Machine learning is a subset of Artificial Intelligence when combined with Data Mining techniques plays a promising role in the field of prediction. We live in an era where data generation is exponential with time but if the generated data is not put to work or not converted to knowledge data, its generation is of no use. Similarly, in Healthcare also, data availability is high, so is the need to extract the information from it for better prognosis, diagnosis, treatment, drug development, and overall healthcare. In this research, we have tried to focus more on diagnosis of Diabetes disease, which is one of the fastest growing chronic diseases all over the world as declared by World Health Organization in the year 2014. We have also tried to show the different techniques like Gradient Boosting, Logistic Regression and Naive Bayes, which can be used for the diagnosis of diabetes disease with attained accuracy as 86% for the Gradient Boosting, 79% for Logistic Regression and 77% for Naive Bayes.

The vast amount of hidden data in huge databases has created tremendous interests in the field of data mining. This paper discusses the data analytical tools and data mining techniques to analyze the medical data as well as spatial data. Spatial data mining includes discovery of interesting and useful patterns from spatial databases by grouping the objects into clusters. This study focuses on discrete and continuous spatial medical databases on which clustering techniques are applied and the efficient clusters were formed. The clusters of arbitrary shapes are formed if the data is continuous in nature. Furthermore, this application investigated data mining techniques such as classical clustering and hierarchical clustering on the spatial data set to generate the efficient clusters. The experimental results showed that there are certain facts that are evolved and can not be superficially retrieved from raw data.

Cloud computing has become buzzword today. It is a digital service where dynamically scalable and virtualized resources are provided as a service over internet. Task scheduling is premier research topic in cloud computing. It is always a challenging task to map variety of complex task on various available heterogenous resources in scalable and efficient way. The very objective of this paper is to dynamically optimize task scheduling at system level as well as user level. This paper relates benefit-fairness algorithm based on weighted-fair Queuing model which is much more efficient than simple priority queuing. In proposed algorithm, we have classified and grouped all tasks as deadline based and minimum cost based constraints and after dynamic optimization, priority of fairness is applied. Here different priority queue (high, mid, low) are implemented in round-robin fashion as per weights assign to them .We recompile the CloudSim and simulate the proposed algorithm and results of this algorithm is compared with sequential task scheduling and simple constraints (cost and deadline) based task scheduling algorithm. The experimental results indicates that proposed algorithm is, not only beneficial to user and service provider, but also provides better efficiency and fairness at priority level, i.e. benefit at system level.

Purpose of Review In the last month of 2019, i.e., December, COVID-19 hit Wuhan city in China. Since then, it has infected more than 210 countries and nearly about 33.4 million people with one million deaths globally. It is a viral disease with flu-like symptoms; hence, prevention and management is the best option to be adopted for its cure. Recent Findings Many healthcare systems, scientists, and researchers are fighting for the cure of this pandemic. Ayurvedic and allopathic treatments have been studied extensively and approached for the cure of COVID-19. In addition to ayurvedic treatments, the Ministry of Ayush, India, has also recommended many remedies to boost up immunity. Allopathic studies involved several antiviral drugs which were used in different combinations for the treatment of COVID-19. Summary Comparative analysis of Ayurveda and allopathic treatment strategies were carried out in the present study. Depending upon the patient’s conditions and symptoms, Ayurveda is useful for the treatment of COVID-19. Allopathic treatments inhibit viral infection by targeting majorly endocytosis, and angiotensin-converting enzyme (Ace) receptor signaling. In this article, we summarize different ayurvedic and allopathic medicines and treatment strategies which have been used for the treatment of COVID-19, a global pandemic.

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CLC-HP-β-CD inclusion complex may potentially be used as a viable formulation of CLC.