A Drilling Debris Tracking and Velocity Measurement Method Based on Fine Target Feature Fusion Optimization by Jinteng Yang, Yu Bao, Zumao Xie, Haojie Zhang, Zhongnian Li, Yonggang Li

“…To facilitate model training and performance evaluation, we establish a drill-cutting splash simulation dataset comprising 3787 images, covering a diverse range of ejection angles, velocities, and material types. …”
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Achieving precision assessment of functional clinical scores for upper extremity using IMU-Based wearable devices and deep learning methods by Weinan Zhou, Diyang Fu, Zhiyu Duan, Jiping Wang, Linfu Zhou, Liquan Guo

“…Trial registration: Clinical trial of telerehabilitation training and intelligent evaluation system, ChiCTR2200061310, Registered 20 June 2022-Retrospective registration.…”
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A Local Dwarf Galaxy Search Using Machine Learning by Huanian Zhang, Guangping Ye, Rongyu Wu, Dennis Zaritsky

“…We introduce the spectrally confirmed training sample, discuss evaluation metrics, investigate features, compare different machine learning algorithms, and find that a seven-class neural network classification model is highly effective in separating the signal (local, low-mass galaxies) from various contaminants, reaching a precision of 95% and a recall of 76%. …”
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Population-based colorectal cancer risk prediction using a SHAP-enhanced LightGBM model by Guinian Du, Hui Lv, Yishan Liang, Jingyue Zhang, Qiaoling Huang, Guiming Xie, Xian Wu, Hao Zeng, Lijuan Wu, Jianbo Ye, Wentan Xie, Xia Li, Yifan Sun

“…BackgroundColorectal cancer (CRC) is a highly frequent cancer worldwide, and early detection and risk stratification playing a critical role in reducing both incidence and mortality. we aimed to develop and validate a machine learning (ML) model using clinical data to improve CRC identification and prognostic evaluation.MethodsWe analyzed multicenter datasets comprising 676 CRC patients and 410 controls from Guigang City People’s Hospital (2020-2024) for model training/internal validation, with 463 patients from Laibin City People’s Hospital for external validation. …”
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Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics by ChenDeng Lao, Wei Wei, JianWen Cheng, ShiJie Liao, XiaoLin Luo, Qian Huang, HengZhen Huang, JinMin Zhao

“…Three machine learning algorithms identified ATF3, CHKA, CLU, CTNNB1, and FASN as potential biomarkers. …”
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Automated interpretation of deep learning-based water quality assessment system for enhanced environmental management decisions by Javed Mallick, Saeed Alqadhi, Majed Alsubih, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…The automated CNN models demonstrated robust performance in predicting water quality indices, with high accuracy (R2 = 0.959 in training and 0.945 in testing) for sodium percentage (Na%). …”
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An MRI-based radiomics nomogram for preoperative prediction of Ki-67 index in nasopharyngeal carcinoma: a two-center study by Yao Wang, Jing Zhang, Qiyuan Li, Li Sun, Yingmei Zheng, Chuanping Gao, Cheng Dong

“…Subsequently, a radiomics nomogram was established using a logistic regression (LR) algorithm. The predictive performance of the nomogram was evaluated using operating characteristics curve (ROC), decision curve analysis (DCA), and the area under the curve (AUC).ResultsFive radiomics features were selected to build the radiomics signature. …”
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InBRwSANet: Self-attention based parallel inverted residual bottleneck architecture for human action recognition in smart cities. by Yasir Khan Jadoon, Muhammad Attique Khan, Yasir Noman Khalid, Jamel Baili, Nebojsa Bacanin, MinKyung Hong, Yunyoung Nam

“…The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. …”
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Diagnosis of Lung Cancer Using Endobronchial Ultrasonography Image Based on Multi-Scale Image and Multi-Feature Fusion Framework by Huitao Wang, Takahiro Nakajima, Kohei Shikano, Yukihiro Nomura, Toshiya Nakaguchi

“…The dataset comprises 1140 EBUS images, with 540 images used for training, and 300 images each for the validation and test sets. …”
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Machine learning-based pipeline for automated intracerebral hemorrhage and drain detection, quantification, and classification in non-enhanced CT images (NeuroDrAIn). by Samer Elsheikh, Ahmed Elbaz, Alexander Rau, Theo Demerath, Elias Kellner, Ralf Watzlawick, Urs Würtemberger, Horst Urbach, Marco Reisert

“…The current standard of practice includes post-operative computer tomography imaging, which is subjectively evaluated. The implementation of an objective, automated evaluation of postoperative studies may enhance diagnostic accuracy and facilitate the scaling of research projects. …”
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Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography by Arpitha Ravi, Philipp Bernhardt, Mathis Hoffmann, Florian Kordon, Siming Bayer, Stephan Achenbach, Andreas Maier

“…An independent test set of 146 sequences was used for evaluation. The multimodal model outperformed the others, achieving an average F1 score of 0.82 and an AUC of 0.87, matching expert evaluations. …”
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Mining hypertension predictors using decision tree: Baseline data of Kharameh cohort study by abbas Rezaianzadeh, Samane Nematolahi, maryam jalali, Shayan Rezaeianzadeh, Masoumeh Ghoddusi Johari, Seyed Vahid Hosseini

“…The study included 2510 hypertensive and 7840 non-hypertensive individuals. 70% of the cases were randomly allocated to the training dataset for establishing the decision tree, while the remaining 30% were used as the testing dataset for performance evaluation of the decision-tree. …”
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Optimized CNN-Bi-LSTM–Based BCI System for Imagined Speech Recognition Using FOA-DWT by Meenakshi Bisla, Radhey Shyam Anand

“…EEG signal is enhanced using firefly optimization algorithm (FOA)–based optimized soft thresholding of high-frequency detail components obtained by DWT decomposition. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…Performance was assessed using multiple evaluation metrics on both training and validation datasets. …”
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Transfer learning for securing electric vehicle charging infrastructure from cyber-physical attacks by Ahmad Almadhor, Shtwai Alsubai, Imen Bouazzi, Vincent Karovic, Monika Davidekova, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…The CICEVSE2024 (EVSE-A and EVSE-B) datasets are used to assess the framework, where one dataset is used to train and store weights, and the second is used to evaluate the learned patterns using transfer learning. …”
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A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…The dataset was split into a 70%:15%:15% ratio for training, testing, and validation, respectively, and five machine learning algorithms were evaluated: AdaBoost, Support Vector Machine, Random Forest, Decision Tree, and Stochastic Gradient Descent. …”
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Enhanced Disc Herniation Classification Using Grey Wolf Optimization Based on Hybrid Feature Extraction and Deep Learning Methods by Yasemin Sarı, Nesrin Aydın Atasoy

“…ResNet50’s residual connections allow for effective training and high-quality feature extraction from input images. …”
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Control strategy of robotic manipulator based on multi-task reinforcement learning by Tao Wang, Ziming Ruan, Yuyan Wang, Chong Chen

“…Finally, the proposed algorithm is evaluated in a multi-task environment of the Meta-World, a benchmark for multi-task reinforcement learning containing robotic manipulation tasks, and the multi-task MUJOCO environment.…”
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Integrated Intelligent Control of Redundant Degrees-of-Freedom Manipulators via the Fusion of Deep Reinforcement Learning and Forward Kinematics Models by Yushuo Chen, Shijie Su, Kai Ni, Cunjun Li

“…Our experimental analysis, applied to 7-DOF and 4-DOF manipulators in simulated and real-world environments, evaluates the FK-DRL strategy’s performance. The results indicate that compared to classical DRL algorithms, the FK-DDPG, FK-TD3, and FK-SAC algorithms improved the success rates of intelligent control tasks for the 7-DOF manipulator by 21%, 87%, and 64%, respectively, and the training convergence speeds increased by 21%, 18%, and 68%, respectively. …”
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Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier by Hong Zheng MS, Wei Chen MS, Jun Liu MD, Lian Jian MD, Tao Luo BS, Xiaoping Yu MD

“…Introduction This study aimed to devise a diagnostic algorithm, termed the Refined Radiomics and Deep Learning Features-Guided CatBoost Classifier (RRDLC-Classifier), and evaluate its efficacy in predicting pathological high-grade patterns in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC). …”
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