R. Subha^1,*, Anandakumar Haldorai¹, Arulmurugan Ramu²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2639-2654, 2023, DOI:10.32604/iasc.2023.028153 - 17 August 2022

Abstract The most significant invention made in recent years to serve various applications is software. Developing a faultless software system requires the software system design to be resilient. To make the software design more efficient, it is essential to assess the reusability of the components used. This paper proposes a software reusability prediction model named Flexible Random Fit (FRF) based on aging resilience for a Service Net (SN) software system. The reusability prediction model is developed based on a multilevel optimization technique based on software characteristics such as cohesion, coupling, and complexity. Metrics are obtained from… More >

M. Manikandakumar^1,*, P. Karthikeyan²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 913-927, 2023, DOI:10.32604/csse.2023.025434 - 01 June 2022

Abstract Weed is a plant that grows along with nearly all field crops, including rice, wheat, cotton, millets and sugar cane, affecting crop yield and quality. Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity. To address this issue, an efficient weed classification model is proposed with the Deep Convolutional Neural Network (CNN) that implements automatic feature extraction and performs complex feature learning for image classification. Throughout this work, weed images were trained using the proposed CNN model with evolutionary computing… More >

V. Rajalakshmi^1,*, S. Ganesh Vaidyanathan²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1335-1348, 2022, DOI:10.32604/iasc.2022.024310 - 24 March 2022

Abstract Traffic flow forecasting is the need of the hour requirement in Intelligent Transportation Systems (ITS). Various Artificial Intelligence Frameworks and Machine Learning Models are incorporated in today’s ITS to enhance forecasting. Tuning the model parameters play a vital role in designing an efficient model to improve the reliability of forecasting. Hence, the primary objective of this research is to propose a novel hybrid framework to tune the parameters of Multilayer Perceptron (MLP) using the Swarm Intelligence technique called Particle Swarm Optimization (PSO). The proposed MLP-PSO framework is designed to adjust the weights and bias parameters… More >