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检索条件"主题词=Machine Learning Algorithms"

共 26130 条记录，以下是2071-2080 订阅

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machine learning Approaches for ECG Analysis: : A Review of algorithms in Diagnosing Cardiac Conditions

Machine Learning Approaches for ECG Analysis: : A Review of ...

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Sudhir Goswami Vijay Anand Kirti Jain School of Research & Technology People’s University Bhopal Bhopal India Sanjeev Agrawal Global Educational University Bhopal Bhopal India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

The electrical activity of the heart is shown graphically by an electrocardiogram, which shows P, QRS, and T waves. It is essential to extract and segment information from an Electrocardiogram to diagnose various heart diseases. This paper’s main goal is to review different machine-learning methods for identifying heart enlargement, hypertrophy (thinning of the heart muscle), arrhythmias (irregular heartbeats), and myocardial infarction (heart attack). Furthermore, we showcase and contrast various machine learning techniques and their results in Electrocardiogram analysis. The goal of the research, the algorithms employed, and the outcomes are just a few of the qualitative and quantitative factors considered when evaluating the current methodologies.

关键词： Heart machine learning algorithms Reviews machine learning Electrocardiography Myocardium Reliability Medical diagnosis Diseases Testing

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A Comparative Study of Classification algorithms for Sentiment Analysis of Covid-19 Vaccine Opinions Using machine learning

A Comparative Study of Classification Algorithms for Sentime...

引用

Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Dilber S Zainulabdeen Mesut Çevik Mohammed Majid Abdulrazzaq Electrical and Computer Engineering Altinbas University Istanbul Turkey Computer Engineering University of Anbar Anbar Iraq

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Textual data analysis and classification into positive, negative, or neutral emotions in natural language processing are complex. This study uses machine learning techniques to classify coronavirus and vaccination attitudes. This study used Random Forest (RF), Gradient Boosting Classifier (GBC), Logistic Regression (LR), and Decision Tree. The RF and GBC algorithms had an impressive 89% accuracy rate, while the LR and DT algorithms had 87%. Word processing and data pre-processing were needed before categorization. The outcomes of this research can help policymakers overcome possible impediments to vaccination initiatives. contain the infection and reduce its public health effect. The examination of Kaggle data, which includes many pandemics and vaccine remarks, emphasizes the necessity for immediate herd immunity against COVID-19. This crucial goal is crucial to limiting the virus's spread and minimizing its negative effects on society. The task requires acknowledging and resolving public concerns and building faith in the immunization campaign. This study uses machine learning approaches and compares their importance. The upcoming study will produce an app that categorizes sickness and vaccination attitudes. To design effective plans for Covid-19 immunization, governments and organizations must understand the challenges. It is important to note that the research only included English tweets. This constraint may reduce the credibility and generalizability of sentiment results. To further understand public perceptions regarding COVID-19 vaccinations, deep learning algorithms might be used to analyze larger Twitter datasets.

关键词： COVID-19 Deep learning machine learning algorithms Social networking (online) Blogs Government Vaccines

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Performance Optimization of machine learning-Driven Image Recognition algorithms for iOS Mobile Platforms

Performance Optimization of Machine Learning-Driven Image Re...

引用

International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ibragim Mamadaev Muslim Mamadaev Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

Today., mobile devices play an important role in the daily life of every person., without which many can no longer imagine their lives. One of the key technologies leading to significant benefits for mobile applications is machine learning. Optimization of machine learning algorithms for mobile devices is an urgent and important task., it is aimed at developing and applying methods that will effectively use the limited computing resources of mobile devices. The paper discusses various ways to optimize image recognition algorithms on mobile devices., such as quantization and compression of models., optimization of initial calculations. In addition to ways to optimize the machine learning model itself., various libraries and tools for using this technology on mobile devices are also being considered. Each of the described methods has its advantages and disadvantages., and therefore., in the results of the work., it is proposed to use not only a combination of the described options., but also an additional method of parallelization of image processing processes.

关键词： Performance evaluation Support vector machines machine learning algorithms Image recognition Recurrent neural networks Optimization methods Generative adversarial networks Mobile handsets Libraries Convolutional neural networks

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Sustainable machine learning: Evaluating the Environmental Cost of AutoML algorithms in AI Development

Sustainable Machine Learning: Evaluating the Environmental C...

引用

Artificial Intelligence (CAI), IEEE Conference on

作者： Marc Schmitt Siemens AG Germany University of Oxford UK

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

This study evaluates the carbon footprint (CF) of Automated machine learning (AutoML) algorithms in AI development, examining three datasets to assess emissions across various run-times and countries. It is shown that the carbon intensity (CI) of these systems is significantly influenced by the energy sources powering the computational infrastructure. A correlation between run-time and model accuracy is observed, showing diminishing returns in accuracy with increased run-time and its environmental cost. The findings highlight the crucial role of geographic location and regional energy mix in determining the carbon footprint of AI operations. In areas with low-carbon or renewable energy sources, AI systems exhibit a reduced carbon footprint, underscoring the importance of infrastructural and environmental context in AI’s ecological impact. This study calls for adopting energy-efficient locations and optimizing ML algorithms to achieve a balance between model accuracy and environmental costs.

关键词： Renewable energy sources machine learning algorithms Costs Accuracy Biological system modeling Green products Energy efficiency

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From algorithms to Alpha: Exploring the Role of machine learning in Financial Markets

From Algorithms to Alpha: Exploring the Role of Machine Lear...

引用

Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Deekshitha Chirumamilla V. M. Manikandan Department of CSE SRM University-AP Andhra Pradesh India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This paper explores how machine learning is transforming the financial markets, specifically in relation to trading strategies and profit-maximizing investment choices. We examine machine learning’s influence on algorithmic trading, revealing how it shapes trading algorithms and guides investment strategies to maximize returns, commonly called "alpha." From there, we explore a wide range of financial applications of machine learning, such as fraud detection, portfolio optimization, and risk management. While highlighting the positive impacts of machine learning, we also acknowledge and address the challenges it presents in finance. Overall, this article shows how machine learning (ML) is changing the financial landscape and emphasizes the need for ethical use to maximize its advantages while minimizing its risks.

关键词： Technological innovation machine learning algorithms Shape Heuristic algorithms Finance machine learning Risk management Optimization Portfolios Investment

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Optimization of Rainfall Prediction Using Satellite Data Through machine learning and Deep learning algorithms

Optimization of Rainfall Prediction Using Satellite Data Thr...

引用

Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Kasarapu Ramani Maddireddy Srilekha Reddy Kelavathi Bhavani Syed Feeza Vellampalli Sai Bavesh Dept. of Data Science Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of CSSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

In recent years, non-periodic rainfall has increased globally. The importance of accurate rainfall prediction model is increased in order to reduce the losses due to floods and drought. Rainfall prediction plays a crucial role in decision-making, flood and drought risk assessment and management. In agriculture, it guides many farmers in optimizing crop planting schedules and irrigation. It ensures better yields and reduces losses from droughts and floods. Accurate rainfall prediction benefits us in effective water resource management and energy sector planning. In India, the rainfall varies rapidly due to the vast geography. There are many factors which effects rainfall like distance from the sea, seasons, the presence of mountains, prevailing winds and precipitation. This research work mainly focuses on developing an efficient ensemble-based machine learning Techniques such as Extreme Gradient boosting algorithm (XG Boost) and Logistic Regression (LR) for predicting rainfall in different regions of India using satellite data and comparing results with the deep learning algorithms such as Convolutional Neural Network (CNN), Multiple Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Radial Basis Function Network (RBF). The aim of this paper is to overcome the drawbacks of existing systems and build improved prediction model with accuracy more than 90% in order to enhance water resource management, early warning systems, improved urban planning, agriculture, and climate change studies.

关键词： Deep learning Rain Accuracy Satellites machine learning algorithms Predictive models Prediction algorithms

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Analysis of in-Processing algorithms Meeting Monotonic Selective Risk Conditions for Fairness and Accuracy in machine learning

Analysis of in-Processing Algorithms Meeting Monotonic Selec...

引用

International Conference on Data and Software Engineering (ICODSE)

作者： Nayotama Pradipta Ir. Windy Gambetta Program Studi Teknik Informatika Sekolah Teknik Elektro dan Informatika Institut Teknologi Bandung Bandung

ISBN: (数字)9798331506407

ISBN: (纸本)9798331506414

In machine learning, balancing fairness and accuracy remains a crucial challenge, especially when applied to models used in decision-making process. This paper explores the application of in-processing algorithms to address this balance, utilizing monotonic selective risk as a guiding parameter. Through experimentation, several algorithms are compared to evaluate their performance on fairness and predictive accuracy. Each model is evaluated on fairness and accuracy using monotonic selective risk, RMSE, and Dalex fairness metrics. Five random seeds are generated on the recruitment data and tested to ensure robustness. Results show that the heteroskedastic neural network with sufficiency-based regularizer outperforms others, reducing RMSE while achieving optimal levels of independence, separation, and sufficiency. This study contributes to the growing field of fair AI by focusing on models that meet the requirements of monotonic selective risk, ensuring reliable and ethical outcomes in practical applications.

关键词： Measurement Accuracy machine learning algorithms Neural networks Software algorithms machine learning Prediction algorithms Robustness Software engineering Recruitment

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Enhancing Investment Decision-Making: A Comprehensive Approach through Fundamental, Technical and Sentiment Analysis with machine learning and Deep learning algorithms

Enhancing Investment Decision-Making: A Comprehensive Approa...

引用

International Conference on Communications and Signal Processing

作者： Hritik Goel L Shyamala SCOPE Vellore Institute of Technology Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In this dynamically changing realm of the Indian financial markets, effective investment strategies drives the need to innovative new methodologies integrating diverse analyses and leveraging advanced technologies. This paper proposes a comprehensive study revolving around the Indian market dynamics, which aims to enhance the investment decision-making via the integration of fundamental, technical, and sentiment analyses using state-of-art machine learning and deep learning algorithms. The core of this methodology implements an Integrated Analysis Framework that amalgamates fundamental ratios, historical and volumetric market trends, various technical indicators, and sentiment variables, leveraging machine learning algorithms to extract meaningful insights from extensive datasets by predictive modeling and sentiment analysis. The framework integrates the fundamental analysis for six different stocks of the Indian market from 2018 to 2023 along with the utilization of Twitter data for sentiment analysis, in combination with pre-trained models, and the calculation of over 80 technical indicators for technical analysis. Results from diverse analyses are synthesized to enhance predictive accuracy of calculating the closing prices and trends using different regression models and time-series analysis. Tailored to the scenario of the Indian market, this methodology aims to provide actionable insights and recommendations for investors.

关键词： Deep learning Sentiment analysis Analytical models machine learning algorithms Heuristic algorithms Signal processing algorithms Predictive models

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Revolutionizing Agriculture: Detection of Plant Disease using machine learning and Deep learning algorithms

Revolutionizing Agriculture: Detection of Plant Disease usin...

引用

Sustainable Expert Systems (ICSES), International Conference on

作者： L Sherin Beevi P. M. Joe Prathap Kolagotla Vishnu Vardhan Reddy Kommini Balaji Mallavaram Nishith W Vinil Dani Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA Department of EEE St. Peter's College of Engg.and Tech. Chennai INDIA

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The threat of plant diseases to global food security highlights the importance of prompt and precise detection. for effective management. Traditional methods are labor-intensive and subjective, prompting the exploration of automated systems. By integrating the advancements in Deep learning and machine learning, this research study investigates the efficacy of algorithms like SVM, Random Forest, CNN, RNN, and ResNet in classifying plant diseases from leaf images. We aim to contribute to enhanced agricultural productivity and food security through automated disease detection.

关键词： Support vector machines Deep learning Training Plant diseases machine learning algorithms Plants (biology) Transfer learning Food security Classification algorithms Random forests

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A Comparative Study of machine learning and Deep learning algorithms for Early Detection of Brain Tumours from MRI Images

A Comparative Study of Machine Learning and Deep Learning Al...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： C. Puvanadevi T. Menakadevi Adhiyamaan College of Engineering (Autonomous) Hosur India Dept of Electronics and Commn Engg Adhiyamaan College of Engineering (Autonomous) Hosur India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Detection of brain tumours at early stages is critical for timely intervention and improved patient outcomes. Magnetic Resonance Imaging (MRI) techniques in medical imaging plays an essential role in facilitating the detection and diagnosis of brain tumours. Over the years, advancements in machine learning and deep learning algorithms have significantly enhanced the accuracy and efficiency of tumour detection from MRI images. However, there remains a need for a comprehensive comparative study to evaluate the performance of these algorithms. The current research work presents a comprehensive comparative study aimed at evaluating the efficacy of such algorithms for early detection of brain tumours from MRI images. A novel algorithm, DT-CNN, is proposed, which combines decision trees (DT) with convolutional neural networks (CNN) to leverage the strengths of both techniques. The study encompasses simulation analyses, employing suitable metrics to assess the performance of DT-CNN alongside existing algorithms. Various simulation metrics such as precision, recall, accuracy and $F 1$-score are applied for rigorous evaluation. Results demonstrate that DT-CNN exhibits promising performance in terms of sensitivity and overall classification accuracy when compared to established algorithms. Moreover, DT-CNN demonstrates robustness in handling complex MRI image data, showcasing its potential as an effective tool for early detection of brain tumours. The study affords valued visions into the strengths and limitations of ML and DL algorithms in the context of medical imaging, facilitating informed decision-making for healthcare professionals.

关键词： Deep learning machine learning algorithms Accuracy Magnetic resonance imaging Decision making Classification algorithms Convolutional neural networks

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