Comparison of Deep Learning-Based Auto-Segmentation Results on Daily Kilovoltage, Megavoltage, and Cone Beam CT Images in Image-Guided Radiotherapy by Zhixing Wang PhD, Chengyu Shi PhD, Carson Wong BS, Seyi M Oderinde PhD, William T Watkins PhD, Kun Qing PhD, Bo Liu PhD, Terence M Williams MD, PhD, An Liu PhD, Chunhui Han PhD

“…Introduction This study aims to evaluate auto-segmentation results using deep learning-based auto-segmentation models on different online CT imaging modalities in image-guided radiotherapy. …”
Get full text

Using artificial neural networks to predict indoor particulate matter and TVOC concentration in an office building: Model selection and method development by Saren Gaowa, Zhen Zhang, Jianchun Nie, Linxiao Li, Han A-ru, Zhili Yu

“…The results in this study can contribute to the use of artificial intelligence algorithm in office buildings aiming at indoor pollutants control.…”
Get full text

Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved... by Hossein MolaAbasi, Satinder Kaur Brar, Amin Tanhadoust, Sepideh Nasrollahpour, Omolbanin Ataee, Moncef L. Nehdi

“…Methods A collection of unconfined compression tests was used to evaluate cemented sand specimens treated with stabilizers including portland cement (at dosages of 2, 4, 6, 8, and 10%) and six dosages of natural zeolite as partial replacement for cement (0, 10, 30, 50, 70, and 90%) at different curing times of 7, 28, and 90 days. …”
Get full text

Review of Artifacts and Related Processing in Ophthalmic Optical Coherence Tomography Angiography (OCTA) by Zhefan Lin, Yitao Hu, Gongpu Lan, Jingjiang Xu, Jia Qin, Lin An, Yanping Huang

“…However, various imaging artifacts may be generated during OCTA data acquisition and processing, originating from a number of factors such as multiple light scattering, tissue motion, improper device operation and signal processing algorithms. Artifacts can detrimentally affect the qualitative interpretation of clinical pathologies and quantitative evaluation of vasculature parameters. …”
Get full text

SpectralEarth: Training Hyperspectral Foundation Models at Scale by Nassim Ait Ali Braham, Conrad M. Albrecht, Julien Mairal, Jocelyn Chanussot, Yi Wang, Xiao Xiang Zhu

“…In tandem, we construct nine downstream datasets for land-cover, crop-type mapping, and tree-species classification, providing benchmarks for model evaluation. Experimental results support the versatility of our models and their generalizability across different tasks and sensors. …”
Get full text

Effect of the time factor on the hydraulic activity of mechanically activated saponin-containing powder by Marina V. Morozova, Maria A. Frolova

“…Based on the obtained potential values (mV), the concentration of calcium ions (Сfact Ca) in the bulk phase of the suspension was calculated using С teor Ca and Сfact Ca, and the hydraulic activity coefficient a predetermined calibration relationship. Further, using the algorithm for calculating the activity parameters of powders, according to potentiometry data, the maximum amount of absorbed calcium hydroxide (∆ССaO ) was determined as the difference between the . …”
Get full text

Large-scale patterns of benthic marine communities in the Brazilian Province. by Anaide W Aued, Franz Smith, Juan P Quimbayo, Davi V Cândido, Guilherme O Longo, Carlos E L Ferreira, Jon D Witman, Sergio R Floeter, Bárbara Segal

Get full text

Constructing a predictive model for acute mastitis in lactating women based on machine learning by Liujing Zhu, Zuyan Huang, Yan Chen, Guangqiu Li, Liwen Liu

“…Prediction models were established using four different ML algorithms. Through analysis, when comparing the four distinct ML models on the test set, the MLP model performed optimally across various evaluation metrics, including the highest area under the receiver operating characteristic (ROC) curve (AUROC) (0.898), sensitivity (0.820), test specificity (0.863), and F1 score (0.849), with an accuracy of 0.840. …”
Get full text

A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce regionZENODO by Rodrigo de Q. Miranda, Rodolfo L.B. Nóbrega, Anne Verhoef, Estevão L.R. da Silva, Jadson F. da Silva, José C. de Araújo Filho, Magna S.B. de Moura, Alexandre H.C. Barros, Alzira G.S.S. Souza, Wanhong Yang, Hui Shao, Raghavan Srinivasan, Feras Ziadat, Suzana M.G.L. Montenegro, Maria do S.B. Araújo, Josiclêda D. Galvíncio

“…We developed a machine learning framework that integrates the Soil-Landscape Estimation and Evaluation Program (SLEEP) with gradient boosting to predict soil properties at regional scales and multiple depths. …”
Get full text

Association of microtubule-based processes gene expression with immune microenvironment and its predictive value for drug response in oestrogen receptor-positive breast cancer by Zhenfeng Huang, Minghui Zhang, Nana Zhang, Mengyao Zeng, Yao Qian, Meng Zhu, Xiangyan Meng, Ming Shan, Guoqiang Zhang, Feng Liu

“…Prognostic risk models were developed via random forest, support vector machines and the least absolute shrinkage and selection operator algorithm. Single-cell analysis revealed differences in the expression levels of key genes among various cell types. …”
Get full text

Artificial Intelligence for Objective Assessment of Acrobatic Movements: Applying Machine Learning for Identifying Tumbling Elements in Cheer Sports by Sophia Wesely, Ella Hofer, Robin Curth, Shyam Paryani, Nicole Mills, Olaf Ueberschär, Julia Westermayr

“…Using triaxial accelerations and rotational speeds, various ML algorithms were employed to classify and evaluate the execution of tumbling manoeuvres. …”
Get full text

A Local Dwarf Galaxy Search Using Machine Learning by Huanian Zhang, Guangping Ye, Rongyu Wu, Dennis Zaritsky

“…We introduce the spectrally confirmed training sample, discuss evaluation metrics, investigate features, compare different machine learning algorithms, and find that a seven-class neural network classification model is highly effective in separating the signal (local, low-mass galaxies) from various contaminants, reaching a precision of 95% and a recall of 76%. …”
Get full text

Improved Key Microbial Biomarker Discovery Using Ensemble Statistical Methods by Walter Pirovano, Yashjit Gangopadhyay, Mirna Lilian Baak, Christiaan Arie de Leeuw, Radhika Bongoni, Eline Suzanne Klaassens

“…However, advanced biostatistical analysis is required to properly combine microbiome taxonomic abundance data with phenotypical metadata and reliably predict disease states. While differential abundance analysis and machine-learning techniques are widely used to perform such analyses, selecting the best method is not trivial due to the complexity and specific characteristics of both the data and the algorithms. …”
Get full text

Does Replication of Native Acetabular Anatomy Satisfy Recommended Hip-Spine Targets for Cup Orientation? by Johnathan Sevick, MSc, MD, Christopher Plaskos, PhD, Andrew Speirs, PhD, Jim Pierrepont, PhD, George Grammatopoulos, MBBS, DPhil, FRCS (Tr & Ortho)

“…Three hip-spine planning algorithms were evaluated for each patient (Optimized Positioning System [OPS], Combined-Sagittal Index [CSI], Hip-Spine Classification). …”
Get full text

An Automated Image-Based Dietary Assessment System for Mediterranean Foods by Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis

“…The proposed volume estimation subsystem uses stereo vision techniques and algorithms, and needs the input of two food images to reconstruct the point cloud of the food and to compute its quantity. …”
Get full text

Residential Electricity Load Model Construction in District Scale by Jieyan XU, Wenyang XU, Yuan CHU, Yuan JIN, Xuyuan KANG, Zheng CHEN

“…To this end, this study proposes a method integrating auto-encoding and neighbor clustering algorithms for data outlier detection for data with high-dimensional characteristics at the spatial and temporal scales. …”
Get full text

Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT by Meysam Dadgar, Amaryllis Verstraete, Jens Maebe, Yves D’Asseler, Stefaan Vandenberghe

“…Conclusion This study conducted a thorough evaluation of deep learning algorithms in the GE Omni Legend PET/CT scanner, demonstrating that these methods enhance image quality, with notable improvements in CRC and CNR, thereby optimizing lesion detectability and offering opportunities to reduce image acquisition time.…”
Get full text

Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization by Waqar Ashiq, Samra Kanwal, Adnan Rafique, Muhammad Waqas, Tahir Khurshaid, Elizabeth Caro Montero, Alicia Bustamante Alonso, Imran Ashraf

“…Evaluation is carried out on two publicly available benchmarks Roman Urdu corpora comprising HS-RU-20 corpus and RUHSOLD hate speech corpus. …”
Get full text

Understanding the Influence of Image Enhancement on Underwater Object Detection: A Quantitative and Qualitative Study by Ashraf Saleem, Ali Awad, Sidike Paheding, Evan Lucas, Timothy C. Havens, Peter C. Esselman

“…Finally, we perform an overlap analysis between the generated enhanced sets to identify cases where the enhanced images of different enhancement algorithms unanimously outperform, equally perform, or underperform the original images. …”
Get full text

The role of artificial intelligence in breast cancer screening as a supportive tool for radiologists by Agata Król, Katarzyna Kwaterska, Karol Kutyłowski, Paweł Łuckiewicz

“…Difficulties, possible errors and people’s opinion were also highlighted. Conclussion AI algorithms find their potential application in breast cancer screening, mainly as a supportive tool for radiologists. …”
Get full text