You Only Attack Once: Single-Step DeepFool Algorithm by Jun Li, Yanwei Xu, Yaocun Hu, Yongyong Ma, Xin Yin

“…This technique is referred to as the “You Only Attack Once” (YOAO) algorithm. Compared to the DeepFool algorithm, this method requires only a single iteration to achieve effective attack results. …”
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Comparative analysis of the performance of selected machine learning algorithms depending on the size of the training sample by Kupidura Przemysław, Kępa Agnieszka, Krawczyk Piotr

“…They also showed that among the tested algorithms, the XGB algorithm is the most sensitive to the size of the training sample, while the least sensitive is SVM, which achieved relatively good results even when using training samples of the smallest sizes. …”
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Long tunnel group driving fatigue detection model based on XGBoost algorithm by Huazhi Yuan, Kun Zhao, Ying Yan, Li Wan, Zhending Tian, Xinqiang Chen

“…A driving fatigue detection model was then developed based on the XGBoost algorithm. The obtained results show that the blink frequency, total blink duration, and mean value of blink duration gradually increase with the deepening of driving fatigue, and the mean value of blink duration is the most sensitive in the tunnel environment. …”
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CNOP-P-Based Parameter Sensitivity Analysis for North Atlantic Oscillation in Community Earth System Model Using Intelligence Algorithms by Bin Mu, Jing Li, Shijin Yuan, Xiaodan Luo, Guokun Dai

“…In this paper, we adopt intelligence algorithms to implement the CNOP-P method and conduct the sensitivity analysis of parameter combinations for NAO events in the Community Earth System Model (CESM). …”
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Assimilating Satellite-Based Biophysical Variables Data into AquaCrop Model for Silage Maize Yield Estimation Using Water Cycle Algorithm by Elahe Akbari, Ali Darvishi Boloorani, Jochem Verrelst, Stefano Pignatti

“…Based on our proposed workflow in previous studies, a Gaussian process regression–particle swarm optimization (GPR-PSO) algorithm and global sensitivity analysis were applied to retrieve the fCover and biomass from Sentinel-2 satellite data and to identify the most sensitive parameters in the AquaCrop model, respectively. …”
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Optimal setting automatic excitation controllers for synchronous generators by R. I. Kanafeev, A. D. Zhirnov, T. G. Klimova

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Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model by Yan Lu, Hongze Zhao

“…Subsequently, a novel Black Kite Algorithm (BKA) was developed to enhance the prediction model of a multilevel perceptron neural network. …”
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Optimization of the flow guiding fins configuration and tube arrangements in a shell and tube heat exchanger: coupling response surface methodology and CFD by Xiangmin Shao, Ali Marzban, Farzad Pourfattah, Omid Ali Akbari, Gholamreza Ahmadi, Nafiseh Emami, Soheil Salahshour

“…To optimize the tube layout and the arrangement of the guide fins, the methods of experiment design, the response surface methodology, and the genetic algorithm have been used. The results of this study show that the horizontal spacing between the tube bundles has no significant effect on heat transfer and the objective functions are most sensitive to Re and the angle of the guide fins. …”
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An intelligent SWMM calibration method and identification of urban runoff generation patterns by Zixin Yang, Zixin Yang, Zixin Yang, Jiahong Liu, Jiahong Liu, Jiahong Liu, Youcan Feng, Youcan Feng, Youcan Feng, Jia Wang, Jia Wang, Hao Wang, Hao Wang, Hao Wang, Changhai Li, Changhai Li, Changhai Li

“…The results show that when the impervious area is less than 10%, the most sensitive parameters are Zero.Imperv, N.Imperv, and Dstore-Imperv, indicating that runoff primarily originates from pervious surfaces. …”
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An advanced CNN-attention model with IFTTA optimization for prediction air consumption of relay nozzles by Shen Min, Shao Ning, Cao Yongbo, Xiong Xiaoshuang, Yang Xuezheng, Wang Zhen, Yu Lianqing

“…Then, the hyperparameters of CNN-Attention model were automatically tuned using an IFTTA algorithm that imitates the collaboration in football team training. …”
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Optimized design of high-strength leaf chain based on response surface methodology by LIU Meiqi, FENG Zengming, ZHOU Zhidi

“…ObjectiveAiming at the insufficient fatigue life of high-strength leaf chains for forklifts under low-speed heavy-load conditions, a collaborative optimization framework integrating Response Surface Methodology (RSM) and multi-objective genetic algorithm (MOGA) is proposed to enhance reliability and optimize stress distribution.MethodsBased on transient dynamic analysis using ANSYS Workbench, the edge of the chain plate hole was identified as the critical stress concentration region. …”
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A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns by Rajamanickam Yuvaraj, Shivam Chadha, A. Amalin Prince, M. Murugappan, Md. Sakib Bin Islam, Md. Shaheenur Islam Sumon, Muhammad E. H. Chowdhury

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Modal Passport Concept for Enhanced Non-Destructive Monitoring and Diagnostics of Wind Turbine Blades by Aleksey Mironov, Pavel Doronkin, Aleksejs Safonovs

“…One of the most sensitive parts of a wind turbine to environmental influences are the rotating blades. …”
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Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging by Sahil Mahey, Hamid Usefi

“…Machine learning (ML) algorithms offer a transformative solution by automating this process, improving efficiency, and enhancing diagnostic accuracy. …”
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Dynamic sensitivity‐based clustering of distributed energy resources using LoRaWAN technology for voltage regulation in distribution networks by Ameen Gargoom, Mohammed Elmusrati, Ahmed Gaouda

“…Abstract As the penetration level of renewable energies increases, many inverter‐based resources (IBRs) managing algorithms are proposed to control the impact of this penetration on the system. …”
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Identifying emergency presentations of chronic liver disease using routinely collected administrative hospital data by Jessica King, Vikram Bains, James Doidge, Jan Van Der Meulen, Kate Walker, William Bernal

“…Impact and implications: The more and most sensitive algorithms are recommended in studies when it is important to minimise the number of patients with CLD erroneously missed from the cohort, such as studies measuring disease burden. …”
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Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a “seek out” machine learning approach by Marianna Rania, Anna Procopio, Paolo Zaffino, Elvira Anna Carbone, Teresa Vanessa Fiorentino, Francesco Andreozzi, Cristina Segura-Garcia, Carlo Cosentino, Franco Arturi

“…Sex, HOMA-IR, HbA1c and plasma glucose in different times, and hypoglycemia events were the most sensitive features for BED diagnosis. This study is the first to use metabolic hallmarks to train ML algorithms for detecting BED in individuals at high risk for metabolic complications. …”
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Integrating single-cell RNA sequencing, WGCNA, and machine learning to identify key biomarkers in hepatocellular carcinoma by Gang Wang, Jiaxing Zhang, Yirong Li, Yuyu Zhang, Weiwei Dong, Hengquan Wu, Jinglan Wang, Peiqing Liao, Ziqiang Yuan, Tao Liu, Wenting He

“…Three machine-learning algorithms were used to identify shared diagnostic genes. …”
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Software Defect Prediction Using Extreme Gradient Boosting(XGBoost) with Tune Hyperparameter by Tariq AL-Hadidi, Safwan Omar Hasoon

“…Software applications have become widely spread in an unprecedented manner in our daily lives, controlling some of the most sensitive and critical aspects within institutions. …”
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Real-time damage detection of bridges using adaptive time-frequency analysis and ANN by V. Ahmadian, S. B. Beheshti Aval, E. Darvishan

“…In this paper, a promising damage detection algorithm is proposed by incorporation of adaptive signal processing and Artificial Neural Network (ANN). …”
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