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  • Open Access

    ARTICLE

    Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles

    Jaeyong Kang1, Chul-Su Kim2, Jeong Won Kang3, Jeonghwan Gwak1,4,5,6,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1-14, 2022, DOI:10.32604/cmc.2022.023641

    Abstract The anomaly detection of the brake operating unit (BOU) in the brake systems on metro vehicle is critical for the safety and reliability of the trains. On the other hand, current periodic inspection and maintenance are unable to detect anomalies in an early stage. Also, building an accurate and stable system for detecting anomalies is extremely difficult. Therefore, we present an efficient model that use an ensemble of recurrent autoencoders to accurately detect the BOU abnormalities of metro trains. This is the first proposal to employ an ensemble deep learning technique to detect BOU abnormalities… More >

  • Open Access

    ARTICLE

    Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

    Masoud Alajmi1, Mohamed K. Nour2, Siwar Ben Haj Hassine3, Mimouna Abdullah Alkhonaini4, Manar Ahmed Hamza5,*, Ishfaq Yaseen5, Abu Sarwar Zamani5, Mohammed Rizwanullah5

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5499-5513, 2022, DOI:10.32604/cmc.2022.026187

    Abstract Recently, cyber physical system (CPS) has gained significant attention which mainly depends upon an effective collaboration with computation and physical components. The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems (CPES). At the same time, the rising ubiquity of wireless sensor networks (WSN) in several application areas makes it a vital part of the design of CPES. Since security and energy efficiency are the major challenging issues in CPES, this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms… More >

  • Open Access

    ARTICLE

    Wireless Intrusion Detection Based on Optimized LSTM with Stacked Auto Encoder Network

    S. Karthic1,*, S. Manoj Kumar2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 439-453, 2022, DOI:10.32604/iasc.2022.025153

    Abstract In recent years, due to the rapid progress of various technologies, wireless computer networks have developed. However, the activities of the security threats and attackers affect the data communication of these technologies. So, to protect the network against these security threats, an efficient IDS (Intrusion Detection System) is presented in this paper. Namely, optimized long short-term memory (OLSTM) network with a stacked auto-encoder (SAE) network is proposed as an IDS system. Using SAE, significant features are extracted from the databases such as input NSL-KDD database and the UNSW-NB15 database. Then extracted features are given as More >

  • Open Access

    ARTICLE

    Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures

    Abdullah S. AL-Malaise AL-Ghamdi1, Mahmoud Ragab2,3,4,*, F. J. Alsolami5, Hani Choudhry3,6, Ibrahim Rizqallah Alzahrani7

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2275-2289, 2022, DOI:10.32604/cmc.2022.026226

    Abstract Industrial Control Systems (ICS) can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate effectively. Internet of Things (IoT) integrates numerous sets of sensors and devices via a data network enabling independent processes. The incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things (IIoT), which find use in water distribution system, power plants, etc. Since the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection, an… More >

  • Open Access

    ARTICLE

    Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

    Manar Ahmed Hamza1,*, Siwar Ben Haj Hassine2, Ibrahim Abunadi3, Fahd N. Al-Wesabi2,4, Hadeel Alsolai5, Anwer Mustafa Hilal1, Ishfaq Yaseen1, Abdelwahed Motwakel1

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2581-2596, 2022, DOI:10.32604/cmc.2022.024764

    Abstract Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature Selection with Optimal Stacked Sparse Autoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAE model involves the design of the ISOFS technique More >

  • Open Access

    ARTICLE

    Improve Representation for Cross-Language Clone Detection by Pretrain Using Tree Autoencoder

    Huading Ling1, Aiping Zhang1, Changchun Yin1, Dafang Li2,*, Mengyu Chang3

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1561-1577, 2022, DOI:10.32604/iasc.2022.027349

    Abstract With the rise of deep learning in recent years, many code clone detection (CCD) methods use deep learning techniques and achieve promising results, so is cross-language CCD. However, deep learning techniques require a dataset to train the models. The dataset is typically small and has a gap between real-world clones due to the difficulty of collecting datasets for cross-language CCD. This creates a data bottleneck problem: data scale and quality issues will cause that model with a better design can still not reach its full potential. To mitigate this, we propose a tree autoencoder (TAE)… More >

  • Open Access

    ARTICLE

    LCF: A Deep Learning-Based Lightweight CSI Feedback Scheme for MIMO Networks

    Kyu-haeng Lee*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5561-5580, 2022, DOI:10.32604/cmc.2022.024562

    Abstract Recently, as deep learning technologies have received much attention for their great potential in extracting the principal components of data, there have been many efforts to apply them to the Channel State Information (CSI) feedback overhead problem, which can significantly limit Multi-Input Multi-Output (MIMO) beamforming gains. Unfortunately, since most compression models can quickly become outdated due to channel variation, timely model updates are essential for reflecting the current channel conditions, resulting in frequent additional transmissions for model sharing between transceivers. In particular, the heavy network models employed by most previous studies to achieve high compression… More >

  • Open Access

    ARTICLE

    Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

    Adel Binbusayyis*

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 501-513, 2022, DOI:10.32604/iasc.2022.022239

    Abstract The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from… More >

  • Open Access

    REVIEW

    Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges

    Gulshan Kumar1,*, Hamed Alqahtani2

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1271-1307, 2022, DOI:10.32604/cmes.2022.018418

    Abstract Cancer is one of the most critical diseases that has caused several deaths in today’s world. In most cases, doctors and practitioners are only able to diagnose cancer in its later stages. In the later stages, planning cancer treatment and increasing the patient’s survival rate becomes a very challenging task. Therefore, it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning. Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases, including cancer disease. However, manual… More >

  • Open Access

    ARTICLE

    Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

    Shakra Mehak1, M. Usman Ashraf2, Rabia Zafar3, Ahmed M. Alghamdi4, Ahmed S. Alfakeeh5, Fawaz Alassery6, Habib Hamam7, Muhammad Shafiq8,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3407-3423, 2022, DOI:10.32604/cmc.2022.022705

    Abstract Breast cancer (BC) is the most widely recognized cancer in women worldwide. By 2018, 627,000 women had died of breast cancer (World Health Organization Report 2018). To diagnose BC, the evaluation of tumours is achieved by analysis of histological specimens. At present, the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness. Pathologists contemplate three elements, 1. mitotic count, 2. gland formation, and 3. nuclear atypia, which is a laborious process that witness's variations in expert's opinions. Recently, some algorithms have been proposed for the detection of mitotic cells, but… More >

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