Piercebonner2718

Experimental results show that MIoU (mean intersection over union) of the proposed model for small object segmentation is 6% higher than that of the original model, the overall MIoU is increased by 3%, and the execution time and memory consumption are only half of the original model, which can be well applied to real-time tracking and segmentation of small particles.Although the digital transformation is advancing, a significant portion of the population in all countries of the world is not familiar with the technological means that allow malicious users to deceive them and gain great financial benefits using phishing techniques. Phishing is an act of deception of Internet users. The perpetrator pretends to be a credible entity, abusing the lack of protection provided by electronic tools and the ignorance of the victim (user) to illegally obtain personal information, such as bank account codes and sensitive private data. One of the most common targets for digital phishing attacks is the education sector, as distance learning became necessary for billions of students worldwide during the pandemic. Many educational institutions were forced to transition to the digital environment with minimal or no preparation. This paper presents a semisupervised majority-weighted vote system for detecting phishing attacks in a unique case study for the education sector. A realistic majority weighted vote scheme is used to optimize learning ability in selecting the most appropriate classifier, which proves to be exceptionally reliable in complex decision-making environments. https://www.selleckchem.com/products/Fulvestrant.html In particular, the voting naive Bayes positive algorithm is presented, which offers an innovative approach to the probabilistic part-supervised learning process, which accurately predicts the class of test snapshots using prerated training snapshots only from the positive class examples.The health status of mechanical bearings concerns the safety of equipment usage. Therefore, it is of crucial importance to monitor mechanical bearings. Currently, deep learning is the mainstream approach for this task. However, in practical situations, the majority of fault samples have the issue of severe class unbalancing, which renders conventional deep learning inapplicable. Targeted at this issue, this paper proposes an invariant temporal-spatial attention fusion network called ITSA-FN for bearing fault diagnosis under unbalanced conditions. First, the proposed method utilizes the invariant temporal-spatial attention representation section, which consists of a pretrained convolutional auto-encoder model, a convolutional block attention module, and a long short-term memory network, to extract independent features and invariant features of spatial-temporal characteristics from input signals. Then, a multilayer perceptron is used to fuse and infer from the extracted features and design a new loss function from the focal loss for network training. Finally, this article validates proposed model's effectiveness through comparative experiments, ablation studies, and generalization performance experiments.Few-Shot Learning has had a significant influence on how people live, work, and learn. Physical education is a requirement for a college diploma. Sports management systems, which focus on data collection, organization, and analysis, as well as timeliness and guidance, are one of the current challenges in the field of physical education at the country's top colleges and universities. The amount of sex in the room is minimal. Time is money when it comes to making college sports decisions, and this paper uses data from physical fitness tests to illustrate this point. Use Few-Shot Learning technology to extract relevant data from the data, allowing teachers to provide more scientific and effective guidance and suggestions to students. The design and implementation of this paper collect data from physical fitness tests in real-time using mobile edge computing, analyze the data, and display the results using machine learning technology, which mines deep features and displays analysis results, can be used to evaluate students' physical fitness. The data and information in the physical fitness analysis system are more readable and time-saving, allowing students to better understand their true level of physical fitness. Because of the results of data mining, teachers can provide more specific guidance and recommendations for each student's physical characteristics.Online learning has changed all elements of teaching of entire learning structure from primary to university level all around the world so that the challenges of online teaching are required to be optimized. The prominent objective of this manuscript is to optimize the issues of online teaching-learning in online education. Twelve issues of online teaching-learning are shortlisted by performing deep reviewing of the literature and grouping into three categories "Students' issues," "Common issues," and "Teachers' issues" using the opinions of expert people. The analytical hierarchy process method is chosen for ranking of issues of online teaching. The findings can become effective in planning to get solution of the challenges of online teaching. These challenges of online teaching may lead to fragmental illness mentally over a long period of time. Because social media platforms may become an efficient tool for incorporating into online education, social media is a vital aspect of online learning. Over time, social media use may have an effect on the human brain in one way or another. The given work's exploration of online teaching-learning challenges could lead to a social media-based examination of mental illness.Person reidentification (ReID) is a challenging computer vision task for identifying or verifying one or more persons when the faces are not available. In ReID, the indistinguishable background usually affects the model's perception of the foreground, which reduces the performance of ReID. Generally, the background of the same camera is similar, whereas that of different cameras is quite different. Based on this finding, we propose a template-aware transformer (TAT) method which can learn intersample indistinguishable features by introducing a learnable template for the transformer structure to cut down the model's attention to regions of the image with low discrimination, including backgrounds and occlusions. In the multiheaded attention module of the encoder, this template directs template-aware attention to indistinguishable features of the image and gradually increases the attention to distinguishable features as the encoder block deepens. We also increase the number of templates using side information considering the characteristics of ReID tasks to adapt the model to backgrounds that vary significantly with different camera IDs. Finally, we demonstrate the validity of our theories using various public data sets and achieve competitive results via a quantitative evaluation.Wiper motor noise has an important impact on vehicle comfort. Accurate prediction of wiper motor noise can obtain motor NVH performance in motor manufacturing or earlier stage and provide necessary support for NVH performance design of parts and vehicles. However, the prediction accuracy of wiper motor noise by the traditional CAE or testing method is low. Data-driven technology provides a new idea for wiper motor noise prediction with its advantages of high efficiency and high precision. This paper studies the wiper motor noise prediction algorithm based on the motor vibration signal, respectively, using the transmission path analysis theory and the support vector machine theory, and carries on the test verification and comparative analysis of the effect. The results show that the method based on support vector machine is more accurate in the prediction of wiper motor noise and has higher practical engineering value.This study aimed to investigate the therapeutic effect of ephedra-forsythia-red bean decoction (a formula of traditional Chinese medicine) addition and subtraction treatment of psoriasis vulgaris based on 22 MHz high-frequency ultrasound, so as to provide reference for the selection of traditional Chinese medicine formulas for psoriasis and the clinical application of ultrasound. 80 patients with psoriasis vulgaris with exterior closing and internal depression syndrome diagnosed and treated in the hospital were divided into an observation group (40 cases) and a control group. Patients in the observation group were received ephedra-forsythia-red bean decoction addition and subtraction; and those in the control group were received traditional Chinese medicine of observation group subtraction of raw ephedra, cinnamomum cassia, addition of fineleaf schizonepeta herb, divaricate saposhnikovia root. 22 MHz high-frequency ultrasonography was also performed. The psoriasis area and severity index (PASI) score and efficacy indicators were compared between the two groups. The results showed that the detection rate of nail malnutrition and psoriasis infiltration in psoriasis by high-frequency ultrasound was significantly higher than that by arthroscopy, and the difference was significant (P less then 0.05). The total PASI score of the two groups after treatment was significantly lower than that before treatment, and the total PASI score of the observation group was lower than that of the control group, and the difference was statistically significant (P less then 0.05). The total effective rate of the observation group (87.5%) was significantly higher than that of the control group (67.5%), and the difference was statistically significant (P less then 0.05). It was found that high-frequency ultrasound can effectively display the condition and prognosis of patients with psoriasis. Ephedra-forsythia-red bean decoction was an effective traditional Chinese medicine formula for the treatment of psoriasis vulgaris.Due to the rapid development of social computerization and smart devices, there is an increasing demand for indoor positioning of mobile robots in the robotics field, so it is very important to realize the autonomous navigation of mobile robots. However, in indoor scenes, due to factors such as dark walls, the global positioning system cannot effectively locate, and the broadband and wired positioning technologies used indoors have problems such as base station laying and delay. Computer vision positioning technology has greatly improved the camera hardware due to its simple equipment and low cost. Compared with other sensor cameras, it is less affected by environmental changes, so visual positioning has received extensive attention. Image matching has become the most critical link in visual positioning. The accuracy, speed, and robustness of image matching directly determine the results of visual positioning, so image matching has become the main topic of this study. In this study, the neural network algorithm is systematically optimized, especially for the robot's local obstacle avoidance, and an obstacle data acquisition method based on VGG16 and fast RCNN is proposed. In order to solve the problem that the semantic image segmentation algorithm based on AlexNet and ResNet is difficult to accurately obtain the information of multiple objects, and an image semantic segmentation algorithm combined with VGG16 is designed to classify the background and road in the image at the pixel level and capture the path boundary line. The collection of robot obstacle path information improves the speed and accuracy of highly automated local obstacle avoidance. This study uses neural network algorithms to systematically optimize computer vision positioning and also studies the accuracy optimization of local obstacle avoidance, aiming to promote its better development.