Developing Machine-Learning Models to Predict Bacteremia in Febrile Adults Presenting to the Emergency Department: A Retrospective Cohort Study from a Large Center by Chia-Ming Fu, Ike Ngo, Pak Sheung Lau, Yaroslav Ivanchuk, Fan-Ya Chou, Chih-Hung Wang, Chien-Yu Lin, Chu-Lin Tsai, Shey-Ying Chen, Tsung-Chien Lu, Hung-Yu Wei

“…We split the dataset into training/validation and testing sets (60-to-40 ratio) and trained five supervised ML models using K-fold cross-validation. …”
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Iterative channel estimation based on the Turbo principle for UWB system by TENG Peng-wei1, WANG Yi-ming1, ZHU Hong-bo2

“…In each iteration, the soft-output of decoder is fed back to the channel estimation module, and used as the prior information, which means no training sequences are used. The performance of the proposed iterative channel estimation algorithm in UWB dense multipath channels is evaluated. …”
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A semantic segmentation-based automatic pterygium assessment and grading system by Qingbo Ji, Qingbo Ji, Wanyang Liu, Wanyang Liu, Qingfeng Ma, Qingfeng Ma, Lijun Qu, Lin Zhang, Hui He

“…This study aims to develop an automated grading system combining deep learning and image processing techniques for precise pterygium evaluation.MethodsThe proposed system integrates two modules: 1) A semantic segmentation module utilizing an improved TransUnet architecture for pixel-level pterygium localization, trained on annotated slit-lamp microscope images from clinical datasets. 2) A severity assessment module employing enhanced curve fitting algorithms to quantify pterygium invasion depth in critical ocular regions. …”
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Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children. by Megan Palmer, James A Seddon, Marieke M van der Zalm, Anneke C Hesseling, Pierre Goussard, H Simon Schaaf, Julie Morrison, Bram van Ginneken, Jaime Melendez, Elisabetta Walters, Keelin Murphy

“…We recommend replicating the methods we describe using a larger chest x-ray dataset from a more diverse population and evaluating the potential role of CAD to replace a human-read chest x-ray within treatment-decision algorithms for paediatric tuberculosis.…”
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The value of a combined model based on ultra-radiomics and multi-modal ultrasound in the benign-malignant differentiation of C-TIRADS 4A thyroid nodules: a prospective multicenter... by Shuai Cui, Qifan Liu, Hailong Wang, Husha Li, Wei Li, Chenlong Li, Leilei Bi, Yang Mu, Wenjing Guo, Jundong Yao, Zhoulong Zhang

“…Based on the enrollment timeline, patients were divided into a training set (n=312) and a test set (n=134) in a 7:3 ratio. …”
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Anoikis-related gene PDK4 and pathogenesis of type 2 diabetes mellitus： A bioinformatics-based study by ZHANG Ke, ZHANG Weiyi, SUN Haitian, CAO Mingfeng, ZHANG Xinhuan

“…Subsequently, key genes were identified using the random forest (RF) and least absolute shrinkage and selection operator (LASSO) algorithms. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the association strength between the expression levels of the identified key genes in pancreas islet tissues and T2DM, followed by validation in the GSE76895 dataset. …”
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Advancing Precision Medicine for Hypertensive Nephropathy: A Novel Prognostic Model Incorporating Pathological Indicators by Yunlong Qin, Jin Zhao, Yan Xing, Zixian Yu, Panpan Liu, Yuwei Wang, Anjing Wang, Yueqing Hui, Wei Zhao, Mei Han, Meng Liu, Xiaoxuan Ning, Shiren Sun

“… Introduction: This study aimed to assess the long-term renal prognosis of patients with hypertensive nephropathy (HN) diagnosed through renal biopsy, utilizing the random survival forest (RSF) algorithm. Methods: From December 2010 to December 2022, HN patients diagnosed by renal biopsy in Xijing Hospital were enrolled and randomly divided into training set and testing set at a ratio of 7∶3. …”
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Intelligent Methods of Operational Response to Accidents in Urban Water Supply Systems Based on LSTM Neural Network Models by Aliaksey A. Kapanski, Nadezeya V. Hruntovich, Roman V. Klyuev, Aleksandr E. Boltrushevich, Svetlana N. Sorokova, Egor A. Efremenkov, Anton Y. Demin, Nikita V. Martyushev

“…The paper presents a data pre-processing algorithm for model training, as well as an analysis of the influence of various architectural parameters, such as the number of LSTM layers, the utilization of Dropout layers for regularization, and the number of neurons in Dense (fully connected) layers. …”
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A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients by Andreea Istrate, Radu Constantinescu, Lithicka Anandavel, Shraddha Rajeshkumar Tandel, Simon Dye, Charlotte Dye

“…Standard accuracy metrics assessed performance during training and testing. The machine learning algorithm showed reasonable accuracy (86%) in classifying the diagnosis from an isolated scan slice but high accuracy (99%) when aggregating over slices taken from a full scan. …”
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LRD-RB: The First Large-Scale Dataset With Rotated Bounding Boxes for Automated Lunar Rockfall Detection by Dingruibo Miao, Jianguo Yan, Zhigang Tu, Jean-Pierre Barriot

“…LRD-RB demonstrates excellent diversity in spatial resolution, solar illumination conditions, and geographical distribution, providing comprehensive feature representations for training deep learning models. We conducted systematic evaluations of several representative rotated object detection models on LRD-RB. …”
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Hybrid Attention-Enhanced Xception and Dynamic Chaotic Whale Optimization for Brain Tumor Diagnosis by Aliyu Tetengi Ibrahim, Ibrahim Hayatu Hassan, Mohammed Abdullahi, Armand Florentin Donfack Kana, Amina Hassan Abubakar, Mohammed Tukur Mohammed, Lubna A. Gabralla, Mohamad Khoiru Rusydi, Haruna Chiroma

“…Our proposed method was evaluated on benchmark datasets achieving remarkable accuracies of 99.67%, 99.09%, and 99.67% compared to the classical algorithms.…”
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Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction by Ahya Radiatul Kamila, Johanes Fernandes Andry, Francka Sakti Lee, Felliks F. Tampinongkol

“…After preprocessing is completed, the prediction model is trained using the Random Forest algorithm to predict employee turnover. …”
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Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning by Liang Wang, PeiPei Zhang, Yanhui Feng, Wenzhi Lv, Xiangde Min, Zhiyong Liu, Jin Li, Zhaoyan Feng

“…We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. …”
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Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics by Wu W, Hu X, Yan L, Li Z, Li B, Chen X, Lin Z, Zeng H, Li C, Mo Y, Wu Y, Wang Q

“…Using 10 classical machine learning algorithms, we developed screening models. Evaluation metrics determined the best model. …”
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A novel AI-based CNN model to predict the structural performance of monopile used for offshore wind energy systems by Sajid Ali, Muhammad Waleed, Daeyong Lee

“…Additionally, comparative assessment of training dataset sizes (100–800) validated increasing model accuracy and reliability with bigger datasets, highlighting the effectiveness of long-term measured data for CNN training. …”
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Image-to-Point Cloud Registration using Camera Motion Generation and Monocular Depth Estimation by M. Kamali, B. Atazadeh, A. Rajabifard, Y. Chen

“…The final registered point cloud is then aligned with the scene point cloud through the Iterative Closest Point (ICP) algorithm, ensuring precise spatial alignment. The proposed method eliminates the need for training or reliance on intrinsic camera parameters, making it robust for diverse and unseen environments. …”
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Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach by Dan Ben Ami, Kobi Cohen, Qing Zhao

“…Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. …”
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Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency by Bruce Pyenson, Maggie Alston, Jeffrey Gomberg, Feng Han, Nikhil Khandelwal, Motoharu Dei, Monica Son, Jaime Vora

“…The study population was then randomly divided into a training subset and a testing subset. The training subset was used to determine the performance metrics of 27 models and to select the highest performing model, and the testing subset was used to evaluate performance of the best machine learning model…”
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Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward Relay by Gangsan Jeong, Xianglan Jin

“…Given the challenge of finding optimal step sizes for the MIMO AQF relay, comparing outcomes across different determination algorithms proves to be a significant obstacle. To address this, we evaluate error performance at the destination for the entire AQF relay communication system by introducing a linear detection method with significantly reduced complexity in the MIMO AQF relay channel. …”
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Classification of Software Engineering Documents Based on Artificial Immune Systems by Nada Saleem, Rasha Saeed

“…After conducting several experiments on a various group of software engineering documents, evaluations results have shown that the accuracy of the innate immunologic method (DCM) has reached (DCM) (95%), whereas Naïve classification method has reached (90 %) with training and classification speed that doesn’t exceed one minutes. …”
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