Prediction of lithium-ion battery SOC based on IGA-GRU and the fusion of multi-head attention mechanism by Pei Tang, Minnan Jiang, Weikai Xu, Zhengyu Ding, Mao Lv

“…In order to more accurately estimate the SOC, this paper proposes a prediction method that combines an immune genetic algorithm, gated recurrent unit, and multi-head attention mechanism (MHA), using battery experimental data from the University of Maryland as the dataset. …”
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Enhancing Agricultural Futures Return Prediction: Insights from Rolling VMD, Economic Factors, and Mixed Ensembles by Yiling Ye, Xiaowen Zhuang, Cai Yi, Dinggao Liu, Zhenpeng Tang

Subjects: “…agricultural futures return prediction…”
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Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features by Xiuyi Li, Yue Zhou, Weiwei Zhao, Chuanyun Fu, Zhuocheng Huang, Nianqian Li, Haibo Xu

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Fault detection of key parts of wind turbine based on BP neural network combination prediction model by Jingjing Zhang, Liming Liu, Lei Wang, Wei Xi

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Machine-Learning Assisted Prediction of Spectral Power Distribution for Full-Spectrum White Light-Emitting Diode by Jiajie Fan, Yutong Li, Irena Fryc, Cheng Qian, Xuejun Fan, Guoqi Zhang

“…Then, the Back Propagation (BP) and Genetic Algorithm-Back Propagation (GA-BP) NNs are proposed to predict the spectral characteristic parameters of LEDs operated under any usage conditions. …”
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A Novel Strategy Coupling Optimised Sampling with Heterogeneous Ensemble Machine-Learning to Predict Landslide Susceptibility by Yongxing Lu, Honggen Xu, Can Wang, Guanxi Yan, Zhitao Huo, Zuwu Peng, Bo Liu, Chong Xu

Subjects: “…landslide susceptibility prediction…”
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Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data by Salha Al-Ahmari, Farrukh Nadeem

“…Machine learning (ML) has demonstrated potential in predicting SSIs; however, the challenge of addressing imbalanced class ratios remains. …”
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Enhancing Heart Attack Prediction: Feature Identification from Multiparametric Cardiac Data Using Explainable AI by Muhammad Waqar, Muhammad Bilal Shahnawaz, Sajid Saleem, Hassan Dawood, Usman Muhammad, Hussain Dawood

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Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

“…Finally, a Graph Convolutional Split-Attention Network (SGCSAN) for promisingly predicting whether the stock prices are going to hit the ground and fly high again or is going to nosedive with Humboldt Squid Optimization Algorithm (HSOA) is introduced to further improve accuracy with lesser error generation. …”
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Constant time lattice reduction in dimension 4 with application to SQIsign by Otto Hanyecz, Alexander Karenin, Elena Kirshanova, Péter Kutas, Sina Schaeffler

“… In this paper we propose a constant time lattice reduction algorithm for integral dimension-4 lattices. …”
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Ensemble-Based Neighborhood Attribute Reduction: A Multigranularity View by Yuan Gao, Xiangjian Chen, Xibei Yang, Pingxin Wang, Jusheng Mi

“…Different from traditional process which computes reduct through employing a fixed granularity, our algorithm aims to obtain reduct from the viewpoint of multigranularity. …”
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Adaptive Receiver-Window Adjustment for Delay Reduction in LTE Networks by N. D. Adesh, A. Renuka

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Reduction of electric energy consumption for heating passenger railway carriages by A. А. Kuznetsov, S. S. Gorobei

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Domain general noise reduction for time series signals with Noisereduce by Tim Sainburg, Asaf Zorea

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Rough decision rules extraction and reduction based on granular computing by Hong-can YAN, Feng ZHANG, Bao-xiang LIU

“…Rule mining was an important research content of data mining,and it was also a hot research topic in the fields of decision support system,artificial intelligence,recommendation system,etc,where attribute reduction and minimal rule set extraction were the key links.Most importantly,the efficiency of extraction was determined by its application.The rough set model and granular computing theory were applied to the decision rule reduction.The decision table was granulated by granulation function,the grain of membership and the concept granular set construction algorithm gener-ated the initial concept granular set.Therefore,attribute reduction could be realized by the distinguish operator of concept granule,and decision rules extraction could be achieved by visualization of concept granule lattice.Experimental result shows that the method is easier to be applied to computer programming and it is more efficient and practical than the existing methods.…”
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Residual-conditioned sparse transformer for photoacoustic image artifact reduction by Xiaoxue Wang, Jinzhuang Xu, Chenglong Zhang, Moritz Wildgruber, Wenjing Jiang, Lili Wang, Xiaopeng Ma

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Model-Order Reduction of Multistage Cascaded Models for Digital Predistortion by Raul Criado, Wantao Li, William Thompson, Gabriel Montoro, Kevin Chuang, Pere L. Gilabert

“…To reduce the computational complexity of these multistage CC behavioral models, a model-order reduction technique based on a greedy algorithm is proposed. …”
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TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies by Pixu Shi, Cameron Martino, Rungang Han, Stefan Janssen, Gregory Buck, Myrna Serrano, Kouros Owzar, Rob Knight, Liat Shenhav, Anru R. Zhang

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Edge Server Placement and Task Allocation for Maximum Delay Reduction by Koki Shibata, Sumiko Miyata

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Multi-Condition Magnetic Core Loss Prediction and Magnetic Component Performance Optimization Based on Improved Deep Forest by Haotian Shi, Zipeng Jin

“…This paper proposes a core loss prediction model based on an improved deep forest algorithm and information entropy-enhanced genetic algorithm. …”
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