scholarly journals Whale Behavior based Rule Optimization on Paraconsistent Neutrosophic Classification Model for Prediction of Dyslexia

2019 ◽  
Vol 8 (2) ◽  
pp. 4597-4604
Keyword(s):  
Paraconsistent Logic ◽  
Classification Model ◽  
False Alarms ◽  
High Detection Rate ◽  
Inference System ◽  
Whale Optimization ◽  
Neutrosophic Logic ◽  
Degree Of Certainty ◽  
Behavior Based ◽  
Rule Optimization

With the advancement in the software field, diagnosing dyslexia in earlier stages among children is highly possible. It helps them to take necessary measures to rise above the problem. This paper intends to develop an uncertainty handling model using neutrosophic logic inference system. This system’s functionality is enhanced by introducing paraconsistent logic with whale behavior based optimization. Paraconsistent logic is used to discover the degree of certainty and contradiction of generated rules. Pruning the population of rules is handled by a nature inspire algorithm known as whale behavior based rule optimization. Dyslexia dataset consists of both vague and crisp values. Treating them as such will often lead to high false alarms in the detection process. To overcome this issue the neutrosophic model is used to denote them in terms of membership degree of truthiness, indeterminacy, and falsity. The paraconsistent analyzer works with the favorable and unfavorable degree of evidence of each rule to handle the inconsistency and uncertainty among dyslexia detection. The potential rules are selected by the encircle prey model of the whale optimization algorithm. The simulation results proved that the performance of the proposed model produces high detection rate in the detection of dyslexia.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

scholarly journals Probabilistic Forecasts of Mesoscale Convective System Initiation Using the Random Forest Data Mining Technique

2016 ◽  
Vol 31 (2) ◽  
pp. 581-599 ◽  
Author(s):  
David Ahijevych ◽  
James O. Pinto ◽  
John K. Williams ◽  
Matthias Steiner
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Weather Prediction ◽  
False Alarms ◽  
Convective System ◽  
Training Set ◽  
Data Mining Technique ◽  
High Detection Rate ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective

Abstract A data mining and statistical learning method known as a random forest (RF) is employed to generate 2-h forecasts of the likelihood for initiation of mesoscale convective systems (MCS-I). The RF technique uses an ensemble of decision trees to relate a set of predictors [in this case radar reflectivity, satellite imagery, and numerical weather prediction (NWP) model diagnostics] to a predictand (in this case MCS-I). The RF showed a remarkable ability to detect MCS-I events. Over 99% of the 550 observed MCS-I events were detected to within 50 km. However, this high detection rate came with a tendency to issue false alarms either because of premature warning of an MCS-I event or in the continued elevation of RF forecast likelihoods well after an MCS-I event occurred. The skill of the RF forecasts was found to increase with the number of trees and the fraction of positive events used in the training set. The skill of the RF was also highly dependent on the types of predictor fields included in the training set and was notably better when a more recent training period was used. The RF offers advantages over high-resolution NWP because it can be run in a fraction of the time and can account for nonlinearly varying biases in the model data. In addition, as part of the training process, the RF ranks the importance of each predictor, which can be used to assess the utility of new datasets in the prediction of MCS-I.

scholarly journals Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

2019 ◽  
Vol 9 (18) ◽  
pp. 3755 ◽  
Author(s):  
Wei Chen ◽  
Haoyuan Hong ◽  
Mahdi Panahi ◽  
Himan Shahabi ◽  
Yi Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Landslide Susceptibility ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Economic Losses ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Prediction Ability ◽  
Inference System ◽  
Whale Optimization

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

scholarly journals An Inference System for Classifying Oil Palm Fungal Diseases

2021 ◽  
Vol 9 (11) ◽  
pp. 611-620
Author(s):  
Olajide Blessing Olajide ◽  
Odeniyi Olufemi Ayodeji ◽  
Olabiyi Olatunji Coker ◽  
Stephen Munu ◽  
Yakubani Yakubu
Keyword(s):  
Oil Palm ◽  
Plant Disease ◽  
Fuzzy Inference ◽  
World Market ◽  
Fungal Disease ◽  
Fungal Diseases ◽  
Classification Model ◽  
Rule Base ◽  
Inference System ◽  
Oil Plant

The oil palm plant is one of the major important cash crops of the Nigerian economy and a significant contributor to the world market for vegetable oils. Unfortunately, infection with fungi has caused a decline in the productivity of oil palms and subsequently the palm oil industry. Hence the need to detect oil palm plant disease earlier before it affects it informed this research to develop a fuzzy inference model to predict the influence of fungal disease on the oil plant plant. Following extensive review of related works, the factors associated with the severity of fungal diseases in the oil palm plant were identified following validation by Botanist. Fuzzy triangular membership functions were used to formulate the input factors identified alongside the target variables for identifying the severity of fungal diseases affecting the oil palm plant. The rule base was formulated using IF-THEN statements to combine the values of the input factors with the respective values of the target severity of oil palm plant disease. The classification model for oil palm plant disease severity was simulated using the Fuzzy Logic Toolbox available in the MATLAB R2015b Software. The results showed that the developed inference system for oil palm plant was capable of classifying and predicting the degree of the fungal disease infection into four groups; no severity, low severity, moderate severity and high severity.

scholarly journals PERANCANGAN BEHAVIOR-BASED ROBOT DENGAN ALGORITMA FUZZY Q-LEARNING (FQL) PADA SISTEM NAVIGASI ROBOT OTONOM BERODA DALAM MEDAN YANG TIDAK TERSTRUKTUR

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Made Santo Gitakarma
Keyword(s):  
Reinforcement Learning ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Q Learning ◽  
Behavior Based

Pada banyak aplikasi robotika, seperti sistem navigasi robot mandiri atau robot otonom yang bergerak dengan mandiri pada lingkungan tidak terstruktur, sangat sulit atau tidak mungkin memperoleh model matematik yang tepat dari interaksi robot dengan lingkungannya. Untuk itu diperlukan pendekatan sistem kendali robot yang dikenal dengan sistem kendali Behavior-Based Robot (BBR). Pada pendekatan ini, sistem diuraikan menjadi beberapa modul yang masing-masingnya bertanggung jawab untuk melakukan satu perilaku (behavior). Salah satu metode pembelajaran yang paling cocok untuk aplikasi robot adalah Reinforcement Learning (RL), dengan jenis algoritma Q-learning. Kombinasi Q-learning dengan Fuzzy Inference System (FIS) dikenal dengan nama Fuzzy Q-Learning (FQL). Berdasarkan percobaan yang dilakukan sebanyak 3 kali pada robot beroda dapat disimpulkan bahwa waktu rata-rata robot kembali ke Homebase yaitu 1 menit 10 detik. Sedangkan waktu rata-rata robot dalam mematikan api lilin adalah 2 detik. Sehingga dapat dikatakan robot yang dibuat mempunyai kinerja yang cukup baik.

Behavior Based Robot Navigation: Resolving Behavior Conflicts Using Fuzzy Inference System

Volume 2 ◽  
2004 ◽  
Author(s):  
S. Parasuraman ◽  
V. Ganapathy ◽  
Bijan Shirinzadeh
Keyword(s):  
Control Systems ◽  
Robot Control ◽  
Fuzzy Inference ◽  
Robot Navigation ◽  
Sensory Information ◽  
System Control ◽  
Inference System ◽  
Control Decision ◽  
Behavior Based ◽  
Perception Action

Conflict resolution is the control decision process, which should be taken as a result of the firing among several fuzzy behavior rules. In the Behavior-based Robot Navigation System, control of a robot is shared between a set of perception-action units, called behaviors selection. In other words, the behavior selection is the way that an agent selects the most appropriate or the most relevant next action to take at a particular moment, when facing a particular problem. Based on selective sensory information, each behavior produces immediate reaction to control the robot with respect to a particular objective, i.e., a narrow aspect of the robot’s overall task such as obstacle avoidance or goal seek. Behaviors with different and possibly incommensurable objectives may produce conflicting actions that are seemingly irreconcilable. The main issue in the design of behavior based robot control systems is the formulation of effective mechanism to coordinate the behavior’s activities without any behavior conflicts during navigation. This paper presents the techniques to design the behaviors and resolve the behaviors conflicts, which are based on the Situation Context of Applicability (SCA) of the environments.

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