scholarly journals Analysis of IPL Match Results using Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 1746-1751
Author(s):  
N. Lokeswari
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Large Set ◽  
Regression Problem ◽  
The Past ◽  
Premier League ◽  
Past Data

Indian Premier League (IPL) is a famous Twenty-20 League conducted by The Board of Control for Cricket in India (BCCI). It was started in 2008 and successfully completed its thirteen seasons till 2020. IPL is a popular sport where it has a large set of audience throughout the country. Every cricket fan would be eager to know and predict the IPL match results.A solution using Machine Learning is provided for the analysis of IPL Match results. This paper attempts to predict the match winner and the innings score considering the past data of match by match and ball by ball. Match winner prediction is taken as classification problem and innings score prediction is taken as regression problem. Algorithms like Support Vector Machine(SVM),Naive Bayes, k-Nearest Neighbour(kNN) are used for classification of match winner and Linear Regression, Decision tree for prediction of innings score. The dataset contains many features in which 7 features are identified in which that can be used for the prediction. Based on those features, models are built and evaluated by certain parameters. Based on the results SVM performed.

scholarly journals A Hybrid Framework for Heart Disease Prediction Using Machine Learning Algorithms

2021 ◽  
Vol 309 ◽  
pp. 01043
Author(s):  
L. Chandrika ◽  
K. Madhavi
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Prediction Model ◽  
Linear Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
The Past ◽  
Past Data

Cardiovascular Diseases (CVDs) are the primary cause for the sudden death in the world today from the past few years the disease has emerged greatly as a most unpredictable problem, not only in India the whole planet facing the criticality. So, there is a desperate need of valid, accurate and practical solution or application to diagnose the CVD problems in time for mandatory treatment. Predicting the CVD is a great challenge in the health care domain of clinical data analysis. Machine learning Algorithms (MLA) and Techniques has been vastly developed and proven to be effective and efficient in predicting the problems using the past data. Using these MLA techniques and taking the clinical dataset which provided by the healthcare industry. Different studies were takes place and tried only a small part into predicting CVD with ML Algorithms. In this thesis, we propose the different novel methodology which concentrates at finding appropriate features by using MLA techniques resulting at finding out the accurate model to predict CVD. In this prediction model we are trying to implement the models with different combinations of features and several known classification techniques such as Deep Learning, Random Forest, Generalised Linear Model, Naïve Bayes, Logistic Regression, Decision Tree, Gradient Boosted trees, Support Vector Machine, Vote and HRFLM and we have got an higher accuracy level and of 75.8%, 85.1%, 82.9%, 87.4%, 85%, 86.1%, 78.3%, 86.1%, 87.41%, and 88.4% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

Regression and Classification Method Based on Gaussian Processes

2014 ◽  
Vol 971-973 ◽  
pp. 1949-1952
Author(s):  
Xing Hui Wu ◽  
Yu Ping Zhou
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Classification Problem ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Regression Problem ◽  
Development Direction

Gaussian processes is a kind of machine learning method developed in recent years and also a promising technology that has been applied both in the regression problem and the classification problem. In this paper, the general principle of regression and classification based on Gaussian process and experimental verification was described. A comparison about accuracy between this method and Support Vector Machine (SVM) is made during the experiments.Finally, it was summarized of the regression and classification of Gaussian process application and future development direction.

scholarly journals Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning

2021 ◽  
Vol 13 (9) ◽  
pp. 4728
Author(s):  
Zinhle Mashaba-Munghemezulu ◽  
George Johannes Chirima ◽  
Cilence Munghemezulu
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Rural Communities ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Subsistence Agriculture ◽  
Smallholder Farms ◽  
Main Driver ◽  
Sentinel 2

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.

Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning

2018 ◽  
Vol 28 (02) ◽  
pp. 1750036 ◽  
Author(s):  
Shuqiang Wang ◽  
Yong Hu ◽  
Yanyan Shen ◽  
Hanxiong Li
Keyword(s):  
Machine Learning ◽  
Surgical Planning ◽  
Diffusion Tensor ◽  
Mean Value ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Control Groups ◽  
Diffusion Tensor Imaging Dti

In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The comparison between selected voxel-based classification and mean value-based classification were performed. A support vector machine (SVM) classifier using a selected voxel-based dataset produced an accuracy of 95.73%, sensitivity of 93.41% and specificity of 98.64%. The efficacy of each index of diffusion for classification was also evaluated. Using the proposed approach, myelopathic areas in CSM are detected to provide an accurate reference to assist spine surgeons in surgical planning in complicated cases.

A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

2021 ◽  
Author(s):  
Julia Kaltenborn ◽  
Viviane Clay ◽  
Amy R. Macfarlane ◽  
Joshua Michael Lloyd King ◽  
Martin Schneebeli
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Snow Layer ◽  
Arctic Sea ◽  
Execution Speed

<p>Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.</p><p>Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.</p><p>We will test and compare the different algorithms and evaluate the algorithms’ effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.</p><p>The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms’ comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation. </p><p> </p><p>King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. <em>The Cryosphere</em>, <em>14</em>(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.</p>

Tremor Identification Using Machine Learning in Parkinson's Disease

2019 ◽  
pp. 128-151
Author(s):  
Angana Saikia ◽  
Vinayak Majhi ◽  
Masaraf Hussain ◽  
Sudip Paul ◽  
Amitava Datta
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Discriminant Analysis ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

MACHINE LEARNING ALGORITHMS FOR IDENTIFICATION OF ABNORMAL GLOW CURVES AND ASSOCIATED ABNORMALITY IN CaSO4:DY-BASED PERSONNEL MONITORING DOSIMETERS

2020 ◽  
Vol 190 (3) ◽  
pp. 342-351
Author(s):  
Munir S Pathan ◽  
S M Pradhan ◽  
T Palani Selvam
Keyword(s):  
Machine Learning ◽  
Glow Curve ◽  
Good Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Computationally Efficient ◽  
Artificial Neural Network Ann ◽  
First Time

Abstract In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO4:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector machine (SVM) are employed for identifying not only the abnormal glow curve but also the type of abnormality. For the first time, the simplest and computationally efficient algorithm based on RF is presented for GC classifications. About 4000 GCs are used for the training and validation of ML algorithms. The performance of all algorithms is compared by using various parameters. Results show a fairly good accuracy of 99.05% for the classification of GCs by RF algorithm. Whereas 96.7% and 96.1% accuracy is achieved using ANN and SVM, respectively. The RF-based classifier is recommended for GC classification as well as in assisting the fault determination of the TLD reader system.

Damage Classification of Composites Using Machine Learning

2019 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Machine Learning ◽  
Composite Materials ◽  
Random Forest ◽  
Condition Monitoring ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Damage Classification ◽  
Combining Data ◽  
Ultrasonic Measurements

Abstract Composite materials have tremendous and ever-increasing applications in complex engineering systems; thus, it is important to develop non-destructive and efficient condition monitoring methods to improve damage prediction, thereby avoiding catastrophic failures and reducing standby time. Nondestructive condition monitoring techniques when combined with machine learning applications can contribute towards the stated improvements. Thus, the research question taken into consideration for this paper is “Can machine learning techniques provide efficient damage classification of composite materials to improve condition monitoring using features extracted from acousto-ultrasonic measurements?” In order to answer this question, acoustic-ultrasonic signals in Carbon Fiber Reinforced Polymer (CFRP) composites for distinct damage levels were taken from NASA Ames prognostics data repository. Statistical condition indicators of the signals were used as features to train and test four traditional machine learning algorithms such as K-nearest neighbors, support vector machine, Decision Tree and Random Forest, and their performance was compared and discussed. Results showed higher accuracy for Random Forest with a strong dependency on the feature extraction/selection techniques employed. By combining data analysis from acoustic-ultrasonic measurements in composite materials with machine learning tools, this work contributes to the development of intelligent damage classification algorithms that can be applied to advanced online diagnostics and health management strategies of composite materials, operating under more complex working conditions.

A Multi-Classification Method Based on Gaussian Processes

2012 ◽  
Vol 198-199 ◽  
pp. 1333-1337 ◽  
Author(s):  
San Xi Wei ◽  
Zong Hai Sun
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Good Accuracy ◽  
Classification Problem ◽  
Decision Time ◽  
Support Vector ◽  
Regression Problem ◽  
Multi Classification

Gaussian processes (GPs) is a very promising technology that has been applied both in the regression problem and the classification problem. In recent years, models based on Gaussian process priors have attracted much attention in the machine learning. Binary (or two-class, C=2) classification using Gaussian process is a very well-developed method. In this paper, a Multi-classification (C>2) method is illustrated, which is based on Binary GPs classification. A good accuracy can be obtained through this method. Meanwhile, a comparison about decision time and accuracy between this method and Support Vector Machine (SVM) is made during the experiments.

scholarly journals FEASIBILITY OF MACHINE LEARNING METHODS FOR SEPARATING WOOD AND LEAF POINTS FROM TERRESTRIAL LASER SCANNING DATA

2017 ◽  
Vol IV-2/W4 ◽  
pp. 157-164 ◽  
Author(s):  
D. Wang ◽  
M. Hollaus ◽  
N. Pfeifer
Keyword(s):  
Machine Learning ◽  
Laser Scanning ◽  
Learning Algorithms ◽  
Terrestrial Laser Scanning ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Area Index ◽  
Training Samples

Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Na¨ıve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an <i>Erytrophleum fordii</i> and a <i>Betula pendula</i> (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

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