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  • 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 - 15 June 2022

    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 More >

  • Open Access

    ARTICLE

    Emotional Vietnamese Speech Synthesis Using Style-Transfer Learning

    Thanh X. Le, An T. Le, Quang H. Nguyen*

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1263-1278, 2023, DOI:10.32604/csse.2023.026234 - 15 June 2022

    Abstract In recent years, speech synthesis systems have allowed for the production of very high-quality voices. Therefore, research in this domain is now turning to the problem of integrating emotions into speech. However, the method of constructing a speech synthesizer for each emotion has some limitations. First, this method often requires an emotional-speech data set with many sentences. Such data sets are very time-intensive and labor-intensive to complete. Second, training each of these models requires computers with large computational capabilities and a lot of effort and time for model tuning. In addition, each model for each… More >

  • Open Access

    ARTICLE

    Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

    C. D. Prem Kumar1,*, S. Saravana Sundaram2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 881-899, 2023, DOI:10.32604/iasc.2023.025766 - 06 June 2022

    Abstract The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content. An effective technique for tampering the identification is the copy-move forgery. Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification. Contrastingly, deep learning (DL) models have demonstrated significant performance over the other statistical techniques. With this motivation, this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection (ODTL-CMFD) technique. The presented ODTL-CMFD technique aims to derive a DL… More >

  • Open Access

    ARTICLE

    Weed Classification Using Particle Swarm Optimization and Deep Learning Models

    M. Manikandakumar1,*, P. Karthikeyan2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 913-927, 2023, DOI:10.32604/csse.2023.025434 - 01 June 2022

    Abstract Weed is a plant that grows along with nearly all field crops, including rice, wheat, cotton, millets and sugar cane, affecting crop yield and quality. Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity. To address this issue, an efficient weed classification model is proposed with the Deep Convolutional Neural Network (CNN) that implements automatic feature extraction and performs complex feature learning for image classification. Throughout this work, weed images were trained using the proposed CNN model with evolutionary computing… More >

  • Open Access

    ARTICLE

    Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

    Joe Louis Paul Ignatius1,*, Sasirekha Selvakumar1, Kavin Gabriel Joe Louis Paul2, Aadhithya B. Kailash1, S. Keertivaas1, S. A. J. Akarvin Raja Prajan1

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 81-97, 2023, DOI:10.32604/csse.2023.025195 - 01 June 2022

    Abstract Tuberculosis (TB) is a severe infection that mostly affects the lungs and kills millions of people’s lives every year. Tuberculosis can be diagnosed using chest X-rays (CXR) and data-driven deep learning (DL) approaches. Because of its better automated feature extraction capability, convolutional neural networks (CNNs) trained on natural images are particularly effective in image categorization. A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets. Ten different deep CNNs (Resnet50, Resnet101, Resnet152, InceptionV3, VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet) are trained and tested for identifying… More >

  • Open Access

    ARTICLE

    Seeded Transfer Learning for Enhanced Attack Trace and Effective Deception

    Jalaj Pateria1,*, Laxmi Ahuja1, Subhranil Som2

    Journal of Cyber Security, Vol.4, No.4, pp. 223-238, 2022, DOI:10.32604/jcs.2023.040186 - 10 August 2023

    Abstract Cyberattacks have reached their peak during COVID-19, and intruders urge to gain the upper hand in the cybersecurity battlefield, even gaining dominance. Now intruders are trying harder to elude behavior analysis techniques, which in turn gets organization security to come for a toss. This phenomenon is even more prevalent in agentless environments (IOT devices, mobile devices), where we do not have any access to edge devices and rely on packet data to predict any attack and its actors. In this paper, we shall be discussing enhancing the accuracy of anomalous behavior detection techniques for efficient… More >

  • Open Access

    ARTICLE

    A Detailed Study on IoT Platform for ECG Monitoring Using Transfer Learning

    Md Saidul Islam*

    Journal on Internet of Things, Vol.4, No.3, pp. 127-140, 2022, DOI:10.32604/jiot.2022.037489 - 12 June 2023

    Abstract Internet of Things (IoT) technologies used in health have the potential to address systemic difficulties by offering tools for cost reduction while improving diagnostic and treatment efficiency. Numerous works on this subject focus on clarifying the constructs and interfaces between various components of an IoT platform, such as knowledge generation via smart sensors collecting biosignals from the human body and processing them via data mining and, in recent times, deep neural networks offered to host on cloud computing architecture. These approaches are intended to assist healthcare professionals in their daily activities. In this comparative research, More >

  • Open Access

    ARTICLE

    Modeling & Evaluating the Performance of Convolutional Neural Networks for Classifying Steel Surface Defects

    Nadeem Jabbar Chaudhry1,*, M. Bilal Khan2, M. Javaid Iqbal1, Siddiqui Muhammad Yasir3

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 245-259, 2022, DOI:10.32604/jai.2022.038875 - 25 May 2023

    Abstract Recently, outstanding identification rates in image classification tasks were achieved by convolutional neural networks (CNNs). to use such skills, selective CNNs trained on a dataset of well-known images of metal surface defects captured with an RGB camera. Defects must be detected early to take timely corrective action due to production concerns. For image classification up till now, a model-based method has been utilized, which indicated the predicted reflection characteristics of surface defects in comparison to flaw-free surfaces. The problem of detecting steel surface defects has grown in importance as a result of the vast range… More >

  • Open Access

    ARTICLE

    Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

    Areej A. Malibari1, Jaber S. Alzahrani2, Marwa Obayya3, Noha Negm4,5, Mohammed Abdullah Al-Hagery6, Ahmed S. Salama7, Anwer Mustafa Hilal8,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6443-6459, 2022, DOI:10.32604/cmc.2022.031324 - 28 July 2022

    Abstract Osteosarcoma is a type of malignant bone tumor that is reported across the globe. Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the detection and classification of malignancies in biomedical images. In this regard, the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning (BOIC-EHODTL) model. The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma. At the initial stage, Gabor Filter (GF) is applied as a pre-processing technique to get rid of the noise from images. In addition,… More >

  • Open Access

    ARTICLE

    An Improved Transfer-Learning for Image-Based Species Classification of Protected Indonesians Birds

    Chao-Lung Yang1, Yulius Harjoseputro2,3, Yu-Chen Hu4, Yung-Yao Chen2,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4577-4593, 2022, DOI:10.32604/cmc.2022.031305 - 28 July 2022

    Abstract This research proposed an improved transfer-learning bird classification framework to achieve a more precise classification of Protected Indonesia Birds (PIB) which have been identified as the endangered bird species. The framework takes advantage of using the proposed sequence of Batch Normalization Dropout Fully-Connected (BNDFC) layers to enhance the baseline model of transfer learning. The main contribution of this work is the proposed sequence of BNDFC that can be applied to any Convolutional Neural Network (CNN) based model to improve the classification accuracy, especially for image-based species classification problems. The experiment results show that the proposed More >

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