The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.2969

An approach to forecast impact of Covid-19 using supervised machine learning model.

Background:  Handling COVID-19 (Corona Virus Disease-2019) in Indonesia was once trending on Twitter. The Indonesian government's handling evoked pros and cons in the community. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter. Objective:  This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective. Methods:  We used tweets from Twitterscraper library. Because they did not have a label, we provided labels using sentistrength_id and experts to be classified into positive, negative, and neutral sentiments. Then, we carried out a pre-processing to eliminate duplicate and irrelevant data. Next, we employed machine learning to predict the sentiments for new data. After that, the machine learning algorithms were evaluated using confusion matrix and K-fold cross-validation. Results:  The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure with the value of 82.00%, 82.24%, 82.01%, and 81.84%, respectively. Conclusion:  From the economic perspective, people seemed to agree with the government’s policies in dealing with COVID-19; but people were not satisfied with the government performance in general. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately.

/pdf/tweets-responding-to-the-indonesian-government-s-handling-of-4pikt006pv.pdf

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications. Keywords—ML, Classification, SVM, Applications.

/pdf/machine-learning-applications-based-on-svm-classification-a-2myfaz80q8.pdf

Machine Learning Applications based on SVM Classification A Review

Dealing with vast amounts of textual data requires the use of efficient systems. Automatic summarization systems are capable of addressing this issue. Therefore, it becomes highly essential to work on the design of existing automatic summarization systems and innovate them to make them capable of meeting the demands of continuously increasing data, based on user needs. This study tends to survey the scientific literature to obtain information and knowledge about the recent research in automatic text summarization specifically abstractive summarization based on neural networks. A review of various neural networks based abstractive summarization models have been presented. The proposed conceptual framework includes five key elements identified as encoder-decoder architecture, mechanisms, training strategies and optimization algorithms, dataset, and evaluation metric. A description of these elements is also included in this article. The purpose of this research is to provide an overall understanding and familiarity with the elements of recent neural networks based abstractive text summarization models with an up-to-date review as well as to render an awareness of the challenges and issues with these systems. Analysis has been performed qualitatively with the help of a concept matrix indicating common trends in the design of recent neural abstractive summarization systems. Models employing a transformer-based encoder-decoder architecture are found to be the new state-of-the-art. Based on the knowledge acquired from the survey, this article suggests the use of pre-trained language models in complement with neural network architecture for abstractive summarization task.

/pdf/a-survey-of-the-state-of-the-art-models-in-neural-3vzbf5li7i.pdf

A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization

Nowadays, Sentiment Analysis is one of the advanced matters of natural language processing. Sentiment analysis determines a particular pole of a paragraph. Our purpose is to find the sentiment from the Bengali paragraph which is happy or sad using various types of machine learning classification analysis algorithms. For doing this we are collecting data from various social network sites, Bengali blogs, etc. To get a compatible result, we passed through many difficulties. Bengali text preprocessing is one of the complex parts of all. After preprocessing the data, we tokenized the data by using Countvectorizer. After that, we applied six different algorithms to predict almost high accuracy. Among them, the Multinomial Naive Bayes provide us the maximum accuracy which is 86.67%.

Sentiment Analysis from Bengali Depression Dataset using Machine Learning

Now a day's sentiment analysis is the most used research topic The sentiment analysis result is based on different investigation for example politics, terrorism, economy, international affairs, movies, fashion, justice, humanity Social media are the main resource for collecting people's opinion and their sentiment about a different trending topic People use many abusing words in social media to express their emotion Using sentiment analysis, we will build a platform where one can easily identify the opinions are either positive or negative or neutral This research paper will contain supervised learning which is under the machine learning approach We run an experiment on different queries from humanity to terrorism and find out an interesting result First of all, we have preprocessed the dataset to convert unstructured airline review into structured review form After that, we convert structured review into a numerical value We have to preprocess the data before using it Stop word removal, @ removal, Hashtag removal, POS tagging, calculating sentiment score have done in preprocessing part Then an algorithm has been applied to classify the opinion as either it is positive or negative In this research paper, we will briefly discuss supervised machine learning Support vector machine as well as Naive Bayes algorithm and compares their overall accuracy, precession, recall value The result shows that in the case of airline reviews Support vector machine gave way better result than Naive Bayes algorithm

Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset

Text summarization is one of the famous problems in natural language processing and deep learning in recent years. Generally, text summarization contains a short note on a large text document. Our main purpose is to create a short, fluent and understandable abstractive summary of a text document. For making a good summarizer we have used amazon fine food reviews dataset, which is available on Kaggle. We have used reviews text descriptions as our input data, and generated a simple summary of that review descriptions as our output. To assist produce some extensive summary, we have used a bi-directional RNN with LSTM's in encoding layer and attention model in decoding layer. And we applied the sequence to sequence model to generate a short summary of food descriptions. There are some challenges when we working with abstractive text summarizer such as text processing, vocabulary counting, missing word counting, word embedding, the efficiency of the model or reduce value of loss and response machine fluent summary. In this paper, the main goal was increased the efficiency and reduce train loss of sequence to sequence model for making a better abstractive text summarizer. In our experiment, we've successfully reduced the training loss with a value of 0.036 and our abstractive text summarizer able to create a short summary of English to English text.

Abstractive method of text summarization with sequence to sequence RNNs

Current world is growing so fast and communication between nation and different type of people with different language became part of our life. Even from buying product to our social life everything is dependent on communication. Therefore language is the most important part of human life. Though still now there is a language barrier for communication between people. But very soon language will be universal and everyone will be able to communicate in any language worldwide using the NLP technology. For that it is necessary to understand each language individually. This research proposes a new type of text generation of Bangla language using the bi-directional RNN. This technique is used to predict the next possible word in a Bangla text.

Bengali Text generation Using Bi-directional RNN

Text summarization is one of the leading problem of natural language processing and deep learning in recent years. Text summarization contains a condensed short note on a large text document. Our purpose is to create an efficient and effective abstractive Bengali text summarizer what can generate an understandable and meaningful summary from a given Bengali text document. To do this we have collected various texts such as newspaper articles, Facebook posts etc. and to generate summary from those text we will be using our model. Our model works with bi-directional RNNs with LSTM in encoding layer and attention model at decoding layer. Our model works as sequence to sequence model to generate summary. There are some challenges we have faced while building this model such as text pre-processing, vocabulary counting, missing words counting, word embedding, unknown words find out and so on. In this model, our main goal was to make an abstractive summarizer and reduce the train loss of that. During our research experiment, we have successfully reduced the train loss to 0.008 and able to generate a fluent short summary note from a given text.

Abu Kaisar Mohammad Masum

Papers

Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset

Abstractive method of text summarization with sequence to sequence RNNs

Sentiment Analysis from Bengali Depression Dataset using Machine Learning

Bengali Text generation Using Bi-directional RNN

Bengali abstractive text summarization using sequence to sequence RNNs