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检索条件"主题词=Machine Learning Algorithms"

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A Comparative Study of the Stock Market using machine learning algorithms

A Comparative Study of the Stock Market using Machine Learni...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： Karthik Deevenapalli Sasi Priyanka Jampani Mohan Venkata Bhagavan Shiva Sai Y Snigdha Sudulakunta Anuradha Chokka Suneetha Bulla Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

The purpose of this review work is to present a strategy for accurate stock price prediction in the face of multiple factors affecting stock prices. The strategy involves the utilization of four efficient machine learning models - K-Nearest Neighbors (KNN), Naive Bayes, SVM classifiers, and Random Forest classifiers - to analyze and forecast stock values under various market conditions. The performance of the proposed strategy is evaluated through the comparison of accuracy, precision, and recall metrics using a stock price dataset. The intention of this review paper is to provide a comprehensive solution to the challenge of stock prediction by utilizing multiple machine learning algorithms in different scenarios. The framework is then assessed and predicted for its classification accuracy, which will help to overcome the complexities in the stock prediction process and provide a robust and reliable approach to stock price forecasting.

关键词： machine learning algorithms Biological system modeling Support vector machine classification Prediction algorithms Market research Reliability Stock markets

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Analysis of machine learning algorithms used in Face Recognition Attendance System

Analysis of Machine Learning algorithms used in Face Recogni...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Gourisha Goel Vinay Bansal Deepa Bura Computer Science & Engineering Manav Rachna International Institute of Research & Studies Faridabad India

Biometric is learning of human features and behavior. The Face Recognition system is used for security. Its purpose is to identify someone’s face with his 2D image, which involves the extraction of his facial features and recognize it regardless of aging, beard, expression or pose which is very difficult. Image Analysis and Computer Vision are the challenges of face recognition for which many techniques have been built which we are going to study in this paper. So how different algorithm work and their comparison is discussed in this paper.

关键词： Solid modeling machine learning algorithms Three-dimensional displays Tracking Face recognition Redundancy Skin

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Opportunities of machine learning algorithms in the Tax Default Prediction - A Survey

Opportunities of Machine Learning Algorithms in the Tax Defa...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： N. Naveen Kumar R. Sridhar U. Uday Prasanna G. Priyanka Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore

To develop the countrywide revenue plans, the tax prediction activities should be monitored and activated efficiently. This research study concentrates on the survey of various machine learning (ML) algorithms to find better solutions. Financial profit ratios and revenue flow statements have been used to find similar occurrences like income manipulations, false cash flows, and the relationship between tax risk and profitability ratio. Also, this research study addresses the financial suggestions of unpaid taxes by practicing an automated technique to anticipate tax default. In the previous analyses, tax default prediction has attained very small consideration. Findings suggested that the existing analyses were unable to predict the information on tax default in the real-world economic data because it fails to report for appropriate data exchanges as well as non-linear relations among tax default behaviours and prior-warning financial indicators. This study aims to predict the best solution for forecasting tax default using cutting-edge ML techniques.

关键词： Surveys Support vector machines machine learning algorithms Finance Clustering algorithms Prediction algorithms Market research

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Classification of Breast Cancer Diagnosis Using machine learning algorithms

Classification of Breast Cancer Diagnosis Using Machine Lear...

引用

International Conference on Computer Communication and Informatics (ICCCI)

作者： Raghav Dwivedi Khush Ramdev Er. Meenu Jain Neha Agrawal Information Technology Maharaja Agrasen Institute of Technology Delhi India

In the developing world, cancer is one of the major causes of death. Breast cancer has the largest share among all types of cancer among women in India [1] and worldwide [2]. Early diagnosis of this can help the patient to increase their chances of survival, as it can provide much needed treatment on time. This paper focuses on classification of breast cancer into benign and malignant using various machine learning methods namely Logistic Regression, Decision Trees, Random Forest, K Nearest Neighbor, Extreme Gradient Boosting and Support Vector Classifier on the basis of Breast Cancer Wisconsin (Diagnostic) dataset. We propose a comparison between these implementations and evaluate them on various parameters. This survey also analyses the effects of dimensionality reduction using Principal Component Analysis and Recursive Feature Elimination on a few of these algorithms.

关键词： Surveys machine learning algorithms Support vector machine classification Sensitivity and specificity Breast cancer Classification algorithms Decision trees

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Applying machine learning algorithms for Opinion Mining on a Digital Internet Service Provider

Applying Machine Learning Algorithms for Opinion Mining on a...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Amalia Utamima Farrel Istihsan Aditya Firdaus W. G. Ashaari Alfito K. Nugraha Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

By.U is the first digital internet service provider in Indonesia that provides complete digital services for all telecommunication requirements. The By.U application on the Google Play Store has garnered many ratings and comments from users. However, the unstructured nature of these reviews makes it challenging to extract and classify valuable information, which is why opinion mining is preferred. This research compares three machine learning algorithms, namely K-Nearest Neighbors (KNN), Support Vector machine (SVM), and Naive Bayes Classifier (NBC), to conduct opinion mining. The research findings suggest that the KNN outperforms the NBC and SVM methods in terms of accuracy, precision, and recall.

关键词： Support vector machines Sentiment analysis machine learning algorithms Web and internet services Digital forensics Companies Internet

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Analysis of Diabetic Prediction Using machine learning algorithms on BRFSS Dataset

Analysis of Diabetic Prediction Using Machine Learning Algor...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： Lakshmi H.N. A. Srinivasa Reddy Kritika Naidu Department of ET CVR College of Engineering Hyderabad India Department of CSIT CVR College of Engineering Hyderabad India

Due to the detrimental effects it has on everyone's health, diabetes is a chronic condition that still poses a serious threat to the global population. It is a metabolic disorder that increases blood sugar levels and increasing the risk of heart disease, kidney failure, stroke, issues with the nerves and heart, among other issues. Over the years, several scholars have sought to create reliable diabetes prediction models. Due to a lack of adequate data sets and prediction techniques, this discipline still faces many unsolved research issues, which forces researchers to apply big data analytics and ML-based methodology. The paper investigates healthcare prediction analytics and addresses the issues using four different machine learning methods. This study has utilized the Early detection and Binary 012 databases. Based on these datasets, the precision, recall, and accuracy of KNNs and Random Forest methods are calculated. The study's findings may be valuable to health professionals, stakeholders, students, and researchers engaged in diabetes prediction research and development because SVM performs better than KNN and Logistic Regression.

关键词： Support vector machines Heart machine learning algorithms Sociology Predictive models Prediction algorithms Diabetes

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Comparison of Performance of machine learning algorithms for Diabetes Detection

Comparison of Performance of Machine Learning Algorithms for...

引用

Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Pranav Dalve Dyuti Bobby Abha Marathe Atharva Dusane Shreya Daga Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune India

In today's society, Diabetes Mellitus affects a large portion of the population. The purpose of this research is to examine a few machine learning (ML) algorithms to assist in prediction of Type 2 diabetes, a disorder that alters the body's blood sugar processing. For the diabetes prediction proposed, the prominent PIMA Indian Diabetes Dataset was utilized. Support Vector machine (SVM), K-Nearest Neighbours (KNN), XGBoost (Extreme gradient boosting), and Random Forest Regression are the techniques that were investigated. SVM yields an accuracy of 84.5%, sensitivity of 88.00% and specificity of 81.00%. KNN yields an accuracy of 80.5% sensitivity of 83.00% and specificity of 78.00%. XGBoost yields an accuracy of 83.41 %, sensitivity of 80.18% and specificity of 86.61%. Random Forest Regression yields an accuracy of 91.26%, sensitivity of 82.76% and specificity of 98.96%. Diabetes may be detected early and treated promptly, slowing the course of the disease. Using an ML model to reliably foresee Type 2 diabetes might prove beneficial. As a result, machine learning and artificial intelligence have found a home in the healthcare industry. The Random Forest method should be used for a far more accurate and specific identification, according to a comparison of the models.

关键词： Support vector machines Sensitivity machine learning algorithms Medical services Prediction algorithms Diabetes Statistics

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Performance Analysis of machine learning algorithms Using Bagging Ensemble Technique for Software Fault Prediction

Performance Analysis of Machine Learning Algorithms Using Ba...

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International Conference on Information Systems and Computer Networks (ISCON)

作者： Roshan Samantaray Himansu Das School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

Software development comes with a lot of challenges. Developers face various issues with performance and bugs. These issues increase with the scale of the project and if fewer individuals work on the development. It has become necessary to fix various bugs during development to ensure better performance and reduce the chances of failure during the deployment of the software. As a result of this, faults in the software must be predicted during the earlier stages of development. This would help in reducing the cost of maintenance of the software post-deployment. Multiple software fault prediction (SFP) approaches have been proposed to tackle this problem. These approaches can be improved by implementing ensemble techniques. In this paper, we study the effect of the bagging technique and how it helps to improve the predictive capability across various datasets. These datasets are provided and made open source by NASA. Decision Tree Classifier (DTC), Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Support Vector Classifier (SVC) were used as base classifiers for the bagging method. Random forest (RFC) is an ensemble learning algorithm that uses the bagging technique. Based on the outcome of the results, it was concluded that RFC was the best-performing algorithm.

关键词： machine learning algorithms Software algorithms NASA Support vector machine classification Static VAr compensators Gaussian processes Prediction algorithms

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An Exploration of the Effectiveness of machine learning algorithms for Text Classification

An Exploration of the Effectiveness of Machine Learning Algo...

引用

Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Ankita Agarwal Aravindan M K Manish Shrivastava S. Sathya Devika A. Verma Jagmeet Sohal Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai - 127 Department of Computer Engineering Centre of Vishwakarma Institute of Information Technology Pune India Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

Text type is an important project in natural language processing (NLP), with programs in facts retrieval, sentiment evaluation, and report categorization. With the exponential increase of virtual information, the call for automatic text category has multiplied. Conventional text category techniques along with rule-based totally structures and statistical methods have obstacles in managing complex and massive datasets. System studying (ML) has emerged as a promising technique to text type, leveraging algorithms which can automatically learn from information and make correct predictions. This paper gives an exploration of the effectiveness of different machine getting to know algorithms for text category responsibilities. We evaluate and compare the performance of famous algorithms consisting of decision bushes, aid vector machines (SVM), ok-nearest pals (KNN), and deep learning fashions which include convolutional neural networks (CNN) and recurrent neural networks (RNN).

关键词： Support vector machines machine learning algorithms Recurrent neural networks Statistical analysis Text categorization Prediction algorithms Natural language processing

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Vehicle Efficiency Prediction using machine learning algorithms

Vehicle Efficiency Prediction using Machine Learning Algorit...

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Smart Data Intelligence (ICSMDI), International Conference on

作者： Parvathi R Ashish Choudhary Pulak Jain Om Kajave School Of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai Tamil Nadu India

The performance analysis and efficiency of a vehicle play a prominent role and a very necessary step to do in today's scenario. There are various instances when the user feels reluctant to discard the vehicle. In such cases where the user is ignorant of the fact to discard the car, the concerned authorities must come forward to check whether the user is using the car beyond the limit. Therefore, there is an increasing need to save the environment and nature to live a sustainable life. The performance analysis of the car is based on the engine type, number of engine cylinders, fuel type, etc. This study predicts the mpg value by using machine learning models like Random Forest (RF), K-Nearest Neighbors (KNN), XG-Boost, Ridge Regression, Lasso Regression, etc. and based on that it is compared with the optimum value of mpg and hence one can reach to a decision to discard the vehicle.

关键词： Training machine learning algorithms Predictive models Prediction algorithms Cleaning Performance analysis Automobiles

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