Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity by ZHANG Xingfu, JIANG Yuanjun, ABI Erdi

“…Inputs such as terrain features, soil characteristics, vegetation indices, and rainfall patterns were utilized to train and evaluate the models. …”
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Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology by Alon Vigdorovits, Gheorghe-Emilian Olteanu, Ovidiu Tica, Andrei Pascalau, Monica Boros, Ovidiu Pop

“…We used this dataset to train two models: a pathomics-based ridge classifier trained on 80 principal components derived from almost 2000 extracted features and a deep convolutional neural network with a modified ResNet18 architecture. …”
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Generating cervical anatomy labels using a deep ensemble multi-class segmentation model applied to transvaginal ultrasound images by Alicia B. Dagle, Yucheng Liu, Madeline Skeel, Gabriel G. Trigo, David Crosby, Helen Feltovich, Michael House, Qi Yan, Kristin M. Myers, Sachin Jambawalikar

“…This study utilizes an ensemble of deep learning-based multi-class segmentation models trained on diverse TVUS data (N = 246) and evaluated on an out-of-distribution dataset (N = 29). …”
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Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech by Julianna Olah, Win Lee Edwin Wong, Atta-ul Raheem Rana Chaudhry, Omar Mena, Sunny X. Tang

“…Linguistic and paralinguistic features were extracted and ensemble learning algorithms (e.g., XGBoost) were used to train models. …”
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Project quality, regulation quality by Elena Mussinelli

“…The objective is far from being achieved, and «necessarily passes through the training of the figures involved, starting with the RUPs». …”
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WSNetDefender: Securing Wireless Sensor Networks Using BBIDNet and Fuzzy-DQN Threat Mitigation System (FD-TMS) by Mohammed A. Aleisa

“…The proposed JWHO is the conceptual synergy of Golden Jackal Optimization and Grey Wolf Optimizer (GJO-GWO), respectively. The extracted JWHO-based features are utilized to train the new BBIDNet, which makes the accurate detection of intruders in WSN. …”
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Artificial Intelligence for Multiclass Rhythm Analysis for Out-of-Hospital Cardiac Arrest During Mechanical Cardiopulmonary Resuscitation by Iraia Isasi, Xabier Jaureguibeitia, Erik Alonso, Andoni Elola, Elisabete Aramendi, Lars Wik

“…A traditional machine learning-based classifier, which incorporates the research conducted over the past two decades in ECG rhythm analysis using more than 90 state-of-the-art features, was used as a point of comparison. …”
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A SVM Approach of Aircraft Conflict Detection in Free Flight by Xu-rui Jiang, Xiang-xi Wen, Ming-gong Wu, Ze-kun Wang, Xi Qiu

“…A potential conflict would be identified, as long as its flight data features are extracted and fed to a classifier which has been trained by a large number of flight datasets. …”
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Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data by Ana Lawry Aguila, Luigi Lorenzini, Mohammed Janahi, Frederik Barkhof, Andre Altmann

“…Methods In this work, we applied a deep learning-based normative model, pre-trained on MRI scans in the UK Biobank, to investigate ageing and identify abnormal age-related decline. …”
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An Efficient Model for Real-Time Traffic Density Analysis and Management Using Visual Graph Networks by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

“…This framework applies transfer learning to adapt pre-trained features to new traffic environments. This enhances vehicle classification in complex urban scenes and improves the model’s ability to distinguish vehicles from non-vehicle objects. …”
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Wheeled Permanent Magnet Climbing Robot for Weld Defect Detection on Hydraulic Steel Gates by Kaiming Lv, Zhengjun Liu, Hao Zhang, Honggang Jia, Yuanping Mao, Yi Zhang, Guijun Bi

“…Finally, an intelligent software system for weld defect detection based on computer vision is developed. This system is demonstrated to conduct defect detection on images of the current weld position using a trained model.…”
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Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis by Alessio Gerussi, Oliver Lester Saldanha, Giorgio Cazzaniga, Damiano Verda, Zunamys I. Carrero, Bastian Engel, Richard Taubert, Francesca Bolis, Laura Cristoferi, Federica Malinverno, Francesca Colapietro, Reha Akpinar, Luca Di Tommaso, Luigi Terracciano, Ana Lleo, Mauro Viganó, Cristina Rigamonti, Daniela Cabibi, Vincenza Calvaruso, Fabio Gibilisco, Nicoló Caldonazzi, Alessandro Valentino, Stefano Ceola, Valentina Canini, Eugenia Nofit, Marco Muselli, Julien Calderaro, Dina Tiniakos, Vincenzo L’Imperio, Fabio Pagni, Nicola Zucchini, Pietro Invernizzi, Marco Carbone, Jakob Nikolas Kather

“…This study investigates the use of a deep learning (DL)-based pipeline for the diagnosis of AIH and PBC to aid differential diagnosis. …”
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A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis by Nikolaos Peppes, Emmanouil Daskalakis, Theodoros Alexakis, Evgenia Adamopoulou

“…The proposed system features a dual-branch architecture: one branch uses a CNN with transfer learning for image-based malware classification, and the other employs a soft-voting ensemble classifier for tabular intrusion detection, both trained on augmented datasets generated by GANs. …”
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Toward Versatile Small Object Detection with Temporal-YOLOv8 by Martin C. van Leeuwen, Ella P. Fokkinga, Wyke Huizinga, Jan Baan, Friso G. Heslinga

“…Deep learning has become the preferred method for automated object detection, but the accurate detection of small objects remains a challenge due to the lack of distinctive appearance features. Most deep learning-based detectors do not exploit the temporal information that is available in video, even though this context is often essential when the signal-to-noise ratio is low. …”
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Automated building typology clustering and identification using a variational autoencoder on digital land cadastres by Jaime de-Miguel-Rodriguez, M.V. Requena-Garcia-Cruz, E. Romero-Sánchez, A. Morales-Esteban

“…Unlike traditional shape clustering approaches, that depend on predefined rules or manual labelling, the method employs unsupervised learning to identify building typologies, based solely on geometric features, derived from roof-print shapes. …”
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Automatic Identification of High-Quality Channels in Distributed Acoustic Sensing Through Implementation of a Channel Quality Index by Tatiana Rodriguez, Aleix Segui, Arantza Ugalde, Melania Cubas Armas, Hugo Latorre, Sergi Ventosa, Tony Monfret

“…Trained on data from four offshore DAS experiments in the Spanish Mediterranean and Canary Islands, our model evaluates temporal, frequency, and energy-based features to assign quality scores to individual channels, reliably identifying high-quality channels with high precision (0.92–0.96) and strong performance in terms of area under the receiver operating characteristic curve (ROC–AUC, 0.92–0.98). …”
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Evaluating Time Series Models for Monthly Rainfall Forecasting in Arid Regions: Insights from Tamanghasset (1953–2021), Southern Algeria by Ballah Abderrahmane, Morad Chahid, Mourad Aqnouy, Adam M. Milewski, Benaabidate Lahcen

“…Historical monthly precipitation data from 1953 to 2020 were used to train and test the models, with lagged observations serving as input features. …”
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CrackNet: A Hybrid Model for Crack Segmentation with Dynamic Loss Function by Yawen Fan, Zhengkai Hu, Qinxin Li, Yang Sun, Jianxin Chen, Quan Zhou

“…Along with the development of deep learning, visual-based crack automatic detection has been widely studied. …”
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Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators. by Girmaw Abebe Tadesse, Laura Ferguson, Caleb Robinson, Shiphrah Kuria, Herbert Wanyonyi, Samuel Murage, Samuel Mburu, Rahul Dodhia, Juan M Lavista Ferres, Bistra Dilkina

“…We further found that machine learning models with satellite-based features alone also outperform Window Averaging baselines, while not needing clinical data at inference time. …”
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Intercultural interferences in technical translation: A glance at Italian and German technical manuals by Karl Gerhard Hempel

“…This paper presents a corpus-based study, the aim of which is to define translation-relevant, culture-bound features of Italian technical manuals and their impact on translation decisions. …”
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