The current landscape of artificial intelligence in computational histopathology for cancer diagnosis by Aaditya Tiwari, Aruni Ghose, Maryam Hasanova, Sara Socorro Faria, Srishti Mohapatra, Sola Adeleke, Stergios Boussios

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A systematic literature review on the role of artificial intelligence in citizen science by Germain Abdul-Rahman, Andrej Zwitter, Noman Haleem

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Design of Intrusion Detection and Response Mechanism for Power Grid SCADA Based on Improved LSTM and FNN by Yu Huang, Liangyuan Su

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TRANSFORMATIONAL PROCESSES IN RESOURCE-RICH COUNTRIES: FROM NATURAL RESOURCES TO INNOVATION AND TECHNOLOGY-BASED ECONOMY by Ihor Hurnyak, Anna Hrytsyshyn, Nataliya Kuzenko, Liana Moskalyk, Olga Tsapko-Piddubna, Lidiya Yemelyanova

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MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images by Jianfeng Li, Yibing Yang, Liutong Yang, Yang Zhao, Qinghua Luo, Chenxu Wang

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Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation by Oliver Watt‐Meyer, Noah D. Brenowitz, Spencer K. Clark, Brian Henn, Anna Kwa, Jeremy McGibbon, W. Andre Perkins, Lucas Harris, Christopher S. Bretherton

“…Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. …”
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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review by Platon S. Papageorgiou, Rafail Christodoulou, Panagiotis Korfiatis, Dimitra P. Papagelopoulos, Olympia Papakonstantinou, Nancy Pham, Amanda Woodward, Panayiotis J. Papagelopoulos

“…Notably, convolutional neural networks have demonstrated exceptional capabilities in pattern recognition, significantly improving tumor detection, segmentation, and differentiation. …”
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A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process by Mohammad Manzurul Islam, Mst. Nasrat Jahan Niva, Abdullahi Chowdhury, Saleh Masum, Rifat Ara Shams, Taskeed Jabid, Md. Sawkat Ali, Md. Mostofa Kamal Rasel, Muhammad Firoz Mridha

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Dysregulated neural coding in the vagus nerve during long sepsis by Joshua J. Strohl, Tomás S. Huerta, Sergio Robbiati, Patricio T. Huerta

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Intra- and peri-tumoral radiomics based on dynamic contrast-enhanced MRI for prediction of benign disease in BI-RADS 4 breast lesions: a multicentre study by Yalan Hu, Zhenhai Cai, Nijiati Aierken, Yueqi Liu, Nan Shao, Yawei Shi, Mengmeng Zhang, Yangling Hu, Xiaoling Zhang, Ying Lin

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Personalized diagnosis of radiation pneumonitis in breast cancer patients based on radiomics by Xiaobo Wen, Xiaobo Wen, Xiaobo Wen, Yutao Zhao, Wen Dong, Congbo Yang, Jinzhi Li, Li Sun, Yutao Xiu, Chang’e Gao, Ming Zhang

“…Five texture-oriented and four first-order features were retained, underscoring the importance of texture-focused extractors [wavelet and local binary pattern (LBP)].ConclusionCT-derived radiomic signatures combined with machine learning classifiers enable the accurate detection of RP in breast cancer patients. …”
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U-shaped association between serum chloride and hypertension risk with nadir around 103 mmol/L: insights from regression and interpretable machine learning (XGBoost/SHAP) using NHA... by Shancheng He, Xuemei Zhong, Guangming Chen, Long Li

“…Interpretation of an XGBoost machine learning model using SHAP values visually corroborated this U-shaped pattern, further indicating that the lowest contribution of serum chloride to the predicted risk of hypertension occurred around the 103 mmol/L level.ConclusionIn conclusion, using NHANES 2017–2018 data, this study revealed a significant U-shaped association between adult serum chloride levels and hypertension risk, with a nadir at 103 mmol/L. …”
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Land use/cover change simulation research: A system literature review based on bibliometric analyses by Meili Chen, Narimah Samat, Mohammad Javad Maghsoodi Tilaki, Liyuan Duan

“…Key developments encompass spatiotemporal pattern analysis, driving mechanism analysis, simulation model analysis, and comprehensive application analysis. …”
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Dementia Scale Score Classification Based on Daily Activities Using Multiple Sensors by Akira Minamisawa, Shogo Okada, Ken Inoue, Mami Noguchi

“…The experimental results show that a maximum accuracy of 0.871 was obtained with a linear support vector machine (SVM) model by fusing the door, location, and sleep features and by clustering activity patterns using the X-means algorithm.…”
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Time Series Modelling and Predictive Analytics for Sustainable Environmental Management—A Case Study in El Mar Menor (Spain) by Rosa Martínez, Ivan Felis, Mercedes Navarro, J. Carlos Sanz-González

“…In this study on data science and machine learning, time series analysis plays a key role in predicting evolving data patterns. …”
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A Data-Driven Approach to Mitigate Evolving Volumetric Attacks in Programmable Networks by Muhammad Saqib, Halima Elbiaze, Roch H. Glitho

“…In-network machine learning (ML) offers a cutting-edge approach for promptly detecting malicious traffic. …”
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BCAST IDS: A Novel Network Intrusion Detection System for Broadcast Networks by Javier Gombao

“…A modern approach to enhancing the capabilities of NIDSs is the use of machine learning (ML) algorithms that predict attacks based on data. …”
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Development of an artificial intelligence system for the forecasting of infectious diseases by A. A. Kuzin, R. I. Glushakov, S. A. Parfenov, K. V. Sapozhnikov, A. A. Lazarev

“…Evolution of data science have led to the emergence of promising artificial intelligence (AI) algorithms and tools for the forecasting of infectious diseases. Employing machine learning algorithms, AI systems can rapidly analyze a large amount of data, extract specific disease patterns, and screen for the most efficient AI instruments in relation to specific tasks, thus contributing to prevention, diagnostics, and treatment of infectious diseases in the context of personalized medicine. …”
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Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel by Christine Ahrends, Mark W Woolrich, Diego Vidaurre

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Disulfide bond-related gene signature development for bladder cancer prognosis prediction and immune microenvironment characterization by Hua Huang, Haiyan Shao, Yifan Wang, Lili Ge

“…By integrating data from TCGA and GEO cohorts, we developed a Disulfide-Related Prognostic Signature (DRPS) using ten machine learning algorithms. Single-cell RNA sequencing (scRNA-seq) elucidated the cell subtype-specific expression patterns of disulfide bond regulatory genes, while immune microenvironment and drug sensitivity analyses validated its clinical translational potential. qRT-PCR experiments confirmed differential expression patterns of core genes in bladder cancer cell lines. …”
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