The main motive behind this research paper is to use the power of social media to observe, examine and analyze the opinion regarding recent Indian government project as the opinion of people plays a vital role in formulating the government policies. By getting into the deeper insights on social media, one can easily analyze the behavior of people regarding various issues and policies, which was otherwise impossible using traditional sources. The case study was done on Statue of Unity. Analysis was done on one of the famous social networking sites i.e. Twitter, using R programming language. Twitter API was used to collect the primary data. Tweets were analyzed by using opinion lexicon and Emotion lexicon-based approaches. Opinion Lexicon based approach categorized the sentiment of tweets in three categories, while Emotion Lexicon based approach refined them into eight more categories. The research work done in this paper will help government to understand the emotions of people regarding their policies and will also enrich people to help them understand majority vote of people
With the advance in technology the focus has shifted from desktops and laptops to handheld devices like tablets, phablets, mobile phones, PDA etc. giving rise in the number of users connected to internet. About 900 million computers are connected to internet [23].In an hour 383 thousand TB of data transmission takes place [24]. As the traffic on internet increases giving rise to the problem of congestion. Several congestion techniques have been proposed, in this paper the summary of several congestion techniques and methods over the years has been discussed. One of the latest approaches to control the congestion is based on Neural Networks is also included in this paper. KeywordsCongestion control, TCP, Neural Networks, ECN, XCN, ATM, MANETS, EWCCP, Back propagation algorithm. INTRODUCTIONA Network is said to be in the state of congestion when the total demands for resources exceeds the total available resources .In other words we can say a network is in a state of congestion when the quality of the service delivered to the user decreases, whenever there is increase of load in a network. Take an example of downloading a 1 GB file .When there is no congestion, the file gets downloaded in few minutes, but on day when there is congestion in a network same file gets downloaded in hours. The problem of congestion has become an important issue these days. BACKGROUNDIn the year 1999 Saverio Mascolo in his paper titled [21] states that there is a large bandwidth delay products in high speed communication networks thus resulting in adverse conditions on closed-loop congestion control algorithm. By using the combination of classical theory and Smiths principle a law for congestion control for high speed communication networks can be designed. This control law is transformed into discrete values and window form for ATM and internet respectively thus guarantying full and stable utilization of queue, links during transit and steady phase.In the year 2001 Deepak Bansal And Hari Balakrishnan in their paper titled [6] states that congestion results in reduction of transmission rate that is problematic for voice and video applications ,therefore an algorithm that is nonlinear in nature is needed naming this algorithm as binomial Algorithm. TheBinomial algorithm makes use of TCP style for Addictive increase and multiple decrease for increasing and decreasing the transmission rate. In addictive increase the rate of increase is inversely proportional to power p of current window size and for multiple decrease, rate of decrease is proportional to power z of current window size. There can be infinite number of such binomial algorithms, those that satisfy p +z =1 and all binomial algorithms converges for the fairness.In the year 2002 J.Alan Bivens in his paper titled [10] predicts the congestion in a network using neural network learning technique, in which neural networks are used to predict the source of congestion, the system has been divided into three programs, one implementation consists of c language used for manipula...
COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.
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