Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM by Xingwang Wang, Xingwang Wang, Xingwang Wang, Can Hu, Xufeng Wang, Hainie Zha, Xueyong Chen, Shanshan Yuan, Jing Zhang, Jianfeng Liao, Zhangying Ye

Get full text

AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

Get full text

EfficientNet-b0-Based 3D Quantification Algorithm for Rectangular Defects in Pipelines by Di Wu, Yong Hong, Jie Wang, Shaojun Wu, Zhihao Zhang, Yizhang Liu

Get full text

Establishment and validation of a model for predicting the SSIGN score and prognosis of patients with clear cell renal cell carcinoma based on CT radiomic features and clinical ind... by ZHI Kaiyue, YANG Zhitao, ZHANG Shuo, ZHU He, WANG Yanmei, ZHAO Lianzi, WANG Ning, ZHAO Xia, LI Xianjun, CHENG Nan, WANG Yicong, CHEN Chengcheng, WANG Nan, NIE Pei

“…Objective To establish and validate a model for predicting the SSIGN score and prognosis of patients with clear cell renal cell carcinoma (ccRCC) based on CT radiomic features and clinical indices. …”
Get full text

Data Compactness Versus Prediction Performance: Achieving Both by Pruning Redundant Samples With Dominant Patterns and Hamming Distance Based Sampling Scheme by Abdul Majeed, Seong Oun Hwang

“…Specifically, we reduce the data size by a reasonable margin while maintaining predictive performance similar to or better than the original data with reduced training time. Our sampling scheme has five key steps: data pre-processing, dominant pattern extraction by exploiting correlations between features, Hamming distance-based data classification into diverse and less diverse parts, data clustering for redundant-sample pruning from less diverse parts, and fine-tuning/synthesizing the final data. …”
Get full text

Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area by Girma Tariku, Isabella Ghiglieno, Andres Sanchez Morchio, Luca Facciano, Celine Birolleau, Anna Simonetto, Ivan Serina, Gianni Gilioli

“…The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conducted a comparative study, using cutting-edge deep-learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. …”
Get full text

Deep Learning-Based Semantic Segmentation for Objective Colonoscopy Quality Assessment by Radu Alexandru Vulpoi, Adrian Ciobanu, Vasile Liviu Drug, Catalina Mihai, Oana Bogdana Barboi, Diana Elena Floria, Alexandru Ionut Coseru, Andrei Olteanu, Vadim Rosca, Mihaela Luca

“…<b>Background:</b> This study aims to objectively evaluate the overall quality of colonoscopies using a specially trained deep learning-based semantic segmentation neural network. …”
Get full text

Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic... by Liang L, Pang J, Zhang B, Que Q, Gao R, Wu Y, Peng J, Zhang W, Bai X, Wen R, He Y, Yang H

“…Feature importance analysis identified LI-RADS classification, MVI, and tumor size as the top three prognostic indicators, while KM survival analysis confirmed the model’s ability to stratify patients into distinct risk groups (training cohort: p &lt; 0.001; validation cohort: p = 0.003).Conclusion: The GBM-based ML model integrating CEUS imaging features and LI-RADS classification demonstrates potential for predicting early postoperative recurrence of HCC, which may assist in guiding follow-up strategies. …”
Get full text

Clinical features and prognostic nomogram development for cancer-specific death in patients with dual primary lung cancer: a population-based study from SEER database by Tenghao Rong, Cheng Ai, Tong Yang, Qingchen Wu, Min Zhang

“…The number of DPLC patients was determined based on the first primary LC (FPLC) and second primary LC (SPLC), and patients were randomly assigned to a training set and a testing set in a 7:3 ratio. …”
Get full text

Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study by Xin Guan, Bo Zhang, Ming Fu, Mengying Li, Xu Yuan, Yaowu Zhu, Jing Peng, Huan Guo, Yanjun Lu

“…Forty-eight clinical and laboratory features were screened with LASSO method. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed death risk prediction model with simple-tree XGBoost model. …”
Get full text

ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection by Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado

“…It is trained using 139 datasets built upon 60 base datasets from 11 diverse domains (finance, healthcare, network security) and 80 ML and DL models composed of 22 base anomaly detection algorithms. …”
Get full text

Machine Learning-Based Detection of Icebergs in Sea Ice and Open Water Using SAR Imagery by Zahra Jafari, Pradeep Bobby, Ebrahim Karami, Rocky Taylor

Get full text

Feature-Driven EnsembleX: An Advanced Ensemble Framework for Enhanced MRI Abdomen Image Classification Using Feature Refinement and Boosting Techniques by Snehal V. Laddha, Rohini S. Ochawar

“…To refine the extracted features, Principal Component Analysis (PCA) was used to reduce dimensionality, followed by Recursive Feature Elimination (RFE) to select the most relevant attributes for classification. …”
Get full text

Noninvasive prediction of failure of the conservative treatment in lateral epicondylitis by clinicoradiological features and elbow MRI radiomics based on interpretable machine lear... by Jianing Cui, Ping Wang, Xiaodong Zhang, Ping Zhang, Yuming Yin, Rongjie Bai

“…Abstract Objectives To develop and validate an interpretable machine learning model based on clinicoradiological features and radiomic features based on magnetic resonance imaging (MRI) to predict the failure of conservative treatment in lateral epicondylitis (LE). …”
Get full text

Maize leaf disease multiclass classification and recognition for sustainable agriculture using multi preceptive deep learning model by Vinay Gautam, Aadam Quraishi, Azzah AlGhamdi, Gaganpreet Kaur, Faisal Alghayadh, Haewon Byeon, Mohammed Wasim Bhatt

“…In tackling the limitations, the current study introduces an AI-based approach. It applied Multi-scaled Xception pre-trained models to extract deep features from images. …”
Get full text

A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions by Yulong Li, Kai Wang

“…DLERKm utilizes pre-trained language models (ESM-2 and RXNFP), molecular fingerprints, and attention mechanisms to extract enzymatic reaction features for the prediction of Km values. …”
Get full text

Study on the factors affecting flight training for trainee pilots by ZHAO Yunxiang, LIANG Zejian, HUANG Hong, LIANG Aimin, XU Haiwen

“…In the field of aviation，the flight skills of trainee pilots are directly related to aviation safety and operational efficiency. Based on the training data of flight trainees from a branch of Civil Aviation Flight University of China，the Pearson correlation coefficient is introduced to evaluate the strength of the relationship between features and target variable. …”
Get full text

Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features by Hongwei Zhang, Kexin Wang, Shurong Liu, Guowei Chen, Yong Jiang, Yingchao Wu, Xiaocong Pang, Xiaoying Wang, Junling Zhang, Xin Wang

Get full text

METHODS FOR ORGANIZATION OF CONTEXTUAL TRAINING OF FUTURE GEOGRAPHY TEACHERS by Karpenko Tetiana, Lakomova Olena, Shyian Daria

“…The purpose of the article is to study the features and determine the crucial principles, approaches, stages and technologies of organizing contextual learning for future geography teachers as an organic part of their professional training. …”
Get full text

Masked pre-training of transformers for histology image analysis by Shuai Jiang, Liesbeth Hondelink, Arief A. Suriawinata, Saeed Hassanpour

“…Our experiments demonstrate that the pre-training procedure enables context-aware understanding of WSIs, facilitates the learning of representative histological features based on patch positions and visual patterns, and is essential for the ViT model to achieve optimal results on WSI-level tasks. …”
Get full text