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  • Open Access


    Novel Multimodal Biometric Feature Extraction for Precise Human Identification

    J. Vasavi1, M. S. Abirami2,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1349-1363, 2023, DOI:10.32604/iasc.2023.032604

    Abstract In recent years, biometric sensors are applicable for identifying important individual information and accessing the control using various identifiers by including the characteristics like a fingerprint, palm print, iris recognition, and so on. However, the precise identification of human features is still physically challenging in humans during their lifetime resulting in a variance in their appearance or features. In response to these challenges, a novel Multimodal Biometric Feature Extraction (MBFE) model is proposed to extract the features from the noisy sensor data using a modified Ranking-based Deep Convolution Neural Network (RDCNN). The proposed MBFE model enables the feature extraction from… More >

  • Open Access


    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


    Detection of Microbial Activity in Silver Nanoparticles Using Modified Convolution Network

    D. Devina Merin1,*, P. Jagatheeswari2

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1849-1860, 2022, DOI:10.32604/iasc.2022.024495

    Abstract The Deep learning (DL) network is an effective technique that has extended application in medicine, robotics, biotechnology, biometrics and communication. The unique architecture of DL networks can be trained according to classify any complex tasks in a limited duration. In the proposed work a deep convolution neural network of DL is trained to classify the antimicrobial activity of silver nanoparticles (AgNP). The process involves two processing steps; synthesis of silver nanoparticles and classification (SEM) of AgNP based on the antimicrobial activity. AgNP images from scanning electron microscope are pre-processed using Adaptive Histogram Equalization in the networking system and the DL… More >

  • Open Access


    Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

    V. Ulagamuthalvi1, G. Kulanthaivel2,*, A. Balasundaram3, Arun Kumar Sivaraman4

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 275-289, 2022, DOI:10.32604/csse.2022.023737

    Abstract One of the fast-growing disease affecting women’s health seriously is breast cancer. It is highly essential to identify and detect breast cancer in the earlier stage. This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately. Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images. Existing methods focused on using various conventional and machine learning methods for processing natural and medical images. It is inadequate for the image where the coarse structure matters… More >

  • Open Access


    Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

    S. Sudha*, A. Srinivasan, T. Gayathri Devi

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 597-609, 2022, DOI:10.32604/cmc.2022.024065

    Abstract Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques alone to detect diabetic retinopathy.… More >

  • Open Access


    Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

    Mohamed Ibrahim Waly1, Mohamed Yacin Sikkandar1, Mohamed Abdelkader Aboamer1, Seifedine Kadry2, Orawit Thinnukool3,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3295-3309, 2022, DOI:10.32604/cmc.2022.020713

    Abstract Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical… More >

  • Open Access


    Breast Cancer Classification Using Deep Convolution Neural Network with Transfer Learning

    Hanan A. Hosni Mahmoud*, Amal H. Alharbi, Doaa S. Khafga

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 803-814, 2021, DOI:10.32604/iasc.2021.018607

    Abstract In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms. We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer. The Deep-CNN is utilized to extract features from mammograms. A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases. The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier. Two texture features are included: texture features of spatial dependency and gradient-based histograms. Both are employed to locate… More >

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