scholarly journals LC–MS peak assignment based on unanimous selection by six machine learning algorithms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroaki Ito ◽  
Takashi Matsui ◽  
Ryo Konno ◽  
Makoto Itakura ◽  
Yoshio Kodera
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Weak Signals ◽  
Accuracy And Precision ◽  
Positive Rate ◽  
Assignment Strategy ◽  
Performance Results

AbstractRecent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance results in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

scholarly journals LC-MS Peak Assignment Based on Unanimous Selection by Six Machine Learning Algorithms

2021 ◽  
Author(s):  
Hiroaki Ito ◽  
Takashi Matsui ◽  
Ryo Konno ◽  
Makoto Itakura ◽  
Yoshio Kodera
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Weak Signals ◽  
Accuracy And Precision ◽  
Peak Assignment ◽  
Positive Rate ◽  
Assignment Strategy

Abstract Recent Mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance resulted in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

Machine Learning for Automated Polyp Detection in Computed Tomography Colonography

2012 ◽  
pp. 830-850
Author(s):  
Abhilash Alexander Miranda ◽  
Olivier Caelen ◽  
Gianluca Bontempi
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
False Positive ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Colorectal Polyps ◽  
Machine Learning Algorithms ◽  
Computed Tomography Colonography ◽  
Positive Rate ◽  
Independent Features

This chapter presents a comprehensive scheme for automated detection of colorectal polyps in computed tomography colonography (CTC) with particular emphasis on robust learning algorithms that differentiate polyps from non-polyp shapes. The authors’ automated CTC scheme introduces two orientation independent features which encode the shape characteristics that aid in classification of polyps and non-polyps with high accuracy, low false positive rate, and low computations making the scheme suitable for colorectal cancer screening initiatives. Experiments using state-of-the-art machine learning algorithms viz., lazy learning, support vector machines, and naïve Bayes classifiers reveal the robustness of the two features in detecting polyps at 100% sensitivity for polyps with diameter greater than 10 mm while attaining total low false positive rates, respectively, of 3.05, 3.47 and 0.71 per CTC dataset at specificities above 99% when tested on 58 CTC datasets. The results were validated using colonoscopy reports provided by expert radiologists.

scholarly journals Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 387
Author(s):  
Nahina Islam ◽  
Md Mamunur Rashid ◽  
Santoso Wibowo ◽  
Cheng-Yuan Xu ◽  
Ahsan Morshed ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Weed Detection ◽  
Learning Techniques ◽  
Positive Rate ◽  
Uav Images

This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.

Machine Learning for Automated Polyp Detection in Computed Tomography Colonography

2010 ◽  
pp. 54-77
Author(s):  
Abhilash Alexander Miranda ◽  
Olivier Caelen ◽  
Gianluca Bontempi
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
False Positive ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Colorectal Polyps ◽  
Machine Learning Algorithms ◽  
Computed Tomography Colonography ◽  
Positive Rate ◽  
Independent Features

This chapter presents a comprehensive scheme for automated detection of colorectal polyps in computed tomography colonography (CTC) with particular emphasis on robust learning algorithms that differentiate polyps from non-polyp shapes. The authors’ automated CTC scheme introduces two orientation independent features which encode the shape characteristics that aid in classification of polyps and non-polyps with high accuracy, low false positive rate, and low computations making the scheme suitable for colorectal cancer screening initiatives. Experiments using state-of-the-art machine learning algorithms viz., lazy learning, support vector machines, and naïve Bayes classifiers reveal the robustness of the two features in detecting polyps at 100% sensitivity for polyps with diameter greater than 10 mm while attaining total low false positive rates, respectively, of 3.05, 3.47 and 0.71 per CTC dataset at specificities above 99% when tested on 58 CTC datasets. The results were validated using colonoscopy reports provided by expert radiologists.

Comparative Study of Various Machine Learning Algorithms for Prediction of Insomnia

2019 ◽  
pp. 234-257 ◽  
Author(s):  
Ravinder Ahuja ◽  
Vishal Vivek ◽  
Manika Chandna ◽  
Shivani Virmani ◽  
Alisha Banga
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
True Positive Rate ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mobility Problem ◽  
Positive Rate ◽  
F Measure

An early diagnosis of insomnia can prevent further medical aids such as anger issues, heart diseases, anxiety, depression, and hypertension. Fifteen machine learning algorithms have been applied and 14 leading factors have been taken into consideration for predicting insomnia. Seven performance parameters (accuracy, kappa, the true positive rate, false positive rate, precision, f-measure, and AUC) are used and for implementation. The authors have used python language. The support vector machine is giving higher performance out of all algorithms giving accuracy 91.6%, f-measure is 92.13, and kappa is 0.83. Further, SVM is applied on another dataset of 100 patients and giving accuracy 92%. In addition, an analysis of the variable importance of CART, C5.0, decision tree, random forest, adaptive boost, and XG boost is calculated. The analysis shows that insomnia primarily depends on the factors, which are the vision problem, mobility problem, and sleep disorder. This chapter mainly finds the usages and effectiveness of machine learning algorithms in Insomnia diseases prediction.

scholarly journals A Framework for Grading of White Chali Type Arecanuts with Machine Learning Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 2782-2788
Keyword(s):  
Machine Learning ◽  
Multinomial Logistic Regression ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Weighted Average ◽  
Machine Learning Algorithms ◽  
Image Feature ◽  
Major Work ◽  
Grading Systems ◽  
Positive Rate

Grading of arecanuts before marketing fetches better prize. There are two universally accepted grading systems for arecanuts namely grading at the producers’ level and grading at the wholesale traders’ level. The major work in this paper is devoted to generation of a standard image database for the White Chali Type arecanuts and constructing a frame work for their grading by exploring the features of White Chali Type arecanut images for the first time. Further, two separate datasets are developed for the above grading systems by employing image feature extraction methods with 3500 and 4132 instances respectively. The arecanuts are graded using popular machine learning algorithms and the results are validated using ten fold cross validation. Multinomial logistic regression as classifier outperformed with classification accuracies of 98.8% and 92.69% for the producers’ level and the whole sale traders’ level grading systems respectively. Also the performances of various machine learning algorithms for the above two datasets are evaluated using weighted average values of True Positive rate, False Positive rate, precision, recall, F-measure and Cohen’s Kappa coefficient.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Tebyan: Fake News Detection System (Preprint)

2021 ◽  
Author(s):  
Lamya Alderywsh ◽  
Aseel Aldawood ◽  
Ashwag Alasmari ◽  
Farah Aldeijy ◽  
Ghadah Alqubisy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arab World ◽  
Detection System ◽  
Learning Algorithms ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Fake News ◽  
Typical Type ◽  
Performance Results

BACKGROUND There is a serious threat from fake news spreading in technologically advanced societies, including those in the Arab world, via deceptive machine-generated text. In the last decade, Arabic fake news identification has gained increased attention, and numerous detection approaches have revealed some ability to find fake news throughout various data sources. Nevertheless, many existing approaches overlook recent advancements in fake news detection, explicitly to incorporate machine learning algorithms system. OBJECTIVE Tebyan project aims to address the problem of fake news by developing a fake news detection system that employs machine learning algorithms to detect whether the news is fake or real in the context of Arab world. METHODS The project went through numerous phases using an iterative methodology to develop the system. This study analysis incorporated numerous stages using an iterative method to develop the system of misinformation and contextualize fake news regarding society's information. It consists of implementing the machine learning algorithms system using Python to collect genuine and fake news datasets. The study also assesses how information-exchanging behaviors can minimize and find the optimal source of authentication of the emergent news through system testing approaches. RESULTS The study revealed that the main deliverable of this project is the Tebyan system in the community, which allows the user to ensure the credibility of news in Arabic newspapers. It showed that the SVM classifier, on average, exhibited the highest performance results, resulting in 90% in every performance measure of sources. Moreover, the results indicate the second-best algorithm is the linear SVC since it resulted in 90% in performance measure with the societies' typical type of fake information. CONCLUSIONS The study concludes that conducting a system with machine learning algorithms using Python programming language allows the rapid measures of the users' perception to comment and rate the credibility result and subscribing to news email services.

A Robust Method to Predict Fluid Properties Based on Big Data and Machine Learning Algorithms

2021 ◽  
Author(s):  
Yingxian Liu ◽  
Cunliang Chen ◽  
Hanqing Zhao ◽  
Yu Wang ◽  
Xiaodong Han
Keyword(s):  
Machine Learning ◽  
Physical Properties ◽  
Learning Algorithm ◽  
Direct Method ◽  
Learning Algorithms ◽  
Small Error ◽  
Machine Learning Algorithms ◽  
Well Test ◽  
Empirical Formulas ◽  
Fluid Properties

Abstract Fluid properties are key factors for predicting single well productivity, well test interpretation and oilfield recovery prediction, which directly affect the success of ODP program design. The most accurate and direct method of acquisition is underground sampling. However, not every well has samples due to technical reasons such as excessive well deviation or high cost during the exploration stage. Therefore, analogies or empirical formulas have to be adopted to carry out research in many cases. But a large number of oilfield developments have shown that the errors caused by these methods are very large. Therefore, how to quickly and accurately obtain fluid physical properties is of great significance. In recent years, with the development and improvement of artificial intelligence or machine learning algorithms, their applications in the oilfield have become more and more extensive. This paper proposed a method for predicting crude oil physical properties based on machine learning algorithms. This method uses PVT data from nearly 100 wells in Bohai Oilfield. 75% of the data is used for training and learning to obtain the prediction model, and the remaining 25% is used for testing. Practice shows that the prediction results of the machine learning algorithm are very close to the actual data, with a very small error. Finally, this method was used to apply the preliminary plan design of the BZ29 oilfield which is a new oilfield. Especially for the unsampled sand bodies, the fluid physical properties prediction was carried out. It also compares the influence of the analogy method on the scheme, which provides potential and risk analysis for scheme design. This method will be applied in more oil fields in the Bohai Sea in the future and has important promotion value.

scholarly journals Risk Monitoring and Quantitative Results of Various Attributes of Machine Learning Algorithms with a Time Series Data

2019 ◽  
Vol 8 (11) ◽  
pp. 4018-4022
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Machine Learning Algorithm ◽  
Risk Modelling ◽  
Risk Monitoring ◽  
Quantitative Results

The aim of this research is to do risk modelling after analysis of twitter posts based on certain sentiment analysis. In this research we analyze posts of several users or a particular user to check whether they can be cause of concern to the society or not. Every sentiment like happy, sad, anger and other emotions are going to provide scaling of severity in the conclusion of final table on which machine learning algorithm is applied. The data which is put under the machine learning algorithms are been monitored over a period of time and it is related to a particular topic in an area

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