Abstract-Exponential growth in mobile technology and mini computing devices has led to a massive increment in social media users, who are continuously posting their views and comments about certain products and services, which are in their use. These views and comments can be extremely beneficial for the companies which are interested to know about the public opinion regarding their offered products or services. This type of public opinion otherwise can be obtained via questionnaires and surveys, which is no doubt a difficult and complex task. So, the valuable information in the form of comments and posts from micro-blogging sites can be used by the companies to eliminate the flaws and to improve the products or services according to customer needs. However, extracting a general opinion out of a staggering number of users' comments manually cannot be feasible. A solution to this is to use an automatic method for sentiment mining. Support Vector Machine (SVM) is one of the widely used classification techniques for polarity detection from textual data. This study proposes a technique to tune the SVM performance by using grid search method for sentiment analysis. In this paper, three datasets are used for the experiment and performance of proposed technique is evaluated using three information retrieval metrics: precision, recall and f-measure.

The world has revolutionized and phased into a new era, an era which upholds the true essence of technology and digitalization. As the market has evolved at a staggering scale, it is must to exploit and inherit the advantages and opportunities, it provides. With the advent of web 2.0, considering the scalability and unbounded reach that it provides, it is detrimental for an organization to not to adopt the new techniques in the competitive stakes that this emerging virtual world has set along with its advantages. The transformed and highly intelligent data mining approaches now allow organizations to collect, categorize, and analyze users' reviews and comments from micro-blogging sites regarding their services and products. This type of analysis makes those organizations capable to assess, what the consumers want, what they disapprove of, and what measures can be taken to sustain and improve the performance of products and services. This study focuses on critical analysis of the literature from year 2012 to 2017 on sentiment analysis by using SVM (support vector machine). SVM is one of the widely used supervised machine learning techniques for text classification. This systematic review will serve the scholars and researchers to analyze the latest work of sentiment analysis with SVM as well as provide them a baseline for future trends and comparisons.

Abstract-Rainfall prediction has extreme significance in countless aspects and scopes. It can be very helpful to reduce the effects of sudden and extreme rainfall by taking effective security measures in advance. Due to climate variations, an accurate rainfall prediction has become more complex than before. Data mining techniques can predict the rainfall through extracting the hidden patterns among weather attributes of past data. This research contributes by exploring the use of various data mining techniques for rainfall prediction in Lahore city. Techniques include: Support Vector Machine (SVM), Naïve Bayes (NB), k Nearest Neighbor (kNN), Decision Tree (J48) and Multilayer Perceptron (MLP). The dataset is obtained from a weather forecasting website and consists of several atmospheric attributes. For effective prediction, pre-processing technique is used which consists of cleaning and normalization processes. Performance of used data mining techniques is analyzed in terms of precision, recall and f-measure with various ratios of training and test data.

Abstract-Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in advance regarding: ongoing construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. Published papers from year 2013 to 2017 from renowned online search libraries are considered for this research. This review will serve the researchers to analyze the latest work on rainfall prediction with the focus on data mining techniques and also will provide a baseline for future directions and comparisons.

Meaningful change in bone mineral density (BMD) should be equal or higher than institutional least significant change (LSC). But some facilities use vendor's LSC which is discouraged by International Society for Clinical Densitometry (ISCD). The aim of this study was to find the impact of scan interpretation upon interval BMD changes using vendors and institutional LSCs. This prospective study was conducted at Joint Commission International-accredited facility of Pakistan from April–June 2017 using Hologic Discovery-A scanner. As per ISCD recommendations, precision error and LSC of two technologists were measured. Serial BMD changes such as deterioration or improvement interpreted based on vendor's and institutional LSCs were compared. Serial BMD changes in 102 patients were included, having a mean age, male:female ratio, and mean body mass index of 63 years, 94%:06%, and 29.274 kg/m2, respectively. Mean menopausal age was 47 years and mean duration between two dual X-ray absorptiometry (DXA) studies was 3 years. BMD changes over hip were found significant in 55% and 53% cases against vendor's and institutional LSCs, respectively (nonsignificant discordance in 2%). BMD changes using vendor's and institutional LSCs were found significant over L1-4 (62% vs. 46%; discordance: 14%) and distal forearm (77% vs. 35%; discordance: 41%), respectively. Interpretations based on vendor's LSCs revealed significantly overestimated deterioration over forearm and improvement over L1-4 BMD values. We conclude that vendor's provided LSC for interpretation of serial DXA is misleading and has a significant negative impact upon patients' management. Every DXA facility must use its own LSC as per ISCD guidelines. Furthermore, ISCD must consider publishing cutoff values for LSC for distal forearm measurement.