This paper links SME performance, with the use of planning and demographics of key person. A model and research frame work has been developed to study the linkage between dependent (SME performance) and independent (use of planning) variables. Structured questionnaire schedule is developed, based on previous research works in this area. A survey is conducted among the representative firms (SMEs in rubber and plastic sector). Statistical test using SPSS and AMOS is conducted and the results are interpreted. Univariate and multivariate tests are used to test the hypotheses formed. Planning, standardization and IT usage by the firms are significantly influencing firm performance. The paper highlights the importance of planning to better the firm performance. For the SMEs to come fourth and to survive in this highly competitive and globalized environment, specific competencies of planning and IT usage are to be attained.
Recently, lots of attempts are done to work on social sites to examine of public sentiment. Most of the efforts are usable to give fine ideas of social public opinions from social media. Hence, there is a need of suitable approach to overcome this problem. Sentiment Analysis (SA) is an action of computationally diagnosing and grouping opinions represented in a particular bunch of text. It is used to recognize opinion of public as feedbacks depending upon the data/domain in social media. Information Gain (IG) is a measure used to identify most impactful words as features in the tweet to classify the opinions using some classification approaches. The purpose of this article is to discuss some approaches for extracting features from tweets and classifying it.
Aim: Machine learning techniques are rapidly used in the area of medical research due to its impressive results in diagnosis and prediction of diseases. The objective of this study is to evaluate the performance of SVM classifier in identification of liver disorder by comparing it with Naive Bayes algorithm. Methods and Materials: A total of 31619 samples are collected from three liver disease datasets available in kaggle. These samples are divided into training dataset (n = 22133 [70%]) and test dataset (n = 9486 [30%]). Accuracy, precision, specificity and sensitivity values are calculated to quantify the performance of the SVM algorithm. Results: SVM achieved accuracy, precision, sensitivity and specificity of 73.64%, 97.82%, 97.56% and 69.77% respectively compared to 57.31%, 41.39%, 94.87% and 37.20% by Naive Bayes algorithm. Conclusion: In this study it is found that the RBF SVM algorithm performed better than the Naive Bayes algorithm in liver disorder detection of the datasets considered.
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