Perspectives on water quality analysis emphasizing indexing, modeling, and application of artificial intelligence for comparison and trend forecasting by Rijurekha Dasgupta, Subhasish Das, Gourab Banerjee, Asis Mazumdar

“…Different methodologies adopted to execute these three approaches are presented in this study, which leads to formulate a comparative discussion. Using statistical operations and soft computing techniques have been done by researchers to combat the subjectivity error in indexing. …”
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Lithium-Ion Battery Degradation Based on the CNN-Transformer Model by Yongsheng Shi, Leicheng Wang, Na Liao, Zequan Xu

“…The final model results indicate that the root mean square error (RMSE) of capacity predictions for the majority of batteries among the 124 batteries is within 3% of the actual values.…”
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Enhancing thermal transport in chemically reacting nanoparticles using the energy source and Cattaneo-Christov heat flux model by Shazia Habib, Saleem Nasir, Zeeshan Khan, Abdallah Berrouk, Saeed Islam, Asim Aamir

“…The important and intriguing feature of this remarkable work is that, for all parameters examined, the heat transfer rate rises with minimal measurement of errors, consistent with the core objective of applying nanofluids to nanotechnology for their prospective implications.…”
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Saturated flow boiling frictional pressure drop inside smooth macro and mini/micro channels: a new predictive tool using CatBoost and XGBoost by Francisco A. Ramírez-Rivera, Alison E. Sánchez-García, Vrindarani Núñez-Ramírez, Néstor F. Guerrero-Rodríguez

“…A total of 22 input parameters were selected for the training process by performing a comparative analysis between several feature selection techniques. The result indicate that the new predictive tool captures 89.52 % of the points within ±30 % error bands with technical statistical metrics MAE = 1.901, MSE = 11.956, RMSE = 3.458, MAPE = 14.204, R2 = 0.992. …”
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Steiner eccentricity: Predictions and applications by Xingfu Li, Zongbo Yang, Siyi Lin, Jialu Wang

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OWL model of multi-agent Smart-system of distance learning for people with vision disabilities by Galina A. Samigulina, Asem S. Shayakhmetova, Adlet Nyusupov

“…The benefits of using of the developed ontological model of smartsystem of distance learning for visually impaired people based on multifunctional agents are: complex approach, based on the use of various intellectual, cognitive and statistical methods; possibility of developing an individual trajectory of learning for visually impaired people including the psychophysiological features of perception information; distance access to the latest technological equipment for performing laboratory and practical works by visually impaired people in the shared laboratories in real time. …”
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Gamified mHealth System for Evaluating Upper Limb Motor Performance in Children: Cross-Sectional Feasibility Study by Md Raihan Mia, Sheikh Iqbal Ahamed, Samuel Nemanich

“…The specific aims were to (1) design and develop novel mHealth gamified software tools to examine theory-driven features of UL movement, (2) analyze spatiotemporal game data with new algorithms and statistical techniques to quantify movement performance as a parameter of speed, accuracy, and precision, and (3) validate assessment methods with healthy participants from schools. …”
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Mitigating the Concurrent Interference of Electrode Shift and Loosening in Myoelectric Pattern Recognition Using Siamese Autoencoder Network by Ge Gao, Xu Zhang, Xiang Chen, Zhang Chen

“…The SAEN model was trained with a variety of shifted-view and masked-view feature maps, which were simulated through feature transformation operated on the original feature maps. …”
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The Spatial Distribution of Tree Dieback Affected by Mistletoe in Relation to their Crown Characteristics by Erfan Boshkar, Ehsan Sayad, Shayeste Gholami

“…These variogram showed positive nugget, which can be explained by sampling error, short range variability, random and inherent variability. …”
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Uncertainty-Aware Predictive Process Monitoring in Healthcare: Explainable Insights into Probability Calibration for Conformal Prediction by Maxim Majlatow, Fahim Ahmed Shakil, Andreas Emrich, Nijat Mehdiyev

“…By incorporating these calibrated probabilities into the CP framework, our multilayer analysis evaluates improvements in prediction region validity, including tighter coverage gaps and reduced minority error contributions. Furthermore, we employ SHAP-based explainability to explain how calibration influences feature attribution for both high-confidence and ambiguous predictions. …”
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Pore size classification and prediction based on distribution of reservoir fluid volumes utilizing well logs and deep learning algorithm in a complex lithology by Hassan Bagheri, Reza Mohebian, Ali Moradzadeh, Behnia Azizzadeh Mehmandost Olya

“…The three feature selection algorithms utilize statistical computations. …”
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Development of a new data management system for the study of the gut microbiome of children who are small for their gestational age by Felix Manske, Magdalena Durda-Masny, Norbert Grundmann, Jan Mazela, Monika Englert-Golon, Marta Szymankiewicz-Bręborowicz, Joanna Ciomborowska-Basheer, Izabela Makałowska, Anita Szwed, Wojciech Makałowski

“…Thus, after initial plausibility checks on the input data to reduce human error, data are stored in a relational database and can be continuously updated over the whole life time of the study. …”
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Wireless Channel Prediction Using Artificial Intelligence With Imperfect Datasets by Gowhar Javanmardi, Ramiro Samano Robles

“…This stress test leads to new conclusions on channel prediction: i) how and why algorithms behave in different ways under diverse conditions (optimality region), ii) derivation of new bounds linked to channel features (coherence time, channel correlation, etc.), iii) optimum parameter settings for ML also linked to channel statistics, and iv) proposal of potential improvements. …”
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Machine learning for battery quality classification and lifetime prediction using formation data by Jiayu Zou, Yingbo Gao, Moritz H. Frieges, Martin F. Börner, Achim Kampker, Weihan Li

“…We extract three classes of features from the raw formation data, considering the statistical aspects, differential analysis, and electrochemical characteristics. …”
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Fast Depth Imaging Denoising With the Temporal Correlation of Photons by Zhenchao Feng, Weiji He, Jian Fang, Guohua Gu, Qian Chen, Ping Zhang, Yuanjin Chen, Beibei Zhou, Minhua Zhou

“…Because of the inevitable noise, which is due to background light and dark counts of the detector, the depth imaging of LiDAR system exists a large estimation error. Our method combines the Poisson statistical model with the different distribution feature of signal and noise in the time axis. …”
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Application of Machine Learning Methods for Gravity Anomaly Prediction by Katima Zhanakulova, Bakhberde Adebiyet, Elmira Orynbassarova, Ainur Yerzhankyzy, Khaini-Kamal Kassymkanova, Roza Abdykalykova, Maksat Zakariya

“…Most prediction errors from all methods were spatially associated with mountainous regions featuring significant elevation changes. …”
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Correlation Analysis and Prediction of the Physical and Mechanical Properties of Coastal Soft Soil in the Jiangdong New District, Haikou, China by Yongchang Yang, Xinying Song, Shuai Zhang, Jun Hu, Ming Ruan, Dongling Zeng, Han Luo, Jiangsi Wang, Zhixin Wang

“…Subsequently, we employed the feature selection guided by the aforementioned data analysis results to establish a random forest model. …”
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Reference-Free Assessment of Speech Intelligibility Using Bispectrum of an Auditory Neurogram. by Mohammad E Hossain, Wissam A Jassim, Muhammad S A Zilany

“…The responses of the auditory-nerve fibers with a wide range of characteristic frequencies were simulated to construct neurograms. The features of the neurograms were extracted using third-order statistics referred to as bispectrum. …”
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Hybrid machine learning applications in pavement engineering: predicting spalling with PSO-GBM by Ali Alnaqbi, Ghazi G. Al-Khateeb, Waleed Zeiada

“…Feature importance analysis identified Age, KESAL, and Initial IRI as the most influential variables. …”
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Genderly: a data-centric gender bias detection system by Wael Khreich, Jad Doughman

“…Error analysis further explored top-performing model strengths and limitations. …”
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