Advanced Plant Phenotyping Technologies for Enhanced Detection and Mode of Action Analysis of Herbicide Damage Management by Zhongzhong Niu, Xuan Li, Tianzhang Zhao, Zhiyuan Chen, Jian Jin

“…The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. …”
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SFMattingNet: A Trimap-Free Deep Image Matting Approach for Smoke and Fire Scenes by Shihui Ma, Zhaoyang Xu, Hongping Yan

“…However, due to the lack of smoke and fire image matting datasets for model training, existing image matting methods exhibit significant errors in predicting the alpha values of smoke and fire targets, leading to unrealistic composite images. …”
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Using Ray Tracing to Improve Bridge Monitoring With High-Resolution SAR Satellite Imagery by Zahra Sadeghi, Tim Wright, Andrew Hooper, Sivasakthy Selvakumaran

“…The results confirm that we can predict overall scattering behavior of a bridge using SAR simulation techniques when we have access to a 3-D model of the structure. …”
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The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases by Artur Świerczek, Dominika Batko, Elżbieta Wyska

“…<b>Results</b>: Pharmacometrics has demonstrated significant potential in optimizing dosing regimens, improving drug safety, and predicting patient-specific responses in AIDs. PBPK and PK/PD models have been instrumental in personalizing treatments, while DisP and QSP models provide insights into disease evolution and pathophysiological mechanisms in AIDs. …”
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A hybrid super learner ensemble for phishing detection on mobile devices by Routhu Srinivasa Rao, Cheemaladinne Kondaiah, Alwyn Roshan Pais, Bumshik Lee

“…Phish-Jam utilizes a super learner ensemble that combines predictions from diverse Machine Learning (ML) algorithms to classify legitimate and phishing websites. …”
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Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA by Sanying Mao, Xiyao Zhao, Lei Wang, Yilong Man, Kaiyuan Li

“…In addition, 25 miRNAs and 55 lncRNAs were predicted to potentially target ZDHHC22, forming the basis for a lncRNA–miRNA–mRNA ceRNA network. …”
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Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteins by He Zhao, Huan Xu, Tao Wang, Guixia Liu

“…Abstract Background Predicting and studying essential proteins not only helps to understand the fundamental requirements for cell survival and growth regulation mechanisms but also deepens our understanding of disease mechanisms and drives drug development. …”
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Genetic subtyping by Whole Exome Sequencing across Diffuse Large B Cell Lymphoma and Plasmablastic Lymphoma. by Aitana Avendaño Pomares, Laura Rodríguez Merino, Sonia González, Jordi Morata, Raúl Tonda, Patricia Arribas, José Revert, Estrella Carrillo, Carlos Grande, Josep Maria Roncero, Jaime Pérez de Oteyza, Concepción Nicolás, Norma Gutierrez, Pau Abrisqueta, Antonio Gutiérrez, Ángel Ramírez-Páyer, Alejandro Martin Garcia-Sancho, Eva González-Barca, Santiago Montes-Moreno, GELTAMO group (Grupo Español de Linfomas y Trasplante Autólogo de Medula Osea), Spain

“…Recently, genomic-based algorithms have been proposed for the classification of this disease. …”
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Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact by A. Canberk Ulusoy, Tuğçe Toprak, M. Alper Selver, Pelin Güneri, Betül İlhan

“…This indicates that ANNs can effectively predict M3M-IAC contact relations and are particularly effective at identifying cases with no contact relation between M3M and IAC compared to other ML methods. …”
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Proteomic profiling of the outer membrane fraction of the obligate intracellular bacterial pathogen Ehrlichia ruminantium. by Amal Moumène, Isabel Marcelino, Miguel Ventosa, Olivier Gros, Thierry Lefrançois, Nathalie Vachiéry, Damien F Meyer, Ana V Coelho

“…These experimental data were compared to the predicted subcellular localization of the entire E. ruminantium proteome, using three different algorithms. …”
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DYNAMIC DISCIPLINE OF PARALLEL SERVICE IN CONCEPT FLIGHT AND FLOW – INFORMATION FOR A COLLABORATIVE ENVIRONMENT by L. E. Rudel'son, S. N. Smorodskiy, V. A. Chernyshyova

“…The essence of ICAO's proposals is in the transition from the "tracking" system which responds to the deviations from the balanced model to the control system which predicts the tendencies to changing the air situation in real time. …”
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Enhancing Air Quality Forecasting Using Machine Learning Techniques by Zeinab Shahbazi, Zahra Shahbazi, Slawomir Nowaczyk

“…In envisioning a future where urban commuting becomes synonymous with eco-friendliness and air quality improvement, a comprehensive platform harnesses the power of data analytics and real-time information to empower commuters and city planners alike. Its intelligent algorithms continuously analyse air quality information, allowing it to predict and address poor air quality. …”
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Determination of the area index of lettuce leaves with a monocular camera by Laimonas Kairiūkštis, Başak Yalçıner, Emre Özkul

“…The integration of Gaussian Mixture Model clustering with the dataset further enhanced the precision of the lettuce growth and harvest predictions. …”
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SIMULATION OF PERIPHERAL DEVICE SECURITY: IMPLEMENTATION AND PRACTICAL EVLUATION OF ADDRESS SPACES PROTECTION IN A TRUSTED MICROPROCESSOR EMULATOR by Mikael A. Kondakhchan, Nikita A. Grevtsev, Peter A. Chibisov

“…This approach aims to predict the operational characteristics of the final product through the analysis of its virtual model, reducing the risk of implementing vulnerable design solutions. …”
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Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates by N. Ragu Ramalingam, K. Johnson, M. Pagani, M. Pagani, M. L. V. Martina

“…The ML model serves as a surrogate, predicting the tsunami waveform on the coast and the maximum inundation depths onshore at the different test sites. …”
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Cryptographic hardness assumptions identification based on discrete wavelet transform by Ke Yuan, Yu Du, Yizheng Liu, Rongjin Feng, Bowen Xu, Gaojuan Fan, Chunfu Jia

“…Experimental results demonstrate that the proposed scheme accurately predicts cryptographic hardness assumptions, achieving an Accuracy, Recall, Precision, and F1 Score of 0.8500, 0.8533, 0.8500, and 0.8493, respectively, in the mixed plaintext scenario.…”
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Revolutionising English language education:empowering teachers with BERT-LSTM-driven pedagogical tools by Sheik Hameed Nagoor Gani, Vijayakumar Selvaraj, Sahidul Islam Md, Sugadev Thalapathy, Mohamed Jalaludeen Abdulkadhar, Kanmani Kalimuthu

“…The outcomes demonstrate that the proposed BERT-LSTM models are highly accurate in predicting errors and possess standard error metrics for scoring. …”
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ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection by Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado

“…ASAD trains an ML model to predict the best candidate from a large pool of models by considering the specific characteristics and requirements of the dataset. …”
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Identification of markers correlating with mitochondrial function in myocardial infarction by bioinformatics. by Wenlong Kuang, Jianwu Huang, Yulu Yang, Yuhua Liao, Zihua Zhou, Qian Liu, Hailang Wu

“…The Cytoscape and miRWalk databases were then used to predict the transcription factors and target miRNAs of the central MitoDEG, respectively. …”
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Automated pipeline for leaf spot severity scoring in peanuts using segmentation neural networks by Joshua Larsen, Jeffrey Dunne, Robert Austin, Cassondra Newman, Michael Kudenov

“…The pipeline was evaluated using field data from plots with varying leaf spot severity, creating a dataset of thousands of images that spanned conventional visual severity scores ranging from 1–9. These predictions were based on the amount of infected leaf area and the presence of defoliated leaves in the surrounding area. …”
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