scholarly journals Train Delay Prediction using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 1312-1315
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Weather Conditions ◽  
Severe Weather ◽  
Average Error ◽  
Machine Learning Method ◽  
Long Distance ◽  
Passenger Trains ◽  
Delay Prediction

Indian Railways operates both long distance and suburban passenger trains and freight services daily in the country. Trains get delayed frequently due to several reasons such as, severe weather conditions such as fog, traffic, signal failure, derailing of trains, accidents, etc, and this delay is propagated from station to station. If we can predict this in advance - it would be of great help for the commuters to plan their journey either for an earlier departure or postpone, and also lets railways to take measures to avoid delays further. In this paper, we used decision tree, a machine learning method used for predicting train delays, and Recurrent Neural Networks distinguished with various fixtures. For predicting train delays, Recurrent Neural networks with 2 layers and 22 neurons per each layer gave best results with an average error of 122 seconds

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

Machine Learning-Based Malware Detection using Recurrent Neural Networks

2019 ◽  
Author(s):  
Miguel Oyler-Castrillo ◽  
Nicolas Bohm Agostini ◽  
Gadiel Sznaier ◽  
David Kaeli
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Malware Detection

scholarly journals Attribution of genetic engineering: A practical and accurate machine-learning toolkit for biosecurity

2020 ◽  
Author(s):  
Ethan C. Alley ◽  
Miles Turpin ◽  
Andrew Bo Liu ◽  
Taylor Kulp-McDowall ◽  
Jacob Swett ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Genetic Engineering ◽  
Recurrent Neural Networks ◽  
International Security ◽  
Responsible Innovation ◽  
Nation State ◽  
Dna Motifs ◽  
Simple Models

AbstractThe promise of biotechnology is tempered by its potential for accidental or deliberate misuse. Reliably identifying telltale signatures characteristic to different genetic designers, termed genetic engineering attribution, would deter misuse, yet is still considered unsolved. Here, we show that recurrent neural networks trained on DNA motifs and basic phenotype can reach 70% attribution accuracy distinguishing between over 1,300 labs. To make these models usable in practice, we introduce a framework for weighing predictions against other investigative evidence using calibration, and bring our model to within 1.6% of perfect calibration. Additionally, we demonstrate that simple models can accurately predict both the nation-state-of-origin and ancestor labs, forming the foundation of an integrated attribution toolkit which should promote responsible innovation and international security alike.

scholarly journals Classifying Radiology Abstracts with Deep Learning

2021 ◽  
Author(s):  
George Shih ◽  
Hongyu Chen
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Natural Language ◽  
Language Processing ◽  
Breast Imaging ◽  
Machine Learning Techniques ◽  
Average Error ◽  
Ensemble Model ◽  
Radiological Society ◽  
Natural Language Programming

Background The Radiological Society of North America (RSNA) receives more than 8000 abstracts yearly for scientific presentations, scientific posters, and scientific papers. Each abstract is assigned manually one of 16 top-level categories (e.g. "Breast Imaging") for workflow purposes. Additionally, each abstract receives a grade from 1-10 based on a variety of subjective factors such as style and perceived writing quality. Using machine learning to automate, at least partially, the categorization of abstract submissions can result in saving many hours of manual labor. Methods A total of 45527 RSNA abstract submissions from 2014 through 2019 were ingested, tokenized, and pre-processed with a standard natural language programming protocol. A bag-of-words (BOW) model was used as a baseline to evaluate two more sophisticated models, convolutional neural networks and recurrent neural networks, and also evaluate an ensemble model featuring all three neural networks. Results ensemble model was able to achieve 73% testing accuracy for classifying the 16 top-level categories, outperforming all other models. The top model for classifying abstract grade was also an ensemble model, achieving a mean average error (MAE) of 1.01. Conclusion While the baseline BOW model was the highest performing individual classifier, ensemble models that included state-of-the-art neural networks were able to outperform it. Our research shows that machine learning techniques can, to a reasonable degree of accuracy, predict both objective factors such as abstract category as well as subjective factors such as abstract grade. This work builds upon previous research involving using natural language processing on scientific abstracts to make useful inferences that address a meaningful problem.

scholarly journals Flight Delay Prediction Using Machine Learning

2021 ◽  
pp. 473-476
Author(s):  
Prajwal Dhone ◽  
Uday Kirange ◽  
Rushabh Satarkar ◽  
Prof. Shashant Jaykar
Keyword(s):  
Machine Learning ◽  
Negative Impact ◽  
Weather Conditions ◽  
Current Data ◽  
Environmental Harm ◽  
Flight Delays ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Flight Delay ◽  
Delay Prediction

In this fast growing world as airplanes continue flying, flight delays are the part of the experience. According to the Bureau Of Statistics(BOS), about 20% of all flights are delayed by 15 minutes or more. Flight delays causes a negative impact, mainly economical for airport authorities, commuters and airline industries as well. Furthermore, in the domain of sustainability, it can even cause environmental harm by the rise in fuel consumption and gas emissions and also some of the important factors including adverse weather conditions, preparing the aircraft, fixing of mechanical issue, getting security clearance, etc. Hence, these are the factors which indicates the necessity it has become to predict the delays of airline problems. To carry out the predictive analysis, which includes a range of statistical techniques from machine learning, this studies historical and current data to make predictions about the future delays, taking help of Regression Analysis using regularization technique used in Python.

Evaluation of Recurrent Neural Network and its Variants for Intrusion Detection System (IDS)

2017 ◽  
Vol 8 (3) ◽  
pp. 43-63 ◽  
Author(s):  
R Vinayakumar ◽  
K.P. Soman ◽  
Prabaharan Poornachandran
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Intrusion Detection ◽  
Recurrent Neural Networks ◽  
Machine Learning Algorithms ◽  
Time Range ◽  
Sequence Information ◽  
Time Lags ◽  
Data Set ◽  
Arbitrary Length

This article describes how sequential data modeling is a relevant task in Cybersecurity. Sequences are attributed temporal characteristics either explicitly or implicitly. Recurrent neural networks (RNNs) are a subset of artificial neural networks (ANNs) which have appeared as a powerful, principle approach to learn dynamic temporal behaviors in an arbitrary length of large-scale sequence data. Furthermore, stacked recurrent neural networks (S-RNNs) have the potential to learn complex temporal behaviors quickly, including sparse representations. To leverage this, the authors model network traffic as a time series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with a supervised learning method, using millions of known good and bad network connections. To find out the best architecture, the authors complete a comprehensive review of various RNN architectures with its network parameters and network structures. Ideally, as a test bed, they use the existing benchmark Defense Advanced Research Projects Agency / Knowledge Discovery and Data Mining (DARPA) / (KDD) Cup ‘99' intrusion detection (ID) contest data set to show the efficacy of these various RNN architectures. All the experiments of deep learning architectures are run up to 1000 epochs with a learning rate in the range [0.01-0.5] on a GPU-enabled TensorFlow and experiments of traditional machine learning algorithms are done using Scikit-learn. Experiments of families of RNN architecture achieved a low false positive rate in comparison to the traditional machine learning classifiers. The primary reason is that RNN architectures are able to store information for long-term dependencies over time-lags and to adjust with successive connection sequence information. In addition, the effectiveness of RNN architectures are shown for the UNSW-NB15 data set.

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