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检索条件"主题词=prediction algorithms"

共 30745 条记录，以下是101-110 订阅

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Performance Analysis for Business Insights prediction Using Machine Learning algorithms

Performance Analysis for Business Insights Prediction Using ...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Steive James Sudarsan Salaja Silas G. Jaspher W. Kathrine Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Predictive analytics is a crucial tool in business decision-making, enabling companies to forecast trends, optimize strategies, and enhance profitability. This study provides a comprehensive performance analysis of three machine learning algorithms-Random Forest (RF), Artificial Neural Networks (ANN), and Gradient Boosting (GB)-for business insights prediction. A structured dataset undergoes preprocessing, including feature engineering and encoding, before model training. Each algorithm is evaluated based on accuracy, precision, recall, Fl-score, and computational efficiency. Results indicate that Gradient Boosting outperforms the others in accuracy (90.2 %), while ANN offers superior generalization. Random Forest provides a balance between interpretability and performance. This comparative analysis aids businesses in selecting appropriate models based on their computational constraints and prediction goals.

关键词： Radio frequency Machine learning algorithms Accuracy Computational modeling Artificial neural networks Predictive models Boosting prediction algorithms Computational efficiency Business

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Employing Machine Learning algorithms for the prediction of Alzhiemer’s Disease

Employing Machine Learning Algorithms for the Prediction of ...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Raja Rajeswari Sethuraman Harshini S Dhaarshieth K R Subhashini M Department of Computer Applications Dr.Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India Swamy Vivekanandha Medical College Hospital and Research Institute Tiruchengode Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Alzheimer's disease is a neurological condition that progressively worsens over time and impairs thinking, memory, social skills, and behavior. Its onset is most commonly seen in the age groups above 65. The World Health Organization's survey indicates that dementia, including AD, is the sixth largest cause of death globally. For older people, this is the most frequent cause of dementia. The word dementia is used to refer to a wide range of brain disorders that are characterized by behavioural abnormalities, loss of memory and reasoning skills, and difficulty carrying out everyday tasks. In order to lower the death rate, early screening for this condition is essential. Machine learning techniques are quite useful in the identification of the relation between risk factors of Alzheimer’s disease and its incidence. This study examines the relationship between several characteristics that lower the risk of Alzheimer's disease using a benchmarking dataset of 2149 Americans. Early detection of AD and associated symptoms is more successful when classifiers such as Random Forest, Support Vector Machine, K-Nearest Neighbours, XGBoost, CatBoost, and Logistic Regression are used. Classifier performance and AD predictions are evaluated using metrics like as F1-score, Precision, Accuracy, and Recall. According to this suggested study, the Random Forest and CatBoost algorithm predicts the test data with 95.5% accuracy. This score is better than the other modern and latest algorithms.

关键词： Support vector machines Surveys Logistic regression Machine learning algorithms Accuracy Refining prediction algorithms Classification algorithms Alzheimer's disease Random forests

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Evaluating the Effectiveness of ML algorithms in Water Potability prediction Aligned with SDGs

Evaluating the Effectiveness of ML Algorithms in Water Potab...

引用

International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Saef Ullah Miah Abhijit Bhowmik Tahmid Alam Sifat Rahman Ahona Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

This research addresses the critical need for accurate water quality prediction, aligning with Clean Water and Sanitation (SDG 6) and Good Health and Well-being (SDG 3). Utilizing a diverse dataset of water quality attributes, we developed and evaluated multiple machine learning models to predict water potability, contributing to SDG 9 (Industry, Innovation, and Infrastructure) through technological advancement. Our primary contribution is a Hyperparameter-Tuned Histogram Gradient Boosting Classifier, which outperformed several other algorithms, achieving an accuracy of 67%, a ROC-AUC score of 70%, and an F1-score of 66%. The study highlights challenges of class imbalance and feature distribution in water quality prediction, addressing aspects of SDG 11 (Sustainable Cities and Communities) by improving urban water management. Our findings have significant implications for water resource management, supporting SDG 12 (Responsible Consumption and Production) through efficient resource utilization. While acknowledging limitations such as single dataset reliance, this research combines modern machine learning with comprehensive analysis to enhance water quality assessment. It contributes to SDG 17 (Partnerships for the Goals) by providing a framework for collaboration between technology and environmental sectors. Future directions emphasize improving model performance and real-world applicability, potentially supporting SDG 13 (Climate Action) by enhancing resilience to water-related climate impacts.

关键词： Histograms Accuracy Signal processing algorithms Water quality Predictive models prediction algorithms Boosting Classification algorithms Water resources Meteorology

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Hybrid Classical-Quantum Generative algorithms for Financial Modelling and prediction

Hybrid Classical-Quantum Generative Algorithms for Financial...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Santanu Ganguly Xing Liang Dimitrios Makris SandboxAQ Surrey UK Kingston University London Surrey UK

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Quantum machine learning (QML) is a cross-disciplinary subject made up of two of the most exciting research areas: quantum computing and classical machine learning (ML), with ML and artificial intelligence (AI) being projected as the first fields that will be impacted by the rise of quantum machines. Quantum computers are being used today in drug discovery, material & molecular modelling and finance. In this work, we explore emerging research areas in the application of hybrid classical quantum machine learning (QML) in finance. We focus on hybrid classical-quantum models that have gained significant interest in the financial world for various applications. We utilise a real-world cryptocurrency dataset to train a quantum generative adversarial networks (qGAN), and implement the real-life NVIDIA stock price data on quantum LSTM (QLSTM), comparing its performance with classical Long Short Term Memory (LSTM) model. Additionally, we investigate the application of quantum circuit Born machine (QCBM) using a toy dataset for applications in finance. For the qGAN, we define quantum circuits for generators and showcase the potential for future quantum advantage in finance through QML, possibly in combination with QLSTM and QCBM. Our findings suggest that, while training classical data is more efficient on LSTM, the test predictions of QLSTM is slightly superior to its classical counterpart making it a promising candidate for financial forecasting and analysis for timeseries type data.

关键词： Training Toy manufacturing industry Time series analysis Finance Machine learning prediction algorithms Generative adversarial networks Data models Long short term memory Financial industry

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Boosting algorithms Empowering Heart Disease prediction for Enhanced Medical Accuracy

Boosting Algorithms Empowering Heart Disease Prediction for ...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Urvashi Sharma Kanak Saxena Computer Science & Engineering Department Samrat Ashok Technological Institute Vidisha Head Computer Science & Engineering Department Samrat Ashok Technological Institute Vidisha India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Heart disease remains a leading cause of mortality worldwide, necessitating accurate and reliable predictive models to aid early diagnosis and treatment. Traditional machine learning methods like LR and DT Classifiers offer reasonable recall rates but suffer from lower precision and accuracy. To address this challenge, we employed advanced boosting algorithms, specifically GradientBoostingClassifier and XGBClassifier, to enhance predictive accuracy in heart disease detection. Our study shows that these algorithms achieve accuracy rates above 93%, with precision exceeding 52%, and a significantly low False Positive Rate below 0.5%. However, the trade-off involves lower recall rates, suggesting some true cases remain undetected. Boosting algorithms thus provide a robust approach for heart disease prediction, though attention to false negatives is essential for comprehensive application.

关键词： Heart Accuracy Machine learning algorithms Predictive models Boosting prediction algorithms Classification algorithms Reliability Medical diagnostic imaging Diseases

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Comparative Analysis of Software Reliability prediction and Optimization using Machine Learning algorithms

Comparative Analysis of Software Reliability Prediction and ...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Prashanth Kumar Bolisetty Midhunchakkaravarthy Faculty of AI AI Computing and Multimedia Lincoln University College Selangor Darul Eshan Malaysia Faculty of AI Computing and Multimedia Lincoln University College Selangor Darul Eshan Malaysia

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Software reliability plays a crucial role in assessing the overall quality of software systems. Developing highly reliable software presents challenges, including ensuring usability, maintaining consistent performance, offering reliable service, and facilitating smooth maintenance. To overcome these limitations, researchers have turned to intelligent prediction and optimization algorithms. This paper presents a comprehensive review of software reliability prediction, emphasizing the application of machine learning techniques. It also provides an analytical evaluation of existing methods to identify areas for improvement and future research opportunities in software reliability prediction.

关键词： Machine learning algorithms Reviews Software algorithms Machine learning Software systems prediction algorithms Software reliability Maintenance Usability Optimization

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Diabetes prediction in Healthcare Utilizing Machine Learning algorithms

Diabetes Prediction in Healthcare Utilizing Machine Learning...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Nrusingha Tripathy Sarbeswara Hota Pranati Satapathy Subrat Kumar Nayak Debasish Swapnesh Kumar Nayak Purnya Prava Nayak Department of Computer Science and Engineering Siksha ‘O' Anusandhan Deemed to be University Bhubaneswar Odisha India Department of Computer Application Siksha ‘O' Anusandhan Deemed to be University Bhubaneswar Odisha India Department of IMCA Utkal University Bhubaneswar Odisha India Department of Computer Science and Engineering Centurion University of Technology and Management Bhubaneswar Odisha India Department of Computer Science and Engineering GIET Baniatangi Bhubaneswar Odisha India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Diabetes mellitus is among the most dangerous conditions that many people suffer from. Age, obesity, poor food, heredity, high blood pressure, and inactivity are some of the variables that might lead to diabetes mellitus. Though there are additional forms of diabetes, such as gestational diabetes, which appears during pregnancy, the two most common types are type 1 and type 2. In data science, machine learning is a relatively new field of study that looks at how machines pick up knowledge via experience. The goal of this work is to build a system that can more accurately forecast a patient's risk of developing diabetes in their early years by utilizing a combination of research data and machine learning techniques. Interpretable findings using logistic regression (LR) show how various risk variables affect the chance of acquiring diabetes. The most crucial characteristics for diabetes prediction may be automatically discovered by neural networks (NN) from unprocessed input data, eliminating the requirement for human feature selection. While compared to previous models, both the logistic regression and the neural network in this study provide a respectable accuracy score following a 10-fold classification.

关键词： Pregnancy Logistic regression Accuracy Machine learning algorithms Machine learning Medical services Artificial neural networks prediction algorithms Diabetes Biomedical monitoring

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Predictive Analysis of Machine Learning and Deep Learning algorithms for Lung Disease prediction

Predictive Analysis of Machine Learning and Deep Learning Al...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Raji Ramachandran Ashina A Jawad Amal U J Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Lung cancer is a significant health issue all over the world, and thus requires effective and precise solutions. Traditionally, lung cancer detection relied on primitive methods such as radiology examination of CT scans, which most of the time leads to delayed diagnosis. These primitive methods were error-prone, inconsistent, and might result in inaccurate results, hence making them hard. This paper discusses predictive analysis of machine learning and deep learning algorithms for prediction of lung cancer. In our approach, using both CSV data and CT scan images, we assess the accuracy and efficiency of different machine learning and deep learning models. While deep learning algorithms process images, machine learning algorithms are applied in CSV data. The paper combines machine learning and deep learning models with hyperparameter tuning to achieve the systems best performance. Evaluation metrics, such as accuracy, recall, precision, and F1-score, are utilized in the evaluation of the comparison purpose. Through model tuning and performance evaluation, the system seeks to uncover the most effective and efficient prediction model.

关键词： Deep learning Machine learning algorithms Accuracy Computed tomography Computational modeling Lung cancer Predictive models prediction algorithms Predictive analytics Tuning

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Classification and prediction-based data mining algorithms to predict visitors to the museum

Classification and prediction-based data mining algorithms t...

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algorithms, Computing and Machine Learning (CACML), Asia Conference on

作者： Yingrui Chi Jiaran Zhang International School of Advanced Studies University of Camerino Ascoli Piceno Italy

ISBN: (数字)9798331543143

ISBN: (纸本)9798331543150

The perception that museums are often perceived as disconnected from everyday life has resulted in a notable reluctance among a substantial segment of the population to visit these institutions. To enhance visitor attendance, it is essential to conduct an analysis of social survey databases. The objective of this study is to identify potential museum visitors within a given population and to predict their likelihood of visiting through the application of a data mining model utilizing classification algorithms. The dataset employed in this research was sourced from the "2021 China General Social Survey." We implemented five classification algorithms using the opensource software WEKA to classify and predict the dataset, specifically Naive Bayes, Logistic, J48, Random Trees, and KStar. Among these algorithms, the J48 classification method yielded the highest accuracy in predicting potential visitors, achieving a success rate of 78.7012%. The findings suggest that lifestyle habits and individual preferences play a significant role in determining the frequency of museum visits. This study demonstrates the effectiveness of classification and prediction algorithms in the context of museum visitor research and proposes several promising avenues for future investigation.

关键词： Surveys Accuracy Computational modeling Software algorithms prediction algorithms Museums Data models Classification algorithms Data mining Standards

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Bone Cancer prediction Using Regression algorithms and Machine Learning Approaches

Bone Cancer Prediction Using Regression Algorithms and Machi...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Abhilash Kokala Kartheek Kalluri Nagaraj Parvatha Naveen Kodakandla Harinagapraveen Gundugolanu Infomatics Corp USA Navy Federal Credit Union USA National Student Clearinghouse USA REI Systems USA

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Bone cancer is an aggressive disease that is often difficult to diagnose and treat and can be very dangerous for the patient. Bone cancer needs to be predicted in time and accurately for successful treatment and good patient care. This study aims to introduce a bone cancer prediction model that combines regression algorithms and machine learning techniques. The model reads data from patient medical records, such as age, sex, or genetic markers, and trains on it to predict the probability of bone cancer. We have tested linear regression, logistic regression, random forest multiple regression algorithm artificially neural networks, and support vector Machine Learning. We used an extensive bone cancer cases and controls dataset from different research centers and hospitals. 6 Our preliminary results show that the new method is more accurate in predicting bone cancer than traditional methods. Regrettably, regression algorithms and machine learning techniques are good enough to model many variables at once while detecting intricate patterns in the data, so as a result, the ability to predict is enhanced. Their model can help healthcare professionals identify PD earlier and make well-informed early treatment choices to improve patient outcomes and quality of life. This is a critical goal given the progressive nature of manystage synucleinopathies. However, more studies and validation are needed before implementing the model clinically.

关键词： Support vector machines Machine learning algorithms Artificial neural networks Predictive models Bones prediction algorithms Genetics Data models Regression tree analysis Cancer

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