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检索条件"主题词=Machine Learning Algorithms"

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Harmony Gradient Boosting Random Forest machine learning algorithms for Sentiment Classification

Harmony Gradient Boosting Random Forest Machine Learning Alg...

引用

Sustainable Energy, Signal Processing and Cyber Security (iSSSC), IEEE International Symposium on

作者： Komarasamy. G Subash Chandra Bose Jaganathan Sridharan. K Anisha Mital Sanika Awal School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh TN Palayam JKK Munirajah College of Technolgy Tamilnadu India

ISBN: (纸本)9781665490573

The exchange of ideas and opinions on social media is increasingly becoming essential. A vast amount of data is produced by the intense analysis of online social media. The harmony random forest machine learning algorithm is suggested in this study as a method for sentiment analysis on social media. Additionally, social media data sentiment analysis employs the gradient boosting harmony algorithm. Compared to other methods, the harmony gradient boosting random forest machine learning technique yields better outcomes. In contrast to the gradient boosting approach, which creates ensembles of weak and shallow succeeding trees, random forest develops ensembles of independent trees. In comparison to the harmonic random forest, the suggested approach has a classification accuracy of 5.68%. Thus, the suggested harmony gradient boosting random forest machine learning is more effective than.

关键词： Radio frequency Sentiment analysis machine learning algorithms Social networking (online) Signal processing algorithms Signal processing Boosting

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Detection of Email Spam using machine learning algorithms: A Comparative Study

Detection of Email Spam using Machine Learning Algorithms: A...

引用

International Conference on Signal Processing and Communication (ICSC)

作者： Prazwal Thakur Kartik Joshi Prateek Thakral Shruti Jain Department of CSE & IT Jaypee University of Information Technology Solan Himachal Pradesh India

ISBN: (纸本)9781665454315

In the digital world a lot of emails are received every day, and most of them are not of any relevance to us, some are containing suspicious links which can cause harm to our system in some way or other. This can be overcome by using spam detection. It is the process of classifying whether the email is a genuine one or if it is some kind of spam. The purpose of spam detection is to deliver relevant emails to the person and separate spam emails. Already every email service provider has spam detection but still, its accuracy is not that much, sometimes they classify useful emails as spam. This paper focuses on the comparative analysis approach, where various machine learning models are applied to the same dataset. The different machine learning models were compared based on accuracy and Precision. Support vector machine results in 98.09% accuracy.

关键词： Support vector machines Analytical models machine learning algorithms Unsolicited e-mail machine learning Signal processing Regression tree analysis

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Trojan Malware Detection using ANN, Naïve Bayes and SVM machine learning algorithms

Trojan Malware Detection using ANN, Naïve Bayes and SVM Mac...

引用

Information and Computing Research (iCORE), International Conference in

作者： Anjelica Castillo Allen Bryant Lineses Brenson Go Rogel Labanan Manolito Octaviano College of Computing and Information Technologies National University Philippines

Trojan malware is one of the most destructive forces in the cyber world that is not easy to detect. In this study, machine learning algorithms namely: Artificial Neural Networks (ANN), Support Vector machine (SVM), and Naïve Bayes were used to detect computer-based DDoS Trojan malware through network traffic flows. Using the CICDDoS2019 dataset, the Correlation Attribute Evaluation feature in Weka was performed to identify the suitable feature set of the network traffic artifacts for malware detection in a computer system. An ablation feature technique was used in the result to understand the effects of different dataset features on the performance of the models. Moreover, the performance of the models was evaluated using Accuracy, Precision, Recall, F-Metric, Misclassification Rate (FPR), and Receiver Operating Characteristic (ROC). Amongst the machine learning models, the result of the ANN model obtained the highest percentage of correctly classified instances of DDoS Trojan malware. Furthermore, the ANN's model evaluation metrics also showed positive results thus, an indication that the model is suitable for performing DDoS Trojan Malware detection using network traffics. Lastly, the model was also evaluated using Local Interpretable Model - Agnostic Explanations (LiME), and live malware samples were tested.

关键词： Support vector machines Measurement machine learning algorithms Computational modeling Telecommunication traffic Artificial neural networks Receivers

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State of Charge Estimation for Lithium-Ion Batteries Using machine learning algorithms

State of Charge Estimation for Lithium-Ion Batteries Using M...

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International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM)

作者： Hamza Mediouni Soumia El Hani Amal Ezzouhri Amina Daghouri Imad Aboudrar Nisrine Naseri EODIC Research Team ENSAM Mohammed V University Rabat Rabat Morocco ERSC Team EMI Mohammed V University Rabat Rabat Morocco

ISBN: (纸本)9781665451697

An effective Battery Management System (BMS) is compulsory for the safe and reliable operation of lithium-ion batteries, which are increasingly being used in Electric Vehicles (EVs). Accurate State of Charge estimation (SoC) is a cumbersome task, since lithium-ion batteries are highly influenced by such random factors including driving loads, operational conditions, and aging. This work presents a comparison study of various machine learning algorithms for SoC estimation. To do this, a 3Ah LGHG2 battery cell was put through a series of temperature and driving cycle tests. The proposed models are evaluated in terms of accuracy and robustness. The simulation results have shown that the Gaussian Process Regression (GPR) model outperforms the other algorithms achieving R 2 and RMSE values of (97 %, 1.3 %) and (95 %, 1.6 %) in normal conditions and in a noisy environment, respectively.

关键词： Lithium-ion batteries machine learning algorithms Simulation Estimation Battery management systems Gaussian processes Electric vehicles

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Performance Analysis and Prediction of Diabetes using Various machine learning algorithms

Performance Analysis and Prediction of Diabetes using Variou...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Saurav Dev Bhawnesh Kumar Dinesh C. Dobhal Harendra Singh Negi Department of Computer Science and Engineering Graphic Era Deemed To Be University Dehradun India

Nowadays, diabetes has become very common. In every house, there should be a person who has diabetes. It is a menacing condition, which means dangerous, and this disease is known as a chronic illness. This happens due to a high level of glucose or sugar in the human body, and then it becomes very indispensable to maintain a level of sugar throughout our lifetime, so it needs to be predicted earlier in the starting days. They are majorly in type 1 and type 2, and gestation is also there, which occurs in pregnancy. Diabetes may cause eye diseases like increasing eye pressure and glaucoma. There may be a chance of heart disease and a slow recovery rate of the pancreas. In this article, we are going to propose and analyze the machine learning algorithms for diabetes prediction by using a dataset of different features (Glucose, BMI, Age, etc.). Outcome of this paper is accuracy, precision, recall and f1-score for various machine learning algorithms. Consideration of implementation on all features, without glucose and without pregnancy for diabetic and non-diabetic. For all features logistic regression has highest accuracy i.e 74.45%, for without glucose feature KNN gives highest accuracy level i.e 68.83% and without pregnancy feature, KNN accuracy with 76.19%.

关键词： Pregnancy Heart machine learning algorithms machine learning Prediction algorithms Diabetes Glucose

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Detecting Diabetes Using machine learning algorithms

Detecting Diabetes Using Machine Learning Algorithms

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Communication and Information Technologies (IICCIT), Iraqi International Conference on

作者： Qabeela Q. Thabit Taqwa O. Fahad Alyaa I. Dawood Basrah Education Directorate Ministry of education Basrah Iraq Biomedical Engineering Department University of Technology-Iraq Baghdad Iraq Engineering Technical College Southern Technical University Basrah Iraq

ISBN: (纸本)9781665472210

machine learning has become a significant and promising approach to research and has proven to be an effective technique. It can significantly help in the early diagnosis and prediction of diabetes, which has spread rapidly throughout the world, demanding more modern techniques to detect and diagnose diabetes. In this regard, artificial intelligence (AI) has proven its efficiency in detecting diseases, especially for critical discovery cases depending on a database approved by medical officials. This study estimates the incidence of diabetes using classification techniques that employ a variety of machine learning algorithms, such as Logistic Regression (LR) or so-called Classifier, Support Vector machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bays (NB) classifiers. These techniques are used to determine the prevalence of diabetes in all methods related to the preprocessing data, including cleaning data, feature selection, scaling data, and splitting of data. Depending on patient electronic health data (Pima Diabetes Database of India), the present work is implemented using Python programming language within the ANACONDA environment under Jupyter (notebook 6.1.4) and Spyder (4.1.5) applications, and the outcomes show its ability to obtain accurate models with excellent results that can predict the presence of diabetes with up to 100% accuracy.

关键词： Support vector machines machine learning algorithms Databases Medical services Predictive models Prediction algorithms Diabetes

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Performance Analysis of machine learning algorithms for COVID-19 Detection

Performance Analysis of Machine Learning Algorithms for COVI...

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IEEE Bombay Section Signature Conference (IBSSC)

作者： Anuradha D. Thakare Pranjali G. Gulhane Shruti M. Chaudhari Hemant Baradkar Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Department of Electronics and Telecommunication Jagdamba College of Engineering and Technology Yavatmal India

ISBN: (纸本)9781665492928

The Novel Coronavirus Illness 2019 (COVID-19) was found in Wuhan, Hubei, China, in December 2019 and has since spread globally. When the patient's corona sickness worsened, his life was in danger. Coronavirus assaults the lungs. Diagnostic kits today only search for viral illnesses, which deceives doctors. All patients receiving the same treatment harm patients with less infection. This publication describes non-invasive treatment for infected people. Dissecting chest X-ray pictures to examine the coronavirus helps investigate and predict COVID-19 patients. We offer a hybrid method for detecting Covid. CNN and SVM identify Covid. Because X-ray pictures are inconsistent, CNN is used for feature extraction. To construct a training dataset before CNN, we used data augmentation. Data augmentation increases the training dataset's amount and quality. SVM is used for classification since it tolerates feature differences. The main goal is to help clinical doctors determine the severity of a chest infection so they can administer life-saving treatment. Deep learning and machine learning-based techniques will determine the degree of chest infection and lead to optimal medication, avoiding expensive treatment for all patients.

关键词： COVID-19 Training Support vector machines Deep learning machine learning algorithms Computational modeling Performance analysis

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Survival Rate Prediction of Blood Cancer (Leukemia) Patients Using machine learning algorithms

Survival Rate Prediction of Blood Cancer (Leukemia) Patients...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International Conference on

作者： Zarish Bilal Wajid Umar Rashid Sajida Zahid Faria Anwar Fahim Gohar Awan Abdul Rauf Anwar Imran Wajid Dept. of Biomedical Engineering University of Engineering & Technology (UET) New Campus Lahore Pakistan Dept. of Electrical Engineering UET New Campus Lahore Pakistan Muhammad Ibn Musa Al-Khwarizmi Research and Development Division Sabz-Qalam Lahore Pakistan Out-Patient Department Mayo Hospital Lahore Pakistan School of Social Sciences Istanbul Commerce University Istanbul Turkey

ISBN: (纸本)9781665492430

Survival rate prediction for medical diseases is a complex task that requires high precision. With a low survival rate among reported patients, leukemia is a type of cancer of blood which is caused by the abnormal growth of white blood cells. It is critical to numerically evaluate the rate of survivability of patients suffering from leukemia. To this end, this paper employs a comprehensive database, namely Surveillance, Epidemiology, and End Results (SEER) maintained by The National Cancer Institute in MD, USA, to construct a survivability model for leukemia patients. To accurately predict the survival months of the patients, we develop a multi-class classification problem by binning the target variable into four bins. The resulting accuracy is improved by utilizing a multi-tier classification framework. Although, the final numerical results hold significance from biological viewpoint, it is recommended that a clinically relevant model be drawn with caution.

关键词： White blood cells machine learning algorithms Biological system modeling Surveillance Predictive models Market research Numerical models

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Fraud detection in Online Payment Transaction using machine learning algorithms

Fraud detection in Online Payment Transaction using Machine ...

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Smart and Sustainable Technologies in Energy and Power Sectors (SSTEPS), International Conference on

作者： Darshan Aladakatti Gagana P Ashwini Kodipalli Shoaib Kamal Computer Science and Engineering Global Academy of Technology Bangalore India Electronics and Communication Engineering Global Academy of Technology Bangalore India Artificial Intelligence and Data Science Global Academy of Technology Bangalore India

Online payment transaction is a transaction in which payment is made using digitalized currency. Customers all over the world prefer online payments to purchase almost everything from furniture to clothing, from food to medicines, from gadgets to appliances, and whatnot. the online transaction has now evolved into many platforms. It is one of the most efficient methods provided by many companies to its customer. It not only helps build the company's revenue but also impacts the growth of the company. Let us not forget that with pros come the cons with the greatest advantage of having tedious transactions done at the fingertips comes the fear of fraudulent activity in which our hard-earned money can get theft within seconds. These activities can be determined by machine learning algorithms by feeding adequate data about these transactions. We have used machine learning algorithms such as SVM (Support Vector machine), LR (Logistic regression), Naive Bayes, Decision tree, and Random Forrest for the same. It is observed that Random Forest classifier has outperformed comparing to other classifiers with the accuracy of 99.94%

关键词： Support vector machines Deep learning machine learning algorithms Clothing Companies Forestry Fraud

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PCOS Perception analysis prediction using machine learning algorithms

PCOS Perception analysis prediction using Machine learning a...

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Recent Advances and Innovations in Engineering (ICRAIE)

作者： Sakthipriya Dhinakaran Chandrakumar Thangavel Shivayavashilaxmipriya S Harinee V S Department of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai India

PCOS-Polycystic ovary syndrome is a prevalent disorder among women in their reproductive years, characterised by irregular periods, pelvic pain, and other symptoms like excessive hair growth on the face, stomach, thighs, and chest, excessive weight gain, and thickened skin patches. According to a large-scale national survey on PCOS, 65 percent of Indian women were unaware of PCOS signs and symptoms. This paper focuses on disseminating knowledge by developing a machine learning-based model for diagnosing whether individuals exhibit PCOS symptoms or not . As PCOS grows, a woman's ovaries and adrenal glands produce an imbalanced amount of hormones. Based on the visible changes in their bodies caused by a hormonal imbalance, this model predicts whether they should take a PCOS test or not. In this paper, we use machine learning algorithms such as logistic regression, random forest, decision tree, K nearest neighbour,naïve bayes and support vector machine (SVM) to diagnose PCOS based on network-obtained data. With the help of performance metrics like accuracy,F-statistics, recall, and precision, the performance of the algorithms was validated after an analysis of the results. The results of logistic regression with an accuracy of 90% have proven to be the most accurate prediction model out of all the possible approaches.

关键词： Support vector machines Measurement Analytical models Technological innovation Stomach machine learning algorithms Thigh

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