An Adversarial Attack via Penalty Method by Jiyuan Sun, Haibo Yu, Jianjun Zhao

“…Deep learning systems have achieved significant success across various machine learning tasks. However, they are highly vulnerable to attacks. …”
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Strength prediction and failure mode classification for SRC shear beams using GA-BP ANN method by Gangfeng Yao, Bingyi Li

“…Considering the advantages of machine-learning (ML) approaches, the back-propagation (BP) artificial neural network (ANN) method combined with genetic algorithm (GA) was employed to the prediction of strength and failure mode of SRC shear beams. …”
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Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies by Mohamed Ahwiadi, Wilson Wang

“…Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. …”
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Evaluation of 3D seed structure and cellular traits in-situ using X-ray microscopy by Marcus Griffiths, Barsanti Gautam, Clara Lebow, Keith Duncan, Xinxin Ding, Pubudu Handakumbura, John C. Sedbrook, Christopher N. Topp

“…Seeds of pennycress (Thlaspi arvense L.) an oilseed cover crop, were scanned and segmented using a machine learning model. Seed morphological analysis and a coat thickness map was applied to compare seed volumes of four genotypes. …”
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Targeted maximum likelihood based estimation for longitudinal mediation analysis by Wang Zeyi, Laan Lars van der, Petersen Maya, Gerds Thomas, Kvist Kajsa, Laan Mark van der

“…To tackle causal and statistical challenges due to the complex longitudinal data structure with time-varying confounders, competing risks, and informative censoring, there exists a general desire to combine machine learning techniques and semiparametric theory. …”
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Plant Leaf Identification Using Feature Fusion of Wavelet Scattering Network and CNN With PCA Classifier by S. Gowthaman, Abhishek Das

“…Unlike traditional machine learning methods that often struggle to capture the intricate features of leaves, CNNs are well-suited for this task. …”
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Parameter Acquisition Study of Mining-Induced Surface Subsidence Probability Integral Method Based on RF-AGA-ENN Model by Jinman Zhang, Liangji Xu, Jiewei Li, Yueguan Yan, Ruirui Xu

“…To obtain more accurate PIM parameters in the absence of observational data, we propose a combined machine learning model (RF-AGA-ENN)—random forest (RF) extracts the best combination of features as the input layer of Elman neural network (ENN); ant colony algorithm (ACO) and genetic algorithm (GA) are combined (called AGA) for the weights and thresholds of ENN optimization. …”
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Design and Evaluation of a Leader–Follower Isomorphic Vascular Interventional Surgical Robot by Pengfei Chen, Yutang Wang, Dapeng Tian

“…We classified operators with different operational experience using machine learning methods. The classification process includes time-frequency domain feature extraction, feature selection based on the Relief method and random forest (RF) method, and a BP neural network (NN) classifier. …”
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Diagnosing and Predicting the Earth’s Health via Ecological Network Analysis by Zi-Ke Zhang, Ye Sun, Chu-Xu Zhang, Kuan Fang, Xiang Xu, Chuang Liu, Xueqi Wang, Kui Zhang

“…Secondly, we identify the importance of each element by a machine learning approach. Thirdly, we use a spreading model to predict the Earth’s health. …”
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The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. by Quan Duy Vo, Yukihiro Saito, Toshihiro Ida, Kazufumi Nakamura, Shinsuke Yuasa

“…The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. …”
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An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment by Yiping Wu, Xiaohua Zhao, Ying Yao, Jian Rong

“…Because of obvious advantage in mining hidden relationship, machine learning was adopted to explore the complicated relationship between driver performance and vehicle fuel consumption and thus to predict the ecolevel of individual driver performance in this study. …”
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Automated Container Terminal Production Operation and Optimization via an AdaBoost-Based Digital Twin Framework by Yu Li, Daofang Chang, Yinping Gao, Ying Zou, Chunteng Bao

“…Digital twin (DT), machine learning, and industrial Internet of things (IIoT) provide great potential for the transformation of the container terminal from automation to intelligence. …”
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Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence by Yi-Ming Chen, Tzu-Hung Hsiao, Ching-Heng Lin, Yang C. Fann

“…Through the synergistic approach of integrating AI across diverse data sets, clinicians gain a holistic view of patient health and potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. …”
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Radial Basis Function Coupling with Metaheuristic Algorithms for Estimating the Compressive Strength and Slump of High-Performance Concrete by Amir Reza Taghavi Khangah, Erfan Khajavi, Hasti Azizi, Amir Reza Alizade Novin

“…The results highlight hybrid machine learning models as the potential to solve complex challenges in civil engineering and provide new approaches toward sustainable and efficient infrastructure development.…”
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Controlling Embedded Systems Remotely via Internet-of-Things Based on Emotional Recognition by Mohammad J. M. Zedan, Ali I. Abduljabbar, Fahad Layth Malallah, Mustafa Ghanem Saeed

“…The methodology is achieved by combining machine learning (for smiling recognition) and embedded systems (for remote control IoT) fields. …”
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<italic>C&#x2099;</italic>&#x00B2; Modeling for Free-Space Optical Communications: A Review by Florian Quatresooz, Claude Oestges

“…Boundary layer <inline-formula> <tex-math notation="LaTeX">$C_{n}^{2}$ </tex-math></inline-formula> models are also addressed, and recent machine learning approaches for <inline-formula> <tex-math notation="LaTeX">$C_{n}^{2}$ </tex-math></inline-formula> modeling are discussed. …”
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Adaptive Learning Algorithms for Low Dose Optimization in Coronary Arteries Angiography: A Comprehensive Review by Komal Tariq, Muhammad Adnan Munir, Hafiza Tooba Aftab, Amir Naveed, Ayesha Yousaf, Sajjad Ul Hassan

“…Results: The extracted data shows a comprehensive data on various techniques that are used for low dose CAA, advancements in image segmentation, noise reduction, and operator dose reduction highlight the potential of machine learning techniques. Innovative methods such as Model-Based Deep Learning (MBDL) and Self-Attention Generative Adversarial Networks (SAGAN) demonstrate efficient reconstruction capabilities. …”
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Detecting travel modes from smartphone-based travel surveys with continuous hidden Markov models by Guangnian Xiao, Qin Cheng, Chunqin Zhang

“…This set of features is expected to help achieve high classification accuracy with few features. Third, as a machine learning approach incorporating high resistance to noise in features, a continuous hidden Markov model is used to classify segments in dataset 1 that comprises Global Positioning System data alone. …”
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Intelligent Prediction of Flood Disaster Risk Levels Based on Knowledge Graph and Graph Neural Networks by Peisheng Yang, Xiaohua Xu, Meilan Shao, Yewei Liu

“…Compared with the three classical models in traditional machine learning, such as RF, SVM and ANN, the performance of this model is improved, and it is better than the traditional model. …”
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Statistical Learning for Semantic Parsing: A Survey by Qile Zhu, Xiyao Ma, Xiaolin Li

“…In this paper, we review recent algorithms for semantic parsing including both conventional machine learning approaches and deep learning approaches. …”
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