In order to solve the problem of coordination between speed and alignment design of expressway in China, and improve the safety of expressway operation. On the basis of determining the operation safety evaluation inde...
详细信息
Identifying the species of animal leather becomes a prominent factor in assessing the quality of the leather goods and also in defending the endangered species. Unlike convolutional neural network (CNN)-based deep lea...
详细信息
the expectation of property prices stands as a vital pursuit within the real estate geography, impacting opinions of buyers, investors, and assiduity professionals likewise. this review paper navigates the multifacete...
the expectation of property prices stands as a vital pursuit within the real estate geography, impacting opinions of buyers, investors, and assiduity professionals likewise. this review paper navigates the multifaceted realm of property price prediction by illuminating its significance and checking the different methodologies and statistical ways employed. the conflation of this converse extends a palpable frame for casing inventors and experimenters, enabling them to navigate the maze of attributes bolstering house prices and offering perceptive recommendations for apt machine learning models. As a capstone, this review underscores the indispensability of nuanced position understanding and advanced prediction methodologies, therefore fortifying the precision of property price predictions within the ever-evolving real estate terrain.
Although multilayer perceptrons (MLPs) present several advantages against other pattern recognition methods, MLP-based speaker verification systems suffer from slow enrollment speed caused by many background speakers ...
详细信息
thermal comfort is an essential aspect for the control and verification of many smart home services. In this research, we design and implement simulation which models thermal environment of a smart house testbed. Our ...
详细信息
this paper presents the age-group classification based on facial images. We perform age-group classification by dividing ages into five age groups according to the incremental regulation of age. Features are extracted...
详细信息
this paper presents the age-group classification based on facial images. We perform age-group classification by dividing ages into five age groups according to the incremental regulation of age. Features are extracted from face images through Active Appearance Model (AAM), which describe the shape and gray value variation of face images. Principle Component Analysis (PCA) is adopted to reduce the dimensions and Support Vector Machine (SVM) classifier with Gaussian Radian Basis Function (RBF) kernel is trained. Experimental results demonstrate that AAM can improve the performance of age estimation.
Training data (TD) are vital for artificial intelligence deep learning or machine learning (AI DL/ML) in remote sensing image interpretation. Withthe proliferation of a large number of Earth Observation (EO) datasets...
Training data (TD) are vital for artificial intelligence deep learning or machine learning (AI DL/ML) in remote sensing image interpretation. Withthe proliferation of a large number of Earth Observation (EO) datasets, the availability of a wide range of datasets has introduced challenges in ensuring the FAIR (Findable, Accessible, Interoperable, Reusable) use of training datasets. this paper proposes an approach that leverages the OGC Training data Markup Language for AI (TrainingDML-AI) standard to make training data ready to be consumable by existing DL frameworks. It presents a training data pipeline approach to integrate TD in DL. the approach enables the retrieval and transformation of training data for compatibility with existing deep learning frameworks.
this paper presents a deep learning approach using Mask R-CNN for detecting and classifying the ripeness of tomatoes in greenhouse imagery withthe goal of enabling automated robotic harvesting. the Mask R-CNN model i...
this paper presents a deep learning approach using Mask R-CNN for detecting and classifying the ripeness of tomatoes in greenhouse imagery withthe goal of enabling automated robotic harvesting. the Mask R-CNN model is trained on a small dataset of 62 annotated tomato images captured under challenging real-world conditions. Despite the limited data, the network achieves promising accuracy in classifying individual tomatoes into unripe, semi-ripe, and ripe categories. To move beyond standalone ripeness classification to robotic picking, density-based clustering with DBSCAN is applied to group tomatoes into harvestable formations based on their spatial and ripeness characteristics. the dual method of Mask R-CNN detection and DBSCAN clustering provides a complete pipeline from ripeness evaluation to tomato localization within dense clusters. Results demonstrate the feasibility of the approach for agricultural applications like selective robotic harvesting based on computer vision and deep learning.
the aim of this paper is to introduce a new methodology for micro-pattern analysis in digital images. the gray-level pixels' structure in an image neighborhood describes a spatial specific context. Edge, line, spo...
详细信息
this paper discusses a collaboration between the Army Research Laboratory (ARL) and the United States Military Academy at West Point in teaching the fundamentals of human factors engineeringthrough assessment and exp...
详细信息
暂无评论