Integrating Renewable Energy with Internet of Things (IoT): Pathways to a Smart Green Planet by Shojib Mia, Firoz Ahmed, Ibrahim Khan, Md. Emamul Kabir, Mehedi H. Roni, Khadijatul Cobra, Anjuman Ara Khatun, Shahriar Mahmud

“…By leveraging data analytics and machine learning, these systems can predict energy consumption, optimize resources, and maintain renewable energy assets proactively. …”
Get full text

Cacao floral traits are shaped by the interaction of flower position with genotype by Seunghyun Lim, Insuck Baek, Seok Min Hong, Yoonjung Lee, Silvas Kirubakaran, Moon S. Kim, Lyndel W. Meinhardt, Sunchung Park, Ezekiel Ahn

“…These findings emphasize the phenotypic diversity of cacao flowers and demonstrate the potential of machine learning in genotype identification, offering valuable insights for breeding and cultivation strategies to enhance cacao productivity.…”
Get full text

Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los Angeles by Yuwei Kong, Karina Jimenez, Christine M. Lee, Sophia Winter, Jasmine Summers-Evans, Albert Cao, Massimiliano Menczer, Rachel Han, Cade Mills, Savannah McCarthy, Kierstin Blatzheim, Jennifer A. Jay

“…Despite limitations from cloud cover and spatial resolution, the findings suggest that integrating satellite data with machine learning can enhance large-scale, efficient turbidity monitoring in coastal waters.…”
Get full text

Analysis of Protein-Ligand Interactions of SARS-CoV-2 Against Selective Drug Using Deep Neural Networks by Natarajan Yuvaraj, Kannan Srihari, Selvaraj Chandragandhi, Rajan Arshath Raja, Gaurav Dhiman, Amandeep Kaur

“…In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. …”
Get full text

Detection of early relapse in multiple myeloma patients by Tereza Růžičková, Monika Vlachová, Lukáš Pečinka, Monika Brychtová, Marek Večeřa, Lenka Radová, Simona Ševčíková, Marie Jarošová, Josef Havel, Luděk Pour, Sabina Ševčíková

“…All results were analyzed by machine learning. Conclusion Mass spectrometry coupled with machine learning shows potential as a reliable, rapid, and cost-effective preliminary screening technique to supplement current diagnostics.…”
Get full text

Photovoltaic Farm Production Forecasting: Modified Metaheuristic Optimized Long Short-Term Memory-Based Networks Approach by Aleksandar Stojkovic, Bosko Nikolic, Miodrag Zivkovic, Nebojsa Bacanin

“…Finally, the applicability of the top-performance models was validated with tiny machine learning (TinyML).…”
Get full text

Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower by Guy Atsmon, Anna Brook, Tom Avikasis Cohen, Fadi Kizel, Hanan Eizenberg, Ran Nisim Lati

“…These VIs, reflecting changes in canopy reflectance over time, were then analyzed using various machine learning models, including a pattern recognition neural network (PRNN). …”
Get full text

2D physically unclonable functions of the arbiter type by V. N. Yarmolik, A. A. Ivaniuk

“…It seems promising to further develop the ideas of constructing two-dimensional physically unclonable functions of the arbiter type, as well as experimental study of their characteristics, as well as resistance to various types of attacks, including using machine learning.…”
Get full text

Association between triglyceride-glucose index and carotid atherosclerosis in Chinese steelworkers: a cross-sectional study by Haoyue Cao, Qinglin Li, Juxiang Yuan

“…In LASSO regression, TyG index and other covariables are screened as important feature variables to be incorporated into the development of machine learning models. The TyG index is associated with an increased risk of CAS among steelworkers, underscoring its potential as a reliable and practical predictive tool for assessing CAS risk in this population. …”
Get full text

A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols by Nicolas Francos, Daniela Heller-Pearlshtien, José A. M. Demattê, Bas Van Wesemael, Robert Milewski, Sabine Chabrillat, Nikolaos Tziolas, Adrian Sanz Diaz, María Julia Yagüe Ballester, Asa Gholizadeh, Eyal Ben-Dor

“…Soil spectral libraries (SSLs) are important big-data archives (spectra associated with soil properties) that are analyzed via machine-learning algorithms to estimate soil attributes. …”
Get full text

Psychological and Behavioral Insights From Social Media Users: Natural Language Processing–Based Quantitative Study on Mental Well-Being by Xingwei Yang, Guang Li

“…We empirically evaluated the effectiveness of our framework by applying machine learning models to detect depression, reporting accuracy, recall, precision, and F1-score using social media status updates from 1047 users along with their associated depression diagnosis questionnaire scores. …”
Get full text

Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction by Zhibo Wang, Xin Chen, Xi Tan, Lingfeng Yang, Kartik Kannapur, Justin L. Vincent, Garin N. Kessler, Boshu Ru, Mei Yang

“…For comparison, we also tested multiple traditional machine learning models including logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost). …”
Get full text

Mood Detection from Physical and Neurophysical Data Using Deep Learning Models by Zeynep Hilal Kilimci, Aykut Güven, Mitat Uysal, Selim Akyokus

“…Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. …”
Get full text

Novel insights into immunopathogenesis and crucial biomarkers between primary open‐angle glaucoma and systemic lupus erythematosus by Yixian Liu, Mengling You, Zhou Zeng, Jing Wang, Rong Rong, Xiaobo Xia

“…The 10 key genes identified through DEA and WGCNA were predominantly involved in immune, inflammatory, and autophagy pathways. Additionally, machine learning identified five biomarkers, and we established associated transcription factors and miRNA regulatory networks. …”
Get full text

Improving drug repositioning with negative data labeling using large language models by Milan Picard, Mickael Leclercq, Antoine Bodein, Marie Pier Scott-Boyer, Olivier Perin, Arnaud Droit

“…We then applied a machine learning ensemble to this new dataset to assess the repurposing potential of the remaining 11,043 drugs in the DrugBank database. …”
Get full text

Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder by Ruimin Ma, Ruitao Xie, Yanlin Wang, Jintao Meng, Yanjie Wei, Yunpeng Cai, Wenhui Xi, Yi Pan

“…With the development of the machine learning and neuroimaging technology, extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging (s-MRI). …”
Get full text

Part of Speech Tagging: Shallow or Deep Learning? by Robert Östling

“… Deep neural networks have advanced the state of the art in numerous fields, but they generally suffer from low computational efficiency and the level of improvement compared to more efficient machine learning models is not always significant. We perform a thorough PoS tagging evaluation on the Universal Dependencies treebanks, pitting a state-of-the-art neural network approach against UDPipe and our sparse structured perceptron-based tagger, efselab. …”
Get full text

A Hybrid System for Subjectivity Analysis by Samir Rustamov

“…We suggested different structured hybrid systems for the sentence-level subjectivity analysis based on three supervised machine learning algorithms, namely, Hidden Markov Model, Fuzzy Control System, and Adaptive Neuro-Fuzzy Inference System. …”
Get full text

Rough sets theory and its extensions for attribute reduction: a review by Sadegh Eskandari

“…Several efforts have been made to make close the rough sets theory and machine learning tasks. In this regard several extensions and modifications of the original theory are proposed. …”
Get full text

The Control Data Method: A New Method of Modeling in Population Dynamics by Lin-Fei Nie, Zhi-Dong Teng

“…Using a the approximation property and the machine learning approach of artificial neural networks, a tuning algorithm of unknown parameters is obtained and the factual data of predator-prey can be asymptotically stabilized using a neural network controller. …”
Get full text