Papers by Keyword: Decision Tree

Abstract: Software-defined networking (SDN) infrastructures are facing a growing menace from cyber threats, which leave them vulnerable to distributed denial of service (DDoS) attacks. This study extends the earlier research [Detecting DDOS attacks in SDN Networks Using Machine Learning Techniques], which extensively investigated vulnerabilities in the SDN architecture for DDoS attacks. We develop and evaluate machine learning methods that are specifically tailored to protect software-defined networks (SDN) from such malicious attacks. The centralized and rigorous operational protocol of SDN has enabled us to develop a range of detecting methods. This study examines the efficacy of different machine learning algorithms, including XGBoost, Native Bayes, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). The algorithm's computational efficiency and precision were evaluated using a dataset specifically created to simulate the intricate and unpredictable characteristics of network traffic. The experimental findings demonstrate that the XGBoost and Random Forest algorithms exhibit commendable performance in terms of both accuracy and speed. The precision varies between 99.26% and 77.0%, depending on the specific detection algorithm employed and the selected features. Consequently, these algorithms are well-suited for promptly dealing with and reducing potential hazards. XGBoost demonstrated exceptional versatility by maintaining accuracy across several testing scenarios while also reaching great processing efficiency. Utilizing machine learning has the potential to significantly enhance the security of SDN systems, as indicated by the results. This revelation has two significant ramifications: This study enhances our comprehension of efficient SDN DDoS mitigation strategies and sets a standard for the next research on integrating machine learning in security frameworks. We offer essential knowledge that aids in preserving vital network infrastructures in an ever more unpredictable digital landscape.

Abstract: Oil palm has become the world’s leading vegetable oil with a tremendous increase in plantations and production. Thailand is among the three largest producers of oil palm. To enhance the oil palm producing potential competitively, the oil palm industry in Thailand has to improve the efficiency of production management among Thai farmers. This work aimed to identify important factors affecting oil palm cultivation based on machine learning and statistical inference methods. The proposed models were evaluated on a data set collected from the local community group for oil palm cultivation and production in Surat Thani and Nakhon Si Thammarat provinces, Thailand. The seedlings’ source and the age of oil palm seedlings were the most significant features according to the analysis.

Abstract: This paper aims to research and predict the expression of formability and compressive strength using machine learning (ML) technology for the composite materials manufactured by mixing CNTs with UHPC. To this end, numerical data of two material properties were collected through related experiments and literature data from a mixing ratio of 0 to 1% manufactured by mixing CNTs with UHPC. Afterwards, in order to predict the material properties of UHPC/CNT composite with various mixing ratios that have not been experimented and studied, the material properties were predicted using ML techniques, k-NN regression and decision tree method based on the collected data. As a result, data analysis with collecting similar kind of research and experimental data, it was confirmed that the formability significantly decreased when the CNTs mixing ratio was 0.4% or more. Also, compressive strengths in the detailed mixing ratio period from 0 to 1% could be predicted. This suggests that the properties of newly developed building materials through this study can be identified with high reliability using ML techniques.

Abstract: In a recent survey, its observed that there is arise in crime rate in India, and due to this, many peoplefeel unsafe in this country. So to reduce it, predicting thecrime before it happens is very important. IndianGovernment uses a software called CCIS (Crime CriminalInformation System), this software is only used to storeinformation, but with the stored information, it doesn’t doanything else, the existing systems predict crime only on aday basis, but it doesn’t predict exactly at what hour is itgoing to occur, to predict it on a hour basis, accuracy isimportant, so to analyze and predict crime, the accuraciesof machine learning algorithms such as KNN, Decision treeand Random forest are compared in order to use the bestfor analysis and prediction.

Abstract: Usually, people really need advice in deciding their choices. Whether it's the movie to watch on Friday night or there are some exciting new things available on e-business. In this distinctive situation, we tend to build a framework which will thus bring down new melodies to shoppers supported their previous history of listening. Now many companies today use the recommendation systems to their advantage such as flip kart and Amazon for the sale of products (e-commerce), ganna.com and music for streaming music, for the sale of clothes, for the films. It assists each business and customers as businesses get financial advantages by attracting customers and users pick up services. Nowadays, everyone uses recommender systems in various forms and they are getting better and better day by day because researchers are trying to cause them to higher and higher each day because of the sturdy competitiveness of the marketplace to provide higher and higher offerings and entice peoples. This project mainly focuses on music only for the music lovers to help them listen to songs they might love. This project allows customers to find new collections or the songs by making the lovey the list accessible for the tuning. Along these lines, the executive can assess which artist or collection would co-ordinate the client's inclinations towards the customers. For the music lovers, music is lifeline and music are a lovely part of the everyone's life because everything in this world can be related to frequency and vibrations. According to all the good things about the music and the high demand for recommendation systems in the market, we chose to do music recommendation system.

Abstract: Stock analysis and forecasting is a very challenging study due to the unpredictable and volatile database environment. However, their patterns are often unique as they are influenced by many uncertainties, such as financial results of companies (Earnings per share), risk transactions, market sentiment, government policies, and conditions such as epidemics. Even though they are challenging our goal is to predict the accurate values within a shorter span of a dataset. In this paper we have compared and analyzed the best ML model that predicts the exact closing amount of the next few days, using three to four months of nifty50 Indian stock from Yahoo Finance. Five regression models are involved in this analysis, Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), SARIMAX (Integrated Seasonal Integrated Season with EXogenous features), Gated Recurrent Unit (GRU – deep learning). The performance metrics like RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) are used. On the basis of our comparison, we would like to conclude that GRU provides a low error value in all three performance metrics and also gives accurate predictions compared to the other five regression models used.

Abstract: Employee attrition rate in Tech industry has become dreadful day by day in all over the world. Meanwhile It has been noticed that churn (attrition) rate in IT industries is growing rapidly than expected especially during pandemic times. This is taken as a foremost issue by each tech industry, to analyze and adapt to the change. The main snag is that, the expenditure of recruiting on a new employee is foremost ineffective than retaining a company trained professional employee. Also retaining an employee will assure certain credibility and work culture of the company than the new employee. Also, the latter will be given access to training modules and code of conduct of the company with lots of Information Overload on a short span of time. It is essential to mention, not every organization has comprehensive training programs for their employees, especially the start-up tech firms, which focuses heavily on skilled workers with experience beforehand. This anonymity causes HR departments to scrutinize and tweak their actions according to current trend in the market. The major goal of this study is to make predictions whether the skillful employee will quit or continue further and predict the reason for quit using supervised classification and machine learning algorithms. Acquainting the human resource team to help them with the required analytics to make decisions based on machine learning.

Abstract: The worldwide society was devastated by the 2019 coronavirus illness (COVID19) epidemic in Wuhan, China, which overloaded advanced medical systems around the world. The World Health Organization (WHO) is constantly monitoring and responding to the pandemic. The current rapid and exponential development in patient numbers necessitates the use of AI technology to forecast possible outcomes of infected individuals in order to provide suitable therapy. The goal is to find the machine learning-based solution that best fits the Covid19 vaccination predictions with the highest accuracy. Variable identification, univariate analysis, bivariate and multivariate analysis, missing value handling and data validation analysis, data cleaning / preparation, and data validation analysis are all accomplished using supervised machine learning technology (SMLT). Various types of data, such as visualisation, are gathered. For the entire given dataset. Proposal of a machine learning-based method for accurately predicting the suitability of Covid19 vaccine prediction.

Abstract: Peak load and compressive failure strength are influent parameters regarding the mechanical properties of concretes. Experiments such as compression tests are usually performed to extract relevant values. It is well known that experimental measurements are relatively costly and energy-consuming. Therefore, it is useful to identify and apply a model prediction from available data. In this work, the influence of the initial size of cylindrical normal-weight concrete considering three different mixtures is presented. Peak loads and associated compressive failure strength of multiple sizes concretes are predicted using machine learning. Decision tree (DT) and random forest (RF) regressors are presented in this work. A comparison between the models is made. The results of the models are found to be consistent with the experimental ones on peak loads (a coefficient of determination of 0.98 is obtained with the DT algorithm and 0.99 with the RF one) and should be improved with respect to the compressive failure strength (a coefficient of determination of 0.77 is obtained).

Abstract: Drilling engineers and operators are stuck with challenges associated with loss circulation of drilling fluids in wellbores during drilling operation. At such times, a clear and careful decision is required in order to minimize cost or save resources that would have been lost in the bid to remedy the situation. This then informs the need to deploy reliable tools that will inform useful decisions as drawn from a thorough risk-analysis coined from the information gathered from the formation characteristics and operating pressure. In this study, a real-time statistic based approach was adopted in carrying out risk-evaluation of loss circulation events in a wellbore. Based on the expected opportunity loss analysis, it is often non-negotiable to consider other options when the analytical solution suggests that the well should be “abandoned”. For the decision tree, at the decision node, D₁, the expected loss of the seal off zone option is $161.25, the expected loss of the drill ahead option is $19.2 and the expected loss of the abandon option is $13.2. Since the expected loss of the abandon option is less than the expected value of both the seal off and the drill ahead option, it is recommended to abandon the well. Furthermore, the risk analysis proved to be a veritable tool considering the cost implications of other options; and can also serve as basis for automated decision-making.