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  • Open Access

    ARTICLE

    A Survey on Stock Market Manipulation Detectors Using Artificial Intelligence

    Mohd Asyraf Zulkifley1,*, 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 >

  • Open Access

    ARTICLE

    Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

    Hala J. Alshahrani1, Khaled Tarmissi2, Ayman Yafoz3, Abdullah Mohamed4, Abdelwahed Motwakel5,*, 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 >

  • Open Access

    ARTICLE

    An Improved Time Feedforward Connections Recurrent Neural Networks

    Jin Wang1,2, Yongsong Zou1, Se-Jung Lim3,*

    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 >

  • Open Access

    ARTICLE

    Hyperparameter Tuning for Deep Neural Networks Based Optimization Algorithm

    D. Vidyabharathi1,*, 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 >

  • Open Access

    ARTICLE

    Improving Performance of Recurrent Neural Networks Using Simulated Annealing for Vertical Wind Speed Estimation

    Shafiqur Rehman1,*, Hilal H. Nuha2, Ali Al Shaikhi3, Satria Akbar2, Mohamed Mohandes1,3

    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 >

  • Open Access

    ARTICLE

    Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition

    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 >

  • Open Access

    ARTICLE

    Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

    Sarah Alzahrani1, Joud Alderaan1, Dalya Alatawi1, Bandar Alotaibi1,2,*

    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 >

  • Open Access

    ARTICLE

    DERNNet: Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

    A. Venkatesh1, S. Asha2,*

    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 >

  • Open Access

    ARTICLE

    An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

    Mohammed Hadwan1,2,*, Hamzah A. Alsayadi3,4, Salah AL-Hagree5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3471-3487, 2023, DOI:10.32604/cmc.2023.033457

    Abstract The attention-based encoder-decoder technique, known as the trans-former, is used to enhance the performance of end-to-end automatic speech recognition (ASR). This research focuses on applying ASR end-to-end transformer-based models for the Arabic language, as the researchers’ community pays little attention to it. The Muslims Holy Qur’an book is written using Arabic diacritized text. In this paper, an end-to-end transformer model to building a robust Qur’an vs. recognition is proposed. The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework. A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic… More >

  • Open Access

    ARTICLE

    Stacking Ensemble Learning-Based Convolutional Gated Recurrent Neural Network for Diabetes Miletus

    G. Geetha1,2,*, K. Mohana Prasad1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 703-718, 2023, DOI:10.32604/iasc.2023.032530

    Abstract Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure. It causes hyperglycemia and chronic multiorgan dysfunction, including blindness, renal failure, and cardiovascular disease, if left untreated. One of the essential checks that are needed to be performed frequently in Type 1 Diabetes Mellitus is a blood test, this procedure involves extracting blood quite frequently, which leads to subject discomfort increasing the possibility of infection when the procedure is often recurring. Existing methods used for diabetes classification have less classification accuracy and suffer from vanishing gradient problems, to overcome these… More >

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