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检索条件"主题词=Machine Learning Algorithms"

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machine learning algorithms in Grassland Monitoring: Utilizing Multi-Temporal Sentinel-1 SAR and Weather Data

Machine Learning Algorithms in Grassland Monitoring: Utilizi...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Alireza Taravat Paolo Cosmo Silvestro Maria P. Gonzalez-Dugo Mariapina Castelli Robert Hinz David Petit Deimos Space UK Oxford UK Deimos Engineering and System Madrid Spain IFAPA Centro Alameda del Obispo Córdoba Spain Eurac Research Institute for Earth Observation Bolzano/Bozen Italy

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study investigates the efficacy of two feed-forward neural networks (MLP and RBF) and an SVM algorithm in monitoring grasslands. Employing six input parameters — Sentinel-1 SAR intensity, texture, temperature, precipitation, global radiation, and evapotranspiration — we aimed to determine their influence on model accuracy. Our research highlights the superior stability and accuracy of MLP and RBF NNs over SVM, with MLP NNs marginally outperforming RBF NNs. The most effective results were achieved using a comprehensive input set. Nevertheless, reducing the number of inputs led to a decrease in data dimensionality and consequently, model accuracy. The research indicates potential accuracy improvements with additional factors like soil type and grassland management. Conducting field studies concurrent with image acquisition is crucial for understanding the diverse grassland conditions and their effects on scattering mechanisms.

关键词： Support vector machines Grasslands Accuracy Precipitation machine learning algorithms Sentinel-1 Scattering

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machine learning algorithms in Quantum Computing: A Survey

Machine Learning Algorithms in Quantum Computing: A Survey

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International Joint Conference on Neural Networks (IJCNN)

作者： Somayeh Bakhtiari Ramezani Alexander Sommers Harish Kumar Manchukonda Shahram Rahimi Amin Amirlatifi Computer Science and Engineering Dept. Mississippi State University Starkville US Dave C. Swalm School of Chemical Eng Mississippi State University Starkville US

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

machine learning (ML) aims at designing models that learn from previous experience, without being explicitly formulated. Applications of machine learning are inexhaustible, including recognizing patterns, predicting future trends and making decisions, and they are capable of handling sizable quantities of multi-dimensional data in the form of large vectors and tensors. To perform these operations on classical computers, however, requires vast time and computational resources. Unlike the classical computers that rely on computations using binary bits, Quantum Computers (QC) benefit from qubits which can hold combinations of 0 and 1 at the same time via superposition and entanglement. This makes QCs powerful at handling and post processing large tensors, making them a prime target for implementing ML algorithms. While several models used for ML on QCs are based on concepts from their classical computing counterparts, utilization of the QC's potential has made them the superior of the two. This paper presents an overview of the current state of knowledge in application of ML on QC, and evaluates the speed up, and complexity advantages of using quantum machines.

关键词： Quantum computing machine learning algorithms Quantum entanglement Complexity theory machine learning Computational modeling Parallel processing

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machine learning algorithms applied to the classification of robotic soccer formations and opponent teams

Machine Learning algorithms applied to the classification of...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Brígida Mónica Faria Luís Paulo Reis Nuno Lau Gladys Castillo Escola Superior Tecn. SaÙde do Porto (ESTSP/IPP) Institute Engineering Electrónica e Telemática de Aveiro (IEETA) Universidade do Porto Universidade de Aveiro Portugal Laboratory Intel. Artificial e Ciência de Computadores (LIACC) Department Engineering Informática Faculty Engineering Universidade do Porto Portugal Institute Engineering Electrónica e Telemática de Aveiro (IEETA) Department Electrical Telecomunicaçōes e Informática (DETI/UA) Universidade de Aveiro Portugal Departamento de Matemática (DMAT/UA) Universidade de Aveiro Portugal

ISBN: (纸本)9781424464999;9781424465026

machine learning (ML) and Knowledge Discovery (KD) are research areas with several different applications but that share a common objective of acquiring more and new information from data. This paper presents an application of several ML techniques in the identification of the opponent team and also on the classification of robotic soccer formations in the context of RoboCup international robotic soccer competition. RoboCup international project includes several distinct leagues were teams composed by different types of real or simulated robots play soccer games following a set of pre-established rules. The simulated 2D league uses simulated robots encouraging research on artificial intelligence methodologies like high-level coordination and machine learning techniques. The experimental tests performed, using four distinct datasets, enabled us to conclude that the Support Vector machines (SVM) technique has higher accuracy than the k-Nearest Neighbor, Neural Networks and Kernel Naïve Bayes in terms of adaptation to a new kind of data. Also, the experimental results enable to conclude that using the Principal Component Analysis SVM achieves worse results than using simpler methods that have as primary assumption the distance between samples, like k-NN.

关键词： machine learning algorithms Robot kinematics Support vector machines machine learning Intelligent robots Artificial intelligence Testing Performance evaluation Artificial neural networks Kernel

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machine learning algorithms Assessment for Snow LWC Retrieval from SAR Data

Machine Learning Algorithms Assessment for Snow LWC Retrieva...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Emanuele Santi Simonetta Paloscia Simone Pettinato Fabrizio Baroni S. Pilia Roberto Colombo Claudia Ravasio Biagio Di Mauro National Research Council – Institute of Applied Physics (CNR-IFAC) Firenze Italy Dept. of Earth and Environmental Sciences University of Milano-Bicocca Italy Institute of Polar Sciences ISP – CNR Venezia Italy

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In this study, the retrieval of snow Liquid Water Content (LWC, %) from C- and X- band SAR data was based on Artificial Neural Network (ANN) and Random Forest (RF). Two approaches were explored for generating a sufficient amount of data to train and test the ANN and RF algorithms: the first strategy was defined as “model-driven”. The second one was defined as “data-driven”. The validation results showed that RF performs better than ANN in terms of correlation coefficient R, regardless of the selected approach ("model driven" R ANN = 0.60, R RF = 0.68; “data-driven” R ANN = 0.50, R RF = 0.88 at X-band). Moreover, the RF implementation trained with the “data-driven” approach outperformed the “model-driven” approach in terms of correlation coefficient R (R RF = 0.88 at X-band).

关键词： Radio frequency Correlation coefficient Training machine learning algorithms Snow Artificial neural networks Data models

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machine learning algorithms for Predicting House Prices in a Smart city using its Real-Time Data

Machine Learning Algorithms for Predicting House Prices in a...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M. Robinson Joel M. Navaneethakrishnan K.P. Sriram S. Priskilla Manonmani Department of Information Technology Kings Engineering College Chennai India Department of Computer Science and Engineering St. Joseph College of Engineering Sriperumbudur Chennai India Department of Information Technology Meenakshi Sundararajan Engineering College Chennai India

Housing prices are an important economic indicator, wherein the property values are important to both buyers and sellers. One of the least transparent industries across the world is real estate. In order to conduct the research, real-time housing data has been collected from Chennai City. Sine this study deals with the continuous outcome variable, regression techniques are highly preferred. The major goal of this research study is to predict housing values by using real-time information. This study performs various house price evaluation process based on various factors. Several regression models like Lasso and Ridge are utilized and their performances are compared in order to select the best model for resolving a specific issue. This study intends to develop an effective machine learning model to predict the price of a property based on input parameters and display the information in a website to reach out to people.

关键词： Training Industries Renewable energy sources machine learning algorithms Smart cities Biological system modeling Linear regression

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machine learning algorithms with Oversampling Methods for Automobile Insurance Fraud Detection

Machine Learning Algorithms with Oversampling Methods for Au...

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Student Conference on Research and Development (SCOReD)

作者： Katarina Prisca Rijanto Diva Amanda Putri Paul Calvin Ivan Sebastian Edbert Derwin Suhartono Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

Some countries regulate purchasing vehicle insurance coverage as mandatory to enable risk transfer and financial protection. With the ever-expanding business-related scope of auto insurance, the crime in fraud insurance is a vital task to fight. The imbalanced dataset makes predicting fraud on automobile insurance claims challenging. Most fraud detection techniques result in inefficient classification models with an imbalance issue. This work is concerned with developing a fraud detection system to examine the increase in model performance with the help of the Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to remove the imbalance. The abilities to predict series machine learning models were compared by tenfold cross-validation, namely Support Vector machine (SVM), Random Forest (RF), Balanced Random Forest (BRF), and Extreme Gradient Boosting (XGBoost) Classifier. The majority voting approach is employed to ensemble numerous classifiers to increase prediction performance in the final stage. As a result, all models achieve satisfactory results, and an F1score of more than 98% has been achieved using Support Vector machine (SVM) model, which employs the SMOTE oversampling method.

关键词： Support vector machines Adaptation models machine learning algorithms Insurance Companies Fraud Automobiles

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machine learning algorithms for URLs Classification

Machine Learning Algorithms for URLs Classification

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Artificial Intelligence of Things (ICAIoT), International Conference on

作者： Sabah Salam Khaduair Al Zirjawi Hawraa Jaafar Murad Kashkool Abdullahi Abdu Ibrahim Mona Idan Ali Al Department of Electrical & Computer Engineering Altinbas University Istanbul Turkey Department of Information Technologies Altinbas University Istanbul Turkey

Phishing is a technique used to collect sensitive data from a user (password or credit card information) for future misuse by posing as a trustworthy source. It often takes advantage of the user’s gullibility in ways that the user will not detect at first look, and in the worst-case scenario, the attacker maintains the user’s data without the user’s awareness. Typically, the URL is the first and simplest piece of information we know about a website. As a result, it is logical to design algorithms for distinguishing harmful from benign URLs. Additionally, accessing and downloading the website’s material may be time-consuming and involves the danger of downloading potentially hazardous information. machine learning techniques are used to train a model on a collection of URLs specified as a set of characteristics and then predict and categorize the URLs as benign or dangerous. This technology enables us to identify and avoid possibly dangerous URLs shortly.

关键词： Uniform resource locators Support vector machines machine learning algorithms Phishing Passwords machine learning Predictive models

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machine learning algorithms for Stress Level Analysis Based on Skin Surface Temperature and Skin Conductance

Machine Learning Algorithms for Stress Level Analysis Based ...

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IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Thien Le Tran Thuan Phuc Khang Nguyen Quynh Nguyen Gia Anh Tu Tran Quoc Khai Le Department of Biomedical Engineering Ho Chi Minh City University of Technology (HCMUT) Ho Chi Minh Vietnam

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

In light of the negative effects of mental stress on health, there is growing scientific attention placed on understanding it better. This study aims to introduce a method that uses biological indicators such as skin surface temperature and skin resistance using easily accessible data to detect and predict stress levels. The publicly available dataset used in this study included the physiological signals of a group of university students whose signals were recorded by a wearable device during three exam sessions (two midterm and one final exam). Features were extracted from heart rate, electrodermal activity, and physiological and skin surface temperature and were input for the classification. In the labeling process, the stress levels were classified as high, low, or moderate. machine learning algorithms such as Support Vector machines (SVM) and K-Nearest Neighbors (KNN) with classifiers were trained and tested with the labeled data The stress level of a subject was predicted with the highest F1 score of up to 0.8 and accuracy of higher than 70%. Such findings highlight the important role of skin conductance and surface temperature in estimating stress levels.

关键词： Support vector machines Accuracy machine learning algorithms Anxiety disorders Human factors Nearest neighbor methods Feature extraction Skin Physiology Wearable devices

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machine learning algorithms used in Crop Yield Prediction for Precision Agriculture

Machine Learning Algorithms used in Crop Yield Prediction fo...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Umamaheswari.R S.Shanmugapriya P.Vimala R.Ganesan Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr. Sagunthala Engineering College Chennai Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Agriculture is a crucial sector that plays a vital role in producing food for human consumption. However, predicting crop yield accurately can be a challenging task due to various natural factors, such as soil quality, climate, rainfall, and resource availability. In the past, farmers relied on their expertise and judgment to determine crop yield, but their traditional methods have become less effective due to the unpredictable nature of these factors. To address this challenge, researchers have developed various algorithms and models for predicting crop yield based on different parameters, such as soil quality, climate, and image analysis. These algorithms can help farmers make informed decisions about when and where to grow their crops, leading to improved productivity and higher yields. Among these algorithms, neural networks are showing great promise as they can analyze vast amounts of data and identify patterns that may not be immediately apparent to human observers. By training neural networks on historical crop yield data, researchers can develop models that accurately predict crop yield based on a variety of factors. Ensuring accurate crop yield prediction is critical to ensuring food security and preventing famine, particularly in developing countries such as India. As technology continues to advance, we can expect even more advanced algorithms and models for predicting crop yield that will help farmers make better decisions and ultimately lead to increased productivity and better outcomes for agriculture.

关键词： Productivity Training Surveys machine learning algorithms Rain Soil Predictive models Prediction algorithms Crop yield Meteorology

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machine learning algorithms to Visualize the Weld Quality and Evaluation of Health Issues during the Welding

Machine Learning Algorithms to Visualize the Weld Quality an...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Rvvsv. Prasad Parkhe Ravindra Ambadas Somesubhra Panda Mohammed Saleh Al Ansari k. Deepthi Department Of Information Technology Swarnandhra College of Engineering & Technology Narsapur India Mechanical Engineering Pravara Rural Engineering Collage Loni India Department of Electrical Engineering Global Institute of Science & Technology Purba Medinipur India Department of Chemical Engineering College of Engineering University of Bahrain Bahrain Mechanical Engineering Avanthi institute of engineering and technology Avev Kotabhogapuram India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

This research explores the potential of machine learning methods to use in the welding sector. Welding operations encounter several challenges that might use machine learning approaches. Welding processes have been found to benefit greatly from the use of machine learning algorithms, which increase both productivity and quality. Industrial robots powered by artificial intelligence may assist in addressing many difficult problems in the manufacturing industry. Many welding procedures depend on human knowledge to choose optimal settings, making them less efficient and more prone to human mistakes. To lessen this reliance, neural networks educate robots and autonomous systems to provide consistent weld quality and increased efficiency. Weld quality is mainly determined by visual examination. As a result, machine learning is also used to provide a visual representation of the welding process. In two dimensions, 12 machine learning pipelines are built: settings for feature engineering and ML techniques. Four feature settings and three machine learning methods were considered. Using data from two active industrial production lines containing 25 welding machines, a machine-learning pipeline was tested. These approaches may also be used to analyze the components using regression analysis to determine which factors relate to certain health concerns.

关键词： Vibrations Visualization machine learning algorithms Service robots Shape Welding Plasma welding

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