Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Application of discriminant function analysis and change-point problem in dating volcanic ashes

The application of Discriminant function analysis (DFA) is not a new idea in the study of tephrochrology. In this paper, DFA is applied to compositional datasets of two different types of tephras from Mountain Ruapehu in New Zealand and Mountain Rainier in USA. The canonical variables from the analysis are further investigated with a statistical methodology of change-point problems in order to gain a better understanding of the change in compositional pattern over time. Finally, a special case of segmented regression has been proposed to model both the time of change and the change in pattern. This model can be used to estimate the age for the unknown tephras using Bayesian statistical calibration

Intake, the discriminant function : a report on the national study on social services for children and their families /

Issued Apr. 1980.

Exact Discriminant Function Design Using Some Optimization Techniques

Some aspects of design of the discriminant functions that in the best way separate points of predefined final sets are considered. The concept is introduced of the nested discriminant functions which allow to separate correctly points of any of the final sets. It is proposed to apply some methods of non-smooth optimization to solve arising extremal problems efficiently.

On classical and other methods of discriminant analysis and estimation of log-odds

For two multinormal populations with equal covariance matrices the likelihood ratio discriminant function, an alternative allocation rule to the sample linear discriminant function when n1 ≠ n2 ,is studied analytically. With the assumption of a known covariance matrix its distribution is derived and the expectation of its actual and apparent error rates evaluated and compared with those of the sample linear discriminant function. This comparison indicates that the likelihood ratio allocation rule is robust to unequal sample sizes. The quadratic discriminant function is studied, its distribution reviewed and evaluation of its probabilities of misclassification discussed. For known covariance matrices the distribution of the sample quadratic discriminant function is derived. When the known covariance matrices are proportional exact expressions for the expectation of its actual and apparent error rates are obtained and evaluated. The effectiveness of the sample linear discriminant function for this case is also considered. Estimation of true log-odds for two multinormal populations with equal or unequal covariance matrices is studied. The estimative, Bayesian predictive and a kernel method are compared by evaluating their biases and mean square errors. Some algebraic expressions for these quantities are derived. With equal covariance matrices the predictive method is preferable. Where it derives this superiority is investigated by considering its performance for various levels of fixed true log-odds. It is also shown that the predictive method is sensitive to n1 ≠ n2. For unequal but proportional covariance matrices the unbiased estimative method is preferred. Product Normal kernel density estimates are used to give a kernel estimator of true log-odds. The effect of correlation in the variables with product kernels is considered. With equal covariance matrices the kernel and parametric estimators are compared by simulation. For moderately correlated variables and large dimension sizes the product kernel method is a good estimator of true log-odds.

A function to predict the risk for death in the neonatal sepsis

This was a prospective study of 43 septic neonates at the NICU of the School of Medicine of Botucatu, São Paulo State University. Clinical and laboratory data of sepsis were analyzed based on outcome divided into two groups, survival and death. We calculated the discriminatory power of the relevant variables for the diagnosis of sepsis in each group, and using software for Discriminant Analysis, a function was proposed. There were 43 septic cases with 31 survivals and 12 deaths. The variables that had the highest discriminatory power were: n(o) of compromised systems, the SNAP, FiO2, and (A-a)O2. The study of these and others variables, such as birth weight, n(o) of risk factors, and pH using a Linear Discriminant Function(LDF) allowed us to identify the high-risk neonates for death with a low error rate (8.33%). The LDF was: F = 0.00043 (birth weight) + 0.30367 (n(o) of risk factors) - 0.1171 (n(o) of compromised systems) + 0.33223 (SNAP) + 2.27972 (pH) - 14.96511 (FiO2) + 0.01814 ((A-a)O2). If F > 22.77 there was high risk of death. This study suggests that the LDF at the onset of sepsis is useful for the early identification of the high-risk neonates that need special clinical and laboratory surveillance.

Statnote 29:discriminant analysis

Discriminant analysis (also known as discriminant function analysis or multiple discriminant analysis) is a multivariate statistical method of testing the degree to which two or more populations may overlap with each other. It was devised independently by several statisticians including Fisher, Mahalanobis, and Hotelling ). The technique has several possible applications in Microbiology. First, in a clinical microbiological setting, if two different infectious diseases were defined by a number of clinical and pathological variables, it may be useful to decide which measurements were the most effective at distinguishing between the two diseases. Second, in an environmental microbiological setting, the technique could be used to study the relationships between different populations, e.g., to what extent do the properties of soils in which the bacterium Azotobacter is found differ from those in which it is absent? Third, the method can be used as a multivariate ‘t’ test , i.e., given a number of related measurements on two groups, the analysis can provide a single test of the hypothesis that the two populations have the same means for all the variables studied. This statnote describes one of the most popular applications of discriminant analysis in identifying the descriptive variables that can distinguish between two populations.

Factors associated with the wish to hasten death : a study of patients with terminal illness

Background. There is considerable debate regarding the clinical issues surrounding the wish to hasten death (WTHD) in the terminally ill. The clinical factors contributing to the WTHD need further investigation among the terminally ill in order to enhance understanding of the clinical assessment and treatment needs that underlie this problem. A more detailed understanding may assist with the development of appropriate therapeutic interventions. Method. A sample of terminally ill cancer patients (N=256) recruited from an in-patient hospice unit, home palliative care service and a general hospital palliative care consulting service from Brisbane Australia between 1998–2001 completed a questionnaire assessing psychological (depression and anxiety), social (family relationship, social support, level of burden on others) and the impact of physical symptoms. The association between these factors and the WTHD was investigated. Results. A high WTHD was reported by 14% of patients. A discriminant function analysis revealed that the following variables were associated with a high WTHD (P<0·001): higher levels of depressive symptoms, being admitted to an in-patient hospice setting, a greater perception of being a burden on others, lower family cohesion, lower levels of social support, higher levels of anxiety and greater impact of physical symptoms. Conclusions. Psychological and social factors are related to a WTHD among terminally ill cancer patients. Greater attention needs to be paid to the assessment of psychological and social issues in order to provide appropriate therapeutic interventions for terminally ill patients.

Discriminating between primary school students with high and low self-esteem.

From an initial sample of 747 primary school students, the top 16 percent (n =116) with high self-esteem (HSE) and the bottom 15 percent (n = I1 I) with low selfesteem (LSE) were se/eeted. These two groups were then compared on personal and classroom variables. Significant differences were found for all personal (self-talk, selfconcepts) and classroom (teacher feedback, praise, teacher-student relationship, and classroom environment) variables. Students with HSE scored more highly on all variables. Discriminant Function Analysis (DFA) was then used to determine which variables discriminated between these two groups of students. Learner self-concept, positive and negative self-talk, classroom environment, and effort feedback were the best discriminators of students with high and low self-esteem. Implications for educational psychologists and teachers are discussed.

Intolerance of uncertainty and meta-worry : relative importance in discriminating GAD

This study explored whether intolerance of uncertainty and/or meta-worry discriminate between non-clinical individuals and those diagnosed with generalised anxiety disorder (GAD group). The participants were 107 GAD clients and 91 university students. The students were divided into two groups (high and low GAD symptom groups). A multivariate analysis of covariance (MANCOVA) adjusting for age indicated that intolerance of uncertainty distinguished between the low GAD symptom group and the high GAD symptom group, and between the low GAD symptom group and the GAD group. Meta-worry distinguished all three groups. A discriminant function including intolerance of uncertainty and meta-worry classified 94.4% of the GAD group and 97.9% of the low GAD symptom group. Only 6.8% of the high GAD symptom group was classified correctly, 77.3% of the high GAD symptom group was classified as GAD. Findings indicated that intolerance of uncertainty and meta-worry may assist with the diagnosis and treatment of GAD.

Substance-related expectancies among men who have sex with men : development of psychometric tools to predict unprotected sexual activity

Significant research has demonstrated direct and indirect associations between substance use and sexual behaviour. Substance use is related to sexual risk-taking and HIV seroconversion among some substance-using MSM. It remains unclear what factors mediate or underlie this relationship, and which substances are associated with greater harm. Substance-related expectancies are hypothesised as potential mechanisms. A conceptual model based on social-cognitive theory was tested, which explores the role of demographic factors, substance use, substance-related expectancies and novelty-seeking personality characteristics in predicting unprotected anal intercourse (UAI) while under the influence, across four commonly used substance types. Phase 1, a qualitative study (N = 20), explored how MSM perceive the effects of substance use on their thoughts, feelings and behaviours, including sexual behaviours. Information was attained through discussion and interviews, resulting in the establishment of key themes. Results indicated MSM experience a wide range of reinforcing aspects associated with substance use. General and specific effects were evident across substance types, and were associated with sexual behaviour and sexual risk-taking. Phase 2 consisted of developing a comprehensive profile of substance-related expectancies for MSM (SEP-MSM) regarding alcohol, cannabis, amyl nitrite and stimulants that possessed sound psychometric properties and was appropriate for use among this group. A cross-sectional questionnaire with 249 participants recruited through gay community networks was used to validate these measures, and involved online data collection, participants rating expectancy items and subsequent factor analysis. Results indicated expectancies can be reliably assessed, and predicted substance use patterns. Phase 3 examined demographic factors, substance use, substance-related expectancies, and novelty-seeking traits among another community sample of MSM (N = 277) throughout Australia, in predicting UAI while under the influence. Using a cross-sectional design, participants were recruited through gay community networks and completed online questionnaires. The SEP-MSM, and associated substance use, predicted UAI. This research extends social-cognitive theory regarding sexual behaviour, and advances understanding of the role of expectancies associated with substance use and sexual risk-taking. Future applications of the SEP-MSM in health promotion, prevention, clinical interventions and research are likely to contribute to reducing harm associated with substance-using MSM (e.g., HIV transmission).

Psychological screening and injury among elite athletes

The purpose of the present investigation was to evaluate the effectiveness of the psychological component of the Queensland Academy of Sport (QAS) Health Screening Questionnaire in screening for injury/illness characteristics among elite athletes. In total, 793 scholarship athletes (409 females and 384 males) ranging in age from 11 to 41 years (M = 19, SD = 4.4) across 20 sports at the QAS in Brisbane, Australia, completed the QAS Health Screening Questionnaire. Psychological risk factors examined were life stress and mood, as measured by the Perceived Stress Scale - 10 (PSS-10) and the Brunel Mood Scale (BRUMS) respectively, in addition to disordered eating behaviours and history of diagnosed psychological disorders. Medical risk factors examined included asthma and back pain. Single-factor MANOVAs showed multivaritate effects for injury, second injury, back pain, asthma, anxiety disorder diagnosis, and fasting. Discriminant function analyses demonstrated that life stress and mood scores had significant utility in correctly classifying injury and second injury status, asthma, back pain, anxiety, and eating disorder diagnosis, in addition to the use of fasting and vomiting as weight control methods. The present findings suggest that the psychology component of the QAS Health Screening Questionnaire demonstrates significant utility as a screening tool regarding injury/illness characteristics among elite athletes.