The flightiness and unpredictability of the financial exchange render it trying to make a significant benefit utilizing any summed up conspire. This paper means to talk about our AI model, which can create a lot of gain in the US financial exchange by performing live exchanging in the Quantopian stage while utilizing assets liberated from cost. Our top methodology was to utilize outfit learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization and Stochastic Gradient Descent, to choose whether to go long or short on a specific stock. As indicated by various examinations, stocks produce more noteworthy returns than different resources. Stock returns mostly come from capital increases and profits. Capital increases are the point at which you sell a specific stock at a more exorbitant cost than at which you bought it. Profits are a portion of the benefit that the organization whose stocks you bought makes, and disseminates it to its investors. As indicated by S&P Dow Jones Indices, beginning around 1926, profits have added to 33% of speculation returns while the other 66% have been contributed by capital increases. The possibility of purchasing shares from generally effective organizations like Apple, Amazon, Facebook, Google, and Netflix, together meant by the renowned abbreviation FAANG, during the beginning phases of stock exchanging can appear to be enticing. Financial backers with a high capacity to bear hazard would incline more towards capital additions for acquiring benefit rather than profits. Other people who lean toward a more safe methodology might decide to stay with stocks which have generally been known to give steady and huge profits.
In modern times every human being rely upon the internet for their scant to hefty needs as internet offers vast amount of information to users, so it’s availability to users is indispensable. Major objectives of security are availability, integrity and confidentiality. Cross Site Scripting (XSS) and SQL Injection Attack (SQLIA) is a generic and critical security issue towards to the web application and database security. In general, not well validated and verified web applications are highly prone and vulnerable by the attackers. Due to the creative and dynamic XSS and SQLIA methods and techniques, users can save their valuable, integral and confidential data in the web site to save their market stability towards their self as well as social enrichment. Many tools and techniques are addressed to the references regarding the XSS and SQL Injection issues, but we are present and used pattern matching techniques in SQL statements to implement the SQLIA and XSS in web application. At the outset pattern matching algorithm is used and gets better solution towards on implementation of SQLIA and XSS attacks and preventions.
In the recent times, the stock markets have emerged as one of the top investment destinations for individual and retail investors due to the lure of huge profits that are possible with stock investments compared to more traditional and conservative forms of investments such as bank deposits, real estate and gold. The stock markets unlike other forms of investment are highly dynamic due to the various variables involved in stock price determination and are complex to understand for a common investor. Individual and small-time investors have to generate a portfolio of common stocks to reduce the overall risk and generate reasonable returns on their investment. This phenomenon has given way too many individual and retail investors incurring huge losses because their decisions are based on speculation and not on sound technical grounds. While there are financial advisory firms and online tools where individual investors can get professional stock investment advice, the reliability of such investment advice in the recent past has been inconsistent and not meeting the rigor of quantitative and rational stock selection process. Many of such stock analysts and the tools mostly rely on short term technical indicators and are biased by the speculation in the market leading to huge variances in their predictions and leading to huge losses for individual investors. While the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques is widely adopted in the financial domain, integration of AI/ML techniques with fundamental variables and long-term value investing is a lacking in this domain. Some of the stock portfolio tools available in the market use AI/ML techniques but are mostly built using technical indicators which makes them only suitable for general trend predictions, intraday trading and not suitable for long term value investing due to wide variances and reliability issues. The availability of a Financial Decision Support System which can help stock investors with reliable and accurate information for selecting stocks and creating an automated portfolio with detailed quantitative analysis is lacking. A Financial Decision Support System (DSS) that can establish a relationship between the fundamental financial variables and the stock prices that can VII automatically create a portfolio of premium stocks shall be of great utility to the individual investment community. As part of this thesis, the researcher has designed and developed a Financial Decision Support System (DSS) for selecting stocks and automatically creating portfolios with minimal inputs from the individual investors. The Financial DSS is based on a System Architecture combining the advantages of Artificial Intelligence (AI), Machine learning (ML) and Mathematical models. The design and development of the Financial DSS is based on the philosophy to combine various independent models and not rely on a single stock price model to increase the accuracy and reliability of the stock selections and increase the overall Return on Investment (ROI) of the stock portfolio. The Machine learning models are used to establish the relationship between fundamental financial variables and the price of the stock, a mathematical model is developed to calculate the intrinsic value of the stock taking in to account the full lifecycle of the stock which involves various phases and a comprehensive model to analyze the financial health of the stocks. The AI/ML stock models are independently trained using historical financial data and integrated with the overall Financial DSS. Finally, the Financial DSS tool with a graphical user interface is built integrating all the three models which shall be able to run on a general-purpose desktop or laptop. To reliably validate the Financial DSS, it has been subjected to wide variety of stocks in terms of market capitalization and industry segments. The Financial DSS is validated for its short term and long-term Return on Investment (ROI) using both historical and current real-time financial data. The researcher has reported that the accuracy of the AI/ML stock price models is greater than 90% and the overall ROI of the stock portfolios created by the Financial DSS is 61% for long term investments and 11.74% for short term investments. This system has the potential to help millions of individual investors who can make their financial decisions on stocks using this system for a fraction of cost paid to corporate financial consultants and value eventually may contribute to a more efficient financial system.
Ongoing investigations propose enrollment derivation (MI) assaults on profound models, where the objective is to surmise if an example has been utilized in the preparation interaction. Regardless of their obvious achievement, these examinations just report exactness, accuracy, and review of the positive class (part class). Subsequently, the presentations of these assaults have not been plainly covered negative class (non-part class). AI (ML) models have been broadly applied to different applications, including picture grouping, text age, sound acknowledgment, and chart information examination. Nonetheless, late investigations have shown that ML models are helpless against participation induction assaults (MIAs), which mean to gather whether an information record was utilized to prepare an objective model or not. MIAs on ML models can straightforwardly prompt a security break. For model, through distinguishing the way that a clinical record that has been utilized to prepare a model related with a specific infection, an assailant can surmise that the proprietor of the clinical record has the sickness with a high possibility. As of late, MIAs have been demonstrated to be compelling on different ML models, e.g., arrangement models and generative models. In the interim, numerous safeguard strategies have been proposed to relieve MIAs.
In recent years, managing the security over the web has gained its importance. Use of appropriate security handling techniques help to solve controversies and to extract interesting scenarios based on the content of the web page. Many varieties of vulnerabilities prevail and Cross-Site Scripting (XSS) vulnerability is ranked among the top ten risks found over the web which is a mandatory issue that requires a solution. XSS vulnerability injects malicious code in many ways that rise during the browsing session. Analysis should be made over the web page to identify whether the page is vulnerable or not. A dataset is formulated that contains malicious and benign data. Malicious data are obtained from the XSS archive [source: www.xssed.com] which contains the vulnerable XSS web pages and benign data are the web pages that are obtained through queries from the Google search engine. The major constraint is the number of Lines of Code (LOC) present in the web page. Five samples from the dataset were considered and algorithms are applied. About 24 attributes are used by the classifier. The samples vary in terms of content and size. Different optimization techniques are applied and the results are analyzed. Evaluation measures like Detection Rate (DR), False Detection Rate (FDR) and F Score (FS) are calculated based on the Confusion Matrix. The final content obtained after the „XSS Handler phase? that is to be displayed on the browser is tested using black box testing technique and also using XSS and SQL Injection Scanner tool. The tool is capable of identifying promising XSS code available in web pages. Based on the experiments, it was observed that the generation of paths using PPACO achieves better results in terms of DR, FDR and FS than other algorithms.
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