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检索条件"主题词=Machine Learning Algorithms"

共 26009 条记录，以下是2031-2040 订阅

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Advanced Bone Fracture Detection Using Hybrid Deep learning algorithms and machine learning Models for Enhanced Medical Imaging Diagnosis

Advanced Bone Fracture Detection Using Hybrid Deep Learning ...

引用

Automation and Computation (AUTOCOM), International Conference on

作者： Nidhi Mishra Ghorpade Bipin Shivaji Department of CS & IT Kalinga University Raipur India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Bone fractures belong to the group of the most frequent pathologies in medical practice and proper diagnosis is crucial for receiving an adequate treatment and recovery. This paper describes the next generation work in the medical imaging diagnostics, advancing the bone fracture detection system based on hybrid Deep learning and machine learning technologies applied on X-ray images. The proposed system was tested using 10,000 X-ray images which include normal, fracture scaphoid, fracture clavicle, fracture humerus, fracture radius/ulna, fracture tibia/fibula, fracture femur, fracture pelvic, fracture spine/vertebra, fracture ankle, fracture knee, fracture jaw images which are annotated in our dataset. The way we take the approach is using transfer learning with pre trained convolutional neural networks (CNN) like ResNet50 and DenseNet121 for robust feature extraction and then using traditional machine learning algorithms such as Support Vector machines (SVM) and Random Forest for classification. Class imbalance was reduced by using techniques in data augmentation and model generalization was enhanced to identify fine fractures that would otherwise be unnoticed. They were meticulously tested with conventional evaluation parameters such as accuracy, precision, recall, and F1 score, demonstrated better performance than traditional diagnosis. In normal fracture bone images the hybrid system is found to be 96.5% accurate in terms of identification.

关键词： Deep learning Support vector machines Accuracy machine learning algorithms Computational modeling Bones Data augmentation Data models Convolutional neural networks X-ray imaging

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Detection of Cardiac Arrhythmias in Patients with Obstructive Sleep Apnea using machine learning and Deep learning algorithms

Detection of Cardiac Arrhythmias in Patients with Obstructiv...

引用

Data Science and Information System (ICDSIS), IEEE International Conference on

作者： K Krithik P. V. Padmashree Nachiketha Nayak S Kushal Rekha Bhandarkar K. S. Shivaprakasha Dept. of ECE N.M.A.M. Institute of Technology NITTE (Deemed to be University) Karnataka India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

This paper presents a pioneering effort to understand the complex relationship between Obstructive Sleep Apnea (OSA) and cardiac arrhythmias, utilizing machine learning (ML) and Deep learning (DL) techniques. OSA, a widespread sleep disorder, is known to influence cardiac health. The approach involves designing ML algorithms and DL models trained on extensive Electrocardiogram (ECG) datasets. These models detect subtle patterns in data, helping us understand the specific traits of arrhythmias in patients with OSA. The paper is validated through rigorous testing, evaluating the accuracy, precision, F1 score, and recall of the developed models. It was seen that the Long Short-Term Memory (LSTM) model gives the best accuracy of 0.85 when compared to a random forest with an accuracy of 0.84 for the combined data of cardiac arrhythmia and OSA. Ultimately, the goal is to refine diagnostic precision, providing clinicians with a deeper understanding of the cardiac implications associated with OSA.

关键词： Deep learning Accuracy machine learning algorithms Arrhythmia Sleep apnea Data models Long short term memory

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algorithms for machine learning to Predict Childbirth Modes

Algorithms for Machine Learning to Predict Childbirth Modes

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： Kavin R Lingesh N Mohammed Sath A V.R. Sadasivam Department of Information Technology K.S. Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Health and well-being of women and bonding created with the newborn are heavily de- pendent on the birth process. What delivery method to choose is a critical decision that often needs to be applied in a timely manner to ensure the well-being of the baby and mother. Errors in this regard may cause serious health consequences for the mother and fetus, making it hard for healthcare professionals to make timely and correct decisions. In this paper, a decision support system based on machine learning for predicting the safest delivery method is presented. Various supervised learning algorithms are tested, including K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector machine (SVM), Decision Tree, and a stochastic classifier. The outcomes of this paper indicate that the Random Forest algorithm has better accuracy in predicting the delivery method and thereby aids healthcare professionals in reducing judgmental errors and improving the safety of neonates and mothers.

关键词： Decision support systems Support vector machines Pediatrics machine learning algorithms Supervised learning Medical services Nearest neighbor methods Prediction algorithms Classification algorithms Random forests

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Comprehensive Analysis of Attention Deficit/Hyperactivity Disorder using machine learning and Deep learning algorithms

Comprehensive Analysis of Attention Deficit/Hyperactivity Di...

引用

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

作者： T. N. Sindhu S. R. Sujatha Department of Computer Science and Engineering Sri Siddhartha Institute of Technology Tumakuru India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects children from childhood to adulthood. The symptoms of ADHD include attention deficit, impulsive behaviour, hyperactivity, and early diagnosis and treatment are imperative, especially for children as it continues into adulthood. ADHD detection is generally carried out using the existing methods such as machine learning (ML) and Deep learning (DL) methods. The ML methods such as Linear Support Vector machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminative Analysis (LDA), Linear Regression (LR), Random Forest (RF), and Least Square SVM (LS-SVM) are used for the detection and analysis of ADHD. The existing DL methods such as Deep Convolutional Neural Network (DCNN), Graph Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLSTM), and Bidirectional LSTM are also employed for the detection of ADHD. The ML and DL algorithms enhance ADHD detection and diagnosis by automatically determining large patterns in data, thereby offering higher accuracy. The performance of the existing techniques is evaluated in terms of evaluation metrics: accuracy, precision, sensitivity, and F1-score to assess their effectiveness.

关键词： Support vector machines Deep learning Radio frequency Accuracy machine learning algorithms Sensitivity Nearest neighbor methods Sensitivity and specificity Convolutional neural networks Random forests

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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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Predicting Thyroid Disease using machine learning: A Comparative Study of algorithms

Predicting Thyroid Disease using Machine Learning: A Compara...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： Khushi Katore Parul Balvir Subodh Daronde Computer Science And Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Predicting thyroid illness has become a significant problem in recent times. Thyroid illness is getting more frequent in women over thirty, but it is also becoming more prevalent in men, women, and children. Long-term effects include difficulties with the heart, eyes, fertility, and pregnancy. Therefore, it remains essential to assess the thyroid data in instruction to detect the disease at an initial stage and take preventative measures against the lethal thyroid cancer condition. Two prevalent thyroid illnesses that affect the thyroid's ability to produce thyroid hormones and control the body's metabolism are hyperthyroidism and hypothyroidism. Data cleaning procedures were utilised to make the data simple sufficient for analytics to demonstration the likelihood of affected role having thyroid illness. Thirteen clinicopathologic characteristics from the Kaggle data set are employed in this paper's analysis and classification models for thyroid illness with the goal of predicting the recurrence of well-differentiated thyroid cancer. An important factor in the process of illness prediction is machine learning. It is important to guarantee a strong foundation of information that container be integrated and employed as a hybrid model for multifaceted knowledge tasks, such medical diagnostic and prognosis tasks. Additionally, we suggested many machine learning methods and diagnostics for thyroid prevention in this research. machine learning algorithms, support vector machine (SVM), K-NN, Decision Trees, Logistic Regression , Random Forest , XGBoost were used to forecast the projected risk on a patient’s chance of obtaining thyroid disease.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Thyroid cancer Data models Decision trees Random forests Thyroid Diseases

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Rock vs Mine Prediction and Detection for Aquatic Systems: A Comparative Analysis of Different machine learning and Deep learning algorithms

Rock vs Mine Prediction and Detection for Aquatic Systems: A...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Ayan Sar Tanupriya Choudhury Anvi Goel Ketan Kotecha Bhasker Pant Bhupesh Kumar Dewangan School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India Symbiosis Center for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune Maharashtra India CSE Dept Graphic Era Deemed to be University Dehradun Uttarakhand Department of Computer Science and Engineering OP Jindal University Raigarh Chhattisgarh India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Deep machine learning approaches can enhance the forecasting and detection of rocks and minerals in water defense systems. This research focused on applying deep machine learning techniques to increase accuracy and efficiency in rock and mineral prediction. The researchers employed a series of watermarks that appeared and then utilized a convolutional neural network (CNN) to extract significant characteristics. Using transfer learning approaches and state-of-the-art optimization algorithms, researchers suggested a multi-class solution to identify between rocks and minerals. The suggested technique accurately predicted and recognized rocks and mines in underwater imagery, even in complicated settings with complicated against and illumination circumstances. The finding was essential for water protection and maritime safety systems, as it can assist in the automatic identification of rocks and minerals, decreasing the time and effort necessary for manual inspection

关键词： Technological innovation machine learning algorithms Transfer learning Watermarking Rocks Prediction algorithms Minerals

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Predicting Student Success in Mathematics Using machine learning Techniques: A Comparative Study of algorithms

Predicting Student Success in Mathematics Using Machine Lear...

引用

Innovative and Intelligent Information Technologies (IC3IT), International Conference on

作者： Zouaoui Samia Zouaoui Nour El Houda Mestour Hareth Computer Science Department University of Batna 2 Batna Algeria LINFI Laboratory of Biskra University Mathematics Department Laboratory of Applied Mathematics University of Biskra Algeria Mathematics Department University of Batna 2 Batna Algeria

ISBN: (数字)9798331541064

ISBN: (纸本)9798331541071

Achieving low results in mathematics is a real and urgent concern, as mathematics is the foundation of science and technology, and any weakness in it reflects negatively on the progress of society in various ways. This study evaluates how different mathematics study methods affect students educational success and achievement. We employed three different machine learning models to analyze data related to mathematics study techniques and student outcomes. These methods included: support vector machine (SVM), A K-nearest neighbors (KNN), and Linear Regression (LR). The results showed that KNN provided the best accuracy rate with 97.6% and an F1-score of 96.7%. These results highlight the significant contribution of forming study groups and trying to cooperate with each other, this factor is considered important and influential in the success of students in mathematics.

关键词： Support vector machines Logistic regression machine learning algorithms Education machine learning Nearest neighbor methods Predictive models Prediction algorithms Mathematical models Object recognition

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Computer Vision algorithms for Microscopy Classification of Nanocomposites Using a Federated machine learning Approach

Computer Vision Algorithms for Microscopy Classification of ...

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International Conference on Actual Problems of Electron Devices Engineering, (APEDE)

作者： Sergey Alekseevich Korchagin Department of Artificial Intelligence Leading Researcher Institute of Digital Technologies Financial University under the Government of the Russian Federation Moscow Russia

ISBN: (数字)9798350393316

ISBN: (纸本)9798350393323

The work solves the problem of classifying nanocomposites using computer vision technology in conditions where microscopy images obtained for training cannot be used centrally for training for various confidentiality reasons. To solve this problem, a computer vision system is implemented and an approach based on federated machine learning is presented, allowing the system to be trained locally considering data confidentiality criteria. The computer vision system is based on a modified convolutional neural network. A horizontal federated machine learning architecture is used to ensure data privacy. A comparison was made of this system with classical machine learning methods. The accuracy of the model was 93.4% according to the accuracy metric.

关键词： Training Measurement Computer vision Data privacy Accuracy machine learning algorithms Federated learning Nanocomposites Electron microscopy Electron devices

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Enhancing Malware Detection Using machine learning: A Comparative Study of Classification algorithms

Enhancing Malware Detection Using Machine Learning: A Compar...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Shalini Singh Suraj Pal Singh Manu Singh Department of Information Technology ABES Engineering College Ghaziabad UP India Department of Computer Science and Engineering Chandigarh University Mohali Punjab India Department of Computer Science ABES Engineering College Ghaziabad UP India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

This research investigates the efficacy of various machine learning algorithms in detecting malware. Utilizing a dataset of 20,000 observations with 33 features,28 classification algorithms are applied and evaluated their performance based on accuracy, balanced accuracy, ROC AUC, F1 score, and execution time. Our results indicate that Extra Trees Classifier, XGB-Classifier, Bagging Classifier, and DecisionTreeClassifier achieved perfect accuracy, with Decision Tree Classifier also demonstrating optimal execution efficiency. This study underscores the potential of decision tree-based models for efficient and accurate malware detection and suggests further exploration with larger datasets and diverse malware types for more generalized findings.

关键词： Accuracy machine learning algorithms Malware Classification algorithms Decision trees Random forests Bagging

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