the rapid growth of urban populations and rising consumerism have led to an unprecedented increase in the volume and diversity of municipal waste, now exceeding 2 billion tons annually. A significant portion, at least...
详细信息
Withthe continuous development of modern astronomical observation methods, the sky survey data obtained through observation has increased exponentially, and machinelearning has gradually replaced traditional scienti...
详细信息
Item to knowledge mapping is a very useful technique in educational dataminingthat assesses to improve the curriculum system and the order learners should perceive new skills. the wide range of item to knowledge map...
详细信息
data Analytic interpretation of plant species is not widely explored. the image variety of plant species can be investigated withthe help of machinelearning and deep learning techniques. machinelearning and deep le...
详细信息
ISBN:
(纸本)9783031235986;9783031235993
data Analytic interpretation of plant species is not widely explored. the image variety of plant species can be investigated withthe help of machinelearning and deep learning techniques. machinelearning and deep learning make the plant species investigation automated and almost accurate. In this paper, we have proposed a novel native and invasive plant species classification technique. We use the pre-trained deep convolution neural networks (transfer learning) to extract features from plant species and classify them into Native and Invasive. Here, state-of-the-art deep convolution neural network models like InceptionV 3, MobileNetV 2, ResNetV 2, VGG16, and Xception have been explored to extract the feature vectors from images. Furthermore, the features of the images have been used for image classification using the Random Forest algorithm. the performance of the proposed system is verified on the proposed dataset (Native-Invasive) with aerial view images of native and invasive species. Results were obtained using cross-validation techniques. Our experiments demonstrate the potential of our proposed method for achieving excellent performance with a 95% accuracy using Xception withdata augmentation, hyper-parameter optimization, and the Random Forest classification technique. the aerial images were captured and labeled by us which is very novel to our experimental setup. After using deep learning models we got promising results. the dataset used to train the plant species classifier will be made available on request.
Withthe continuous advancement of artificial intelligence and machinelearning technologies, this paper proposes a novel method to enhance safety management on power engineering construction sites. By leveraging obje...
详细信息
How to obtain effective and valuable topic information from massive texts has always been a hot topic in text mining. this paper attempts to use the improved algorithm to model massive high-dimensional text data. On t...
详细信息
Emotion recognition is a technique for identifying and classifying human emotions by combining biosensing, face recognition, speech and voice recognition, machinelearning, and patternrecognition. the electroencephal...
详细信息
Strokes of the heart are responsible for an increasing number of deaths that occur on a daily basis, and it is anticipated that this pattern will continue for the foreseeable future. It is unfortunate that determining...
详细信息
Nowadays a large number of traffic passes through the network. However, the network system is unreliable and has safety issues. Different attacking activities may arise on the network traffic. Again the performance, p...
详细信息
暂无评论