Relevant bibliographies by topics / Pre-predator model

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Academic literature on the topic 'Pre-predator model'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Pre-predator model"

1

Ramos-Jiliberto, Rodrigo, Ernesto Frodden, and Adriana Aránguiz-Acuña. "Pre-encounter versus post-encounter inducible defenses in predator–prey model systems." Ecological Modelling 200, no. 1-2 (2007): 99–108. http://dx.doi.org/10.1016/j.ecolmodel.2006.07.023.

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2

Li, Yong, Muhammad Rafaqat, Tariq Javed Zia, Imran Ahmed, and Chahn Yong Jung. "Flip and Neimark-Sacker Bifurcations of a Discrete Time Predator-Pre Model." IEEE Access 7 (2019): 123430–35. http://dx.doi.org/10.1109/access.2019.2937956.

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3

Srinivasarao, Thota. "Prey-Predator Model for Awash National Park, Oromia, Ethiopia and Its Stability Analysis with Simulations." Journal of Science and Sustainable Development 7, no. 2 (2019): 15–21. https://doi.org/10.20372/au.jssd.7.2.2019.0133.

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In this paper, we discuss a model of two-space food chain consisting of the population of ecology of foxes (the predator) and rabbits (prey) in Awash National park, Ethiopia. The study is based on formulation of a mathematical model to study the dynamics of the population densities and analyzing the stability of equilibrium points of the prey-predator model. The aim of this model is to explore the behavior of a simple model by considering a population of foxes, and rabbits. This model is constituted by a system of nonlinear decoupled ordinary differential equations. By using perturbed method, we identify the nature of the system at each equilibrium point. The stability analysis of a prey-predator model is discussed with numerical simulations.
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4

Faux, David A., and Peter Bassom. "The game of life as a species model." American Journal of Physics 91, no. 7 (2023): 561. http://dx.doi.org/10.1119/5.0150858.

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Conway's classic game of life is a two-dimensional cellular automaton in which each cell is alive or dead and evolves according to simple rules that depend solely on the number of live cells in its immediate neighborhood. The emergence of complex multi-cellular objects provides a fascinating vehicle for exploration. A variant of the classic game of life is presented, the generalized semi-classical game of life, in which each cell contains a qubit that evolves by repeated application of birth, death, and survival operators. Species are characterized by just two parameters: a preferred neighborhood liveness representing the tendency to herd and a resilience parameter representing species' vulnerability to environmental changes. This generalized model provides the opportunity to model the fortune of species and to compare to available data. The model is shown to mimic environmental catastrophes and is illustrated by the model's prediction of a return to the pre-hunting level of the global whale population by 2140. A student-designed predator–prey model is shown to qualitatively describe the fate of strongly- and weakly coupled predator–prey systems (snowshoe hare/lynx and rabbit/fox, respectively) and sudden and slow predatory impact (dodo and diprotodon, respectively).
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Song, Woncheol, Sang-im Lee, and Piotr G. Jablonski. "Evolution of switchable aposematism: insights from individual-based simulations." PeerJ 8 (April 10, 2020): e8915. http://dx.doi.org/10.7717/peerj.8915.

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Some defended prey animals can switch on their normally hidden aposematic signals. This switching may occur in reaction to predators’ approach (pre-attack signals) or attack (post-attack signals). Switchable aposematism has been relatively poorly studied, but we can expect that it might bring a variety of benefits to an aposmetic organism. First, the switching could startle the predators (deimatism). Second, it could facilitate aversive learning. Third, it could minimize exposure or energetic expense, as the signal can be switched off. These potential benefits might offset costs of developing, maintaining and utilizing the switchable traits. Here we focused on the third benefit of switchability, the cost-saving aspect, and developed an individual-based computer simulation of predators and prey. In 88,128 model runs, we observed evolution of permanent, pre-attack, or post-attack aposematic signals of varying strength. We found that, in general, the pre-attack switchable aposematism may require moderate predator learning speed, high basal detectability, and moderate to high signal cost. On the other hand, the post-attack signals may arise under slow predator learning, low basal detectability and high signal cost. When predator population turnover is fast, it may lead to evolution of post-attack aposematic signals that are not conforming to the above tendency. We also suggest that a high switching cost may exert different selection pressure on the pre-attack than the post-attack switchable strategies. To our knowledge, these are the first theoretical attempts to systematically explore the evolution of switchable aposematism relative to permanent aposematism in defended prey. Our simulation model is capable of addressing additional questions beyond the scope of this article, and we open the simulation software, program manual and source code for free public use.
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Deepthi .S. "Non Hodgkin's Lymphoma Classification using Improved Predator Optimization Based Densenet121 Model." Journal of Electrical Systems 20, no. 3 (2024): 2937–48. http://dx.doi.org/10.52783/jes.4638.

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CAD system (computer aided diagnosis) assists medical experts in NHL (Non Hodgkin's lymphoma) diagnosis for making better decisions. In the case of NHL diagnosis, the manual analysis required more time. A few issues with the current methods included low accuracy, increased computational complexity, low dependability, increased feature dimensionality, and increased time consumption due to inadequate hyperparameter optimization. Hence, this work presents a CAD approach that ensures efficient and accurate diagnosis of NHL at the beginning stage. The proposed work has the different stages like pre-processing, optimal feature extraction and classification. The input images are resized and the features from the images are extracted and classified. This process is carried out by the DL (deep learning) model DenseNet121 with IPO (improved predator optimization) approach. The hyperparameters like batch size, neurons in the dense layer and learning rate are optimized by the IPO which minimizes the over-fitting and complexity. The analysis is demonstrated on the malignant lymphoma classification dataset and achieved a better accuracy of 98.6%.
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7

Fleitz, Julie, Manfred R. Enstipp, Emilie Parent, Jonathan Jumeau, Yves Handrich, and Mathilde L. Tissier. "Improving the success of reinforcement programs: effects of a two-week confinement in a field enclosure on the anti-predator behaviour of captive-bred European hamsters." PeerJ 11 (September 1, 2023): e15812. http://dx.doi.org/10.7717/peerj.15812.

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Captive breeding programs are an important pillar in biodiversity conservation, aiming to prevent the extinction of threatened species. However, the establishment of self-sustaining populations in the wild through the release of captive-bred animals is often hampered by a high mortality upon release. In this study, we investigated how a 2-week confinement period within a large field enclosure affected the anti-predator behaviour of ‘naive’ captive-bred hamsters and how potential modifications persisted over time. During three consecutive tests, hamsters were confronted with a moving predator model (a red fox mount, Vulpes vulpes) and their behaviour was filmed. After the initial round of confrontation with the predator model, one group of hamsters (field group) was released into a field enclosure protected from predators, while the other group (control) remained in their individual laboratory cages. After 2 weeks, hamsters from the field group were recaptured and individuals of both groups underwent a second confrontation test. A total of 1 month after their return from the field enclosure, field hamsters were subjected to a last confrontation test. Video analysis, investigating four behavioural variables, revealed that field hamsters significantly modified their behavioural response following the 2 weeks confinement in the enclosure, while this was not the case for control hamsters. In addition, most behavioural modifications in field hamsters persisted over 1 month, while others started to revert. We suggest that an appropriate pre-release period inside a field enclosure will enable naive (captive-bred) hamsters to develop an adequate anti-predator behaviour that will increase their immediate survival probability upon release into the wild. We believe that such measure will be of great importance for hamster conservation programs.
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8

Abdul Kareem, Baydaa, Salah L. Zubaidi, Hussein Mohammed Ridha, Nadhir Al-Ansari, and Nabeel Saleem Saad Al-Bdairi. "Applicability of ANN Model and CPSOCGSA Algorithm for Multi-Time Step Ahead River Streamflow Forecasting." Hydrology 9, no. 10 (2022): 171. http://dx.doi.org/10.3390/hydrology9100171.

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Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, and mitigating flood damage. This research developed a novel methodology that involves data pre-processing and an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast the monthly water streamflow. The monthly streamflow data of the Tigris River at Amarah City, Iraq, from 2010 to 2020, were used to build and evaluate the suggested methodology. The performance of CPSOCGSA was compared with the slim mold algorithm (SMA) and marine predator algorithm (MPA). The principal findings of this research are that data pre-processing effectively improves the data quality and determines the optimum predictor scenario. The hybrid CPSOCGSA-ANN outperformed both the SMA-ANN and MPA-ANN algorithms. The suggested methodology offered accurate results with a coefficient of determination of 0.91, and 100% of the data were scattered between the agreement limits of the Bland–Altman diagram. The research results represent a further step toward developing hybrid models in hydrology applications.
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9

Raghavendra, Vijith, and Pundikala Veeresha. "Analysing the market for digital payments in India using the predator-prey mode." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 13, no. 1 (2023): 104–15. http://dx.doi.org/10.11121/ijocta.2023.1306.

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Technology has revolutionized the way transactions are carried out in economies across the world. India too has witnessed the introduction of numerous modes of electronic payment in the past couple of decades, including e-banking services, National Electronic Fund Transfer (NEFT), Real Time Gross Settlement (RTGS) and most recently the Unified Payments Interface (UPI). While other payment mechanisms have witnessed a gradual and consistent increase in the volume of transactions, UPI has witnessed an exponential increase in usage and is almost on par with pre-existing technologies in the volume of transactions. This study aims to employ a modified Lotka-Volterra (LV) equations (also known as the Predator-Prey Model) to study the competition among different payment mechanisms. The market share of each platform is estimated using the LV equations and combined with the estimates of the total market size obtained using the Auto-Regressive Integrated Moving Average (ARIMA) technique. The result of the model predicts that UPI will eventually overtake the conventional digital payment mechanism in terms of market share as well as volume. Thus, the model indicates a scenario where both payment mechanisms would coexist with UPI being the dominant (or more preferred) mode of payment.
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10

Zafar, Amad, Jawad Tanveer, Muhammad Umair Ali, and Seung Won Lee. "BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization." Bioengineering 10, no. 7 (2023): 825. http://dx.doi.org/10.3390/bioengineering10070825.

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Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women. Various pre-trained networks are used to extract the deep features of the BU images. Ten wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier. Furthermore, a network selection algorithm was employed to determine the best pre-trained network. An online BU dataset was used to test the proposed framework. After comprehensive testing and analysis, it was found that the EO algorithm produced the highest classification rate for each pre-trained model. It produced the highest classification accuracy of 96.79%, and it was trained using only a deep feature vector with a size of 562 in the ResNet-50 model. Similarly, the Inception-ResNet-v2 had the second highest classification accuracy of 96.15% using the EO algorithm. Moreover, the results of the proposed framework are compared with those in the literature.
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