Research progress and prospect of intelligent prediction and disaster risk assessment of open-pit mining surface deformation by LI Hui, ZHU Wancheng, XU Xiaodong, SONG Qingwei, HAN Xiaofei, GENG Huikai

“…Specifically, by reviewing the intelligent monitoring technologies of mine surface deformation, this study indicates that the choice of intelligent monitoring methods should factor in data accuracy, installation cost and post-processing speed, reviews the intelligent modeling methods of surface deformation prediction regarding the methodological combination of traditional deformation prediction and intelligent optimization, machine learning and deep learning, and summarizes the mechanism behind the typical risk assessment method of mine deformation hazards. …”
Get full text

Animal Species Identification in Historical Parchments by Continuous Wavelet Transform–Convolutional Neural Network Classifier Applied to Ultraviolet–Visible–Near-Infrared Spectros... by Nicolas Roy, Henry Pièrard, Julie Bouhy, Alexandre Mayer, Olivier Deparis, David Gravis

“…In this study, we propose a contactless methodology based on reflectance spectrophotometry (ultraviolet–visible–near-infrared) and a machine learning approach for data analysis. Spectra were recorded from both historical and modern parchments crafted from calf, goat, and sheep skins. …”
Get full text

Research on credit card transaction security supervision based on PU learning by Renfeng CHEN, Hongbin ZHU

“…The complex and ever-evolving nature of credit card cash out methods and the emergence of various forms of fake transactions present challenges in obtaining accurate transaction information during customer interactions.In order to develop an accurate supervision method for detecting fake credit card transactions, a PU (positive-unlabeled learning) based security identification model for single credit card transactions was established.It was based on long-term transaction label data from cashed-up accounts in commercial banks’ credit card systems.A Spy mechanism was introduced into sample data annotation by selecting million positive samples of highly reliable cash-out transactions and 1.3 million samples of transactions to be labeled, and using a learner to predict the result distribution and label negative samples of non-cash-out transactions that were difficult to identify, resulting in 1.2 million relatively reliable negative sample labels.Based on these samples, 120 candidate variables were constructed, including credit card customer attributes, quota usage, and transaction preference characteristics.After importance screening of variables, nearly 50 candidate variables were selected.The XGBoost binary classification algorithm was used for model development and prediction.The results show that the proposed model achieve an identification accuracy of 94.20%, with a group stability index (PSI) of 0.10%, indicating that the single credit card transaction security identification model based on PU learning can effectively monitor fake transactions.This study improves the model discrimination performance of machine learning binary classification algorithm in scenarios where high-precision sample label data is difficult to obtain, providing a new method for transaction security monitoring in commercial bank credit card systems.…”
Get full text

A human-on-the-loop approach for labelling seismic recordings from landslide site via a multi-class deep-learning based classification model by Jiaxin Jiang, David Murray, Vladimir Stankovic, Lina Stankovic, Clement Hibert, Stella Pytharouli, Jean-Philippe Malet

“…Recent advances in machine learning have introduced algorithms for classifying seismic events associated with landslides, such as earthquakes, rockfalls, and smaller quakes. …”
Get full text

Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers by Bentegri Houcine, Rabehi Mohamed, Kherfane Samir, Boukansous Sarra

“…Using various validation criteria such as coefficient of determination (R), root mean squared error (RMSE) and mean absolute error (MAE), the ANN model was validated and compared to two machine learning (ML) Random Forest (RF) techniques and Multilayer Perceptron (MLP). …”
Get full text

Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review by Kaelan Lockhart, Juan Sandino, Narmilan Amarasingam, Richard Hann, Barbara Bollard, Felipe Gonzalez

“…Despite the potential of established Machine-Learning (ML) classifiers such as Random Forest, K Nearest Neighbour, and Support Vector Machine, and gradient boosting in the semantic segmentation of UAV-captured images, there is a notable scarcity of research employing Deep Learning (DL) models in these extreme environments. …”
Get full text

Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors by C. L. Bachand, C. L. Bachand, C. Wang, B. Dafflon, L. N. Thomas, L. N. Thomas, I. Shirley, S. Maebius, S. Maebius, C. M. Iversen, K. E. Bennett

“…We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. …”
Get full text

Prediction of ischemic stroke in patients with H-type hypertension based on biomarker by Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao, Hui Yuan

“…Logistic regression, least absolute shrinkage and selection operator regression, and best subset selection analysis were used to assess the contribution of variables to ischemic stroke, and models were derived using four machine learning algorithms. Area Under Curve (AUC), calibration plot and decision-curve analysis respectively evaluated the discrimination and calibration of four models, then external validation and visualization of the best-performing model. …”
Get full text

Two-Level Automatic Adaptation of a Distributed User Profile for Personalized News Content Delivery by Maria Papadogiorgaki, Vasileios Papastathis, Evangelia Nidelkou, Simon Waddington, Ben Bratu, Myriam Ribiere, Ioannis Kompatsiaris

“…It involves the use of machine learning algorithms applied to the implicit and explicit user feedback. …”
Get full text

Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian Embryos by Alexander Heidrich, Jana Schmidt, Johannes Zimmermann, Hans Peter Saluz

“…The classification engine was implemented using the WEKA machine learning tool. Results. Our system reduces analysis time and observer bias while maintaining high accuracy. …”
Get full text

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing by Jing Chen, Yinglong Wang

“…However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts. Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling. …”
Get full text

Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings by Özge Öztürk Hacar, Müslüm Hacar, Fatih Gülgen, Luca Pappalardo

“…The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. …”
Get full text

Federated Learning Lifecycle Management for Distributed Medical Artificial Intelligence Applications: A Case Study on Post-Transcatheter Aortic Valve Replacement Complication Predi... by Min Hyuk Jung, InSeo Song, KangYoon Lee

“…Federated learning is a paradigm that enables the training of machine learning models in a decentralized manner without transferring data to a central repository, allowing model development while preserving data privacy across medical and other industries. …”
Get full text

Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages by Muhammad Kashif Nazir, Cm Nadeem Faisal, Muhammad Asif Habib, Haseeb Ahmad

“…Additionally, the proposed model outperformed other transformer-based models, as well as deep learning and machine learning algorithms in sentiment extraction from code-mixed data. …”
Get full text

Human Trajectory Imputation Model: A Hybrid Deep Learning Approach for Pedestrian Trajectory Imputation by Deb Kanti Barua, Mithun Halder, Shayanta Shopnil, Md. Motaharul Islam

“…Previous attempts to address this issue, such as statistical inference and machine learning approaches, have shown promise. Yet, the landscape of deep learning is rapidly evolving, with new and more robust models emerging. …”
Get full text

Assessing Driving Risk Level: Harnessing Deep Learning Hybrid Model With Intercity Bus Naturalistic Driving Data by Wei-Hsun Lee, Che-Yu Chang

“…Additionally, the high-risk level prediction F1-score reaches 0.728 for the proposed model, which is up to 9.3 times better than the performance of the machine learning baseline model. This breakthrough in driving risk prediction not only represents a major advancement in traffic safety management but also has practical implications for fleet scheduling management among transportation companies in the future. …”
Get full text

Identification of Proteins and Genes Associated with Hedgehog Signaling Pathway Involved in Neoplasm Formation Using Text-Mining Approach by Nadezhda Yu. Biziukova, Sergey M. Ivanov, Olga A. Tarasova

“…For recognition of the Hedgehog pathway proteins and genes and neoplastic diseases we use a dictionary-based named entity recognition approach, while for all other proteins and genes machine learning method is used. For association extraction, we develop a set of semantic rules. …”
Get full text

A Performance Analysis of Business Intelligence Techniques on Crime Prediction by Ivan, Niyonzima, Emmanuel Ahishakiye, Elisha Opiyo Omulo, Ruth Wario

“…The dataset was acquired from UCI machine learning repository website with a title ‘Crime and Communities’. …”
Get full text

Global insights into MRSA bacteremia: a bibliometric analysis and future outlook by Jia-Yi Lin, Jia-Yi Lin, Jia-Kai Lai, Jia-Kai Lai, Jian-Yi Chen, Jian-Yi Chen, Jia-Yu Cai, Jia-Yu Cai, Zhan-Dong Yang, Zhan-Dong Yang, Liu-Qingqing Yang, Liu-Qingqing Yang, Liu-Qingqing Yang, Ze-Tao Zheng, Ze-Tao Zheng, Xu-Guang Guo, Xu-Guang Guo, Xu-Guang Guo

“…Future research will rely on integrating genomics, AI, and machine learning to drive personalized treatment. Strengthening global cooperation, particularly in resource-limited countries, will be key to effectively addressing MRSA BSIs.…”
Get full text

Common lipidomic signatures across distinct acute brain injuries in patient outcome prediction by Santtu Hellström, Antti Sajanti, Abhinav Srinath, Carolyn Bennett, Romuald Girard, Aditya Jhaveri, Ying Cao, Johannes Falter, Janek Frantzén, Fredrika Koskimäki, Seán B. Lyne, Tomi Rantamäki, Riikka Takala, Jussi P. Posti, Susanna Roine, Sulo Kolehmainen, Kenneth Nazir, Miro Jänkälä, Jukka Puolitaival, Melissa Rahi, Jaakko Rinne, Anni I. Nieminen, Eero Castrén, Janne Koskimäki

“…Lipidomic profiling of 1153 lipids, analyzed using statistical and machine learning methods, identified 153 lipids with late-stage significant outcome differences. …”
Get full text