The Value of Reducing Inconclusive and False-Positive Newborn Screening Results for Congenital Hypothyroidism, Congenital Adrenal Hyperplasia and Maple Syrup Urine Disease in The N... by Rosalie C. Martens, Anita Boelen, Michèle H. van der Kemp, Annet M. Bosch, Eveline M. Berghout, Gert Weijman, Nitash Zwaveling-Soonawala, Rendelien K. Verschoof-Puite, Robert de Jonge, Sabine E. Hannema, Judith E. Bosmans, Annemieke C. Heijboer

“…The current study estimates the financial consequences of these improved algorithms related to the reduction in inconclusive results and false-positives. …”
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DCLTV: An Improved Dual-Condition Diffusion Model for Laser-Visible Image Translation by Xiaoyu Zhang, Laixian Zhang, Huichao Guo, Haijing Zheng, Houpeng Sun, Yingchun Li, Rong Li, Chenglong Luan, Xiaoyun Tong

“…Existing cross-modal image translation algorithms are plagued with issues, including difficult training and color bleeding. …”
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Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury by Aref Andishgar, Maziyar Rismani, Sina Bazmi, Zahra Mohammadi, Sedighe Hooshmandi, Behnam Kian, Amin Niakan, Reza Taheri, Hosseinali Khalili, Roohallah Alizadehsani

“…Nineteen variables were assessed using ML algorithms to predict outcomes. Data preparation addressed missing values and balancing methods corrected imbalances. …”
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Malware Detection in Forensic Memory Dumps: The Use of Deep Meta-Learning Models by Yalçın Özkan

“…First, it was subjected to the classification process with a deep learning algorithm, and a predictive model was acquired. The predictive model was found to have an accuracy metric of 98.25%. …”
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Identification of immune and major depressive disorder-related diagnostic markers for early nonalcoholic fatty liver disease by WGCNA and machine learning by Yuyun Jia, Yanping Cao, Qin Yin, Xueqian Li, Xiu Wen

“…Immune cell infiltration levels were quantified using single-sample gene set enrichment analysis (ssGSEA). A predictive model for SS/NASH was developed by evaluating nine machine-learning algorithms with 10-fold cross-validation on the datasets.ResultsFourteen genes strongly linked to both the immune system and the two conditions were identified. …”
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Landslide hazard early warning method for rock slopes using a hybrid LSTM-SARIMA data-driven model. by Yongxin Dai, Zijian Li, Jingbiao Lu

“…This paper proposes a comprehensive technical system for landslide early hazard warning in open-pit mine slopes, encompassing the full process of monitoring data acquisition and processing, analysis of influencing mechanisms, intelligent algorithm-based prediction, and the construction of early hazard warning indicators. …”
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Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers by Mao Yang, Xiaoxuan Shen, Dawei Huang, Xin Su

“…Then, parallel factor algorithm is used to reduce prediction model redundancy, which can reduce high-dimensional numerical weather prediction feature matrix to extract relevant features. …”
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Abilities and limitations in counteracting urban heat islands by Michał Musiał, Anna Pomykała, Lech Lichołai

“…While AI models can expedite predictions, their precision may still lag behind traditional numerical weather prediction methods. …”
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Deep Learning-Based Detection and Identification Method for Sports Health Video Dissemination by Yajun Pang

“…Our method also performs better overall in the same type of algorithms.…”
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Methods for Change Parallelism in Process of High-level VLSI Synthesis by Igor Nikolaevich Ryzhenko, Oleg Vladimirovich Nepomnyaschy, Aleksandr Ivanovich Legalov, Vladimir Viktorovich Shaidurov

“…An algorithm for calculating the reduction coefficients and estimating the required resources is proposed. …”
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Workplace Preference Analytics Among Graduates by Sin-Yin Ong, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham

“…Notably so, results from this study demonstrate that using Random Forest Algorithm resulted in better performance in predicting employment status with an accuracy rate of 99.40%, predicting company type with 66.60% and lastly predicting company sector with 30.80% when compared to other predictive models utilized during our research work's project lifecycle phase.…”
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Grey wolf‐based heuristic methods for accurate parameter extraction to optimize the performance of PV modules by Seyit Alperen Celtek, Seda Kul, Manish Kumar Singla, Jyoti Gupta, Murodbek Safaraliev, Hamed Zeinoddini‐Meymand

“…In this study, the parameter prediction of a four‐diode PV model was carried out using the Improved Grey Wolf Optimization (IGWO) algorithm, which builds upon the Grey Wolf Optimization (GWO) algorithm. …”
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Hybrid Machine Learning in Hydrological Runoff Forecasting: An Exploration of Extreme Gradient-Boosting and Categorical Gradient Boosting Optimization in the Russian River Basin by Reza Seifi Majdar, Ali Rahnamaei, Vahid Babazadeh

“…Recent advances in machine learning (ML) have opened new opportunities to improve prediction accuracy. This study focuses on evaluating commonly used ML methods for runoff prediction, with an emphasis on simplicity and comparability to more advanced models. …”
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Comprehensive datasets for RNA design, machine learning, and beyond by Jan Badura, Agnieszka Rybarczyk, Tomasz Zok

“…We demonstrated the potential of the dataset by testing it with several popular open-source RNA design algorithms. Furthermore, we illustrated how our dataset can be used to train machine learning models that consider both RNA sequence and structure, potentially advancing RNA design and prediction capabilities.…”
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Rapid Lactic Acid Content Detection in Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging by Xiaoyu Xue, Haiqing Tian, Kai Zhao, Yang Yu, Ziqing Xiao, Chunxiang Zhuo, Jianying Sun

“…To minimize model redundancy, three algorithms, such as competitive adaptive reweighted sampling (CARS), were used to extract the characteristic wavelengths of the three dyes, and the combination of the characteristic wavelengths obtained by each algorithm was used as an input variable to build an analytical model for quantitative prediction of the lactic acid content by support vector regression (SVR). …”
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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…These technologies enable precise early-stage diagnosis of plant diseases and facilitate the prediction of their progression, allowing for timely implementation of crop protection measures. …”
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Investigation of the Pathogenesis of Liver Fibrosis Associated with Type 2 Diabetes Mellitus via Bioinformatic Analysis by Zhiyu Xiong, Kan Shu, Yingan Jiang

“…The co-differentially expressed genes (co-DEGs) were identified and subsequently analyzed through functional enrichment, protein–protein interaction (PPI) network construction, transcription factor prediction, and drug prediction. Machine learning algorithms were then applied to identify key genes. …”
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Comparative Study and Real-World Validation of Vertical Load Estimation Techniques for Intelligent Tire Systems by Ti Wu, Xiaolong Zhang, Dong Wang, Weigong Zhang, Deng Pan, Liang Tao

“…Vertical load prediction algorithms are developed using Support Vector Machine (SVM) and linear regression, considering variables like contact length, vehicle speed, and tire pressure. …”
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A feature selection method utilizing path accumulation cost, redundancy minimization, and interaction maximization for the diagnosis of coronary heart disease by Jiayao Jiang, Zheng Yue, Hongling Zhu, Yan Wang, Hongsen Cai, Wenguang Hou

“…Results Experimental outcomes demonstrate that our algorithm outperforms several classical algorithms in terms of classification accuracy and feature dimension reduction, while also identifying highly correlated feature subsets. …”
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LightGBM-Based Human Action Recognition Using Sensors by Yinuo Liu, Ziwei Chen

“…Moreover, after searching for the model parameters using grid search, the prediction accuracy of LightGBM can be increased to 95.35%. …”
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