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Providing assurance for security is the major issue in today's cloud atmosphere. In cloud computing one of the main issues is "Cloud Privacy (CP)". As the whole cloud user don't have the same anxiety concerning CP. Only some of the users are fulfilled with the present policy whereas rest will comparatively involve in affecting the equivalent privacy. Based in the essential cloud design it's typically deployed for imparting three core service models illustrated to be "Software as a Service (SaaS)", "Platform as a Service (PaaS)" and "Infrastructure as a Service (IaaS)". Though these entire delivery services will tend to model and expose variety of security level attacks through the intelligent intruder. In this paper we are proposing a new algorithm which improves the security of the data over the cloud. [ABSTRACT FROM AUTHOR]

The concept of Cloud computing will be evolving towards the future generation for imparting the technology over various IT enterprises with distinct features that provides additional procedures of demand based on infrastructure as it facilitates various primary services. It provides distinct types of services like need based storage over web which includes cloud services based on the user requirements which is a challenging concern as it is based on information square measure with interconnected resource pool which is scattered over distinct global locations that comprises of unauthorized user who tends to access knowledge by means of virtual machines which is a negative characteristic of the cloud storage for acquiring the knowledge. On the same hand due to this the established insecurity will create many drawbacks for end users. One of the major drawbacks in cloud computing is securing the knowledge as the AES is considered to be the chief standard for development of higher performance AES in the maximum utilized applications. [ABSTRACT FROM AUTHOR]

In today's generation, there has been a lot of technological advancement as well as limitations in the healthcare field. As healthcare organizations must store large amounts of data, they are seeking to use cloud-based software development to meet their objectives and make their jobs easier. As a result, we're working on a Cloud-Based Framework for Healthcare Services project. This project will be secure, adaptable, and private, as well as cost-effective. Hospitals will save data in files manually, which has a number of disadvantages. Due to availability of earlier health system which is deemed as the unsuccessful one as per the number of issues are concerned, due o to inclusion of poor storage capabilities as the process requires higher maintenance expenses. And while performing the integration process which is replaced with the "Electronic Health Information System". We propose a framework system that will allow residents to adopt a well-developed healthcare sector that is accountable for providing the efficient and reliable shared services. As a result, the best method is to keep any type of data in the cloud, which can be readily shared and improves healthcare services. [ABSTRACT FROM AUTHOR]

The proposed application must assist a targeted user to obtaining real estate property information with elegant correctness and precise output. By implementing the proposed algorithmic prediction process, which will forecast various property prices based on several criterions. We're going to use datasets from the landmark we're going to forecast, and this landmark can be changed according to the area we want to predict. We'll use a linear regression technique to predict, and by utilizing Flask framework more accurate results can be acquired. We discovered that by training data to the maximum, we were able to achieve absolute outcomes. We can forecast working in this subject by leveraging technologies such as Python, which allows us to train and collect more entertaining data. In comparison to other investments, real estate property prices are unaffordable. We can say that we are able to predict. [ABSTRACT FROM AUTHOR]

In the present era most of the people are effected with hypothyroid disease. People who are of age 13 and above are more effected with this disease and day by day it is transforming into a dangerous disease. The prediction of disease at earlier stage is very crucial so superior treatment is contributed by doctors. In this paper, hypothyroid disease prediction is implemented through Ensemble Machine Learning Algorithms and through RapidMiner Tool. The algorithms like Random Forest, AdaBoost, and Gradient Boosting and Bagging are implemented. These models were compared and evaluated using evaluation metrics like Accuracy, Precision, and Recall. As multiple models are combined so ensemble method is more robust and accurate. It removes unrelated data in the medical dataset and produces accurate and precise data. It proves that the ensemble method is very efficient than using a single classifying method. Hence implementing the ensemble method, we can predict the patient's disease efficiently. [ABSTRACT FROM AUTHOR]

Since heavy rain can beget numerous disasters, downfall vaticination is pivotal. The cast should be accurate, and it also encourages people to take preventives. For nations like India, whose frugality is largely dependent on husbandry, the delicacy of the downfall statement is veritably important. Due to the atmosphere's dynamic character, applied mathematics ways are unfit to guarantee dependable perfection for a statement about rush. Retrogression may be used in the vaticination of rush utilising machine learning approaches. The thing of this design is to make the styles and approaches used in the field of rush vaticination accessible tonos-experts while also furnishing a comparison of the colourful machine learning algorithms. [ABSTRACT FROM AUTHOR]

Researchers in this study recommend a changed Transformer model to look at the conceivable outcomes of this model for SMS spam ID. As a feature of the UtkMl's Twitter Spam Detection Competition dataset, our recommended spam Transformer is tried against laid out AI classifiers and cutting edge SMS spam location draws near. ' According to our tests on SMS spam distinguishing proof, the adjusted spam Transformer introduced here has the best exhibition of the multitude of applicants, with precision, review, and Fl-Score upsides of 98%, 0.945l, and 0.9613, separately. Additionally, the suggested model performs well on the UtkMl Twitter dataset, indicating that it may be applied to other issues of a similar kind. [ABSTRACT FROM AUTHOR]

Expecting sadness be capable of alleviate catastrophe. Many mechanisms encompass been projected up to now by means of Machine-Learning (ML) methods. The aspire of this learning is to recognize significant changeable utilized in sadness forecast, modern sadness screening implements accepted, and the most modern ML methods exercised. These concerned supplies investigators by means of the primary modules necessary to forecast on sadness. Social media channels, such as Facebook, Twitter, and Instagram, have distorted our humankind everlastingly. Public are currently ever more associated than increasingly and make known a kind of digital character. Even though communal medium positively has numerous extraordinary features, the demerits are indisputable additionally. Modern learning has designated an association connecting far above the ground practice of social media platforms and amplified sadness. The current learning aspire to develop ML methods for distinguish a feasible unhappy Twitter customer based on together, his/her system activities and tweets. For this reason, we skilled and experienced classifiers to differentiate whether a customer is unhappy or not utilizeing characteristics take ousted from his/her behavior in the system and tweets. The consequences demonstrated that the additional characteristics are utilized; the advanced are the accurateness and F-measure achieves in become aware of discouraged customers. This technique is a information ambitious, extrapolative come within attain of for near the beginning discovery of sadness or additional psychological sickness. This learning's major role is the examination division of the characteristics and its collision on notice the sadness stage. [ABSTRACT FROM AUTHOR]