Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology by Raoof Nopour

“…The chosen machine learning algorithms, including LightGBM, XG-Boost, random forest, artificial neural network, support vector machine, decision tree, K-Nearest Neighbor and logistic regression, were utilized to establish prediction models for predicting metastasis among colorectal cancer patients. …”
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Next Generation Power System Planning and Operation With Quantum Computation by Priyanka Arkalgud Ganeshamurthy, Kumar Ghosh, Corey O'Meara, Giorgio Cortiana, Jan Schiefelbein-Lach, Antonello Monti

“…Next, we present the fundamental principles of quantum computing, introduce key quantum algorithms, and offer a comparison of the computational load between classical and quantum approaches for few important mathematical problems, indicating their relevance to various power system applications. …”
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Professional competence of the person in the Smart-society by Nina V. Komleva

“…Monitoring of competencies, including the monitoring in the centers of the qualification independent assessment should be carried out with the use of adaptive testing algorithms and test tasks continuously updated to meet the changing needs of the Smart-society.…”
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Peripheral myelin protein 2 is underexpressed in early-onset colorectal cancer and inhibits metastasis by Zhiyu Yu, Zhiyu Yu, Sen Wang, Sen Wang, Peng Xu, Cheng Zhang, Cheng Zhang

“…We identified four feature genes by analyzing these genes using machine learning algorithms and taking the intersection: LINC02268, AC092652.1, GRIK1, and PMP2. …”
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Deep sea spy: An online citizen science annotation platform for science and ocean literacy by Marjolaine Matabos, Pierre Cottais, Riwan Leroux, Yannick Cenatiempo, Charlotte Gasne-Destaville, Nicolas Roullet, Jozée Sarrazin, Julie Tourolle, Catherine Borremans

“…Although deep learning offers an alternative to human processing, training algorithms requires large annotated reference datasets. …”
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Secure Lattice-Based Signature Scheme for Internet of Things Applications by Sunil Prajapat, Deepika Gautam, Pankaj Kumar, Srinivas Jangirala, Ashok Kumar Das, Biplab Sikdar

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Analytical review of visualization methods for launch and landing of spacecraft with consideration of 64-bit system boundary value issues by Oleksii Vynokur, Iryna Perova, Polina Zhernova

“…The need for further research and development of new algorithms and data structures to ensure high precision and support for large datasets was identified. …”
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Round, just-below, or precise prices? Cultural differences in the prevalence of price endings in E-commerce by Alfio Ventura, Alfio Ventura, Alfio Ventura, Eve Sarah Troll, Meikel Soliman, David D. Loschelder

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Towards accurate L4 ocean colour products: Interpolating remote sensing reflectance via DINEOF by Christian Marchese, Simone Colella, Vittorio Ernesto Brando, Maria Laura Zoffoli, Gianluca Volpe

“…Our outcomes show that this “upstream interpolation” method can generate a consistent Rrs dataset, thereby improving the accuracy of L4 Chl predictions when used as input in algorithms for remote Chl estimation. We anticipate further improvements in L4 Rrs accuracy using richer spectral information from upcoming hyperspectral satellite missions. …”
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Adverse events associated with gepants: a pharmacovigilance analysis based on the FDA adverse event reporting system by Qi Song, Siyuan Gao, Yaqian Tan

“…Disproportionality analysis and subsequent sensitivity analysis were employed to evaluate the risk signals of the gepants utilizing the algorithms of reporting odds ratio (ROR), proportional reporting ratio (PRR), and information component (IC). …”
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Early Recognition of Secondary Asthma Caused by Lower Respiratory Tract Infection in Children Based on Multi-Omics Signature: A Retrospective Cohort Study by Rao Z, Zhang S, Xu W, Huang P, Xiao X, Hu X

“…Bacterial isolation and culture were performed on all children, and drug sensitivity tests were conducted on the isolated pathogens; And according to whether the child developed secondary asthma during treatment, they were divided into asthma group (n = 116) and non-asthma group (n = 659); Using logistic regression model to analyze the risk factors affecting secondary asthma in children with LTRIs, and establishing machine learning (ie nomogram and decision tree) prediction models; Using ROC curve analysis machine learning algorithms to predict AUC values, sensitivity, and specificity of secondary asthma in children with LTRIs.Results: 792 pathogenic bacteria were isolated from 775 children with LTRIs through bacterial culture, including 261 Gram positive bacteria (32.95%) and 531 Gram negative bacteria (67.05%). …”
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Prediction of microbe-drug associations using a CNN-Bernoulli random forest model by Zihao Song, Qingnuo Li, Jincheng Zhao, Qinggang Bu, Zekang Bian, Jia Qu

“…The dual use of the Bernoulli distribution in BRF ensures algorithmic consistency and contributes to superior performance. …”
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Using machine learning to predict the rupture risk of multiple intracranial aneurysms by Junqiang Feng, Chunyi Wang, Yu Wang, He Liu, He Liu

“…Therefore, we constructed a risk prediction model for the rupture of MIAs by machine learning algorithms.…”
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Structural-methodical model of computer program for control of theoretical knowledge of cadets by V. V. Bulgakov

“…Basically, readymade computer programs are used, which are universal and are intended for wide application, without taking into account the specifics of Universities.Research and development of structural and methodological model was carried out with the use of theoretical analysis of the provisions of pedagogy on the problems of control, evaluation and analysis of the level of theoretical knowledge, methods of theory of algorithms and decision-making, methods of synthesis and analysis of information processes. …”
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Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

“…Two novel scheduling algorithms, Dynamic Renewable Energy Allocation (DREA) and Energy-Aware Priority Scheduling (EAPS), enhance energy efficiency. …”
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Quinary Classification of Human Gait Phases Using Machine Learning: Investigating the Potential of Different Training Methods and Scaling Techniques by Amal Mekni, Jyotindra Narayan, Hassène Gritli

“…Preprocessing methods such as Min–Max Scaling (MMS), Standard Scaling (SS), and Principal Component Analysis (PCA) were applied to the dataset to ensure optimal performance of the machine learning models. Several algorithms were implemented, including <i>k</i>-Nearest Neighbors (<i>k</i>-NNs), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (Gaussian, Bernoulli, and Multinomial) (NB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). …”
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Impact of monitor unit optimization in volumetric modulated arc therapy planning for nasopharyngeal carcinoma. by Huaqu Zeng, Zhen Li, Zongyou Chen, Shukui Tang, Qifu Lin, Minzhi Zhong, Zunbei Wen

“…Dual-arc VMAT plan were designed using photon optimization algorithms without the monitor unit objective (MUO) tool, denoted as the base plan. …”
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Machine learning model for diagnosing salivary gland adenoid cystic carcinoma based on clinical and ultrasound features by Huan-Zhong Su, Zhi-Yong Li, Long-Cheng Hong, Yu-Hui Wu, Feng Zhang, Zuo-Bing Zhang, Xiao-Dong Zhang

“…The least absolute shrinkage and selection operator (LASSO) regression identified optimal features, which were subsequently utilized to construct predictive models employing five ML algorithms. The performance of the models was evaluated across a comprehensive array of learning metrics, prominently the area under the receiver operating characteristic curve (AUC). …”
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Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning approach by Eun Namgung, Young Sun Kim, Sun U. Kwon, Dong-Wha Kang, Dong-Wha Kang

“…Four machine learning algorithms were trained, Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), and logistic regression, to predict cognitive decliners, defined as a decline of ≥3 K-MMSE points over 9 months, and ranked variable importance using the SHapley Additive exPlanations methodology.ResultsCatBoost outperformed the other models in classifying cognitive decliners within 9 months. …”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…Furthermore, decision tree, random forest, support vector machine (SVM), logistic regression, XGBoost, blending ensemble, and convolutional neural network (CNN) algorithms with corresponding optimized hyperparameters and synthetic minority oversampling technique (SMOTE) have been applied for learning behavior classification. …”
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