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检索条件"主题词=Machine Learning Algorithms"

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Classification of Plant Disease using machine learning algorithms

Classification of Plant Disease using Machine Learning Algor...

引用

Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Majji V Appalanaidu G Kumaravelan Department of Computer Science Engineering Hyderabad Institute of Technology and Management Hyderabad India Department of Computer Science Pondicherry University Karaikal Campus Karaikal

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

The identification of plant diseases has become a key topic in smart agriculture research in recent years. The productivity of agricultural products will undoubtedly increase with early plant disease identification. For the farmers, it is difficult to determine the specific diseases affecting the plant leaves. Therefore, the development of an automated system becomes an essential practice for detecting diseases. So that the remedies can be suggested according to the disease’ s severity. In this situation, image processing methods combined with machine learning (ML) algorithms have lately offered an accurate and dependable way of identifying and categorizing many plant diseases. Hence, this work investigates the importance of the deployment of various ML algorithms such as Naïve Bayes (NB), Decision Tree (DT), Artificial Neural Networks (ANN), K-Nearest Neighbor (KNN), and Support Vector machine (SVM) in automatic classification of plant diseases on the benchmark plant village dataset.

关键词： Support vector machines Smart agriculture Productivity Plant diseases machine learning algorithms Image processing Artificial neural networks

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Data Models based on Time-domain Multi-parameter Information Fusion and machine learning algorithms

Data Models based on Time-domain Multi-parameter Information...

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Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Yisheng Wang Wuhan University of Technology Wuhan China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In the case of uncertain system parameters, it is not possible to directly perform state estimation. The state estimation of the system model requires known model parameters and noise variance. Therefore, before conducting state estimation, it is necessary to identify the unknown parameters in the model and input the identification results into the system model for state estimation. This article applied various new recognition technologies to fuse time-domain multi-parameter information, thereby reducing data dimensions and improving recognition accuracy. However, the current data obtained is often not accurate and comprehensive enough to fully reflect system characteristics. Therefore, it is particularly important to use information fusion methods for state estimation. This study introduced a mathematical model based on principal component analysis (PCA) and clustering algorithm, which combined PCA and genetic algorithm (GA) to improve the clustering algorithm and enhance the effectiveness of data preprocessing and analysis. The effectiveness of the introduced GA-PCA model in improving classification accuracy and computational efficiency was verified through simulation experiments on multiple datasets.

关键词： Dimensionality reduction Accuracy machine learning algorithms Feature extraction Mathematical models State estimation Time-domain analysis Optimization Principal component analysis Genetic algorithms

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Determining Positive-Negative Emotions in Male and Female Based on EEG Signals using machine learning algorithms

Determining Positive-Negative Emotions in Male and Female Ba...

引用

International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Yuri Pamungkas Edwin Nugroho Njoto Medical Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Medicine Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Emotionsare vital in everyday human life as a controller of behavior, decision-making and as a means to determine product marketing strategy/ market research. All of these things are very dependent on human emotional conditions. In addition, in the development of the computational affective field, brain signal-based emotion recognition (EEG) has become a trending topic of current research. Therefore, we attempted to compare positive-negative emotions in men and women based on EEG using a machine learning algorithm in this study. A total of 20 male and 20 female participants recorded their EEG signals in the frontopolar and frontal areas of the brain. Then the EEG data is processed by filtering, removing artifact, and decomposing it into three sub-bands (alpha, beta, and gamma). The extracted signal features are Mean Absolute Deviation and Power Spectral Density. Based on the signal feature analysis results, it is known that the signal feature values (MAD and PSD) for women tend to be higher than for men. Meanwhile, several algorithms are used to classify positive and negative emotions, such as Naive Bayes, K-Nearest Neighbor, Support Vector machine, and Random Forest. Based on the results of classification, the best accuracy rate was 95.8% (on positive emotions for male & female gender), 92.2% (on negative emotions for male & female gender), and 79.8% (on positive-negative emotions for male & female gender) using Random Forest algorithm.

关键词： Support vector machines Emotion recognition machine learning algorithms Accuracy Nearest neighbor methods Feature extraction Market research Electroencephalography Classification algorithms Random forests

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Revolutionizing Culinary Quality: An Intelligent Food Grading System Employing Advanced machine learning algorithms

Revolutionizing Culinary Quality: An Intelligent Food Gradin...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： C Benin Pratap Aren D'Souza Athulya Amblish Jane Mary Mathew Srivenu K Leno Varghese Babu Department of Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Food Processing Technology Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In this pioneering research, a transformative paradigm in culinary quality assessment through the implementation of an Intelligent Food Grading System (IFGS) powered by state-of-the-art machine learning algorithms is introduced. The primary objective is to revolutionize conventional food quality evaluation methods by introducing a dynamic, precise, and automated approach. The IFGS employs advanced machine learning techniques to comprehensively analyze diverse food attributes, including taste, texture, freshness, and nutritional content. This system provides a standardized and objective framework for evaluating food quality. Moreover, it caters to consumer expectations by ensuring transparency and reliability in the assessment of food products. The study delves into the intricacies of the machine learning algorithms incorporated in the IFGS, emphasizing their adaptability and continuous learning capabilities. The synergy between technological innovation and culinary expertise presented in this work encourages a broader conversation within the culinary community, inviting chefs, researchers, and enthusiasts to embrace a future where cutting-edge technology enhances and refines the culinary experience.

关键词： Technological innovation Privacy machine learning algorithms Prevention and mitigation Oral communication machine learning Quality assessment Reliability Optimization Testing

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A Comparative Study of machine learning algorithms for Classifying Personality Type based on Carl Jung's Cognitive Function

A Comparative Study of Machine Learning Algorithms for Class...

引用

International Electronics Symposium (IES)

作者： Entin Martiana Kusumaningtyas Tirta Rumy Swandaru Tri Hadiah Muliawati Iqbal Bimantoko Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Clinical Psychology Puskesmas Balas Klumprik Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

According to Carl Jung, every human being has a personality preference from each dichotomy dimension of cognitive function to live life. Cognitive function is mental attitudes and actions that always coexist with humans. This theory is the basis for developing personality assessment instruments, such as the Myers-Briggs Type Indicator (MBTI). On the other hand, machine learning (ML) approach is often used in psychology to classify personality types automatically. However, selecting the most suitable algorithm and developing the instruments for data collection is not an easy task. This paper aims to conduct experiments choose the most appropriate algorithm for classifying personality types based on Carl Jung's theory of cognitive function. In collaboration with a psychologist, we adapt Carl Jung's theory of cognitive function into a text-based personality assessment instrument consisting of several items. The experimental results show that the Stochastic Gradient Descent algorithm with hyperparameter tuning consistently yields good performance in carrying out cognitive function personality type classification tasks.

关键词： Support vector machines machine learning algorithms Accuracy Instruments Psychology Stochastic processes Prediction algorithms

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Improving Performance of Supervised machine learning algorithms on Small Datasets

Improving Performance of Supervised Machine Learning Algorit...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： B.Rajesh Kumar R. Ramya Albin George Kurian Jithin J Ram Prasad Sana Fathima Ameesha Suresh Dept.of CSE Dhanalakshmi Srinivasan College of Engineering Coimbatore Tamilnadu

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In most cases, machine learning involves working with large volumes of data in order to perform optimally. However, in some cases, there is only a limited amount of training data available, resulting in a decrease in the performance of the model. The current research aims to enhance the effectiveness of machine learning models, which have been developed utilizing limited services. In this case study, the focus is on learning using a multi-iteration approach along with Gaussian Mixture Model GMM-based Synthetic Data Generation, which were suggested as solutions to the problem of data scarcity. The research explores how much these methods boost a model's performance in the performance metrics

关键词： Measurement Accuracy machine learning algorithms Active learning Training data machine learning Ubiquitous computing Data models Gaussian mixture model Synthetic data

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Benchmarking Multiple machine learning algorithms for Sentiment Analysis on Sexual Violence

Benchmarking Multiple Machine Learning Algorithms for Sentim...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Ririn Nurdiyanti Ema Utami Bachelor of Informatics Universitas Amikom Yogyakarta Yogyakarta Indonesia Dept. of Informatics Doctorate Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Sexual violence is not a new phenomenon in Indonesian society. It has occurred among women, men, and adolescents in various global conflicts. YouTube comments are a good source of data for sentiment analysis because they provide a large amount of text-based input from users. The data used in this study were collected from YouTube comments through a crawling process conducted on April 3, 2024. From crawling the comments on 6 videos, the data were combined into a single CSV file with a total of 11,628 entries, which was reduced to 11,133 entries after the cleansing stage. The results of this study show that the SVM model achieved the highest accuracy at 82.50%, followed by Logistic Regression with an accuracy of 82.18%. The Random Forest and XGBoost models had almost identical accuracies, at 81.64% and 81.60% respectively. The Naive Bayes model had an accuracy of 80.11%, while KNN showed the lowest accuracy among all models, at 78.39%.

关键词： Support vector machines Sentiment analysis Accuracy machine learning algorithms Video on demand Social networking (online) Nearest neighbor methods Classification algorithms Web sites Random forests

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A Comparative Study of machine learning algorithms and Explainable AI for Fitness Club Attendance Classification

A Comparative Study of Machine Learning Algorithms and Expla...

引用

IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Divyasri S Deepa Gupta Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

The growing interest in health and fitness has resulted in an increase in the number of people joining fitness clubs. However, maintaining member engagement remains a challenge for fitness club operators. The goal of this study is to create a binary classification model that predicts whether a member is likely to attend a fitness club or not. In this paper, we propose a framework to explain how good the fitness club attendance classification performance will be under various machine learning models by understanding its metrics. In this study by employing several classification models to determine Fitness Club attendance Classification using machine learning algorithms such as Random Forest, Gradient Boosting, Light GBM, etc. The Random Forest classifier performs with a higher accuracy of 0.87, and Explainable AI has been used to reveal the most significant feature.

关键词： Measurement Graphics Accuracy machine learning algorithms Explainable AI Receivers Forestry Predictive models Random forests Testing

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Exploring machine learning algorithms for Myocardial Infarction Diagnosis

Exploring Machine Learning Algorithms for Myocardial Infarct...

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Intelligent Systems and Computer Vision (ISCV)

作者： Sara El Omary Souad Lahrache Rajae El Ouazzani IMAGE Laboratory ISNET Team Higher School of Technology Moulay Ismail University of Meknes Meknes Morocco LabSIV Faculty of Sciences Ibn Zohr University Agadir Morocco

ISBN: (数字)9798350350180

ISBN: (纸本)9798350350197

Cardiovascular diseases, including Myocardial Infarction, remain a leading cause of global morbidity and mortality [1]. Myocardial Infarction is a medical condition characterized by abnormal heart function resulting from factors such as arterial and venous blockages [2]. machine learning has emerged as a highly effective approach for developing predictive models in various healthcare sectors. In this study, we investigate the application of machine learning algorithms for Myocardial Infarction detection using the MIT-BIH dataset. We conduct a thorough performance evaluation of four distinct algorithms including K-Nearest Neighbors, Decision Trees, Random Forest, and Support Vector Classifier. Notably, Random Forest emerges as the top performing algorithm, achieving an accuracy rate of 97.68%. This study not only demonstrates the effectiveness of ensemble learning in enhancing Myocardial Infarction detection accuracy but also highlights the potential of computational techniques in cardiac health diagnostics.

关键词： machine learning algorithms Accuracy Recurrent neural networks Myocardium Nearest neighbor methods Feature extraction Vectors

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Fraud Detection in Credit Card through the machine learning algorithms

Fraud Detection in Credit Card through the Machine Learning ...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Sandhya Kummarikuta Ganesh Karthik Muppagowni Keerthi Patam Mounika Pennera Upendra Patimeedi Poreddy Chandra Harshith Reddy Dept. AI&ML School of Computing Mohan Babu University Tirupati India Department of CSE GITAM School of Technology GITAM University-Bengaluru Campus Bengaluru India Dept. Computer Science and Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The threat of credit card fraud is high and so there should be efficient ways to combat it. The research is being employed to focus on how to enhance credit card fraud detection, machine learning methodologies can be employed. The major benefit of this structured design model is that it observes many transaction variables and find out any potential threat transaction since its trained to do so. To start the project, a large dataset of both authentic and fraudulent credit card transactions will be collected and preprocessed. For the machine to learn how we want it to extract correct location, time, date and users' behavior and an important amount of transaction has been made. This improves our dataset by adding relevant information. This research is based on identifying fake activities it will discuss how to XGBoost, CatBoost and Logistic Regression used for this purpose.

关键词： Logistic regression machine learning algorithms machine learning Credit cards Fraud

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