The impact of the Covid-19 pandemic on the inventory management of the Medicine supply chain is an essential part of inventory management in the area and has become an important concept for the overall profitability of the industrial scenario. It consists of several levels in which the material goes through different phases in order to reach the end customer. The impact of the Covid-19 pandemic on the inventory management of the three-tiered Medicine supply chain includes a Medicine Manufacturing sites, Medicine warehouse and medical centers that bear the costs. A coordinated approach between levels is necessary so that the chain is precisely tuned for the lowest inventory and minimum cost, and therefore, maximum profit. In this article, we consider a three-level coordinated impact of the Covid-19 pandemic on inventory management of the Medicine supply chain with a single Medicine collection point providing a single type of product to distribution centers individual Medicine, then to individual medical centers. A mathematical model is being developed for the coordinated effects of the Covid-19 pandemic on inventory management of the Medicine supply chain, which is solved by using the travelling salesman problem to optimize the ant colony for optimal values of decision variables and target functions. A numerical example is provided and the results obtained here are compared for these techniques.
Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and expressions based on emotional recognition. By identifying or recognising facial expressions that elicit human responses, it is possible for computers to make more accurate predictions about a person's mental state and to provide more tailored responses. As a result, we examine how a deep learning technique that employs a convolutional neural network might improve the detection of emotions based on facial features (CNN). Multiple facial expressions are included in our dataset, which consists of about 32,298 photos for testing and training. The preprocessing system aids in removing noise from the input image, and the pretraining phase aids in revealing face detection after noise removal, including feature extraction. As a result, the existing paper generates the classification of multiple facial reactions like the seven emotions of the facial acting coding system (FACS) without using the optimization technique, but our proposed paper reveals the same seven emotions of the facial acting coding system.
People are actively expressing their views and opinions via the use of visual pictures and text captions on social media platforms, rather than just publishing them in plain text as a consequence of technical improvements in this field. With the advent of visual media such as images, videos, and GIFs, research on the subject of sentiment analysis has expanded to encompass the study of social interaction and opinion prediction via the use of visuals. Researchers have focused their efforts on understanding social interaction and opinion prediction via the use of images, such as photographs, films, and animated GIFs (graphics interchange formats). The results of various individual studies have resulted in important advancements being achieved in the disciplines of text sentiment analysis and image sentiment analysis. It is recommended that future studies investigate the combination of picture sentiment analysis and text captions in more depth, and further research is necessary for this field. An intermodal analysis technique known as deep learning-based intermodal (DLBI) analysis is discussed in this suggested study, which may be used to show the link between words and pictures in a variety of scenarios. It is feasible to gather opinion information in numerical vector form by using the VGG network. Afterward, the information is transformed into a mapping procedure. It is necessary to predict future views based on the information vectors that have been obtained thus far, and this is accomplished through the use of active deep learning. A series of simulation tests are being conducted to put the proposed mode of operation to the test. When we look at the findings of this research, it is possible to infer that the model outperforms and delivers a better solution with more accuracy and precision, as well as reduced latency and an error rate, when compared to the alternative model (the choice).
Abstract In this article, we establish some inequalities for invariant submanifolds involving totally real sectional curvature and the scalar curvature. The equality cases are also discussed.
The best practitioners of Spatial cluster analysis the whole of ideas, innovation, convey the practices and challenges, so be spread is very important, the logistics industry of Spatial cluster analysis, a unique interdisciplinary effort, epidemiologists apply the statistics. In this study, reviewed the scope of the retrieval spatial cluster analysis method employed by systematically individual level, a peer-reviewed journal database to study the data obtained from the address position or coordinates. Advanced Resource management (ARM) for logistics are widely used in such consumer transport monitoring. Because of its reduced instruction set, require effective few transistors smaller for the Integrated Circuit (IC) which is used to logistics monitoring in transportation. The existing system does not have proper result for less accuracy of logistics industry spatial cluster analysis and then less poor performance of spatial cluster analysis for prediction and classification. The proposed method gives correct result of more accuracy of logistics industry spatial cluster analysis and then high performance of spatial cluster analysis in this section using Random Support Vector System (RSVS) algorithm. The proposed system processes the data collection and analysis, then use the classification process, Advanced RISC Machines (ARM) Heterogeneous and Spatial cluster analysis and predict the result.
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e)
'%3e%3ccircle r='120' fill='%230f47af'/%3e%3cg id='a'%3e%3cpath fill='%23fcdd09' d='m0-96-4.206 12.944 17.348 53.39h-23.13l-2.599 8h74.163l11.011-8H21.553z'/%3e%3cpath stroke='%23fcdd09' stroke-width='4' d='m25.863-35.597 30.564-42.069'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(72)'/%3e%3cuse xlink:href='%23a' transform='rotate(144)'/%3e%3cuse xlink:href='%23a' transform='rotate(216)'/%3e%3cuse xlink:href='%23a' transform='rotate(288)'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cpath fill='%23fff' d='m8.751-17.959 10.11 11.373L3.997-9.844l13.94-6.1-7.692 13.129z'/%3e%3c/svg%3e)