Spectral estimation of the aboveground biomass of cotton under water–nitrogen coupling conditions by Shunyu Qiao, Jiaqiang Wang, Fuqing Li, Jing Shi, Chongfa Cai

“…The optimal spectral model based on RT had higher R2 values and lower RMSE values at the bud stage, with R2 = 0.79, RMSE = 0.24 g and RPD = 16.18. …”
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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…The calculation method of the effective specific cutting energy is given, which is calculated by the cutter motor power, slurry concentration, and slurry flow rate. Based on the machine learning framework, a model framework for predicting the specific cutting energy according to the relevant parameters of the suction-lifting system is constructed. …”
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Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques by Shoayee Dlaim Alotaibi, Abeer A. K. Alharbi

“…As the number of dementia cases continues to rise, delivering optimal care becomes more complex. Machine learning (ML) plays a crucial role in addressing this challenge by utilizing medical data to enhance care planning and management for individuals at risk of various types of dementia. …”
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Data Augmentation for Enhanced Fish Detection in Lake Environments: Affine Transformations, Neural Filters, SinGAN by Kidai Watanabe, Thao Nguyen-Nhu, Saya Takano, Daisuke Mori, Yasufumi Fujimoto

“…Recent advancements in high-resolution cameras and machine learning technologies have facilitated image analysis automation, promoting remote fish tracking. …”
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Advances in photoelectrochemical glucose sensors: A critical review by Oluwaseun Adepoju, Mahabubur Chowdhury, Tunde V. Ojumu

“…It also explores the growing role of density functional theory (DFT)-based computational simulations and machine learning (ML) models in predicting, optimizing, screening, and understanding sensor performance. …”
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Design and Development of Miniature Measuring Instrument for Parachute Cords Dynamic Load for Stepless Parachute Opening by Wei Liang, Xin Zhao, Pengpeng Wu, Yuxin Li, Shuai Lv

“…The software of the parachute cords tension acquisition system based on the miniature measuring instrument is developed. The Load Cell is modeled by using SolidWorks 2022 and statically analyzed by using Ansys 2022 R1 Workbench finite element analysis software. …”
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An Optimised CNN Hardware Accelerator Applicable to IoT End Nodes for Disruptive Healthcare by Arfan Ghani, Akinyemi Aina, Chan Hwang See

“…In the evolving landscape of computer vision, the integration of machine learning algorithms with cutting-edge hardware platforms is increasingly pivotal, especially in the context of disruptive healthcare systems. …”
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Functional Asymmetries and Force Efficiency in Elite Junior Badminton: A Controlled Trial Using Hop Test Metrics and Neuromuscular Adaption Indices by Mariola Gepfert, Artur Gołaś, Adam Maszczyk, Kajetan Ornowski, Przemysław Pietraszewski

“…Contrary to initial hypotheses, traditional asymmetry metrics showed no significant correlations with performance variables (r < 0.1). Machine learning models (Random Forest) using FER and AII failed to classify responders reliably (AUC = 0.50). …”
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Design Guidelines for Material Extrusion of Metals (MEX/M) by Karim Asami, Mehar Prakash Reddy Medapati, Titus Rakow, Tim Röver, Claus Emmelmann

“…These design guidelines apply specifically to the machine and filament type utilized in this study.…”
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ENHANCING 〖PM〗_(2.5) PREDICTION IN KEMAYORAN DISTRICT, DKI JAKARTA USING DEEP BILSTM METHOD by Nabila Karin, Gumgum Darmawan, Triyani Hendrawati

“…The optimal parameters, which comprised 24 timesteps, Adam optimizers with a learning rate of 0.001, 16 batch sizes, 1000 epochs, and ReLU activation functions across multiple BiLSTM layers, showcased the model’s effectiveness in forecasting the concentration in Kemayoran District, DKI Jakarta, on November , 2023.…”
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Structural Damping with Friction Beams by L. Gaul, J. Roseira, J. Becker

“…Numerical simulation of the friction interface requires non-linear models. The response of the double beam system is simulated using a numerical algorithm programmed in MATLAB which models point-to-point friction with the Masing friction model. …”
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Detection of Tomato Leaf Pesticide Residues Based on Fluorescence Spectrum and Hyper-Spectrum by Jiayu Gao, Xuhui Yang, Simo Liu, Yufeng Liu, Xiaofeng Ning

“…Qualitative analysis was established based on the characteristic wavelengths of hyper-spectrum and fluorescence spectrum extracted by the SPA algorithm, and the accuracy of the training sets of the optimal qualitative model reached 94.9% and 95.7%, respectively, and the accuracy of the test sets both reached 100%. …”
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Meta-Ensemble Learning for Heart Disease Prediction: A Stacking-Based Approach With Explainable AI by Mehwish Naz, Aqsa Khalid, Abdul Hameed, Rabia Taj, Waleed Mumtaz, Faiz Abdullah Alotaibi, Mrim M. Alnfiai

“…Similarly, to ensure optimal model performance and generalizability, k-fold cross-validation was employed for hyperparameter tuning, enabling systematic evaluation and fine-tuning of model parameters while reducing the risk of overfitting. …”
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System Identification and Navigation of an Underactuated Underwater Vehicle Based on LSTM by Changhao Li, Zetao Hu, Desheng Zhang, Xin Wang

“…To mitigate position divergence in dead reckoning (DR) caused by LSTM, a Nonlinear Explicit Complementary Filter (NECF) is integrated for attitude estimation, providing accurate yaw computation and reliable localization without dependence on acoustic sensors or machine vision. Finally, validation and evaluation are conducted to demonstrate model accuracy, EKF convergence, and the reliability of LSTM-based navigation.…”
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A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM by Wei-Li Qin, Wen-Jin Zhang, Chen Lu

“…Finally, the GS and CV algorithms are employed to train a one-versus-one SVM for fault classification, thus obtaining the optimal model parameters and assuring the generalization of the trained SVM, respectively. …”
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Performance Evaluation of Neighbors-Based Learning Methods for Network Intrusion Detection System by Ngoc Thien Nguyen

“…Consequently, intrusion detection technologies are being actively researched and optimized to enhance threat detection and prevention capabilities. …”
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Data Filtering in Content Industries: The Case of Recommender Systems in the Scientific and Technical Information (STI) Sphere by Amine Sennouni

“…In our research, we aim to design a recommendation system model to optimize the selection and access of research users to document resources on online catalogs and Scientific and Technical Information (STI) platforms based on machine learning techniques using a constructivist engineering method which proceeds to the elaboration of a model associated to a prototype. …”
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The Application of the Gestalt Theory in Music Psychotherapy for Piano by Fukun Cai

“…The wavelet energy entropy (WEE) characteristics of EEG signals were extracted as the input of the designed and optimized deep belief network model, and the average emotional classification accuracy of EEG signals in the left and right brain regions could be obtained as 84.20% and 83.07%, respectively, under the condition of distinguishing brain regions and different music environments. …”
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A Fault Identification Method of Mechanical Element Action Unit Based on CWT-2DCNN by Hongyu Ge, Yujiao Guo, Tianyu Luo, Manzhi Yang, Chuanwei Zhang

“…By collecting the vibration signals of four kinds of fault states and normal states of the worm rotation unit of the CNC machine tool, the data are preprocessed and identified. …”
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Bayesian Regularization Backpropagation Neural Network for Glass Beams in Lateral–Torsional Buckling by Saddam Hussain, Chiara Bedon, Gaurav Kumar, Zaheer Ahmed

“…Additionally, the Bayesian regularization backpropagation (trainbr) algorithm is used to optimize the input–output relationship accuracy. The suitability of present modeling strategy for LG beams in LTB is quantitatively discussed based on error and performance trends.…”
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