False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review. by Anastasia Chalkidou, Michael J O'Doherty, Paul K Marsden

“…After applying appropriate statistical corrections, an average type-I error probability of 76% (range: 34-99%) was estimated with the majority of published results not reaching statistical significance. …”
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Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features by Meng Wang, Juanle Wang, Mingming Yu, Fei Yang

“…Our model consistently achieves lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values across various prediction intervals (3, 6, 9, and 12 h) compared to other models, supported by the meteorological station data from 2019 to 2021. …”
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Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…Research investigations demonstrate that the ELNET-BDE model attains significantly lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) than contesting Machine Learning (ML) algorithms like Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). …”
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An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection by Farouq Zitouni, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed, Saad Harous

“…The evaluation covered 22 datasets of varying sizes, ranging from 9 to 856 features, and included the utilization of six distinct evaluation metrics related to accuracy, classification error rate, number of selected features, and completion time to facilitate comprehensive comparisons. …”
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Design and application of a music recommendation system based on user behavior and feature recognition by Ji Lu, Minjun Wu

“…Experimental comparisons with traditional systems show that the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) fluctuate between 0 and 1, with recommendation accuracy exceeding 87.5 % and peaking at 99 %, indicating excellent recommendation performance.…”
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Application Framework and Optimal Features for UAV-Based Earthquake-Induced Structural Displacement Monitoring by Ruipu Ji, Shokrullah Sorosh, Eric Lo, Tanner J. Norton, John W. Driscoll, Falko Kuester, Andre R. Barbosa, Barbara G. Simpson, Tara C. Hutchinson

“…Insights on optimal features for monitoring structural dynamic response are discussed based on statistical analysis of the error characteristics for the various reference target patterns used to track the structural displacements.…”
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Genotypic and Phenotypic Association of Agronomic Features in Triticale Genotypes under Drought Stress Conditions by Hassan Basiri, Omid Alizadeh, Forud Bazrafshan, Mehdi Zare, Mohammad Yazdani

“…The data obtained from this experiment were first subjected to the composite analysis of variance, and year variance, environmental variance, genotypic variance, phenotypic variance, and test error variance were estimated based on these calculations. …”
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Age estimation from morphometric features of maxillary central incisors using CBCT images by Sara Hilmi Kazzaz, Tavga Mustafa Faris, Miwan Salahalddin Abdulrahman, Mohammed Taha Ahmed Baban, Ranjdar Mahmood Talabani, Kazhan Omer Abdalrahman

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Features of providing specialized medical care to adult patients with neoplasms of the parotid salivary glands by V. A. Belchenko, I. V. Chantyr

“…We analyzed the statistical data of specialized medical care provided to patients with PSG neoplasms in institutions of the Department of Health of the city of Moscow. …”
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Bi-temporal Gaussian feature dependency guided change detection in remote sensing images by Yi Xiao, Bin Luo, Jun Liu, Xin Su, Wei Wang

“…However, existing CD methods still struggle to mitigate pseudo-changes resulting from domain information differences in multi-temporal images and instances of detail errors caused by the loss and contamination of detail features during the upsampling process in the network. …”
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Applying Mixed-Effects Models in Research on Second Language Acquisition: A Tutorial for Beginners by Marc Brysbaert

“…Subsequently, we introduce the gamlj package, highlighting its intuitive interface and error-prevention features. To illustrate the application of the package, we employ toy datasets that can be easily replicated and used with other statistical software. …”
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Application of Non-Linear System Model Updating Using Feature Extraction and Parameter Effects Analysis by John F. Schultze, François M. Hemez, Scott W. Doebling, Hoon Sohn

“…The approach investigates several mechanisms to assist the analyst in updating an analytical model based on experimental data and statistical analysis of parameter effects. The first is a new approach at data reduction called feature extraction. …”
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A Review of Facial Thermography Assessment for Vital Signs Estimation by Syaidatus Syahira Ahmad Tarmizi, Nor Surayahani Suriani, Fadilla 'Atyka Nor Rashid

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Improvement of point cloud feature extraction and alignment algorithms and lidar slam in coal mine underground by Guanghui XUE, Zhenghao ZHANG, Guiyi ZHANG, Ruixue LI

“…Initially, the method constructs kd-tree structures for the source and target point clouds, reduces point cloud numbers through statistical and voxel filtering, extracts point cloud surface normal, and computes fast point feature histogram descriptors for key points. …”
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A multimodal retina‐iris biometric system using the Levenshtein distance for spatial feature comparison by Vincenzo Conti, Leonardo Rundo, Carmelo Militello, Valerio Mario Salerno, Salvatore Vitabile, Sabato Marco Siniscalchi

“…To provide comprehensive results, detection error trade‐off‐based metrics, as well as statistical analyses for assessing the authentication performance, were considered. …”
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Features of application of sensitivity theory for analysis of influence of parametric disturbances on dynamic properties electromechanical converters by N. A. Malev, O. V. Pogoditsky, A. M. Cvetkovich

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Аssessment of the Effectiveness of Etiotropic Prophylaxis of Tick-Borne Infections: the Systematization of concepts and methodological features by N. A. Penyevskaya, N. V. Rudakov

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Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution by Il-Youp Kwak, Sungsu Kwag, Junhee Lee, Youngbae Jeon, Jeonghwan Hwang, Hyo-Jung Choi, Jong-Hoon Yang, So-Yul Han, Jun Ho Huh, Choong-Hoon Lee, Ji Won Yoon

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Robust Hybrid Data-Level Approach for Handling Skewed Fat-Tailed Distributed Datasets and Diverse Features in Financial Credit Risk by Musara Keith R, Ranganai Edmore, Chimedza Charles, Matarise Florence, Munyira Sheunesu

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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…The engineering characteristics encompassed Los Angeles abrasion value, aggregate crushing value, aggregate impact value, specific gravity, water absorption, and unconfined compressive strength, whereas petrographic examination of thin sections quantified the mineralogical composition. Statistical methods and machine learning models have been applied to elucidate the relationships between the petrographic and engineering features of the aggregates and establish potential predictive capability. …”
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