A Novel Deeply-Learned Image Quality Analysis Algorithm for Clustering by Zhongzhe Chen, Xing Gao

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Automatic eddy detection in the MIZ based on YOLO algorithm and SAR images by Nikita Sandalyuk, Eduard Khachatrian

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PERFORMANCE COMPARISON OF CLASSICAL ALGORITHMS AND DEEP NEURAL NETWORKS FOR TUBERCULOSIS PREDICTION by Gilgen Mate Landry, Rodolphe Nsimba Malumba, Fiston Chrisnovi Balanganayi Kabutakapua, Bopatriciat Boluma Mangata

“…The sample is divided into two sets: 80% for training and 20% for testing. The algorithms evaluated include Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest and Convolutional Neural Networks (CNN). …”
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Research on Polyphonic Music Generation Algorithm Based on GPT Large Model by Lin Zhu, Wenjuan Zhang

“…Along with the rapid technological progress in the field of artificial intelligence, music generation algorithms based on large-scale pre-trained models have increasingly become the focus of academic attention. …”
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An improved traffic lights recognition algorithm for autonomous driving in complex scenarios by Ziyue Li, Qinghua Zeng, Yuchao Liu, Jianye Liu, Lin Li

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GENETIC ALGORITHM-PID CONTROLLER FOR MODEL ORDER REDUCTION PANTOGRAPHCATENARY SYSTEM by Nasir A. Al-Awad, Izz K. Abboud, Muaayed F. Al-Rawi

“…All the synthesis steps of the manipulate law are given and a formal evaluation of the closed loop stability indicates an asymptotic monitoring of a nominal steady contact force. …”
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Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training by M. Funk Drechsler, T. A. Fiorentin, H. Göllinger

“…In this way, the present research proposes and evaluates an open-loop training process, which permits the data acquisition without the control interaction and an open-loop training of the neural networks. …”
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Boosting Crowdsourced Annotation Accuracy: Small Loss Filtering and Augmentation-Driven Training by Yanming Fu, Weigeng Han, Jingsang Yang, Haodong Lu, Xin Yu

“…SLNC has been evaluated using 16 simulated and two real-world datasets. …”
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Effectiveness of AdaBoost and XGBoost Algorithms in Sentiment Analysis of Movie Reviews by I Gusti Ayu Nandia Lestari, Ni Made Rai Masita Dewi, Komang Gita Meiliana, I Komang Agus Ady Aryanto

“…Based on the results of model training with cross-validation, the accuracy of the XGBoost model is 85% and AdaBoost is 77%. …”
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Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials by Mahmood Ahmad, Ramez A. Al-Mansob, Kazem Reza Kashyzadeh, Suraparb Keawsawasvong, Mohanad Muayad Sabri Sabri, Irfan Jamil, Arnold C. Alguno

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Enhanced Butterfly Optimization and Deep Learning Algorithm for Student Placement Prediction by T. Kavi Priya, N. Kumar

“…It would be extremely beneficial to develop Deep Learning (DL)-based algorithms that can assist individuals in getting placement guidance, analyses labor market trends, and help educational institutions evaluate opportunities and expanding fields. …”
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Postpartum depression risk prediction using explainable machine learning algorithms by Xudong Huang, Lifeng Zhang, Chenyang Zhang, Jing Li, Chenyang Li

“…Feature selection was performed using LASSO regression and the Boruta algorithm. Eight machine learning algorithms were then employed to construct the prediction models. …”
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Dataset resulting from the user study on comprehensibility of explainable AI algorithms by Szymon Bobek, Paloma Korycińska, Monika Krakowska, Maciej Mozolewski, Dorota Rak, Magdalena Zych, Magdalena Wójcik, Grzegorz J. Nalepa

“…In the advent of the area of rapid development of XAI techniques, the need for a multidisciplinary qualitative evaluation of explainability is one of the emerging topics in the community. …”
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Research on rock strength prediction model based on machine learning algorithm by Xiang Ding, Mengyun Dong, Wanqing Shen

“…By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). Finally, four regression evaluation indicators, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2), were used to evaluate the predictive performance of the established regression models. …”
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Mechanism–Data Collaboration for Characterizing Sea Clutter Properties and Training Sample Selection by Wenhao Chen, Yong Zou, Zhengzhou Li, Shengrong Zhong, Haolin Gan, Aoran Li

“…The experiments based on field data are included to evaluate the effectiveness of the proposed method including sea clutter characterization accuracy and training sample selection across various scenarios. …”
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Novel three‐dimensional ECG algorithm for reliable screening for cardiac amyloidosis by Amir A. Mahabadi, Jan Knobeloch, Viktoria Backmann, Lars Michel, Markus S. Anker, Reza Wakili, Christian Fach, Stefan D. Anker, Tienush Rassaf

“…Consecutively, an AI algorithm was trained in the derivation cohort (n = 66 amyloidosis cases, n = 89 controls). …”
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Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension by Wei Zhao, Zhihua Huang, Xiaolin Diao, Zhan Yang, Zhihui Zhao, Yun Xia, Qing Zhao, Zhaohong Sun, Qunying Xi, Yanni Huo, Ou Xu, Jiahui Geng, Xin Li, Anqi Duan, Sicheng Zhang, Luyang Gao, Yijia Wang, Sicong Li, Qin Luo, Zhihong Liu

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A Closer Look at Invalid Action Masking in Policy Gradient Algorithms by Shengyi Huang, Santiago Ontañón

“…In this paper, we 1) show theoretical justification for such a practice, 2) empirically demonstrate its importance as the space of invalid actions grows, and 3) provide further insights by evaluating different action masking regimes, such as removing masking after an agent has been trained using masking.…”
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N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning by Juliana Manrique-Cordoba, Miguel Ángel de la Casa-Lillo, José María Sabater-Navarro

“…The algorithm is evaluated in 2D and 3D environments with datasets combining position and velocity. …”
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Machine learning algorithms to predict the tensile strength of novel composite materials by S. Sathees Kumar, P. Shyamala, Pravat Ranjan Pati

“…This study develops machine learning (ML) models to predict NFRP tensile strength using publicly available datasets containing parameters such as epoxy group content, density, elastic modulus, curing agent amount, resin consumption, surface density, and matrix–filler ratio. Five regression algorithms such as polynomial regression, bagging regression, random forest, XGBoost, and gradient boosting were trained and evaluated using five-fold cross-validation and standard error metrics. …”
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