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  • Open Access


    Drug–Target Interaction Prediction Model Using Optimal Recurrent Neural Network

    G. Kavipriya*, D. Manjula

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1675-1689, 2023, DOI:10.32604/iasc.2023.027670

    Abstract Drug-target interactions prediction (DTIP) remains an important requirement in the field of drug discovery and human medicine. The identification of interaction among the drug compound and target protein plays an essential process in the drug discovery process. It is a lengthier and complex process for predicting the drug target interaction (DTI) utilizing experimental approaches. To resolve these issues, computational intelligence based DTIP techniques were developed to offer an efficient predictive model with low cost. The recently developed deep learning (DL) models can be employed for the design of effective predictive approaches for DTIP. With this motivation, this paper presents a… More >

  • Open Access


    Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

    M. Mohan1,*, P. C. Kishore Raja2, P. Velmurugan3, A. Kulothungan4

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1151-1163, 2023, DOI:10.32604/iasc.2023.026255

    Abstract Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss. The proposed model uses a real time dataset of fifteen Stocks as input into the system and based on the data, predicts or forecast future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting… More >

  • Open Access


    Detection of DDoS Attack in IoT Networks Using Sample Selected RNN-ELM

    S. Hariprasad1,*, T. Deepa1, N. Bharathiraja2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1425-1440, 2022, DOI:10.32604/iasc.2022.022856

    Abstract The Internet of Things (IoT) is a global information and communication technology which aims to connect any type of device to the internet at any time and in any location. Nowadays billions of IoT devices are connected to the world, this leads to easily cause vulnerability to IoT devices. The increasing of users in different IoT-related applications leads to more data attacks is happening in the IoT networks after the fog layer. To detect and reduce the attacks the deep learning model is used. In this article, a hybrid sample selected recurrent neural network-extreme learning machine (hybrid SSRNN-ELM) algorithm that… More >

  • Open Access


    Mutation Prediction for Coronaviruses Using Genome Sequence and Recurrent Neural Networks

    Pranav Pushkar1, Christo Ananth2, Preeti Nagrath1, Jehad F. Al-Amri5, Vividha1, Anand Nayyar3,4,*

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

    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 been cultured and stored as… More >

  • Open Access


    Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

    Nasebah Almufadi1, Ali Mustafa Qamar1,2,*

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1255-1270, 2022, DOI:10.32604/csse.2022.025029

    Abstract Currently, mobile communication is one of the widely used means of communication. Nevertheless, it is quite challenging for a telecommunication company to attract new customers. The recent concept of mobile number portability has also aggravated the problem of customer churn. Companies need to identify beforehand the customers, who could potentially churn out to the competitors. In the telecommunication industry, such identification could be done based on call detail records. This research presents an extensive experimental study based on various deep learning models, such as the 1D convolutional neural network (CNN) model along with the recurrent neural network (RNN) and deep… More >

  • Open Access


    Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

    Ravindra Parshuram Bachate1, Ashok Sharma2, Amar Singh3, Ayman A. Aly4, Abdulaziz H. Alghtani4, Dac-Nhuong Le5,6,*

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 439-454, 2022, DOI:10.32604/csse.2022.024214

    Abstract Communication is a significant part of being human and living in the world. Diverse kinds of languages and their variations are there; thus, one person can speak any language and cannot effectively communicate with one who speaks that language in a different accent. Numerous application fields such as education, mobility, smart systems, security, and health care systems utilize the speech or voice recognition models abundantly. Though, various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional languages. It is necessary to understand… More >

  • Open Access


    Arrhythmia Detection and Classification by Using Modified Recurrent Neural Network

    Ajina Mohamed Ameer*, M. Victor Jose

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1349-1361, 2022, DOI:10.32604/iasc.2022.023924

    Abstract This paper presents a novel approach for arrhythmia detection and classification using modified recurrent neural network. In medicine and analytics, arrhythmia detections is a hot topic, specifically when it comes to cardiac identification. In the research methodology, there are 4 main steps. Acquisition and pre-processing of data, electrocardiogram (ECG) feature extraction utilizing QRS (Quick Response Systems) peak, and ECG signal classification using a Modified Recurrent Neural Network (Modified RNN) for arrhythmia diagnosis. The Massachusetts Institute of Technology-Beth Israel Hospital. (MIT-BIH) Arrhythmia database was used, as well as the image accuracy. Medium filter is used in the pre-processing. Feature extraction is… More >

  • Open Access


    Parkinson's Detection Using RNN-Graph-LSTM with Optimization Based on Speech Signals

    Ahmed S. Almasoud1, Taiseer Abdalla Elfadil Eisa2, Fahd N. Al-Wesabi3,4, Abubakar Elsafi5, Mesfer Al Duhayyim6, Ishfaq Yaseen7, Manar Ahmed Hamza7,*, Abdelwahed Motwakel7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 871-886, 2022, DOI:10.32604/cmc.2022.024596

    Abstract Early detection of Parkinson's Disease (PD) using the PD patients’ voice changes would avoid the intervention before the identification of physical symptoms. Various machine learning algorithms were developed to detect PD detection. Nevertheless, these ML methods are lack in generalization and reduced classification performance due to subject overlap. To overcome these issues, this proposed work apply graph long short term memory (GLSTM) model to classify the dynamic features of the PD patient speech signal. The proposed classification model has been further improved by implementing the recurrent neural network (RNN) in batch normalization layer of GLSTM and optimized with adaptive moment… More >

  • Open Access


    Optimized LSTM with Dimensionality Reduction Based Gene Expression Data Classification

    S. Jacophine Susmi*

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1139-1152, 2022, DOI:10.32604/iasc.2022.023865

    Abstract The classification of cancer subtypes is substantial for the diagnosis and treatment of cancer. However, the gene expression data used for cancer subtype classification are high dimensional in nature and small in sample size. In this paper, an efficient dimensionality reduction with optimized long short term memory, algorithm (OLSTM) is used for gene expression data classification. The main three stages of the proposed method are explicitly pre-processing, dimensional reduction, and gene expression data classification. In the pre-processing method, the missing values and redundant values are removed for high-quality data. Following, the dimensional reduction is done by orthogonal locality preserving projections… More >

  • Open Access


    Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

    S. Manikandan1,*, B. Nagaraj2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 761-775, 2022, DOI:10.32604/iasc.2022.023398

    Abstract Weather forecasting is primarily related to the prediction of weather conditions that becomes highly important in diverse applications like drought discovery, severe weather forecast, climate monitoring, agriculture, aviation, telecommunication, etc. Data-driven computer modelling with Artificial Neural Networks (ANN) can be used to solve non-linear problems. Presently, Deep Learning (DL) based weather forecasting models can be designed to accomplish reasonable predictive performance. In this aspect, this study presents a Hyper Parameter Tuned Bidirectional Gated Recurrent Neural Network (HPT-BiGRNN) technique for weather forecasting. The HPT-BiGRNN technique aims to utilize the past weather data for training the BiGRNN model and achieve the effective… More >

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