International Journal of Information Technology 

About: International Journal of Information Technology is an academic journal. The journal publishes majorly in the area(s): Cloud computing & Routing protocol. It has an ISSN identifier of 0218-7957. Over the lifetime, 803 publications have been published receiving 3059 citations.

Machine learning based approaches for detecting COVID-19 using clinical text data.

Abstract: Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naive Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.

Survey on SVM and their application in image classification

Abstract: Life of any living being is impossible if it does not have the ability to differentiate between various things, objects, smell, taste, colors, etc. Human being is a good ability to classify the object easily such as different human face, images. This is time of the machine so we want that machine can do all the work like as a human, this is part of machine learning. Here this paper discusses the some important technique for the image classification. What are the techniques through which a machine can learn for the image classification task as well as perform the classification task with efficiently. The most known technique to learn a machine is SVM. Support Vector machine (SVM) has evolved as an efficient paradigm for classification. SVM has a strongest mathematical model for classification and regression. This powerful mathematical foundation gives a new direction for further research in the vast field of classification and regression. Over the past few decades, various improvements to SVM has appeared, such as twin SVM, Lagrangian SVM, Quantum Support vector machine, least square support vector machine, etc., which will be further discussed in the paper, led to the creation of a new approach for better classification accuracy. For improving the accuracy as well as performance of SVM, we must aware of how a kernel function should be selected and what are the different approaches for parameter selection. This paper reviews the different computational model of SVM and key process for the SVM system development. Furthermore provides survey on their applications for image classification.

Group Key Management Protocols: A Novel Taxonomy

Abstract: Group key management is an important functional building block for any secure multicast architecture. Thereby, it has been extensively studied in the literature. In this paper we present relevant group key management protocols. Then, we compare them against some pertinent performance criteria.

Counting the cost of COVID-19.

Abstract: Coronavirus disease 2019 (COVID-19) is the name given by the World Health Organization (WHO) to the highly contagious and infectious disease caused by the Novel Corona Virus or SARS-CoV-2, which was first reported on 31 December 2019 in Wuhan city of the capital of China's Hubei province. Due to the rapid increase in the number of infections worldwide, the WHO in March 2020, declared COVID-19 as a pandemic. Historically, first coronavirus had surfaced in 1965 with symptoms of common cold. Since then five different strands of this virus have emerged, most lethal of them was the Severe Acute Respiratory Syndrome (SARS), which infected about eight thousand people, killing ten percent of them. The COVID-19 is not the most deadly pandemic world has ever witnessed as the Spanish influenza pandemic, during 1918-19, killed more than fifty million people. Indeed COVID-19 has turned out to be the most lethal of all coronaviruses as it has infected at least three million people killing more than two hundred thousands of them in the first 4 months of its spread. Many politicians and social scientists have dubbed the depression, being caused by COVID-19, worse than that caused by the Second World War. In this article, we shall analyze economic, social, cultural, educational and political impact of the COVID-19.

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.