Research on Actuator Control System Based on Improved MPC by Qingjian Zhao, Qinghai Zhang, Shuang Zhao, Xiaoqian Zhang, Shilei Lu, Yang Guo, Liqiang Song, Zhengxu Zhao

“…The system uses an STM32 controller as the core processing unit, integrating high-precision position sensors to build a multi-level control architecture. An improved model predictive control algorithm is proposed, which introduces extended state observers and multi-objective optimization strategies to estimate system states and external disturbances in real-time, achieving precise disturbance compensation. …”
Get full text

Snow Depth Extraction From Time‐Lapse Imagery Using a Keypoint Deep Learning Model by C. M. Breen, W. R. Currier, C. Vuyovich, Z. Miao, L. R. Prugh

“…At 20 snow pole locations from Grand Mesa, CO (n = 9,722 images), our model successfully predicts the top and bottom of the pole with a mean absolute error (MAE) of 1.30 cm. …”
Get full text

Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research by Parveen Kumar, Benu Chaudhary, Preeti Arya, Rupali Chauhan, Sushma Devi, Punit B. Parejiya, Madan Mohan Gupta

“…Seasonal flu, Ebola, Zika, COVID-19, tuberculosis, and outbreak predictions were made using deep computation and artificial intelligence. …”
Get full text

SIGNIFICANCE OF LABORATORY PARAMETERS IN PROGNOSIS OF OUTCOMES OF ACUTE MYOCARDIAL INFARCTION by I. E. Belaya, V. I. Kolomiets, E. K. Musaeva

“…The following set of parameters predicts unfavorable outcome (death) with 95.56 % accuracy: NO2  < level 22.755 mmol/l, triglycerides ≥ 1.565 mmol/l, erythrocytes < 4.91 M/uL, alanine aminotransferase < 1.23 mmol/l, urea < 7.05 mmol/l, very low-density lipoproteins < 0.965 mmol/l, creatinine ≥ 91.55 µmol/l, NO2 level ≥ 22.755 mmol/l predicts a favorable outcome with 95.56 % accuracy.…”
Get full text

The role of FOXK2–FBXO32 in breast cancer tumorigenesis: Insights into ribosome‐associated pathways by Fuben Liao, Jinjin Zhu, Junju He, Zheming Liu, Yi Yao, Qibin Song

“…Method FOXK2 genes were analyzed using single‐cell sequencing in pan‐cancer bulk RNA‐seq from the TCGA database. We used algorithms to predict their immune infiltration. Functional enrichment and ChIP‐seq identified potential downstream gene, FBXO32. …”
Get full text

Multi-level User Interest and Multi-intent Fusion for Next Basket Recommendation by WEI Chuyuan, YUAN Baojie, WANG Changdong

“…Finally, user interests and intents from different levels are fused in the predict layer for the next basket of predictions. …”
Get full text

Enhancing Yield Estimation and Field Zoning Accuracy in Precision Agriculture Using Solar-Powered Drone-Based Remote Sensing by Abbas Haider Mohammed, Obaid Mohammed Kadhim, Vittalaiah A.

“…The results highlight the effectiveness of the proposed precision agriculture system, showcasing its potential for improving crop yield predictions, optimizing resource distribution, and enhancing overall farm productivity.…”
Get full text

Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review by Dhekre Saber Saleh, Mohd Shahizan Othman

“…Regardless, this study predicts that ML-based thyroid disease detection with imbalanced data and other novel approaches may reveal numerous unrealised possibilities in the future …”
Get full text

Explore potential immune-related targets of leeches in the treatment of type 2 diabetes based on network pharmacology and machine learning by Tairan Hu, Zhaohui Fang

“…Finally, we employed LASSO regression, SVM-RFE, XGBoost, and random forest algorithms to further predict potential targets, followed by validation through molecular docking.ResultsLeeches may influence cellular immunity by modulating immune receptor activity, particularly through the activation of RGS10, CAPS2, and OPA1, thereby impacting the pathology of Type 2 Diabetes Mellitus (T2DM).DiscussionHowever, it is important to note that our results lack experimental validation; therefore, further research is warranted to substantiate these findings.…”
Get full text

Identification of developmental and reproductive toxicity of biocides in consumer products using ToxCast bioassays data and machine learning models by Donghyeon Kim, Siyeol Ahn, Jinhee Choi

“…This study suggested the potential of ToxCast bioassays and machine learning models in predicting DART potential, offering a promising approach to address data-gap in consumer product safety.…”
Get full text

Combined Allocation of Renewable-Based Distributed Generators and Shunt Capacitor Banks in Distribution Networks with Electric Vehicle Load Penetration by Abhilasha Pawar, Rajkumar Viral, Divya Asija, Mohit Bansal

“…Compared to other optimization algorithms, the APO shows superior performance. …”
Get full text

SCoralDet: Efficient real-time underwater soft coral detection with YOLO by Zhaoxuan Lu, Lyuchao Liao, Xingang Xie, Hui Yuan

“…By incorporating soft labels and soft central region loss, our model is guided to prioritize high-quality, well-aligned predictions. We evaluate SCoralDet on the Soft-Coral dataset, achieving an inference latency of 9.52 ms and an mAP50 of 81.9. …”
Get full text

Imaging genomics of cancer: a bibliometric analysis and review by Xinyi Gou, Aobo Feng, Caizhen Feng, Jin Cheng, Nan Hong

“…It has been widely applied to explore heterogeneity and predict responsiveness and disease progression in cancer. …”
Get full text

High-parameter immunophenotyping reveals distinct immune cell profiles in pruritic dogs and cats by Erin McDonald, Eric Kehoe, Darcy Deines, Mary McCarthy, Brie Wright, Susan Huse

“…IntroductionImmunophenotyping is a powerful tool for grading disease severity, aiding in diagnosis, predicting clinical response, and guiding the development of novel therapeutics.MethodsThis pilot study employs high parameter immunophenotyping panels (15 markers for dog, 12 for cat) and leverages unsupervised clustering to identify immune cell populations. …”
Get full text

Advanced Soft Computing Ensemble for Modeling Contaminant Transport in River Systems: A Comparative Analysis and Ecological Impact Assessment by Jafar Chabokpour

“…The research employed various techniques, including Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Support Vector Regression (SVR), and Genetic Algorithms (GA), to predict pollutant concentrations and estimate transport parameters. …”
Get full text

A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence by Abdullah Alabdulatif

“…The proposed model combines an Artificial Neural Network and Support Vector Machine as base classifiers and employs a Random Forest as a meta-classifier to fuse predictions, improving detection performance. Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model’s decisions. …”
Get full text

Resolving Covid-19 with Blockchain and AI by Suyogita Singh, Satya Bhushan Verma

“…In that review, many potential blockchain applications were discovered; however, the majority of them were still in their infancy, and it couldn’t yet be predicted how they could contribute to the fight against COVID-19 through the use of platforms, access kinds, and consensus algorithms. …”
Get full text

3Mont: A multi-omics integrative tool for breast cancer subtype stratification. by Miray Unlu Yazici, J S Marron, Burcu Bakir-Gungor, Fei Zou, Malik Yousef

“…Improving survival time and predicting prognosis for distinct molecular profiles is substantial. …”
Get full text

Driver drowsiness shield (DDSH): a real-time driver drowsiness detection system by Archita Bhanja, Dibyajyoti Parhi, Dipankar Gajendra, Kreetish Sinha, Arup Kumar Sahoo

“…An algorithm has been proposed to classify eye states as open or closed using Transfer Learning based on the MobileNet architecture. …”
Get full text

Identification of common genetic variation that modulates alternative splicing. by Jeremy Hull, Susana Campino, Kate Rowlands, Man-Suen Chan, Richard R Copley, Martin S Taylor, Kirk Rockett, Gareth Elvidge, Brendan Keating, Julian Knight, Dominic Kwiatkowski

“…The functional consequences of these SNPs could not be predicted using bioinformatic algorithms. Our findings suggest that phenotypic variation in splicing patterns is determined by the presence of SNPs within flanking introns or exons. …”
Get full text