FORECASTING STOCK MARKET LIQUIDITY WITH MACHINE LEARNING: AN EMPIRICAL EVALUATION IN THE GERMAN MARKET by Bogdan Ionut ANGHEL

“…The study benchmarks four machine-learning algorithms— Random Forest, XGBoost, CatBoost and Long Short-Term Memory (LSTM) networks—for forecasting stock market liquidity in Germany’s DAX equity market. …”
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Multiple-Image Encryption Based on Compressive Ghost Imaging and Coordinate Sampling by Xianye Li, Xiangfeng Meng, Xiulun Yang, Yongkai Yin, Yurong Wang, Xiang Peng, Wenqi He, Guoyan Dong, Hongyi Chen

“…In the encryption process, random phase-only masks are first generated with the modified logistic map algorithm; multiple secret images are transformed to be sparsed by the 2-D discrete cosine transformation (DCT) operation and scrambled by different random sequences; the scrambled images are then grouped to one combined image with the help of the coordinate sampling matrices; finally, putting the combined image in the object plane of the compressive ghost imaging system, the ciphertext will be obtained from the buck detector and transferred to the receivers. …”
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Whole genome resequencing reveals genetic diversity, population structure, and selection signatures in local duck breeds by Pengwei Ren, Yongdong Peng, Liu Yang, Muhammad Zahoor Khan, Yadi Jing, Chao Qi, Zhansheng Liu, Shuer Zhang, Nenzhu Zheng, Meixia Zhang, Xiang Liu, Zhiming Zhu, Mingxia Zhu

“…Conclusions This study, utilizing genome sequencing data and machine learning algorithms, provides a comprehensive evaluation of the genetic resources of Shandong’s local duck breeds. …”
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Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

“…The predictive model was implemented using a two-stage approach: the classical ARIMA algorithm was used for baseline forecasting, while an LSTM architecture with two recurrent layers and regularization was trained on 24-hour sequences. …”
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Machine learning-based predictive analysis of energy efficiency factors necessary for the HIFU treatment of adenomyosis by Ziyan Liu, Ziyi Liu, Yuan Wang, Xiyao Wan, Xiaohua Huang

“…EEF values were calculated based on T2WI fat suppression (T2WI-FS) sequences, and radiomics features were extracted. Predictive features were selected using minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) methods, and two joint—based on decision tree and random forest algorithms—models were developed for EEF prediction.ResultsThe decision tree model achieved a mean absolute error (MAE) of 8.095 on the test set, while the random forest model exhibited an MAE of 8.231. …”
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Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

“…Nine machine learning algorithms (Decision Tree, Gradient Boosting Machine, K-Nearest Neighbors, Linear Discriminant Analysis, Logistic Regression, Multilayer Perceptron, Naive Bayes, Random Forest, and Support Vector Machine) were combined with selected features, generating 45 unique model combinations. …”
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Solving the RNA design problem with reinforcement learning. by Peter Eastman, Jade Shi, Bharath Ramsundar, Vijay S Pande

“…After training it on randomly generated targets, we test it on the Eterna100 benchmark and find it outperforms all previous algorithms. …”
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Hashing technique based on SHA-3 using chaotic maps1 by A. V. Sidorenko, M. S. Shishko

“…Introduced algorithm was tested for        resistance against collisions, statistical analysis of output sequences was performed, hashing performance was evaluated. …”
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‎A Stochastic-Process Methodology for Detecting Anomalies at Runtime in Embedded Systems‎ by Alfredo Cuzzocrea, Enzo Mumolo, Islam Belmerabet, Abderraouf Hafsaoui

“…The memory reference sequence is divided into randomly-selected blocks and spectrally described with the Discrete Cosine Transform (DCT) [36]. …”
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Automatic Vehicles’ Trajectories Optimization on Highway Exclusive Lanes by Lingjuan Chen, Yangbo Ruan, Yiquan Gou

“…Dividing the time zone into discrete pieces, the model is analyzed as a large-scale discrete problem influenced by the randomness of the sequence of vehicles. A two-phase algorithm combined with upper evolution strategies and lower dynamic programming is developed to diminish stochastics and reduce computation step by step and solve the trajectories optimization model. …”
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Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…LASSO regression selected optimal features, followed by model construction via five algorithms (logistic regression, decision tree, random forest, SVM, AdaBoost). …”
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A Three-Dimensional OFDM System with PAPR Reduction Method for Wireless Sensor Networks by Zhenxing Chen, Seog Geun Kang

“…In the proposed algorithm, the components of 3D signals are assigned to different subblocks using a diagonal rule (DR), increasing randomness of the signals in the disjoint subblocks forcibly. …”
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Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning by Mustapha Mouzai, Mohamed Amine Riahla, Amor Keziou, Hacène Fouchal

“…Then, we design tree-based machine learning algorithms, namely decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), for the purpose of classifying signal types and to recognize spoofing attacks. …”
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An Extended Production and Inspection Model with Nonrigid Demand by Neng-Hui Shih, Chih-Hsiung Wang

“…Products are produced by an imperfect process that may shift randomly from the IN state to the OUT state. When the process is in the OUT state, it has a higher probability of producing a nonconforming product than when it is in the IN state. …”
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A combined method of formation of a cryptographic key with secret modification of the results of synchronization of artificial neural networks by M. L. Radziukevich

“…In this regard, it is proposed to prematurely interrupt the synchronization process at the first stage of the combined method and make changes to the resulting binary sequence by randomly inverting a certain number of bits. …”
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A Novel Active Learning Approach for Improving Classification of Unlabeled Video Based on Deep Learning Techniques by Mohamed Salama

“…Unlike passive learning methods that randomly select samples for labeling, our approach actively identifies the most informative unlabeled instances to be annotated. …”
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Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine learning by Changjin Wei, Yongfeng Zhu, Caiming Chen, Feipeng Li, Li Zheng

“…Inflammatory-related COPD feature genes were selected using Lasso regression and random forest algorithms, and a COPD risk prediction model was constructed. …”
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AI for rapid identification of major butyrate-producing bacteria in rhesus macaques (Macaca mulatta) by Annemiek Maaskant, Donghyeok Lee, Huy Ngo, Roy C. Montijn, Jaco Bakker, Jan A. M. Langermans, Evgeni Levin

“…Results By integrating fecal image data with corresponding metagenomic sequencing information, the deep learning (DL) and machine learning (ML) algorithms successfully predicted 16 individual bacterial genera (area under the curve (AUC) > 0.7) among the 50 most abundant genera in rhesus macaques (Macaca mulatta). …”
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A Framework of State Estimation on Laminar Grinding Based on the CT Image–Force Model by Jihao Liu, Guoyan Zheng, Weixin Yan

“…Then, the similarity between the prediction sequences and the segmented milling signal is derived by the dynamic time warping (DTW) algorithm. …”
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Decoding ferroptosis in ischemic stroke: key genes and the therapeutic potential of acupuncture by Chunxiao Wu, Chunxiao Wu, Zhirui Xu, Qizhang Wang, Shuping Zhu, Chunzhi Tang

“…The top 10 hub DE-FRGs selected through the PPI analysis and random forest algorithm included SLC3A2, FTH1, MAP1LC3A, SLC40A1, TFRC, TMSB4X NRAS CD82 CD44, and PTPN18. …”
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