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  • Open Access

    ARTICLE

    SMOGN, MFO, and XGBoost Based Excitation Current Prediction Model for Synchronous Machine

    Ping-Huan Kuo1,2, Yu-Tsun Chen1, Her-Terng Yau1,2,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2687-2709, 2023, DOI:10.32604/csse.2023.036293 - 03 April 2023

    Abstract The power factor is the ratio between the active and apparent power, and it is available to determine the operational capability of the intended circuit or the parts. The excitation current of the synchronous motor is an essential parameter required for adjusting the power factor because it determines whether the motor is under the optimal operating status. Although the excitation current should predict with the experimental devices, such a method is unsuitable for online real-time prediction. The artificial intelligence algorithm can compensate for the defect of conventional measurement methods requiring the measuring devices and the… More >

  • Open Access

    ARTICLE

    An Improvement in Power Quality and By-Product of the Run-Off River Micro Hydro Power Plant

    Ignatius Riyadi Mardiyanto1, Jangkung Raharjo2,*, Sri Utami1, Wahyu Budi Mursanto1, Agoeng Hardjatmo Rahardjo1

    Energy Engineering, Vol.120, No.6, pp. 1295-1305, 2023, DOI:10.32604/ee.2023.027756 - 03 April 2023

    Abstract Utilization of Micro Hydro Power Plant at the Gunung Halu case study type run-off the river is a household use only in the afternoon, around 5 pm until bedtime at about 10 pm. Therefore, more than 75% of the energy is lost. This case study lost power which can be used as a by-product, such as for drying coffee beans. In this case study, a design was carried out to obtain by-products and improve power quality in the electrical system. In addition, they complain about the poor quality of power controlled by frequency using Triac-Based More > Graphic Abstract

    An Improvement in Power Quality and By-Product of the Run-Off River Micro Hydro Power Plant

  • Open Access

    ARTICLE

    Simulation Study of the Control Strategy of a DC Inverter Heat Pump Using a DC Distribution Network

    Siwei Han1,*, Xianglong Li2, Wei Zhao1, Linyu Wang1, Anqi Liang2, Shuang Zeng2

    Energy Engineering, Vol.120, No.6, pp. 1421-1444, 2023, DOI:10.32604/ee.2023.027094 - 03 April 2023

    Abstract Photovoltaics, energy storage, direct current and flexibility (PEDF) are important pillars of achievement on the path to manufacturing nearly zero energy buildings (NZEBs). HVAC systems, which are an important part of public buildings, play a key role in adapting to PDEF systems. This research studied the basic principles and operational control strategies of a DC inverter heat pump using a DC distribution network with the aim of contributing to the development and application of small DC distribution systems. Along with the characteristics of a DC distribution network and different operating conditions, a DC inverter heat… More >

  • 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 - 31 March 2023

    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… More >

  • Open Access

    ARTICLE

    A Cyber-Attack Detection System Using Late Fusion Aggregation Enabled Cyber-Net

    P. Shanmuga Prabha*, S. Magesh Kumar

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3101-3119, 2023, DOI:10.32604/iasc.2023.034885 - 15 March 2023

    Abstract Today, securing devices connected to the internet is challenging as security threats are generated through various sources. The protection of cyber-physical systems from external attacks is a primary task. The presented method is planned on the prime motive of detecting cybersecurity attacks and their impacted parameters. The proposed architecture employs the LYSIS dataset and formulates Multi Variant Exploratory Data Analysis (MEDA) through Principle Component Analysis (PCA) and Singular Value Decomposition (SVD) for the extraction of unique parameters. The feature mappings are analyzed with Recurrent 2 Convolutional Neural Network (R2CNN) and Gradient Boost Regression (GBR) to 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 - 15 March 2023

    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… More >

  • Open Access

    ARTICLE

    Generalized Jaccard Similarity Based Recurrent DNN for Virtualizing Social Network Communities

    R. Gnanakumari1,*, P. Vijayalakshmi2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2719-2730, 2023, DOI:10.32604/iasc.2023.034145 - 15 March 2023

    Abstract In social data analytics, Virtual Community (VC) detection is a primary challenge in discovering user relationships and enhancing social recommendations. VC formation is used for personal interaction between communities. But the usual methods didn’t find the Suspicious Behaviour (SB) needed to make a VC. The Generalized Jaccard Suspicious Behavior Similarity-based Recurrent Deep Neural Network Classification and Ranking (GJSBS-RDNNCR) Model addresses these issues. The GJSBS-RDNNCR model comprises four layers for VC formation in Social Networks (SN). In the GJSBS-RDNNCR model, the SN is given as an input at the input layer. After that, the User’s Behaviors… More >

  • Open Access

    ARTICLE

    Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

    Mrim M. Alnfiai1,2, Nabil Almalki1,3, Fahd N. Al-Wesabi4,*, Mesfer Alduhayyem5, Anwer Mustafa Hilal6, Manar Ahmed Hamza6

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2639-2652, 2023, DOI:10.32604/iasc.2023.034069 - 15 March 2023

    Abstract Text-To-Speech (TTS) is a speech processing tool that is highly helpful for visually-challenged people. The TTS tool is applied to transform the texts into human-like sounds. However, it is highly challenging to accomplish the TTS outcomes for the non-diacritized text of the Arabic language since it has multiple unique features and rules. Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language. But, such signs are not frequently used in the texts written in the Arabic language since its speakers… 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 - 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 >

  • Open Access

    ARTICLE

    Speech Separation Algorithm Using Gated Recurrent Network Based on Microphone Array

    Xiaoyan Zhao1,*, Lin Zhou2, Yue Xie1, Ying Tong1, Jingang Shi3

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3087-3100, 2023, DOI:10.32604/iasc.2023.030180 - 15 March 2023

    Abstract Speech separation is an active research topic that plays an important role in numerous applications, such as speaker recognition, hearing prosthesis, and autonomous robots. Many algorithms have been put forward to improve separation performance. However, speech separation in reverberant noisy environment is still a challenging task. To address this, a novel speech separation algorithm using gate recurrent unit (GRU) network based on microphone array has been proposed in this paper. The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost. The proposed algorithm extracts the sub-band steered… More >

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