Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home by Yan Hu, Bingce Wang, Yuyan Sun, Jing An, Zhiliang Wang

“…In this article, the authors propose a real-time online activity recognition approach based on the genetic algorithm–optimized support vector machine classifier. …”
Get full text

The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms by Hongxia Yan, Yixin Tan, Fan Qiao, Zhuotong Zeng, Yaqian Shi, Xueqin Zhang, Lu Li, Ting Zeng, Yi Zhan, Ruixuan You, Xinglan He, Rong Xiao, Xiangning Qiu

“…RFE identified the top predictive factors: dermoscopy vascular pattern, immediate fluorescence intensity (IFI) after HMME-PDT, the facial port-wine stain area and severity index score, and age. …”
Get full text

Comparative Analysis of Machine Learning Algorithms and Statistical Techniques for Data Analysis in Crop Growth Monitoring with NDVI by M. Arunachalam, S. Sekar, A. M. Erdmann, V. V. Sajith Variyar, R. Sivanpillai

“…Results generated from ML algorithms were compared to the output generated by the ISODATA algorithm. …”
Get full text

Identifying Diagnostic Biomarkers for Electroacupuncture Treatment of Rheumatoid Arthritis Using Bioinformatic Analysis and Machine Learning Algorithms by Sun Y, Dong G, Gao H, Yao Y, Yang H

“…A rat model of RA was established using Complete Freund’s Adjuvant (CFA), and quantitative real-time PCR was performed to confirm the differential expression of identified diagnostic biomarkers and assess the modulatory impact of EA on these genes.Results: Twenty-six genes were identified as differentially expressed following EA treatment. Three machine learning algorithms converged on ARHGAP17 and VEGFB as potential diagnostic biomarkers for RA, exhibiting robust diagnostic performance (AUC > 0.75) and consistent expression patterns across multiple RA cohorts (GSE17755, GSE205962 and GSE93272). …”
Get full text

Identification of potential biomarkers and mechanisms for keloid disorder based on comprehensive bioinformatics analysis and machine learning algorithms by Bowen Zheng, Jianxiong Qiao, Xiaoping Yu, Hanghang Zhou, Anqi Wang, Xuanfen Zhang

Get full text

Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms by Rashadul Islam Sumon, Md Ariful Islam Mozumdar, Salma Akter, Shah Muhammad Imtiyaj Uddin, Mohammad Hassan Ali Al-Onaizan, Reem Ibrahim Alkanhel, Mohammed Saleh Ali Muthanna

“…In contrast, the Random Forest, Support Vector Machine, and K-means algorithms yielded lower segmentation performance metrics. …”
Get full text

CONSTRUCTING METAMORPHOSIS OF IMAGES FOR THE OBJECTS ON THE BASIS OF SOLVING EULER-POINCARE EQUATIONS by S. V. Leichter

“…The considered problem of comparing two images can be used for constructing optimal metamorphosis of images, when there is no exact correspondence between the target image and the final image of the diffeomorphism. The designed algorithms can be used through a biometrical system, in images and subjects classification systems, machine vision systems, images and patterns recognition, tracking systems.…”
Get full text

Synergistic use of satellite, legacy, and in situ data to predict spatio-temporal patterns of the invasive Lantana camara in a savannah ecosystem by Lilly Theresa Schell, Emma Evers, Sarah Schönbrodt-Stitt, Konstantin Müller, Maximilian Merzdorf, Drew Arthur Bantlin, Insa Otte

“…In this study, we modeled the suitable habitat and potential distribution of the notorious invader Lantana camara in the Akagera National Park (1,122 km²), a savannah ecosystem in Rwanda. Spatiotemporal patterns of Lantana camara from 2015 to 2023 were predicted at a 30-m spatial resolution using a presence-only species distribution model, implementing a Random Forest classification algorithm and set up in the Google Earth Engine. …”
Get full text

A Novel Local Binary Patterns-Based Approach and Proposed CNN Model to Diagnose Breast Cancer by Analyzing Histopathology Images by Mehmet Gul

“…The histopathology images improved with the QS-LBP method were then analyzed with the most commonly used Random Forest and Optimized Forest algorithms among machine learning algorithms. The BreaKHis dataset contains images with 40X, 100X, 200X, and 400X magnification resolutions and contains approximately 7924 images. …”
Get full text

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System by Michael Olumuyiwa Adio, Ogunmakinde Jimoh Ogunwuyi, Mayowa Oyedepo Oyediran, Adebimpe Omolayo Esan, Olufikayo Adepoju Adedapo

“…With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. …”
Get full text

MAPE-ViT: multimodal scene understanding with novel wavelet-augmented Vision Transformer by Muhammad Waqas Ahmed, Touseef Sadiq, Hameedur Rahman, Sulaiman Abdullah Alateyah, Mohammed Alnusayri, Mohammed Alatiyyah, Dina Abdulaziz AlHammadi

Get full text

Integrating Machine Learning Workflow into Numerical Simulation for Optimizing Oil Recovery in Sand-Shale Sequences and Highly Heterogeneous Reservoir by Dung Bui, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Adewale Amosu, William Ampomah

“…This paper aims to evaluate the efficiency of various machine learning algorithms integrating with numerical simulations in optimizing oil production for a highly heterogeneous reservoir. …”
Get full text

Deep Learning-Based Dzongkha Handwritten Digit Classification by Yonten Jamtsho, Pema Yangden, Sonam Wangmo, Nima Dema

“…With the advancement in deep learning technology, many machine learning algorithms were developed to tackle the problem of pattern recognition. …”
Get full text

Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm by Fatemeh Hataminia, Anahita Azinfar

Get full text

Leveraging Radiomics and Genetic Algorithms to Improve Lung Infection Diagnosis in X-Ray Images Using Machine Learning by A. Beena Godbin, S. Graceline Jasmine

“…Radiomics, an emerging discipline in medical imaging, focuses on extracting detailed quantitative features from images to unveil subtle patterns imperceptible to the naked eye. This study specifically employs radiomics and machine learning techniques to discern cases of viral pneumonia and COVID-19. …”
Get full text

Combining the SHAP Method and Machine Learning Algorithm for Desert Type Extraction and Change Analysis on the Qinghai–Tibetan Plateau by Ruijie Lu, Shulin Liu, Hanchen Duan, Wenping Kang, Ying Zhi

“…In this work, five different machine learning algorithms are used to classify different desert types on the Qinghai–Tibetan Plateau (QTP), and their classification performance is evaluated on the basis of their classification results and classification accuracy. …”
Get full text

Subject based feature selection for hybrid brain computer interface using genetic algorithm and support vector machine by Nida Mateen, Mehreen Naeem, Muhammad Jawad Khan, Talha Yousaf, Ahsan Ali, Wael A. Altabey, Mohammad Noori, Sallam A Kouritem

“…The framework outperforms traditional filter- and wrapper-based feature selection methods on representative subjects, confirming its robustness and adaptability across individual neural patterns. These results highlight the importance of personalized feature selection in hybrid BCIs and demonstrate the viability of evolutionary algorithms for real-time, low-latency brain–machine applications.…”
Get full text

Effect of geographical parameters on PM10 pollution in European landscapes: a machine learning algorithm-based analysis by Seyedehmehrmanzar Sohrab, Nándor Csikós, Péter Szilassi

Get full text

Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…The purpose of this study was to evaluate the performance accuracy of the proposed machine learning algorithms by spatial cross-validation method in combination with the cellular automata model to simulate urban growth.Material and methods: In this study, to analyze urban land-use changes, Landsat satellite images related to the years 1997, 2006, and 2015 were classified using the support vector machine algorithm. …”
Get full text

Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors by Heba-Allah Ibrahim El-Azab, R. A. Swief, Noha H. El-Amary, H. K. Temraz

“…Where the whole database is split into four seasons based on demand patterns. This article’s integrated model is built on techniques for machine and deep learning methods: Adaptive Neural-based Fuzzy Inference System, Long Short-Term Memory, Gated Recurrent Units, and Artificial Neural Networks. …”
Get full text