The purpose of this study is to investigate how investor's money attitudes shape their stock market participation (SMP) decisions. This study followed the theory of planned behavior (TPB), and a survey was conducted to collect the responses from active investors. Structural equation modeling (SEM) was used for the analysis of proposed relationships among the constructs, and a confirmatory factor analysis (CFA) was conducted to check the interrelation of the variables and validity of the constructs. This research has concluded that investor's money attitudes are significant to affect their stock market participation decisions. Further, it was found that risk attitudes partially mediate the relationship between money attitudes and stock market participation. Moreover, financial knowledge and financial self-efficacy positively moderated the relationship between money attitudes and stock market participation. This research is one of the early attempts at studying the money attitudes of investors and introduces financial self-efficacy as a moderating construct between money attitudes and stock market participation. The sample size for this study was 250 respondents which can be increased in future research, and the same relationships can be tested by using a larger sample. Moreover, this study has used money attitudes as predictors of stock market participation. Still, many other variables, like personal value, can also be taken to investigate their influence on stock market participation.

Smart cities require the development of information and communication technology to become a reality (ICT). A “smart city” is built on top of a “smart grid”. The implementation of numerous smart systems that are advantageous to the environment and improve the quality of life for the residents is one of the main goals of the new smart cities. In order to improve the reliability and sustainability of the transportation system, changes are being made to the way electric vehicles (EVs) are used. As EV use has increased, several problems have arisen, including the requirement to build a charging infrastructure, and forecast peak loads. Management must consider how challenging the situation is. There have been many original solutions to these problems. These heavily rely on automata models, machine learning, and the Internet of Things. Over time, there have been more EV drivers. Electric vehicle charging at a large scale negatively impacts the power grid. Transformers may face additional voltage fluctuations, power loss, and heat if already operating at full capacity. Without EV management, these challenges cannot be solved. A machine-learning (ML)-based charge management system considers conventional charging, rapid charging, and vehicle-to-grid (V2G) technologies while guiding electric cars (EVs) to charging stations. This operation reduces the expenses associated with charging, high voltages, load fluctuation, and power loss. The effectiveness of various machine learning (ML) approaches is evaluated and compared. These techniques include Deep Neural Networks (DNN), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) (DNN). According to the results, LSTM might be used to give EV control in certain circumstances. The LSTM model’s peak voltage, power losses, and voltage stability may all be improved by compressing the load curve. In addition, we keep our billing costs to a minimum, as well.

One of the important factors in generating low cost electrical power is the accurate forecasting of electricity consumption called load forecasting. The major objective of the load forecasting is to trim down the error between actual load and forecasted load. Due to the nonlinear nature of load forecasting and its dependency on multiple variables, the traditional forecasting methods are normally outperformed by artificial intelligence techniques. In this research paper, a robust short term load forecasting technique for one to seven days ahead is introduced based on particle swarm optimization (PSO) and Levenberg Marquardt (LM) neural network forecast model, where the PSO and LM algorithm are used for the training process of neural network. The proposed methods are tested to predict the load of the New England Power Pool region's grid and compared with the existing techniques using mean absolute percentage errors to analyze the performance of the proposed methods. Forecast results confirm that the proposed LM and PSO-based neural network schemes outperformed the existing techniques.

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<span>SQL injections attacks have been rated as the most dangerous vulnerability of web-based systems over more than a decade by OWASP top ten. Though different static, runtime and hybrid approaches have been proposed to counter SQL injection attacks, no single approach guarantees flawless prevention/ detection for these attacks. Hundreds of components of open source and commercial software products are reported to be vulnerable for SQL injection to CVE repository every year. In this mapping study, we identify different existing approaches in terms of the cost of computation and protection offered. We found that most of the existing techniques claim to offer protection based on the testing on a very small or limited scale. This study dissects each proposed approach and highlights their strengths and weaknesses and categorizes them based on the underlying technology used to detect or counter the injection attacks.</span>