Scalable Machine Learning Approach to Classifying Transportation Noise at Two Urban Sites in Greater Boston, Massachusetts

2021 ◽  
Vol 263 (1) ◽  
pp. 4962-4974
Author(s):  
Tiange Wang ◽  
Ruijie Jiang ◽  
YuLun (Elain) Lin ◽  
Kyle Monahan ◽  
Douglas Leaffer ◽  
...  
Keyword(s):  
Urban Communities ◽  
Digital Signal ◽  
Training Data ◽  
Neural Net ◽  
Fine Particulates ◽  
Class Model ◽  
Vehicle Class ◽  
Transportation Noise ◽  
Machine Learning Approach ◽  
Audio Files

The goal of this study was to characterize transportation noise by vehicle class in two urban communities, to inform studies of transport noise and ultra-fine particulates. Data were collected from April to September 2016 (150 days) of continuous recording in each urban community using high-resolution microphones. Training data was created for airplanes, trucks/buses, and train events by manual listening and extraction of audio files. Digital signal processing using STFT and Hanning windowing was performed in MATLAB, creating audio spectrograms with varying frequency: log vs linear frequency scales, and 4K vs 20K max frequency. For each of the four spectrogram sets, a neural net model using PyTorch was trained via a compute cluster. Initial results for a multi-class model provide an accuracy of 85%. Comparison between a selection of frequency scales and expanding to longer time periods is ongoing. Validation with airport transport logs and local bus and train schedules will be presented.

scholarly journals Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

2021 ◽  
Vol 11 (7) ◽  
pp. 885
Author(s):  
Maher Abujelala ◽  
Rohith Karthikeyan ◽  
Oshin Tyagi ◽  
Jing Du ◽  
Ranjana K. Mehta
Keyword(s):  
Machine Learning ◽  
Environmental Conditions ◽  
Brain Activity ◽  
Memory Task ◽  
Classification Problem ◽  
Brain Regions ◽  
Training Data ◽  
Information Encoding ◽  
Machine Learning Approach ◽  
Encoding And Retrieval

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

scholarly journals Using Brain Activity Patterns to Differentiate Real and Virtual Attended Targets during Augmented Reality Scenarios

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 226
Author(s):  
Lisa-Marie Vortmann ◽  
Leonid Schwenke ◽  
Felix Putze
Keyword(s):  
Augmented Reality ◽  
Eye Tracking ◽  
Brain Activity ◽  
Activity Patterns ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Neural Net ◽  
Dependent Manner ◽  
Tracking Data ◽  
Average Accuracy

Augmented reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) and eye tracking data collected in augmented reality scenarios. A shallow convolutional neural net classified 3 second EEG data windows from 20 participants in a person-dependent manner with an average accuracy above 70% if the testing data and training data came from different trials. This accuracy could be significantly increased to 77% using a multimodal late fusion approach that included the recorded eye tracking data. Person-independent EEG classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a brain–computer interface is high enough for it to be treated as a useful input mechanism for augmented reality applications.

scholarly journals The LAILAPS Search Engine: Relevance Ranking in Life Science Databases

2010 ◽  
Vol 7 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Matthias Lange ◽  
Karl Spies ◽  
Joachim Bargsten ◽  
Gregor Haberhauer ◽  
Matthias Klapperstück ◽  
...  
Keyword(s):  
Neural Network ◽  
Search Engine ◽  
Life Science ◽  
Expert Knowledge ◽  
Feature Model ◽  
Training Data ◽  
Relevance Ranking ◽  
User Interactions ◽  
Daily Work ◽  
Machine Learning Approach

SummarySearch engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases.With a set of features, extracted from each database hit in combination with user relevance preferences, a neural network predicts user specific relevance scores. Using expert knowledge as training data for a predefined neural network or using users own relevance training sets, a reliable relevance ranking of database hits has been implemented.In this paper, we present the LAILAPS system, the concepts, benchmarks and use cases. LAILAPS is public available for SWISSPROT data at http://lailaps.ipk-gatersleben.de

scholarly journals Reviewing Sentiment Analysis at the Shallow End

2020 ◽  
Vol 8 (4) ◽  
pp. 47-62
Author(s):  
Francisca Oladipo ◽  
Ogunsanya, F. B ◽  
Musa, A. E. ◽  
Ogbuju, E. E ◽  
Ariwa, E.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Information Exchange ◽  
Training Data ◽  
Data Set ◽  
The Social ◽  
Machine Learning Approach ◽  
Media Space ◽  
Social Media Platforms

The social media space has evolved into a large labyrinth of information exchange platform and due to the growth in the adoption of different social media platforms, there has been an increasing wave of interests in sentiment analysis as a paradigm for the mining and analysis of users’ opinions and sentiments based on their posts. In this paper, we present a review of contextual sentiment analysis on social media entries with a specific focus on Twitter. The sentimental analysis consists of two broad approaches which are machine learning which uses classification techniques to classify text and is further categorized into supervised learning and unsupervised learning; and the lexicon-based approach which uses a dictionary without using any test or training data set, unlike the machine learning approach.  

scholarly journals A Comprehensive Analysis of Deep Neural-Based Cerebral Microbleeds Detection System

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2208
Author(s):  
Maria Anna Ferlin ◽  
Michał Grochowski ◽  
Arkadiusz Kwasigroch ◽  
Agnieszka Mikołajczyk ◽  
Edyta Szurowska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Cerebral Microbleeds ◽  
Training Data ◽  
Learning Approach ◽  
Dimensional Problem ◽  
Reliable System ◽  
Machine Learning Approach

Machine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved, it would streamline the radiologists work. To deal with this complex three-dimensional problem, we propose a machine learning approach based on a 2D Faster RCNN network. We aimed to achieve a reliable system, i.e., with balanced sensitivity and precision. Therefore, we have researched and analysed, among others, impact of the way the training data are provided to the system, their pre-processing, the choice of model and its structure, and also the ways of regularisation. Furthermore, we also carefully analysed the network predictions and proposed an algorithm for its post-processing. The proposed approach enabled for obtaining high precision (89.74%), sensitivity (92.62%), and F1 score (90.84%). The paper presents the main challenges connected with automatic cerebral microbleeds detection, its deep analysis and developed system. The conducted research may significantly contribute to automatic medical diagnosis.

Research on Aircraft Aluminum Alloy Structure Corrosion Fatigue Life Based on Artificial Neural Net Theory

2012 ◽  
Vol 184-185 ◽  
pp. 1159-1162
Author(s):  
Zhi Guo Liu ◽  
Zhi Tao Mu ◽  
Zeng Jie Cai ◽  
Shi Lu Zhang
Keyword(s):  
Fatigue Life ◽  
Corrosion Fatigue ◽  
Development Trend ◽  
Training Data ◽  
Neural Net ◽  
Function Fitting ◽  
Artificial Neural Net ◽  
Result Show ◽  
Artificial Neural ◽  
Model Training

Ther BP neural net model which is a branch of artificial neural net theory was adopted to analyse the problem of aircraft LY12CZ structure corrosion fatigue life considering the effect of stress level and corrosion environment,and Newton interpolation algorithm was used to increase the quantity and enhance the quality of BP net model training data. The application result show that the BP net model can simulate the value and development trend of corrosion fatigue life with higher precision contrast to the traditional method of exponential function fitting.

RTID-08. MACHINE LEARNING TO UNCOVER SIGNATURES OF VULNERABILITY IN GLIOBLASTOMA UMBRELLA SIGNATURE TRIAL (GUST)

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi194-vi194
Author(s):  
Sen Peng ◽  
Matthew Lee ◽  
Nanyun Tang ◽  
Manmeet Ahluwalia ◽  
Ekokobe Fonkem ◽  
...  
Keyword(s):  
Machine Learning ◽  
Specific Treatment ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Class Model ◽  
Mutation Status ◽  
Optimal Thresholds ◽  
Investigational Treatment ◽  
Whole Transcriptome ◽  
Larger Sample

Abstract Glioblastoma is characterized by intra- and inter-tumoral heterogeneity. A glioblastoma umbrella signature trial (GUST) posits multiple investigational treatment arms based on corresponding biomarker signatures. A contingency of an efficient umbrella trial is a suite of orthogonal signatures to classify patients into the likely-most-beneficial arm. Assigning optimal thresholds of vulnerability signatures to classify patients as “most-likely responders” for each specific treatment arm is a crucial task. We utilized semi-supervised machine learning, Entropy-Regularized Logistic Regression, to predict vulnerability classification. By applying semi-supervised algorithms to the TCGA GBM cohort, we were able to transform the samples with the highest certainty of predicted response into a self-labeled dataset and thus augment the training data. In this case, we developed a predictive model with a larger sample size and potential better performance. Our GUST design currently includes four treatment arms for GBM patients: Arsenic Trioxide, Methoxyamine, Selinexor and Pevonedistat. Each treatment arm manifests its own signature developed by the customized machine learning pipelines based on selected gene mutation status and whole transcriptome data. In order to increase the robustness and scalability, we also developed a multi-class/label classification ensemble model that’s capable of predicting a probability of “fitness” of each novel therapeutic agent for each patient. Such a multi-class model would also enable us to rank each arm and provide sequential treatment planning. By expansion to four independent treatment arms within a single umbrella trial, a “mock” stratification of TCGA GBM patients labeled 56% of all cases into at least one “high likelihood of response” arm. Predicted vulnerability using genomic data from preclinical PDX models correctly placed 4 out of 6 models into the “responder” group. Our utilization of multiple vulnerability signatures in a GUST trial demonstrates how a precision medicine model can support an efficient clinical trial for heterogeneous diseases such as GBM. Surgical Therapies

scholarly journals A Machine Learning Approach to Fitting Prescription for Hearing Aids

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 736
Author(s):  
Mondol ◽  
Lee
Keyword(s):  
Hearing Loss ◽  
Hearing Aids ◽  
Real Life ◽  
Training Data ◽  
Prescription Database ◽  
Data Set ◽  
Promising Tool ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Two Phases

A successful Hearing-Aid Fitting (HAF) is more than just selecting an appropriate HearingAid (HA) device for a patient with Hearing Loss (HL). The initial fitting is given by the prescriptionbased on user’s hearing loss; however, it is often necessary for the audiologist to readjust someparameters to satisfy the user demands. Therefore, in this paper, we concentrated on a new applicationof Neural Network (NN) combined with a Transfer Learning (TL) strategy to develop a fittingalgorithm with the prescription database for hearing loss and readjusted gain to minimize the gapbetween fitting satisfaction. As prior information, we generated the data set from two popularhearing-aid fitting software, then fed the training data to our proposed model, and verified theperformance of the architecture. Pondering real life circumstances, where numerous fitting recordsmay not always be accessible, we first investigated the number of minimum fitting records requiredfor possible sufficient training. After that, we evaluated the performance of the proposed algorithmin two phases: (a) NN with refined hyper parameter showed enhanced performance in compareto state-of-the-art DNN approach, and (b) the TL approach boosted the performance of the NNalgorithm in a broad way. Altogether, our model provides a pragmatic and promising tool for HAF.

scholarly journals A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens

2020 ◽  
Vol 6 (39) ◽  
pp. eaba9338 ◽  
Author(s):  
George W. Ashdown ◽  
Michelle Dimon ◽  
Minjie Fan ◽  
Fernando Sánchez-Román Terán ◽  
Kathrin Witmer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanism Of Action ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Cross Resistance ◽  
Learning Approach ◽  
Imaging Data ◽  
Drug Induced ◽  
Effective Prevention ◽  
Machine Learning Approach

Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. These methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human- and machine-labeled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semi-supervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Our approach also reproducibly detects and clusters drug-induced morphological outliers by mechanism of action, demonstrating the potential power of machine learning for accelerating cell-based drug discovery.

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