Machine Learning Algorithm

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 124170 Experts worldwide ranked by ideXlab platform

A. Cekin - One of the best experts on this subject based on the ideXlab platform.

  • A supervised Machine Learning Algorithm for arrhythmia analysis
    Computers in Cardiology 1997, 1997
    Co-Authors: Halil Altay Güvenir, Gulsen Demiroz, Baver Acar, A. Cekin
    Abstract:

    A new Machine Learning Algorithm for the diagnosis of cardiac\narrhythmia from standard 12 lead ECG recordings is presented. The\nAlgorithm is called VF15 for Voting Feature Intervals. VF15 is a\nsupervised and inductive Learning Algorithm for inducing classification\nknowledge from examples. The input to VF15 is a training set of records.\nEach record contains clinical measurements, from ECG signals and some\nother information such as sex, age, and weight, along with the decision\nof an expert cardiologist. The knowledge representation is based on a\nrecent technique called Feature Intervals, where a concept is\nrepresented by the projections of the training cases on each feature\nseparately. Classification in VF15 is based on a majority voting among\nthe class predictions made by each feature separately. The comparison of\nthe VF15 Algorithm indicates that it outperforms other standard\nAlgorithms such as Naive Bayesian and Nearest Neighbor classifiers

P Teluguntla - One of the best experts on this subject based on the ideXlab platform.

  • a 30 m landsat derived cropland extent product of australia and china using random forest Machine Learning Algorithm on google earth engine cloud computing platform
    Isprs Journal of Photogrammetry and Remote Sensing, 2018
    Co-Authors: Prasad S. Thenkabail, P Teluguntla, J. Xiong, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, A Oliphant, Alfredo Huete
    Abstract:

    Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) Machine Learning Algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF Algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282.

Changyeon Won - One of the best experts on this subject based on the ideXlab platform.

  • Searching magnetic states using an unsupervised Machine Learning Algorithm with the Heisenberg model
    Physical Review B, 2019
    Co-Authors: H. Y. Kwon, Namkwon Kim, Chanki Lee, Changyeon Won
    Abstract:

    Magnetism is a canonical example of a spontaneous symmetry breaking. The symmetry of a magnetic state below the Curie temperature is spontaneously broken even though the Hamiltonian is invariant under symmetry. Recently, Machine Learning Algorithms have been successfully utilized to study topics in physics. We applied unsupervised Machine Learning Algorithms to find the magnetic ground states of the Heisenberg model. A fully connected neural network was used to generate the spin configuration from randomly coded features, and the magnetic energy was selected as the cost to be minimized during the Machine Learning process. We found that ground states solved by the unsupervised Learning process are consistent with the theoretical solution. Also, we compared the results with those from traditional computational methods and found that the Machine Learning Algorithm provides an efficient method to solve the magnetic state numerically.

Mohammad Ehteram - One of the best experts on this subject based on the ideXlab platform.

  • Operating a reservoir system based on the shark Machine Learning Algorithm
    Environmental Earth Sciences, 2018
    Co-Authors: M.f. Allawi, Firdaus Mohamad Hamzah, Othman Jaafar, Mohammad Ehteram, Md. Shabbir Hossain
    Abstract:

    The operating process of a multi-purpose reservoir needs to develop models that have the ability to overcome the challenges facing the decision makers. Therefore, the development of a mathematical optimization model is crucial for selecting the optimal policies for the reservoir operation. In the current study, the shark Machine Learning Algorithm (SMLA) is proposed to develop an optimal rule for operating the reservoir. The SMLA began with a group of randomly produced potential solutions and later interactively executed the search for the optimal solution. The procedure for the SMLA is suitable to be applied to a reservoir system due to its ability to tackle the stochastic features of dam and reservoir systems. The major purpose of the proposed models is to generate an operation rule that could minimize the absolute value of the differences between water release and water demand. The proposed model has been examined using the data of the Aswan High Dam, Egypt as the case study. The performance of the SMLA was compared with the performance of the most widespread evolutionary Algorithms, namely, the genetic Algorithm (GA). Comprehensive analysis of the results was performed using three performance indicators, namely, resilience, reliability, and vulnerability. This work concluded that the performance of the SMLA model was better than the GA model in generating the optimal policy for reservoir operation. The result showed that the SMLA succeeded in providing high reliability (99.72%), significant resilience (1) and minimum vulnerability (20.7% of demand).

  • Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System
    Water Resources Management, 2018
    Co-Authors: Mohammad Ehteram
    Abstract:

    It is remarkable that several hydrological parameters have a significant effect on the reservoir operation. Therefore, operating the reservoir system is complex issue due to existing the nonlinearity hydrological variables. Hence, determining modern model has high ability in handling reservoir operation is crucial. The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) to provide optimal operational rules. The major objective for the proposed model is minimizing the deficit volume between water releases and the irrigation water demand. The current study compared the performance of the SML model with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The proposed models have been utilized of finding the optimal policies to operate Timah Tasoh Dam, which is located in Malaysia. The study utilized considerable statistical indicators to explore the efficiency of the models. The simulation period showed that SMLA approach outperforms both of conventional Algorithms. The SMLA attained high Reliability and Resilience (Rel. = 0.98%, Res. = 50%) and minimum Vulnerability (Vul. = 21.9 of demand). It is demonstrated that shark Machine Learning Algorithm would be a promising tool in handling the long-term optimization problem in operation a reservoir system.

Halil Altay Güvenir - One of the best experts on this subject based on the ideXlab platform.

  • A supervised Machine Learning Algorithm for arrhythmia analysis
    Computers in Cardiology 1997, 1997
    Co-Authors: Halil Altay Güvenir, Gulsen Demiroz, Baver Acar, A. Cekin
    Abstract:

    A new Machine Learning Algorithm for the diagnosis of cardiac\narrhythmia from standard 12 lead ECG recordings is presented. The\nAlgorithm is called VF15 for Voting Feature Intervals. VF15 is a\nsupervised and inductive Learning Algorithm for inducing classification\nknowledge from examples. The input to VF15 is a training set of records.\nEach record contains clinical measurements, from ECG signals and some\nother information such as sex, age, and weight, along with the decision\nof an expert cardiologist. The knowledge representation is based on a\nrecent technique called Feature Intervals, where a concept is\nrepresented by the projections of the training cases on each feature\nseparately. Classification in VF15 is based on a majority voting among\nthe class predictions made by each feature separately. The comparison of\nthe VF15 Algorithm indicates that it outperforms other standard\nAlgorithms such as Naive Bayesian and Nearest Neighbor classifiers