Cloud computing is a technology that provides resources and utility services based on user demand. Due to this demand, efficient cloud security protocols are highly required, especially at the time of data communication for user authentication and data aggregation. The data communication scenarios are majorly affected by the security threats in the cloud computing environment. This article provides a practical approach to developing an efficient and empirical cloud framework in terms of cloud protocol. The framework uses fuzzy c-means (FCM) algorithm to group data, and calculation is done individually or associatively to rank the text data. Uploaded data are passed to a simple additive weighting (SAW) algorithm for ranking and making decision selection. The framework executes in three phases, namely data preprocessing, clustering, and automatic data security with an alert mechanism. The process is completely automated so there is no need of considering the individual files for the processing and the data held will be appropriately correlated with the sharing inter-cloud environment. To inspect security issues, the proposed framework is secured by three different security algorithms. The encryption process is completed by Rivest Cipher 6 (RC6); the substitution process is done by Advanced Encryption Standard (AES); and key generation is done by RC6, AES, and Rivest-Shamir-Adleman (RSA) approaches collectively. Based on the given situations, these standard approaches were automatically applied separately or collectively. Unauthorized access trapping and data deletion mechanism are also provided in the proposed framework. The experimental results with a comparative study depicted the effectiveness of the proposed work.

The water deficiency for agriculture around India has pushed a need for optimal utilization of it. Internet of Things (IoT) is today’s emerging trend which bridges the gap of real world with the technology. An IoT based automatic irrigation control systems helps in the optimal water utilization for the crop fields. This article proposes an automatic irrigation control system which is based on Internet of Things to predict water requirement of a crop field by processing the wireless sensor network’s (WSN) sensor data like relative humidity, moisture of soil, temperature of soil, and radiation of light (Ultraviolet) along with the Indian Meteorological Department’s weather forecast. Considering the sensed data from the environment with the weather forecasting data, a machine learning algorithm for soil moisture prediction is developed to evaluate the water requirement for a particular area of agricultural land for that crop yield. Real time information of the agriculture landscape is provided through a smart information processing and decision support system which is implemented using schematic web service. This article discusses and describes the system implementation in detail. The results by the automatic irrigation control system based on the soil moisture prediction algorithm are presented. The irrigation control system is completely automated and soil moisture prediction results are nearest to reality.

This paper presents a concise preface for the Cloud ecosystem, logical cloud security challenges and Confidence Mechanism concept, (REO) architecture and opportunities are presented briefly. To noticeably and resourcefully discuss security intimidation, risks, controls, agreement and other safety desires within the cloud framework, this paper also discuss cloud service providers, cloud vendors and cloud tenants with a framework. Role of security in cloud service ecosystem is further elaborate in the paper.

Caused by a shortage of oxygen-rich blood, cardiac artery disease occurs when coronary arteries get blocked. Arterial occlusion decreases the heart's blood flow and hence its oxygen supply. Plaque buildup is the leading cause of arterial obstruction. Both cholesterol and calcium can be found in plaque. Atherosclerosis refers to the development of plaque within the arteries. It is possible to have total ischemia due to an arterial clot. As arterial plaque ruptures, blood clots form. A diet rich in cholesterol can contribute to the development of plaque in a person's arteries. Cholesterol is transported in the bloodstream via lipoproteins, which are protein complexes. Lipoproteins come in two main types, high-density (HDL) and low-density (LDL). Plaque builds up in the arteries when levels of LDL are too high. The term “bad cholesterol” (LDL) is also commonly used. HDL is responsible for transporting cholesterol from the blood to the liver. HDL is the “good” or “healthy” cholesterol. Maintaining healthy levels of LDL and HDL is crucial for avoiding CAD. This research study has developed a ETC (Extra Tree Classification) approach for CAD diagnosis. The proposed approach employed the Z-Alizadeh Sani CAD dataset and employed SMOTE to address the issue of class imbalance. Classification was carried out utilizing classifiers such as XGBoost (extreme gradient boosting), KNN (K-nearest neighbour), SVM-Linear (support vector machine - linear), and SVM-RBF (support vector machine - radial basis function). After employing the GridSearch optimization approach, the hyper-parameters were fine-tuned to an accuracy of 95.16 percent.

In this paper a cloud computing security analysis based on RC6, AES and RSA algorithms in client-cloud environment has proposed. This framework is divided in 2 phases. Data pre-processing is first phase applied based on the weight assigned. Based on the data values obtainable from the uploaded textual file in cloud environment weight assigned is calculated. Now in second phase fuzzy c-means has been applied in cloud on the uploaded data for clustering. First the score in terms of aggregated assessment ranking have been calculated based on simple additive weighting (SAW). Then the decision performances have been arranged. Different random sets of data values have been obtained and also the aggregated values have been obtained. These aggregated sets will determine the security operation and the risk associated with the data uploaded with the cloud environment. The maximum key unpredictability and validation shows the privileged security in terms of the elevated threat opportunity. Proposed automated FCM based clustered and multi layer security standard algorithms have potential to deal with multifaceted security.