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  • Open Access

    ARTICLE

    Lamport Certificateless Signcryption Deep Neural Networks for Data Aggregation Security in WSN

    P. Saravanakumar1, T. V. P. Sundararajan2, Rajesh Kumar Dhanaraj3, Kashif Nisar4,*, Fida Hussain Memon5,6, Ag. Asri Bin Ag. Ibrahim4

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1835-1847, 2022, DOI:10.32604/iasc.2022.018953 - 24 March 2022

    Abstract Confidentiality and data integrity are essential paradigms in data aggregation owing to the various cyberattacks in wireless sensor networks (WSNs). This study proposes a novel technique named Lamport certificateless signcryption-based shift-invariant connectionist artificial deep neural networks (LCS-SICADNN) by using artificial deep neural networks to develop the data aggregation security model. This model utilises the input layer with several sensor nodes, four hidden layers to overcome different attacks (data injection, compromised node, Sybil and black hole attacks) and the output layer to analyse the given input. The Lamport one-time certificateless signcryption technique involving three different processes… More >

  • Open Access

    ARTICLE

    Prediction of Intrinsically Disordered Proteins Based on Deep Neural Network-ResNet18

    Jie Zhang, Jiaxiang Zhao*, Pengchang Xu

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 905-917, 2022, DOI:10.32604/cmes.2022.019097 - 14 March 2022

    Abstract Accurately, reliably and rapidly identifying intrinsically disordered (IDPs) proteins is essential as they often play important roles in various human diseases; moreover, they are related to numerous important biological activities. However, current computational methods have yet to develop a network that is sufficiently deep to make predictions about IDPs and demonstrate an improvement in performance. During this study, we constructed a deep neural network that consisted of five identical variant models, ResNet18, combined with an MLP network, for classification. Resnet18 was applied for the first time as a deep model for predicting IDPs, which allowed More >

  • Open Access

    ARTICLE

    A Blockchain-Based Architecture for Enabling Cybersecurity in the Internet-of-Critical Infrastructures

    Mahmoud Ragab1,2,3,*, Ali Altalbe1

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1579-1592, 2022, DOI:10.32604/cmc.2022.025828 - 24 February 2022

    Abstract Due to the drastic increase in the number of critical infrastructures like nuclear plants, industrial control systems (ICS), transportation, it becomes highly vulnerable to several attacks. They become the major targets of cyberattacks due to the increase in number of interconnections with other networks. Several research works have focused on the design of intrusion detection systems (IDS) using machine learning (ML) and deep learning (DL) models. At the same time, Blockchain (BC) technology can be applied to improve the security level. In order to resolve the security issues that exist in the critical infrastructures and… More >

  • Open Access

    ARTICLE

    Criminal Persons Recognition Using Improved Feature Extraction Based Local Phase Quantization

    P. Karuppanan1,*, K. Dhanalakshmi2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1025-1043, 2022, DOI:10.32604/iasc.2022.023712 - 08 February 2022

    Abstract Facial recognition is a trending technology that can identify or verify an individual from a video frame or digital image from any source. A major concern of facial recognition is achieving the accuracy on classification, precision, recall and F1-Score. Traditionally, numerous techniques involved in the working principle of facial recognition, as like Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Subspace Decomposition Method, Eigen Feature extraction Method and all are characterized as instable, poor generalization which leads to poor classification. But the simplified method is feature extraction by comparing the particular facial features of the… More >

  • Open Access

    ARTICLE

    Political Ideology Detection of News Articles Using Deep Neural Networks

    Khudran M. Alzhrani*

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 483-500, 2022, DOI:10.32604/iasc.2022.023914 - 05 January 2022

    Abstract Individuals inadvertently allow emotions to drive their rational thoughts to predetermined conclusions regarding political partiality issues. Being well-informed about the subject in question mitigates emotions’ influence on humans’ cognitive reasoning, but it does not eliminate bias. By nature, humans tend to pick a side based on their beliefs, personal interests, and principles. Hence, journalists’ political leaning is defining factor in the rise of the polarity of political news coverage. Political bias studies usually align subjects or controversial topics of the news coverage to a particular ideology. However, politicians as private citizens or public officials are… More >

  • Open Access

    ARTICLE

    Deep Neural Network with Strip Pooling for Image Classification of Yarn-Dyed Plaid Fabrics

    Xiaoting Zhang1, Weidong Gao2,*, Ruru Pan2

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1533-1546, 2022, DOI:10.32604/cmes.2022.018763 - 30 December 2021

    Abstract Historically, yarn-dyed plaid fabrics (YDPFs) have enjoyed enduring popularity with many rich plaid patterns, but production data are still classified and searched only according to production parameters. The process does not satisfy the visual needs of sample order production, fabric design, and stock management. This study produced an image dataset for YDPFs, collected from 10,661 fabric samples. The authors believe that the dataset will have significant utility in further research into YDPFs. Convolutional neural networks, such as VGG, ResNet, and DenseNet, with different hyperparameter groups, seemed the most promising tools for the study. This paper… More >

  • Open Access

    ARTICLE

    Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

    Abhishek Kumar Shukla*, Sujoy Das

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3557-3570, 2022, DOI:10.32604/cmc.2022.022411 - 07 December 2021

    Abstract The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining, Natural language processing, Image processing, and Information retrieval etc. Word embedding has been applied by many researchers for Information retrieval tasks. In this paper word embedding-based skip-gram model has been developed for the query expansion task. Vocabulary terms are obtained from the top “k” initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user… More >

  • Open Access

    ARTICLE

    Optimization of Deep Learning Model for Plant Disease Detection Using Particle Swarm Optimizer

    Ahmed Elaraby1,*, Walid Hamdy2, Madallah Alruwaili3

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4019-4031, 2022, DOI:10.32604/cmc.2022.022161 - 07 December 2021

    Abstract Plant diseases are a major impendence to food security, and due to a lack of key infrastructure in many regions of the world, quick identification is still challenging. Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities, motivating our mission. Because of the large range of diseases, identifying and classifying diseases with human eyes is not only time-consuming and labor intensive, but also prone to being mistaken with a high error rate. Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and… More >

  • Open Access

    ARTICLE

    Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

    N. Venkateswaran1,*, K. Umadevi2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 1-14, 2022, DOI:10.32604/csse.2022.021217 - 02 December 2021

    Abstract Huge data over the cloud computing and big data are processed over the network. The data may be stored, send, altered and communicated over the network between the source and destination. Once data send by source to destination, before reaching the destination data may be attacked by any intruders over the network. The network has numerous routers and devices to connect to internet. Intruders may attack any were in the network and breaks the original data, secrets. Detection of attack in the network became interesting task for many researchers. There are many intrusion detection feature… More >

  • Open Access

    ARTICLE

    Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

    Kexin Zhang1, Bin Lin2,*, Jixin Chen1, Xinlong Wu1, Chao Lu3, Desheng Zheng1, Lulu Tian4

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 351-360, 2022, DOI:10.32604/csse.2022.021132 - 02 December 2021

    Abstract Deep learning techniques have outstanding performance in feature extraction and model fitting. In the field of aero-engine fault diagnosis, the introduction of deep learning technology is of great significance. The aero-engine is the heart of the aircraft, and its stable operation is the primary guarantee of the aircraft. In order to ensure the normal operation of the aircraft, it is necessary to study and diagnose the faults of the aero-engine. Among the many engine failures, the one that occurs more frequently and is more hazardous is the wheeze, which often poses a great threat to… More >

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