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Cowpea genetic diversity, population structure and genome-wide association studies in Malawi: insights for breeding programs
Published 2025-01-01“…The study assessed the effects of genotype, location, and their interactions on morphological traits. The Fixed and Random Model Circulating Probability Unification (FarmCPU) algorithm was used to identify significant MTAs.ResultsThe morphological traits showed significant genotype, location, and interaction effects. …”
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Hierarchical Classification of Variable Stars Using Deep Convolutional Neural Networks
Published 2022-04-01Get full text
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Neoantigen prioritization based on antigen processing and presentation
Published 2024-11-01Get full text
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225
UAV-Multispectral Based Maize Lodging Stress Assessment with Machine and Deep Learning Methods
Published 2024-12-01“…The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. …”
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Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8
Published 2025-03-01Get full text
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227
ProCAPTCHA: A profile-based CAPTCHA for personal password authentication.
Published 2024-01-01“…ProCAPTCHA leverages keystroke dynamics and personal information to create unique CAPTCHAs that are difficult for intruders to solve. ProCAPTCHA's algorithm generates CAPTCHA based on the user's profile data, ensuring randomness and uniqueness for each login. …”
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TS-PAID: A Two-Stage PUF-Based Lightweight Authentication Protocol for Internet of Drones
Published 2025-01-01Get full text
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data
Published 2025-06-01“…Despite advancements in satellite-based radiometry such as NASA’s Soil Moisture Active Passive (SMAP), significant challenges persist in coastal SSS retrieval due to radio frequency interference (RFI), land-sea contamination, and complex interactions of nearshore dynamic processes.MethodThis study proposes a deep neural network (DNN) framework that integrates SMAP L-band brightness temperature data with ancillary oceanographic and geographic parameters such as sea surface temperature, the shortest distance to the coastline (dis) to enhance SSS estimation accuracy in the Yellow and East China Seas. The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach
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Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques
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Robust fault detection and classification in power transmission lines via ensemble machine learning models
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Fog Service Placement Optimization: A Survey of State-of-the-Art Strategies and Techniques
Published 2025-03-01“…To solve this problem, various authors proposed different algorithms like the randomized algorithm, heuristic algorithm, meta heuristic algorithm, machine learning algorithm, and graph-based algorithm for finding the optimal placement. …”
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Wearable sensor-based fall detection for elderly care using ensemble machine learning techniques
Published 2025-06-01Get full text
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Machine Learning Models in the Detection of MB2 Canal Orifice in CBCT Images
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Using the β/α Ratio to Enhance Odor-Induced EEG Emotion Recognition
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AI-powered IoT and UAV systems for real-time detection and prevention of illegal logging
Published 2024-12-01Get full text
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Residential Electricity Load Model Construction in District Scale
Published 2020-08-01Get full text
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