Development of an Intelligent Tablet Press Machine for the In-Line Detection of Defective Tablets Using Machine Learning and Deep Learning Models by Sun Ho Kim, Su Hyeon Han

“…The TPM was verified by sorting defective tablets in-line using a pretrained defect-detection algorithm. <b>Results:</b> The RF model demonstrated the highest predictive accuracy at 93.7% with an Area Under the Curve (AUC) of 0.895, while the ANN model achieved an accuracy of 92.6% with an AUC of 0.878. …”
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Load forecasting of microgrid based on an adaptive cuckoo search optimization improved neural network by Liping Fan, Pengju Yang

“…The mean absolute percentage error (MAPE) of the ICS-BP forecasting model was 1.13%, which was very close to an ideal prediction model, and was 52.3, 32.8, and 42.3% lower than that of conventional BP, cuckoo search improved BP, and particle swarm optimization improved BP, respectively, and the root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) of ICS-BP were reduced by 75.6, 70.6, and 94.0%, respectively, compared to conventional BP. …”
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Camouflage Target Detection Method with Mutual Compensation of Local-Global Features by HE Wenhao, GE Haibo

“…In the field of camouflage object detection (COD), the latest proposed methods mainly use the local features of the camouflaged target to complete the COD task. The output prediction map has problems such as rough target contours and incomplete objects. …”
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Application of support vector machine system introducing multiple submodels in data mining by Weinan Tang

“…Under the maximum data scale experiment, the research model improved prediction accuracy by 21 percentage points with an acceptable additional time cost of about 4 min only. …”
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Co-Optimization of Vibration Suppression and Data Efficiency in Robotic Manipulator Dynamic Modeling by Xiaowei Han, Kunru Wu, Nanmu Hui

“…Simulation results demonstrate that under typical motion conditions of the manipulator, the proposed method exhibits excellent capability in capturing nonlinear disturbances, maintaining joint prediction errors below 6 × 10⁻¹² N·m. This significantly improves the accuracy and robustness of the feedforward vibration suppression control. …”
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STUDY ON ENERGY ABSORPTION CHARACTERISTIC OF SINUSOIDAL BELLOWS FILLED EMULSION ABSORBER by ZHANG JianZhuo, ZHANG WanJiu, PAN YiShan, GUO Hao, WANG ShuWen

“…The pre⁃folded energy absorber is the only energy absorber structure used in rock burst mine at present.It has excellent energy absorption characteristics,but there are some problems in the buckling deformation process such as large load fluctuation and spark due to friction.In view of the above situation,a sinusoidal bellows(reduced diameter round pipe)filled with emulsion was designed to form a solid⁃liquid coupling body energy absorption component(composite component).The effects of three factors,namely wall thickness of the bellows,amplitude of the busbar and diameter of the liquid outlet,on the energy absorption characteristics of the energy absorption component were studied.The prediction model of average load,load fluctuation coefficient and specific energy absorption was established.The smoothed partiole hydrodynamics(SPH)particle algorithm of Abaqus finite element software was used for fluid⁃structure coupling analysis of the model.The structural parameters of energy absorption components were optimized by response surface method and the optimization results were obtained.The results show that when the wall thickness of corrugated pipe is 3.959 mm,the amplitude of busbar is 3.721 mm,and the diameter of outlet is 22.161 mm,the energy absorption characteristics of the member are the best.The average load is 438.684 kN,the load fluctuation coefficient is 1.178,and the specific energy absorption is 12.123 kJ/kg.Through comparison and verification,the results have high reliability,which provides a superior energy absorption component for the energy absorption link of anti⁃impact support equipment.…”
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Bathymetry Inversion Using a Deep‐Learning‐Based Surrogate for Shallow Water Equations Solvers by Xiaofeng Liu, Yalan Song, Chaopeng Shen

“…The inversion loss due to flow prediction error and the two regularizations play dominant roles in the initial and final stages, respectively. …”
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Research on Sugar Concentration Sensing Based on Real-Time Polarization and Interaction Effects by Qiong Gong, Tongxiao Lyu, Song Ye, Xinqiang Wang

“…The addition of the interaction effect model significantly improves the prediction accuracy, with an RMSE of 0.36934 and a relative error within ±5%. …”
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Evaluation of inflammatory bowel disease-related sleep disorders based on an interpretable machine learning approach: a multicenter study in China by Jiayi Sun, Junhai Zhen, Chuan Liu, Changqing Jiang, Jie Shi, Kaichun Wu, Weiguo Dong

“…Conclusion: The construction of risk assessment models using ML has high clinical value in the prediction of IBD-related sleep disorders, and the efficacy of its application suggests it can serve as a promising evaluation tool in clinical work.…”
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Migrative armadillo optimization enabled a one-dimensional quantum convolutional neural network for supply chain demand forecasting. by Mohamed Irhuma, Ahmad Alzubi, Tolga Öz, Kolawole Iyiola

“…The Migrative Armadillo Optimization (MAO) algorithm effectively optimizes the hyperparameters of the model. …”
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Optimization of a Heavy-Duty Hydrogen-Fueled Internal Combustion Engine Injector for Optimum Performance and Emission Level by Murat Ozkara, Mehmet Zafer Gul

“…This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). The CFD model was validated using particle image velocimetry (PIV) data from non-reacting flow experiments conducted in an optically accessible research engine developed by Sandia National Laboratories, ensuring accurate prediction of in-cylinder flow structures. …”
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A 37 years [1984–2021] Landsat/Sentinel-2 derived snow cover time-series for Switzerland by Charlotte Poussin, Pascal Peduzzi, Bruno Chatenoux, Gregory Giuliani

“…Remote sensing technologies have emerged as key tools for monitoring snow cover, providing critical data for climate models, hazard prediction, and resource planning. In Switzerland, snow cover is monitored using ground-based measurements, remote sensing, and climate models, with datasets from satellites like Landsat and Sentinel-2 offering valuable insights despite challenges such as cloud obstruction. …”
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Construction of Industrial Economic Management Decision Model Based on Three Parameter Interval Grey Numbers by Pao-Ching Lin, Tzu-Jung Wu, Jui-Chan Huang

“…In addition, through comparative experiments with other decision models, this model has shown superiority in improving profit margins, increasing sales, reducing unit costs, and reducing prediction errors. Therefore, the proposed industrial management decision model can effectively handle uncertainty factors, enhance the scientific and effective nature of industrial economic management decisions, and provide more accurate and reliable solutions for industrial enterprises.…”
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Intelligent anti-jamming communication technology with electromagnetic spectrum feature cognition. by Hui Zhao, Guobin Zhao, Xichun Wang, Zhonghui Zhang, Xianchao Xun

“…This study proposes an intelligent anti-interference algorithm that combines deep neural networks and game theory, and constructs a model based on near-end strategy optimization. …”
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Incorporating Semi-Automated Approach for Effective Software Requirements Prioritization: A Framework Design by Fang-Fang Chua, Tek-Yong Lim, Bushra Tajuddin, Amarilis Putri Yanuarifiani

“…SARiP concentrates on the areas related to prediction of requirements priority group and ranks requirements using classification tree and ranking algorithm. …”
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Study on Tooth Surface Roughness Modeling of Form Grinding Considering the Change of the Local Grinding Condition by Chen Xiaoqi, Tang Cheng, Liao Xianggui, Zhou Weihua, Tang Jinyuan

“…The results show that the prediction model of the surface roughness of formed grinding teeth presented in this study agrees with the experimental results. …”
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Electric Vehicle and Soft Open Points Co-Planning for Active Distribution Grid Flexibility Enhancement by Jie Fang, Wenwu Li, Dunchu Chen

“…Firstly, this paper establishes a charging pile day-ahead dispatchable prediction model and a real-time dispatchable potential assessment model through Monte Carlo sampling simulation. …”
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Simulation-Based Design and Machine Learning Optimization of a Novel Liquid Cooling System for Radio Frequency Coils in Magnetic Hyperthermia by Serhat Ilgaz Yöner, Alpay Özcan

“…The performance index was also developed and modeled using Gaussian Process Regression, enabling rapid performance prediction without requiring extensive numerical studies. …”
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Evaluating the level of digitalization of the innovation process with artificial intelligence approach in the digital transformation of knowledge-based companies by ali Bagheri, reza radfar, sepehr ghazinoory

“…As a subset of artificial intelligence, machine learning algorithms create a mathematical model based on sample data or "training data" in order to predict or make decisions without overt planning (Du et al., 2019). …”
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Terminal ileitis: Questions of differential diagnosis and prospects for treatment. A review by Alexey M. Osadchuk, Oleg V. Golovenko, Irina D. Loranskaia

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