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Human Populations
Published in Gary S. Moore, Kathleen A. Bell, Living with the Earth, 2018
Gary S. Moore, Kathleen A. Bell
The science of demography has categorized two different reproductive strategies for living organisms. One strategy is known as a k-strategy in which large organisms with relatively long life spans have only a few offspring, but devote their energies to protecting and nurturing the offspring to enhance their individual survival until they can reproduce. Examples falling into this group would be elephants, deer, large cats, swans, and humans. These populations normally stabilize at the carrying capacity of their environments where density dependent factors limit growth. Density dependent factors include such items as food supply, which becomes more limiting as the size of the population grows. The pattern for mortality of these species can be demonstrated by plotting a survivorship curve in which the fate of young individuals is followed to the point of death in order to describe mortality at different ages.12 This curve can be used to predict the probability that a newborn will reach a specific age which is useful in determining the life expectancy within a population The “type I” curve typical for the k-strategy survival population shows that the majority of infants reach sexual maturity and many survive to maturity eventually dying of diseases from old age (Figure 2.9). This is in sharp contrast to r-strategy populations that are typically small, short-lived organisms, which produce large numbers of offspring and receive little or no parental care. This is characteristic of species of invertebrates, fish, mollusks, plants, fungi, gypsy moths, locusts, grasshoppers, and some rodents. Large numbers are produced, but few offspring survive. There is high mortality among the offspring as depicted by the Type III survivor curve (Figure 2.9). These organisms are limited by density-independent factors such as a drought that dries up a pond, or sudden climactic changes such as El Niño, which alters the temperature of the water, making it uninhabitable for certain species. Such populations often demonstrate wildly fluctuating numbers with population explosions and crashes.
Research challenges and future directions towards medical data processing
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Anusha Ampavathi, Vijaya Saradhi T
The noteworthy performance metrics considered for the analysis of disease prediction models in given in Table 5. The most common measure is accuracy, which is used in 45.7% of the total work. Secondly, AUC is utilised in 31%, MSE is analysed in 2.8%, and ROC is employed in 17%, and RMSE is validated in 5.7% of the works. AUC measures the complete two-dimensional area under the total ROC, while ROC is a graphical plot to illustrate the diagnostic ability of a binary classifier model. Sensitivity is considered as the number of true positives. FDR is the number of false positives in completely reduced hypotheses. Specificity shows the number of true negatives that are attained in accurate way. The ratio among true positives, false negatives, true negatives, and false positives are given as MCC to specify the better prediction results. Survival curve or survivorship curve is a graph to show the proportion or number of person’s survived at each age for a specific groups. FPR is ‘the ratio of count of false positive predictions to the entire count of negative predictions’. Precision is ‘the ratio of positive observations’ that are predicted exactly to the total number of observations, which are absolutely predicted. The positive and negative predictive values (PPV and NPV, respectively) depict the performance of a diagnostic test. Recall is the measure of quantity. A measure of prediction accuracy of a forecasting approach in statistics is named as Mean absolute percentage Error (MAPE). Computational time is the length of time required to perform a computational process. Scalability is defined as the ability of algorithms, data, models and infrastructure to operate at the size, speed and complexity required for the mission. Jitter is defined as the idea of adding small amounts of noise to your data to generate new, artificially corrupted examples.