Intelligent approach of score-based artificial fish swarm algorithm (SAFSA) for Parkinson's disease diagnosis

PurposeConventional diagnostic techniques, on the other hand, may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify, potentially resulting in misdiagnosis. Meanwhile, early nonmotor signs of Parkinson’s disease (PD) can be mild and may be due to variety of other conditions. As a result, these signs are usually ignored, making early PD diagnosis difficult. Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD (like, movement disorders or other Parkinsonian syndromes).Design/methodology/approachMedical observations and evaluation of medical symptoms, including characterization of a wide range of motor indications, are commonly used to diagnose PD. The quantity of the data being processed has grown in the last five years; feature selection has become a prerequisite before any classification. This study introduces a feature selection method based on the score-based artificial fish swarm algorithm (SAFSA) to overcome this issue.FindingsThis study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database. Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant. According to a few objective functions, features subset chosen should provide the best performance.Research limitations/implicationsIn many situations, this is an Nondeterministic Polynomial Time (NP-Hard) issue. This method enhances the PD detection rate by selecting the most essential features from the database. To begin, the data set's dimensionality is reduced using Singular Value Decomposition dimensionality technique. Next, Biogeography-Based Optimization (BBO) for feature selection; the weight value is a vital parameter for finding the best features in PD classification.Originality/valuePD classification is done by using ensemble learning classification approaches such as hybrid classifier of fuzzy K-nearest neighbor, kernel support vector machines, fuzzy convolutional neural network and random forest. The suggested classifiers are trained using data from UCI ML repository, and their results are verified using leave-one-person-out cross validation. The measures employed to assess the classifier efficiency include accuracy, F-measure, Matthews correlation coefficient.

Path planning and smoothing of mobile robot based on improved artificial fish swarm algorithm

An algorithm that integrates the improved artificial fish swarm algorithm with continuous segmented Bézier curves is proposed, aiming at the path planning and smoothing of mobile robots. On the one hand, to overcome the low accuracy problems, more inflection points and relatively long planning paths in the traditional artificial fish swarm algorithm for path planning, feasible solutions and a range of step sizes are introduced based on Dijkstra's algorithm. To solve the problems of poor convergence and degradation that hinder the algorithm's ability to find the best in the later stage, a dynamic feedback horizon and an adaptive step size are introduced. On the other hand, to ensure that the planned paths are continuous in both orientation and curvature, the Bessel curve theory is introduced to smooth the planned paths. This is demonstrated through a simulation that shows the improved artificial fish swarm algorithm achieving 100% planning accuracy, while ensuring the shortest average path in the same grid environment. Additionally, the smoothed path is continuous in both orientation and curvature, which satisfies the kinematic characteristics of the mobile robot.

Assessment of risk factors in medical data using improved Binary Artificial Fish Swarm Algorithm with Classification upon Evaluation from F-Test

The objective of this research work is to effectively deploy improved Binary Artificial Fish Swarm optimization Algorithm (BAFSA) with the data classification techniques. The improvement has been made with accordance to the condition of visual scope and the movement of fish to update towards the central position and chasing behavior towards best point of movement has been modified among the given population. The experimental results show that feature selection by BAFSA and classification by Decision trees and Gaussian Naïve bayes algorithm provides an improved accuracy of about 89.6% for Pima Indian diabetic dataset, 91.1% for lenses dataset and 94.4% for heart disease dataset. Statistical analysis has also been made using Fisher’s F-Test for two sample variance and the selected risk factors such as glucose, insulin level, blood pressure for diabetics datasets, spectacle prescription, tear production rate for lenses dataset and trestbps, cholesterol level, thalach, chest pain type for heart disease dataset are found to be significant with R2<0.001 respectively.

Assessment of risk factors in medical data using improved Binary Artificial Fish Swarm Algorithm with Classification upon Evaluation from F-Test

The objective of this research work is to effectively deploy improved Binary Artificial Fish Swarm optimization Algorithm (BAFSA) with the data classification techniques. The improvement has been made with accordance to the condition of visual scope and the movement of fish to update towards the central position and chasing behavior towards best point of movement has been modified among the given population. The experimental results show that feature selection by BAFSA and classification by Decision trees and Gaussian Naïve bayes algorithm provides an improved accuracy of about 89.6% for Pima Indian diabetic dataset, 91.1% for lenses dataset and 94.4% for heart disease dataset. Statistical analysis has also been made using Fisher’s F-Test for two sample variance and the selected risk factors such as glucose, insulin level, blood pressure for diabetics datasets, spectacle prescription, tear production rate for lenses dataset and trestbps, cholesterol level, thalach, chest pain type for heart disease dataset are found to be significant with R2<0.001 respectively.

Optimization of Esophageal Ultrasound under Artificial Fish Swarm Algorithm and Its Adoption in Treatment of Ventricular Septal Defect

This work was aimed at exploring the adoption value of the optimized and upgraded esophageal ultrasound in the treatment of patients with ventricular septal defect (VSD) by artificial fish swarm algorithm. A model was built based on artificial fish swarm algorithm. A random ultrasonic optical signal in the database was decomposed several times and sparsity was optimized to complete partial optimization, which was then extended to global optimization. A total of 100 patients with ventricular septal defect were divided into control group who underwent cardiopulmonary bypass under the guidance of three-dimensional thoracic ultrasound and experimental group of ventricular septal defect occlusion under the guidance of esophageal ultrasound based on artificial fish swarm algorithm. The results showed that the number of successful cases in the experimental group was 12 cases of perimembranous type, 10 cases of septal type, 7 cases of simple membranous type, 13 cases of muscular type, 4 cases of subdry type, and 2 cases of ridge type. The average length of operation after surgery was 70.65 minutes, the average length of ventilator ventilation was 125.8 minutes, and the average length of intensive care unit was 377.9 minutes. The average length of hospital stay after surgery was 5.6 days, and the average total length of hospital stay was 8.2 days, which were better than the control group in many aspects, with statistical significance ( P < 0.05 ). In short, the artificial fish swarm algorithm for esophageal ultrasound-guided ventricular septal defect closure had short operation time and good postoperative effect, which was of high application value in the clinical treatment of patients with ventricular septal defect.

Low voltage abnormal user identification based on improved fish swarm algorithm

The power consumption readings of sub meter and total meter of distribution transformer of low-voltage users follow the law of conservation of energy. The meter power loss rate of abnormal low-voltage users must also be abnormal. This paper studies the solution of the meter power loss rate under the four abnormal power consumption scenarios of single (multi) user and full (partial) period. The traditional linear solution method has accurate identification effect for the abnormal power consumption scenario of full period, but it cannot identify the abnormal power consumption scenario of partial period. In this paper, an improved artificial fish swarm algorithm is proposed. By adjusting the fixed step to the adaptive step, the power loss rate of each sub meter is obtained, and the abnormal power users are pinpointed. The research results are verified by simulation examples on IEEE European Low Voltage Test Feeder. The results show that the improved artificial fish swarm algorithm in this paper can identify abnormal power users for the above four abnormal electric field scenarios. The algorithm provides a new alternative for the identification of abnormal low voltage users.