Emotion is considered to be one of the significant human interaction factors. Recognizing human behavior based on emotion is often misinterpreted. However, that has not prevented the analysts from attempting to extract the information from a speech called speech recognition. In this paper, we analyzed the different algorithm's performance in the field of speech signal classification. Considering accuracy and precision as factors, ANN showed higher results with around 80% accuracy and correctness, followed by SVM and CNN competing for each other for accuracy in between 75 and 80 percent. SVM showed close approximation between accuracy and precision, leaving random forest classifier in the last place. Our experimentation showed neural networks performed substantially in the field of speech and signal processing.

The whole world facing a huge crisis because of Corona virus also known as COVID-2019, identified first in December 2019 in the city of Wuhan located in China. The detection of persons infected with the virus is most important as it can be spread easily from him to others and also the person infected with the virus may not know that he is infected until a number of symptoms fallout from him. In this paper the virus detection is done using deep learning and machine learning algorithms using the X-ray images. A dataset is created with three classes consisting of normal, corona virus, and pneumonia images. The proposed method uses ResNet50 and SVM, deep learning features are extracted using ResNet50 and classification is done using SVM classifier. The classification accuracy obtained from the model is 100% when testing on the Corona virus and normal images, whereas the results obtained from the model is 94% when it is tested on the dataset consisting of normal, Corona virus and pneumonia images and performed well compared to VGG16.

Among all the reasons for the occurrence of road accidents, the human state of drowsiness and Underage driving contributes a major share. With the rigid implementation of traffic rules and national schemes, it does not result in decreasing the accidents. Hence, there is a need for automation of surveillance which strictly restricts the teen driving and fatigue driving. In this paper, we introduce a face image descriptor-based combination of deep learning model i.e., convolution neural network (CNN) with ResNet50 architecture to predict age and a recurrent neural network (RNN) with LSTM architecture to detect the drowsiness in driver and alert them when they are drowsy. The algorithm is based on face recognition for age prediction and the blink frequency for detecting the fatigue. Image processing techniques are utilized to obtain the feature-based extraction for prediction. The proposed model developed could give a validation accuracy of 96% thus providing the promising results. This automation model thus could help the road safety authorities in their work and also decreases the occurrence of road accidents.

No abstract

Terrorism is a major issue facing the world today. It has negative impact on the economy of the nation suffering terrorist attacks from which it takes years to recover. Many developing countries are facing threats from terrorist groups and organizations. This paper examines various terrorist factors using data mining from the historical data to predict the terrorist groups most likely to attack a nation. In this paper we focus on sampled data primarily from India for the past two decades and also consider International database. To create meaningful insights, data mining, machine learning techniques and algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine, Ensemble methods, Random Forest Classification are implemented to analyze comparative based classification results. Patterns and predictions are represented in the form of visualizations with the help of Python and Jupyter Notebook. This analysis will help to take appropriate preventive measures to stop Terrorism attacks and to increase investments, to grow the economy and tourism.