Algorithms and probabilistic models of parameters of operation of in-plant power supply by E. I. Gracheva, O. V. Naumov, A. N. Gorlov, Z. M. Shakurova

“…The article considers the features of the functioning of low-voltage networks of intra-plant power supply systems. …”
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Devanagari Character Recognition: A Comprehensive Literature Review by Sandhya Arora, Latesh Malik, Sonakshi Goyal, Debotosh Bhattacharjee, Mita Nasipuri, Ondrej Krejcar

“…This literature review examines the evolution of handwritten Devanagari character recognition (HDCR), exploring early template matching and feature extraction methods that struggled with the script’s intricacy. …”
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Parathyroid Carcinoma: An Up-to-Date Retrospective Multicentric Analysis by Francesco Quaglino, Luca Manfrino, Luca Cestino, Massimo Giusti, Enrico Mazza, Alessandro Piovesan, Nicola Palestini, Corrado Lauro, Elena Castellano

“…A meticulous presurgical evaluation of PHPT patients showing PC’s evocative features is mandatory to obtain a complete disease extirpation.…”
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Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling by Jing Zhang, Tingyi Tan, Yuhao Jiang, Congming Tan, Liangliang Hu, Daowen Xiong, Yikang Ding, Guowei Huang, Junjie Qin, Yin Tian

“…Working memory, a fundamental cognitive function of the brain, necessitates the evaluation of cognitive load intensity due to limited cognitive resources. …”
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Solar Energy Datasets of Deep Learning Models Incorporating with GK-2A and ASOS Ground Measurements by Jong-Sung Ha, Seungtaek Jeong, Seyun Min, Yejin Lee, Suhwan Kim, Doehee Han, Jong-Min Yeom

“…This study presents the construction and evaluation of a dataset for estimating solar energy using the GK-2A satellite and deep learning. …”
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Adaptive meta-modeling of evapotranspiration in arid agricultural regions of Saudi Arabia using climatic factors, drought indices and MODIS data by Osama Elsherbiny, Salah Elsayed, Obaid Aldosari, Muhammad Sohail Memon, Ahmed Elbeltagi

“…Machine learning models, including the backpropagation neural network (BPNN) and XGBoost Regressor (XGB), enhanced with an adaptive meta-model (AMM) strategy, were evaluated for predicting monthly AET. …”
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Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques by C. Zhang, X. Niu, H. Wu, Z. Ding, K. L. Chan, J. Kim, T. Wagner, C. Liu, C. Liu, C. Liu

“…In this study, we successfully incorporate geostationary satellite observations into a neural network model (GeoNet) to forecast full-coverage surface nitrogen dioxide (NO<span class="inline-formula">₂</span>) concentrations over eastern China at 4 h intervals for the next 24 h. …”
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Identification of High-Photosynthetic-Efficiency Wheat Varieties Based on Multi-Source Remote Sensing from UAVs by Weiyi Feng, Yubin Lan, Hongjian Zhao, Zhicheng Tang, Wenyu Peng, Hailong Che, Junke Zhu

“…Vegetation indices (VIs) and texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), and BP Neural Network (BPNN)) were employed to construct predictive models for nine photosynthetic indices across multiple growth stages. …”
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Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…Comprehensive evaluation metrics confirmed its robust performance: precision 0.98, recall 0.97, F1-score 0.98, MCC 0.95, ROC-AUC 0.99, and PR-AUC 0.99. …”
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OPTISTACK: A Hybrid Ensemble Learning and XAI-Based Approach for Malware Detection in Compressed Files by Khaled Mahmud Sujon, Rohayanti Binti Hassan, M. Abdullah-Al-Wadud, Jia Uddin

“…SHAP-based interpretability analysis further identifies the 25th quartile entropy as the most influential feature across all models. Additionally, we introduce an entropy network graph-based vulnerability analysis that reveals ZIP and RAR as the most malware-prone formats. …”
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AI-assisted anatomical structure recognition and segmentation via mamba-transformer architecture in abdominal ultrasound images by Shih-Fang Chang, Shih-Fang Chang, Po-Yi Wu, Ming-Chang Tsai, Ming-Chang Tsai, Vincent S. Tseng, Chi-Chih Wang, Chi-Chih Wang

“…Recognizing and extracting anatomical landmarks facilitates lesion evaluation and enhances diagnostic interpretation. Recent artificial intelligence (AI) segmentation methods employing deep neural networks (DNNs) and transformers encounter computational efficiency challenges to balance the preservation of feature dependencies information with model efficiency, limiting their clinical applicability.MethodsThe anatomical structure recognition framework, MaskHybrid, was developed using a private dataset comprising 34,711 abdominal ultrasound images of 2,063 patients from CSMUH. …”
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Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations by Brendan D. Adkinson, Matthew Rosenblatt, Javid Dadashkarimi, Link Tejavibulya, Rongtao Jiang, Stephanie Noble, Dustin Scheinost

“…Predictive modeling potentially increases the reproducibility and generalizability of neuroimaging brain-phenotype associations. Yet, the evaluation of a model in another dataset is underutilized. …”
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Genome-scale metabolic modelling reveals interactions and key roles of symbiont clades in a sponge holobiont by Shan Zhang, Weizhi Song, Geogios Marinos, Silvio Waschina, Johannes Zimmermann, Christoph Kaleta, Torsten Thomas

“…Abstract Sponges harbour complex microbiomes and as ancient metazoans and important ecosystem players are emerging as powerful models to understand the evolution and ecology of symbiotic interactions. Metagenomic studies have previously described the functional features of sponge symbionts, however, little is known about the metabolic interactions and processes that occur under different environmental conditions. …”
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AI‐Based Digital Rocks Augmentation and Assessment Metrics by Lei Liu, Bernard Chang, Maša Prodanović, Michael J. Pyrcz

“…Compared to most traditional dimensionality reduction methods that process images into a flattened vector, we propose topological image analysis for dimensionality reduction while preserving the essential geometric and topological features of the high‐dimensional data. To demonstrate our proposed approach, we evaluate the generated images starting from four distinct digital rock samples, sorted sandstone, synthetic sphere pack, limestone, and poorly sorted sandstone, using Minkowski functionals, image graph network‐based measures, graph Laplacian‐based measures, local trend maps, and a homogeneity‐heterogeneity classifier. …”
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Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model by Xianmei Zhou, Shanliang Zhu, Wentao Jia, Hengkai Yao

“…Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of ST and SS across different depths with Argo data, demonstrating the model’s ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model demonstrates strong adaptability to seasonal variations. …”
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Millimeter waves in communication systems as a prospect for the development and application of robotic means and complexes by Vladislav R. Skrynskij

“…The high degree of radio wave absorption in the atmosphere creates unique opportunities for the formation of interference-proof wireless local and personal networks. These factors emphasize the need for in-depth study of the features and advantages of millimeter-wave band application in radio communications. …”
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Algorithmic Shadow Spectroscopy by Hans Hon Sang Chan, Richard Meister, Matthew L. Goh, Bálint Koczor

“…We present shadow spectroscopy as a simulator-agnostic quantum algorithm for estimating energy gaps using very few circuit repetitions (shots) and no extra resources (ancilla qubits) beyond performing time evolution and measurements. The approach builds on the fundamental feature that every observable property of a quantum system must evolve according to the same harmonic components: we can reveal them by postprocessing classical shadows of time-evolved quantum states to extract a large number of time-periodic signals N_{o}∝10^{8}, whose frequencies correspond to Hamiltonian energy differences with precision limited as ϵ∝1/T for simulation time T. …”
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An AI method to predict pregnancy loss by extracting biological indicators from embryo ultrasound recordings in early pregnancy by Lijue Liu, Yuan Zang, Huimu Zheng, Siya Li, Yu Song, Xue Feng, Xiyuan Zhang, Yaoxu Li, Lulu Cao, Guanglin Zhou, Tingting Dong, Qi Huang, Teng Pan, Jinhai Deng, Danling Cheng

“…Then an ensemble learning model predicted pregnancy loss risk based on these features. Dice, IOU and Precision were used to evaluate the measurement results, and sensitivity, AUC etc. were used to evaluate the predict results. …”
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Recognizing Special Art Pieces Through EEG: A Journey in Neuroaesthetics Classification by Maurizio Palmieri, Marco Avvenuti, Francesco Marcelloni, Alessio Vecchio

“…The first two methods exploit classical machine learning approaches based on various sets of features extracted using different techniques for EEG analysis. …”
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HCAP: Hybrid cyber attack prediction model for securing healthcare applications. by Mohanad Faeq Ali, Mohammed Shakir Mohmood, Ban Salman Shukur, Rex Bacarra, Jamil Abedalrahim Jamil Alsayaydeh, Masrullizam Mat Ibrahim, Safarudin Gazali Herawan

“…The extracted features are fed into the lion-optimization technique to fine-tune the hyperparameters of the recurrent neural networks, enhancing the model's ability to efficiently predict cybersecurity threats with a maximum recognition rate in IoMT environments. …”
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