Water Quality Prediction Method Based on Reinforcement Learning Graph Neural Network by Mingming Yan, Zhe Wang

“…However, existing methods face two main challenges: the interaction between water quality variables and the environment is often overlooked, and even when considered, it is not effectively utilized. …”
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On variational formulations of inner boundary value problems for infinite systems of elliptic equations of special kind by Yu. A. Muzychuk, R. S. Chapko

“…We consider boundary value problems for infinite triangular systems of elliptic equations with variable coefficients in 3d Lipschitz domains. Variational formulations of Dirichlet, Neumann and Robin problems are received and their well posedness in corresponding Sobolev spaces is established. …”
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MDMU-Net: 3D multi-dimensional decoupled multi-scale U-Net for pancreatic cancer segmentation by Lian Lu, Miao Wu, Gan Sen, Fei Ren, Tao Hu

“…First, depthwise separable convolution is employed to reduce model complexity. …”
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Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…This limitation becomes increasingly problematic in dynamic environments where temporal relevance and variable interdependencies fluctuate significantly. …”
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Real-time classroom student behavior detection based on improved YOLOv8s by Xiaojing Sheng, Suqiang Li, Sixian Chan

“…However, the field still faces specific challenges, primarily concerning the accuracy of identifying student behaviors within complex and variable classroom environments, as well as the real-time capabilities of detection algorithms. …”
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A Lightweight Person Detector for Surveillance Footage Based on YOLOv8n by Qicheng Wang, Guoqiang Feng, Zongzhe Li

“…Next, a heterogeneous PAFPN with improved MSBlock was formed using heterogeneous convolution kernels. Finally, AKConv, a variable kernel convolution, was applied to further reduce the number of parameters and the computational cost while improving accuracy. …”
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Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations by Deepanshu Lakra, Deepanshu Lakra, Shobhit Pipil, Prashant K. Srivastava, Suraj Kumar Singh, Manika Gupta, Rajendra Prasad

“…Soil moisture is a fundamental variable in the Earth’s hydrological cycle and vital for development of agricultural water management practices. …”
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An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN by Mohamed Abdelkader Aboamer, Mohamed Yacin Sikkandar, Sachin Gupta, Luis Vives, Kapil Joshi, Batyrkhan Omarov, Sitesh Kumar Singh

“…Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, “convolutional neural network” or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. …”
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OPTIMAL CONTROL OF INVESTMENTS AROUND COURNOT POINT by Y. Aganin

“…The equations of dynamics of variables for equilibrium, developing and crisis markets in a linear approximation are obtained. …”
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Application of Modified Laplace Variational Iteration Hybrid Approach for Solving Time-Fractional Fourth-Order Parabolic PDEs by Mehari Fentahun Endalew, Xiaoming Zhang

“…The current study is aimed at obtaining analytical solutions of fourth-order parabolic partial differential equations of time-fractional derivative with variable coefficients. The modified Laplace variational iteration approach and the homotopy perturbation method were used to treat nonlinear, fourth-order, time-fractional partial differential equations with time-fractional derivatives. …”
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A Wind Power Forecasting Method Based on Lightweight Representation Learning and Multivariate Feature Mixing by Chudong Shan, Shuai Liu, Shuangjian Peng, Zhihong Huang, Yuanjun Zuo, Wenjing Zhang, Jian Xiao

“…However, the forecasting of wind power is subject to the complex influence of multiple variable features and their interrelationships, which poses challenges to traditional forecasting methods. …”
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Study on Color Detection of Korla Fragrant Pears by Near-Infrared Spectroscopy Combined with PLSR by Yifan Xia, Yang Liu, Hong Zhang, Jikai Che, Qing Liang

“…The full-spectrum data were pre-processed using six methods: Savitzky–Golay convolution smoothing (SGCS), Savitzky–Golay convolution derivative (SGCD), multiplicative scatter correction (MSC), vector normalization (VN), min–max normalization (MMN), and standard normal variate transformation (SNV). …”
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Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms by Hasan Alkahtani, Theyazn H. H. Aldhyani

“…In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short-term memory (LSTM), and a hybrid convolution neural network with the long short-term memory (CNN-LSTM) model. …”
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SIGNAL DETECTION WITH UNKNOWN PARAMETERS by Y. A. Sidorkina, V. V. Antipov

“…The effectiveness of this algorithm is determined by the sampling distribution of the maximum values of random variables distributed according to Rayleigh.…”
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A time-domain finite element formulation of the equivalent fluid model for the acoustic wave equation by Maurerlehner Paul, Mayrhofer Dominik, Kaltenbacher Manfred, Schoder Stefan

“…When transforming the acoustic wave equation for the EF model from the frequency domain to the time domain, convolution integrals arise. The auxiliary differential equation (ADE) method is used to circumvent the direct calculation of these convolution integrals. …”
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Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry by ZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai

“…After preprocessing the NIR data through 5-point 2-fold convolutional smoothing, spectral feature wavelengths were selected using the competitive adaptive reweighted sampling (CARS) algorithm; combining Spearman’s rank correlation coefficient, maximum information coefficient (MIC) and random forest (RF) variable importance, the key flavor components (KC) identified by GC-MS affecting the grading of raw Baijiu were determined. …”
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Design a new scheme for image security using a deep learning technique of hierarchical parameters by Khazaal Yasmine M., Falih Mohanaed Ajmi, Majeed Abbas Hamid

“…DL technology was used to encrypt and decrypt images, and based on hierarchical variables to complicate the encryption process. Convolutional neural networks are used in automatic learning to extract hierarchical features from an image, and to ensure adaptability, the model is trained on a variety of images. …”
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A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction by Dawei Wang, Jingwei Guo, Chunyang Zhang

“…This paper proposes a novel hybrid deep learning model composed of convolutional neural networks (CNN) and temporal convolutional networks (TCN), named the CNN + TCN model, for predicting train delays in railway systems. …”
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Laparoscopic Suture Gestures Recognition via Machine Learning: A Method for Validation of Kinematic Features Selection by Juan M. Herrera-Lopez, Alvaro Galan-Cuenca, Antonio J. Reina, Isabel Garcia-Morales, Victor F. Munoz

“…For that purpose, this work models the laparoscopic suturing manoeuvre as a set of simpler gestures to be recognized and, using the ReliefF algorithm on the JIGSAWS dataset’s kinematic data, presents a study of significance of the different kinematic variables. To validate this study, three classification models based on the multilayer perceptron and on Hidden Markov Models have been trained using both the complete set of variables and a reduced selection including only the most significant. …”
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Analysis of Nonviscous Oscillators Based on the Damping Model Perturbation by Mario Lázaro, César F. Casanova, Ignacio Ferrer, Pedro Martín

“…The dissipative forces of these systems are characterized by convolution integrals with kernel functions, which in turn contain a set of damping parameters. …”
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