scholarly journals Acoustic differentiation and classification of wild belugas and narwhals using echolocation clicks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marie J. Zahn ◽  
Shannon Rankin ◽  
Jennifer L. K. McCullough ◽  
Jens C. Koblitz ◽  
Frederick Archer ◽  
...  
Keyword(s):  
Strong Support ◽  
Predictive Performance ◽  
Single Species ◽  
Classification Model ◽  
Delphinapterus Leucas ◽  
Classification Rate ◽  
Frequency Spectra ◽  
Significant Difference ◽  
Spectral Peaks ◽  
Passive Acoustic

AbstractBelugas (Delphinapterus leucas) and narwhals (Monodon monoceros) are highly social Arctic toothed whales with large vocal repertoires and similar acoustic profiles. Passive Acoustic Monitoring (PAM) that uses multiple hydrophones over large spatiotemporal scales has been a primary method to study their populations, particularly in response to rapid climate change and increasing underwater noise. This study marks the first acoustic comparison between wild belugas and narwhals from the same location and reveals that they can be acoustically differentiated and classified solely by echolocation clicks. Acoustic recordings were made in the pack ice of Baffin Bay, West Greenland, during 2013. Multivariate analyses and Random Forests classification models were applied to eighty-one single-species acoustic events comprised of numerous echolocation clicks. Results demonstrate a significant difference between species’ acoustic parameters where beluga echolocation was distinguished by higher frequency content, evidenced by higher peak frequencies, center frequencies, and frequency minimums and maximums. Spectral peaks, troughs, and center frequencies for beluga clicks were generally > 60 kHz and narwhal clicks < 60 kHz with overlap between 40–60 kHz. Classification model predictive performance was strong with an overall correct classification rate of 97.5% for the best model. The most important predictors for species assignment were defined by peaks and notches in frequency spectra. Our results provide strong support for the use of echolocation in PAM efforts to differentiate belugas and narwhals acoustically.

A DECISION TREE-BASED CLASSIFICATION APPROACH TO RULE EXTRACTION FOR SECURITY ANALYSIS

2006 ◽  
Vol 05 (01) ◽  
pp. 227-240 ◽  
Author(s):  
N. REN ◽  
M. ZARGHAM ◽  
S. RAHIMI
Keyword(s):  
Decision Tree ◽  
High Performance ◽  
Security Analysis ◽  
Predictive Performance ◽  
Classification Model ◽  
Stock Selection ◽  
Selection Rules ◽  
Stock Portfolios ◽  
Decision Tree Classification ◽  
C4.5 Decision Tree

Stock selection rules are extensively utilized as the guideline to construct high performance stock portfolios. However, the predictive performance of the rules developed by some economic experts in the past has decreased dramatically for the current stock market. In this paper, C4.5 decision tree classification method was adopted to construct a model for stock prediction based on the fundamental stock data, from which a set of stock selection rules was derived. The experimental results showed that the generated rules have exceptional predictive performance. Moreover, it also demonstrated that the C4.5 decision tree classification model can work efficiently on the high noise stock data domain.

scholarly journals SOIL PROTECTION BY USED OF COVER CROPS CONSORTIA AND SINGLE CULTIVATION

2020 ◽  
Vol 5 (7) ◽  
Author(s):  
Daiana Jungbluth ◽  
Ana Regina Dahlem Ziech ◽  
Camila Roberta Pereira ◽  
Márcia Cristina Dos Santos ◽  
Patrick Machado
Keyword(s):  
Cover Crops ◽  
Soil Cover ◽  
Block Design ◽  
Single Species ◽  
Coverage Rate ◽  
Soil Protection ◽  
Rotation System ◽  
Randomized Block Design ◽  
Significant Difference ◽  
Technological University

The no-till system has been growing over the years and for this system to be successful, it is essential to maintain permanent vegetation cover over the soil, an adequate crop rotation system with minimal overturning. A strategy for soil protection is to introduce species of cover crops in winter under single or intercropping. The objective was to evaluate the rate of soil cover by intercropping between black oats (Avena strigosa L.) and forage turnip (Raphanus sativus L.) at different sowing densities, as well as the isolated species in terms of soil protection under no-tillage. The study was conducted at the Federal Technological University of Paraná (UTFPR), campus Santa Helena, with a randomized block design, with five treatments and three repetitions. The treatments were: 100% black oats (BO); 100% forage turnip (FT); 75% BO + 25% FT; 50% BO + 50% FT and 25% BO + 75% FT. The cover crops were sown in May 2019. The percentage of soil cover from 21 to 91 days after sowing (DAS) was evaluated using the photographic method, with weekly collection of images in an area delimited by a metallic frame (25 m²), positioned on the ground at two fixed points per plot. The coverage rate quantification was estimated by overlaying a grid with 100 points of intersection over each image. The rate of soil cover by consortia and single crops did not show a statistically significant difference. To 49 days, consortia had coverage equal to or greater than 70%, while for single species, this percentage was reached at 56 DAS and 70 DAS, for BO and FT, respectively. All treatments showed high potential for soil protection and coverage rate from 70 DAS.

Acoustic detections of Arctic marine mammals near Ulukhaktok, Northwest Territories, Canada

2019 ◽  
Vol 97 (1) ◽  
pp. 72-80 ◽  
Author(s):  
W.D. Halliday ◽  
M.K. Pine ◽  
S.J. Insley ◽  
R.N. Soares ◽  
P. Kortsalo ◽  
...  
Keyword(s):  
Northwest Territories ◽  
Marine Mammals ◽  
Bowhead Whale ◽  
The Arctic ◽  
Delphinapterus Leucas ◽  
Anthropogenic Activity ◽  
Marine Animals ◽  
Vocal Activity ◽  
Beluga Whale Delphinapterus Leucas ◽  
Passive Acoustic

The Arctic marine environment is changing rapidly through a combination of sea ice loss and increased anthropogenic activity. Given these changes can affect marine animals in a variety of ways, understanding the spatial and temporal distributions of Arctic marine animals is imperative. We use passive acoustic monitoring to examine the presence of marine mammals near Ulukhaktok, Northwest Territories, Canada, from October 2016 to April 2017. We documented bowhead whale (Balaena mysticetus Linnaeus, 1758) and beluga whale (Delphinapterus leucas (Pallas, 1776)) vocalizations later into the autumn than expected, and we recorded bowhead whales in early April. We recorded ringed seal (Pusa hispida (Schreber, 1775)) vocalizations throughout our deployment, with higher vocal activity than in other studies and with peak vocal activity in January. We recorded bearded seals (Erignathus barbatus (Erxleben, 1777)) throughout the deployment, with peak vocal activity in February. We recorded lower bearded seal vocal activity than other studies, and almost no vocal activity near the beginning of the spring breeding season. Both seal species vocalized more when ice concentration was high. These patterns in vocal activity document the presence of each species at this site over autumn and winter and are a useful comparison for future monitoring.

Study on the syndrome characteristics and classification model of non-small cell lung cancer based on tongue and pulse data (Preprint)

2021 ◽  
Author(s):  
Yulin Shi ◽  
Jiayi Liu ◽  
Xiaojuan Hu ◽  
Liping Tu ◽  
Ji Cui ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Deficiency Syndrome ◽  
Small Cell ◽  
Classification Model ◽  
Syndrome Differentiation ◽  
Small Cell Lung ◽  
Yin Deficiency ◽  
Significant Difference

BACKGROUND Lung cancer is a common malignant tumor that affects people's health seriously. Traditional Chinese medicine (TCM) is one of the effective methods for the treatment of advanced lung cancer, accurate TCM syndrome differentiation is essential to treatment. When the symptoms are not obvious, the traditional symptom-based syndrome differentiation cannot be carried out. There is a close relationship between syndrom and index of western medicine, the combination of micro index and macro symptom can assist syndrome differentiation effectively. OBJECTIVE To explore the characteristics of tongue and pulse data of non-small cell lung cancer (NSCLC) with Qi deficiency syndrome and Yin deficiency syndrome, and to establish syndromes classification model based on tongue and pulse data by using machine learning method, and to evaluate the feasibility of the model. METHODS Tongue and pulse data of non-small cell lung cancer (NSCLC) patients with Qi deficiency syndrome (n=163), patients with Yin deficiency syndrome (n=174) and healthy controls (n=185) were collected by using intelligent Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument, respectively. The characteristics of tongue and pulse data were analyzed, the correlation analysis was also made on tongue and pulse data. And four machine learning methods, namely Random Forest, Logistic Regression, Support Vector Machine and Neural Network, were used to establish the classification models based on symptoms, tongue & pulse data, and symptoms & tongue & pulse data, respectively. RESULTS Significant difference indexes of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll and the tongue coating texture indexes including TC-Con, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indexes of pulse diagnosis were t4 and t5. The classification performance of each model based on different data sets was as follows: model of tongue & pulse data <model of symptom < model of symptom & tongue & pulse data. The Neural Network model had a better classification performance for the symptom & tongue & pulse data, with an area under the ROC curve and accuracy rate were 0.9401 and 0.8806. CONCLUSIONS This study explored the characteristics of tongue and pulse data of NSCLC with Qi deficiency syndrome and Yin deficiency syndrome, and established syndromes classification model. It was feasible to use tongue and pulse data as one of the objective diagnostic indexes in Qi deficiency syndrome and Yin deficiency syndrome of NSCLC. CLINICALTRIAL Trial registration number: ChiCTR1900026008; ChiCTR-IOR-15006502 Date of registration: Jun. 04th, 2015 URL of trial registry record: http://www.chictr.org.cn/showprojen.aspx?proj=11119; http://www.chictr.org.cn/edit.aspx?pid=38828&htm=4 (This is a retrospective registration)

Effects of decoupling on spectra of seismic waves

1962 ◽  
Vol 52 (1) ◽  
pp. 123-131
Author(s):  
D. E. Willis ◽  
James T. Wilson
Keyword(s):  
Seismic Waves ◽  
High Explosive ◽  
Atomic Energy Commission ◽  
University Of Michigan ◽  
Frequency Spectra ◽  
Spherical Cavities ◽  
Significant Difference ◽  
The University ◽  
Relationship Of ◽  
The Relationship

Abstract A series of controlled high explosive shots were conducted by the Atomic Energy Commission in a salt mine near Winnfield, Louisiana, to investigate seismic decoupling theories. Two recording stations were used by the University of Michigan at various distances between 1.1 and 14.7 kilometers for a majority of these shots. Frequency analyses of the magnetic tape recordings were made and the results are presented showing the relationship of the frequency spectra as a function of charge size, distance from the source, and coupled vs decoupled shots. The smaller decoupled shots detonated in the large spherical cavities were observed to have somewhat higher predominate frequencies than the equivalent size coupled shots. A change in cavity size produced no significant difference in the shape of the spectra of the large decoupled shots.

Using the Kullback-Leibler Divergence and Kolmogorov-Smirnov Test to Select Input Sizes to the Fault Diagnosis Problem Based on a CNN Model

2021 ◽  
Vol 18 (2) ◽  
pp. 16-26
Author(s):  
Rodrigo Paula Monteiro ◽  
◽  
Carmelo Jose Albanez Bastos-Filho ◽  
Mariela Cerrada ◽  
Diego Cabrera ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Classification Model ◽  
Data Sets ◽  
Frequency Spectra ◽  
Signal Representations ◽  
Input Size ◽  
Leibler Divergence ◽  
Classifier Performance ◽  
Kolmogorov Smirnov ◽  
Smirnov Test

Choosing a suitable size for signal representations, e.g., frequency spectra, in a given machine learning problem is not a trivial task. It may strongly affect the performance of the trained models. Many solutions have been proposed to solve this problem. Most of them rely on designing an optimized input or selecting the most suitable input according to an exhaustive search. In this work, we used the Kullback-Leibler Divergence and the Kolmogorov-Smirnov Test to measure the dissimilarity among signal representations belonging to equal and different classes, i.e., we measured the intraclass and interclass dissimilarities. Moreover, we analyzed how this information relates to the classifier performance. The results suggested that both the interclass and intraclass dissimilarities were related to the model accuracy since they indicate how easy a model can learn discriminative information from the input data. The highest ratios between the average interclass and intraclass dissimilarities were related to the most accurate classifiers. We can use this information to select a suitable input size to train the classification model. The approach was tested on two data sets related to the fault diagnosis of reciprocating compressors.

Prognosticating Traumatic Brain Injury (TBI): Analysis of CRASH, IMPACT Models and Patient Age in a Cohort of TBI Patients

Neurosurgery ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. E271-E272 ◽  
Author(s):  
Conor Gillespie ◽  
Catherine McMahon
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Survival Rates ◽  
Predictive Performance ◽  
Good Survival ◽  
Post Injury ◽  
Patient Age ◽  
Patient Âgé ◽  
Significant Difference ◽  
Impact Models

Abstract INTRODUCTION Both CRASH and IMPACT models have been developed in recent years to predict the outcome of Traumatic Brain Injury (TBI). However, there is no clear evidence as to how these models perform in a modern cohort of UK-patients. There is also predictive uncertainty with regards to survival rates and functional outcome in elderly (>65 yr) patients. METHODS Patients referred to a tertiary neuroscience center from December 2014 to January 2016 with a suspected TBI were retrospectively examined. For each model, the predicted survival and overall outcome were compared to the actual outcome on admission and at 6 mo post injury, stratified by patient age (>65 yr vs ≤65 yr). RESULTS A total of 161 patients met the initial criteria; mean age 65 yr (SD = 21) and 110 male. Both CRASH and IMPACT correctly predicted 6-mo mortality rates and functional outcomes in most patients (range 61.7%-82.4%), with better predictive performance for patients not accepted to the center (range 84%-98%). There was no significant difference in the initial survival of elderly patients if accepted (78% [95% CI 50.6-104.0] vs 81% [95% CI 67.8-94.8] but were lower for those not accepted (24% [95% CI 4.2-43.7] vs 76% [95% CI 63.5-88.5], P = .027). CONCLUSION Patients >65 yr admitted to tertiary neuroscience center had good survival rates on admission and at 6 mo. The lesser ability of CRASH and IMPACT models to predict poorer outcomes when accepted suggests that acceptance to specialist centers may be able to improve outcome and suggests more optimistic treatment and acceptance of appropriate over 65 yr should be considered.

scholarly journals THE EFFECT OF CURCUMA XANTHORRHIZA ETHANOL EXTRACT ON THE VIABILITY OF STREPTOCOCCUS MUTANS AND AGGREGATIBACTER ACTINOMYCETEMCOMITANS (DENTAL BIOFILM RESEARCH: IN VITRO STUDY)

2017 ◽  
Vol 10 (17) ◽  
pp. 30
Author(s):  
Royan Diana ◽  
Hedijanti Joenoes ◽  
Ariadna A Djais
Keyword(s):  
Streptococcus Mutans ◽  
In Vitro Study ◽  
Ethanol Extract ◽  
Single Species ◽  
Aggregatibacter Actinomycetemcomitans ◽  
Vitro Study ◽  
Dental Biofilm ◽  
Significant Difference ◽  
Curcuma Xanthorrhiza

Objective: This study aimed to compare the effect of Curcuma xanthrorrhiza ethanol extract to the viability of Streptococcus mutans and Aggregatibacter  actinomycetemcomitans using single- and dual-species biofilm at different phases of formation.Methods: Biofilm models were incubated for 4, 12, and 24 hrs, then exposed to the extract at a concentration of 0.525%.Results: The viability of the single-species S. mutans biofilm was low (p<0.05), and no significant difference (p>0.05) was found between singlespeciesA. actinomycetemcomitans and dual-species biofilm.Conclusions: Curcuma xanthorrhiza ethanol extract is more effective for decreasing the viability of single-species S. mutans biofilm.

scholarly journals Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model

2020 ◽  
Vol 12 (4) ◽  
pp. 646 ◽  
Author(s):  
Jamie Barwick ◽  
David William Lamb ◽  
Robin Dobos ◽  
Mitchell Welch ◽  
Derek Schneider ◽  
...  
Keyword(s):  
Visual Observation ◽  
Classification Model ◽  
Moving Window ◽  
State Classification ◽  
Sensor Orientation ◽  
Data Segment ◽  
Axial Acceleration ◽  
Widespread Agreement ◽  
Significant Difference ◽  
Continuous Stream

Behaviour is a useful indicator of an individual animal’s overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a ‘clean state’ moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments.

The role of radiomics in the preoperative diagnosis of renal cell carcinoma with sarcomatoid dedifferentiation (sRCC).

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 621-621
Author(s):  
Chalairat Suk-Ouichai ◽  
Aikaterini Kotrotsou ◽  
Tagwa Idris ◽  
Srishti Abrol ◽  
Eric Umbreit ◽  
...  
Keyword(s):  
Tumor Size ◽  
Clear Cell ◽  
Clinical Care ◽  
External Validation ◽  
Second Order ◽  
Response To Therapy ◽  
Classification Model ◽  
Experienced Radiologist ◽  
Contrast Enhanced Ct ◽  
Significant Difference

621 Background: sRCC is an aggressive renal malignancy, with poor survival and limited response to therapy. Preoperative identification of sRCC would be helpful for counselling patients, and clinical trial enrollment. This study aims at assessing the potential of radiomics to discriminate clear cell sRCC from non-sarcomatoid clear cell RCC (nsRCC). Methods: The study included 49 sRCC and 41 nsRCC patients treated with surgery between 2007-2016, who had contrast-enhanced CT available. An experienced radiologist delineated the entire tumor using 3D Slicer ( http://www.slicer.org ). The extracted 3D region of interest was imported in our in-house radiomic pipeline. A total of 310 features (10 histogram-based and 300 second-order features) were calculated. Second-order radiomic features were calculated using the Grey Level Cooccurrence Matrix (GLCM) and 20 Haralick features were obtained from the GLCM. To account for directionality, the mean, variance and range of the features across different directions were calculated. Finally, different number of gray levels were also considered in the analysis (N = 8, 16, 32, 64, 256). Core features were obtained using a feature selection based on Least Absolute Shrinkage and Selection Operator (LASSO). Selected features were used to build a classification model for prediction of sRCC versus nsRCC (XGboost). To evaluate the robustness of the estimates, Leave One Out Cross-Validation (LOOCV) was conducted on the patient set. Results: Overall, median tumor size was 10.0 cm and most patients had pT3a (68%). There was no significant difference of age, gender, race, tumor size and stages between sRCC and nsRCC cohorts. The prediction of sRCC using LOOCV was significant with p-value < 0.0001. Area under the curve, sensitivity, and specificity for identification of sRCC were 96.8%, 92.6% and 93.8% respectively. Conclusions: This study demonstrates that CT radiomic features can accurately discriminate between sRCC and nsRCC. The proposed tool has the potential to advance clinical management strategies. In addition to being noninvasive, this methodology can be applied to scans obtained during routine clinical care. Further external validation is warranted.

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