A multitask framework based on CA-EfficientNetV2 for the prediction of glioma molecular biomarkers by Qian Xu, Feng Ning Liang, Ya Ru Cao, Jin Duan, Teng Cui, Teng Zhao, Hong Zhu

“…Initially, unlabeled MR images were annotated using K-means clustering to generate pseudolabels, which were subsequently refined using a Vision Transformer (ViT) network to improve labeling accuracy. Then, the Fruit Fly Optimization Algorithm (FOA) was employed to assign optimal weights to the pseudolabeled data. …”
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Research on Active–Passive Training Control Strategies for Upper Limb Rehabilitation Robot by Yongming Yang

“…By utilizing neural networks to train sample data during rehabilitation training, the fuzzy rules and membership functions in fuzzy intention recognition algorithm are optimized to improve the accuracy of intention recognition during training. …”
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Stepwise Corrected Attention Registration Network for Preoperative and Follow-Up Magnetic Resonance Imaging of Glioma Patients by Yuefei Feng, Yao Zheng, Dong Huang, Jie Wei, Tianci Liu, Yinyan Wang, Yang Liu

“…This methodology leverages preoperative and follow-up MRI scans as fixed images and moving images, respectively, and employs a multi-level registration strategy that establishes a precise and holistic correspondence between images, from coarse to fine. Furthermore, our model introduces a corrected attention module into the multi-level registration network that can generate an attention map at the local level through the deformation fields of the upper-level registration network and pathological areas of preoperative images segmented by a mature algorithm in BraTS, serving to strengthen the registration accuracy of non-correspondence areas. …”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…Moreover, applying DANN for fault diagnosis significantly improved diagnostic performance in noisy environments by overcoming environmental differences between the source and target domains. …”
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P-Band PolInSAR Sub-Canopy Terrain Retrieval in Tropical Forests Using Forest Height-to-Unpenetrated Depth Mapping by Chuanjun Wu, Jiali Hou, Peng Shen, Sai Wang, Gang Chen, Lu Zhang

“…A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. …”
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Ensemble Methods for Parameter Estimation of WRF‐Hydro by Arezoo RafieeiNasab, Michael N. Fienen, Nina Omani, Ishita Srivastava, Aubrey L. Dugger

“…Results show a large improvement in the model performance. In summary, our study demonstrates the efficacy of employing iES alongside differential weighting of observations, highlighting its potential to enhance hydrological model parameter estimation.…”
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Research on ionospheric parameters prediction based on deep learning by Yuntian FENG, Xia WU, Xiong XU, Rongqing ZHANG

“…For ionospheric parameter prediction, the short-term and daily mean value prediction of ionospheric parameters was established by long short-term memory (LSTM) predictive neural network modeling.Two methods of point-by-point prediction and sequence prediction were utilized.Furthermore, in order to predict the hourly and daily changes of ionospheric parameters, the proposed scheme was optimized by multidimensional prediction and empirical mode decomposition (EMD) algorithm.Finally, the feasibility of the proposed optimization algorithm in improving the prediction accuracy of ionospheric parameters is verified.…”
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Air-ground collaborative multi-source orbital integrated detection system: Combining 3D imaging and intrusion recognition. by Mengyuan Yan, Xingyu Yang, Wei Gao, Lifan Rong, Shengbo Li, Yuan Xiong

“…The system employs a lightweight rail inspection vehicle equipped with dual LiDARs and an Astro camera, synchronized with an unmanned aerial vehicle (UAV) carrying industrial-grade LiDAR. We propose an improved LiDAR odometry and mapping with sliding window (LOAM-SLAM) algorithm enables real-time dynamic mapping, while an optimized iterative closest point (ICP) algorithm achieves high-precision point cloud registration and colorization. …”
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Deep coal fluidization mining ropeless hoisting system and its cooperative driving control strategy by Shuai LIU, Jiusheng BAO, Jinhao DAI, Yan YIN, Liang ZHAO, Zhongbin WANG, Shirong GE

“…The research results show that the fuzzy PI loop coupling control algorithm can optimize the motor response characteristics and improve the following and synchronization performance among multiple motors. …”
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Research on Electric Spot Trading of Electric Vehicles Based on Block Chain by ZHANG Zhening, ZHANG Xinyu, GONG Gangjun

“…Finally,by designing intelligent contract, the optimization process of trading scheme was validated by a genetic algorithm.The simulation results show that through the efficient operation of the smart contract of block chain, the formulation of the optimal trading scheme to meet the objectives of multiple participants is achieved, which can provide a reference for improving the safety, reliability and efficient coordination of the electric vehicle charging market.…”
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TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance by Xuanbai Yu, Olivier Caspary

“…These findings offer new insights to support reliable predictive maintenance in industrial settings and provide a new perspective for future research into active vibration control, where vibration signal analysis, feature extraction, and mathematical modeling play key roles in optimizing control algorithms and enhancing the efficiency of adaptive control systems.…”
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Distributed Photovoltaic Distribution Voltage Prediction Based on eXtreme Gradient Boosting and Time Convolutional Networks by Fang Yuan, Yong Lu, Zhi Xie, Shenxiang Dai

“…Then, the model is improved and optimized using residual module with bottle sea sheath algorithm. …”
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A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management by Sheng-Wei Wang, Yen-Yu Chen, Shu-Han Hsu, Yu-Hsuan Kao, Masaomi Kimura, Li-chiu Chang, Tzi-Wen Pan, Chuen-Fa Ni

“…This study develops a groundwater level prediction model using the extreme gradient boosting (XGB) algorithm, employing power consumption, precipitation, and groundwater level data as input features. …”
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Predicting Trip Duration and Distance in Bike-Sharing Systems Using Dynamic Time Warping by Ahmed Ali, Ahmad Salah, Mahmoud Bekhit, Ahmed Fathalla

“…For the two datasets, the obtained results show that the best improvements of the predictive model’s accuracy are 30% and 42% on average for predicting trip duration and distance of the trip, respectively.…”
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Statistical Evaluation of Smartphone-Based Automated Grading System for Ocular Redness Associated with Dry Eye Disease and Implications for Clinical Trials by Rodriguez JD, Hamm A, Bensinger E, Kerti SJ, Gomes PJ, Ousler III GW, Gupta P, De Moraes CG, Abelson MB

“…The optimal generalized model improved predictive accuracy with horizontality such that 93.0% of images were predicted with an absolute error less than one unit difference in grading.Conclusion: This study demonstrates that fully automating image analysis allows thousands of images to be graded efficiently. …”
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A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology by Yanni Wang, Wisit Cheungpasitporn, Hatem Ali, Jianbo Qing, Charat Thongprayoon, Wisit Kaewput, Karim M. Soliman, Zhengxing Huang, Min Yang, Zhongheng Zhang

“…AI-driven models utilize diverse datasets—including electronic health records, imaging, and biomarkers—to improve clinical decision-making. …”
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Predicting Diabetic Retinopathy and Nephropathy Complications Using Machine Learning Techniques by D. R. Manjunath, J. J. Lohith, S. Selva Kumar, Abhijit Das

“…This paper shows the possibility of machine learning based frameworks in diabetic complication management by predicting accurately and in time. These models can be integrated into clinical decision support systems (CDSS) to give insights to clinicians, improve patient outcomes through personalized interventions and optimize resource allocation. …”
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Effective Breast Cancer Classification Using Deep MLP, Feature-Fused Autoencoder and Weight-Tuned Decision Tree by Nagham Rasheed Hameed Alsaedi, Mehmet Fatih Akay

“…This paper presents an advanced machine learning algorithm designed to improve classification accuracy in breast cancer diagnosis. …”
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Neuro-fuzzy controller based adaptive control for enhancing the frequency response of two-area power system by M.S. Elborlsy, Samir A. Hamad, Fayez F.M. El-Sousy, R.M. Mostafa, H.E. Keshta, Mohamed A. Ghalib

“…FPI and PI controllers' parameters are optimally tuned using a recent optimization technique known as the Coati Optimization Algorithm (COA). …”
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Hierarchical energy management of distribution network with multi-microgrids based on double Stackelberg game by XIE Yuanhao, LIN Shenghong, ZHU Jianquan

“…By applying the Karush-Kuhn-Tucker (KKT) conditions, the tri-level model is reformulated into a bi-level model, and iterative process combining heuristic algorithm and solver is adopted to solve the model. …”
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