The area of stock market prediction has attracted a great deal of attention during the past decade especially after multiple market crashes. By analyzing market price fluctuations, we can achieve valuable insight regarding future trends. This research proposes a novel method for prediction using pattern analysis and classification. For the first part of the research, a trend analysis algorithm, Elliot wave theory, is used to classify price patterns for DJIA, S&P500, and NASDAQ into three categories: LONG, SHORT, and HOLD. After labeling patterns, classification learning algorithms including decision tree, naïve Bayes, and support vector machine (SVM) are used to learn from the patterns and make a prediction for the future. The algorithm is implemented during the market crashes of May 2010 and August 2015, and the obtained results show that it correctly identifies the market volatility by issuing HOLD and SHORT signals during those crashes.

The recent outbreak of COVID-19 has caused disruption all over the world including in the US. It seems that this pandemic is going to stay for a while. Under current circumstances, what matters most is to mitigate its impact to get back to the daily routine as fast as possible. One answer to this challenge is to turn to technology, especially ICT (information and communication technology). Relying on ICT requires a dependable ICT infrastructure that can handle the fast-growing number of users transitioning to online mode. NRI (Network Readiness Index) is a composite index to measure the multi-faceted impact of ICT on society and development. It is a holistic framework measuring the impact of ICT on four fundamental dimensions of society: technology, people, governance, and impact. In this article, a thorough study of the NRI pillars in 2019 is conducted to highlight the strengths and weaknesses of network readiness in the US. The results of the analysis will provide insight into the trend of digital transformation in the USA.

The extensive use of the internet and digital technology in the workplace, including universities, has transformed the work style of employees, faculty members, and students. On the one hand, it has helped employees communicate, coordinate, and collaborate on a 24/7 basis around the world, which in turn has improved productivity and work performance. However, on the other hand, it has made employees vulnerable to monitoring and invasion of privacy. Many faculty members and students feel that surveillance and computer monitoring are compromising their intellectual freedom, the right to free inquiry, and digital privacy. This study addresses how the use of computer monitoring affects the morale and performance of faculty members and students for their intellectual and free inquiry. The study uses the survey method to interview professors and students to analyze their responses with regards to monitoring their online activities.

In this research paper, a novel study on the perception of the users regarding the role of cookies, online advertisements, and their impact on users' shopping behavior is conducted.Cookies are text files used to collect customers' information, especially by E-commerce websites, and for providing better services to online customers.The results of this research will provide online businesses an insight into online advertisements and how to target customers more effectively.This research presents that older people tend to perceive advertisements delivered to them as less relevant and therefore less effective in changing their online behavior.

The stock market prediction is an interesting topic, especially for traders and investors. One important aspect of predicting the stock market is identifying price patterns which may result in a market crash. With the advancement of computer technology, particularly in the area of artificial intelligence, a large number of new models have been proposed. The proposed method in this article is based on identifying the normal behaviour of a crowd in the stock market using exponential moving average and then classifying the price fluctuations into three categories BUY, SELL, and STOP. An artificial neural network (ANN) with five input neurons, ten hidden neurons, and three output neurons is then used to learn from the price fluctuations and predict one day ahead. The final results show that the algorithm is capable of identifying the market crashes in advance by issuing STOP labels.

Vehicular Ad-hoc Networks (VANET) has attracted a great deal of attention during the last decade. This type of wireless network is predicted to play a key role in future automotive innovation. VANET as a foundation for Intelligent Transportation System (ITS) promises many improvements in terms of safety, resource efficiency and passenger assistance services. Among these three main categories, safety applications are the most important ones because they deal with the lives of large numbers of people who drive every day. Safety applications are classified as real-time applications; they must act within a certain period of time, otherwise their implementation will be worthless. As a result, providing Quality of Service (QoS) is critical for this type of network. Various methods of improving QoS in the different layers of VANET, such as physical and Medium Access Control (MAC) have been proposed so far. In this paper the main focus will be the network layer. Two important routing protocols, Ad-hoc On-demand Distance Vector (AODV) and Destination-Sequenced Distance Vector (DSDV) are compared regarding their QoS parameters including delay, packet loss, and overhead in simulation scenarios.