Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.
The major objective of this research is to identify the presence of brain cancer along with the incidence levels of beginning stage to advanced stage using collaborative analysis of big data and data mining techniques. The dataset collected from secondary sources had few errors and rectified using preprocessing techniques in MATLAB. Further, the testing dataset is processed with k-means algorithm to form cluster analysis and identify the presence of brain cancer in three levels of well, fair and poor levels using degree of difference between the normal and cancer cells in brain. The algorithm is modified according to the needs of the medical analysis of the current dataset. The results indicates the presence of brain cancer in various three levels under cluster values of initial stage (54%), Curable stage (38%) and incurable stage (8%), respectively. The accuracy of prediction is 93.4% and the error identification is 9.3% whereas the sensitivity and specificity accounts to 0.8 and 0.7, respectively. Hence, further analysis is conducted in tableau big data tool and the sheets with story boards are formed. This research indicates the occurrence of brain cancer is influenced by gender and age factors along with regular activities and streams. Thus, brain cancer is considered as one of the challenging prediction as the cell contains mixed patterns with variations according to gender and age of human beings.
Emtricitabine is a nucleoside reverse transcriptase inhibitor for the prevention of
HIV infection. This drug's, clinical and pharmaceutical analysis requires effective analytical
procedures and stability studies for quality control and pharmacodynamics and pharmacokinetic
studies. A comprehensive literature survey published in various journals related to analytical
and pharmaceutical chemistry was conducted and instrumental analytical methods were
developed and used in bulk drugs and pharmaceutical dosage form as single and combined with
other drugs. This review will critically examine UV spectroscopy analytical methods
(simultaneous equation method, derivative spectrophotometric method, absorption ratio and Qbased
method), High-performance liquid chromatography (HPLC), High-performance thinlayer
chromatography (HPTLC), Liquid chromatography coupled with tandem mass
spectrometry (LC-MS).
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