Toward Automated Air Leak Localization: A Machine Learning-Enhanced Ultrasonic and LiDAR-SLAM Framework for Industrial Environments by Anthony Schenck, Walter Daems, Jan Steckel

“…To this end, we trained a series of classifiers using real-world experiment data, and show that, by using the best performing classifiers in our leak detection algorithm, we are able to drastically reduce the amount of false-positive leaks, without any meaningful impact on the true-positive leak detection performance. …”
Get full text

A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet by Kaizhe Chen, Jin Liu, Wenjing Lyu, Tianyuan Wang, Jinxi Wen

“…Therefore, this research develops a Neural Network (NN) trained with the Adaptive Moment Estimation (Adam) algorithm, based on 2.14 million charging events. …”
Get full text

A multimodal travel route recommendation system leveraging visual Transformers and self-attention mechanisms by Zhang Juan, Jing Zhang, Ming Gao

“…Based on this fused representation, a classification or regression model is trained using real travel datasets to recommend optimal travel routes.Results and discussionThe algorithm was rigorously evaluated through experiments conducted on real-world travel datasets, and its performance was benchmarked against other route recommendation methods. …”
Get full text

Development and validation of an explainable machine learning model for predicting prognosis in sepsis patients with a history of cancer who were admitted to the intensive care uni... by Xiang Luo, Xiuji Kan, Dongliang Wang, Yu Shi, Siqi Zhu, Zhenyu Chen, Congcong Wang, Wenqi Zhu, Xiangtong Wang, Wenwen Sun

“…Eight machine learning algorithms, such as random forest and extreme gradient boosting, were trained and evaluated using five-fold cross-validation. …”
Get full text

Machine learning approach for prediction of safe mud window based on geochemical drilling log data by Hongchen Cai, Yunliang Yu, Yingchun Liu, Xiangwei Gao

“…Traditional geomechanical methods for SMW determination face challenges in handling complex, nonlinear relationships within drilling datasets.PurposeThis study aims to develop robust machine learning (ML) models to predict two key SMW parameters—Mud Pressure below shear failure (MWsf) and tensile failure (MWtf)—using geochemical drilling log data from Middle Eastern carbonate reservoirs.MethodsHybrid ML models combining Least Squares Support Vector Machine (LSSVM) and Multilayer Perceptron (MLP) with optimization algorithms (Gray Wolf Optimization, GWO; Grasshopper Optimization Algorithm, GOA) were trained on 2,820 data points from three wells. …”
Get full text

Enhancing Mobile App Recommendations With Crowdsourced Educational Data Using Machine Learning and Deep Learning by Naadiya Mirbahar, Kamlesh Kumar, Asif Ali Laghari

“…The objective of this study is to recommend apps to university students by category based on app usage patterns. Data was used to evaluate these 806 university students to train the Collaborative Filtering (CF) and Contemporary Deep Learning (DL) models. …”
Get full text

Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. …”
Get full text

Do more with less: Exploring semi-supervised learning for geological image classification by Hisham I. Mamode, Gary J. Hampson, Cédric M. John

“…But an alternative exists: there often is a large corpus of unlabeled data which may enhance the learning process. To evaluate this potential for subsurface data, we compare a high-performance semi-supervised learning (SSL) algorithm (SimCLRv2) with supervised transfer learning on a Convolutional Neural Network (CNN) in geological image classification.We tested the two approaches on a classification task of sediment disturbance from cores of International Ocean Drilling Program (IODP) Expeditions 383 and 385. …”
Get full text

A Pilot Study: Sleep and Activity Monitoring of Newborn Infants by GRU-Stack-Based Model Using Video Actigraphy and Pulse Rate Variability Features by Ádám Nagy, Zita Lilla Róka, Imre Jánoki, Máté Siket, Péter Földesy, Judit Varga, Miklós Szabó, Ákos Zarándy

“…However, we developed a system that automatizes the preceding evaluations in a non-contact way using deep learning algorithms. …”
Get full text

A Method for Searching for Defective Solar Panels in Telemetry Data of a Power Plant Based on the Results of a Digital Twin by K. S. Dzik

“…The research is aimed at developing and evaluating the effectiveness of a new methodology and software algorithm for searching for anomalies in the operation of solar panels based on the results of a digital twin created and trained using telemetry data from a solar power plant. …”
Get full text

Zero-Shot Sand-Dust Image Restoration by Fei Shi, Zhenhong Jia, Yanyun Zhou

“…Extensive experiments are performed and evaluated both qualitatively and quantitatively. The results show that the proposed method works better than the state-of-the-art algorithms for enhancing and restoring sand-dust images.…”
Get full text

Classification of periodontitis stage and grade using natural language processing techniques. by Nazila Ameli, Tahereh Firoozi, Monica Gibson, Hollis Lai

“…Then, we fine-tuned the pre-trained BERT model on 70% of our data. The performance of the model was evaluated on 32 unseen new patients' clinical notes. …”
Get full text

Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China by Tiezhu Li, Qidi Huang, Qigang Chen

“…To assess the models’ robustness, the trained models were applied to the neighboring Mao County for cross-regional validation. …”
Get full text

Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions by H. S. Gowri Yaamini, Swathi K J, Manohar N, Ajay Kumar G

“…There are several state-of-art You Only Live Once (YOLO) models trained on benchmark datasets which fails to cater the challenges of Indian roads. …”
Get full text

Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images by Hafiz Muhammad Danish, Zobia Suhail, Faiza Farooq

“…Four widely used fully convolutional neural networks: UNet, UNet++, DeepLabV3, and ResUNet were modified by replacing their encoders with a pre-trained ResNet50. These models were trained and evaluated using 5-fold cross-validation to identify the optimal approach for GS segmentation. …”
Get full text

Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data by Fuyong Wang, Xianmu Hou

“…Machine learning models are trained and evaluated to predict carbonate rock properties. …”
Get full text

Machine learning-based estimation of seismic structural damage via an accessible web application by Vasile Calofir, Mircea-Ștefan Simoiu, Ruben-Iacob Munteanu, Emil Calofir, Sergiu-Stelian Iliescu

“…The platform utilizes gradient boosting, a machine learning algorithm selected as the most effective after evaluating several alternatives. …”
Get full text

Parallel Multi-Scale Semantic-Depth Interactive Fusion Network for Depth Estimation by Chenchen Fu, Sujunjie Sun, Ning Wei, Vincent Chau, Xueyong Xu, Weiwei Wu

“…Furthermore, we also employ a metric loss based on semantic edges to take full advantage of semantic geometric information. Our network is trained and evaluated on KITTI datasets. The experimental results show that the methods achieve satisfactory performance compared to other existing methods.…”
Get full text

Model-Based AUV Path Planning Using Curriculum Learning and Deep Reinforcement Learning on a Simplified Electronic Navigation Chart by Łukasz Marchel, Rafał Kot, Piotr Szymak, Paweł Piskur

“…Deep Reinforcement Learning (DRL)-based algorithms have demonstrated substantial effectiveness in tackling complex control problems for autonomous underwater vehicles (AUVs). …”
Get full text

Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey by Ziyan Zhang, Chuheng Wei, Guoyuan Wu, Matthew J. Barth

“…Furthermore, the survey examines benchmark datasets used to train and evaluate VRU detection models. Alongside innovative detection models and sufficient datasets, key challenges and emerging trends in algorithm development and dataset collection are also discussed. …”
Get full text