scholarly journals Stochastic Numerical Investigations for a Food Web System of Prey-Predation, Competition and Commensalism

2021 ◽  
Author(s):  
Zulqurnain Sabir ◽  
Seshagiri Rao N ◽  
Kalyani K
Keyword(s):  
Neural Network ◽  
Food Web ◽  
Prey Species ◽  
Testing Strategies ◽  
Web System ◽  
The Neural Network ◽  
Frame Work ◽  
Numerical Computing ◽  
High Predation ◽  
Numerical Program

Abstract Employing Levenberg-Marquardt backpropagation(LMB) neural network, the system of three species nonlinear equations are illuminated by designing an integrated numerical computing-based plot. The proposed dynamical system comprises of two competing species which are growing logistically in nature and, the third species is predating with Holling type II functional response mode on second species and also acts host for the first prey species. Besides, the prey species protect themselves to refuge high predation. The designed LMB neural network has been used to exhibits the solutions of the dynamical frame work. In each case of the species, a reference dataset of the planned LMB neural network is initiated in comparison of Adam numerical program. The approximate results of the food web system are displayed within the training, confirmation and testing strategies to redesign the neural network to minimize the mean square error (MSE) function employing the designed LMB. The investigations depend on the corresponding achievements and the examinations based on MSE out comes, correlation, regression and error histograms signify the proficiency, rightness as well as the potency of the suggested LMB neural network conspire.

scholarly journals Computational Intelligent Paradigms to Solve the Nonlinear SIR System for Spreading Infection and Treatment Using Levenberg–Marquardt Backpropagation

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 618
Author(s):  
Muhammad Umar ◽  
Zulqurnain Sabir ◽  
Muhammad Asif Zahoor Raja ◽  
Manoj Gupta ◽  
Dac-Nhuong Le ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Scheme ◽  
System Of Differential Equations ◽  
Reference Dataset ◽  
Testing Procedures ◽  
The Neural Network ◽  
Levenberg Marquardt ◽  
Numerical Computing ◽  
The Mean ◽  
Computational Intelligent

The current study aims to design an integrated numerical computing-based scheme by applying the Levenberg–Marquardt backpropagation (LMB) neural network to solve the nonlinear susceptible (S), infected (I) and recovered (R) (SIR) system of differential equations, representing the spreading of infection along with its treatment. The solutions of both the categories of spreading infection and its treatment are presented by taking six different cases of SIR models using the designed LMB neural network. A reference dataset of the designed LMB neural network is established with the Adam numerical scheme for each case of the spreading infection and its treatment. The approximate outcomes of the SIR system based on the spreading infection and its treatment are presented in the training, authentication and testing procedures to adapt the neural network by reducing the mean square error (MSE) function using the LMB. Studies based on the proportional performance and inquiries based on correlation, error histograms, regression and MSE results establish the efficiency, correctness and effectiveness of the proposed LMB neural network scheme.

Long survival of “living fossils” with low taxonomic diversities in an evolving food web

Paleobiology ◽  
2002 ◽  
Vol 28 (4) ◽  
pp. 464-473 ◽  
Author(s):  
Katsuhiko Yoshida
Keyword(s):  
Food Web ◽  
Feeding Preference ◽  
Evolutionary Rates ◽  
Long Survival ◽  
Web System ◽  
Living Fossils ◽  
Long Time ◽  
High Predation ◽  
Taxonomic Groups ◽  
Sufficient Food

Living fossils are taxonomic groups surviving for a long time without any remarkable morphological change. Most of them retain low taxonomic diversities. Although some of them have survived in refuges to avoid predators and competitors, not all living fossils live in refuges. The survival of these groups, therefore, should be discussed in the context of biological interaction. I carried out computer simulations of a model food web system, in which each species feeds on others according to its feeding preference. The system evolves via evolution of species. In the simulation, some clades, like “living fossils,” survived for a long time with low species diversities. Such clades consisted of species with low evolutionary rates, which result in high predation pressure and intraclade competition for food. Nevertheless, the clades sustainably utilize prey clades and are consequently provided with sufficient food. In addition, because of the low species diversities of the clades, predators of the clades soon become extinct through lack of food. This study strongly suggests that in an evolving food web system, the low evolutionary rates of living fossils allow the long survival of those groups with low taxonomic diversities.

Immune Computation of Anti-Worm Static Web System

2011 ◽  
Vol 48-49 ◽  
pp. 603-606
Author(s):  
Tao Gong ◽  
Song Wang ◽  
Lei Yao
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
The Self ◽  
Normal Model ◽  
Computation Model ◽  
Learning Mechanism ◽  
Web System ◽  
The Neural Network

A normal model and an immune computation model were modelled to detect recognize and eliminate worms in a static Web system. Immune computation included detecting, recognizing, learning and eliminating non-selfs. The self/non-self detection was based on querying in the self database and the self database was built on the normal model of the static Web system. After the detection, the recognition of known non-self was based on querying in the non-self database and the recognition of unknown non-self was based on learning unknown non-self. The learning algorithm was designed on the neural network or the learning mechanism from examples. The last step was elimination of all the non-self and failover of the damaged Web system. The immunization of the static Web system was programmed with Java to test effectiveness of the approach. Some worms infected the static Web system, and caused the abnormity. The results of the immunization simulations show that, the immune program can detect all worms, recognize known worms and most unknown worms, and eliminate the worms. The damaged files of the static Web system can all be repaired through the normal model and immunization. The normal model & immune computation model are effective in some anti-worm applications.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

Sign in / Sign up

Export Citation Format

Share Document