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检索条件"主题词=Machine Learning Algorithms"

共 25882 条记录，以下是1151-1160 订阅

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

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

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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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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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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...

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

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

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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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Improving the Accuracy of Heart Disease Prediction Approach of machine learning algorithms

Improving the Accuracy of Heart Disease Prediction Approach ...

引用

Delhi Section Conference (DELCON), IEEE

作者： Md. Belal Hossain Mohammed Nasir Uddin Syada Tasmia Alvi Chowdhury Abida Anjum Era Dept. of CSE Jagannath University Dhaka Bangladesh Dept. of CSE Daffodil International University Dhaka Bangladesh

The work is about forecasting heart disease. First and foremost, we gathered data from various sources and divided it into two portions, one of which is 80% and the other is 20%, where the first part is for training and the remainder is reserved for the test dataset. After collecting this dataset, we applied the pre-processing formula and different classifier algorithms. K-Nearest Neighbor, Support Vector machine, Decision Tree, Random Forest, Naive Bayes & Logistic Regression are the techniques utilized here. When compared to other algorithms, Logistic Regression, KNN, and SVM provided the same or superior accuracy. Precision, Recall, F1 score, and ERR are used to measure accuracy. Gender, Glycogen, BP, and Heartrate are some of the prefixes used while training and found to be different major vulnerable factors of heart diseases. The direction of this work is real-life experiments and clinical trials using different devices.

关键词： Heart Training Support vector machines machine learning algorithms IEEE Sections Predictive models Classification algorithms

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Classification and Contrast of Supervised machine learning algorithms

Classification and Contrast of Supervised Machine Learning A...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Ramakrishnan Raman Rejuwan Shamim Shaik Vaseem Akram Lalit Thakur Biju G. Pillai R. Ponnusamy Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering with Data Science Maharishi University of Information Technology Noida Uttaranchal Institute of Technology Uttaranchal University Department of CIVIL Parul University Department of IT & Business Analytics BIIB & Dean - Faculty of Management Sri Balaji University Pune (SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

Supervised Classification (SML) is the pursuit of systems that reasoning from externally given instances to generate broad hypotheses, which subsequently generate predictions for future instances. One of the jobs performed by intelligent systems most commonly is supervised classification. Based on the data set, number of occurrences and variables in this study, the most efficient classification algorithm is selected. It is comprehensively described and contrasted with various supervised learning methods. Seven distinct machine learning algorithms were taken into consideration utilizing the Waikato Environment for Data Analysis (WEDA) machine learning tool. For the identification process, the data set was used with 780 instances, eight independent variables (quality), and one dependent variable (variable). According to the data, SVM was the technique with the highest degree of precision and accuracy. Accordingly, the next accurate classification algorithms after SVM were determined to be Naive Bayes and Random Forest. The study demonstrates that precision (accuracy) and model construction time are two factors, whereas kappa statistics and mean error percentage (MAE) are two other factors. Therefore, controlled predictive machine learning requires precision, accuracy, and minimal error in ML algorithms.

关键词： Support vector machines machine learning algorithms Supervised learning Training data Prediction algorithms Time measurement Classification algorithms

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Performance Evaluation of machine learning algorithms to Predict the Medication Prescription Errors in Intensive Care Units

Performance Evaluation of Machine Learning Algorithms to Pre...

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Advancement in Technology (ICONAT), International Conference for

作者： Vineetha Pais Santhosha Rao Balachandra Muniyal Department of Information & Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

Medication error can be considered as one of the major issues in healthcare. Critical care unit is considered as the most crucial unit where medication errors if occurred can be dangerous at the cost of life of a patient. Artificial intelligence has the capacity to significantly reduce prescription errors by helping to identify potential mistakes before they take place. This study is an attempt to compare and choose a machine learning algorithm for the machine learning model that will help the doctors and clinicians working in the intensive care unit to reduce the prescription errors in intensive care unit. In this study machine learning classification techniques have been applied to choose the best machine learning algorithm for the problem of predicting the prescription errors in intensive care unit. The result shows that K Nearest Neighbors (KNN) shows the best accuracy (99.13 %).

关键词： Performance evaluation Drugs Deep learning machine learning algorithms Costs Hospitals Measurement uncertainty

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Comparative Performance Analysis of Quantum Algorithm with machine learning algorithms on Diabetes Mellitus

Comparative Performance Analysis of Quantum Algorithm with M...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： BBNS. Prakash B. Naveen MD. Akhiluzzama Pothuraju Rajarajeswari Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

This paper shows the distinction between Quantum and Classical machine learning techniques appeal to a diabetic mellitus dataset. Diabetes mellitus is like series of diseases that affect the body and deplete blood sugar levels (insulin). In our bodies, glucose is an important source of energy for powering mitochondria, the cells' function that make muscles and tissues strong. We use many machine learning classifiers such as Support Vector machine, Kernel Principal Component Analysis, Bayesian Network and Decision Tree etc. These models are able predict a certain amount of data, while in case of large data there is significant amount of error and low accuracy rate, A new method known as quantum machine learning (QML) is nothing but the collaboration of machine learning in the way of quantum algorithms the term used for machine learning algorithms executed in a quantum computer for analysis of classical data is known as quantum-enhanced machine learning. In this method we are predicting diabetes mellitus using quantum machine learning algorithms which able to handle the huge data with high accuracy rate of 97% and F1-Score of 68%.Our study having implemented models of Predicted & Enhanced models.

关键词： Support vector machines machine learning algorithms Quantum algorithm Computational modeling machine learning Predictive models Data models

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