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  • 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


    An Innovative Approach Utilizing Binary-View Transformer for Speech Recognition Task

    Muhammad Babar Kamal1, Arfat Ahmad Khan2, Faizan Ahmed Khan3, Malik Muhammad Ali Shahid4, Chitapong Wechtaisong2,*, Muhammad Daud Kamal5, Muhammad Junaid Ali6, Peerapong Uthansakul2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5547-5562, 2022, DOI:10.32604/cmc.2022.024590

    Abstract The deep learning advancements have greatly improved the performance of speech recognition systems, and most recent systems are based on the Recurrent Neural Network (RNN). Overall, the RNN works fine with the small sequence data, but suffers from the gradient vanishing problem in case of large sequence. The transformer networks have neutralized this issue and have shown state-of-the-art results on sequential or speech-related data. Generally, in speech recognition, the input audio is converted into an image using Mel-spectrogram to illustrate frequencies and intensities. The image is classified by the machine learning mechanism to generate a classification transcript. However, the audio… 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


    Artificial Intelligence Based PID Controller for an Eddy Current Dynamometer

    İhsan Uluocak1,*, Hakan Yavuz2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1229-1243, 2022, DOI:10.32604/iasc.2022.023835

    Abstract This paper presents a design and real-time application of an efficient Artificial Intelligence (AI) method assembled with PID controller of an eddy current dynamometer (ECD) for robustness due to highly nonlinear system by reason of some magnetism phenomena such as skin effect and dissipated heat of eddy currents. PID Control which is known as the most popular conventional control method in industry is inadequate for such nonlinear systems. On the other hand, Adaptive Neural Fuzzy Interference System (ANFIS), Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), and Radial Basis Neural Network (RBNN) are examples used as artificial… 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 >

  • Open Access


    Automated Facial Expression Recognition and Age Estimation Using Deep Learning

    Syeda Amna Rizwan1, Yazeed Yasin Ghadi2, Ahmad Jalal1, Kibum Kim3,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5235-5252, 2022, DOI:10.32604/cmc.2022.023328

    Abstract With the advancement of computer vision techniques in surveillance systems, the need for more proficient, intelligent, and sustainable facial expressions and age recognition is necessary. The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments. The proposed system first takes an input image pre-process it and then detects faces in the entire image. After that landmarks localization helps in the formation of synthetic face mask prediction. A novel set of features are extracted and passed to… More >

  • Open Access


    Multi-Model CNN-RNN-LSTM Based Fruit Recognition and Classification

    Harmandeep Singh Gill1,*, Osamah Ibrahim Khalaf2, Youseef Alotaibi3, Saleh Alghamdi4, Fawaz Alassery5

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 637-650, 2022, DOI:10.32604/iasc.2022.022589

    Abstract Contemporary vision and pattern recognition issues such as image, face, fingerprint identification, and recognition, DNA sequencing, often have a large number of properties and classes. To handle such types of complex problems, one type of feature descriptor is not enough. To overcome these issues, this paper proposed a multi-model recognition and classification strategy using multi-feature fusion approaches. One of the growing topics in computer and machine vision is fruit and vegetable identification and categorization. A fruit identification system may be employed to assist customers and purchasers in identifying the species and quality of fruit. Using Convolution Neural Network (CNN), Recurrent… More >

  • Open Access


    Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

    Asrar Bajaber1,*, Mai Fadel1, Lamiaa Elrefaei2

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 465-481, 2022, DOI:10.32604/csse.2022.022003

    Abstract Touch gesture biometrics authentication system is the study of user's touching behavior on his touch device to identify him. The features traditionally used in touch gesture authentication systems are extracted using hand-crafted feature extraction approach. In this work, we investigate the ability of Deep Learning (DL) to automatically discover useful features of touch gesture and use them to authenticate the user. Four different models are investigated Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) combined with LSTM (CNN-LSTM), and CNN combined with GRU(CNN-GRU). In addition, different regularization techniques are investigated such as Activity Regularizer, Batch Normalization… More >

  • Open Access


    Optimized Fuzzy Enabled Semi-Supervised Intrusion Detection System for Attack Prediction

    Gautham Praveen Ramalingam1, R. Arockia Xavier Annie1, Shobana Gopalakrishnan2,*

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1479-1492, 2022, DOI:10.32604/iasc.2022.022211

    Abstract Detection of intrusion plays an important part in data protection. Intruders will carry out attacks from a compromised user account without being identified. The key technology is the effective detection of sundry threats inside the network. However, process automation is experiencing expanded use of information communication systems, due to high versatility of interoperability and ease off 34 administration. Traditional knowledge technology intrusion detection systems are not completely tailored to process automation. The combined use of fuzziness-based and RNN-IDS is therefore highly suited to high-precision classification, and its efficiency is better compared to that of conventional machine learning approaches. This model… More >

  • Open Access


    Hybrid Approach for Taxonomic Classification Based on Deep Learning

    Naglaa. F. Soliman1,*, Samia M. Abd-Alhalem2, Walid El-Shafai2, Salah Eldin S. E. Abdulrahman3, N. Ismaiel3, El-Sayed M. El-Rabaie2, Abeer D. Algarni1, Fatimah Algarni4, Amel A. Alhussan5, Fathi E. Abd El-Samie1,2

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1881-1891, 2022, DOI:10.32604/iasc.2022.017683

    Abstract Recently, deep learning has opened a remarkable research direction in the track of bioinformatics, especially for the applications that need classification and regression. With deep learning techniques, DNA sequences can be classified with high accuracy. Firstly, a DNA sequence should be represented, numerically. After that, DNA features are extracted from the numerical representations based on deep learning techniques to improve the classification process. Recently, several architectures have been developed based on deep learning for DNA sequence classification. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are the default deep learning architectures used for this task. This paper presents a… More >

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