Geological Stratification Technology Based on Artificial Intelligence Algorithms and Its Application Effects by GAO Yuan, YAO Weihua, CAI Shaofeng, LI Liang, XUE Yuan, ZHAO Peipei, WANG Xiaoyang, WEI Wei

“…Good prediction results are achieved, and exploration and evaluation wells are used to predict development wells. …”
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Identification of soil texture and color using machine learning algorithms and satellite imagery by Jiyang Wang

“…For future research, it is recommended to explore the combination of SVR with optimization techniques such as genetic algorithms to further improve the accuracy of soil texture and color predictions.…”
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An Intelligent Gestational Diabetes Mellitus Recognition System Using Machine Learning Algorithms by Rasool Jader, Sadegh Aminifar

“…The healthcare system is one of the many parts of our daily lives that are being rethought thanks to the creation of intelligent systems by machine learning algorithms. In this article, a hybrid prediction model is suggested as a way to find out if a woman has gestational diabetes. …”
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Algorithms Facilitating the Observation of Urban Residential Vacancy Rates: Technologies, Challenges and Breakthroughs by Binglin Liu, Weijia Zeng, Weijiang Liu, Yi Peng, Nini Yao

“…The ensemble learning algorithm combines multiple prediction models to improve the prediction accuracy and stability. …”
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Quantum Computing-Accelerated Kalman Filtering for Satellite Clusters: Algorithms and Comparative Analysis by Shreyan Prakash, Raj Bhattacherjee, Sainath Bitragunta, Ashutosh Bhatia, Kamlesh Tiwari

“…The increasing demand for high-precision real-time data processing in satellite clusters requires efficient algorithms to manage inherent uncertainties in space-based systems. …”
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Performance Comparison of Random Forest and Decision Tree Algorithms for Anomaly Detection in Networks by Rafiq Fajar Ramadhan, Wahid Miftahul Ashari

“…However, in multi-class data, Random Forest showed slightly better performance, though it required significantly more time for training and prediction. Despite the small difference in accuracy, Decision Tree demonstrated faster prediction times, making it more efficient for time-sensitive applications. …”
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Freedom under algorithms: how unpredictable and asocial management erodes free choice by Robert Donoghue

“…This issue is further complicated by the fact that as algorithms become more resilient and useful, their outputs grow increasingly opaque and unpredictable—what some refer to as the resilience-predictability paradox. …”
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Estimating latent heat flux of subtropical forests using machine learning algorithms by Harekrushna Sahu, Pramit Kumar Deb Burman, Palingamoorthy Gnanamoorthy, Qinghai Song, Yiping Zhang, Huimin Wang, Yaoliang Chen, Shusen Wang

“…By harnessing diverse datasets, we employ various machine learning regression algorithms. We find the support vector regression superior to linear, lasso, random forest, adaptive boosting and gradient boosting algorithms. …”
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Multivariate machine learning algorithms for energy demand forecasting and load behavior analysis by Farhan Hussain, M. Hasanuzzaman, Nasrudin Abd Rahim

“…This article presents deep learning frameworks for predicting electricity demand in the Western region of Bangladesh, utilizing Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithms. …”
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A brief comparison of algorithms and methods for optimization of electric vehicles charging strategies by Arpita Bharti, Rajeev Kumar Chauhan

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Security situational awareness of power information networks based on machine learning algorithms by Chao Wang, Jia-han Dong, Guang-xin Guo, Tian-yu Ren, Xiao-hu Wang, Ming-yu Pan

“…To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. …”
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Medical decision support systems for diagnosing diseases based on ensemble learning algorithms by Luma Jarallah

“…This paper proposes a stacked learning model derived from multiple ensembles learning algorithms, including Random Forest, Catboost and XGBoost. …”
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Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting by Aslı BOR, Merve OKAN

“…Our findings indicate that the estimated capability of the Bayesian Regularization algorithm were close to with Levenberg-Marquardt algorithm for training and testing, respectively. …”
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Multi-domain rule-based phenotyping algorithms enable improved GWAS signal by Abigail Newbury, Ahmed Elhussein, Gamze Gürsoy

“…Here, we assess the impact of various rule-based phenotyping algorithms on GWAS outcomes, examining factors such as power, heritability, replicability, functional annotations, and polygenic risk score prediction accuracy across seven diseases in the UK Biobank. …”
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THE TECHNIQUE OF THE COMPARATIVE ANALYSIS OF THE ROUTE TRACKING INFORMATION ALGORITHMS DURING THE TERTIARY PROCESSING by A. A. Belous

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Evaluation of the Performance of Unsupervised Learning Algorithms for Intrusion Detection in Unbalanced Data Environments by Gutierrez-Portela Fernando, Almenares Mendoza Florina, Calderon-Benavides Liliana

“…Results showed that K-means++ achieved 95% purity with 95% and 99% prediction accuracies for normal and abnormal data, respectively, while I-forest delivered similar results and excelled in computational efficiency, consuming only 10% of CPU resources compared to 16% for other algorithms. …”
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A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks by Vanja Smailovic, Vedran Podobnik, Ignac Lovrek

“…However, these approaches introduce uncertainty in calculating (i.e., predicting) the value of social influence. Hence, a methodology is proposed for evaluating algorithms that calculate social influence in complex social networks; this is done by identifying the most accurate and precise algorithm. …”
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Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis by Mario Muñoz, Adrián Rubio, Guillermo Cosarinsky, Jorge F. Cruza, Jorge Camacho

“…Real-time post-processing algorithms further refine prediction accuracy by reducing false-positives and false-negatives, augmenting interpretational clarity and obtaining a final processing rate of up to 20 frames per second with accuracy levels of 89% for consolidation, 92% for B-lines, 66% for A-lines, and 92% for detecting normal lungs compared with an expert opinion.…”
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Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep Learning by Sira Astour, Ahmad Hasan

“…Extensive experiments on a large, balanced dataset containing 491,530 URLs, equally distributed between benign and malicious, showed that ensemble learning models significantly outperform other algorithms. The Bagging classifier, which uses decision trees as the base classifier, achieved an accuracy of 99.01%, a training time of 23.84 seconds, and a prediction time of 0.86 seconds. …”
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Genetic Algorithms Applied to Optimize Neural Network Training in Reference Evapotranspiration Estimation by Eluã Ramos Coutinho, Jonni G.F. Madeira, Robson Mariano da Silva, Angel Ramon Sanchez Delgado, Alvaro L.G.A. Coutinho

“…This confirms that employing Genetic Algorithms (GA) to automate the training and optimization of the model is effective and enhances the neural network's capacity to predict ETo.…”
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