Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering

With the progress of network technology, there are more and more digital images of the internet. But most images are not semantically marked, which makes it difficult to retrieve and use. In this paper, a new algorithm is proposed to automatically annotate images based on particle swarm optimization (PSO) and support vector clustering (SVC). The algorithm includes two stages: firstly, PSO algorithm is used to optimize SVC; secondly, the trained SVC algorithm is used to annotate the image automatically. In the experiment, three datasets are used to evaluate the algorithm, and the results show the effectiveness of the algorithm

How can sustainable public transport be improved? A traffic sign recognition approach using convolutional neural network

Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic sign recognition is the key technology of driverless public transport. It is particularly important to adopt innovative algorithms to optimize the accuracy of traffic sign recognition and build sustainable public transport. Therefore, this paper proposes a convolutional neural network (CNN) based on k-means to optimize the accuracy of traffic sign recognition, and it proposes a sparse maximum CNN to identify difficult traffic signs through hierarchical classification. In the rough classification stage, k-means CNN is used to extract features, and improved support vector machine (SVM) is used for classification. Then, in the fine classification stage, sparse maximum CNN is used for classification. The research results show that the algorithm improves the accuracy of traffic sign recognition more comprehensively and effectively, and it can be effectively applied in unmanned driving technology, which will also bring new breakthroughs for the sustainable development of public transport

Visual attention mechanism and support vector machine based automatic image annotation.

Automatic image annotation not only has the efficiency of text-based image retrieval but also achieves the accuracy of content-based image retrieval. Users of annotated images can locate images they want to search by providing keywords. Currently most automatic image annotation algorithms do not consider the relative importance of each region in the image, and some algorithms extract the image features as a whole. This makes it difficult for annotation words to reflect salient versus non-salient areas of the image. Users searching for images are usually only interested in the salient areas. We propose an algorithm that integrates a visual attention mechanism with image annotation. A preprocessing step divides the image into two parts, the salient regions and everything else, and the annotation step places a greater weight on the salient region. When the image is annotated, words relating to the salient region are given first. The support vector machine uses particle swarm optimization to annotate the images automatically. Experimental results show the effectiveness of the proposed algorithm

A Pre-Computing Solution for Online Advertising Serving

Click-Through Rate (CTR) prediction plays a key role in online advertising systems and online advertising. Constrained by strict requirements on online inference efficiency, it is often difficult to deploy useful but computationally intensive modules such as long-term behaviors modeling. Most recent works attempt to mitigate the online calculation issue of long historical behaviors by adopting two-stage methods to balance online efficiency and effectiveness. However, the information gaps caused by two-stage modeling may result in a diminished performance gain. In this work, we propose a novel framework called PCM to address this challenge in the view of system deployment. By deploying a pre-computing sub-module parallel to the retrieval stage, our PCM effectively reduces overall inference time which enables complex modeling in the ranking stage. Comprehensive offline and online experiments are conducted on the long-term user behaviors module to validate the effectiveness of our solution for the complex models. Moreover, our framework has been deployed into a large-scale real-world E-commerce system serving the main interface of hundreds of millions of active users, by deploying long sequential user behavior model in PCM. We achieved a 3\% CTR gain, with almost no increase in the ranking latency, compared to the base framework demonstrated from the online A/B test. To our knowledge, we are the first to propose an end-to-end solution for online training and deployment on complex CTR models from the system framework side

Chemical profiling and investigation of molecular mechanisms underlying anti-hepatocellular carcinoma activity of extracts from Polygonum perfoliatum L.

Polygonum perfoliatum L. is an herbal medicine that has been extensively used in traditional Chinese medicine to treat various health conditions ranging from ancient internal to surgical and gynecological diseases. Numerous studies suggest that P. perfoliatum extract elicits significant anti-tumor, anti-inflammatory, anti-bacterial, and anti-viral effects. Nevertheless, the underlying mechanisms of its anti-liver cancer effects remain poorly understood. Our study suggests that P. perfoliatum stem extract (PPLA) has a favorable safety profile and exhibits a significant anti-liver cancer effect both in vitro and in vivo. We identified that PPLA activates the cGMP-PKG signaling pathway, and key regulatory genes including ADRA1B, PLCB2, PRKG2, CALML4, and GLO1 involved in this activation. Moreover, PPLA modulates the expression of genes responsible for the cell cycle. Additionally, we identified four constituents of PPLA, namely taxifolin, myricetin, eriodictyol, and pinocembrin, that plausibly act via the cGMP-PKG signaling pathway. Both in vitro and in vivo experiments confirmed that PPLA, along with its constituting compounds taxifolin, myricetin, and eriodictyol, exhibit potent anti-cancer activities and hold the promise of being developed into therapeutic agents