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Search Results (19)
  • Open Access

    ARTICLE

    Intrusion Detection System for Big Data Analytics in IoT Environment

    M. Anuradha1,*, G. Mani2, T. Shanthi3, N. R. Nagarajan4, P. Suresh5, C. Bharatiraja6

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 381-396, 2022, DOI:10.32604/csse.2022.023321

    Abstract In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS… More >

  • Open Access

    ARTICLE

    Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks

    Atif Mehmood1,*, Ahed Abugabah1, Ahmed Ali AlZubi2, Louis Sanzogni3

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 305-315, 2022, DOI:10.32604/csse.2022.018520

    Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder, causing the most common dementia in the elderly peoples. The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA. Magnetic resonance imaging (MRI) is the leading modality used for the diagnosis of AD. Deep learning based approaches have produced impressive results in this domain. The early diagnosis of AD depends on the efficient use of classification approach. To address this issue, this study proposes a system using two convolutional neural networks (CNN) based approaches for an early diagnosis of AD automatically. In the proposed… More >

  • Open Access

    ARTICLE

    An Enhanced Privacy Preserving, Secure and Efficient Authentication Protocol for VANET

    Safiullah Khan1, Ali Raza2,3, Seong Oun Hwang4,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3703-3719, 2022, DOI:10.32604/cmc.2022.023476

    Abstract Vehicular ad hoc networks (VANETs) have attracted growing interest in both academia and industry because they can provide a viable solution that improves road safety and comfort for travelers on roads. However, wireless communications over open-access environments face many security and privacy issues that may affect deployment of large-scale VANETs. Researchers have proposed different protocols to address security and privacy issues in a VANET, and in this study we cryptanalyze some of the privacy preserving protocols to show that all existing protocols are vulnerable to the Sybil attack. The Sybil attack can be used by malicious actors to create fake… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

    Anwer Mustafa Hilal1, Hadeel Alsolai2, Fahd N. Al-Wesabi3, Mohammed Abdullah Al-Hagery4, Manar Ahmed Hamza1,*, Mesfer Al Duhayyim5

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6289-6304, 2022, DOI:10.32604/cmc.2022.021522

    Abstract In present digital era, data science techniques exploit artificial intelligence (AI) techniques who start and run small and medium-sized enterprises (SMEs) to have an impact and develop their businesses. Data science integrates the conventions of econometrics with the technological elements of data science. It make use of machine learning (ML), predictive and prescriptive analytics to effectively understand financial data and solve related problems. Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations. At the same time, it is needed to develop an effective tool which can assist small to medium sized… More >

  • Open Access

    ARTICLE

    Superposition of Functional Contours Based Prosodic Feature Extraction for Speech Processing

    Shahid Ali Mahar1, Mumtaz Hussain Mahar1, Javed Ahmed Mahar1, Mehedi Masud2, Muneer Ahmad3, NZ Jhanjhi4,*, Mirza Abdur Razzaq1

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 183-197, 2021, DOI:10.32604/iasc.2021.015755

    Abstract Speech signal analysis for the extraction of speech elements is viable in natural language applications. Rhythm, intonation, stress, and tone are the elements of prosody. These features are essential in emotional speech, speech to speech, speech recognition, and other applications. The current study attempts to extract the pitch and duration from historical Sindhi sound clips using the functional contours model’s superposition. The sampled sound clips contained the speech of 273 undergraduates living in 5 districts of the Sindhi province. Several Python libraries are available for the application of this model. We used these libraries for the extraction of prosodic data… More >

  • Open Access

    ARTICLE

    Detection of COVID-19 Enhanced by a Deep Extreme Learning Machine

    Aaqib Inam1,*, Zhuli1, Ayesha Sarwar1, Salah-ud-din2, Ayesha Atta3, Iftikhar Naaseer4, Shahan Yamin Siddiqui5,6, Muhammad Adnan Khan7

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 701-712, 2021, DOI:10.32604/iasc.2021.014235

    Abstract The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for the diagnosis of disease are… More >

  • Open Access

    REVIEW

    Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review

    H. Asha Gnana Priya1, J. Anitha1, Daniela Elena Popescu2, Anju Asokan1, D. Jude Hemanth1, Le Hoang Son3,4,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2771-2786, 2021, DOI:10.32604/cmc.2021.012907

    Abstract Diabetic Retinopathy (DR) is an eye disease that mainly affects people with diabetes. People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage. Once the vision is lost, it cannot be regained but can be prevented from causing any further damage. Early diagnosis of DR is required for preventing vision loss, for which a trained ophthalmologist is required. The clinical practice is time-consuming and is not much successful in identifying DR at early stages. Hence, Computer-Aided Diagnosis (CAD) system is a suitable alternative for screening and grading… More >

  • Open Access

    ARTICLE

    3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems

    Jing Zhang1,2, Keping Yu3,*, Zheng Wen4, Xin Qi3, Anup Kumar Paul5

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2087-2104, 2021, DOI:10.32604/cmc.2020.014220

    Abstract The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images. Generally, during the acquisition of images in real-time, motion blur, caused by camera shaking or human motion, appears. Deep learning-based intelligent control applied in vision can help us solve the problem. To this end, we propose a 3D reconstruction method for motion-blurred images using deep learning. First, we develop a BF-WGAN algorithm that combines the bilateral filtering (BF) denoising theory with a Wasserstein generative adversarial network (WGAN) to remove motion blur. The bilateral filter… More >

  • Open Access

    ARTICLE

    Fused and Modified Evolutionary Optimization of Multiple Intelligent Systems Using ANN, SVM Approaches

    Jalal Sadoon Hameed Al-bayati1,*, Burak Berk Üstündağ2

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1479-1496, 2021, DOI:10.32604/cmc.2020.013329

    Abstract The Fused Modified Grasshopper Optimization Algorithm has been proposed, which selects the most specific feature sets from images of the disease of plant leaves. The Proposed algorithm ensures the detection of diseases during the early stages of the diagnosis of leaf disease by farmers and, finally, the crop needed to be controlled by farmers to ensure the survival and protection of plants. In this study, a novel approach has been suggested based on the standard optimization algorithm for grasshopper and the selection of features. Leaf conditions in plants are a major factor in reducing crop yield and quality. Any delay… More >

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