Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model by Elhoucine Ouassam, Yassine Dabachine, Nabil Hmina, Belaid Bouikhalene

“…To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. …”
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Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis by Lengyun Wei, Dashuai Li, Hongjin Chen, Yajing Pu, Qun Wang, Jintao Li, Meng Zhou, Chenfeng Liu, Pengpeng Long

“…Furthermore, we used machine learning methods to perform feature selection and reduction, which generated a clinically applicable scoring system consisting of the seven hub genes for predicting clinical outcomes in BC patients. …”
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Experimental and machine learning based analysis of pervious concrete enhanced with fly ash and silica fume by Siva Shanmukha Anjaneya Babu Padavala, Siva Avudaiappan, Venkatesh Noolu

“…SVM achieved the highest predictive accuracy (R2 = 0.98), while KNN and ANN showed lower performance (R2 = 0.69 and 0.71, respectively). …”
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Classification of finger movements through optimal EEG channel and feature selection by Murside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Matjaž Perc, Matjaž Perc, Matjaž Perc, Matjaž Perc, Yalcin Isler

“…Additionally, for almost all feature sets, the statistical significance-based feature reduction method improves the prediction performance in the most of classifiers, contributing elaborate EEG channel and feature analysis. …”
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Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope by Ling Guo, PhD, Gregg S. Pressman, MD, Spencer N. Kieu, BS, Scott B. Marrus, MD, PhD, George Mathew, PhD, John Prince, PhD, Emileigh Lastowski, MS, Rosalie V. McDonough, MD, MSc, Caroline Currie, BA, John N. Maidens, PhD, Hussein Al-Sudani, MD, Evan Friend, BA, Deepak Padmanabhan, MD, Preetham Kumar, MD, Edward Kersh, MD, Subramaniam Venkatraman, PhD, Salima Qamruddin, MD

“…Results: The CNN model demonstrated an area under the receiver operating characteristic curve of 0.85, with a sensitivity of 77.5%, specificity of 78.3%, positive predictive value of 20.3%, and negative predictive value of 98.0%. …”
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Development of an Artificial Intelligent Lighting System for Protected Crops by Basil Mohammed Al-Hadithi, Cecilia E. García Cena, Raquel Cedazo León, Carlos Loor Loor

“…It also allows selecting the appropriate control strategy with a choice of selecting a predictive control or PD control system. Both algorithms make use of a mathematical model of the lamps which is responsible for transforming the signals generated by the drivers in digital signals that govern the operation of the implemented electronic system. …”
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Artificial Intelligence and Internet of Things Integration in Pharmaceutical Manufacturing: A Smart Synergy by Reshma Kodumuru, Soumavo Sarkar, Varun Parepally, Jignesh Chandarana

“…<b>Results:</b> Applications discussed herein focus on industrial predictive analytics and quality, underpinned by case studies showing improvements in product quality and reductions in downtime. …”
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A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks by Hatice Büşra Lüleci, Dilara Uzuner, Müberra Fatma Cesur, Atılay İlgün, Elif Düz, Ecehan Abdik, Regan Odongo, Tunahan Çakır

“…The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. …”
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Monitored reconstruction improved by post-processing neural network by A.V. Yamaev

“…A novel training method specifically designed for neural network algorithms within the Monitored reconstruction framework is proposed. …”
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Lactylation Modification as a Promoter of Bladder Cancer: Insights from Multi-Omics Analysis by Yipeng He, Lingyan Xiang, Jingping Yuan, Honglin Yan

“…Multiple omics data of BLAC were obtained from the GEO database and TCGA database. The Lasso algorithm was used to establish a prognostic model related to lactylation modification, and its predictive ability was tested with a validation cohort. …”
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The Military Aleatory: Weaponizing Winds by Ryan Bishop

“…From signal interpretation with remote sensing systems to telecommunciations to algorithms, media and information theory, and the weather, the unintended consequences of both dynamic systems in nature and technological attempts to control them led to innovations and accidents, developments and prediction advances that furnish large swaths of our current global sensorial domain. …”
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A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data by Qingmiao Wang, Jinghe Feng, Shuguang Li

“…K-means clustering algorithms and the LSTM neural network prediction model are used to obtain the space mean speed and vehicle steering ratio of the intersection sections. …”
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Enhancing Sports Team Management Through Machine Learning by Ling Zhang, Yifan An

“…The 34 performance characteristics identified achieved a prediction accuracy of 63.4% for match outcomes.…”
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Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment by Yue Li, Yue Li, Shengxiao Nie, Lei Wang, Dongsheng Li, Shengmiao Ma, Ting Li, Hong Sun

“…BackgroundMachine learning is pivotal for predicting Peripherally Inserted Central Catheter-related venous thrombosis (PICC-RVT) risk, facilitating early diagnosis and proactive treatment. …”
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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

“…The results suggested that our novelty, SMOTEENN-ENC, integrated with the XGBoost algorithm demonstrated superiority and stability in the predictive performance when applied to skewed fat-tailed distributed datasets with inherent diverse features.…”
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Enhanced Projection Method for the Solution of the System of Nonlinear Equations Under a More General Assumption than Pseudo-Monotonicity and Lipschitz Continuity by Kanikar Muangchoo, Auwal Bala Abubakar

“…Some benchmark test problems, which included monotone and pseudo-monotone problems, were considered for the experiments. Lastly, the algorithm was utilized to solve the logistic regression (prediction) model.…”
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Study on Rapid Inversion Method for Chlorophyll Content in Ginseng Leaves Based on Reflectance Spectroscopy by Jinyu Wang, Jin Yang, Jiaqi Chen, Shulong Feng, Zitong Zhao, Mingjia Wang, Nan Song, Wei Zhang, Ci Sun

“…To address the problem that the existing means of detecting chlorophyll in ginseng leaves are time-consuming and disruptive and cannot meet the demand for rapid detection of chlorophyll in ginseng leaves, this study firstly establishes a variety of prediction models for chlorophyll in ginseng leaves based on the hyperspectral reflectance data and the vegetation index, respectively, and determines the strengths and weaknesses of the models by comparing the RMSE and the MAE of the test sets; Secondly, the analytical model construction process used for ginseng leaf chlorophyll content prediction was obtained through comparison and summary, and a fast and non-destructive ginseng leaf chlorophyll prediction method based on hyperspectral imaging technology and combining vegetation indices with machine learning algorithms was proposed; The experimental results showed that the final VI-SPA-RFR ginseng leaf chlorophyll prediction model had the best prediction performance, which had an RMSE of 1.1568 and an MAE of 0.9936 in the test set. …”
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Least Square Regularized Regression for Multitask Learning by Yong-Li Xu, Di-Rong Chen, Han-Xiong Li

“…The study of multitask learning algorithms is one of very important issues. This paper proposes a least-square regularized regression algorithm for multi-task learning with hypothesis space being the union of a sequence of Hilbert spaces. …”
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Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function by Hailun Wang, Daxing Xu

“…Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.…”
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Benchmark Investigation of SARS-CoV-2 Mutants’ Immune Escape with 2B04 Murine Antibody: A Step Towards Unraveling a Larger Picture by Karina Kapusta, Allyson McGowan, Santanu Banerjee, Jing Wang, Wojciech Kolodziejczyk, Jerzy Leszczynski

“…Three essentially different algorithms were employed: forced placement based on a template, followed by two steps of extended molecular dynamics simulations; protein–protein docking utilizing PIPER (an FFT-based method extended for use with pairwise interaction potentials); and the AlphaFold 3.0 model for complex structure prediction. …”
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