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  • Open Access

    ARTICLE

    The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

    S. Navaneethan1,*, P. Siva Satya Sreedhar2, S. Padmakala3, C. Senthilkumar4

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 125-135, 2023, DOI:10.32604/csse.2023.034546

    Abstract The pupil recognition method is helpful in many real-time systems, including ophthalmology testing devices, wheelchair assistance, and so on. The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size, occlusion of eyelids, and eyelashes. Deep Convolutional Neural Networks (DCNN) are being used in pupil recognition systems and have shown promising results in terms of accuracy. To improve accuracy and cope with larger datasets, this research work proposes BOC (BAT Optimized CNN)-IrisNet, which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm… More >

  • Open Access

    ARTICLE

    Applications Classification of VPN Encryption Tunnel Based on SAE-2dCNN Model

    Jie Luo*, Qingbing Ji, Lvlin Ni

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 133-142, 2022, DOI:10.32604/jai.2022.031800

    Abstract How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique. Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel. Various VPN protocols make the feature engineering of machine learning extremely difficult. Deep learning has the advantages that feature extraction does not rely on manual labor and has a good early application in classification. This article uses deep learning technology to classify the applications of VPN encryption tunnel based on the SAE-2dCNN model. SAE can effectively reduce the dimensionality of the data, which not only improves the training efficiency of 2dCNN,… More >

  • Open Access

    ARTICLE

    Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

    Sureshkumar Rajagopal1, Tamilvizhi Thanarajan2,*, Youseef Alotaibi3, Saleh Alghamdi4

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2093-2109, 2023, DOI:10.32604/csse.2023.032488

    Abstract Automated segmentation of brain tumors using Magnetic Resonance Imaging (MRI) data is critical in the analysis and monitoring of disease development. As a result, gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods. It is intended to extract characteristics from an image using the Gray Level Co-occurrence (GLC) matrix feature extraction method described in the proposed work. Using Convolutional Neural Networks (CNNs), which are commonly used in biomedical image segmentation, CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor. Using two segmentation networks, a… More >

  • Open Access

    ARTICLE

    Millimeter Wave Massive MIMO Heterogeneous Networks Using Fuzzy-Based Deep Convolutional Neural Network (FDCNN)

    Hussain Alaaedi*, Masoud Sabaei

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 633-646, 2023, DOI:10.32604/iasc.2023.032462

    Abstract Enabling high mobility applications in millimeter wave (mmWave) based systems opens up a slew of new possibilities, including vehicle communications in addition to wireless virtual/augmented reality. The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links. In this research work, the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated. The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output (MIMO) which is utilized in a hyperdense… More >

  • Open Access

    ARTICLE

    Deep-BERT: Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

    Md. Anwar Hussen Wadud1, M. F. Mridha1, Jungpil Shin2,*, Kamruddin Nur3, Aloke Kumar Saha4

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.027841

    Abstract Offensive messages on social media, have recently been frequently used to harass and criticize people. In recent studies, many promising algorithms have been developed to identify offensive texts. Most algorithms analyze text in a unidirectional manner, where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences. In addition, there are many separate models for identifying offensive texts based on monolingual and multilingual, but there are a few models that can detect both monolingual and multilingual-based offensive texts. In this study, a detection system has been developed for both monolingual and multilingual offensive texts by… More >

  • Open Access

    ARTICLE

    An Optimized Transfer Learning Model Based Kidney Stone Classification

    S. Devi Mahalakshmi*

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1387-1395, 2023, DOI:10.32604/csse.2023.027610

    Abstract The kidney is an important organ of humans to purify the blood. The healthy function of the kidney is always essential to balance the salt, potassium and pH levels in the blood. Recently, the failure of kidneys happens easily to human beings due to their lifestyle, eating habits and diabetes diseases. Early prediction of kidney stones is compulsory for timely treatment. Image processing-based diagnosis approaches provide a greater success rate than other detection approaches. In this work, proposed a kidney stone classification method based on optimized Transfer Learning(TL). The Deep Convolutional Neural Network (DCNN) models of DenseNet169, MobileNetv2 and GoogleNet… More >

  • Open Access

    ARTICLE

    Qualitative Abnormalities of Peripheral Blood Smear Images Using Deep Learning Techniques

    G. Arutperumjothi1,*, K. Suganya Devi2, C. Rani3, P. Srinivasan4

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1069-1086, 2023, DOI:10.32604/iasc.2023.028423

    Abstract In recent years, Peripheral blood smear is a generic analysis to assess the person’s health status. Manual testing of Peripheral blood smear images are difficult, time-consuming and is subject to human intervention and visual error. This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques. Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images. In order to mitigate this issue Deep Convolution Neural Network (DCNN) based automatic classification technique is introduced with the classification of eight groups… More >

  • Open Access

    ARTICLE

    A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

    Sara A. Alameen*, Areej M. Alhothali

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 895-912, 2023, DOI:10.32604/csse.2023.024643

    Abstract Today, fatalities, physical injuries, and significant economic losses occur due to car accidents. Among the leading causes of car accidents is drowsiness behind the wheel, which can affect any driver. Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents. This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos. This model depends on integrating a 3D convolutional neural network (3D-CNN) and long short-term memory (LSTM). The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames. The learned… More >

  • Open Access

    ARTICLE

    A Steganography Model Data Protection Method Based on Scrambling Encryption

    Xintao Duan1,*, Zhiqiang Shao1, Wenxin Wang1, En Zhang1, Dongli Yue1, Chuan Qin2, Haewoon Nam3

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5363-5375, 2022, DOI:10.32604/cmc.2022.027807

    Abstract At present, the image steganography method based on CNN has achieved good results. The trained model and its parameters are of great value. Once leaked, the secret image will be exposed. To protect the security of steganographic network model parameters in the transmission process, an idea based on network model parameter scrambling is proposed in this paper. Firstly, the sender trains the steganography network and extraction network, encrypts the extraction network parameters with the key shared by the sender and the receiver, then sends the extraction network and parameters to the receiver through the public channel, and the receiver recovers… More >

  • Open Access

    ARTICLE

    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 >

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