A Combined Prediction Model for Hog Futures Prices Based on WOA-LightGBM-CEEMDAN by Xiang Wang, Shen Gao, Yibin Guo, Shiyu Zhou, Yonghui Duan, Daqing Wu

“…An integrated hog futures price forecasting model based on whale optimization algorithm (WOA), LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. …”
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Identification of Perceptual Phonetic Training Gains in a Second Language Through Deep Learning by Georgios P. Georgiou

“…Results: The results demonstrated good model performance across a range of metrics, confirming that learners’ gains in phonetic training could be effectively detected by the algorithm. Conclusions: This research underscores the potential of deep learning techniques to track improvements in phonetic training, offering a promising and practical approach for evaluating language learning outcomes and paving the way for more personalized, adaptive language learning solutions. …”
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Effectiveness of the Spatial Domain Techniques in Digital Image Steganography by Rosshini Selvamani, Yusliza Yusoff

“…This broadens the range of steganographic technique development and often concentrates the implementation of adaptive techniques. As a result, this study helps to analyze the fundamentals of image steganography, a comparative review on the spatial domain algorithms. …”
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基于QPSO-SVM的轴承故障诊断方法 by 杨光春, 蹇清平

“…Due to the importance of rolling bearing as one of the most widely used in rotating machines,bearing failures have adverse effects on the safe operation of the equipment,in order to diagnosing the fault of rolling bearing effectively,a fault diagnosis model of support vector machine（SVM）optimized by quantum particle swarm optimization（QPSO）algorithm is proposed.First,fault vibration signals are decomposed into several intrinsic mode functions（IMFs）using empirical mode decomposition（EMD）method,then the instantaneous amplitudes of the IMFs that have the fault characteristics are extracted and regarded as the features vector,finally the SVM model optimized by QPSO is used for the failure mode identification.The experimental results indicate that the proposed bearing fault diagnosis method has good capability for adaptive features extraction as well as high fault diagnostic accuracy.…”
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Defect detection method of red globe grapes bunches based on near infrared camera imaging by GAO Sheng

“…The edges of the samples and the edges of the defective parts were first extracted by applying the Sobel algorithm to the NIR images （NIR）, and then the images were binarized by the adaptive thresholding algorithm to achieve the segmentation of the images. …”
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Generate vector graphics of fine-grained pattern based on the Xception edge detection. by Anqi Chen, Yicui Peng, Meng Li, Hao Chen, Chang Liu, Jinrong Hu, Xiang Wen, Guo Huang

“…With higher autonomy, the machine learning algorithms are able to accurately extract the image information, understand and convey the concept contained in it. …”
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The perceptual and biomechanical effects of scaling back exosuit assistance to changing task demands by Jinwon Chung, D. Adam Quirk, Jason M. Cherin, Dennis Friedrich, Daekyum Kim, Conor J. Walsh

“…This study investigates the perceptual and biomechanical impacts of a SLACK suit (non-assistive) controller versus three controllers with varying adaptability: a Weight-Direction-Angle adaptive (WDA-ADPT) that scales assistance based on the weight of the boxes using a chest-mounted camera and machine learning algorithm, movement direction, and trunk flexion angle, and standard Direction-Angle adaptive (DA-ADPT) and Angle adaptive (A-ADPT) controllers. …”
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IQGO: Iterative Quantum Gate Optimiser for Quantum Data Embedding by Tautvydas Lisas, Ruairi de Frein

“…To address these challenges, we introduce an adaptive quantum embedding optimisation algorithm, namely the Iterative Quantum Gate Optimiser (IQGO), which is suited to the task of tabular data classification. …”
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Partial Discharge Signal Capture of High Voltage Cable Under Deep Belief Network by GAO Baoqi, MA Hanchao, BI Yanbing, ZHANG Xiaojun, WU Suzhou

“… In order to monitor the running state of high-voltage cable in real time and ensure the safe transportation of power, a partial discharge signal capture method of high-voltage cable based on deep belief network is studied.The improved variational modal decomposition algorithm is used to remove the internal noise of the original local signal; the deep belief network capture model is established, and the limited Boltzmann machine is pre trained by contrast divergence algorithm to obtain the pre training network parameters of the capture model; using the global fine tuning capture model of adaptive moment estimation algorithm, the denoised signal is inputted into the trained capture model and the partial discharge signal is outputted.Experimental results show that the proposed method can effectively remove the internal noise from partial discharge signals.In the case of different interferences, the capture probability of this method is above 0.9, which has excellent anti-interference performance and accurately captures the partial discharge signal types caused by insulation defects.…”
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Non-Destructive Identification of Wool and Cashmere Fibers Based on Cascade Optimizations of Interval-Wavelength Selection Using NIR Spectroscopy by Xin Chen, Qingle Lan, Yaolin Zhu, Jinni Chen

“…Then, the backward interval partial least squares (BiPLS) algorithm is applied for the preliminary selection of spectral intervals, followed by the application of three different variable selection algorithms, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and whale optimization algorithm (WOA), for secondary wavelength optimization, respectively. …”
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Triple equivalence for the emergence of biological intelligence by Takuya Isomura

“…Consequently, canonical neural networks can biologically plausibly perform variational Bayesian inferences of external Turing machines. Applying Helmholtz energy minimisation at the species level facilitates deriving active Bayesian model selection inherent in natural selection, resulting in the emergence of adaptive algorithms. …”
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Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS by Ahmed M. Elshewey, Enas Selem, Amira Hassan Abed

“…The performance of the proposed model is compared with another machine learning models, namely, random forest, decision tree, bagging classifier, adaptive boosting, and k-nearest neighbor, and the performance of the models is evaluated using accuracy, sensitivity, specificity, F-score, and area under the ROC curve. …”
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Hierarchical genetic structure in an evolving species complex: Insights from genome wide ddRAD data in Sebastes mentella. by Atal Saha, Matthew Kent, Lorenz Hauser, Daniel P Drinan, Einar E Nielsen, Jon-Ivar Westgaard, Sigbjørn Lien, Torild Johansen

“…We identified a SNP panel with only 21 loci to discriminate populations in mixed samples based on a machine-learning algorithm. This first SNP based investigation clarifies the population structure of S. mentella, and provides novel and high-resolution genomic tools for future investigations. …”
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Prediction of Snacking Behavior Involving Snacks Having High Levels of Saturated Fats, Salt, or Sugar Using Only Information on Previous Instances of Snacking: Survey- and App-Base... by Shaima Dammas, Tillman Weyde, Katy Tapper, Gerasimos Spanakis, Anne Roefs, Emmanuel M Pothos

“…However, the palatability of these snacks means that people can sometimes struggle to reduce their intake. Machine learning algorithms could help in predicting the likely occurrence of HFSS snacking so that just-in-time adaptive interventions can be deployed. …”
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Variable Admittance Control of High Compatibility Exoskeleton Based on Human–Robotic Interaction Force by Jian Cao, Jianhua Zhang, Chang Wang, Kexiang Li, Jianjun Zhang, Guihua Wang, Hongliang Ren

“…The inner loop of the controller adopts the PID control algorithm, and the outer loop adopts the adaptive admittance control algorithm based on human–robot interaction force (HRI). …”
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Personalization of Robot Behavior Using Approach Based on Model Predictive Control by Mateusz Jarosz, Bartlomiej Sniezynski

“…Experiments assessed the performance of five machine-learning algorithms generating user models in a simulated environment, with the Light Gradient Boosting Machine (LGBM) achieving the best results, closely followed by Random Forest (RF). …”
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A Novel Behavioral Strategy for RoboCode Platform Based on Deep Q-Learning by Hakan Kayakoku, Mehmet Serdar Guzel, Erkan Bostanci, Ihsan Tolga Medeni, Deepti Mishra

“…This paper addresses a new machine learning-based behavioral strategy using the deep Q-learning algorithm for the RoboCode simulation platform. …”
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Bearing fault diagnosis for high-speed train based on improved VMD and APSO-SVM by ZHANG Qingsong, ZHANG Bing, QIN Yi

“…Aiming at the problem that the fault information of high-speed train wheel bearing is weak and difficult to extract, a fault feature extraction and recognition model for vibration signal of high-speed train bearing based on variational mode decomposition and adaptive particle swarm optimization-support vector machine was proposed. …”
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Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model by Fusheng Zha, Yizhou Liu, Xin Wang, Fei Chen, Jingxuan Li, Wei Guo

“…The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (EM) algorithm is proposed. …”
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A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques by Rizal Bakri, Bobur Sobirov, Niken Probondani Astuti, Ansari Saleh Ahmar, Pawan Kumar Singh

“…Fuzzy C-Means (FCM) offers a more adaptive approach by allowing each school to simultaneously have a degree of membership in several clusters. …”
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