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检索条件"主题词=Machine Learning Algorithms"

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Microarray Data Classification using machine learning algorithms

Microarray Data Classification using Machine Learning Algori...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Sanjay Prajapati Himansu Das Mahendra Kumar Gourisaria School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

Microarray data contains a huge amount of features with less number of instances. Extraction of significant information and knowledge from such a huge amount of features that are concealed in this may not be found without the right approaches and instruments. As a result, techniques that can be handled and explored these big amount of data are required. For the analysis and prediction of such massive data sets, the field of machine learning has many kind of approaches and tools for analysis. Here, we will be examining the microarray datsets using traditional machine learning algorithms and models such as Decision Tree (DT), k-nearest neighbor (KNN), Support Vector machine (SVM), logistic regression (LR), and random forest (RF).The accuracy of the five machine learning algorithms was compared to determine the outcome. We will also see the various performance metrics as it tells us about how our model has performed and accordingly we can make suitable changes in our models to make it better for the given dataset. From the result it was observed that KNN, SVM, LR, and RF have been shown the accuracies more than 80% and in some cases 100%.

关键词： Radio frequency Measurement machine learning algorithms Instruments Support vector machine classification Forestry Feature extraction

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Customer Churn Prediction Based on Interpretable machine learning algorithms in Telecom Industry

Customer Churn Prediction Based on Interpretable Machine Lea...

引用

Computer Simulation and Modeling, Information Security (CSMIS), International Conference on

作者： Liwen Ou College of Arts and Science New York University New York United States of America

ISBN: (数字)9798350343724

ISBN: (纸本)9798350343731

Research regarding the interpretability and feature importance of customer churn prediction is rare compared to study on its performance. Nevertheless, knowing what type of customer information are the most significant for predicting churn is critical for firms to layout cost-efficient investment strategies. In this work, feature importance for churn prediction in telecom industry is investigated using a dataset with nearly 40 customer features, and 3 interpretable, tree-based machine learning models. In particular, the data is trained in random forest, decision tree and extra tree classifier algorithms, and uses their built-in feature importance functions to interpret the significance of each feature. The feature importance scores from all three models are compared to analyze which features are the most significant overall. As the result, all models yield optimal prediction accuracy, with decision tree model's slightly lower than the others. The resulting top features from all three models have large overlaps, indicating all models are suitable for the purpose of this study. Random forest and extra tree classifier, as the more complicated models, produce the most satisfactory results, which demonstrate that features related to customer's tenure, consumption in the firm and referral amount are the most important to make accurate churn prediction. This analysis result is reasonable since all these features imply customers' personal preference towards the company.

关键词： Industries machine learning algorithms Layout Companies Predictive models Prediction algorithms Telecommunications

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Sentiments Analysis using machine learning algorithms

Sentiments Analysis using Machine Learning Algorithms

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： Vivek Joshi Sanjay Patel Radhika Agarwal Himanshu Arora School of Information Technology Artificial Intelligence and Cyber Security Rashtriya Raksha University Gandhinagar Gujrat India Department of CSE Nirma University Ahmedabad Gujrat India Maharaja Sawai Man Singh Vidyalaya Jaipur Rajasthan India Department of CSE Arya Institute of Engineering & Technology Jaipur Rajasthan India

The analysis of the sentiments can be determined if a piece of message or text or comment has a decent, negative or neutral feeling or sentiment. Analysis of sentiments is a subtype of natural language processing that incorporates data extraction. For this purpose, numerous types of techniques and algorithms are used. In this paper used the previous twitter data for sentimental analysis and categorization. It is necessary for sentiment analysis to make use of and execute appropriate data visualization and preprocessing. For the analysis of positive or negative sentiments machine learning algorithm is used. In this work used the naive byes classifier for the analysis of the sentiments.

关键词： Sentiment analysis Renewable energy sources machine learning algorithms Social networking (online) Databases Blogs Data visualization

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Design of Enterprise Financial and Economic Risk Early Warning System Based on machine learning algorithms

Design of Enterprise Financial and Economic Risk Early Warni...

引用

Computer Simulation and Modeling, Information Security (CSMIS), International Conference on

作者： Hanjun Li Donghong Zhang Department of Financial Management Department of Accounting Wenhua College Wuhan Hubei China

ISBN: (数字)9798350343724

ISBN: (纸本)9798350343731

The design of enterprise financial and economic risk (ER) warning systems based on machine learning algorithms has important background significance. In the current fierce market competition and complex financial environment, enterprises are facing unprecedented financial and ER. Traditional early warning systems often have problems such as low timeliness, missed reports, and false positives. Therefore, introducing machine learning algorithms can more accurately predict and identify potential financial risks, and help enterprises take timely measures to reduce risks. Taking the collected financial data of a manufacturing enterprise as an example, a financial and ER warning model was constructed through the training and validation of machine learning algorithms. In the past five years, the company's operating revenue fluctuated between 9 million to 13 million yuan, and profits fluctuated between 400000 to 700000 yuan; the total assets increased from 18 million yuan to 24 million yuan, and the total liabilities increased from 10 million yuan to 14 million yuan. The designed warning system is based on real-time monitoring and historical data analysis, which can detect potential financial and ER as early as possible in the event of abnormal changes in financial indicators. By establishing a feedback mechanism, the system continuously optimizes accuracy and reliability. This system design provides enterprises with a more accurate and timely financial and ER warning mechanism, which helps to maintain the sustainable development and stable operation of the enterprise.

关键词： Economics machine learning algorithms Data analysis Current measurement Alarm systems Data models Real-time systems

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Diabetes Prediction in Teenagers using machine learning algorithms

Diabetes Prediction in Teenagers using Machine Learning Algo...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Jayavrinda Vrindavanam Raye Haarika Sindhu MG Kilari Sumanth Kumar Department of Computer Science & Engineering (AI & ML) School of Engineering Dayananda Sagar University Bengaluru India

Diabetes Mellitus (DM) is a hazardous condition that can lead to worldwide health problems owing to an increase in blood glucose levels, age, lack of movement, high blood pressure, poor nutrition, and other factors. The aim of this study is to develop a system that can predict diabetes in individuals aged 10 to 30 by merging the results of different machine-learning algorithms. machine learning is used for increasing performance and forecasting accuracy. Some of the methods used to detect diabetes early include Logistic Regression (LR), Random Forest (RF), Support Vector machine (SVM), K Nearest Neighbours (KNN), and XGBoost. In comparison to the other methods, the random forest and XGBoost Classifiers algorithm outperformed all other ML algorithms, with a maximum accuracy of 86

关键词： Support vector machines Radio frequency machine learning algorithms Computational modeling Forestry Prediction algorithms Feature extraction

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Prognosis of Diabetes Mellitus Based on machine learning algorithms

Prognosis of Diabetes Mellitus Based on Machine Learning Alg...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Ayasha Malik Veena Parihar Jaya Srivastava Harpreet Kaur Shafiqul Abidin Department of CSE Delhi Technical Campus (DTC) GGSIPU Greater Noida India Department of CSE(AI) KIET Group of Institutions Delhi-NCR Ghaziabad India Department of CSE ABES Engineering College Ghaziabad India Department of Computer Science Aligarh Muslim University Aligarh Uttar Pradesh India

One of the life-threatening and deep root diseases leading to raise the level of sugar in the blood is Diabetes Mellitus (DM) and if it is kept anonymous and untouched then many difficulties have to be faced as a result. Generally, when it is identified a patient visits a diagnostic center and takes a consultation with the doctor. Now, this critical problem can be solved using machine learning (ML) approaches as it is booming. A model has been designed in this study that can prognosticate the prediction of DM in patients with a certain level of accuracy. Hence 3 ML algorithms based on classification namely Decision tree, Support Vector machine (SVM), and Naïve Bayes are used in our study for early detection of DM. Database which is sourced from UCI ML repository namely Pima Indians Diabetes Database (PIDD) is used for performing experiments. Measurement of the performance of all 3 algorithms is based on various criteria like Accuracy, Recall, Precision, and F- Measure. Measurement of Accuracy is done on both correct and incorrect classified instances. After comparative analysis, it has been found that Naïve Bayes is having the highest accuracy among other algorithms. For verification of results Receiver Operating Characteristic (ROC) curves are traced in an organized manner.

关键词： Support vector machines machine learning algorithms Databases Receivers Diabetes Classification algorithms Decision trees

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Prediction and Analysis of Heart Attack using Various machine learning algorithms

Prediction and Analysis of Heart Attack using Various Machin...

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

作者： Ochin Sharma Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

The healthcare industry is typically thought of as "information rich" yet "knowledge poor." The healthcare systems include a vast amount of data. However, the lack of efficient analysis tools makes the employability challenging to uncover hidden linkages and patterns in the data. In the business and scientific realms, data mining and information retrieval have various applications. The use of data mining methods in the health service can produce insightful information. The possible applications for rule based, tree structure, Bayesian networks, and artificial neural networks classification are briefly discussed in this research based data mining approaches to a large volume of healthcare data. Huge amounts of healthcare data are gathered by the industry, but they are regrettably not "mined" to find hidden information. Heart attack is a primary causes of unexpected mortality, especially in women, heart attack prediction is crucial in nations with low incomes. Despite using common clinical techniques like electrocardiography and the research goal is to identify the finest machine learning algorithm for predicting heart attacks.

关键词： Industries Analytical models machine learning algorithms Cardiac arrest Medical services machine learning Electrocardiography

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A Study To Detect Emotions From Twitter Text Using machine learning algorithms

A Study To Detect Emotions From Twitter Text Using Machine L...

引用

Innovation in Technology (INOCON), IEEE International Conference for

作者： Anusha Savitha A Shenoy S V Harish Department of Computer Science and Engineering MIT Manipal Udupi Department of Computer Science and Engineering SMVITM Bantakal Udupi

One of the main factor contributing to mental illness, which has been linked to an increased risk of dying young is depression. Additionally, it significantly contributes to suicide ideation. Although there are many underlying reasons of depression, social networking sites play a key part in raising the likelihood of depression. In recent years social media has become the integral part of our daily lives. User reflects his internal life in the content he shares in his social media platform like twitter. People share happy incidents, joyful memories and sad moments through tweets. Thus it is possible to forecast depression in people using Twitter data. Various machine learning techniques have been employed to analyze these data. The algorithms employed are Naïve Bayes and Logistic Regression. Those algorithms will produce intended outcomes.

关键词： Technological innovation machine learning algorithms Social networking (online) Blogs machine learning Depression Information exchange

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Exploring the Impact of Homomorphic Encryption on the Performance of machine learning algorithms 23

Exploring the Impact of Homomorphic Encryption on the Perfor...

引用

Proceedings of the 12th Latin-American Symposium on Dependable and Secure Computing

作者： Clayton Matias Naghmeh Ivaki Regina Moraes State University of Campinas Brazil University of Coimbra Portugal

ISBN: (纸本)9798400708442

The evolution of the internet and the popularization of access to high-speed connections increased the need for data sharing, especially between business partners. For this reason, the importance of secure data communication, storage, and processing grows. New encryption techniques, such as homomorphic encryption, have been studied in this context, allowing companies to share and analyze data without violating data privacy laws. Nonetheless, it is important to consider potential impacts or overhead associated with these techniques. In this study, we aim to examine the use of machine learning (ML) algorithms on homomorphically-encrypted data. We compare four ML algorithms and analyze the impact of encryption on performance (i.e., in terms of accuracy, precision, recall, and F1-Score) and processing time using a health dataset available on the Kaggle platform. Our analysis demonstrates that it is possible to use ML on data encrypted with homomorphic techniques without significant performance loss. However, it is important to consider the trade-off of longer processing times associated with ML-based solutions working with encrypted data.

关键词： Homomorphic Encryption machine learning algorithms Performance Privacy Processing time

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Predicting Chronic Kidney Disease Using machine learning algorithms

Predicting Chronic Kidney Disease Using Machine Learning Alg...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Afia Farjana Fatema Tabassum Liza Parth Pratim Pandit Madhab Chandra Das Mahadi Hasan Fariha Tabassum Md. Helal Hossen Department of Computer Science University of South Dakota Vermillion South Dakota USA Computer Science Florida State University Tallahassee Florida USA Mechanical and Manufacturing Engineering Miami University Oxford Ohio USA Electrical and Electronic Engineering University of information and technology Dhaka Bangladesh Data Analytics The University of Tennessee at Chattanooga Chattanooga Tennessee USA Department of Sociology Western Michigan University Kalamazoo Michigan USA Department of Computer Science The University of Texas at Dallas TX USA

In the modern era, everyone tries to be aware of their health, but because of their workload and hectic schedules, they only pay attention to it when certain symptoms appear. However, because CKD (Chronic Kidney disease) is a disease with no symptoms or, in some cases, no symptoms at all, it is difficult to predict, detect, and prevent such a disease, which could result in long-term health damage. However, machine learning (ML) offers hope in this situation because it excels at prediction and analysis. In this paper, we proposed nine ML approaches, such as K-nearest neighbors (KNN), support vector machines (SVM), logistic regression (LR), Naïve Bayes, Extra tree classifiers, AdaBoost, Xgboost, and LightGBM. These predictive models are built using a dataset on chronic kidney disease with 14 attributes and 400 records to choose the best classifier for predicting chronic kidney illness. The dataset was gathered via ***. Additionally, this study has compared how well these model's function. With the LightGBM model, we could predict kidney illness more accurately than ever before, with a 99.00% accuracy level.

关键词： Support vector machines Schedules machine learning algorithms Sociology Static VAr compensators Predictive models Chronic kidney disease

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