Sentiment Analysis and Classification on Amazon Products using Improved Support Vector Machine for Multiclass Classification

There is a huge increase in number of peoples who have been accessing many social networking sites especially user post or reviews for a specific product, company, brand, individual, forums and movies etc. These reviews are helpful in judging customer perception on certain thing. The development of algorithms that could automate the categorization of distinct comments based on feedback from consumers became an analyst project, and this automated classification process is known as sentiment analysis. This research main goal is to analyze Amazon product reviews using an approach to Machine Learning (ML) built around TF-IDF and then employ the Support Vector Machine (SVM) algorithm to categorize the sentiment scores and sentences. SVM can handle binomial classification but the customer reviews is mostly classified into positive, negative and neutral and in some applications, it is fine grained into star ratings such 1-5 or sometimes 1-10. Also, in some applications features or attributes are high in number in which some are irrelevant. Hence, this work applies feature subset algorithm and improves the existing SVM to handle multiclass classification. The Sentiment analysis, Rapidminer tool is considered for classification and the results are visualized, assessed with suitable classification metrics

Significant Feature Selection Method for Health Domain using Computational Intelligence- A Case Study for Heart Disease

In the medical field, the diagnosing of cardiovascular disease is that the most troublesome task. The diagnosis of heart disease is difficult as a decision relied on grouping of large clinical and pathological data. Due to this complication, the interest increased in a very vital quantity between the researchers and clinical professionals regarding the economical and correct heart disease prediction. In case of heart disease, the correct diagnosis in early stage is important as time is the very important factor. Heart disease is the principal supply of deaths widespread, and the prediction of Heart Disease is significant at an untimely phase. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the best support for predicting disease with correct case of training and testing. The main idea behind this work is to find relevant heart disease feature among the large number of feature using rough computational Intelligence approach. The proposed feature selection approach performance is better than traditional feature selection approaches. The performances of the rough computation approach is tested with different heart disease data sets and validated with real-time data sets

Opinion mining in Machine Learning for High Perfomance using Sentimental Analysis

Opinion mining refers to the use of the natural language processing in which it is used for linguistics to identify and extract information .Opinion mining has been an indispensible part of present scenario. Due to large amount of online app development and processing of all data through internet Opinion has become one of the major part in reviewing through online. A various kinds of probabilistic topic modeling technique are available to analyze and extract the idea behind the probability distribution over words. In proposed review system, a review of a particular product that brought in is Amazon, opinion review dataset of a particular product by UPC database and it is pre-processed to give a result by machine learning to get specific opinion word using sentimental analyses. LDA model is applied into the machine learning technique to analyses. It also determine the large amount of time required for determining the opinion of a particular product that is purchased. Experimental evaluation shows that our proposed techniques are efficient and perform better than previously proposed technique, however, the proposed technique can be used by any other languages

FABRICATION OF SODIUM ALGINATE/GUM GHATTI IPN MICROBEADS INTERCALATED WITH KAOLIN NANO CLAY FOR CONTROLLED RELEASE OF CURCUMIN

Objective: The objective of this study is to fabricate sodium alginate (SA)/gum ghatti (GG) microbeads intercalated with Kaolin (KA) nano clay for the sustained release of curcumin (CUR). Methods: The microbeads were prepared by a simple ionotropic gelation technique. The developed beads were characterized by fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), X-ray diffraction (X-RD), and scanning electron microscopy (SEM). Swelling studies and in vitro release studies were investigated under both pH 7.4 and pH 1.2 at 37 °C. Results: The developed microbeads were characterized by FTIR, which confirms the interaction between CUR, polymeric matrix and KA. DSC and XRD analysis reveals that the CUR has molecularly dispersed in the polymer matrix. In vitro results illustrated that microbeads were influenced by the pH of test media, which might be suitable for intestinal drug delivery. The drug release mechanism was analyzed by fitting the release data into different kinetic equations and n values are obtained in the range of 0.609-0.640, suggesting that the developed microbeads showed the non-Fickian diffusion type drug release. Conclusion: These results clearly illustrated that the developed KA intercalated polymeric microbeads are potential drug carriers for the controlled release of CUR

A Domestic Case Studies Probability to Overcome Software Failures

Computers are the pervasive technology of our time. As computer become critically tied to human life, it also becomes more important that interactions with them are under control. They are no longer a novelty, but are integrated into the fabric of our world, performing both high and low-level tasks. That is, computers may be used to eliminate heavy, redundant work and more. Sophisticated machines have been deployed to perform remote surgery or detect subterranean landmines in repopulated civilian areas. The increasing importance of computers in our lives means that it is essential that the design of computer systems incorporates techniques that can ensure reliability, safety and security. This paper will examine technological mishaps involving the use of computers. This review will include notorious software bugs that have affected finance, communication, transit, defense, health and medicine and others systems or industries. The sequence and etiology of these accidents will be discusses as well as how catastrophes may be avoided in the future through lessons and practices based on research

Gall bladder ejection fraction as a marker of autonomic neuropathy in type 2 diabetes mellitus

Background: Diabetic neuropathy is one of the commonest complications of diabetes mellitus and associated with considerable morbidity and mortality. The influence of diabetes on gall bladder function was not demonstrated in many studies. In this study, the association of fasting gall bladder volume and gall bladder ejection fraction with degree of cardiac autonomic neuropathy was assessed and correlated with duration of diabetes and severity of diabetes..Methods: The study was conducted in Government Sivagangai Medical College Hospital, Sivagangai, Madurai during a period of January 2018 to September 2018 as a Prospective observational study conducted among 100 patients in study group and 50 healthy subjects in control group. The aim of the study was to find out the incidence of autonomic neuropathy in study group by simple bed side tests, to determine the fasting gall bladder ejection fraction in diabetics, comparison of gall bladder volume in both study and control group, correlation of gall bladder ejection fraction with autonomic neuropathy.Results: The incidence of CAN is found to be high with longer duration of the disease and the degree is also correlated with duration of the disease. The correlation coefficient of this association is 0.792 which indicates high correlation. The correlation of severity of DM   with incidence and degree of CAN was 0.81 which indicates high correlation and also the study showed an increase in the FGBV and a decrease in the GBEF with increase in the severity of cardiac autonomic neuropathy.Conclusions: In patients with type 2 diabetes mellitus, the gall bladder ejection fraction is  significantly  related  to  the  duration  of diabetes mellitus and degree of hyperglycemia in addition to cardiac autonomic neuropathy(CAN). Similarly,  fasting  gall  bladder  volume (FGBV)  is  significantly increased  in  type 2  diabetes  mellitus  patients  with  cardiac autonomic neuropathy