بدائل البحث:
boosting algorithm » routing algorithm (توسيع البحث), twisting algorithm (توسيع البحث), modeling algorithm (توسيع البحث)
element boosting » element bonding (توسيع البحث), element modeling (توسيع البحث)
coding algorithm » cosine algorithm (توسيع البحث), modeling algorithm (توسيع البحث), finding algorithm (توسيع البحث)
study algorithm » wsindy algorithm (توسيع البحث), td3 algorithm (توسيع البحث), seu algorithm (توسيع البحث)
element study » relevant study (توسيع البحث), present study (توسيع البحث), recent study (توسيع البحث)
level coding » level according (توسيع البحث), level modeling (توسيع البحث), level using (توسيع البحث)
boosting algorithm » routing algorithm (توسيع البحث), twisting algorithm (توسيع البحث), modeling algorithm (توسيع البحث)
element boosting » element bonding (توسيع البحث), element modeling (توسيع البحث)
coding algorithm » cosine algorithm (توسيع البحث), modeling algorithm (توسيع البحث), finding algorithm (توسيع البحث)
study algorithm » wsindy algorithm (توسيع البحث), td3 algorithm (توسيع البحث), seu algorithm (توسيع البحث)
element study » relevant study (توسيع البحث), present study (توسيع البحث), recent study (توسيع البحث)
level coding » level according (توسيع البحث), level modeling (توسيع البحث), level using (توسيع البحث)
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121
C2f structure.
منشور في 2025"…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. …"
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122
Experimental environment configuration.
منشور في 2025"…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. …"
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123
Ablation experiment results table.
منشور في 2025"…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. …"
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124
YOLOv8 identification results.
منشور في 2025"…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. …"
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125
LSKA module structure diagram.
منشور في 2025"…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. …"
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126
Comparison of mAP curves in ablation experiments.
منشور في 2025"…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. …"
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127
FarsterBlock structure.
منشور في 2025"…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. …"
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128
Sample augmentation and annotation illustration.
منشور في 2025"…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. …"
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129
YOLOv8 model architecture diagram.
منشور في 2025"…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. …"
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130
FLMP-YOLOv8 architecture diagram.
منشور في 2025"…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. …"
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131
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132
Mechanomics Code - JVT
منشور في 2025"…At the beginning of the code, there is a help section that explains how to use it.…"
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133
Visualization of algorithm detection results.
منشور في 2025"…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …"
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134
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135
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136
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137
Parameters of stimuli used in the study.
منشور في 2025"…<div><p>This study introduces a neurobiologically inspired computational model based on the predictive coding algorithm, providing insights into coherent motion detection processes. …"
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138
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139
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140
The run time for each algorithm in seconds.
منشور في 2025"…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . …"