Sourcing efficacy – The role of supportive intelligence by Aleksandar Erceg, Santhosh Joseph

“…Technological advances such as machine learning (ML) and artificial intelligence (AI) and their integration in FBN are significant transformative steps. …”
Get full text

Role of artificial intelligence in digital pathology for gynecological cancers by Ya-Li Wang, Song Gao, Qian Xiao, Chen Li, Marcin Grzegorzek, Ying-Ying Zhang, Xiao-Han Li, Ye Kang, Fang-Hua Liu, Dong-Hui Huang, Ting-Ting Gong, Qi-Jun Wu

Get full text

Radiomics features from the peritumoral region can be associated with the epilepsy status of glioblastoma patients by Yeong Chul Yun, Yeong Chul Yun, Yeong Chul Yun, Johann M. E. Jende, Johann M. E. Jende, Katharina Holz, Katharina Holz, Sabine Wolf, Sabine Wolf, Freya Garhöfer, Freya Garhöfer, Anja Hohmann, Philipp Vollmuth, Martin Bendszus, Heinz-Peter Schlemmer, Felix Sahm, Felix Sahm, Sabine Heiland, Wolfgang Wick, Varun Venkataramani, Varun Venkataramani, Felix T. Kurz, Felix T. Kurz, Felix T. Kurz

“…The training cohort was used for feature selection with ElasticNet and model optimization. Various machine learning models, including logistic regression (LR), were used to predict epilepsy status. …”
Get full text

System frequency response model and droop coefficient setting considering renewable energy participation in frequency regulation by Yuyan Song, Yongjie Zhang, Shuai Zhang, Fang Liu, Yunche Su, Yang Liu

“…Furthermore, the required equivalent droop coefficients are proposed for various sending-end system capacities and operating conditions. Finally, the model’s validity and accuracy are confirmed through a modified WSCC 4-machine 10-bus system, offering theoretical underpinnings for stable system operation and optimized operational planning.…”
Get full text

DWE: Research on Water Quality Inversion and Scalability in Small Watersheds Using UAV Remote Sensing Data by Hao Li, Zhijun Xie, Xing Jin, Changchun Peng, Cheng Ren, Yunfei Mao, Kefan Zhou, Yingying Li

“…Therefore, this study applies the idea of integrated modeling to remote sensing inversion of water quality parameters (WQPs), and proposes an adaptive dynamic weighted ensemble learning (DWE) model, aiming to integrate the advantages of different machine learning models, improve the accuracy of WQP inversion, and adaptively assign dynamic weights to different machine learning models, thereby achieving the scalability of the model in time and space. …”
Get full text

Future of Connectivity: A Comprehensive Review of Innovations and Challenges in 7G Smart Networks by Vinay Chamola, Mritunjay Shall Peelam, Mohsen Guizani, Dusit Niyato

“…The review also examines the role of Large Language Models (LLMs) in enabling real-time actionable intelligence and optimizing edge devices within 7G. …”
Get full text

A Multiplatform Approach for Chlorophyll Level Estimation for Irish Lakes by Minyan Zhao, Fiachra O'Loughlin

“…In the first stage, three machine learning models (random forest, extreme gradient boosting, and support vector machine) were built directly between chlorophyll levels and remote sensing reflectance from Sentinel-2, Landsat-8, Moderate Resolution Imaging Spectroradiometer (MODIS) Terra, and MODIS Aqua. …”
Get full text

Voice analysis and deep learning for detecting mental disorders in pregnant women: a cross-sectional study by Hikaru Ooba, Jota Maki, Hisashi Masuyama

“…Conclusion We developed a lightweight machine learning model to analyze pregnant women's voices for screening various mental disorders, achieving high sensitivity and demonstrating the potential of voice analysis as an effective and objective tool in perinatal mental health care.…”
Get full text

Advanced sentiment analysis in online shopping: Implementing LSTM models analyzing E-commerce user sentiments by Lu Liyuan

“…This article elaborately contrasts long short-term memory (LSTM)-based models with traditional machine learning models, like support vector machines (SVM), random forest, and Naive Bayes classifiers. …”
Get full text

Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities by Paul Iacobescu, Virginia Marina, Catalin Anghel, Aurelian-Dumitrache Anghele

“…Advanced preprocessing techniques, such as SMOTE–ENN for addressing class imbalance and hyperparameter optimization through Grid Search Cross-Validation, were applied to enhance the reliability and performance of these models. …”
Get full text

Advanced Financial Fraud Malware Detection Method in the Android Environment by Jaeho Shin, Daehyun Kim, Kyungho Lee

“…This study proposes a machine learning (ML) model based on static analysis to detect malware. …”
Get full text

Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large‐Scale Structures of the Solar Corona by Tingyu Wang, Bingxian Luo, Jingjing Wang, Xianzhi Ao, Liqin Shi, Qiuzhen Zhong, Siqing Liu

“…Abstract With the increasing number of large constellations, it is crucial to accurately predict satellite positions and movements using upper atmosphere models driven by geomagnetic indices. Machine learning (ML) can quickly provide geomagnetic index predictions. …”
Get full text

Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction by Xinyue Lv, Youli Li, Lili Zhangzhong, Chaoyang Tong, Yibo Wei, Guangwei Li, Yingru Yang

“…Subsequently, Spearman correlation analysis was utilized to select the combination of canopy coverage and environmental data, followed by the random forest feature importance ranking method to identify the most optimal feature variables. We constructed average fusion, weighted fusion, and stacking fusion models based on RandomForest, LightGBM, and CatBoost machine learning algorithms to accurately predict the water requirements of greenhouse tomato crops. …”
Get full text

Non-destructive Identification of Moldy Walnuts by Fusing X-Ray and Visual Image Features by NING Xinyue, ZHANG Hui, JI Shuai, LAI Lisi

“…The experimental results showed that the ELM model developed using SPA optimized feature set had the best performance. …”
Get full text

The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease by Mohammed A. Chowdhury, Rodrigue Rizk, Conroy Chiu, Jing J. Zhang, Jamie L. Scholl, Taylor J. Bosch, Arun Singh, Lee A. Baugh, Jeffrey S. McGough, KC Santosh, William C.W. Chen

“…The application of artificial intelligence (AI) and machine learning (ML) in medicine and healthcare has been extensively explored across various areas. …”
Get full text

Research Progress and Applications of Artificial Intelligence in Agricultural Equipment by Yong Zhu, Shida Zhang, Shengnan Tang, Qiang Gao

“…This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). …”
Get full text

Prediction of Myocardial Infarction Based on Non-ECG Sleep Data Combined With Domain Knowledge by Changyun Li, Yonghan Zhao, Qihui Mo, Zhibing Wang, Xi Xu

“…The experiments demonstrate that the model’s accuracy reaches its optimal level by combining the age rule, improving to 73.1%. …”
Get full text

Mechanism of Influence of Spatial Perception on Residents’ Emotion in Child-Friendly Urban Streets of Fuzhou City by Shaofeng CHEN, Zhengyan CHEN, Yuhan XU, Zheng DING

“…Three machine learning architectures are deployed: CNN-BiLSTM Hybrid Model, FCN-RF Semantic Segmentation, and XGBoost-SHAP Interpretability Framework. …”
Get full text

Data-Driven Carbon Emission Dynamics Under Ship In-Port Congestion by Weiyu Liu, Bowei Xu, Junjun Li

Get full text

Advancing brain tumor diagnosis: Deep siamese convolutional neural network as a superior model for MRI classification by Gowtham Murugesan, Pavithra Nagendran, Jeyakumar Natarajan

“…This study evaluated five state‐of‐the‐art classification models to determine the optimal model for brain tumor classification and diagnosis using MRI. …”
Get full text