Application of Machine Learning in Perovskite Solar Cell Crystal Size Distribution Analysis

MRS Advances ◽  
2019 ◽  
Vol 4 (14) ◽  
pp. 793-800 ◽  
Author(s):  
Thomas Chen ◽  
Yuchen Zhou ◽  
Miriam Rafailovich
Keyword(s):  
Machine Learning ◽  
Solar Cell ◽  
Edge Detection ◽  
True Positive Rate ◽  
Perovskite Solar Cell ◽  
Higher Order ◽  
True Positive ◽  
Degree Polynomial ◽  
Positive Rate ◽  
Crystal Grains

AbstractThis research automates edge detection for perovskite crystal grains using machine learning (ML). Once the edges of the crystal grains are located, a flood-fill algorithm can be used to find the distribution of crystal grain areas. The ML algorithm uses GNU Octave to run a regularized logistic regression algorithm that classifies each pixel of an input image as part of an edge or not part of an edge. The basic features used for the algorithm are each pixel’s grayscale intensity, its Sobel derivative. Higher order Sobel derivatives, higher degree polynomial terms, and intensities convolved by various kernels were used as additional features to improve the program’s accuracy and true-positive rate. Training data is obtained by using non-ML Canny Edge Detection to annotate the edges an SEM image of a pure perovskite solar cell (PSC). The classifier exhibits an 85.58% accuracy and produces an edge mask that clearly outlines the crystals visually. The ML edge mask exhibits far fewer false-positive mis-classifications for pixels in the middle of the crystals than Canny. However, the ML mask’s edges are fainter, owing to a lower true-positive classification rate. Using more kernels, higher order derivatives, and higher degree polynomial terms all significantly increased the true positive rate of the classifier, leading to thicker edges. This algorithm can greatly accelerate perovskite solar cell research (and potentially any research requiring particle size analysis), automating a process scientists previously had to perform by hand. This will facilitate the search for a solution for the world’s growing demands for renewable energy.

scholarly journals An Improved Particle Swarm Optimization based classification model for high dimensional medical disease prediction

2018 ◽  
Vol 7 (2.7) ◽  
pp. 546
Author(s):  
P R.Sudha Rani ◽  
Dr K.Kiran Kumar
Keyword(s):  
Machine Learning ◽  
True Positive Rate ◽  
High Dimensional ◽  
Disease Prediction ◽  
True Positive ◽  
Learning Models ◽  
Swarm Optimization ◽  
Hybrid Classifier ◽  
Medical Disease ◽  
Positive Rate

Recently, machine learning techniques have become popular and widely accepted for medical disease detection and classification on high dimensional datasets. Classification models is one of the essential model in machine learning models for medical disease prediction due to its fast processing speed, high efficiency and noisy datasets. Traditional machine learning models are failed to estimate the disease patterns with high true positive rate due to large number of features and data size. In this paper, a novel particle swarm optimization based hybrid classifier was implemented for medical disease prediction with high dimensions. The main objective of the feature selection based hybrid classifier is to classify the high dimensional data for large medical feature set. Proposed filtered based hybrid classifier is usually designed and implemented to improve the medical prediction rate on high dimensional data. In this work, we have used different ensemble learning models such ACO+NN, PSO+ELM, PSO+WELM to analyze the performance of proposed model(IPSO+WELM). Experimental results are evaluated on different types of medical datasets including lung cancer, diabetes, ovarian, and DLBCL-Stanford. Performance results show that proposed IPSO+WELM with ensemble model has high computational efficiency in terms of true positive rate, error rate and accuracy.  

scholarly journals Hadoop based Parallel Machine Learning Algorithms for Intrusion Detection System

2019 ◽  
Vol 9 (1) ◽  
pp. 1152-1156
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Naive Bayes ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
True Positive ◽  
Positive Rate ◽  
Bayes Algorithm

Web use and digitized information are getting expanded each day. The measure of information created is likewise getting expanded. On the opposite side, the security assaults cause numerous security dangers in the system, sites and Internet. Interruption discovery in a fast system is extremely a hard undertaking. The Hadoop Implementation is utilized to address the previously mentioned test that is distinguishing interruption in a major information condition at constant. To characterize the strange bundle stream, AI methodologies are used. Innocent Bayes does grouping by a vector of highlight esteems produced using some limited set. Choice Tree is another Machine Learning classifier which is likewise an administered learning model. Choice tree is the stream diagram like tree structure. J48 and Naïve Bayes Algorithm are actualized in Hadoop MapReduce Framework for parallel preparing by utilizing the KDDCup Data Corrected Benchmark dataset records. The outcome acquired is 89.9% True Positive rate and 0.04% False Positive rate for Naive Bayes Algorithm and 98.06% True Positive rate and 0.001% False Positive rate for Decision Tree Algorithm.

scholarly journals Epileptic Seizures Prediction Using Machine Learning Methods

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Syed Muhammad Usman ◽  
Muhammad Usman ◽  
Simon Fong
Keyword(s):  
Machine Learning ◽  
Epileptic Seizures ◽  
True Positive Rate ◽  
Noise Removal ◽  
Machine Learning Techniques ◽  
True Positive ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Positive Rate ◽  
Anticipation Time

Epileptic seizures occur due to disorder in brain functionality which can affect patient’s health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures’ sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects.

Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomno graphy Resources?

2017 ◽  
Vol 56 (04) ◽  
pp. 308-318 ◽  
Author(s):  
Asli Bostanci ◽  
Murat Turhan ◽  
Selen Bozkurt
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Classification Models ◽  
True Positive ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Obstructive Sleep ◽  
Positive Rate

SummaryObjectives: The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic variables: 1) clinical data, 2) symptoms and 3) physical examination.Methods: In order to produce classification models for OSA severity, five different machine learning methods (Bayesian network, Decision Tree, Random Forest, Neural Networks and Logistic Regression) were trained while relevant variables and their relationships were derived empirically from observed data. Each model was trained and evaluated using 10-fold cross-validation and to evaluate classification performances of all methods, true positive rate (TPR), false positive rate (FPR), Positive Predictive Value (PPV), F measure and Area Under Receiver Operating Characteristics curve (ROC-AUC) were used.Results: Results of 10-fold cross validated tests with different variable settings promisingly indicated that the OSA severity of suspected OSA patients can be classified, using non-polysomnographic features, with 0.71 true positive rate as the highest and, 0.15 false positive rate as the lowest, respectively. Moreover, the test results of different variables settings revealed that the accuracy of the classification models was significantly improved when physical examination variables were added to the model.Conclusions: Study results showed that machine learning methods can be used to estimate the probabilities of no, mild, moderate, and severe obstructive sleep apnea and such approaches may improve accurate initial OSA screening and help referring only the suspected moderate or severe OSA patients to sleep laboratories for the expensive tests.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 166
Author(s):  
Jakub T. Wilk ◽  
Beata Bąk ◽  
Piotr Artiemjew ◽  
Jerzy Wilde ◽  
Maciej Siuda
Keyword(s):  
Gas Sensors ◽  
Laboratory Tests ◽  
Test Chamber ◽  
Ambient Air ◽  
True Positive Rate ◽  
True Positive ◽  
Positive Rate ◽  
Parameters Of Accuracy ◽  
Selection Of

Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The study aimed to compare the responses of sensors to the odor of old worker bees (3–6 weeks old), young ones (0–1 days old), and those from long-term queenless colonies. From the experimental colonies, 10 samples of 100 workers were taken for each group and placed successively in the research chambers for the duration of the study. Old workers came from outer nest combs, young workers from hatching out brood in an incubator, and laying worker bees from long-term queenless colonies from brood combs (with laying worker bee’s eggs, humped brood, and drones). Each probe was measured for 10 min, and then immediately for another 10 min ambient air was given to regenerate sensors. The results were analyzed using 10 different classifiers. Research has shown that the devices can distinguish between the biological status of bees. The effectiveness of distinguishing between classes, determined by the parameters of accuracy balanced and true positive rate, of 0.763 and 0.742 in the case of the best euclidean.1nn classifier, may be satisfactory in the context of practical beekeeping. Depending on the environment accompanying the tested objects (a type of insert in the test chamber), the introduction of other classifiers as well as baseline correction methods may be considered, while the selection of the appropriate classifier for the task may be of great importance for the effectiveness of the classification.

The Heartbeat Classification Based on PCA

2012 ◽  
Vol 195-196 ◽  
pp. 402-406
Author(s):  
Xue Qin Chen ◽  
Rui Ping Wang
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Recognition Task ◽  
Principal Component ◽  
True Positive Rate ◽  
True Positive ◽  
Heartbeat Classification ◽  
Positive Rate ◽  
Electrocardiogram Ecg ◽  
Pattern Recognition Task

Classify the electrocardiogram (ECG) into different pathophysiological categories is a complex pattern recognition task which has been tried in lots of methods. This paper will discuss a method of principal component analysis (PCA) in exacting the heartbeat features, and a new method of classification that is to calculate the error between the testing heartbeat and reconstructed heartbeat. Training and testing heartbeat is taken from the MIT-BIH Arrhythmia Database, in which 8 types of arrhythmia signals are selected in this paper. The true positive rate (TPR) is 83%.

scholarly journals The influence of digital PET/CT on diagnostic certainty and interrater reliability in [68Ga]Ga-PSMA-11 PET/CT for recurrent prostate cancer

2021 ◽  
Author(s):  
Ian Alberts ◽  
Jan-Niklas Hünermund ◽  
Christos Sachpekidis ◽  
Clemens Mingels ◽  
Viktor Fech ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Interrater Reliability ◽  
True Positive Rate ◽  
True Positive ◽  
Recurrent Prostate Cancer ◽  
Pathological Lesions ◽  
Diagnostic Certainty ◽  
Pet Ct ◽  
Positive Rate ◽  
Digital Pet

Abstract Objective To investigate the impact of digital PET/CT on diagnostic certainty, patient-based sensitivity and interrater reliability. Methods Four physicians retrospectively evaluated two matched cohorts of patients undergoing [68Ga]Ga-PSMA-11 PET/CT on a digital (dPET/CT n = 65) or an analogue scanner (aPET/CT n = 65) for recurrent prostate cancer between 11/2018 and 03/2019. The number of equivocal and pathological lesions as well as the frequency of discrepant findings and the interrater reliability for the two scanners were compared. Results dPET/CT detected more lesions than aPET/CT (p < 0.001). A higher number of pathological scans were observed for dPET/CT (83% vs. 57%, p < 0.001). The true-positive rate at follow-up was 100% for dPET/CT compared to 84% for aPET/CT (p < 0.001). The proportion of lesions rated as non-pathological as a total of all PSMA-avid lesions detected for dPET/CT was comparable to aPET/CT (61.8% vs. 57.0%, p = 0.99). Neither a higher rate of diagnostically uncertain lesions (11.5% dPET/CT vs. 13.7% aPET/CT, p = 0.95) nor discrepant scans (where one or more readers differed in opinion as to whether the scan is pathological) were observed (18% dPET/CT vs. 17% aPET/CT, p = 0.76). Interrater reliability for pathological lesions was excellent for both scanner types (Cronbach’s α = 0.923 dPET/CT; α = 0.948 aPET/CT) and interrater agreement was substantial for dPET/CT (Krippendorf’s α = 0.701) and almost perfect in aPET/CT (α = 0.802). Conclusions A higher detection rate for pathological lesions for dPET/CT compared with aPET/CT in multiple readers was observed. This improved sensitivity was coupled with an improved true-positive rate and was not associated with increased diagnostic uncertainty, rate of non-specific lesions, or reduced interrater reliability. Key Points • New generation digital scanners detect more cancer lesions in men with prostate cancer. • When using digital scanners, the doctors are able to diagnose prostate cancer lesions with better certainty • When using digital scanners, the doctors do not disagree with each other more than with other scanner types.

scholarly journals Face Recognition Using the SR-CNN Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4237 ◽  
Author(s):  
Yu-Xin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fang-Qing Wen ◽  
Guan-Qun Sheng ◽  
...  
Keyword(s):  
Face Recognition ◽  
Target Position ◽  
Operation Time ◽  
True Positive Rate ◽  
Processing Unit ◽  
True Positive ◽  
Face Database ◽  
Acceleration Ratio ◽  
Computational Performance ◽  
Positive Rate

In order to solve the problem of face recognition in complex environments being vulnerable to illumination change, object rotation, occlusion, and so on, which leads to the imprecision of target position, a face recognition algorithm with multi-feature fusion is proposed. This study presents a new robust face-matching method named SR-CNN, combining the rotation-invariant texture feature (RITF) vector, the scale-invariant feature transform (SIFT) vector, and the convolution neural network (CNN). Furthermore, a graphics processing unit (GPU) is used to parallelize the model for an optimal computational performance. The Labeled Faces in the Wild (LFW) database and self-collection face database were selected for experiments. It turns out that the true positive rate is improved by 10.97–13.24% and the acceleration ratio (the ratio between central processing unit (CPU) operation time and GPU time) is 5–6 times for the LFW face database. For the self-collection, the true positive rate increased by 12.65–15.31%, and the acceleration ratio improved by a factor of 6–7.

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