An enhanced machine learning based approach for failures detection and diagnosis of PV systems

Electroencephalogram (EEG) is one of the most commonly used tools for epilepsy detection. In this paper we have presented two methods for the diagnosis of epilepsy using machine learning techniques.EEG waveforms have five different kinds of frequency bands. Out of which only two namely theta and gamma bands carry epileptic seizure information. Our model determines the statistical features like mean, variance, maximum, minimum, kurtosis, and skewness from the raw data set. This reduces the mathematical complexities and time consumption of the feature extraction method. It then uses a Logistic regression model and decision tree model to classify whether a person is epileptic or not. After the implementation of the machine learning models, parameters like accuracy, sensitivity, and recall have been found. The results for the same are analyzed in detail in this paper. Epileptic seizures cause severe damage to the brain which affects the health of a person. Our key objective from this paper is to help in the early prediction and detection of epilepsy so that preventive interventions can be provided and precautionary measures are taken to prevent the patient from suffering any severe damage

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Machine Learning Applications in Breast Cancer Diagnosis

Handbook of Research on Machine Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2229-4.ch020 ◽

2017 ◽

pp. 465-490

Author(s):

Syed Jamal Safdar Gardezi ◽

Mohamed Meselhy Eltoukhy ◽

Ibrahima Faye

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Feature Extraction ◽

Machine Learning Algorithms ◽

Learning Tools ◽

Extraction Techniques ◽

Cad Systems ◽

Cad System ◽

Machine Learning Applications ◽

Detection And Diagnosis

Breast cancer is one of the leading causes of death in women worldwide. Early detection is the key to reduce the mortality rates. Mammography screening has proven to be one of the effective tools for diagnosis of breast cancer. Computer aided diagnosis (CAD) system is a fast, reliable, and cost-effective tool in assisting the radiologists/physicians for diagnosis of breast cancer. CAD systems play an increasingly important role in the clinics by providing a second opinion. Clinical trials have shown that CAD systems have improved the accuracy of breast cancer detection. A typical CAD system involves three major steps i.e. segmentation of suspected lesions, feature extraction and classification of these regions into normal or abnormal class and further into benign or malignant stages. The diagnostics ability of any CAD system is dependent on accurate segmentation, feature extraction techniques and most importantly classification tools that have ability to discriminate the normal tissues from the abnormal tissues. In this chapter we discuss the application of machine learning algorithms e.g. ANN, binary tree, SVM, etc. together with segmentation and feature extraction techniques in a CAD system development. Various methods used in the detection and diagnosis of breast lesions in mammography are reviewed. A brief introduction of machine learning tools, used in diagnosis and their classification performance on various segmentation and feature extraction techniques is presented.

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A distributed sensor-fault detection and diagnosis framework using machine learning

Information Sciences ◽

10.1016/j.ins.2020.08.068 ◽

2021 ◽

Vol 547 ◽

pp. 777-796

Author(s):

Sana Ullah Jan ◽

Young Doo Lee ◽

In Soo Koo

Keyword(s):

Machine Learning ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Sensor Fault ◽

Distributed Sensor ◽

Sensor Fault Detection ◽

Detection And Diagnosis

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Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques

Energies ◽

10.3390/en12163158 ◽

2019 ◽

Vol 12 (16) ◽

pp. 3158 ◽

Cited By ~ 5

Author(s):

Ngoc Thien Le ◽

Watit Benjapolakul

Keyword(s):

Machine Learning ◽

Investment Decision ◽

High Volume ◽

Machine Learning Techniques ◽

Energy Yield ◽

Pv System ◽

Useful Knowledge ◽

Pv Systems ◽

Learning Techniques ◽

The Difference

Rooftop photovoltaics (PV) systems are attracting residential customers due to their renewable energy contribution to houses and to green cities. However, customers also need a comprehensive understanding of system design configuration and the related energy return from the system in order to support their PV investment. In this study, the rooftop PV systems from many high-volume installed PV systems countries and regions were collected to evaluate the lifetime energy yield of these systems based on machine learning techniques. Then, we obtained an association between the lifetime energy yield and technical configuration details of PV such as rated solar panel power, number of panels, rated inverter power, and number of inverters. Our findings reveal that the variability of PV lifetime energy is partly explained by the difference in PV system configuration. Indeed, our machine learning model can explain approximately 31 % ( 95 % confidence interval: 29–38%) of the variant energy efficiency of the PV system, given the configuration and components of the PV system. Our study has contributed useful knowledge to support the planning and design of a rooftop PV system such as PV financial modeling and PV investment decision.

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Machine Learning Approach improves the Quality of the MRI Images in Tumor Detection and Diagnosis: A PSO based Cluster Analysis

2018 International Conference on Intelligent Computing and Communication for Smart World (I2C2SW) ◽

10.1109/i2c2sw45816.2018.8997145 ◽

2018 ◽

Author(s):

V. Sivakumar ◽

N. Janakiraman ◽

S. Naganandhini

Keyword(s):

Machine Learning ◽

Cluster Analysis ◽

Tumor Detection ◽

Learning Approach ◽

Machine Learning Approach ◽

Detection And Diagnosis

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Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network

Symmetry ◽

10.3390/sym11020256 ◽

2019 ◽

Vol 11 (2) ◽

pp. 256 ◽

Cited By ~ 9

Author(s):

Jiangyong An ◽

Wanyi Li ◽

Maosong Li ◽

Sanrong Cui ◽

Huanran Yue

Keyword(s):

Neural Network ◽

Machine Learning ◽

Drought Stress ◽

Convolutional Neural Network ◽

Extraction Process ◽

Deep Convolutional Neural Network ◽

Gradient Boosting ◽

Moderate Drought ◽

Detection And Diagnosis

Drought stress seriously affects crop growth, development, and grain production. Existing machine learning methods have achieved great progress in drought stress detection and diagnosis. However, such methods are based on a hand-crafted feature extraction process, and the accuracy has much room to improve. In this paper, we propose the use of a deep convolutional neural network (DCNN) to identify and classify maize drought stress. Field drought stress experiments were conducted in 2014. The experiment was divided into three treatments: optimum moisture, light drought, and moderate drought stress. Maize images were obtained every two hours throughout the whole day by digital cameras. In order to compare the accuracy of DCNN, a comparative experiment was conducted using traditional machine learning on the same dataset. The experimental results demonstrated an impressive performance of the proposed method. For the total dataset, the accuracy of the identification and classification of drought stress was 98.14% and 95.95%, respectively. High accuracy was also achieved on the sub-datasets of the seedling and jointing stages. The identification and classification accuracy levels of the color images were higher than those of the gray images. Furthermore, the comparison experiments on the same dataset demonstrated that DCNN achieved a better performance than the traditional machine learning method (Gradient Boosting Decision Tree GBDT). Overall, our proposed deep learning-based approach is a very promising method for field maize drought identification and classification based on digital images.

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