Song Yu^*, Chen Wang

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5555-5571, 2023, DOI:10.32604/cmc.2023.038162

Abstract PM_2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM_2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM_2.5 flow between cities when analyzing the impact of inter-city PM_2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such as altitude and… More >

Guangxia Xu^1,*, Lei Liu², Jingnan Dong³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 903-922, 2023, DOI:10.32604/cmes.2023.026627

Abstract In recent years, with the great success of pre-trained language models, the pre-trained BERT model has been gradually applied to the field of source code understanding. However, the time cost of training a language model from zero is very high, and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at present. In this paper, we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained language model BERT and connected to a bidirectional gate recurrent unit model. The downstream neural network adopts… More >

Mohd Asyraf Zulkifley^1,*, Ali Fayyaz Munir², Mohd Edil Abd Sukor³, Muhammad Hakimi Mohd Shafiai⁴

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4395-4418, 2023, DOI:10.32604/cmc.2023.036094

Abstract A well-managed financial market of stocks, commodities, derivatives, and bonds is crucial to a country’s economic growth. It provides confidence to investors, which encourages the inflow of cash to ensure good market liquidity. However, there will always be a group of traders that aims to manipulate market pricing to negatively influence stock values in their favor. These illegal trading activities are surely prohibited according to the rules and regulations of every country’s stock market. It is the role of regulators to detect and prevent any manipulation cases in order to provide a trading platform that is fair and efficient. However,… More >

Hala J. Alshahrani¹, Khaled Tarmissi², Ayman Yafoz³, Abdullah Mohamed⁴, Abdelwahed Motwakel^5,*, Ishfaq Yaseen⁵, Amgad Atta Abdelmageed⁵, Mohammad Mahzari⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3139-3155, 2023, DOI:10.32604/iasc.2023.034841

Abstract Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns. Emails stay in the leading positions for business as well as personal use. This popularity grabs the interest of individuals with malevolent intentions—phishing and spam email assaults. Email filtering mechanisms were developed incessantly to follow unwanted, malicious content advancement to protect the end-users. But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced. Thus, this study provides a solution related to email message body text automatic classification into phishing… 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

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 branch was introduced for the… More >

D. Vidyabharathi^1,*, V. Mohanraj²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2559-2573, 2023, DOI:10.32604/iasc.2023.032255

Abstract For training the present Neural Network (NN) models, the standard technique is to utilize decaying Learning Rates (LR). While the majority of these techniques commence with a large LR, they will decay multiple times over time. Decaying has been proved to enhance generalization as well as optimization. Other parameters, such as the network’s size, the number of hidden layers, dropouts to avoid overfitting, batch size, and so on, are solely based on heuristics. This work has proposed Adaptive Teaching Learning Based (ATLB) Heuristic to identify the optimal hyperparameters for diverse networks. Here we consider three architectures Recurrent Neural Networks (RNN),… 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

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), using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Secondly,… More >

Junjie Zhou, Hongkui Xu^*, Zifeng Zhang, Jiangkun Lu, Wentao Guo, Zhenye Li

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2277-2297, 2023, DOI:10.32604/csse.2023.036419

Abstract Fraud cases have been a risk in society and people’s property security has been greatly threatened. In recent studies, many promising algorithms have been developed for social media offensive text recognition as well as sentiment analysis. These algorithms are also suitable for fraudulent phone text recognition. Compared to these tasks, the semantics of fraudulent words are more complex and more difficult to distinguish. Recurrent Neural Networks (RNN), the variants of RNN, Convolutional Neural Networks (CNN), and hybrid neural networks to extract text features are used by most text classification research. However, a single network or a simple network combination cannot… More >

Sarah Alzahrani¹, Joud Alderaan¹, Dalya Alatawi¹, Bandar Alotaibi^1,2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 651-667, 2023, DOI:10.32604/cmc.2023.035173

Abstract Internet of Things (IoT) devices incorporate a large amount of data in several fields, including those of medicine, business, and engineering. User authentication is paramount in the IoT era to assure connected devices’ security. However, traditional authentication methods and conventional biometrics-based authentication approaches such as face recognition, fingerprints, and password are vulnerable to various attacks, including smudge attacks, heat attacks, and shoulder surfing attacks. Behavioral biometrics is introduced by the powerful sensing capabilities of IoT devices such as smart wearables and smartphones, enabling continuous authentication. Artificial Intelligence (AI)-based approaches introduce a bright future in refining large amounts of homogeneous biometric… More >

A. Venkatesh¹, S. Asha^2,*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1375-1392, 2023, DOI:10.32604/csse.2023.030944

Abstract A Wireless Sensor Network (WSN) is constructed with numerous sensors over geographical regions. The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy. As sensor nodes are resource constrained in nature, novel techniques are essential to improve lifetime of nodes in WSN. Nodes energy is considered as an important resource for sensor node which are battery powered based. In WSN, energy is consumed mainly while data is being transferred among nodes in the network. Several research works are carried out focusing on preserving energy of nodes in the network and made network… More >