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Search Results (15)
  • Open Access

    ARTICLE

    A Feature Learning-Based Model for Analyzing Students’ Performance in Supportive Learning

    P. Prabhu1, P. Valarmathie2,*, K. Dinakaran3

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2989-3005, 2023, DOI:10.32604/iasc.2023.028659

    Abstract Supportive learning plays a substantial role in providing a quality education system. The evaluation of students’ performance impacts their deeper insight into the subject knowledge. Specifically, it is essential to maintain the baseline foundation for building a broader understanding of their careers. This research concentrates on establishing the students’ knowledge relationship even in reduced samples. Here, Synthetic Minority Oversampling TEchnique (SMOTE) technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy. When the initial processing is not done substantially, it leads to misleading prediction accuracy. This research concentrates on modelling an efficient… More >

  • Open Access

    ARTICLE

    Deep Learning Based Face Detection and Identification of Criminal Suspects

    S. Sandhya1, A. Balasundaram2,*, Ayesha Shaik1

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2331-2343, 2023, DOI:10.32604/cmc.2023.032715

    Abstract Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person’s face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network (DNN) model which employs a… More >

  • Open Access

    ARTICLE

    An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

    Passent El-kafrawy1,2, Maie Aboghazalah2,*, Abdelmoty M. Ahmed3, Hanaa Torkey4, Ayman El-Sayed4

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 909-926, 2023, DOI:10.32604/cmes.2022.021713

    Abstract Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption of medical images is very important to secure patient information. Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem. In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted.… More >

  • Open Access

    ARTICLE

    Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

    Amal H. Alharbi1, S. Karthick2, K. Venkatachalam3, Mohamed Abouhawwash4,5, Doaa Sami Khafaga1,*

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2773-2787, 2023, DOI:10.32604/iasc.2023.030763

    Abstract Recent security applications in mobile technologies and computer systems use face recognition for high-end security. Despite numerous security techniques, face recognition is considered a high-security control. Developers fuse and carry out face identification as an access authority into these applications. Still, face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user. In the existing spoofing detection algorithm, there was some loss in the recreation of images. This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of… More >

  • Open Access

    ARTICLE

    Early Detection of Heartbeat from Multimodal Data Using RPA Learning with KDNN-SAE

    A. K. S. Saranya1,*, T. Jaya2

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 545-562, 2023, DOI:10.32604/csse.2023.029975

    Abstract Heartbeat detection stays central to cardiovascular an electrocardiogram (ECG) is used to help with disease diagnosis and management. Existing Convolutional Neural Network (CNN)-based methods suffer from the less generalization problem thus; the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed. In contrast, this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders (KDNN-SAE) that computes the disease before the exact heart rate by combining features from multiple ECG Signals. Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.… More >

  • Open Access

    ARTICLE

    An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

    P. Sherubha1,*, S. P. Sasirekha2, A. Dinesh Kumar Anguraj3, J. Vakula Rani4, Raju Anitha3, S. Phani Praveen5,6, R. Hariharan Krishnan5,6

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 149-166, 2023, DOI:10.32604/csse.2023.024424

    Abstract The Cloud system shows its growing functionalities in various industrial applications. The safety towards data transfer seems to be a threat where Network Intrusion Detection System (NIDS) is measured as an essential element to fulfill security. Recently, Machine Learning (ML) approaches have been used for the construction of intellectual IDS. Most IDS are based on ML techniques either as unsupervised or supervised. In supervised learning, NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns. Similarly, the unsupervised model fails to provide a satisfactory outcome. Hence, to boost the functionality of… More >

  • Open Access

    ARTICLE

    Swarm Optimization and Machine Learning for Android Malware Detection

    K. Santosh Jhansi1,2,*, P. Ravi Kiran Varma2, Sujata Chakravarty3

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6327-6345, 2022, DOI:10.32604/cmc.2022.030878

    Abstract Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential features. The nature-inspired wrapper-based algorithms… More >

  • Open Access

    ARTICLE

    Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

    Kyamelia Roy1, Sheli Sinha Chaudhuri1, Sayan Pramanik2, Soumen Banerjee2,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 647-662, 2023, DOI:10.32604/csse.2023.024997

    Abstract In recent years, computer vision finds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture. Automatic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies. Waterways being an important medium of transport require continuous monitoring for protection of national security. The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea. This paper proposes a deep learning based model capable enough to… More >

  • Open Access

    ARTICLE

    Deep Learning Based Intrusion Detection in Cloud Services for Resilience Management

    S. Sreenivasa Chakravarthi1,*, R. Jagadeesh Kannan2, V. Anantha Natarajan3, Xiao-Zhi Gao4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5117-5133, 2022, DOI:10.32604/cmc.2022.022351

    Abstract In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology, and invest in building infrastructure. All the developed and developing countries focus on building resilient infrastructure and promote sustainable developments by fostering innovation. At this juncture the cloud computing has become an important information and communication technologies model influencing sustainable development of the industries in the developing countries. As part of the innovations happening in the industrial sector, a new concept termed as ‘smart manufacturing’ has emerged, which employs the benefits of emerging technologies like internet of things and cloud computing.… More >

  • Open Access

    ARTICLE

    Building Graduate Salary Grading Prediction Model Based on Deep Learning

    Jong-Yih Kuo*, Hui-Chi Lin, Chien-Hung Liu

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 53-68, 2021, DOI:10.32604/iasc.2021.014437

    Abstract Predicting salary trends of students after employment is vital for helping students to develop their career plans. Particularly, salary is not only considered employment information for students to pursue jobs, but also serves as an important indicator for measuring employability and competitiveness of graduates. This paper considers salary prediction as an ordinal regression problem and uses deep learning techniques to build a salary prediction model for determining the relative ordering between different salary grades. Specifically, to solve this problem, the model uses students’ personal information, grades, and family data as input features and employs a multi-output deep neural network to… More >

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