Durvalumab and tremelimumab with or without stereotactic body radiation therapy in relapsed small cell lung cancer: a randomized phase II study by Chao Zhang, Shuhua Wang, Suresh Ramalingam, Sagar Lonial, Walter Curran, Edmund K Waller, Bassel Nazha, Taofeek K Owonikoko, Conor Steuer, Suchita Pakkala, Kristin Higgins, Zhengjia Chen, Gabriel Sica, Guojing Zhang, Mohammad S Hossain, Tyler Beardslee, Fadlo R Khuri

“…We evaluated circulating lymphocyte repertoire in serial peripheral blood samples and tumor infiltrating lymphocytes (TILs) from on-treatment biopsies as pharmacodynamic markers.Results Eighteen patients were randomized to arms A and B (n=9 each): median age 70 years; 41.2% women. …”
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The T-DBSCAN Algorithm for Stopover Site Identification of Migration Birds Based on Satellite Positioning Data by Xinwu He, Xiqun Liu, Jiajia Liu, Youwen Li, Zhenggang Xu, Ping Mo, Tian Huang

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A synchronous compression and encryption method for massive electricity consumption data privacy preserving by Ruifeng Zhao, Jiangang Lu, Zhiwen Yu, Kaiwen Zeng

“…This mitigates high-frequency sampling overload and ensures data confidentiality. …”
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Statistical considerations for enhanced forest resource mapping by Sara Franceschi, Caterina Pisani, Lorenzo Fattorini, Piermaria Corona

“…Traditionally, in forest surveys estimates of averages and totals are obtained using design-unbiased estimators, with known variance expressions that can be easily estimated using standard sampling methodologies. The paper emphasizes the prominent role of kNN and Random Forest techniques in forest mapping while addressing the methodological limitations identified over more than thirty years of forest literature in efforts to estimate map precision. …”
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Sure Independence Screening for Ultrahigh-Dimensional Additive Model with Multivariate Response by Yongshuai Chen, Baosheng Liang

“…We also developed an iterative sure independence screening algorithm for convenient and efficient implementation. Extensive finite-sample simulations and a real data example demonstrate the superiority of the proposed procedure over 58–100% of existing candidates. …”
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Inversion of Crop Water Content Using Multispectral Data and Machine Learning Algorithms in the North China Plain by Zhenghao Zhang, Gensheng Dou, Xin Zhao, Yang Gao, Saisai Liu, Anzhen Qin

“…Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations.…”
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AdaBoost algorithm based on target perturbation by Shufen ZHANG, Yanling DONG, Jingcheng XU, Haoshi WANG

“…Aiming at the problem that the multi-round iteration process in the AdaBoost algorithm will amplify the noise added to achieve differential privacy protection, which leads to slow model convergence and greatly reduced data availability, an AdaBoost algorithm based on target perturbation—DPAda was proposed.Target perturbation was used to add noise to sample weights, accurately calculated their sensitivity, and a dynamic privacy budget was given.In order to solve the problem of excessive noise superposition, three noise injection algorithms based on swing sequence, random response and improved random response were proposed.The experimental results show that compared with DPAda_Random and DPAda_Swing, DPAda_Improved achieves the privacy protection of data, has higher classification accuracy, as well as better than other differential privacy AdaBoost algorithm, and can also solve the problem of excessive noise caused by continuous noise addition.…”
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The synergistic effects of circuit training and diet on weight loss, body fat percentage, and BMI in obese Indonesian females: a randomized controlled trial by Ali Muthahhari Rahim, Ahmad Nasrulloh

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Probabilistic analysis of active earth pressures in spatially variable soils using machine learning and confidence intervals by Tran Vu-Hoang, Tan Nguyen, Jim Shiau, Duy Ly-Khuong, Hung-Thinh Pham-Tran

“…A two-phase optimization approach, combining Random Search and Adaptive Sampling, is employed to refine the hyperparameters of the machine learning model. …”
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An electrocardiogram signal classification using a hybrid machine learning and deep learning approach by Faramarz Zabihi, Fatemeh Safara, Behrouz Ahadzadeh

“…The second subsystem uses several feature extraction methods and a random forest to classify the ECG signals. Furthermore, it employs a Synthetic Minority Over-Sampling Technique to improve dataset balance and overall performance. …”
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Artificial intelligence and machine learning to enhance critical mineral deposit discovery by Rhys S. Davies, McLean Trott, Jaakko Georgi, Alexander Farrar

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Improving Satellite-Based Retrieval of Maize Leaf Chlorophyll Content by Joint Observation with UAV Hyperspectral Data by Siqi Yang, Ran Kang, Tianhe Xu, Jian Guo, Caiyun Deng, Li Zhang, Lulu Si, Hermann Josef Kaufmann

“…Additional field measurements sampled at other farming areas were applied to validate the method’s transferability and generalization. …”
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GOVERNMENT EXPENDITURES WITHOUT GROWTH: THE CASE OF TURKEY by Orçun Söylemez, Özkan Zülfüoğlu

“…Five important variables that might affect government expenditures from different angles are evaluated regarding their contributions to the out-of-sample predictions of government expenditures using random forest methodology. …”
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Machine learning-based process quality control of screen-printed titanium dioxide electrodes by Anesu Nyabadza, Lola Azoulay-Younes, Mercedes Vazquez, Dermot Brabazon

“…AI can analyze a printed electrode and classify it as either good or bad quality within milliseconds, much faster than humans and conventional methods (random sampling and control charts). Herein, machine learning methods including Random Forest (RF), Support Vector Machine (SVM), and Feedforward Neural Network (FNN) are used to address a quality control problem involving the classification of screen-printed TiO2 electrodes based on image data. …”
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Evaluation of preceding variables affecting behavioral use and acceptance of chord-enabled keyboard among students by Ardvin Kester S. Ong, Calil C. Aceron, Warrick Jathniel S. Quimpo, Derek Tyler U. Ong, John Francis T. Diaz, Josephine D. German

“…To fill this gap, this study aimed to examine the factors that affect the acceptance and adoption of chord-enabled keyboards through Random Forest Classifier and Neural Network ensemble. …”
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A comprehensive diagnostic framework for hepatitis C using structured data and predictive analytics by Behnaz Motamedi, Balázs Villányi

“…To test this theory, we established an extensive four-phase preparation pipeline: Baseline imputes missing values using class-specific means; Refine mitigates outliers through class-specific medians and normalization; Balanced addresses class imbalance across five stages employing localized random affine shadow-sampling; and Augmented incorporates a clustering-based feature derived from an ensemble of K-means and Gaussian mixture models, combined with principal component analysis. …”
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Leveraging machine learning to predict residential location choice: A comparative analysis by Vahid Noferesti, Hamid Mirzahossein

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Machine learning in assessing the association between the size and structure of the ascending aortic wall in patients with aortic dilatation of varying severity by V. E. Uspenskiy, V. L. Saprankov, V. I. Mazin, D. G. Zavarzina, A. B. Malashicheva, O. B. Irtyuga, O. M. Moiseeva, M. L. Gordeev

“…To predict aortopathies, thoracic aorta diameters indexed to body surface area should not be used. Aortic wall sampling (circular section) followed by a continuous pathological examination may be promising.…”
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Modeling of informal employment factors by Yu. A. Metel, O. A. Lepekhin

“…Wage  determinants  in  the  informal  sector  are  identified  through the  analysis  of  fixed  and  random  effects  panel  data. Results  and  discussion. …”
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Research and Analysis of Short-term Load Forecasting Based on Frequency Domain Decomposition by Yuan MA, Qian ZHANG, Guoli LI, Jinhui MA, Jinjin DING

“…The low-frequency part is selected as the training sample and is combined with the Elman neural network to predict the high-frequency components. …”
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