Nowadays, People share their opinions through social media. This information may be informative or non-informative. To filtering the informative information from the social media plays a challenging issue. Nevertheless, in social media especially when a disaster been occurs the peoples will interact more on that particular disaster event. They share their opinion through some textual information such as tweets or posts. In this work, we are proposing a generalized approach for categorizing the informative and non-informative on twitter media. We collected the seven natural disaster events from the crisisNLP. These datasets are different disaster events which contains the people’s opinions on that specific event. We preprocess the information which converts the tweet information into machine understandable vectors. These vectors been processed by the different machine learning algorithms. We consider the individual performance of each ML algorithm on different disaster datasets upon chosen the best five algorithms for voting techniques. We tested the performance with matrices such as accuracy, precision, recall and f1-score. We compared our results with existing models in which our proposed model performed better than other existing state of art models.

In Present days, one of the most appealing technologies is cloud computing in the current time. Now a day, different organizations are offering various cloud services concerning their platforms. These organizations are profitable in cost-efficiency to the end-users in various factors that include process, storage, and applications instance. Most of the end-users choose the services from the cloud service provider, whereas some medium-scale organizations are also depending on the service providers even though they have enough resources. This causes the problem due to a lack of knowledge on design and deployment issues. In this paper, we proposed a private cloud that offers cloud services with limited resources so that small and medium scale organizations can be benefited. In this work, we proposed a novel three tier architecture that contains Domain Controller (DC), Virtual Machine Manager (VMM), and Member Servers (MSs) at a different level of functioning and also have their responsibilities. In this Novel three tier architecture was designed in a hierarchal fashion in which the upper layer accesses the lower layer users. The top layer, which deals with the domain controller whose responsibility, is to control the domain-specific issues like adding the member in the network, granting permissions, creating network groups, etc. The middle layer has various administrative users like Virtual Machine Manager (VMM), Storage Area Network (SAN), Disaster Recovery (D/R), and Operational Manager (SCOM), which deals with their responsibilities in cloud services. In the bottom layer, the physical servers exist and are configured by DC and get access by the middle layer users. This architecture was tested with various applications with different instances concerning storage and process utilities.

Nowadays, people share their opinions through social media. This information may be informative or non-informative. Filtering informative information from social media plays a challenging issue. Nevertheless, people will interact more with that particular disaster event on social media, primarily when a disaster occurs. They share their opinion through some textual information such as tweets or posts. In this work, we propose a generalized approach for categorizing the informative and non-informative media on Twitter. We collected the seven natural disaster events from the crisisNLP. These datasets are different disaster events containing people’s opinions on that specific event. We pre-process the information, which converts the tweet information into machine-understandable vectors. Various machine learning algorithms have processed these vectors. We consider the individual performance of each ML algorithm on different disaster datasets upon choosing the best five algorithms for voting techniques. We tested the performance with matrices such as accuracy, precision, recall, and F1-score. We compared our results with existing models in which our proposed model performed better than other existing state of the art models.

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