A Systematic Modular Approach for the Coupling of Deep-Learning-Based Models to Forecast Urban Flooding Maps in Early Warning Systems by Juliana Koltermann da Silva, Benjamin Burrichter, Andre Niemann, Markus Quirmbach

“…Deep learning (DL) approaches to forecast precipitation and inundation areas in the short-term forecast horizon have up until now been treated as independent research problems from the model development perspective. …”
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Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks by Deependra Rastogi, Prashant Johri, Massimo Donelli, Seifedine Kadry, Arfat Ahmad Khan, Giuseppe Espa, Paola Feraco, Jungeun Kim

“…In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients’ overall survival rate. …”
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Aggregated Housing Price Predictions with No Information About Structural Attributes—Hedonic Models: Linear Regression and a Machine Learning Approach by Joanna Jaroszewicz, Hubert Horynek

“…This makes it necessary to include detailed information about structural attributes when predicting prices using regression models and machine learning algorithms and makes it difficult to study the influence of external attributes. …”
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Using machine learning algorithms to evaluate the performance of electrocoagulation with membrane bioreactor (EC-MBR) for treatment of organic matters in domestic wastewater by Yousif Zakoor, Hatem Gzar

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Neuroprotective Effect of Leaf Extract of Moringa Oleifera And Fenofibrate on Learning and Memory in Wistar Rats Subjected to Water Immersion Restraint Stress by Stephen, Olawale Ajayi, Iliya, Ezekiel, Iliya, Andesire, Agnes, Igimi Odey, Ajayi, Damilola Ife, Ejike, Daniel Eze

“…In the present study, the neuroprotective effect of Moringa oleifera leaf extract and Fenofibrate was investigated to evaluate their ameliorative effects on learning and memory in Wistar Rats subjected to water immersion restraint stress (WIS). …”
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Application of deep learning based on convolutional neural network model in multimodal ultrasound diagnosis of unexplained cervical lymph node enlargement by Shanshan Jiang, Naiqian Zhang, Chen Li, Lingxia Tong, Xiuhua Yang

“…This study retrospectively analyzed the multimodal ultrasound features and clinical characteristics of 586 patients with unexplained cervical lymphadenopathy who were treated at three hospitals between October 2019 and December 2022. …”
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HoloMoA: a holography and deep learning tool for the identification of antimicrobial mechanisms of action and the detection of novel MoA by Zohreh Sedaghat, Benoît Courbon, Héloïse Botrel, Hélène Dugua, Pawel Tulinski, Laethitia Alibaud, Lucia Pagani, Derry Mercer, Cyril Guyard, Christophe Védrine, Sophie Dixneuf

“…Our rapid, robust, affordable and versatile tool is based on the combination of time-lapse Digital Inline Holographic Microscopy (DIHM) and Deep Learning (DL). In combination with hologram reconstruction. …”
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Advanced skin cancer prediction with medical image data using MobileNetV2 deep learning and optimized techniques by Tuğçe Öznacar, Nuray Varol Kayapunar

“…The chances of successful treatment are greatly reduced if the melanoma is not treated in its early stages because it could spread aggressively. …”
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Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysis by Mosab Kaseem, Talitha Tara Thanaa, Krishna Kumar Yadav, Hagar H. Hassan, Arash Fattah-alhosseini

“…This research explores the anticorrosive performance of AZ31 magnesium alloy treated with plasma electrolytic oxidation (PEO), further enhanced by layered double hydroxides (LDHs) containing Co, Ni, or Zn. …”
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Hybridizing spatial machine learning to explore the fine-scale heterogeneity between stunting prevalence and its associated risk determinants in Rwanda by Gilbert Nduwayezu, Ali Mansourian, Jean Pierre Bizimana, Petter Pilesjö

“…This study assesses the performance of hybrid spatial machine learning techniques in predicting stunting among children below the age of five in Rwanda. …”
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PCA- and PLSR-Based Machine Learning Model for Prediction of Urea-N Content in Heterogeneous Soils Using Near-Infrared Spectroscopy by Damiano Crescini, Gabriele Mascialino, Nicola Moggia, Giordano Piubeni, Mauro Serpelloni, Emilio Sardini

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A novel machine learning-based approach to determine the reduction factor for punching shear strength capacity of voided concrete slabs by Alireza Mahmoudian, Mussa Mahmoudi, Mohammad Yekrangnia, Nima Tajik, Mostafa Mohammadzadeh Taleshi

“…In this study, an innovative application of machine learning is proposed to address this challenge. Initially, a predictive model for punching shear strength in conventional slabs is established using the Random Forest Regressor (RFR). …”
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Effectiveness of a distance-learning vs standard training in the integrated management of childhood illnesses: a cluster randomized controlled trial by Saidul Abrar, Assad Hafeez, Sana Rahim, Suhail A. R. Doi, Muhammad Naseem Khan

“…The World Health Organization (WHO) introduced the concept of distance learning IMCI in 2014 to improve uptake of the strategy. …”
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Comparing machine learning models for predicting preoperative DVT incidence in elderly hypertensive patients with hip fractures: a retrospective analysis by Xue Ge, Lan Yao, Yan Liu, Yi Wang, Fang Zhang

“…Among the multiple machine learning predictive models, the LightGBM model exhibited the best performance, achieving an AUC of 0.910. …”
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Establishment and assessment of an early screening model for cervical cancer based on single-cell Raman spectroscopy combined with machine learning algorithms by MA Dongmei, ZHAO Wenjie, LIU Shihai, XU Haicang, CAI Duo, JI Yuetong, XU Jian, GUO Cancan, MA Bo, PAN Huazheng

“…Objective To establish an early screening model for cervical cancer based on single-cell Raman spectroscopy (SCRS) combined with machine learning algorithms, and to assess the performance of the model. …”
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Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț

“…Traditional machine learning (ML) algorithms have been widely used for this purpose; however, recent advancements in deep learning, such as autoencoders, offer the potential to enhance predictive accuracy. …”
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Refining Intra-Arterial Therapy Selection for Large Hepatocellular Carcinoma: A Deep Learning Approach Based on Covariate Interaction Analysis by An C, Li L, Luo Y, Zuo M, Liu W, Li C, Wu P

“…However, optimizing IAT selection for large HCC remains challenging due to tumor heterogeneity and varying patient responses.Aim: To develop and validate a deep learning (DL) model for guidance of decision-making between TACE and HAIC for unresectable HCC.Methods: We conducted a retrospective, multi-center study involving 900 patients with large HCC treated with IATs. …”
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Correlation Between Learning and Memory Impairment and Changes in Cerebral White Matter Nerve Fibers and Pericytes in Rats with Chronic Cerebral Ischemia by HUANG Jia, WANG Mengxue, NI Jinglei, LIANG Shengxiang, LIN Bingbing

“…ObjectiveTo analyze the correlation between the changes of learning and memory and the progressive degeneration of white matter nerve fibers and pericytes in the corpus callosum and hippocampus of rats with chronic cerebral ischemia, then to explore the mechanism of learning and memory impairment caused by chronic cerebral ischemia, and provide a possible predictive target for vascular cognitive impairment.MethodsA total of 24 SPF male SD rats were divided randomly into sham operation group, 28 days after operation group and 56 days after operation group, with eight rats in each group. …”
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Screening and Management of Obstructive Sleep Apnea and Daytime Sleepiness Among Professional Drivers in Tunisia: Protocol for a Machine Learning Study by Sameh Msaad, Nesrine Kammoun, Rahma Gargouri, Rim Khemakhem, Amira Triki, Narjes Abid, Sonia Fehri, Kaouthar Kallel, Rim Kammoun, Leila Douik EL Gharbi, Sonia MaalejBellaj, Heni Bouhamed, Ahmed Abdelghani, Chiraz Aichaouia, Mohamed Turki, Samy Kammoun

“…After one year of follow-up, all participants will be re-evaluated with self-report questionnaires. For those treated with CPAP, they will undergo the Maintenance of Wakefulness Test (MWT). …”
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A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective by Dung Nguyen Tien, Huong Thi Thu Bui, Tram Hoang Thi Ngoc, Thuy Thi Pham, Dac Trung Nguyen, Huyen Nguyen Thi Thu, Thi Thu Hang Vu, Thi Lan Anh Luong, Lan Thu Hoang, Ho Cam Tu, Nina Körber, Tanja Bauer, Lam Khanh Ho

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