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检索条件"主题词=Machine Learning Algorithms"

共 25882 条记录，以下是931-940 订阅

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AlgoXSSF: Detection and Analysis of Cross-Site Request Forgery (XSRF) and Cross-Site Scripting (XSS) Attacks via machine learning algorithms

AlgoXSSF: Detection and Analysis of Cross-Site Request Forge...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Naresh Kshetri Dilip Kumar James Hutson Navneet Kaur Omar Faruq Osama School of Business & Technology Emporia State University Emporia KS USA Laboratory of Applied Cryptology and Security (LACS) Indian Institute of Technology Mandi India Department of Art History & Culture Lindenwood University Saint Charles Missouri Department of Computer Science University of Missouri St. Louis Missouri USA Department of Systems Science and Industrial Engineering Binghamton University SUNY Binghamton NY USA

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

The global rise in online users and devices has led to a corresponding surge in cybercrimes and attacks, demanding advanced technology and algorithms like Artificial Intelligence (AI), Deep learning, and machine learning to bolster web security. The unprecedented increase rate of cybercrime and website attacks urged for new technology consideration to protect data and information online. There have been recent and continuous cyberattacks on websites, web domains with ongoing data breaches including - GitHub account hack, data leaks on Twitter, malware in WordPress plugins, vulnerability in Tomcat server to name just a few. Extensive research has been conducted to detect and analyze prevalent cyber threats like cross-site request forgery (XSRF) and cross-site scripting (XSS) attacks. Leveraging machine learning algorithms, particularly through the developed cyber defense framework - algoXSSF, offers numerous benefits. AlgoXSSF enhances detection accuracy, facilitates real-time threat response, autonomously adapts to evolving threats, minimizes false positives, and streamlines security operations. By harnessing the power of machine learning, algoXSSF serves as a robust defense mechanism against malicious attacks, including Man-in-the-Middle attacks, reinforcing websites and online platforms against cyber threats.

关键词： machine learning algorithms Social networking (online) Cross-site scripting Surge protection Forgery Security Servers

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Diabetes Prediction Using machine learning algorithms

Diabetes Prediction Using Machine Learning Algorithms

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Nikita Sharma Shrddha Sagar Department of CSE GU Greater Noida Uttar Pradesh India GU Greater Noida Uttar Pradesh India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Overall the world, we have seen that many health diseases problems have arise like as Obesity, High-Blood Pressure, Heart-Attacks and Diabetes which is very harmful for Human life and have a greats of impact in daily living *** is a major of *** is at No.2 with an estimated 77million people with diabetes(2nd highest in the world) CHINA is at No.1 with an estimated 160 million diabetes patients,which is very dangerous diseases and does not able to *** it disease affects once, it will maintain in life *** groups of diseases in which here major one a group of diabetes that happen due to the insufficiency of insulin in our body. The Objective of the Research paper is to help curing in the prediction & structured identification of diabetes. Growthly, machine-learning is becoming an important tool for diagnosing the problems of medical ***, To predict the highest accuracy score of diabetes, we applied machine learning algorithms. Also, We outline the methodology, train, test and predict the machine learning algorithm using logistic regression, decision tree and Support Vector machine.

关键词： Support vector machines Obesity Logistic regression machine learning algorithms machine learning Diabetes Object recognition Insulin Medical diagnostic imaging Diseases

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A Comparative Study of machine learning algorithms for Predicting Customer Churn: Analyzing Sequential, Random Forest, and Decision Tree Classifier Models

A Comparative Study of Machine Learning Algorithms for Predi...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Ratnam Dodda C. Raghavendra Marpu Aashritha Hima Varshini Macherla Anusha Reddy Kuntla Department of CSE (AI&ML) CVR College of Engineering Hyderabad India Department of CSE (DS) CVR College of Engineering Hyderabad India Department of Computer Science and Information Technology CVR College of Engineering Hyderabad India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Predicting customer churn is vital for businesses aiming to retain their customer base and enhance profitability. This study investigates the effectiveness of three prominent machine learning algorithms Sequential, Random Forest, and Decision Tree Classifier in forecasting customer churn. Utilizing a dataset encompassing historical customer information such as demographics, transaction records, and interaction patterns, we preprocess the data by addressing missing values, encoding categorical variables, and scaling numerical features. Subsequently, each model is trained on the processed dataset, and their performance is assessed using accuracy as the evaluation metric. Our findings demonstrate promising predictive capabilities, with the Sequential model achieving an accuracy score of 94%, followed by Random Forest with 92%, and Decision Tree Classifier with 91%. Additionally, we conduct a comparative analysis of these models, focusing on interpretability, computational complexity, and robustness. While the Sequential model excels in predictive accuracy by capturing temporal dependencies in sequential data, Random Forest and Decision Tree Classifiers offer simplicity and interpretability, catering to scenarios where model transparency is paramount.

关键词： Analytical models Accuracy machine learning algorithms Computational modeling Predictive models Prediction algorithms Data models Numerical models Decision trees Random forests

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Development for Electrical Fault Detection and Classification Analysis Model based on machine learning algorithms

Development for Electrical Fault Detection and Classificatio...

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Conference on Technologies for Sustainability (Sustech)

作者： Junho Kim Sunhwa Sim Seokjun Kim Seokheon Cho Changhee Han Department of Robot Engineering Keimyung University Daegu Republic of Korea Department of Medical IT Convergence Engineering Kumoh National Institute of Technology Gumi Republic of Korea Department of Semiconductor Systems Engineering Kumoh National Institute of Technology Gumi Republic of Korea Qualcomm Institute University of San Diego La Jolla USA Department of Electrical Engineering Gyeongsang National University Jinju Republic of Korea

ISBN: (数字)9798350394344

ISBN: (纸本)9798350394351

With the development of industry and technology in modern society, many industries and houses require a sufficient electricity supply. As demand for electricity increases, rapid detection of the type and location of electrical faults within the power system is critical to ensure the reliable operation of power systems. Since the traditional fault detection method has low accuracy and takes much time to detect the fault type and location, we propose a new electrical fault detection model based on machine learning algorithms. MATLAB Simulink collects the line current and bus voltage data during power system fault events. We consider two machine learning algorithms, Random Forest and K-Nearest Neighbor (K-NN) algorithms, as electrical fault detection and classification models. The data collected from the power system simulation is processed in various ways and then applied to the machine learning algorithms. As a result, we verify that the learning model based on the Random Forest algorithms, using the peak-to-peak value of the line current and bus voltage as training data, shows the best performance for detecting and predicting electrical faults.

关键词： machine learning algorithms Fault detection Voltage Power system stability Predictive models Electrical fault detection Mathematical models

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Predicting Photovoltaic Power Generation in Pakistan Using machine learning algorithms

Predicting Photovoltaic Power Generation in Pakistan Using M...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Roshan Kumar Muhammad Osama Aziz Ramesh Kumar Ayyasamy Syed Mustafa Ali Amjad Khan Daniyal Shakir Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Malaysia Buildings Energy Research Center University of Engineering and Technology Peshawar Pakistan Department of Engineering Management University of Alabama Birmingham AL USA Department of Electrical Engineering University of Engineering and Technology Peshawar Pakistan S&P Global Islamabad Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Photovoltaic (PV) power forecasting is central to energy management and integrating renewable energy into the electricity grid network. This study considered environmental data, namely, Global Horizontal Irradiance (GHI), temperature, relative humidity, wind speed, and rainfall data obtained from NASA's Prediction of Worldwide Energy Resources (POWER) project to estimate the PV power generation in Pakistan. The PV generation dataset of Pakistan is trained using three machine learning algorithms: XGBoost, LightGBM, and Random Forest. The performance of the proposed machine learning algorithm is assessed by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared (R 2 ). The evaluations suggest that LightGBM has the smallest RMSE (0.7116), the lowest MAE (0.3922), and the highest R 2 (0.99997), which proves its enhanced accuracy and robustness. The study reveals that environmental factors are crucial in solar PV power forecasting and identified GHI and temperature as the most important predictors of PV power output.

关键词： Photovoltaic systems Renewable energy sources Temperature distribution machine learning algorithms Rain Wind speed Robustness Forecasting Root mean square Random forests

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Determination of Calorific Value in Coal Using Laser-Induced Breakdown Spectroscopy Combined with machine learning algorithms

Determination of Calorific Value in Coal Using Laser-Induced...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Hongyan Dang Xiaoli Liu Jinlan An Tianyun Ren Shaanxi Energy Quality Supervision and Inspection Institute Xi'an The People's Republic of China School of Water and Environment Chang'an University Xi'an The People's Republic of China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Rapid analysis of calorific value is of great significance for the efficient and clean application of coal. In this work, laser induced breakdown spectroscopy (LIBS) coupled with different machine learning method was employed to analyze calorific value in coal. Principal component regression (PCR) algorithm, partial least squares regression (PLSR) algorithm, and support vector machine regression (SVR) algorithm were used to establish analysis models separately. The pretreatment methods and important model parameters were investigated to improve predictive performance of models. Compared with PCR and SVR, PLSR method exhibited superior predictive performance when the spectral data was preprocessed with standard normal transformation and the number of latent variable was set to 15. For the PLSR model, the root mean square error of prediction set (RMSEP) was 1.0010, and the correlation coefficient of prediction set (R p ) was 0.9734. The results show that LIBS coupled with PLSR is a potential method for rapid analysis of calorific value in coal.

关键词： Support vector machines Spectroscopy Analytical models machine learning algorithms Electric breakdown Statistical analysis Data preprocessing Coal Predictive models Prediction algorithms

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Heart Disease Prediction Using machine learning algorithms

Heart Disease Prediction Using Machine Learning Algorithms

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International Conference on Smart Communications and Networking (SmartNets)

作者： Dina Jrab Derar Eleyan Amna Eleyan Tarek Bejaoui Department of Computer Science Faculty of Information Technology Palestine Technical University - Kadoorie Tulkarem State of Palestine Faculty of Telecomm. and Information Technology Nablus University for Technical and Vocational Education Nablus Palestine Department of Computing and Mathematics Manchester Metropolitan University Manchester Uk Computer Engineering Department University of Carthage Tunisia

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Heart disease is a prevalent and complex condition that affects numerous individuals worldwide. Timely and accurate diagnosis of heart disease is of utmost importance in cardiology. In this research article, we propose an efficient and precise system for heart disease diagnosis, employing machine learning techniques. The system is designed based on various classification algorithms, including Support Vector machine, Logistic Regression, Decision Tree, and Random Forest. Standard feature selection algorithms such as ANDV A, Chi-Squared, and Mutual Information Feature Selection (MIFS) are utilized to eliminate unrelated features. Furthermore, we introduce a novel fast experiment that contains a conditional mutual information feature selection algorithm, ANDVA feature selection algorithms, and a Chi-squared feature selection algorithm to address the feature selection challenge. These feature selection algorithms enhance classification model accuracy and reduce the compile time in the classification ML model. The cross-validation method evaluates the models and optimizes hyperparameters, ensuring reliable model assessment. Performance measuring metrics are utilized to assess the classifiers' performance. The classifiers are estimated based on the selected features determined by the feature selection algorithms. The experimental results show that the ANDV A F -test feature selection algorithm along with the Support Vector machine classifier, is a viable approach for developing an advanced intelligent system that can identify heart disease. The proposed model can also be easily implemented in healthcare to facilitate heart disease identification.

关键词： Heart Support vector machines Accuracy machine learning algorithms System performance Feature extraction Prediction algorithms

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Maximizing Security and Privacy of machine learning algorithms in Automated Farming

Maximizing Security and Privacy of Machine Learning Algorith...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Debanjan Ghosh Sunita Bishnoi Naveen Kumar Rajendran Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India Department of Chemistry Vivekananda Global University Jaipur India Department of Aerospace Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper offers an evaluation of the current literature associated with automated farming and the safety and privacy implications of device learning algorithms used in those systems. Gadget-studying algorithms are becoming increasingly important in the automation of certain farming duties, such as detecting weeds, rejecting vegetation, and managing water usage. But, the safety and privacy risks related to those algorithms have to be understood if you want to maximize their protection and limit their ability for misuse or abuse. It has existing techniques for comfortable gadgets, gaining knowledge of structures, as well as the modern-day nation of vulnerability control in the automatic farming sector. Moreover, this paper provides recommendations for maximizing protection and privacy in computerized farming programs, inclusive of encryption techniques, statistics minimization strategies, and at-ease improvement lifecycle threat-modeling strategies.

关键词： Knowledge engineering Privacy machine learning algorithms Vegetation Minimization Encryption Reliability Personnel Protection Farming

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Web-Interfaced Diagnosis System of Diabetes Prediction Using machine learning algorithms

Web-Interfaced Diagnosis System of Diabetes Prediction Using...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： E. V. Krishna Rao Narendra Pamula Snigdha Battula Manaswitha Guntaka ECE dept Lakireddy Bali Reddy College of Engineering JNTUK Kakinada Mylavaram India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

This study aims to develop a comprehensive solution for predicting diabetes using machine learning (ML) algorithms. Due to rising cases of diabetes worldwide, early detection is imperative for effective health management and prevention of their complications. The objective of this research is to construct a strong model capable of accurately predicting diabetes based on diverse health data that are included in the Pima Indians Diabetes Dataset (PIDD). We explore multiple ML models, such as SVM, KNN, Bagging-DT, RF, and XGBOOST to enhance diabetic predictive accuracy. In addition, we leveraged the concept of Feature Set Creation to optimize ML model performance. Feature Sets are created by considering the various combinations of diabetic attributes. Based on this Feature Set creation, we emphasize the importance of accuracy, efficiency, and adaptability while selecting ML models for healthcare applications. Our proposed classifier, utilizing the XGBoost algorithm, demonstrates outstanding performance by achieving an accuracy of 95.29% sensitivity of 89.28%, specificity of 98.2%, and a notable F1 score of 92.59% compared with other classifiers. Additionally, a user-friendly web page has been developed to allow them to enter their BMI, age, insulin levels, and other relevant data to determine their likelihood of being diabetic or not. This website also provides specific diet recommendations for diabetic patients, emphasizing the significance of customized food plans in controlling their blood sugar, enhancing health conditions, and minimizing associated diabetic complications.

关键词： Support vector machines Adaptation models Accuracy machine learning algorithms Web pages Medical services Predictive models

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Performance Enhancement of machine learning algorithms for Predicting Stroke

Performance Enhancement of Machine Learning Algorithms for P...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Vinod Jain Anuj Mangal Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Stroke is very common heath problem now. Early prediction of stroke is very essential to decrease the impact of this disease. machine learning (ML) algorithms are very useful in making predictions for various diseases. In this paper, the power of machine learning algorithms is used for stroke prediction. Even after a lot of research the accuracy of machine learning algorithms is a challenging issue. Many researchers worked on improving the accuracy of ML models. machine learning models worked on data sets. For stroke prediction using machine learning, a well-known data set is considered in this paper. The data set consist of many important factors which may affect the chances of stroke in a person. Logistic Regression, Decision Tree and Random Forest are three machine learning algorithms which are very popular and effective in prediction of stroke. In this work these three machine learning algorithms are applied for stroke prediction and their performance is checked and compared. From the results it is observed that the performance of Logistic Regression is best with 95.34% accuracy.

关键词： Heating systems Logistic regression machine learning algorithms Accuracy Forestry Prediction algorithms Data models

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