Asian Journal of Research in Computer Science

A stock market is a place where company shares are traded to the stockbrokers. Stock price prediction is one of the most challenging problems as a high level of accuracy is the key factor in predicting a stock market. Many methods are used to predict the price in the stock market but none of those methods are proved as a consistently acceptable prediction tool due to its volatile nature. In this paper, we proposed Artificial Neural Network (ANN) technique because ANN can generalize and predict data after learning and analyzing from the initial inputs and their relationships. We used feed forward network and backward propagation algorithm to predict stock prices. In this paper, we introduced a method that can find out the future value of stock prices in a particular day based on some input using ANN back propagation algorithm.

The vulnerabilities in most web applications enable hackers to gain access to confidential and private information. Structured query injection poses a significant threat to web applications and is one of the most common and widely used information theft mechanisms. Where hackers benefit from errors in the design of systems or existing gaps by not filtering the user's input for some special characters and symbols contained within the structural query sentences or the quality of the information is not checked, whether it is text or numerical, which causes unpredictability of the outcome of its implementation. In this paper, we review PHP techniques and other techniques for protecting SQL from the injection, methods for detecting SQL attacks, types of SQL injection, causes of SQL injection via getting and Post, and prevention technology for SQL vulnerabilities.

Whether it's for work or personal well-being, keeping secrets or private information has become part of our everyday existence. Therefore, several researchers acquire an entire focus on secure transmitting secret information. Confidential information is collectively referred to as Steganography for inconspicuous digital media such as video, audio, and images. In disguising information, Steganography plays a significant role. Traditional Steganography faces a further concern of discovery as steganalysis develops. The safety of present steganographic technologies thus has to be improved. In this research, some of the techniques that have been used to hide information inside images have been reviewed. According to the hiding domain, these techniques can be divided into two main parts: The spatial Domain and Transform Domain. In this paper, three methods for each Domain have been chosen to be studied and evaluated. These are; Least Significant Bit (LSB), Pixel Value Difference (PVD), Exploiting Modification Direction (EMD), contourlet transform, Discrete Wavelet Transformation (DWT), and, Discrete Cosine Transformation (DCT). Finally, the best results that have been obtained in terms of higher PSNR, Capacity, and more robustness and security are discussed.

Thermally coupled distillation is a new energy-saving method, but the traditional thermally coupled distillation simulation calculation process is complicated, and the optimization method based on the traditional simulation process is difficult to obtain a good feasible solution. The neural network algorithm has the advantages of fast learning and can approach nonlinear functions arbitrarily. For the problems in complex process control systems, neural network control does not require cumbersome control structures or precise mathematical models. When training the network, only the input and output samples it needs are given, so that the dynamics of the system can be controlled. In this way, the dynamic performance of the system can be approximated. This method can effectively solve the mathematical model of the thermally coupled distillation process, and quickly obtain the solution of the optimized variables and the objective function. This article summarizes the research progress of artificial neural network and the optimization control of thermally coupled distillation and the application of neural network in thermally coupled distillation.

Nanotechnology is a field of study that aims to make our lives easier, safer, and more environmentally friendly. With current upgrades and alterations to available networking and communication paradigms, incorporating Wireless Nano Sensor Networks (WNSN) with various products, sensors, and devices would introduce new network paradigms. The Internet of Nano Things is a term for this concept (IoNT), To achieve seamless interconnection between Nano networks and existing communication networks and the Internet, many topologies and communication paradigms must be developed while technological hurdles are addressed. The amount of data accessible limits how much research and decision-making can be done. This research visualizes a wide range of nanotechnology applications. The goal of this project was to show in more than way this research method may be used to information recommendation services. The routing protocol is critical in WNSN and IoNT because of the many nanoscale constraints. While ensuring the flow of data and information, this routing protocol must take into account the specific features of nanoscale communication.This research aims to provide insight into the WNSN (Wireless Nano Sensor Networks) and IoNT (The Internet of Nano Things) paradigms, as well as a detailed assessment of a large number of current routing protocols that are tailored to the characteristics and features of nano communication. Big data applications with their features and characteristics in general also use cloud computing. This paper explains different hands based on neural networks and implemented on FPGAs (which is Field-Programmable Gate Array) and other genetic algorithms and neural networks. Many more approaches and models compare.