Navaneetha Krishnan Muthunambu¹, Senthil Prabakaran², Balasubramanian Prabhu Kavin³, Kishore Senthil Siruvangur⁴, Kavitha Chinnadurai¹, Jehad Ali^5,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172 - 26 March 2024

Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent… More >

Zhaoan Wang¹, Shaoping Xiao^1,*, Cheryl Reuben², Qiyu Wang², Jun Wang²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 285-297, 2023, DOI:10.32604/cmc.2023.044366 - 31 October 2023

Abstract This paper presents designing sequence-to-sequence recurrent neural network (RNN) architectures for a novel study to predict soil NOx emissions, driven by the imperative of understanding and mitigating environmental impact. The study utilizes data collected by the Environmental Protection Agency (EPA) to develop two distinct RNN predictive models: one built upon the long-short term memory (LSTM) and the other utilizing the gated recurrent unit (GRU). These models are fed with a combination of historical and anticipated air temperature, air moisture, and NOx emissions as inputs to forecast future NOx emissions. Both LSTM and GRU models can… More >

Loc Vu-Quoc^1,*, Alexander Humer²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130 - 26 June 2023

Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting… More >

Jin Wang^1,2, Yongsong Zou¹, Se-Jung Lim^3,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2743-2755, 2023, DOI:10.32604/iasc.2023.033869 - 15 March 2023

Abstract Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel… More >

Shafiqur Rehman^1,*, Hilal H. Nuha², Ali Al Shaikhi³, Satria Akbar², Mohamed Mohandes^1,3

Energy Engineering, Vol.120, No.4, pp. 775-789, 2023, DOI:10.32604/ee.2023.026185 - 13 February 2023

Abstract An accurate vertical wind speed (WS) data estimation is required to determine the potential for wind farm installation. In general, the vertical extrapolation of WS at different heights must consider different parameters from different locations, such as wind shear coefficient, roughness length, and atmospheric conditions. The novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks (RNN) model to estimate WS at different heights using measurements from lower heights. The first optimization of the RNN is performed to minimize a differentiable cost function, namely, mean squared error (MSE),… More >

Elumalaivasan Poongavanam^1,*, Padmanathan Kasinathan², Kulothungan Kanagasabai³

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 249-265, 2023, DOI:10.32604/iasc.2023.030101 - 29 September 2022

Abstract The nation deserves to learn what India’s future energy demand will be in order to plan and implement an energy policy. This energy demand will have to be fulfilled by an adequate mix of existing energy sources, considering the constraints imposed by future economic and social changes in the direction of a more sustainable world. Forecasting energy demand, on the other hand, is a tricky task because it is influenced by numerous micro-variables. As a result, an macro model with only a few factors that may be predicted globally, rather than a detailed analysis for… More >

Pranav Pushkar¹, Christo Ananth², Preeti Nagrath¹, Jehad F. Al-Amri⁵, Vividha¹, Anand Nayyar^3,4,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1601-1619, 2022, DOI:10.32604/cmc.2022.026205 - 18 May 2022

Abstract The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community. The recent ongoing SARS-Cov2 (Severe Acute Respiratory Syndrome) pandemic proved the unpreparedness for these situations. Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently. One major way to find out more information about such pathogens is by extracting the genetic data of such viruses. Though genetic data of viruses have… More >

Dheeraj Kumar Dixit^*, Amit Bhagat, Dharmendra Dangi

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5733-5750, 2022, DOI:10.32604/cmc.2022.023628 - 14 January 2022

Abstract In recent years, social media platforms have gained immense popularity. As a result, there has been a tremendous increase in content on social media platforms. This content can be related to an individual's sentiments, thoughts, stories, advertisements, and news, among many other content types. With the recent increase in online content, the importance of identifying fake and real news has increased. Although, there is a lot of work present to detect fake news, a study on Fuzzy CRNN was not explored into this direction. In this work, a system is designed to classify fake and More >

Aurora Gonzalez-Vidal¹, Fernando Terroso-Sáenz^2,*, Antonio Skarmeta¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4355-4375, 2022, DOI:10.32604/cmc.2022.021492 - 14 January 2022

Abstract Nowadays, the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces. The purpose of our work is to study, design and develop a parking-availability predictor that extracts the knowledge from human mobility data, based on the anonymized human displacements of an urban area, and also from weather conditions. Most of the existing solutions for this prediction take as contextual data the current road-traffic state defined at very high temporal or spatial resolution. However, access to this type of fine-grained location data is usually quite limited due… More >

Abdullah Ali Salamai^1,*, Ather Abdulrahman Ageeli¹, El-Sayed M. El-kenawy²

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5091-5106, 2022, DOI:10.32604/cmc.2022.021268 - 11 October 2021

Abstract E-commerce refers to a system that allows individuals to purchase and sell things online. The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase. They will purchase the item online and have it delivered to their home within a few days. The goal of this research was to develop machine learning algorithms that might predict e-commerce platform sales. A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search (SFS) based on a Guided Whale Optimization Algorithm… More >