Rajasthan Technical University

About: Rajasthan Technical University is a education organization based out in Kota, Rajasthan, India. It is known for research contribution in the topics: Photovoltaic system & PID controller. The organization has 716 authors who have published 1084 publications receiving 4530 citations. The organization is also known as: RTU.

Analysis on novel coronavirus (COVID-19) using machine learning methods

Abstract: In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy.

Optimizing semantic LSTM for spam detection

Abstract: Classifying spam is a topic of ongoing research in the area of natural language processing, especially with the increase in the usage of the Internet for social networking. This has given rise to the increase in spam activity by the spammers who try to take commercial or non-commercial advantage by sending the spam messages. In this paper, we have implemented an evolving area of technique known as deep learning technique. A special architecture known as Long Short Term Memory (LSTM), a variant of the Recursive Neural Network (RNN) is used for spam classification. It has an ability to learn abstract features unlike traditional classifiers, where the features are hand-crafted. Before using the LSTM for classification task, the text is converted into semantic word vectors with the help of word2vec, WordNet and ConceptNet. The classification results are compared with the benchmark classifiers like SVM, Naive Bayes, ANN, k-NN and Random Forest. Two corpuses are used for comparison of results: SMS Spam Collection dataset and Twitter dataset. The results are evaluated using metrics like Accuracy and F measure. The evaluation of the results shows that LSTM is able to outperform traditional machine learning methods for detection of spam with a considerable margin.

A Survey on Parallel Particle Swarm Optimization Algorithms

Abstract: Most of the complex research problems can be formulated as optimization problems. Emergence of big data technologies have also commenced the generation of complex optimization problems with large size. The high computational cost of these problems has rendered the development of optimization algorithms with parallelization. Particle swarm optimization (PSO) algorithm is one of the most popular swarm intelligence-based algorithm, which is enriched with robustness, simplicity and global search capabilities. However, one of the major hindrance with PSO is its susceptibility of getting entrapped in local optima and; alike other evolutionary algorithms the performance of PSO gets deteriorated as soon as the dimension of the problem increases. Hence, several efforts are made to enhance its performance that includes the parallelization of PSO. The basic architecture of PSO inherits a natural parallelism, and receptiveness of fast processing machines has made this task pretty convenient. Therefore, parallelized PSO (PPSO) has emerged as a well-accepted algorithm by the research community. Several studies have been performed on parallelizing PSO algorithm so far. Proposed work presents a comprehensive and systematic survey of the studies on PPSO algorithms and variants along with their parallelization strategies and applications.