Vol. 43 · No. 4

Objective: This study aims to screen osteoarthritis (OA) endoplasmic reticulum (ER) stress signature genes using a machine learning approach to provide new insights and methods for OA treatment. Methods: We obtained GSE55235 and GSE98918 datasets from the gene expression omnibus (GEO) database and identified ER stress-related genes from the GeneCard database. We used R software to perform data batch correction, extract OA endoplasmic reticulum stress-related genes, and conduct differential analysis. We performed functional Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis, and gene set enrichment analysis (GSEA) on differentially expressed genes (DEGs). Additionally, we used machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, SVM-RFE, and weighted gene co-expression network analysis (WGCNA), to screen OA endoplasmic reticulum stress signature genes. Human chondrocytes were selected for OA model establishment, cells without any treatment were served as the control. Results: We obtained 236 DEGs related to OA ER stress. GO and KEGG enrichment analysis showed that these genes were mainly involved in the positive regulation of leukocyte activation, collagen-containing extracellular matrix, phagosome, and other biological functions or signaling pathways. GSEA-GO analysis revealed that ER stress genes were significantly enriched in the negative regulation in metabolic processes of nucleobase-containing compounds (NES = -2.50, P < 0.001), while OA ER stress genes were significantly enriched in the processing and presentation of peptide antigens (NES = 2.40, P < 0.001). Using WGCNA analysis, LASSO regression analysis, and SVM-RFE analysis of intersection, we identified KLF9 and FOSL2 as potential OA endoplasmic reticulum stress signature genes, which were found to be more accurate as OA signature genes after validation. KLF9 expression in OA group was higher than that in control group, while FOSL2 expression was lower (P < 0.05). Conclusion: Machine learning and co-expression network analysis can effectively identify the genes and potential factors characteristic of ER stress in OA, which can help elucidate its pathogenesis and provide a new direction for better clinical treatment.

When processing the lost data of web learning resource information flow, the noise in the data signal cannot be eliminated, resulting in inaccurate detection of the lost data of web learning resource information flow in the later stage. Therefore, a data mining algorithm is proposed based on weighted depth forest for web learning resource information flow loss. Based on building a brand-driven Web data acquisition model to collect data, this method uses clustering analysis technology to extract the lost data feature information of web learning resource information flow. It carries out wavelet threshold denoising on it. According to the characteristics of lost data, the lost data mining of web learning resource information flow is completed. Experimental results show that the proposed algorithm has a low error rate, high accuracy, high labour intensity, high efficiency and high performance.

Speaker verification aims to verify whether an input speech corresponds to the claimed speaker, and conventionally, this kind of system is deployed based on single-stream scenario, wherein the feature extractor operates in full frequency range. In this paper, we hypothesize that machine can learn enough knowledge to do classification task when listening to partial frequency range instead of full frequency range, which is so called frequency selection technique, and further propose a novel framework of multi-stream Convolutional Neural Network (CNN) with this technique for speaker verification tasks. The proposed framework accommodates diverse temporal embeddings generated from multiple streams to enhance the robustness of acoustic modeling. For the diversity of temporal embeddings, we consider feature augmentation with frequency selection, which is to manually segment the full-band of frequency into several sub-bands, and the feature extractor of each stream can select which sub-bands to use as target frequency domain. Different from conventional single-stream solution wherein each utterance would only be processed for one time, in this framework, there are multiple streams processing it in parallel. The input utterance for each stream is pre-processed by a frequency selector within specified frequency range, and post-processed by mean normalization. The normalized temporal embeddings of each stream will flow into a pooling layer to generate fused embeddings. We conduct extensive experiments on VoxCeleb dataset, and the experimental results demonstrate that multi-stream CNN significantly outperforms single-stream baseline with 20.53

We present MIDWRSeg, a simple semantic segmentation model based on neural network architecture. For complex road scenes, a large receptive field gathered at multiple scales is crucial for semantic segmentation tasks. Currently, there is an urgent need for the CNN architecture to establish long-range dependencies (large receptive fields) akin to the unique attention mechanism employed by the Transformer architecture. However, the high complexity of the attention mechanism formed by the matrix operations of Query, Key and Value cannot be borne by real-time semantic segmentation models. Therefore, a Multi-Scale Convolutional Attention (MSCA) block is constructed using inexpensive convolution operations to form long distance dependencies. In this method, the model adopts a Simple Inverted Residual (SIR) block for feature extraction in the initial encoding stage. After downsampling, the feature maps with reduced resolution undergo a sequence of stacked MSCA blocks, resulting in the formation of multi-scale long-range dependencies. Finally, in order to further enrich the size of the adaptive receptive field, an Internal Depth Wise Residual (IDWR) block is introduced. In the decoding stage, a simple decoder similar to FCN is used to alleviate computational consumption. Our method has formed a competitive advantage with existing real-time semantic segmentation models for encoder-decoder on Cityscapes and CamVid datasets. Our MIDWRSeg achieves 74.2

Due to its potential applications in numerous industries, Unmanned Aerial Vehicles (UAVs) have gained considerable attention recently. UAV networks that are autonomous and decentralized have various practical uses, such as in disaster recovery, environmental monitoring, and security surveillance. Due to frequent route distractions and traffic congestion at high node speeds, the performance of routing systems in these networks drops considerably. UAVs with a mission to gather sensory data from various sources require meticulous route planning to decrease traffic congestion effectively. Due to flight time, range, and coverage area limitations, efficient route planning is crucial for maximizing the efficiency of UAV data collection. Optimal route planning and a delicate balancing act between these critical parameters are two of the biggest obstacles in sensory data gathering. This study presents a new method for dealing with these issues by developing an Intelligent Route Planning for Sensory Data Collection (IRP-SDC) system to optimize autonomous UAV route planning with congestion-aware modelling by considering time, distance, and area coverage limits. The IRP-SDC framework uses Multi-Objective Grey Wolf Optimization and Deep Q-Learning (MOGWO-DQL) for smart UAV route planning. The MOGWO algorithm, developed after observing the hunting techniques of grey wolves, can perform a worldwide search, which helps determine the most efficient paths to take after gathering information. DQL, on the other hand, has adaptive learning capabilities that can modify the UAV's flight path in response to alterations in its external environment. The suggested framework combines the two techniques to maximize the usefulness of UAVs in gathering sensory data. Extensive trials were carried out to prove the efficacy of the proposed technique. The IRP-SDC system beats previous approaches concerning time, distance, and area coverage by providing an ideal route for a UAV to acquire sensory data.

As culture continues to evolve, the field of visual communication design faces new challenges in the era of new media. To address these challenges, a paper proposed innovative ideas for the industry's growth and development. Specifically, the paper suggested that incorporating visual communication design and color balance into multimedia image analysis can enhance the visual impact of images. Results showed that images analyzed under this approach received a visual effect score 10

The image structuring process was mainly divided into three stages: model training, model prediction, and report structuring. In the report structure stage, based on the feature annotation sequence, this paper associated the text sequence with the corresponding table structure and stored the text sequence in the corresponding database in the background. In dataset 1, the accuracy rate of removing visual information submodel was 30

With the rapid development of technology, unmanned vehicles have become a hot research topic in the field of intelligent transportation. Unmanned vehicles have many advantages, such as improving traffic efficiency, reducing traffic accidents, and reducing energy consumption. However, the controllability and safety of unmanned vehicles have always been a key issue in research. The Internet of Things can achieve information exchange and data sharing between vehicles, transportation facilities, traffic management centers, etc., providing real-time traffic and road condition information, and providing accurate data support for intelligent decision-making and path planning of unmanned vehicles. Therefore, the artificial intelligence unmanned vehicle control device based on IoT intelligent transportation has become an important research direction. This paper used deep reinforcement learning as the decision-making control algorithm, and designed a set of unmanned vehicle control system based on the DDPG (Deep Deterministic Policy Grad) algorithm, supplemented by meta DDPG algorithm, which is the knowledge of meta learning. Through the test of the simulation platform, it was concluded that the control system has good generalization. This study combined the Internet of Things and artificial intelligence algorithms, which has certain guiding significance for the development and application of unmanned vehicles in the future.

With the comprehensive arrival of the fifth generation mobile communication technology (5G) era, the Internet of Things (IoT) has developed rapidly and is widely used in various industries. As one of the modern transportation signs, highways play an irreplaceable role in social and economic development. The research on Intelligent Traffic Systems (ITS) on highways has always been a hot topic. With the continuous improvement of the highway network and the increasing mileage, the efficiency of the existing highway ITS in processing information and solving problems is relatively low. Introducing new technologies to achieve breakthroughs has become a top priority. Edge computing is widely used in the Internet of Things technology because of its low latency and high response speed. Based on the characteristics of edge computing technology, this paper conducted in-depth research on expressway ITS, and analyzed the specific functions of intelligent system through IoT technology and edge computing technology. Through analysis and experiment, it is concluded that in the experimental highway area, the ITS using edge computing technology has slightly improved compared with the traditional ITS in all aspects. The response speed of the monitoring system has increased by 4.9

The traditional two-dimensional data visualization method is difficult to accurately express and display the three-dimensional information of the data, while the 3D interactive image technology can visually display the data in the three-dimensional space. At the same time, it can also operate and edit the data through interactive methods to help people better understand and use the data. Intelligent algorithms are an important supplement to 3D interactive imaging technology, which can improve visualization and analysis accuracy through calculations, model building, and other methods, achieving more accurate data display and analysis. This article aimed to explore the application of intelligent algorithms and 3D interactive imaging technology in the field of spatial visualization. Intelligent algorithms can help analyze data features and relationships, extract data attributes, and optimize data display effects. 3D interactive imaging technology can present processed data in a virtual three-dimensional space, enhancing the interaction and visualization effect between users and data. The combination of the two can achieve better data analysis and presentation results. This article conducted simulation experiments on spatial visualization based on intelligent algorithms and 3D interactive imaging technology, and scored some experimental indicators using a scoring system. The experimental results of this article indicate that the intelligent algorithm had a time efficiency of 46.5 seconds and an accuracy of 95

With the rapid development of computer technology, intelligent cockpit has increasingly become a trend in on-board related equipment. The intelligent cockpit system can not only provide auxiliary support for users in the driving environment, but also greatly improve driving efficiency. With the advancement of technology and the improvement of people's material lives, people's demand for driving environment safety and intelligence is gradually increasing. In order to effectively enhance the interactivity of the intelligent cockpit and ensure the safety of users' driving, this article effectively analyzed the performance and functional requirements of the intelligent cockpit, and combined speech recognition algorithms to construct an artificial intelligence (AI) speech interaction system. On this basis, this paper provides an in-depth study of intelligent cockpit design. To verify the effectiveness of the intelligent cockpit application, it was tested from four aspects: system operation accuracy, command response time, system security, and user experience. The test findings showed that at the level of system command response time, the response time of driving assistance commands, entertainment media commands, and traffic information commands was basically concentrated below 300 milliseconds. From the specific test results, it can be seen that the intelligent cockpit based on the artificial intelligence voice interaction system can quickly respond to users' different command needs through precise speech recognition.