Fusion of non-iterative deep neural network feature extraction with kernel extreme learning machine for plant disease classification by Kirti Kirti, Navin Rajpal, Virendra P. Vishwakarma, Pramod Kumar Soni

“…All these issues have emerged from the expansion of hidden layers and their parameters. In this work, a novel hybrid approach is proposed using a ResNet-50 based deep neural network integrated with a Kernel Extreme Learning Machine (KELM) classifier for efficient and accurate plant disease classification. …”
Get full text

Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning by Min Liang, Zhiwen Zhang, Langming Wu, Mafeng Chen, Shifan Tan, Jian Huang

“…Results We extracted a total of 9121 eligible patients from the database, identifying eleven clinical parameters that significantly influenced OS prognostication. …”
Get full text

Integrating machine learning and reliability analysis: A novel approach to predicting heavy metal removal efficiency using biochar by Mohammad Sadegh Barkhordari, Chongchong Qi

“…This research introduces an advanced machine learning (ML) framework, utilizing deep forest (DF) algorithms, to predict and optimize the efficiency HM removal through biochar applications. …”
Get full text

Predictive modeling of hydrogen production and methane conversion from biomass-derived methane using machine learning and optimisation techniques by Adegboyega Bolu Ehinmowo, Bright Ikechukwu Nwaneri, Joseph Oluwatobi Olaide

“…The predicted methane conversion using the firefly-optimized support vector machine regressor was 72%, with the actual conversion being 68%. …”
Get full text

Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model by Md. Siddikur Rahman, Miftahuzzannat Amrin, Md. Abu Bokkor Shiddik

“…To address this, we propose an interpretable tree‐based machine learning (ML) model for dengue early warning systems and outbreak prediction in Bangladesh based on climatic, sociodemographic, and landscape factors. …”
Get full text

High-resolution energy consumption forecasting of a university campus power plant based on advanced machine learning techniques by Saad A. Alsamraee, Sanjeev Khanna

“…To bridge this gap, this study introduces an advanced machine learning (ML) framework leveraging an extensive hourly energy consumption dataset from the University of Missouri campus over a period of six years from 2017 to 2022. …”
Get full text

Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer by Lidan Liu, Bo Liu, Huimei Wu, Qiuying Gan, Qianyi Huang, Mujun Li

“…Research questionCan machine learning models accurately predict the risk of early miscarriage following single vitrified-warmed blastocyst transfer (SVBT)?…”
Get full text

MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects by Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Rossella Donghia, Gianluigi Giannelli, Angela Lombardi, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci

“…This selection is achieved through Recursive Feature Elimination (RFE) using a Random Forest (RF) to train Machine Learning (ML) algorithms and provide a mortality risk (Yes/No) output. …”
Get full text

High-resolution soil temperature and soil moisture patterns in space, depth and time: An interpretable machine learning modelling approach by Maiken Baumberger, Bettina Haas, Sindhu Sivakumar, Marvin Ludwig, Nele Meyer, Hanna Meyer

“…By applying interpretable machine learning techniques, we investigated the detailed influence of all drivers and discussed overlapping effects that led to the prediction patterns.…”
Get full text

A novel committee machine and reviews of neural network and statistical models for currency exchange rate prediction: An experimental analysis by Trilok Nath Pandey, Alok Kumar Jagadev, Satchidananda Dehuri, Sung-Bae Cho

“…Moreover, our newly proposed committee machine is drawing a clear line over all the models while predicting exchange rate of GBP/USD.…”
Get full text

Development of a technique to identify μm-sized organic matter in asteroidal material: An approach using machine learning by Rahul Kumar, Katsura Kobayashi, Christian Potiszil, Tak Kunihiro

“…We found that identifying OM is more accurate by classification with machine learning than by clustering. On the approach of classification with machine learning, five algorithms were tested. …”
Get full text

Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm by Bihui Zhang, Zhuqi Li, Bingjie Li, Jingbo Zhan, Songtao Deng, Yi Fang

“…The TAR-2 dataset is inputted into a Support Vector Machine (SVM) optimized using a hybrid algorithm and used to infer the risk of urban traffic crashes. …”
Get full text

Color-Based Lifetime Estimation of LEDs Using Spectral Power Distribution Prediction Through Analytical and Machine Learning Models by J. Lokesh, Savitha G. Kini, M. G. Mahesha, Anjan N. Padmasali

“…The study also compares chromaticity coordinate projections with the TM-35-19 projection method and explores lifetime predictions using CCT binning, as outlined in ANSI C78.377, to assess long-term stability. Both analytical and machine learning (ML) models are employed for SPD prediction, with the support vector machine demonstrating superior performance. …”
Get full text

Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms by Hyungkyung Shin, Kwang Jin Ko, Wei-Jin Park, Deok Hyun Han, Ikjun Yeom, Kyu-Sung Lee

“…Purpose This study aimed to develop and evaluate machine learning models, specifically CatBoost and extreme gradient boosting (XGBoost), for diagnosing lower urinary tract symptoms (LUTS) in male patients. …”
Get full text

A Pilot Study on Thermal Comfort in Young Adults: Context-Aware Classification Using Machine Learning and Multimodal Sensors by Bibars Amangeldy, Timur Imankulov, Nurdaulet Tasmurzayev, Serik Aibagarov, Nurtugan Azatbekuly, Gulmira Dikhanbayeva, Aksultan Mukhanbet

Get full text

Establishment and Validation of a Machine‐Learning Prediction Nomogram Based on Lymphocyte Subtyping for Intra‐Abdominal Candidiasis in Septic Patients by Jiahui Zhang, Wei Cheng, Dongkai Li, Guoyu Zhao, Xianli Lei, Na Cui

“…We assessed the clinical characteristics and lymphocyte subsets at the onset of IAI. A machine‐learning random forest model was used to select important variables, and multivariate logistic regression was used to analyze the factors influencing IAC. …”
Get full text

Experimental assessment and data-driven hybrid machine learning quantification with parametric optimization of compressive strength of ceramic waste concrete by Mihir Mishra, Md. Habibur Rahman Sobuz, Md. Kawsarul Islam Kabbo, M Jameel, Turki S. Alahmari, Naim Ahmad, Md. Munir Hayet Khan

“…This study aims to develop hybrid machine learning (ML) models for ceramic waste-based concrete (CWC) to predict its compressive strength (CS) and analyze the mix parameters that significantly impact its strength. …”
Get full text

Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques by Hazem Samih Mohamed, Tang Qiong, Haytham F. Isleem, Rupesh Kumar Tipu, Ramy I. Shahin, Saad A. Yehia, Pradeep Jangir, Arpita, Mohammad Khishe

“…Then, a parametric and analytical study was performed to explore the influence of geometric and material parameters on the load-carrying capacity of elliptical CFST short columns. …”
Get full text

Prediction of biological evolution following blood product transfusion during liver transplantation: the contribution of machine learning to decision-making by Jacques Creteur, Thibault Martinez, Valerio Lucidi, Turgay Tuna, Florian Blanchard, Olivier Duranteau, Benjamin Popoff, Axel Abels, Eric Savier, Patrizia Loi, Desislava Germanova, Anne Demulder

“…Despite the use of a large healthcare database, a rigorous statistical methodology and an academic machine learning methodology, most models showed limited generalisability (R² < 0.5).Discussion Key limitations included the small dataset size relative to machine learning requirements, lack of advanced haemostatic parameters (eg, ROtational ThromboElastoMetry (ROTEM) or Thromboelastography (TEG)) and the variability introduced by evolving surgical practices over the 20-year study period. …”
Get full text

Machine Learning-Enhanced Discrimination of Gamma-Ray and Hadron Events Using Temporal Features: An ASTRI Mini-Array Analysis by Valentina La Parola, Giancarlo Cusumano, Saverio Lombardi, Antonio Alessio Compagnino, Antonino La Barbera, Antonio Tutone, Antonio Pagliaro

“…The model incorporates feature importance analysis to select the most discriminating temporal parameters from a comprehensive set of time-based features. …”
Get full text