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7101
Design and Demonstration of a Hybrid FES-BCI-Based Robotic Neurorehabilitation System for Lower Limbs
Published 2025-07-01“…To enable these individuals to exercise, we developed a novel hybrid rehabilitation system combining a brain–computer interface (BCI), functional electrical stimulation (FES), and a robotic device. …”
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7102
A Labor Division Artificial Gorilla Troops Algorithm for Engineering Optimization
Published 2025-02-01Get full text
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7103
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7104
Aerodynamic Prediction and Design Optimization Using Multi-Fidelity Deep Neural Network
Published 2025-03-01Get full text
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7105
Measuring biases in AI-generated co-authorship networks
Published 2025-05-01“…In this study, we investigate this issue through the concrete task of reconstructing real-world co-authorship networks of computer science (CS) researchers using two widely used LLMs—GPT-3.5 Turbo and Mixtral 8x7B. …”
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7106
Application of AI in Date Fruit Detection—Performance Analysis of YOLO and Faster R-CNN Models
Published 2025-06-01“…Presented study evaluates and compares two deep learning models, i.e., YOLOv8n and Faster R-CNN, for automated detection of date fruits in natural orchard environments. …”
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7107
Activity prediction of anti-cancer drug candidate ERα inhibitor
Published 2022-09-01Get full text
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7108
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7109
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7110
Ripen banana dataset: A comprehensive resource for carbide detection and ripening stage analysis to enhance food quality and agricultural efficiencyMendeley Data
Published 2025-06-01“…This was made it by evaluating these images for their sharpness and brightness levels to ensure they meet the quality standards that are required. …”
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7111
A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients
Published 2025-06-01“…Computed tomography (CT) is the imaging method of choice for evaluating the canine nasal cavity, being invaluable in determining disease extent, guiding sampling, and planning treatment. …”
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7112
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7113
A Comparative Study of One‐Step and Multi‐Step Numerical Methods for Solving Ordinary Differential Equations in Water Tank Drainage Systems
Published 2025-03-01“…MATLAB scripts and Python (Google Colab) were used to evaluate each method's performance by calculating local and global errors, with detailed analyses of error versus step size, error versus computational effort, and computational effort versus step size. …”
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7114
Can Radiomics of Dynamic PET Imaging with 11C-methionine Predict EGFR Amplification Status in Glioblastoma?
Published 2024-11-01Get full text
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7115
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7116
QKDTI A quantum kernel based machine learning model for drug target interaction prediction
Published 2025-07-01“…Abstract Drug-target interaction (DTI) prediction is a critical task in computational drug discovery, enabling drug repurposing, precise medicine, and large-scale virtual screening. …”
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7117
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7118
Radiological analysis of maxillary sinus anatomy in the 1st molar region in different ages and genders- A Cone Beam CT study
Published 2025-01-01“…Aims and Objectives: Cone-beam computed tomography (CBCT) aids in estimating the key surgical anatomic structures of the MS in the first molar region among males and females in different age groups that can be helpful in preventing perioperative and after complications. …”
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7119
Effect of Head and Tongue Posture on the Pharyngeal Airway Dimensions and Morphology in Three-Dimensional Imaging: a Systematic Review
Published 2016-03-01“…Objectives: Natural head position is recommended to be optimal at cone-beam computed tomography acquisition. For standardization purposes in control of treatment outcome, it is clinically relevant to discuss, if a change of posture from natural head position may have an effect on the pharyngeal airway dimensions and morphology, during computed tomography, cone-beam computed tomography or magnetic resonance imaging acquisition. …”
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7120
Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRS
Published 2024-12-01“…Four publicly available datasets (A, B, C, and D) were utilized for evaluating the proposed model. Datasets A and B were MA type, while datasets C and D were MI type. …”
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