AI-Based Forecasting in Renewable-Rich Microgrids: Challenges and Comparative Insights by Martins Osifeko, Josiah Lange Munda

“…The study demonstrates that, with effective feature engineering, classical ML models can rival deep learning counterparts in forecasting accuracy. …”
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Artificial Intelligence Approach in Hip Prosthesis Identification and Addressing Radiographic Outcome Measures by Omar Musbahi, MSc, ChM, Savvas Hadjixenophontos, MEng, Saran S. Gill, Iris Soteriou, Bsc, Kyriacos Pouris, Bsc, Takuro Ueno, PhD, Justin P. Cobb, MCh

“…Mean absolute error (MAE) and R-squared values were calculated with and without the NN model to identify the model's accuracy and variability. …”
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SPECIFIC ASPECTS OF BENEFIT-RISK EVALUATION OF HERBAL MEDICINAL PRODUCTS: ANALYSIS OF REGISTRATION DOSSIERS by N. G. Olenina, N. S. Mikheeva, N. M. Krutikova

“…Based on the results of the analysis the authors elucidate the main mistakes in the preparation of the necessary documents for registration dossiers for herbal medicinal products, namely: lack of complete information on the preclinical toxicological study of the product; inconsistencies in the product composition as specified in different documents; lack of statistical analysis of the results of studies; errors in draft patient information leaflets for herbal medicinal products. …”
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Ice volume and thickness of all Scandinavian glaciers and ice caps by Thomas Frank, Ward Jan Jacobus van Pelt

“…We calibrate the modelled thicknesses against >11 000 ice thickness observations, resulting in a final ice volume estimate of 302.7 km3 for Norway, 18.4 km3 for Sweden and 321.1 km3 for the whole of Scandinavia with an error estimate of ~$\pm 11\%$. The validation statistics computed indicate good agreement between modelled and observed thicknesses (RMSE = 55 m, Pearson's r = 0.87, bias = 0.8 m), outperforming all other ice thickness maps available for the region. …”
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Dual-Channel Deepfake Audio Detection: Leveraging Direct and Reverberant Waveforms by Gunwoo Lee, Jungmin Lee, Minkyo Jung, Joseph Lee, Kihun Hong, Souhwan Jung, Yoseob Han

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HMSTNet: A Deep Learning Multimodal Approach for Personalized English Literature Recommendations by Xiaopeng Wang, Xue Liang

“…Multiple metrics demonstrate the efficiency of this model because they measure highest MBD with KNN model of 6.8m along with Theil’s U-statistic of 0.04 and 90th percentile error is 8.41 to confirm minimized prediction errors and enhanced accuracy and fail-safe capability. …”
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Analyzing the Accuracy of Satellite-Derived DEMs Using High-Resolution Terrestrial LiDAR by Aya Hamed Mohamed, Mohamed Islam Keskes, Mihai Daniel Nita

“…To quantify the average difference, root mean square error (RMSE) values were calculated as 10.43 m for ALOS and 5.65 m for the SRTM. …”
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Predictive modeling of oil rate for wells under gas lift using machine learning by Famin Ma, Farag M. A. Altalbawy, Pinank Patel, R. Manjunatha, Rishiv Kalia, Shoira Formanova, P. Raja Naveen, Kamal Kant Joshi, Aashna Sinha, Abdolali Yarahmadi Kandahari, Taqi Mohammed Khattab Al-Rubaye, Mohammad Mahtab Alam

“…Multiple machine learning models (Decision Tree, AdaBoost, Random Forest, Ensemble Learning, CNN, SVR, MLP-ANN, and Lasso Regression) were trained and evaluated using 5-fold cross-validation and key statistical metrics (R², MSE, AARE%). The Random Forest model demonstrated superior performance, achieving a test R² of 0.867 and the lowest prediction errors (MSE: 18502 and AARE: 8.76%) for the testing phase, while other models were prone to overfitting or underfitting. …”
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The Finno-Ugric Peoples of the Middle Volga and Southern Urals Based on the 1920 All-Russian Census: New Data by Leyla F. Sayfullina

“…Because of the insufficient source base of the Civil War period in the country, further study of the materials of the statistical research of 1920 will allow us to open new horizons in analyzing both the composition of the peasant family and the peculiarities of the economy (including the specifics of the introduction of agriculture, animal husbandry, poultry farming, etc.) of different ethnic groups of variable geographical areas of residence, as well as to determine the common and special features of each group of the population.…”
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DeepSeek-AI-enhanced virtual reality training for mass casualty management: Leveraging machine learning for personalized instructional optimization. by Zhe Li, Lei Shi, Mingyu Pei, Wan Chen, Yutao Tang, Guozheng Qiu, Xibin Xu, Liwen Lyu

“…The DeepSeek AI framework was employed to analyze the data, utilizing clustering analysis, principal component analysis (PCA), and random forest models. Descriptive statistics, error rates, and correlation analyses were performed using R software (version 4.1.2). …”
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A Combined RSM-FEM Analysis of Electric Field Distribution in a Novel Design of an Inclined-Plane Electrostatic Separator by Abdelkader Nadjem, Karim Rouagdia

“…The correlation between simulated data and model predictions was strong (R² > 0.99), with prediction errors not exceeding 5.83%. Comparative analysis revealed that our model enhanced E-field parameters by approximately 65% compared to conventional designs.…”
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Evaluation of the Finis Swimsense® and the Garmin Swim™ activity monitors for swimming performance and stroke kinematics analysis. by Robert Mooney, Leo R Quinlan, Gavin Corley, Alan Godfrey, Conor Osborough, Gearóid ÓLaighin

“…It is reasonable to expect that this level of error would increase when the devices are used by recreational swimmers rather than elite swimmers. …”
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Application of Three-Dimensional Hierarchical Density-Based Spatial Clustering of Applications with Noise in Ship Automatic Identification System Trajectory-Cluster Analysis by Shih-Ming Wang, Wen-Rong Yang, Qian-Yi Zhuang, Wei-Hong Lin, Mau-Yi Tian, Te-Jen Su, Jui-Chuan Cheng

“…While numerous studies have explored methods for optimizing ship trajectory clustering, such as narrowing dynamic time windows to prevent errors in time warp calculations or employing the Mahalanobis distance, these methods enhance DBSCAN (Density-Based Spatial Clustering of Applications with Noise) by leveraging trajectory similarity features for clustering. …”
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Impact of Developer Queries on the Effectiveness of Conversational Large Language Models in Programming by Viktor Taneski, Sašo Karakatič, Patrik Rek, Gregor Jošt

“…The results reveal that students who queried LLMs for error fixing (EF) were statistically more likely to have runnable code, regardless of prior knowledge. …”
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The Estimating Parameter and Number of Knots for Nonparametric Regression Methods in Modelling Time Series Data by Autcha Araveeporn

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Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids by Cristian Rojas, Doménica Muñoz, Ivanna Cordero, Belén Tenesaca, Davide Ballabio

“…The performance of the models was quantified by means of the non-error rate (<i>NER</i>) statistical parameter.…”
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Edge-optimized multimodal cross-fusion architecture for efficient crop disease detection by Thomas Kinyanjui Njoroge, Kelvin Mugoye Shindu, Rachael Kibuku

“…Traditional diagnostic methods, such as manual inspections, are often inefficient and error-prone. Existing deep learning models (e.g., ResNet50, Inception V3) struggle with computational inefficiency and poor generalizability in real-world farming contexts. …”
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Modeling methylene blue removal using magnetic chitosan carboxymethyl cellulose multiwalled carbon nanotube composite with genetic algorithms and regression techniques by Mahmood Yousefi, Saeid Fallahizadeh, Yosra Maleki, Amir Sheikhmohammadi, Alieh Rezagholizade-shirvan

“…The adsorbent showed stability in residuals in the training set equal to Mean Residual = 0, and Root Mean Square Error of 0.68, while testing gave the Mean residual = 0.15, and the Root Mean Square Error of 2.33. …”
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A hybrid Hadoop-based sentiment analysis classifier for tweets associated with COVID-19 utilizing two machine learning algorithms: CNN, and fuzzy C4.5 by Fatima Es-sabery, Ibrahim Es-sabery, Junaid Qadir, Beatriz Sainz-de-Abajo, Begonya Garcia-Zapirain

“…The results showed that the model performs exceptionally well on the COVID-19_Sentiments dataset, surpassing other classification algorithms with a precision rate of 94.56%, false-negative rate of 5.28%, classification rate of 95.15%, F1-score of 94.63%, kappa statistic of 95.12%, execution time of 11.81 s, false-positive rate of 4.26%, error rate of 4.26%, specificity of 95.74%, recall of 94.72%, stability with a mean deviation standard of 0.09%, convergence starting around the 75th round, and significantly reduced complexity in terms of time and space.…”
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Ensemble Machine Learning, Deep Learning, and Time Series Forecasting: Improving Prediction Accuracy for Hourly Concentrations of Ambient Air Pollutants by Valentino Petrić, Hussain Hussain, Kristina Časni, Milana Vuckovic, Andreas Schopper, Željka Ujević Andrijić, Simonas Kecorius, Leizel Madueno, Roman Kern, Mario Lovrić

“…The utilisation of surface atmospheric ERA5-Land datasets within the models as model features showed high feature post hoc importance in the best (hybrid) models per pollutant and site. …”
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