Cleaning of Abnormal Wind Speed Power Data Based on Quartile RANSAC Regression by Fengjuan Zhang, Xiaohui Zhang, Zhilei Xu, Keliang Dong, Zhiwei Li, Yubo Liu

“…The cleaning accuracy improvement is particularly significant when dealing with a high proportion of stacked anomaly data, thereby bringing significant value to wind power applications such as wind power prediction, condition assessment, and fault detection.…”
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Abnormal noise detection of electric machines based on HPSS-CIS and CNN-CBAM by Zhao Qingsong, Wang Xiufeng, Luo Kun, He Dan, Liu Xiang

“…To solve these problems, a new prediction method based on the algorithm of harmonic percussion sound separation (HPSS) and continuous interphase sampling (CIS) of cochlear implants and the CNN-CBAM (Convolutional neural network based on Convolutional Block Attention Module) model, is proposed in this paper. …”
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iSubgraph: integrative genomics for subgroup discovery in hepatocellular carcinoma using graph mining and mixture models. by Bahadir Ozdemir, Wael Abd-Almageed, Stephanie Roessler, Xin Wei Wang

“…The robustness analysis of the mixture model showed that the class predictions are highly stable. Moreover, the Kaplan-Meier survival analysis revealed that the HCC subgroups identified by the algorithm have different survival characteristics. …”
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Identification and validation of TUBB, CLTA, and FBXL5 as potential diagnostic markers of postmenopausal osteoporosis by Yue Tan, Yujing Wang, Qin Zhu, Yan Xue, Xuhao Ji, Zhenkun Li, Jiawen Shen, Chengming Sun, Shiqi Ren, Chenlin Zhang, Jianfeng Chen

“…The genetic markers identified in this study hold potential for accurately predicting the risk of PMOP in patients. The findings contribute to understanding the underlying molecular mechanisms of CLTA, TUBB, and FBXL5 in PMOP and may facilitate the development of novel therapeutic strategies and improved monitoring of the disease. …”
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Internet of medical things and trending converged technologies: A comprehensive review on real-time applications by Shiraz Ali Wagan, Jahwan Koo, Isma Farah Siddiqui, Muhammad Attique, Dong Ryeol Shin, Nawab Muhammad Faseeh Qureshi

“…It also discusses various applied machine learning algorithms available in the healthcare industry. It compares a variety of disease predictions and the accuracy of the equipment and decision recommendations. …”
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Application of Path Planning and Tracking Control Technology in Mower Robots by Lixing Liu, Hongjie Liu, Xu Wang, Jianping Li, Pengfei Wang, Shuteng Liu, Jinxuan Zou, Xin Yang

“…Meanwhile, future development trends are predicted.…”
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Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management by Chenxi Wang, Chenxi Wang, Xinyue Yuan, Wei Jing

“…We focus on two dominant AI-EEG application models: supportive AI (augmenting clinical decisions) and predictive AI (anticipating seizures or outcomes).ResultsAI-based EEG analysis demonstrates significant potential in improving epilepsy detection, monitoring, and therapeutic evaluation. …”
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In silico exploration of novel EGFR-targeting compounds: integrative molecular modeling, docking, pharmacokinetics, and MD simulations for advancing anti-cervical cancer therapeuti... by Auwal Salisu Isa, Adamu Uzairu, Umar Meleh Umar, Muhammad Tukur Ibrahim, Abdullahi Bello Umar, Kamal Tabti, Abdussamad Mukhtar Mohammed

“…This study investigates the potential of 2,4-diphenyl indenol [1,2-b] pyridinol derivatives as inhibitors targeting the epidermal growth factor receptor (EGFR) through computational drug discovery methods. A genetic algorithm-multiple linear regression (GA-MLR) model was created, achieving strong predictive accuracy with R² = 0.9243, Q² = 0.8957, CCC = 0.9021, and MAE = 0.034. …”
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Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach by Illia Mushta, Sulev Koks, Anton Popov, Oleksandr Lysenko

“…To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. …”
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Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning by Nikolaos A. Fountas, Dimitrios E. Manolakos, Nikolaos M. Vaxevanidis

“…The results indicated that the feed rate was the dominant parameter affecting both objectives, namely the main cutting force and surface roughness, while the NSGA-II algorithm was capable of delivering advantageous solutions for enhancing machinability with less than 10% error predictions when comparing simulated and actual machining results.…”
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China’s county-level monthly CO2 emissions during 2013–2021 by Ming Gao, Chaofan Tu, Miaomiao Liu, Jiandong Chen, Xingyu Chen, Hong Zou, Thomas Shiu Tong, Long Chen, Shuke Fu

“…After the main feature variables were identified, a hybrid regression algorithm combining deep neural networks and CatBoost was constructed to generate instrumental variable for predicting CO2 emissions. …”
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Estimating Economic Insights: A Machine Learning Method for Estimating the Shanghai Stock Exchange by Reza Seifi Majdar, Seyed Hadi Seyed Hatami

“…This work aims to create an accurate hybrid model for predicting stock prices which includes Adaptive Boosting, Slime mould algorithm, and Empirical mode decomposition (EMD) to forecast the stock market values. …”
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Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis. by Leonardo V Castorina, Filippo Grazioli, Pierre Machart, Anja Mösch, Federico Errica

“…This could then be used to estimate a confidence score on predictions on novel and unseen peptides, based on how different they are from the training ones. …”
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Fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and RF optimization by Shuwen Wang, Taisi Chen, Zimin Li, Haitao Sang

“…The accuracy rate in biomass prediction and environmental data classification was as high as 98%, highlighting its potential to completely change mangrove monitoring through precise land feature identification and ecological protection applications. …”
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From Tweets to Threats: A Survey of Cybersecurity Threat Detection Challenges, AI-Based Solutions and Potential Opportunities in X by Omar Alsodi, Xujuan Zhou, Raj Gururajan, Anup Shrestha, Eyad Btoush

“…The findings indicate that current studies often lack comprehensive evaluations of critical aspects such as prediction scope, types of cybersecurity threats, feature extraction techniques, algorithm complexity, information summarization levels, scalability over time, and performance measurements. …”
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Network intrusion detection model using wrapper based feature selection and multi head attention transformers by Muhammad Umer, Muhammad Tahir, Muhammad Sardaraz, Muhammad Sharif, Hela Elmannai, Abeer D. Algarni

“…The model uses a wrapper-based feature selection technique using machine learning algorithms to select the best features, which are then combined and fed into a Multi-Head Attention-based transformer for getting the predictions. …”
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Honeybee Colony Growth Period Recognition Based on Multivariate Temperature Feature Extraction and Machine Learning by Chuanqi Lu, Lin Li, Denghua Li, Qiuying Huang, Wei Hong

“…The results demonstrate that the proposed features can effectively characterize the growth period of bee colonies, and the BP method performs best in predicting growth period categories, with an MAE of only 1.45%. …”
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Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling by Javier Arduengo, Nicolas Hascoet, Francisco Chinesta, Jean-Yves Hascoet

“…Thus, the control system offers predictability and adaptability capabilities to ensure the consistent production of high-quality bioprinted structures. …”
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Dim and Small Target Detection Based on Improved Bilateral Filtering and Gaussian Motion Probability Estimation by Fan Xiangsuo, Qin Wenlin, Feng Gaoshan, Huang Qingnan, Min Lei

“…Six scenes and eleven comparison algorithms are selected for experiments, experimental results show the effectiveness and robustness of the proposed algorithm.…”
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Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification by Long Li, Xiaoming Tao, Hui Song, Xiaolong Li, Zhilong Ye, Yao Jin, Qiuyu He, Shiyin Wei, Wenli Chen

“…Finally, a probabilistic generative model based on a deep belief network (DBN) is developed to predict damage locations and degrees. The proposed methods are validated using vibration data from a numerical three-span continuous bridge subjected to random vehicle excitations. …”
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