Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…Furthermore, these workflows lay the foundation for implementing long-term learning algorithms, a pivotal increment for monitoring initiatives. …”
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Evaluating Recycling Initiatives for Landfill Diversion in Developing Economies Using Integrated Machine Learning Techniques by Muyiwa Lawrence Adedara, Ridwan Taiwo, Olusola Olaitan Ayeleru, Hans-Rudolf Bork

“…It evaluates the recycling diversion rate (RDR) of household recyclables (HSRs) across local government areas using field surveys and population data. Machine learning algorithms (logistic regression, random forest, XGBoost, and CatBoost) refined with Bayesian optimisation were employed to predict household recycling motivation. …”
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Contrastive cross-domain sequential recommendation with attention-aware mechanism by Wei Zhao, Bo Li, Xian Mo

“…Abstract Cross-domain sequential recommendation (CDSR) aims to predict future sequential interactions in a target domain by analyzing historical sequence data from different domains. …”
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Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning by Nehir Uyar, Azize Uyar

“…The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing Landsat satellite imagery and Google Earth Engine (GEE) for spatial and temporal analysis. Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. …”
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Mantle Flow and Anisotropy in Subduction Zones: Modeling and Clustering of Olivine Textures by Yijun Wang, Ágnes Király, Clinton P. Conrad, Valerie Maupin

“…We compare olivine texture evolution predicted using different methods in both retreating and stationary‐trench settings. …”
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Machine learning and deep learning in medicine and neuroimaging by Iván Sánchez Fernández, Jurriaan M. Peters

“…Machine learning is the subfield of artificial intelligence in which computers have the ability to learn and iteratively improve their performance without being explicitly programmed. Deep learning algorithms learn by processing the data with increasing levels of abstraction in each layer. …”
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Stability of the Subaxial Spine after Penetrating Trauma: Do Classification Systems Apply? by Jackson Rucker Staggers, Thomas Elliot Niemeier, William E. Neway, Steven Michael Theiss

“…Blunt spinal trauma classification systems are well established and provide reliable treatment algorithms. To date, stability of the spine after civilian gunshot wounds (CGSWS) is poorly understood. …”
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Identification of Fake Comments in E-Commerce Based on Triplet Convolutional Twin Network and CatBoost Model by Juanjuan Peng

“…At the same time, the accuracy of the four features was 0.8931, 0.9012, 0.8846, and 0.8961, respectively, which verified the excellent predictive recognition performance. Compared with the other two comparison algorithms, the detection time required by the proposed model was the shortest at 115us and consumed the least amount of resources. …”
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Chemoreactomic study of fonturacetam effects: molecular mechanisms of influence on adipose tissue metabolism by O. A. Gromova, I. Yu. Torshin

“…Analysis of pharmacological capabilities of molecules within the framework of chemoreactomic methodology is carried out by comparing the chemical structure of racetam molecules with the structures of molecules for which pharmacological properties were studied using training artificial intelligence algorithms based on big data information presented in PubChem, HMDB, STRING, PharmGKB databases. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…Furthermore, these workflows lay the foundation for implementing long-term learning algorithms, a pivotal increment for monitoring initiatives. …”
Get full text

RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

“…Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. …”
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Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. by Maggy T Sikulu-Lord, Michael D Edstein, Brendon Goh, Anton R Lord, Jye A Travis, Floyd E Dowell, Geoffrey W Birrell, Marina Chavchich

“…The spectra were analysed using machine learning to develop predictive models for infection.<h4>Findings</h4>Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). …”
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A combined immune and exosome-related risk signature as prognostic biomakers in acute myeloid leukemia by Zenghui Fang, Jiali Fu, Xin Chen

“…The variations in immune cell infiltrations among risk groups were assessed through four algorithms. Expression of hub gene in specific cell was analyzed by single-cell RNA seq.Results A total of 85 immune-ERGs associated with prognosis were identified, enabling the construction of a risk model for AML. …”
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Unlocking the structural, vibrational, electronic, optical and thermoelectric properties of K2X (X=S, Se, Te) monolayers via DFT and ML by G. Sneha, R.D. Eithiraj

“…AFLOW-PLMF model was deployed for the electronic band gap value predictions. Machine learning algorithms namely Decision Tree Regressor and Gradient Boosting Regressor out-performs the other ML models with least root mean square error (RMSE) and accurate R2 values. …”
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Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention. by Dovilė Kurmanavičiūtė, Hanna Kataja, Lauri Parkkonen

“…Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. …”
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The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection by Pak Hung Chan, Boda Li, Gabriele Baris, Qasim Sadiq, Valentina Donzella

“…Assisted and automated driving functions will rely on machine learning algorithms, given their ability to cope with real-world variations, e.g. vehicles of different shapes, positions, colors, and so forth. …”
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Attention-Based Color Difference Perception for Photographic Images by Hua Qiang, Xuande Zhang, Jinliang Hou

“…Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. …”
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Artificial intelligence in environmental monitoring: in-depth analysis by Emran Alotaibi, Nadia Nassif

“…In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. …”
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Damage Scene Change Detection Based on Infrared Polarization Imaging and Fast-PCANet by Min Yang, Jie Yang, Hongxia Mao, Chong Zheng

“…Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. …”
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Countermeasuring Anti-Ship Missiles for Surface Naval Platforms: A Machine Learning Approach With Explainable Artificial Intelligence by Murat Ertop, Ali Oter, Ali Kara

“…This model has been used to predict target parameters. The simulator includes parameters for the ship, missile, and chaff/flare. …”
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