A Comprehensive Method for Anomaly Detection in Complex Dynamic IoT Systems by Andrii Liashenko, Larysa Globa

“…Anomalies are subsequently identified through significant reconstruction errors, which serve as indicators of deviations from typical patterns. …”
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Use of a spatially distributed in-phase antenna to increase the noise immunity of signal reception by G. V. Kulikov, Yu. A. Polevoda, M. S. Kostin

“…The probability of a bit error when receiving discrete information using the proposed antenna is estimated.Conclusions. …”
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In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning by Ehsan Chatraei Azizabadi, Mohamed El-Shetehy, Xiaodong Cheng, Ali Youssef, Nasem Badreldin

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Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding by Jiayi Su, Xiangfeng Kong, Wenliang Wang, Qian Xie, Chengming Wang, Bie Tan, Jing Wang

“…A total of 10,089 observations were collected from 17 trial pig farms across eight provinces in China. Eleven statistical and machine learning (ML) regression algorithms were employed, incorporating stratified sampling and the recursive feature elimination method for feature selection. …”
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Positioning method for roadheaders based on fusion of LiDAR and inertial navigation by LIU Jing, WEI Zhiqiang, CAI Chunmeng, LIU Yang

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Generation and Classification of Novel Segmented Control Charts (SCC) Based on Hu’s Invariant Moments and the K-Means Algorithm by Roberto Baeza-Serrato

“…Control charts (CCs) are one of the most important techniques in statistical process control (SPC) used to monitor the behavior of critical variables. …”
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Evaluation of Patients with Severe Combined Immunodeficiency Due to Adenosine Deaminase Deficiency: A Single-Center Experience by Sevgi Bilgic Eltan, Selcen Bozkurt, Asena Pınar Sefer, Ezgi Yalçın Güngören, Melek Yorgun Altunbaş, Salim Can, Razin Amirov, Necmiye Öztürk, Safa Barış, Ahmet Özen, Elif Karakoç Aydıner

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On the generalisation capabilities of Fisher vector‐based face presentation attack detection by Lázaro J. González‐Soler, Marta Gomez‐Barrero, Christoph Busch

“…In contrast, for more realistic scenarios, existing algorithms face difficulties in detecting unknown PAI species which are only included in the test set. A feature space based on Fisher Vectors computed from compact binarised statistical image features histograms, which allows discovering semantic feature subsets from known samples to enhance the detection of unknown attacks is presented. …”
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Predictive performance and uncertainty analysis of ensemble models in gully erosion susceptibility assessment by Congtan Liu, Haoming Fan, Yixuan Wang

“…The ensemble model Transformer-RF-CNN employing PEWM demonstrated superior performance, validated by 10-fold cross-validation and 8 metrics: Efficiency (E), True Positive Rate (TPR), False Positive Rate (FPR), True Skill Statistics (TSS), Kappa coefficient (K), Area Under the receiver operating characteristic Curve (AUC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). …”
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Sparse Robust Weighted Expectile Screening for Ultra-High-Dimensional Data by Xianjun Wu, Pingping Han, Mingqiu Wang

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Is a surgeon protected when treating complications after a previous operation? (a case study of treating patients with injuries and strictures of the extrahepatic bile ducts) by V. I. Belokonev

“…The authors emphasize the need for regular analysis of medical errors and improvement of the system of training surgeons.…”
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Development of algorithms and software for classification of nucleotide sequences by V. R. Zakirava, D. A. Syrakvash, S. V. Hileuski, P. V. Nazarov, M. M. Yatskou

“…The difference between coding and non-coding fragments of nucleotide sequences was established. An error of the coding and non-coding sequences classification using the random forests method on a set of the 23 most informative features is 2,93 %.…”
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Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting by Andrey K. Gorshenin, Anton L. Vilyaev

“…It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. …”
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Remaining Available Energy Prediction for Energy Storage Batteries Based on Interpretable Generalized Additive Neural Network by Ji Qi, Pengrui Li, Yifan Dong, Zhicheng Fu, Zhanguo Wang, Yong Yi, Jie Tian

“…Finally, the model is trained and validated on the feature dataset. The validation results show that the model achieves an average absolute error of 2.39%, indicating that it effectively captures the energy variation characteristics within the 0.2 C to 0.6 C dynamic current range. …”
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A High-Precision Method for Warehouse Material Level Monitoring Using Millimeter-Wave Radar and 3D Surface Reconstruction by Wenxin Zhang, Yi Gu

“…To improve grain surface identification, an anomalous signal correction method based on angle–range feature fusion is introduced, mitigating errors caused by weak reflections and multipath effects. …”
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Implementation of MF block in CNN for advanced REB fault diagnosis by M. Pandiyan, Narendiranath Babu T.

“…The proposed C-CNN model has been found to well perform other classifiers in accurately recognizing bearing faults, achieving an excellent accuracy of 95% and 93.5% on 12,800 and 5120 Hz datasets, respectively. Statistical significance tests and error bars validate the robustness of the model’s performance. …”
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Activity prediction of anti-cancer drug candidate ERα inhibitor by XIA Yulan, XIE Jiming, WANG Yajing, LU Mengyuan, WANG Jinrui, QIN Yaqin

“…The results show that compared with the GBDT integrated learning method, the prediction effect of Mul-BHO-Bi-LSTM integrated machine learning prediction model is better, and the model error indexes MSE, NRMSE, error mean, and error std are less than 0.15, and the correlated indicators R2 and r are above 0.99, indicating that the integrated machine learning predictionmodel of Mul-BHO-Bi-LSTM has the good robustness and generalization, and the model can provide a method for the screening and design of anti-breast cancer drugs.…”
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A New Breast Cancer Discovery Strategy: A Combined Outlier Rejection Technique and an Ensemble Classification Method by Shereen H. Ali, Mohamed Shehata

“…It achieves 0.987 accuracy, 0.013 error, 0.98 recall, 0.984 precision, and a run time of 3 s, outperforming all other methods from the literature.…”
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Explaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods by Hassan Hassani, Esfandiar Malekian Kallehbasti, Yahya Kamyabi

“…To achieve this goal, 180 companies admitted to the Tehran Stock Exchange were selected as a statistical sample from 2010 to 2021. Also, to test the hypotheses, the criteria of average accuracy and type I and type ΙΙ errors were used. …”
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Underwater Modulation Classification Using Discrete Wavelet Transform and Genetic Algorithm by Ali Çimen, Erdoğan Aldemir, Timur Düzenli

“…The results show that the approximation and detail coefficient energies provide higher classification performance in the classification of modulated signals according to statistical features such as mean, variance, and standard deviation. …”
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