Ming-xin Jiang, Chao Deng, Zhi-geng Pan, Lan-fang Wang, Xing Sun

“…In this paper, we propose a multiobject tracking algorithm in videos based on long short-term memory (LSTM) and deep reinforcement learning. …”
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Comparative Analysis of The Combined Model (Spatial and Temporal) and Regression Models for Predicting Murder Crime by Laith S. Ibrahim, Ghadeer Jasim Mohammed

“… This research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. …”
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Collagen gene signature in the tumor microenvironment predicts survival and guides prognosis in bladder cancer by Yi Huang, Shuogui Fang, Weibin Xie, Yitong Zou, Hui Zhuo, Gang Shen, Hua Zhou, ChunPing Mao, Cong Lai, Jianqiu Kong, Xinxiang Fan

“…Model construction employed the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. The model's performance was thoroughly assessed, including its discrimination, calibration, and clinical utility. …”
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The Hessian by blocks for neural network by backward propagation by Radhia Bessi, Nabil Gmati

“…In addition, the introduction of original operators, for the calculation of second derivatives, facilitates the reading and allows the parallelization of the backward-looking algorithm. To study the practical performance of Newton's method, we apply the proposed algorithm to train two classical neural networks for regression and classification problems and display the associated numerical results.…”
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Comparative Assessment of Several Effective Machine Learning Classification Methods for Maternal Health Risk by Md Nurul Raihen, Sultana Akter

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Enhancing clinical decision-making in closed pelvic fractures with machine learning models by Dian Wang, Yongxin Li, Li Wang

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Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 s... by Marwan T. Mezher, Alejandro Pereira, Rusul Ahmed Shakir, Tomasz Trzepieciński

“…However, the random forest algorithm gave the second best prediction of the MSE while the CatBoost and gradient boosting algorithms were third and fourth, respectively. …”
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Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with neural networks by Stefan Hildebrand, Sandra Klinge

“…An extendable, efficient and explainable Machine Learning approach is proposed to represent cyclic plasticity and replace conventional material models based on the Radial Return Mapping algorithm. High accuracy and stability by means of a limited amount of training data is achieved by implementing physics-informed regularizations and the back stress information. …”
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Enhanced Yolov8 network with Extended Kalman Filter for wildlife detection and tracking in complex environments by Langkun Jiang, Li Wu

“…Initially, the Stable Diffusion model augments the dataset, establishing a basis for training data. Subsequently, enhancements to the Yolov8n model are implemented through the incorporation of the deformable convolutional network DCNv3 and the utilization of the C2f_DCNV3 layer to augment feature extraction efficacy, while addressing detection challenges associated with small targets and intricate backgrounds by integrating the EMGA attention mechanism and the ASPFC feature fusion module. …”
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GOME-NGU: Visual Navigation Under Sparse Reward via Goal-Oriented Memory Encoder With Never Give Up by Ji Sue Lee, Jun Moon

“…In this paper, we propose the Goal-Oriented Memory Encoder (GOME) with Never Give Up (NGU) algorithm to enhance visual navigation in sparse-reward environments. …”
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