The diagnostic accuracy of the ESC 0/1-hour algorithm in non-ST-segment elevation myocardial infarction in a crowded emergency department: a real-world experience from a single-cen... by Emre Kudu, Ahmet Enes Kucukardali, Mustafa Altun, Ümit Öztürk, Erhan Altunbaş, Sinan Karacabey, Erkman Sanri, Arzu Denizbasi

“…This study aimed to evaluate its diagnostic accuracy in a high-volume ED setting in Türkiye. …”
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Development and validation of a nomogram for predicting lung cancer based on acoustic–clinical features by Zhou Lu, Zhou Lu, Jiaojiao Sha, Xunxia Zhu, Xiaoyong Shen, Xiaoyu Chen, Xin Tan, Rouyan Pan, Shuyi Zhang, Shi Liu, Tao Jiang, Jiatuo Xu

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Using Segment Anything Model 2 for Zero-Shot 3D Segmentation of Abdominal Organs in Computed Tomography Scans to Adapt Video Tracking Capabilities for 3D Medical Imaging: Algorithm... by Yosuke Yamagishi, Shouhei Hanaoka, Tomohiro Kikuchi, Takahiro Nakao, Yuta Nakamura, Yukihiro Nomura, Soichiro Miki, Takeharu Yoshikawa, Osamu Abe

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Service quality evaluation of integrated health and social care for older Chinese adults in residential settings based on factor analysis and machine learning by Zhihan Liu, Caini Ouyang, Nian Gu, Jiaheng Zhang, Xiaojiao He, Qiuping Feng, Chunguyu Chang

“…Methods This study employs three machine learning algorithms—Backpropagation Neural Networks (BPNN), Feedforward Neural Networks (FNN), and Support Vector Machines (SVM)—to train and validate an evaluative item system. …”
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Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study by Haoyan Li, Zhenpeng Chen, Shuaiyi Gao, Jiaqi Hu, Zhihao Yang, Yun Peng, Jihang Sun

“…The evaluation set used 5 mGy VMIs reconstructed with various reconstruction algorithms: FBP, ASIR-V50%, ASIR-V100%, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. …”
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Evaluating the Effect of Thermal Treatment on Phenolic Compounds in Functional Flours Using Vis–NIR–SWIR Spectroscopy: A Machine Learning Approach by Achilleas Panagiotis Zalidis, Nikolaos Tsakiridis, George Zalidis, Ioannis Mourtzinos, Konstantinos Gkatzionis

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Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models by Junjie Zhao, Diyuan Li, Jian Zhou, Danial J. Armaghani, Aohui Zhou

“…A total of 102 data sets with seven input parameters (spacing‐to‐burden ratio, hole depth‐to‐burden ratio, burden‐to‐hole diameter ratio, stemming length‐to‐burden ratio, powder factor, in situ block size, and elastic modulus) and one output parameter (rock fragment mean size, X50) were adopted to train and validate the predictive models. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination ( R 2) were used as the evaluation metrics. …”
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Improved Deviation Sparse Fuzzy C-Means-2D Cumulative Sum Average Filter and Modified Sine Cosine Crow Search Algorithm-Wavelet Extreme Learning Machine for Brain Tumor Detection a... by Suvashisa Dash, Satyasis Mishra, Mohammed Siddique, Demissie J. Gelmecha, Ram Sewak Singh

“…The hybrid modified sine cosine algorithm-crow search algorithm (MSCA-CSA)–based WELM model is proposed to classify the brain tumor. …”
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Portable X-ray fluorescence sensor for ecofriendly, low-cost, and fast assessment of eucalypt charcoal attributes by Renata Andrade, Lucas Benedet, Marcelo Mancini, Sérgio Henrique Godinho Silva, Camila da Silva Freitas, Marco Aurélio Carbone Carneiro, Nilton Curi

“…The objective of this study was to evaluate the use of pXRF data in machine-learning models trained to predict attributes of eucalypt charcoal. pXRF data (elemental contents) from 276 charcoal samples were used to train predictive models using six machine-learning algorithms. …”
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Traffic Classification in Software-Defined Networking Using Genetic Programming Tools by Spiridoula V. Margariti, Ioannis G. Tsoulos, Evangelia Kiousi, Eleftherios Stergiou

“…This is most evident in the feature construction method where at each generation of the genetic algorithm, a set of learning models is required to be trained to evaluate the generated artificial features.…”
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Machine Learning with Administrative Data for Energy Poverty Identification in the UK by Lin Zheng, Eoghan McKenna

“…This data is selected to closely resemble what might be available in national administrative databases, incorporating variables such as household socio-demographics and building physical characteristics. We evaluate multiple classification algorithms, including Random Forest and XGBoosting, applying resampling and class weighting techniques to address the inherent class imbalance in energy poverty classification. …”
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Federated learning and GWO-enabled consumer-centric healthcare internet of things for pancreatic tumour by Nuha Alruwais, Ghada Moh. Samir Elhessewi, Muhammad Kashif Saeed, Menwa Alshammeri, Othman Alrusaini, Abdulwhab Alkharashi, Samah Al Zanin, Yahia Said

“…This study aims to address this problem by introducing a unified framework that integrates the inherent capabilities of Federated Learning (FL) with the unique characteristics of the Grey Wolf Optimisation algorithm. The pancreatic tumour dataset is used to evaluate the GWO-enabled FL framework. …”
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The Cart-Pole Application as a Benchmark for Neuromorphic Computing by James S. Plank, Charles P. Rizzo, Chris A. White, Catherine D. Schuman

“…We propose achievement levels for AI agents that are trained with these settings. Next, we perform an experiment that employs the benchmark and its difficulty levels to evaluate the effectiveness of eight neuroprocessor settings on success with the application. …”
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Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data by Ali Asghar Zolfaghari, Maryam Raeesi, Giuseppe Longo-Minnolo, Simona Consoli, Miles Dyck

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Construction of a deep learning-based predictive model to evaluate the influence of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts by Ruozu Xiao, Haowei Zhou, Zhen Shi, Rong Huang, Yuheng Zhang, Jing Li

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CLASSYFYING STUDENTS DECISION MAKING ABILITY USING K-NEAREST NEIGHBOR FOR DETERMINING STUDENTS SUPPLEMENTARY LEARNING by Giyanti Giyanti, Rina Oktaviyanthi, Usep Sholahudin

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Application of smart technologies for predicting soil erosion patterns by Rana Muhammad Adnan Ikram, Mo Wang, Hossein Moayedi, Atefeh Ahmadi Dehrashid, Shiva Gharibi, Jing-Cheng Han

“…It is imperative to examine the impact of water-induced erosion on cultivated lands, as it can cause significant damage. This study evaluates the effectiveness of four data-driven approaches (biogeography-based optimization, earthworm optimization algorithm, symbiotic organisms search, and whale optimization algorithm) combined with artificial neural network models for the assessment of erosion susceptibility. …”
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Reducing the acquisition time for magnetic resonance imaging using super-resolution image generation and evaluating the accuracy of hippocampal volumes for diagnosing Alzheimer’s d... by Nobukiyo Yoshida, Nobukiyo Yoshida, Hajime Kageyama, Hajime Kageyama, Hiroyuki Akai, Satoshi Kasai, Satoshi Kasai, Kei Sasaki, Noriko Sakurai, Naoki Kodama

“…The hippocampal volume was measured using brain anatomical analysis with diffeomorphic deformation software, which employs machine learning algorithms and performs voxel-based morphometry. Peak signal-to-noise ratio (PSNR) and Multiscale structural similarity (MS-SSIM) score were used to objectively evaluate the generated images.ResultsAt λ = e3, the PSNR and MS-SSIM score of the generated images were 27.91 ± 1.78 dB and 0.96 ± 0.0045, respectively. …”
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SOC Estimation of Large Capacity Lithium Batteries Based on LWOA-LSTM by Hongzhong MA, Wenjing XUAN, Muyu ZHU, Yuelin CHEN

“…Firstly, the LSTM neural network and LWOA algorithm are analyzed, and the LWOA-LSTM model is constructed to optimize the parameters. …”
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Research on somatic interactive exhibition design of lacquer artwork based on multi-source data analysis by Ke Ni

“…First, it explores the theoretical foundation of multi-source data and related algorithms, with a focus on ACS and ACO algorithms within neural networks, providing strong theoretical support for the model design. …”
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