Automatic grading of admitted patients using genetic algorithm

2020 ◽  
pp. 1-10
Author(s):  
Zaixing Zhuang ◽  
lijie Zhong ◽  
Xianwu Zhu ◽  
Miao Miao ◽  
Cheng Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Initial Population ◽  
Research Model ◽  
Current Application ◽  
Intelligent Algorithms ◽  
Hospital Resources ◽  
Automatic Grading ◽  
Random Method ◽  
Genetic Algorithm Operator

The current application of intelligent algorithms has achieved certain applications in smart medical, but its application in the automatic grading of admitted patients is in a blank, which makes it difficult to allocate hospital resources effectively. In order to improve the efficiency and accuracy of automatic classification of patients admitted to hospital, this study builds the corresponding genetic algorithm operator based on genetic algorithm. At the same time, this paper uses the random method to generate the initial population and uses the inversion mutation operator to perform the mutation operation. In addition, this article combines image processing to automatically classify patient types and patient levels. Finally, this paper uses the data collection method to verify the model and input the data into the research model. The research shows that the model proposed in this paper has certain effects, which can realize the automatic grading of patients admitted, and can provide theoretical reference for subsequent related research.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

scholarly journals Classification of SSVEP-based BCIs using Genetic Algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamideh Soltani ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Keivan Maghooli
Keyword(s):  
Genetic Algorithm ◽  
Dimension Reduction ◽  
Classification Accuracy ◽  
Bayesian Method ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Effective Dimension ◽  
Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

The Walking Robot Control System that is Adaptive to Changes in the Kinematics

2021 ◽  
Vol 22 (11) ◽  
pp. 601-609
Author(s):  
A. S. Samoylova ◽  
S. A. Vorotnikov
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Robot Control ◽  
Control Algorithm ◽  
Human Life ◽  
Support Surface ◽  
Machine Modeling ◽  
Cargo Delivery ◽  
Formal Classification

The walking mobile robots (WMR) have recently become widely popular in robotics. They are especially useful in the extreme cases: search and rescue operations; cargo delivery over highly rough terrain; building a map. These robots also serve to explore and describe a partially or completely non-deterministic workspace, as well as to explore areas that are dangerous to human life. One of the main requirements for these WMR is the robustness of its control system. It allows WMR to maintain the operability when the characteristics of the support surface change as well as under more severe conditions, in particular, loss of controllability or damage of the supporting limb (SL). We propose to use the principles of genetic programming to create a WMR control system that allows a robot to adapt to possible changes in its kinematics, as well as to the characteristics of the support surface on which it moves. This approach does not require strong computational power or a strict formal classification of possible damage to the WMR. This article discusses two main WMR control modes: standard, which accord to a serviceable kinematics, and emergency, in which one or more SL drives are damaged or lost controllability. As an example, the structure of the control system of the WMP is proposed, the kinematics of which is partially destroyed in the process of movement. We developed a method for controlling such robot, which is based on the use of a genetic algorithm in conjunction with the Mealy machine. Modeling of modes of movement of WMR with six SL was carried out in the V-REP program for two cases of injury: absent and not functioning limb. We present the results of simulation of emergency gaits for these configurations of WMP and the effectiveness of the proposed method in the case of damage to the kinematic scheme. We also compared the performance of the genetic algorithm for the damaged WMR with the standard control algorithm.

scholarly journals Genetic Algorithm for Exam Timetabling Problem - A Specific Case for Japanese University Final Presentation Timetabling

2020 ◽  
Author(s):  
Jiawei LI ◽  
Tad Gonsalves
Keyword(s):  
Genetic Algorithm ◽  
Real Data ◽  
Initial Population ◽  
Exam Timetabling ◽  
Timetabling Problem ◽  
Training Approach ◽  
Chromosome Representation ◽  
Genetic Algorithm Approach ◽  
Hard And Soft Constraints ◽  
Microsoft Office

This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.

scholarly journals USE OF GENETIC ALGORITHM IN DEEP NEURAL NETWORKS CONFIGURATION FOR THE PURPOSES OF COMPUTER ATTACKS CLASSIFICATION

2020 ◽  
pp. 104-117
Author(s):  
O.S. Amosov ◽  
◽  
S.G. Amosova ◽  
D.S. Magola ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Neural Network Architecture ◽  
Computer Attacks ◽  
Neural Network Technology

The task of multiclass network classification of computer attacks is given. The applicability of deep neural network technology in problem solving has been considered. Deep neural network architecture was chosen based on the strategy of combining a set of convolution and recurrence LSTM layers. Op-timization of neural network parameters based on genetic algorithm is proposed. The presented results of modeling show the possibility of solving the network classification problem in real time.

scholarly journals Karakter Morfologi dan Identifikasi Molekuler dengan Menggunakan Marka Gen 12S rRNA pada Ikan Baronang (Siganus spp.) di Perairan Laut Selatan Pulau Lombok

2020 ◽  
Vol 6 (1) ◽  
pp. 105
Author(s):  
Mahrus Mahrus ◽  
Abdul Syukur
Keyword(s):  
Fragment Size ◽  
Morphological Characters ◽  
Rrna Genes ◽  
12S Rrna ◽  
Morphological Classification ◽  
Number Of Species ◽  
South Sea ◽  
Random Method ◽  
Molecular Characters

This study aims to obtain the certainty of the number of species of baronang fish (Siganus spp.) Siganidae family in the waters of the south sea of Lombok Island.  This study used a sample of 90 fish from the fishermen's catch. Sampling used a random method three times. Observation of morphological characters used 90 and nine fishes for molecular characters-12S rRNA genes. The study used a descriptive technique for data analysis. The results showed that the fish is only one species and suspected consists of 3 variants. Three variants of fish have the same 12S rRNA genes with a fragment size of approximately 1000 bp. The morphological classification of the three groups of fish does not indicate the number of species, but no more as genetic variants.

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