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检索条件"主题词=Classification Algorithms"

共 26406 条记录，以下是521-530 订阅

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Performance Comparison of Multinomial and Bernoulli Naïve Bayes algorithms with Laplace Smoothing Optimization in Fake News classification

Performance Comparison of Multinomial and Bernoulli Naïve B...

引用

Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics (ICoABCD), International Conference on

作者： Fikri Fauzan Sabiq Alam Rahmatulloh Irfan Darmawan Randi Rizal Rohmat Gunawan Erna Haerani Department of Informatics Siliwangi University Tasikmalaya Indonesia Department of Information Systems Telkom University Bandung Indonesia Department of Information Systems Siliwangi University Tasikmalaya Indonesia

ISBN: (数字)9798350368451

ISBN: (纸本)9798350368468

In this digital era, there is a lot of information available, one of which is news information. In the dissemination of this news information, there is real news and fake news. This can have a negative impact that can be felt by the public in making decisions based on wrong information and reporting. Therefore, it is important to develop effective methods and algorithms in identifying and classifying fake news. One approach commonly used in fake news classification is the Naïve Bayes algorithm. This algorithm is based on probability theory and could classify texts into fake news or real news categories. In Naïve Bayes there are several variants that are commonly used, namely the Multinomial Naïve Bayes algorithm and the Bernoulli Naïve Bayes algorithm. In this research, the two algorithms with and without Laplace smoothing be compared. The difference in accuracy is 0.003%. The performance of the model using the multinomial naïve Bayes algorithm is superior to Bernoulli naïve Bayes in terms of accuracy if both do not use Laplace smoothing. However, if Laplace smoothing is carried out on both models, the resulting accuracy has the same value and increases. With a model that uses the multinomial Naïve Bayes algorithm, it increases by 0.002%. Thus, multinomial naïve Bayes is better at classifying hoax news than Bernoulli naïve Bayes and Laplace smoothing can increase the accuracy of both models.

关键词： Cloud computing Smoothing methods Accuracy Data analysis Computational modeling Data models Bayes methods classification algorithms Fake news Optimization

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Performance Evaluation and Comparison of Machine Learning algorithms in classification of CDC Diabetes Health Indicators Dataset by WEKA

Performance Evaluation and Comparison of Machine Learning Al...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Kezban Alpan Bardia Arman Kamil Dimililer Department of HND Digital Technologies David Game Higher Education Centre Research Center for Technology and Engineering (BILTEM) Near East University London United Kingdom Department of Information Technology Business Intelligence and Analytics Department Near East Technology Cyprus International University Nicosia North Cyprus Department of Electrical and Electronic Engineering Applied Artificial Intelligence Research Centre (AAIRC) Research Center for Technology and Engineering (BILTEM) Near East University Nicosia North Cyprus

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

Diabetes Mellitus is a chronic disease that occurs when the body loses its ability to produce insulin. When left uncontrolled and untreated, the sugar that accumulates in the veins causes complications that will reduce the quality of life and shorten life expectancy. Early diagnosis is important in diabetes to prevent these complications. In this study, indicators that play an important role in the early diagnosis of diabetes were classified into 3 different groups such as medical indicators, behavioural/lifestyle and overall indicators which combine two groups and machine learning methods applied to dataset groups for successful classification of diabetes. As a result, J48 performed 84.58 % accuracy for the classification of the medical indicator group, while SVM performed 84.05 % accuracy for the behavioural/lifestyle indicator group and Random Tree performed 84.25 % accuracy for the overall dataset. The results obtained showed that behavioural/lifestyle data are equally important as medical data in the early diagnosis of diabetes.

关键词： Support vector machines Performance evaluation Accuracy Machine learning algorithms Prediction algorithms Diabetes classification algorithms Naive Bayes methods Medical diagnostic imaging Sugar

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Binary and Ternary Human Gait Phase classification Using Machine Learning algorithms

Binary and Ternary Human Gait Phase Classification Using Mac...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Amal Mekni Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Higher Institute of Information and Communication Technologies University of Carthage ENIT University of Tunis El Manar Borj Cedria Tunis Tunisia Department of Mechanical Engineering Thapar Institute of Engineering and Technology Patiala Punjab India Department of Computing Imperial College London UK Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) ENIT University of Tunis El Manar Le Belvédère Tunis Tunisia ISTIC University of Carthage Borj Cedria Tunis Tunisia

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

Accurately identifying gait phases offers vital in-sights into medical diagnostics, requiring precise biomechanical assessment. Despite various machine learning (ML) methods, achieving high precision is challenging. This study uses ML techniques to classify the gait phases of 100 individuals (average age 41.91 ± 5.3 years). classification algorithms considered in this work are: k-Nearest Neighbor (k-NN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayesian (NB). The performance of these algorithms is evaluated using two training methods while classifying binary (stance/swing) and ternary (stance-I1stance-Illswing) subphases. In the first method, 80% of the data from binary or ternary phases is randomly selected for training and the rest for testing. In the second method, data is divided by participants, with 80% used for training and 20% for testing. The RF algorithm consistently outperforms others in both training methods, achieving 99.5% (first method) and 99.9% (second method) accuracy in binary classification and 99.4% (first method) and 99.5% (second method) in ternary classification. Despite the reduced performance while transitioning from bi-nary to ternary classification, consistency is maintained for all ML algorithms. Moreover, the second training method is more effective for classifying binary and ternary gait phases.

关键词： Training Support vector machines Radio frequency Technological innovation Machine learning algorithms Nearest neighbor methods classification algorithms Medical diagnosis Random forests Testing

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Drug classification Using Machine Learning algorithms

Drug Classification Using Machine Learning Algorithms

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Veeravalli Kavya Sree Peddapally Sai Ridhi Rajidi Likitha Reddy Polisetty Swetha Rontala Rishitha Ganesh Regulwar Dept. of Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Personalized medicine is based on the categorization of medications based on each patient’s unique needs. But traditional approaches have drawbacks, especially when it comes to scaling and confidentiality. In this work, we present a new machine learning (ML) method to improve and automate processes related to medicine classification. The study’s dataset came from the open repository Kaggle. The objective point is the kind of medication, and the point settings are coitus, age, BP, cholesterol, and the ratio of sodium to potassium. As part of our process, we replace drug names with placeholders such as DrugX, DrugY, etc. to anonymize them. The patient’s age, sex, blood pressure, cholesterol, and blood salt-to-potassium ratio are also considered. Our approach is evaluated on benchmark datasets to demonstrate its efficacy. Using state-of-the-art machine learning techniques like random forests and neural networks, we extract numerous features from pharmaceutical substances. The results are competitive and demonstrate improved performance over conventional techniques.

关键词： Drugs Machine learning algorithms Accuracy Machine learning Nearest neighbor methods Feature extraction Robustness classification algorithms Decision trees Random forests

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Training Object Detection algorithms for Vehicle Type classification from Public CCTV Camera by Using Custom Dataset

Training Object Detection Algorithms for Vehicle Type Classi...

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Conference on Power, Energy and Innovations (ICPEI)

作者： Prajuab Inrawong Mongkol Kupimai Wuttichai Sa-nga-ngam Kritsada Tasuntia Trairat Jariyatum Phongpraphat Choohirunwat Faculty of Engineering and Technology Rajamangala University of Technology Isan Nakhon Ratchasima Thailand Department of Engineering Nakhon Ratchasima Municipality Nakhon Ratchasima Thailand

ISBN: (数字)9798350356779

ISBN: (纸本)9798350356786

Computer vision technology has gained popularity and been widely applied across various fields. Object detection algorithms are pivotal in these applications. Traditional algorithms have been developed to classify over 80 general object types. However, they still fall short of comprehensive object classification in specialized domains. This research presents the training of object detection algorithms to classify vehicle types using a custom dataset from collected data from multiple public CCTV cameras. Performance evaluation results demonstrate that Precision, Mean Average Precision (mAP@50), mAP@50–95, Recall and F1-Score achieve values 0.83, 0.72, 0.58, 0.58, and 0.69 respectively. Testing results indicate that the proposed algorithm achieves 93% higher accuracy in classifying vehicle types compared to traditional algorithms. This algorithm can be further refined and applied to traffic management systems to maximize future benefits.

关键词： Training Performance evaluation Technological innovation Accuracy Rain Object detection Cameras Prediction algorithms classification algorithms Testing

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Exploring the Use of Machine Learning algorithms in Text classification

Exploring the Use of Machine Learning Algorithms in Text Cla...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Preeti Naval Krishnan Batri D. Nirmala Pawan S. Wawage Narendra Kumar Jain Jatin Khurana Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Technology Pune INDIA Department of Civil Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The use of device mastering algorithms has become increasingly popular in text type. The essential benefits of using these algorithms are their ability to perceive trends in text statistics and examine the information they may be uncovered. Textual content classification entails sorting textual content into predefined categories or lessons using diverse algorithms. Specific device studying strategies, including assist vector machines (SVMs) and artificial neural networks (ANNs), have been carried out for textual content class duties. Those algorithms help identify the applicable topics in extensive text collections and allow the categorization of files into nicely-defined topic clusters. This paper explores the software of machine studying algorithms in text category obligations. Further, a number of the popular algorithms and their applications on this subject and their blessings and limitations are discussed. Sooner or later, conclusions and destiny paintings are discussed.

关键词： Machine learning algorithms Software algorithms Text categorization Clustering algorithms Vectors Software classification algorithms Sorting Postal services Painting

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Performance Comparative Analysis of OvA, AvA, and OvO algorithms in Multi-Class classification

Performance Comparative Analysis of OvA, AvA, and OvO Algori...

引用

International Conference on Management of Large-Scale System Development (MLSD)

作者： Daniyar A. Wolf Rinat R. Galin Laboratory of Cyber-Physical Systems V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

The paper presents the result of comparative analysis of the main approaches to multi-class classification, synthesis of their mathematical models based on the considered algorithms is carried out. Their features, advantages, and disadvantages are presented, and the accuracy of multi-class classification is evaluated. Their features, advantages, and disadvantages are presented, and the accuracy of multi-class classification is evaluated. The dependence on accuracy and computational resource requirements is shown. The OvA algorithm can be a better way to solve problems with a small number of classes and limited resources. For tasks that require high classification accuracy and sufficient computational resources, AvA or OvO algorithms can be chosen. Synthesis of mathematical models and systematic generalization was carried out.

关键词： Accuracy Machine learning algorithms Systematics Machine learning Mathematical models classification algorithms Large-scale systems

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Comparative Analysis of Deep Learning algorithms for Image classification

Comparative Analysis of Deep Learning Algorithms for Image C...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Ginjupalli Pranay Chowdary Rajakumar K Sri Satya Narendra Yalla Varthala Charith Yadav Sai Bhargav Kasetty SCOPE V.I.T University Vellore Tamilnadu India SCOPE V.I.T University Vellore Tamilnadu

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Image classification is one of the highly explored fields in artificial intelligence and computer vision, due to its widespread applications such as medical image analysis, autonomous cars, geographical classification of satellite photos, and facial recognition. Generally, conventional image classification models depend upon manual feature extraction, but due to the exponential rise in data volume, manual adjustment is no longer feasible or efficient. This difficulty has led to the adoption of deep learning algorithms, which automate feature extraction and provide benefits such as high processing capacity, efficient feature filtering, and improved classification speed and accuracy. This research compares multiple deeplearning techniques for image classification. The study examines the results, offering a fundamental analysis and discussion of the findings where CNN proved to perform better when compared to other algorithms like GAN, RNN, and DBN with the highest accuracy of 92.8%.

关键词： Deep learning Solid modeling Accuracy Signal processing algorithms Manuals Signal processing Feature extraction Data models classification algorithms Image classification

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Improvements of Flower classification Using Deep Neural Networks with Various Solver Optimization algorithms

Improvements of Flower Classification Using Deep Neural Netw...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Bui Hai Phong Son Nguyen Le Anh Chuong Tran Hoang Thy Thy Nguyen Tien Dung Tran Nam Tran Tran Mac Dang Danh Nguyen Ngoc Thuy Tien Pham Van Khuong Faculty of Information Technology Hanoi Architectural University Hanoi Vietnam Hanoi University of Science and Technology Hanoi Vietnam Le Quy Don High School Da Nang Vietnam Nguyen Thuong Hien High School Ho Chi Minh City Vietnam HUS High School for Gifted Students Hanoi Vietnam Cranbrook Schools Michigan USA Kim Lien High School Hanoi Vietnam Scotch AGS Ho Chi Minh City Vietnam BKStar center Hanoi Vietnam

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Flowers hold significant importance across various fields due to their multifaceted roles and impact, such as agriculture manufacturing and pharmacological research. Therefore, the precise classification of flowers has extensive implications in real life and has attracted more and more researchers. However, the accuracy of the classifiers still needs to be improved. In this work, we present an enhancement in the pipeline of flower image classification, where some state-of-the-art models are performed and compared. In our proposed process, we fine-tuned various Deep neural network models (DNNs) to classify flower images. To improve the accuracy of the classifier, various optimization algorithms are performed and evaluated. We also implemented some traditional approaches to compare the efficiency of classification methods. These methods are evaluated on four public datasets (Oxford-17, Oxford-102, Kaggle-5, and Iris flower datasets). The experimental results showed that the Densenet-121 network combined with the RMSProp optimizer had a competitive advantage in terms of accuracy compared with traditional approaches.

关键词： Iris Accuracy Machine learning algorithms Pipelines Artificial neural networks Machine learning Flowering plants classification algorithms Manufacturing Optimization

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Optimizing Feature Selection for Brain Tumor classification using a Hybrid ACCSA and Utilizing Machine Learning algorithms for classification

Optimizing Feature Selection for Brain Tumor Classification ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Vinoth Nageshwaran Association for Computer Machinery(ACM)

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This research addresses the challenges associated with brain tumor classification by proposing a novel hybrid approach that combines feature selection and machine learning techniques. A median filter is employed to remove noise from MRI images, followed by feature extraction using the Grey Level Co-Occurrence Matrix (GLCM). To enhance classification accuracy, a hybrid Ant Colony Optimization with Crow Search Optimization Algorithm (HACCSA) is introduced for feature selection, identifying the most relevant features from the extracted set. The selected features are then used to train various machine learning classifiers, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). The classification performance is evaluated using k-fold cross-validation, demonstrating a significant improvement of $\mathbf{9 7 \%}$ compared to existing methods. This research contributes to the advancement of brain tumor classification by providing an effective and efficient approach that leverages feature selection and machine learning techniques.

关键词： Support vector machines Radio frequency Ant colony optimization Accuracy Machine learning algorithms Noise Nearest neighbor methods Feature extraction classification algorithms Optimization

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