The decisions and approaches of renowned personality used to impress the real world are to a great extent adapted to how others have seen or assessed the world with opinion and sentiment. Examples could be any opinion and sentiment of people view about Movie audits, Movie surveys, web journals, smaller scale websites, and informal organizations. In this research classifies the movie review into its correct category, classifier model is proposed that has been trained by applying feature extraction and feature ranking. The focus is on how to examine the sentiment expression and classification of a given movie review on a scale of (–) negative and (+) positive sentiments analysis for the IMDB movie review database. Due to the lack of grammatical structures to comments on movies, natural language processing (NLP) has been used to implement proposed model and experimentation is performed to compare the present study with existing learning models. At the outset, our approach to sentiment classification supplements the existing movie rating systems used across the web to an accuracy of 97.68%.
Query optimization is the most critical phase in query processing. Query optimization in distributed databases explicitly needed in many aspects of the optimization process, this is not only increases the cost of optimization, but also changes the trade-offs involved in the optimization process significantly .This paper describes the synthetically evolution of query optimization methods from uniprocessor relational database systems to parallel database systems. We point out a set of parameters to characterize and compare query optimization methods, mainly: (i) type of algorithm (static or dynamic), (ii) working environments (re-optimization or re-scheduling) and (iii) level of modification.The major contributions of this paper are: (I) Understanding the mechanisms of query optimization methods with respect to the considered environments and their constraints (e.g. parallelism, distribution, heterogeneity, large scale, dynamicity of nodes). (ii) Study the problem of query optimization particular in term of heterogeneously environment and pointing out their main characteristics, which allow comparing them and help to Implement new query optimization algorithm and model. These contributions is led to performance enhancement of query optimization in distributed database system through classify by different QEPs and minimize the response time.
Nowadays, computational technology is given great importance in the health
care system to understand the importance of advanced computational technologies.
Skin cancer or skin disease (melanoma) has been considered in this chapter. As we
know, the detection of skin lesions caused by exposure to UV rays over the human
body would be a difficult task for doctors to diagnose in the initial stages due to the low
contrast of the affected portion of the body. Early prediction campaigns are expected to
diminish the incidence of new instances of melanoma by lessening the populace's
openness to sunlight. While beginning phase forecast campaigns have ordinarily been
aimed at whole campaigns or the public, regardless of the real dangers of disease
among people, most specialists prescribe that melanoma reconnaissance be confined to
patients who are in great danger of disease. The test for specialists is the way to
characterise a patient's real danger of melanoma since none of the rules, in actuality,
throughout the communities offer an approved algorithm through which melanoma risk
may be assessed. The main objective of this chapter is to describe the employment of
the deep learning (DL) approach to predict melanoma at an early stage. The
implemented approach uses a novel hair removal algorithm for preprocessing. The k.means clustering technique and the CNN architecture are then used to differentiate
between normal and abnormal skin lesions. The approach is tested using the ISIC
International Skin Imaging Collaboration Archive set, which contains different images
of melanoma and non-melanoma.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.