Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization by Luyu Tian, Chaoyu Dong, Rui Wang, Yunfei Mu, Hongjie Jia

“…After denoising by the autoencoder, the prediction results improved by 22% compared to non‐local mean denoising and by about 32% compared to noisy images.…”
Get full text

ANALISIS KINERJA MODEL STACKING BERBASIS RANDOM FOREST DAN SVM DALAM KLASIFIKASI RUMAH TANGGA BERDASARKAN GARIS KEMISKINAN MAKANAN DI PROVINSI JAWA BARAT by Ghardapaty Ghaly Ghiffary, Nabila Tri Amanda, Rizky Ardhani, Bagus Sartono, Aulia Rizki Firdawanti

“…The stacking method is an ensemble technique in machine learning that combines predictions from several base models to improve classification accuracy. …”
Get full text

Vibration Diagnostic Methods from Methodsof Obtaining Data to Processing It Using Modern Means by Anton O. Zhuravlev, Alexey O. Polyakov, Denis A. Andrikov

“…Thus, self-diagnosis, combined with a high level of automated analytics, makes it possible to predict a malfunction with a high degree of probability, warn about the timing of its occurrence and methods of preventive elimination. …”
Get full text

Integrating feedback control for improved human-structure interaction analysis by Santiago A. Lopez, Daniel Gomez, Albert R. Ortiz, Sandra Villamizar

“…The results of this study indicate that feedback controllers accurately predict the experimental structural response for different subjects. …”
Get full text

Integrating Multi-Variable Driving Factors to Improve Land Use & Land Cover Classification Accuracy using Machine Learning Approaches: A Case Study from Lombok Island by Miftahul Irsyadi Purnama, Hüseyin Oğuz Çoban

“…In field validation assessments, comparing the predictions of these machine learning models with ground truth data, Random Forest was the most reliable, achieving an overall accuracy of 88%. …”
Get full text

Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports by Ümit Gökkuş, Mehmet Sinan Yıldırım, Metin Mutlu Aydin

“…Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt Algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic Algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). …”
Get full text

A Novel Admixture-Based Pharmacogenetic Approach to Refine Warfarin Dosing in Caribbean Hispanics. by Jorge Duconge, Alga S Ramos, Karla Claudio-Campos, Giselle Rivera-Miranda, Luis Bermúdez-Bosch, Jessicca Y Renta, Carmen L Cadilla, Iadelisse Cruz, Juan F Feliu, Cunegundo Vergara, Gualberto Ruaño

“…<h4>Results</h4>The admixture-adjusted, genotype-guided warfarin dosing refinement algorithm developed in Caribbean Hispanics showed better predictability (R2 = 0.70, MAE = 0.72mg/day) than a clinical algorithm that excluded genotypes and admixture (R2 = 0.60, MAE = 0.99mg/day), and outperformed two prior pharmacogenetic algorithms in predicting effective dose in this population. …”
Get full text

BETASCAN: probable beta-amyloids identified by pairwise probabilistic analysis. by Allen W Bryan, Matthew Menke, Lenore J Cowen, Susan L Lindquist, Bonnie Berger

“…BETASCAN suggests multiple alternate folding patterns and assigns relative a priori probabilities based solely on amino acid sequence, probability tables, and pre-chosen parameters. The algorithm compares favorably with the results of previous algorithms (BETAPRO, PASTA, SALSA, TANGO, and Zyggregator) in beta-structure prediction and amyloid propensity prediction. …”
Get full text

Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Frimpong Twum, Gaddafi Abdul-Salaam

“…The focus on contextual nonbiomedical measurements using a supervised machine learning modeling technique is aimed at creating an understanding of the reasons why treatment default occurs, including identifying important contextual parameters that contribute to treatment default. The predicted accuracy scores of four supervised machine learning algorithms, namely, gradient boosting, logistic regression, random forest, and support vector machine were 0.87, 0.90, 0.81, and 0.77, respectively. …”
Get full text

Automated Fillet Weld Inspection Based on Deep Learning from 2D Images by Ignacio Diaz-Cano, Arturo Morgado-Estevez, José María Rodríguez Corral, Pablo Medina-Coello, Blas Salvador-Dominguez, Miguel Alvarez-Alcon

“…Following an extensive review of available solutions, algorithms, and networks based on this convolutional strategy, it was determined that the You Only Look Once algorithm in its version 8 (YOLOv8) would be the most suitable for object detection due to its performance and features. …”
Get full text

Deep Mining on the Formation Cycle Features for Concurrent SOH Estimation and RUL Prognostication in Lithium-Ion Batteries by Dongchen Yang, Weilin He, Xin He

“…By integrating diverse datasets with advanced algorithms and models, we perform correlation analyses of parameters such as capacity, voltage, temperature, pressure, and strain, enabling precise SOH estimation and RUL prediction. …”
Get full text

Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. by Shayla Naznin, Md Jamal Uddin, Ishmam Ahmad, Ahmad Kabir

“…Projections indicate further reductions in under-5 mortality to 29.87 per 1,000 live births by 2030 and 26.21 by 2035.…”
Get full text

Application of Neural Networks to Analyse the Spatial Distribution of Bicycle Traffic Before, During and After the Closure of the Mill Road Bridge in Cambridgeshire, United Kingdom by Shohel Amin

“…The application of wireless sensors and machine learning algorithms can enhance the analysis and prediction ability of traffic distribution and characteristics in the proximity of road closures. …”
Get full text

Enhanced Multi-Criteria DNA Sequence Analysis Using Neutrosophic Logic and Deep Learning: An Integrated Approach for Comparison and Classification by Romany M. Farag, Mahmoud Y. Shams, Dalia A. Aldawody, Hazem M. El-Bakry, Huda E. Khalid, Ahmed K. Essa, A. A. Salama

“…Traditionally, traditional methods such as multiple sequence alignment (MSA) algorithms or the NeedlemanWinch algorithm become extremely cumbersome when faced with large or unclear data. …”
Get full text

SurVIndel2: improving copy number variant calling from next-generation sequencing using hidden split reads by Ramesh Rajaby, Wing-Kin Sung

“…We also show that SurVIndel2 is able to complement small indels predicted by Google DeepVariant, and the two software used in tandem produce a remarkably complete catalogue of variants in an individual. …”
Get full text

Machine Learning Applications in Gray, Blue, and Green Hydrogen Production: A Comprehensive Review by Xuejia Du, Shihui Gao, Gang Yang

“…Among these, green hydrogen—particularly via water electrolysis and biomass gasification—received the most attention, reflecting its central role in decarbonization strategies. ML algorithms such as artificial neural networks (ANNs), random forest (RF), and gradient boosting regression (GBR) have been widely applied to predict hydrogen yield, optimize operational conditions, reduce emissions, and improve process efficiency. …”
Get full text

Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

“…Using the proposed system, it was demonstrated that predictions of diseases like powdery mildew and blight are improved compared to traditional methods both in terms of accuracy as well as in the speed of response. …”
Get full text

A classification modeling approach for determining metabolite signatures in osteoarthritis. by Jason S Rockel, Weidong Zhang, Konstantin Shestopaloff, Sergei Likhodii, Guang Sun, Andrew Furey, Edward Randell, Kala Sundararajan, Rajiv Gandhi, Guangju Zhai, Mohit Kapoor, Mohit Kapoor

“…Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. …”
Get full text

ClassRoom-Crowd: A Comprehensive Dataset for Classroom Crowd Counting and Cross-Domain Baseline Analysis by Wenqian Jiang, Xiaohua Huang, Qun Zhao, Sheng Liu

“…Furthermore, to enhance crowd movement predictions in real-world applications, these methods must demonstrate temporal coherence. …”
Get full text

Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors by Ehsan Masoudian, Ali Mirzaei, Hossein Bagheri

“…Utilizing advanced remote sensing, geospatial information system (GIS) processing techniques such as cloud computing, and machine learning algorithms, this research analyzed the impact of climatic parameters, topographic features, and human-related factors on wildfire susceptibility assessment and prediction in Iran. …”
Get full text