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检索条件"主题词=Machine Learning Algorithms"

共 25808 条记录，以下是531-540 订阅

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Predicting Acute Myocardial Infarction Using machine learning algorithms

Predicting Acute Myocardial Infarction Using Machine Learnin...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Md Rahat Kader Khan Tamador Mohaidat Kasem Khalil Department of Electrical and Computer Engineering University of Mississippi Mississippi USA

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Acute myocardial infarction remains the leading cause of mortality in both developed and developing countries. This severe medical condition requires prompt hospitalization and often requires immediate diagnosis and treatment in emergency departments to prevent fatal outcomes. While numerous studies have explored the prognosis of AMI using hemogram parameters, none have employed an interpretable artificial intelligence-based clinical approach to investigate these parameters for diagnosing AMI. In this paper, we present a study that leverages potential hemogram parameters to detect acute myocardial infarction. By implementing a diverse range of machine learning algorithms, our research highlights the superior performance of boosting algorithms over classical machine learning methods. Among these, the Light Gradient Boosting machine algorithm, combined with hemogram parameters, establishes a new benchmark, achieving an accuracy of 85.31% and a score of 85.28% for the Area Under the Receiver Operating Characteristic Curve, indicating its robust capability to distinguish between AMI patients and healthy individuals.

关键词： Performance evaluation machine learning algorithms Accuracy Simulation Receivers Myocardium Benchmark testing Prediction algorithms Boosting Diseases

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Autism Spectrum Disorder Analysis Using Different machine learning algorithms

Autism Spectrum Disorder Analysis Using Different Machine Le...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Anusha Tiwari Akanksha Singh Daksh Rawat S. Vats V. Sharma Computer Science and Engineering Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

ASD (autism spectrum disorder) affects how people communicate, socialize, and behave. It usually starts showing up in childhood but can affect people of any age. While ASD can't be cured completely, early detection and intervention can help manage its symptoms effectively, Spotting ASD early can be tough because its symptoms vary and can be similar to those of other mental health issues. Advanced technology like deep learning and different machine learning algorithms can assist doctors in diagnosing ASD more accurately. In our study, we analyse different algorithm and suggest using support vector classifier and analyse lots of different features and figure out the most important ones for predicting ASD early on. This algorithm will make best predictions and more accurate. Finding ASD early is really important for improving how patients do so that they don’t have to suffer more later and saving money in healthcare. machine learning methods have shown promise in predicting autism by looking at complicated data patterns and figuring out risk factors. By using data from various websites and analysing and thinking about various categories on which evaluation can be done, we can make better models for predicting autism in any age group of people.

关键词： Support vector machines Autism machine learning algorithms Accuracy Medical services Prediction algorithms Feature extraction Data models Complexity theory Classification algorithms

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Exploring machine learning algorithms for Early Diagnosis of Plant Disease

Exploring Machine Learning Algorithms for Early Diagnosis of...

引用

Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Rev Ojha Saru Dhir Raghav Betty Paulraj Sumita Gupta Amity University Noida Uttar Pradesh

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Plant leaf diseases threaten agricultural productivity and food security, affecting global crop production. Traditional detection methods are often time-consuming and error-prone. This paper explores using machine learning to automate plant leaf disease detection, improving speed and accuracy. Paper elaborates the execution of a comprehensive database of plant leaf diseases, detailing symptoms, environmental conditions, and affected species, to train. The experimental results generated through the implementation were tested with the state-of-the-art methodologies and found to be over 90% accurate. The proposed method outperformed the existing methods. The performance measures like the accuracy and other evaluation metric proved the efficiency of the methodology presented with improved accuracy. The outcome of the paper served as a socio-beneficial so as the reformation of agricultural practices have been emerged out. The new revolution in the modern agricultural practices had emerged owing to the inclusion of machine learning (ML) and the Deep learning models in the early disease prediction in plant.

关键词： Support vector machines Productivity Measurement Plant diseases Accuracy machine learning algorithms Plants (biology) Food security Predictive models Nearest neighbor methods

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Prognostic Modeling of Diabetes Mellitus in Women: A comparitive analysis of machine learning algorithms integrating Clinical and Genetic Data

Prognostic Modeling of Diabetes Mellitus in Women: A compari...

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Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS), International Conference on

作者： M. Venkat Prashanth Nikhil Tom Jose Konda Hemesh Verma B. Sreevidya M. Rajesh Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Diabetes is considered as one of the most commonly observed medical condition among human beings. Millions of people worldwide are affected by a chronic condition that goes by the name of diabetes. There are 3 sorts of diabetes and they comprise of Type 1, Type 2 and Gestational diabetes. Early prediction of diabetes helps to avoid complications like diabetic retinopathy which leads to blindness. There is a 13% higher risk of death among people with diabetes, especially in women, cardiovascular disease caused by diabetes attributes to 50% higher risk of death, coronary heart disease caused by diabetes contributes to a 58% higher risk of death. The proposed research work aims in predicting diabetes from the symptoms a patient exhibit. The proposed system is a healthcare assisting system which is accessible to patients as well as medical practitioners. The system is developed as a web application using the features of Django python framework along with suitable database management system. The system is powered with gradient boosting machine learning algorithm for predictions. By using the web application along with a blood test to find out the insulin levels and glucose levels in blood, any user can predict whether he/she would be affected by diabetes in future and can avoid the complications caused by diabetes.

关键词： Heart machine learning algorithms Computational modeling Predictive models Boosting Diabetes Glucose

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Application of machine learning algorithms in Personalized Marketing

Application of Machine Learning Algorithms in Personalized M...

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Recent Trends in Computer Science and Technology (ICRTCST), International Conference on

作者： P G Thirumagal Dr. Kishore Bhattacharjee Rajesh Dorbala Manesh R. Palav Viji S Vaishali Mahajan Department of MBA VISTAS Chennai Tamil Nadu Amity University Patna Bihar Mittal School of Business Lovely Professional University Phagwara Punjab India MBA Department Global Business School & Research Centre Pune Maharashtra India Department of Commerce Kalasalingam Academy of Research and Education Srivilliputtur Tamil Nadu Department of Marketing Symbiosis Centre for Management & Human Resource Development (SCMHRD) Pune Maharashtra

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

The win-win relationship between artificial intelligence and the cutting-edge field of targeted advertising. Companies nowadays are employing machine learning algorithms to develop targeted marketing strategies because they simply cannot afford to ignore their clients. This article delves into significant machine learning approaches such collaborative filtering, content-based filtering, clustering, and predictive modelling to illuminate their roles in offering personalised experiences. Examining real-world applications across many industries, such as e-commerce, social media, email marketing, and retail, highlights successful implementations. Although the piece focuses on the positives, such increased conversion rates and satisfied consumers, it also examines the negatives, like concerns about data privacy and algorithmic biases. The discusses the ethical concerns brought up by highly personalised advertising and foresees future advances such as natural language processing and reinforcement learning. Lastly, this article provides excellent research on the ways in which machine learning algorithms have changed personalised marketing, which can help businesses adapt to the dynamic nature of customer engagement.

关键词： Industries machine learning algorithms Social networking (online) Filtering Heuristic algorithms Reinforcement learning Predictive models

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Transformer equipment fault detection based on multiple machine learning algorithms

Transformer equipment fault detection based on multiple mach...

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作者： Zhongping Ning School of Mechanical and Automotive Engineering Anhui Industrial Economics Vocational and Technical College

Transformer is an indispensable equipment in the power system, and its normal operation plays a vital role in the stable operation of the power system and power ***, due to the complexity of the working environment of the transformer, its internal structure is also more complex, so the transformer failure frequency is higher. Therefore,timely detection and early warning of transformer failure is of great importance. In this paper, a machine learning algorithm is introduced to automatically discover the laws and patterns in the data by learning and analysing a large amount of data, and use these laws and patterns to make predictions and decisions. In this paper, the winding temperature indicator, oil temperature indicator alarm, oil temperature indicator trip and magnetic oil level indicator are taken as the target variables, and the current and voltage parameters are taken as the input variables, and the dataset is divided into training set, validation set and test set according to the ratio of 6:2: *** dataset is divided into training set, validation set and test set, and 12 kinds of machine learning models are introduced to predict the winding temperature indicator, the oil temperature indicator alarm, the oil temperature indicator trip, and the magnetic oil level indicator, respectively. indicator alarm, oil temperature indicator trip, and magnetic oil level indicator values, respectively, to observe the operating status of the transformer in real time by predicting the transformer state parameters. The results show that the machine learning model has high application value in transformer condition monitoring. Through the input of current and voltage parameters, the machine learning model can accurately predict and monitor the important indicators of the transformer, and discover the abnormal state of the transformer in time, so as to ensure the safe operation of the transformer. In the prediction of winding temperature, oil temperature and oil temp

关键词： Transformer equipment fault detection machine learning algorithms Prediction accuracy

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Enhancing Early Diagnosis of Autism With machine learning algorithms Using Postural Control Features

Enhancing Early Diagnosis of Autism With Machine Learning Al...

引用

IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Maya Nachega Sugru A. Yahaya Ila Wallace Ting Liu Damian Valles Rachel M. Koldenhoven Yumen Li Dept. of Biomedical Engineering Vanderbilt University Nashville USA Weldon School of Biomedical Engineering Purdue University West Lafayette USA Dept. of Computer Science Texas State University San Marcos USA College of Graduate Studies Texas A&M University-San Antonio San Antonio USA Ingram School of Engineering Texas State University San Marcos USA Dept. of Health & Human Performance Texas State University San Marcos USA

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder whose biological cause is still not well understood. Due to this, as well as the gradual onset patterns of the disorder’s symptoms, early identification and diagnosis of ASD proves to be challenging. This study aimed to provide a potential early diagnostic tool for ASD by training machine learning classifiers with linear and nonlinear postural control data features. For the research methodology, center of pressure (COP) data for 38 children ages 5-16 was collected using a force plate during both eyes opened and eyes closed conditions. Each trial was 20 seconds, and three trials were completed for each condition. The raw data was preprocessed, and six COP variables were calculated for each trial: anteroposterior (AP) displacement, mediolateral (ML) displacement, elliptical sway area, COP travel distance, AP multiscale entropy complexity, and finally, ML multiscale entropy complexity. After preprocessing, these data features were trained to seven machine learning models to classify participants as having ASD or typical development (TD). Our experiment showed that all seven machine learning models could appropriately classify participants with an accuracy of $79 \%$ and above. The best classification performance had an accuracy of almost $97 \%$ and was done by the model that utilized the random forest algorithm for classification. Additionally, the data feature that was the most important across machine learning algorithms was AP complexity. These results have strengthened the validity of a machine learning approach for early ASD diagnosis. In future research, more data should be collected beyond the 38 participants obtained in this study, or data augmentation should be done to generate more data to train the models. Future research should also put efforts into obtaining more robust and representative datasets, exploring best multiscale entropy parameter values, and exploring why certain data featu

关键词： Training Autism machine learning algorithms Accuracy machine learning Data augmentation Entropy Data models Complexity theory Random forests

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Detecting Alzheimer’s Disease (AD) Using machine learning algorithms

Detecting Alzheimer’s Disease (AD) Using Machine Learning A...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Aayushi Saxena Arpita Soni Singh School Of CSE Lovely Professional University Phagwara Punjab India Department of Computer Science and Engineering Lovely Professional University Jalandhar Phagwara Punjab India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Alzheimer’s is a neurological condition that worsens over time and affects cognitive and memory abilities. Because AD is so common, prompt intervention and therapy depend on early detection. Conventional diagnostic techniques, such neuroimaging and clinical evaluations, frequently have poor specificity and sensitivity, which can result in delayed or incorrect diagnoses. This paper investigates the use of ML techniques for the fast detection of AD. Using a longitudinal dataset of 150 people having age ranging from 60 to 96, this study examines seven ML algorithms named Support Vector machine (SVM), Voting Classifier, Decision Tree , Gradient Booster, Random Forest Classifier, Gaussian Naive Bayes, and the MLP i.e. Multi layer perceptron—to assess their efficacy in identifying AD. Biomarkers such as the Normalized Whole Brain Volume, Clinical Dementia Rating i.e. CDR and Mini-Mental State Examination i.e. MMSE are included in the dataset. The results show that MLP has the lowest accuracy at 56.25%, while Random Forest and Gradient Boosting classifiers obtain the best accuracy of roughly 83.92%. This comparison investigation shows that AD detection can become more precised with the machine learning algorithms. Additionally, this work implies that future detection systems might benefit from increased accuracy through the integration of various models and the combination of textual and image data. These revelations support continued efforts to create effective and trustworthy Alzheimer’s disease diagnostic instruments.

关键词： Support vector machines Neuroimaging Technological innovation machine learning algorithms Accuracy Sensitivity Instruments Medical treatment Alzheimer's disease Random forests

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Analyzing HR Management and its Impact using machine learning algorithms

Analyzing HR Management and its Impact using Machine learnin...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ritika Tiwari Garima Bhardwaj Pooja Tiwari Amity University Greater Noida Uttar Pradesh India Sharda University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

AI is a discipline that combines computer science with wide-ranging datasets to empower problem-solving capabilities. Application of AI in Human resource management increases the efficiency and performance of HRM. AI will reduce the burden of mundane works associated with HRM. Efficient HR management tools have the capacity to automate routine tasks, reducing the administrative burden on HR professionals. This not only saves time but also enables organizations to redirect their focus and resources towards core business objectives. By automating processes related to recruitment, onboarding, performance management, and more, these tools enhance overall operational efficiency, allowing organizations in the IT industry to adapt swiftly to the evolving landscape and capitalize on emerging opportunities. AI will help to take strategic decisions which made a huge impact on every organization here it is used [1].

关键词： Training Leadership machine learning algorithms Resumes Organizations Problem-solving Artificial intelligence Interviews Recruitment Investment

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Enhance fault identification in rotary equipment using machine learning algorithms

Enhance fault identification in rotary equipment using Machi...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： V Sangeetha Shilpa Shashikant Chaudhari R Tanupriya K Theertha S D Varsha C Vishnupriya Department of CSE Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

Rotating machinery is essential to production and is utilized across many sectors. Compressor, pump, and motor productivity all impact an industry’s profitability. A single industry can lose ${\$}$2.3 million annually due to unplanned downtime. In some industries, these costs are even higher, with unplanned downtime losses reaching tens of thousands of dollars per minute. To address these types of issues, the proposed system uses machine learning algorithms to identify the faults in rotary equipment. In this paper, the proposed model applies machine-learning algorithms to classify the faults in the model. Fast Fourier transform (FFT) analysis is carried out on vibration signals to separate them into constituent spectral components. A Decision tree and Random Forest classifier are used to predict anomalies and classify them into different fault types. The proposed system predicts specific fault types based on vibration diagnosis which compared to the general features of temperature, speed, and acceleration gives a much more accurate analysis. The proposed models provide users with previously unattainable levels of insight that accurately predict specific potential failures and replace the failing components just before they are due to fail. The results indicate that the Multi-fault classification using random forest classifier achieves higher accuracy of 86 percent when compared to decision tree classifier of 84 percent.

关键词： Industries Vibrations Fault diagnosis machine learning algorithms Costs Fast Fourier transforms Predictive models

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