scholarly journals An experimental study investigating the ability of volunteers to identify squirrel species from tail–hair samples

2014 ◽  
Vol 37 (2) ◽  
pp. 145-147
Author(s):  
L. Shearer ◽  
◽  
R. Bray ◽  
C. Toner ◽  
Keyword(s):  
Training Session ◽  
Accurate Method ◽  
Identification Accuracy ◽  
Sciurus Carolinensis ◽  
Correct Identification ◽  
Grey Squirrel ◽  
Red Squirrels ◽  
Identification Rate ◽  
Hair Samples ◽  
Correct Identification Rate

Hair–tubes, collecting nape hairs, are widely used for establishing the presence of red (Sciurus vulgaris) and grey (Sciurus carolinensis) squirrels. However it is time–consuming and prone to identification errors. An alternative is to collect tail hairs from sticky pads on baited poles. However, there is no evidence concerning identification accuracy of tail hairs. This study reports an experiment in which subjects underwent a short training session before identifying hair samples from four species. There was a 96.5% correct identification rate for grey squirrel hairs, and 77.5% for red squirrels, which suggests that tail hairs collection may provide a quick, easy and accurate method of identification for both species.

Fuel Identification Based on the Least Squares Support Vector Machines

2011 ◽  
Vol 317-319 ◽  
pp. 1237-1240 ◽  
Author(s):  
Yao Song Huang ◽  
Shi Liu ◽  
Jie Li ◽  
Lei Jia ◽  
Zhi Hong Li
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Power Plants ◽  
Detection System ◽  
Support Vector ◽  
Correct Identification ◽  
Identification Rate ◽  
Vector Machines ◽  
Flame Detection ◽  
Correct Identification Rate

The identification of the fuel types plays an important role in ensuring the safety and economics of the power plants. In order to obtain the flame signal in the process of combustion, a flame detection system is designed and a laboratorial platform is constructed. This paper extracts the signal parameters—the mean, the peak-peak value, the flicker frequency, and the flicker intensity —and takes them as the characteristic quantities of the flame signal. Based on the least squares support vector machines (LSSVM), an efficient method of identifying the flame types is developed. The result of the identification is more ideal, with the correct identification rate up to 100%. This shows that the method combined the four characteristic quantities with the LSSVM can obtain a good result in the identification of the fuel types.

Authentication of Whole and Ground Coffee Beans by near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 2 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Gerard Downey ◽  
Jerôme Boussion ◽  
Dominique Beauchêne
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Correct Identification ◽  
Correct Classification Rate ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Identification Rate ◽  
Coffee Beans ◽  
Correct Identification Rate ◽  
Nir Reflectance Spectroscopy

The potential of NIR reflectance spectroscopy for discriminating between pure Arabica and pure Robusta coffees and blends of these two was investigated. Studies were performed on whole and ground beans using a factorial discriminant procedure. For whole beans, in the absence of blended samples, a correct classification rate of 96.2% was achieved. Inclusion of blended samples reduced this figure to between 82.9 and 87.6%. In the case of ground samples, including blends, a correct identification rate of 83.02% was achieved. The molecular basis for discrimination is discussed.

scholarly journals Natural strongholds for red squirrel conservation in Scotland

2021 ◽  
Vol 43 ◽  
pp. 93-108
Author(s):  
Andrew Slade ◽  
Andy White ◽  
Kenny Kortland ◽  
Peter W. W. Lurz
Keyword(s):  
Management Practice ◽  
Management Policy ◽  
Sciurus Carolinensis ◽  
Red Squirrel ◽  
Grey Squirrel ◽  
Case Scenario ◽  
Red Squirrels ◽  
Worst Case ◽  
Standard Management ◽  
Grey Squirrels

The Eurasian Red Squirrel (Sciurus vulgaris) is under threat from the invasive North American eastern Grey Squirrel (Sciurus carolinensis) with 80% of the remaining red squirrel populations in the British Isles found in Scotland. In this study we develop a spatially explicit mathematical model of the red and grey squirrel system and use it to assess the population viability of red squirrels across Scotland. In particular, we aim to identify existing forests – natural strongholds for red squirrels – that can successfully support red squirrels under UK Forestry Standard management and protect them from potential disease-mediated competition from grey squirrels. Our model results indicate that if current levels of grey squirrel control, which restrict or reduce the distribution of grey squirrels, are continued then there will be large expanses of forests in northern Scotland that support viable red squirrel populations. Model results that represent (hypothetical) scenarios where grey squirrel control no longer occurred indicated that grey squirrel range expansion and the process of red squirrel replacement would be slow. Model results for an assumed worst-case scenario where grey squirrels have expanded to all regions in Scotland identified forest regions – denoted natural strongholds – that could currently support red squirrels under UK Forestry Standard management practice. The results will be used to inform forest management policy and support a strategic review of red squirrel management by land management agencies and other stakeholders.

The Design of a Car License Plate Identification System Based on AdaBoost Algorithm

2011 ◽  
Vol 181-182 ◽  
pp. 588-593
Author(s):  
Hong Wang ◽  
Xian Li ◽  
Shuang Liu
Keyword(s):  
False Positive Rate ◽  
Identification System ◽  
License Plate ◽  
Correct Identification ◽  
Integral Image ◽  
Large Database ◽  
Identification Rate ◽  
Adaboost Algorithm ◽  
Positive Rate ◽  
Correct Identification Rate

Design and implement a car license plate identification system with the applications of Viola and Jones algorithm. This algorithm which is based on the AdaBoost method is trained and optimized for the best performance using large database of car license plate images. The final license plate identification system obtained a cascade of classifiers consisting of 8 stages with 1310 Haar-like features. Once the license plates have sufficient visibility and there are no other objects similar to the plate in images, this system operates perfectly and shows high correct identification rate with low false positive rate. And as integral image allows the Haar-like features to be calculated very fast, the system also finished the identification rapidly.

scholarly journals Determination of Geographical Origin of Wuchang Rice with the Geographical Indicator by Multielement Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Lili Qian ◽  
Feng Zuo ◽  
Hongyan Liu ◽  
Caidong Zhang ◽  
Xiaoxing Chi ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Geographical Origin ◽  
Soil Samples ◽  
Multielement Analysis ◽  
Correct Identification ◽  
Linear Discriminant ◽  
Identification Rate ◽  
Correct Identification Rate ◽  
Rice Samples

The study aims to investigate whether the multielement analysis result can be used as a fingerprint to identify the geographical origin of Wuchang rice. The element contents of rice and soil samples from three regions in China (Wuchang, Qiqihar, and Jiamusi) were analyzed. The concentrations of 16 elements (Na, Mg, Al, K, Ca, V, Mn, Fe, Co, Cu, Zn, As, Rb, Sr, Cd, and Pb) in 194 rice samples and 112 soil samples from the harvest season in 2013 and 2014 were determined. The analysis of variance and linear discriminant analysis were performed to analyze the variation among regions and rice genotypes and classify the geographical origins of rice. Only the element of Cu showed significant differences among different genotypes. In the discriminant analysis, the overall correct identification rates of the rice samples obtained in 2013 and 2014 were, respectively, 96.6% and 89.6% and the overall correct identification rate for Wuchang rice reached 100%.

Fusion-Based Hand Geometry Recognition Using Dempster–Shafer Theory

2015 ◽  
Vol 29 (05) ◽  
pp. 1556005 ◽  
Author(s):  
Asish Bera ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri
Keyword(s):  
Final Decision ◽  
Correct Identification ◽  
Maximum Probability ◽  
Identification Rate ◽  
Dempster Shafer Theory ◽  
Correct Identification Rate ◽  
A New Technique ◽  
Hand Geometry ◽  
Shafer Theory ◽  
Theory Of Evidence

This paper presents a new technique for user identification and recognition based on the fusion of hand geometric features of both hands without any pose restrictions. All the features are extracted from normalized left and right hand images. Fusion is applied at feature and also at decision level. Two probability-based algorithms are proposed for classification. The first algorithm computes the maximum probability for nearest three neighbors. The second algorithm determines the maximum probability of the number of matched features with respect to a thresholding on distances. Based on these two highest probabilities initial decisions are made. The final decision is considered according to the highest probability as calculated by the Dempster–Shafer theory of evidence. Depending on the various combinations of the initial decisions, three schemes are experimented with 201 subjects for identification and verification. The correct identification rate is found to be 99.5%, and the false acceptance rate (FAR) of 0.625% has been found during verification.

Clinical photographs as teaching aids: how reliable are they?

1995 ◽  
Vol 109 (8) ◽  
pp. 741-743 ◽  
Author(s):  
Andrew McCombe ◽  
Peter Williamson ◽  
Michael Hawke
Keyword(s):  
Teaching Tool ◽  
Correct Identification ◽  
Median Score ◽  
Identification Rate ◽  
Teaching Aids ◽  
Chi Squared ◽  
Correct Identification Rate ◽  
Natural Inclination ◽  
The Uk ◽  
Huge Variation

AbstractA study to investigate the value and reliability of clinical photographs as teaching aids was undertaken. Twenty colour photographs were taken using the StarMed video-otoscopic system. The pictures, which were a mixture of normal and abnormal ears, were shown to 21 experienced otolaryngologists from the UK and Canada. These clinicians were asked to identify the abnormality if any.The median score for correctly identified pictures was 15 (range 12–18). This score was identical for both the UK and Canadian subgroups. Although the abnormalities were consistently well recognized with an average correct identification rate of 90 per cent (range 67–100 per cent), the ‘normals’ were recognized significantly less well at only 41 per cent (range 5–71 per cent) (chi-squared = 110.6; 1 df; p<0.001).This result is probably due to failure of the camera to capture the huge variation and subtleties in the range of normal, and the clinicians' natural inclination to identify pathology, when in doubt. We would conclude that as long as this failing is recognized, clinical photographs, and specifically those from the video-otoscope, represent a useful and reliable teaching tool.

Application of Deep Learning to Community-Science-Based Mosquito Monitoring and Detection of Novel Species

2021 ◽  
Author(s):  
Ali Khalighifar ◽  
Daniel Jiménez-García ◽  
Lindsay P Campbell ◽  
Koffi Mensah Ahadji-Dabla ◽  
Fred Aboagye-Antwi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mosquito Species ◽  
Identification System ◽  
Correct Identification ◽  
Reference Library ◽  
Infected Female ◽  
Identification Rate ◽  
Correct Identification Rate ◽  
Community Science ◽  
Health Burdens

Abstract Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.

The Italian External Quality Assessment (EQA) program on urinary sediment: results of the period 2012–2015

2015 ◽  
Vol 53 (s2) ◽  
Author(s):  
Sandra Secchiero ◽  
Giovanni B. Fogazzi ◽  
Fabio Manoni ◽  
MariaGrazia Epifani ◽  
Giuseppe Garigali ◽  
...  
Keyword(s):  
Clinical History ◽  
High Rate ◽  
Correct Identification ◽  
Laboratory Findings ◽  
Urinary Sediment ◽  
Identification Rate ◽  
Correct Identification Rate ◽  
Clinical Meaning ◽  
Micro Organisms ◽  
Clinical Cases

AbstractManual microscopy still represents the gold standard for urinary sediment (US) examination. We report the results obtained in the period 2012–2015 by the EQA Italian program on US, which today involves about 260 laboratories.The program includes four surveys per year. In two surveys, participants are asked to supply identification and clinical association of US particles. In two other surveys, they are asked to supply the diagnosis of clinical cases, presented with images, some key laboratory findings and a short clinical history. Sixty-six images of US particles (21 cells, 2 lipids, 21 casts, 10 crystals, 3 microorganisms, 15 contaminants) and seven clinical cases were presented.The correct identification rate for each category of particles, in decreasing order, was: micro-organisms (mean±SD: 92.4%±4.5%), lipids (92.0%±1.8%), casts (82.8%±8.8%), crystals (79.4%±29.8%), cells (77.3%±13.5%), and contaminants (70.9%±22.2%). For 13 particles, a correct clinical association was indicated by 91.5%±11.7% of participants, while it was 52.7% for particles associated with urinary tract infection. For clinical cases, due to a high rate of particles misidentification, only 44.3%±10.1% of participants achieved access to clinical diagnosis, which was then correctly indicated by 92.5%±5.3% of them.The results of the EQA program confirm that, while some US particles are well known in terms of identification, clinical association and clinical meaning, others particles still are not, and this represents an important reason to encourage EQA programs on US.

The Algorithm of Digital Modulation Recognition Based on Stochastic

2013 ◽  
Vol 333-335 ◽  
pp. 531-534
Author(s):  
Mei Ying Xiong ◽  
Shui Ying Xiong
Keyword(s):  
Stochastic Resonance ◽  
Support Vector ◽  
Correct Identification ◽  
Modulation Recognition ◽  
Digital Modulation ◽  
Identification Rate ◽  
Signal Characteristics ◽  
Correct Identification Rate ◽  
Good Identification ◽  
High Order Cumulants

The traditional digital modulation recognition methods have high requirements for the SNR. This paper presented a good identification method. The quadratic stochastic resonance for signal processing cascade was proposed, then the signal characteristics of high order cumulants were extracted and the support vector machine was used for classifier, simulation results showed that the correct identification rate was up to 88% or more when the SNR was low as-8dB. The bistable stochastic resonance treatment can effectively improve the SNR.

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