Comparison of three one-dimensional time-domain electromagnetic forward algorithms by Frederik Alexander Falk, Anders Vest Christiansen, Thomas Mejer Hansen

“…Accurate, efficient, and accessible forward modeling of geophysical processes is essential for understanding them and for inversion of geophysical data. Various algorithms are available for predicting data with the time domain electromagnetic method (TDEM). …”
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Machine Learning Algorithms for Nondestructive Sensing of Moisture Content in Grain and Seed by Arthur P. LeBlanc, Samir Trabelsi, Khaled Rasheed, John A. Miller

“…Performance of this model is investigated and compared with models trained on an individual grain or seed by using different algorithms, including artificial neural network (NN), support vector regression (SVR), ElasticNet, among other algorithms. …”
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Increasing clinicians’ suspicion of ATTR amyloidosis using a retrospective algorithm by Jessica Ammon, John Alexander, Woodson Petit-Frere, Deya Alkhatib, Aranyak Rawal, Grace Newman, Oguz Akbiligic, Brian Borkowski, John Jefferies, Isaac B. Rhea

“…Conclusion The results of this study suggest that using this algorithm, despite it not being independently predictive of ATTR, did result in our clinicians having a lower threshold for testing for ATTR. …”
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Algorithm for Recognizing the Type of Modulation and Measuring Parameters of Radar Signals with Chirp by D. V. Arkhipenkov

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Cross-validation for training and testing co-occurrence network inference algorithms by Daniel Agyapong, Jeffrey Ryan Propster, Jane Marks, Toby Dylan Hocking

“…We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. …”
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Improved Hypernymy Detection Algorithm Based on Heterogeneous Graph Neural Network by Li Ren, Jing Huang, Hai-Tao Jia, Shu-Bao Sun, Kai-Shi Wang, Yi-Le Wu

“…These tasks often require understanding the semantic relations between concepts or entities in text for more accurate analysis and reasoning. Currently, algorithms for identifying and detecting hyponymy–hypernymy semantic relations face two main challenges: first, candidate hyponymy–hypernymy relation tuples do not exist in the same contextual sentence, failing to meet the co-occurrence requirement; second, distributed algorithms have issues with lexical memory. …”
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An evaluation of flow-routing algorithms for calculating contributing area on regular grids by A. B. Prescott, J. D. Pelletier, S. Chataut, S. Ananthanarayan

“…We revisit the purported excess dispersion of the multiple-flow-direction (MFD) algorithm of Freeman (1991) that motivated the development of <span class="inline-formula"><i>D</i>∞</span> and demonstrate that MFD is superior to <span class="inline-formula"><i>D</i>∞</span> when tested against analytic solutions for the contributing areas of idealized landforms and the predictions of the shallow-water equation solver FLO-2D for more complex landforms in which the water surface slope is closely approximated by the bed slope. …”
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Induction of Convolutional Decision Trees for Semantic Segmentation of Color Images Using Differential Evolution and Time and Memory Reduction Techniques by Adriana-Laura López-Lobato, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes

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CLINICAL AND EXPERIMENTAL ARGUMENTATION OF MODIFIED METHOD FOR PERI-IMPLANT BONE REDUCTION EVALUATION DURING VARIOUS SCHEMES OF PROSTHETIC REHABILITATION by A.T. Kenyuk

“…Circular definition of bone changes was highlighted by color difference for each of the analyzed objects, after which was conducted calculation of conventional parameters corresponded to the volumetric reduction of bone. Bone tissue as an object of research and superimposition of tomographic evaluation results, is unique in terms of analysis capacities that by its structure allows the efficient use of surface parameters and the voxel characteristics algorithms for image overlaying. …”
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Hybrid vegetation-seawall coastal systems for wave hazard reduction: analytics for cost-effective design from optimized features by Erfan Amini, Reza Marsooli, Somayeh Moazeni, Bilal M. Ayyub

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Red-KPLS Feature Reduction with 1D-ResNet50: Deep Learning Approach for Multiclass Alzheimer’s Staging by Syrine Neffati, Ameni Filali, Kawther Mekki, Kais Bouzrara

“…The proposed method integrates discrete wavelet transform (DWT) for multi-scale feature extraction, a novel reduced kernel partial least squares (Red-KPLS) algorithm for feature reduction, and ResNet-50 for classification. …”
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Massive Scenario Reduction Based Distribution-Level Power System Planning Considering the Coordination of Source, Network, Load and Storage by Jinsen LIU, Ning LUO, Jie WANG, Chang XU, Yi Cao, Zhiwen Liu

“…In order to solve the problem of coordination between the massive operation data of renewable power generation and the coordinated planning of the source-network-load-storage, this paper proposes a coordinated planning method of the source-network-load-storage based on the massive scenario dimension reduction. Firstly, the dimensionality reduction clustering is carried out on the wind-light-load mass high-dimensional scenarios by the principal component Gaussian mixture clustering algorithm, and the typical scenario set of wind and power loads is obtained; then, a source-network-load-storage coordination planning model of distribution network for massive scenarios is constructed, and the second-order cone relaxation technique is adopted to convert the non-convex constraints to convex ones; finally, the effectiveness of the proposed massive scenario dimension reduction clustering method and distribution network planning model is verified on the Portugal 54-node distribution network.…”
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Outage reduction in 5G uplink cooperative NOMA using PSO-optimized relay selection and successive interference cancelation by David S. Bhatti, M. Asif Haroon, Haeung Choi, Seungmin Kim, Heung-No Lee

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An Adaptive Noise Reduction Method Based on Improved Dislocation Superposition Method for Abnormal Noise Fault Component of Automotive Engine by Jiaoyi Hou, Pengwei Guo, Aoyu Xu, Dayong Ning, Shengtao Chen, Zengmeng Zhang, Yongjun Gong, Yinglong Chen, Hao Tian, Hongwei Du

“…This study proposes an adaptive noise reduction method based on the dislocation superposition method (DSM), which can realize the adaptive noise reduction and the extraction of fault a component from the automobile engine abnormal noise signal of low SNR. …”
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Improving automated scoring of prosody in oral reading fluency using deep learning algorithm by Kuo Wang, Xin Qiao, George Sammit, Eric C. Larson, Joseph Nese, Akihito Kamata

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ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions by Yirga Yayeh Munaye, Wasyihun Admass, Yenework Belayneh, Atinkut Molla, Mekete Asmare

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Health poverty reduction effect of medical insurance in China’s middle-aged and elderly populations: a cross-sectional study by Haijun Yang, Rui Chen, Ye Li, Qunhong Wu, Linghan Shan, Bing Wu, Fang Yin, Yiyun Zhang, Yongqiang Lai, Xinwei Liu, Wanxin Tian, Xiyu Zhang, Yujuan Xu, Wenqing Miao, Baoguo Shi, Feifei Leng

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AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images by Csaba Csikos, Sándor Barna, Ákos Kovács, Péter Czina, Ádám Budai, Melinda Szoliková, Iván Gábor Nagy, Borbála Husztik, Gábor Kiszler, Ildikó Garai

“…This study aimed to assess the performance of an AI-based bone scan noise-reduction filter on noisy, low-count images in a routine clinical environment. …”
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Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts by Miaobing Zheng, Yuxin Zhang, Rachel A Laws, Peter Vuillermin, Jodie Dodd, Li Ming Wen, Louise A Baur, Rachael Taylor, Rebecca Byrne, Anne-Louise Ponsonby, Kylie D Hesketh

“…ObjectiveLeveraging machine learning (ML) algorithms, this study aimed to develop and validate risk prediction models to identify infant RWG by the age of 1 year. …”
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A machine learning-based predictive model for predicting early neurological deterioration in lenticulostriate atheromatous disease-related infarction by Zhuangzhuang Jiang, Dongjuan Xu, Hongfei Li, Xiaolan Wu, Yuan Fang, Chen Lou

“…Background and aimThis study aimed to develop a predictive model for early neurological deterioration (END) in branch atheromatous disease (BAD) affecting the lenticulostriate artery (LSA) territory using machine learning. …”
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