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The Conversation Among Malware as well as Intestinal tract Microbiota: A Review.
This research discusses an interesting topic, using artificial intelligence methods to predict the score of powerlifters. We collected the characteristics of powerlifters, and then used the reservoir computing extreme learning machine to build a predictive model. In order to further improve the prediction results, we propose a method to optimize the reservoir computing extreme learning machine using the whale optimization algorithm. Experimental results show that our proposed method can effectively predict the score of powerlifters with the coefficient of determination value is 0.7958 and root-mean-square error of prediction value is 16.73. This provides a reliable basis for experts to judge the results before the competition.With the wide application of unmanned ground vehicles (UGV) in a complex environment, the research on the obstacle avoidance system has gradually become an important research part in the field of the UGV system. Aiming at the complex working environment, a sensor detection system mounted on UGV is designed and the kinematic estimation model of UGV is studied. In order to meet the obstacle avoidance requirements of UGVs in a complex environment, a fuzzy neural network obstacle avoidance algorithm based on multi-sensor information fusion is designed in this paper. MATLAB is used to simulate the obstacle avoidance algorithm. By comparing and analyzing the simulation path of UGV's obstacle avoidance motion under the navigation control of fuzzy controller and fuzzy neural network algorithm, the superiority of the proposed fuzzy neural network algorithm was verified. Finally, the superiority and reliability of the obstacle avoidance algorithm are verified through the obstacle avoidance experiment on the UGV experimental platform.Deep neural networks(DNN)have achieved good results in the application of Named Entity Recognition (NER), but most of the DNN methods are based on large numbers of annotated data. Electronic Medical Record (EMR) belongs to text data of the specific professional field. The annotation of this kind of data needs experts with strong knowledge of the medical field and time labeling. To tackle the problems of professional medical areas, large data volume, and annotation difficulties of EMR, we propose a new method based on multi-standard active learning to recognize entities in EMR. Our approach uses three criteria the number of labeled data, the cost of sentence annotation, and the balance of data sampling to determine the choice of active learning strategy. We put forward a more suitable way of uncertainty calculation and measurement rule of sentence annotation for NER's neural network model. Also, we use incremental training to speed up the iterative training in the process of active learning. Finally, the named entity experiment of breast clinical EMRs shows that it can achieve the same accuracy of NER results under the premise of obtaining the same sample's quality. Compared with the traditional supervised learning method of randomly selecting labeled data, the method proposed in this paper reduces the amount of data that needs to be labeled by 66.67%. Besides, an improved TF-IDF method based on Word2Vec is also proposed to vectorize the text by considering the word frequency.The combination of Unmanned Aerial Vehicle (UAV) technologies and computer vision makes UAV applications more and more popular. Computer vision tasks based on deep learning usually require a large amount of task-related data to train algorithms for specific tasks. Since the commonly used datasets are not designed for specific scenarios, in order to give UAVs stronger computer vision capabilities, large enough aerial image datasets are needed to be collected to meet the training requirements. In this paper, we take low-altitude aerial image object detection as an example to propose a framework to demonstrate how to construct datasets for specific tasks. Firstly, we introduce the existing low-altitude aerial images datasets and analyze the characteristics of low-altitude aerial images. On this basis, we put forward some suggestions on data collection of low-altitude aerial images. Then, we recommend several commonly used image annotation tools and crowdsourcing platforms for data annotation to generate labeled data for model training. In addition, in order to make up the shortage of data, we introduce data augmentation techniques, including traditional data augmentation and data augmentation based on oversampling and generative adversarial networks.As the number of various sensors grows fast in real applications such as smart city and intelligent agriculture, context-aware systems would acquire raw context information from dynamic, asynchronous and heterogeneous context providers, but multi-source information usually leads to the situation uncertainty of the system entities involved, which is harmful to appropriate services, and specially the inconsistency is a kind of main uncertainty problems and should be processed properly. A new inconsistent context fusion algorithm based on back propagation (BP) neural network and modified Dempster-Shafer theory (DST) combination rule is proposed in this paper to eliminate the inconsistency to the greatest extent and obtain accurate recognition results. Through the BP neural network, the situations of entities can be recognized effectively, and based on the modified combination rule, the recognition results can be fused legitimately and meaningfully. In order to verify the performance of the proposed algorithm, several experiments under different error rates of context information sources are conducted in the personal identity verification (PIV) application scenario. The experimental results show that the proposed BP neural network and modified DST based inconsistent context fusion algorithm can obtain good performance in most cases.The preceptorship model is an education-focused model for teaching and learning within a clinical environment in nursing. It formulates a professional educational relationship between a staff nurse (preceptor) and student nurse and is based on the provision of providing patient care. GSK046 solubility dmso Preceptorship is widely acknowledged in the literature as a positive pedagogical approach in clinical nursing education in terms of knowledge and skill acquisition, confidence, and professional socialisation of undergraduate nursing students. However, the literature also widely reports negative interpersonal experiences within this professional educational relationship resulting in negative educational experiences and in some cases, negative patient experiences. Therefore, the authors set out to examine what teaching strategies are being implemented by nurse educators to encourage the development of interpersonal and communication skills in facilitating positive interpersonal relationships between the preceptor, nursing student and patient.
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