Volume 10 | Issue 6 |

 Abstract

The purpose of our proposed system is to predict the stock market from previous years stock data. Increase in uncertainty in stock market gives us the motive for our proposed system to make it easy and available for common people to know and avoid losses using other technologies available today. The approach consists of the following steps - Data pre-processing, clustering, classification and visualization. Classification and correlation of data set makes it easy to understand similarities & dissimilarities amongst the data objects. In our proposed system data collection was done from yfinance. The significance of our proposed system is to give the commoner an insight in stock market pattern and awareness to avoid losses in prior. Stock prices cannot be predicted exactly since it is neither systematic nor random. Even though we cannot predict exact price changes due to uncertainty but can predict the price patterns. The predicted results cannot be 100% accurate but the results show that our application helps in reducing occurrence of losses to a certain extent by providing idea about stock market price pattern. We are collecting stock data from source yfinance. This huge data is used as a record for creating a yfinance stock market analysis. So, the main challenge in front of us is developing a better, efficient stock market pattern detection tool to identify stock patterns effectively and give any suggestion to the viewer on how to proceed further

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 Keywords

STOCK MARKETING PREDICTION USING DEEP LEARNING

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Lane detection plays an important role in intelligent vehicle systems. Therefore, this paper presents a robust road lane marker detection algorithm to detect the left and right lane markers. The algorithm consists of optimization of Canny edge detection and Hough Transform. The system captures images from a front viewing vision sensor placed facing the road behind the windscreen as input. Then a series of image processing is applied to generate the road model. Canny edge detection performs features recognition then followed by Hough Transform lane generation. The algorithm detects visible left and right lane markers on the road based on real-time video processing.

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LANE LINE DETECTION WITH COMPUTER VISION USING ARTIFICIAL INTELLIGENCE

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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PREDICTION OF CYBER ATTACKS USING MACHINE LEARNING TECHNIQUE

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Creative Commons Attribution 4.0 and The Open Definition

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To design a recommendation system for the users to recommend the best topics among the users and to develop a system that promotes the interest of the user by recommending them with customized topics. LDA model is used for extracting all the topics of the user. Popular topics are then analysed and extracted. Influence analysis is carried out to find the influenced topics for the users and the topics are classified to positive and negative topics.Those topics are then ranked using SVM algorithm. Finally recommendation of topics are given to the users.

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PERSONALIZED RECOMMENDATION OF TOPIC BY INFLUENCE ANALYSIS USING SUPPORT VECTOR MACHINE ALGORITHM

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cloud data loss or disruption can occur because of a natural disaster, human error, malicious activity, or other causes. It can damage your reputation, cause client attrition, and lead to unnecessary delays and expenses. Still, most backup systems have been designed and optimized for outdated environments and use cases. That fact, generates frustration over currently backup challenges and leads to a greater willingness to modernize and to consider new technologies. This proposed system set up a flexible data backup operation, using Disaster Backup as a Service (BaaS) solutions, mixing them up with FogDrive local storage system.

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FOG DRIVE DISASTER BACKUP AS A SERVICE FOR CLOUD SERVER USING IOT AND FOG COMPUTING

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the past decades, there is an increasing interest in predicting markets among economists, policymakers, academics and market makers. The objective of the proposed work is to study and improve the supervised learning algorithms to predict the stock price.Stock Market Analysis of stocks using data mining will be useful for new investors to invest in stock market based on the various factors considered by the software. Stock market includes daily activities like Sensex calculation, exchange of shares. The exchange provides an efficient and transparent market for trading in equity, debt instruments and derivatives. Our aim is to create software that analyses previous stock data of certain companies, with help of certain parameters that affect stock value. We are going to implement these values in data mining algorithms and we will be able to decide which algorithm gives the best result. This will also help us to determine the values that particular stock will have in near future. We will determine the patterns in data with help of machine learning algorithms.

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STOCK MARKET PRICE PREDICTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The on-demand service system is built based on service-oriented architecture that is going to be a business model through which Normal people can connect with the domestic workers and provide features. Providing services to the people who are in seek for service. Providing jobs to the domestic workers. Allows the administrator to control the entire system with a web enabled back-end user interface. The System consist of two interfaces for the user the web enabled application and an android application for variable access. The On-demand is a system that connects the service providers and people who seek for service. It is based on SOA architecture and also have an advantage of location-based service. Implementation this application, there is no need of much expensive hardware and software, internet connection and a desktop with basic operating system and a browser will be sufficient. The entire system is built for an organization known as passhouse. This system was built in an intention to showcase the efficient utilization of the services which will be very useful for the developers to save time in doing CRUD operations in common language. This can be easily achieved by modifying the data model as per the requirements.

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ON-DEMAND SERVICE SYSTEM USING SOA

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Data de duplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. To protect the confidentiality of sensitive data while supporting de duplication, the convergent encryption technique has been proposed to encrypt the data before outsourcing. To better protect data security, this proposed system makes the first attempt to formally address the problem of authorized data de duplication. Different from traditional de duplication systems, the differential privileges of users are further considered induplicate check besides the data itself. We also present several new de duplication constructions supporting authorized duplicate check in hybrid cloud architecture. Security analysis demonstrates that our scheme is secure in terms of the definitions specified in the proposed security model. As a proof of concept, the proposed work implements a prototype of our proposed authorized duplicate check scheme and conduct test bed experiments using our prototype. The proposed work shows that our proposed authorized duplicate check scheme incurs minimal overhead compared to normal operations.

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IMPROVING ACCESSING EFFICIENCY OF CLOUD STORAGE USING DEDUPLICATION SCHEMES

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

????? ??? ???? ?? (Goods and Service Tax- GST) ?? ?? ?????????? ?? ??? ?? ?????? 1 ???? 2017 ???? ???? ??????? ??? ????. ?????? ????? ??? ???? ?? ???? ???? ?? ?? ???? ???? ?????????? ??? ????????? ?? ????????? ???????? ?????????? ?????? ?? ???????????? ???? ????. ?????, ????? ??? ???? ?? ?? ?????????? ?????? ???? ???? ??? ????? ??? ???? ?? ?????? ????? ?????? ????? ??? ???? ?? ?????? ?????? ???? ??????? ??? ????????? ????? ????? ?????. ???? ?? ????, ?????????????? ??????, ???? ??????? ????, ITC ??????, ????? ??? ???? ?? ?? ??? ?-?? ??? ??????? ????????? ???????????, ??????? SMEs ?? ??????? ?????? ???? ???. ?? ????? ?????? ?????? ??? ??????? ??????????? ?????? ?????? ???? ????? ??????? ??????? ???? ???.

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Creative Commons Attribution 4.0 and The Open Definition

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GST Good and Services Tax

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The evolution and growth of social networking and modern web technology have made an individual's online presence permanent. People frequently express their thoughts, ideas, and emotions through social networking links, with the most popular activity being the discussion of everyday events, which may include private or public conversations. Bullying that involves technology is referred to as cyber-bullying. Bullying attacks target teenagers and young people who spent lot of time on social networking sites. The rise of social media, especially Twitter, has caused confusion about the meaning of free expression, which has given rise to a number of worries. Cyber-bullying is one of these issues, a severe global problem that has an impact on both people and societies. There have been numerous reported attempts to intervene, prevent, or lessen cyber-bullying; however, these efforts are unworkable since they depend on the victims' interactions. Victims of this behavior may experience hopelessness and other potentially fatal issues. It is necessary to create monitoring and detection procedures for potentially hazardous Internet behavior. By using machine learning, we can create algorithms to automatically identify cyber-bullying content and recognize language patterns used by bullies and their victims. Therefore, it is crucial to identify cyber-bullying without the victims' participation. Additionally, numerous machine learning classifiers were applied, including the K-nearest neighbor technique, linear regression, decision trees, and the Support Vector Machine classifiers, Random Forest, Naive Bayes, and AdaBoost. The most precise supervised learning algorithm was used to identify communications that contained cyber-bullying.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

cyber-bully, cybercrime, social media, traditional bullying, social Networking sites, Harassment.

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Creative Commons Attribution 4.0 and The Open Definition