scholarly journals Hybrid Optimization Driven RideNN for Software Reusability Estimation

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Ramu Vankudoth ◽  
◽  
Dr.Shireesha P ◽  
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Hybrid Optimization ◽  
Software Components ◽  
Estimation Model ◽  
Hybrid Optimization Algorithm ◽  
Software Reusability ◽  
Prime Concern ◽  
Over Time

Measuring the software reusability has become a prime concern in maintaining the quality of the software. Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges. This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time. Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor. Initially, nine software related metrics are extracted from the software. Then, a holoentropy based log function identifies the Measuring the software reusability has become a prime concern in maintaining the quality of the software. Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges. This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time. Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor. Initially, nine software related metrics are extracted from the software. Then, a holoentropy based log function identifies the normalized metric function and provides it to the proposed Cat Swarm Rider Optimization based Neural Network (C-RideNN) algorithm for the software reusability estimation. The proposed C-RideNN algorithm uses the existing Cat Swarm Optimization (CSO) along with the Rider Neural Network (RideNN) for the training purpose. Experimentation results of the proposed C-RideNN are evaluated based on metrics, such as Magnitude of Absolute Error (MAE), Mean Magnitude of the Relative Error (MMRE), and Standard Error of the Mean (SEM). The simulation results reveal that the proposed C-RideNN algorithm has improved performance with 0.0570 as MAE, 0.0145 as MMRE, and 0.6133 as SEM.

Adaptive neural network with hybrid optimization oriented localization in wireless sensor network: A multi-objective model

2020 ◽  
pp. 2050064
Author(s):  
P. Purusothaman ◽  
M. Gunasekaran
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Sensor Node ◽  
Optimization Algorithms ◽  
Sensor Nodes ◽  
Hybrid Optimization ◽  
Wireless Sensor ◽  
Hybrid Optimization Algorithm ◽  
Localization Model ◽  
Anchor Nodes

The localization strategy is broadly utilized in Wireless Sensor Networks (WSNs) to detect the present location of the sensor nodes. A WSN comprises of multiple sensor nodes, which makes the employment of GPS on each sensor node costly, and GPS does not give accurate localization outcomes in an indoor environment. The process of configuring location reference on each sensor node manually is also not feasible in the case of a large dense network. Hence, this proposal plans to develop an intelligent model for developing localization pattern in WSN with a group of anchor nodes, rest nodes, and target nodes. The initial step of the proposed node localization model is the selection of the optimal location of anchor nodes towards the target nodes using the hybrid optimization algorithm by concerning the constraints like the distance between the nodes. The second step is to optimally determine the location of the rest node by reference to the anchor nodes using the same hybrid optimization algorithm. Here, the weight has to be determined for each anchor sensor node based on its Received Signal Strength (RSS), and RSS threshold value with the assistance of Neural Network. The hybrid optimization algorithms check the direction to where the concerned node has to be moved by merging the beneficial concepts of two renowned optimization algorithms named as Rider Optimization Algorithm (ROA), and Chicken Swarm Optimization Algorithm (CSO) to solve the localization problem in WSN. The newly developed hybrid algorithm is termed as Rooster Updated Attacker-based ROA (RUA-ROA). Finally, the comparative evaluation indicates a significant improvement in the proposed localization model by evaluating the convergence and statistical analysis.

Optimized recurrent neural network with fuzzy classifier for data prediction using hybrid optimization algorithm: Scope towards diverse applications

2020 ◽  
pp. 2050074
Author(s):  
A. S. Prakaash ◽  
K. Sivakumar
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Optimization Algorithm ◽  
Fuzzy Regression ◽  
Hybrid Optimization ◽  
Hybrid Optimization Algorithm ◽  
Data Prediction ◽  
Fuzzy Regression Model

Today, data processing has become a challenging task due to the significant increase in the amount of data collected using various sensors. To put up knowledge and forecast the data, the existing data mining techniques compute all numerical attributes in the memory simultaneously. However, the over-abundance of entire factors in the data makes accurate prediction infeasible. This paper attempts to implement a new data prediction model using an optimized machine learning algorithm. The proposed data prediction model involves four main phases: (a) data acquisition, (b) feature extraction, (c) data normalization, and (d) prediction. Initially, few data from the UCI repository like Bike Sharing Dataset, Carbon Nanotubes, Concrete Compressive Strength, Electrical Grid Stability Simulated Data, and SkillCraft-1 Master Table are collected. Further, the feature extraction process extracts the first-order statistics like mean, median, standard deviation, the maximum value of entire data, and the minimum value of entire data, and the second-order statistics like kurtosis, skewness, energy, and entropy. Next, the data or feature normalization is done to arrange the data within a certain limit. The normalized features are then subjected to a hybrid prediction system by integrating the Recurrent Neural Network (RNN) and Fuzzy Regression model. As a modification, the number of hidden neurons in the RNN and membership limits of the Fuzzy Regression model are optimized by a hybrid optimization algorithm by merging the concepts of Whale Optimization Algorithm (WOA) and Cat Swarm Optimization (CSO), which is called the Whale Updated Seek Mode-based CSO (WS-CSO) algorithm. Then, the efficiency of the optimized hybrid classifier for all-time prediction of data in different applications is confirmed based on its valuable performance and comparative analysis.

Hybrid Optimization Algorithm to Combine Neural Network for Rainfall-Runoff Modeling

2016 ◽  
Vol 15 (03) ◽  
pp. 1650015
Author(s):  
Jiansheng Wu
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Bp Algorithm ◽  
Hybrid Optimization ◽  
Economic Losses ◽  
Rainfall Runoff ◽  
Hybrid Optimization Algorithm ◽  
Runoff Forecasting ◽  
Runoff Modeling ◽  
Searching Ability

Rainfall-runoff modeling is very important for Water Resources Management because accurate and timely prediction can avoid accidents, such as the life risk, economic losses, etc. This paper proposed the novel hybrid optimization algorithm to combine Neural Network (NN) for rainfall-runoff modeling, namely HGASA-NN. Firstly, a novel and specialized hybrid optimization strategy by incorporating Simulated Annealing algorithm (SA) into Genetic Algorithm (GA) was used to train the initial connection weights and thresholds of NN. Secondly, the Back Propagation (BP) algorithm was adjusted the final weights and biases. Finally, a numerical example of daily rainfall-runoff data was used to elucidate the forecasting performance of the proposed HGASA-NN model. The HGASA-NN can make use of not only strong global searching ability of the GASA, but also strong local searching ability of the BP algorithm. The forecasting results indicate that the proposed model yields more accurate forecasting results than the BP-NN and pure GA training NN model. Therefore, the HGASA-NN model is a promising alternative for rainfall-runoff forecasting.

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