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  • Open Access

    ARTICLE

    Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

    Zulqar Nain1, B. Shahana2, Shehzad Ashraf Chaudhry3, P. Viswanathan4, M.S. Mekala1, Sung Won Kim1,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5705-5721, 2023, DOI:10.32604/cmc.2023.033194

    Abstract Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and… More >

  • Open Access

    ARTICLE

    Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

    S. Aanjanadevi1,*, S. Aanjankumar2, K. R. Ramela3, V. Palanisamy4

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2351-2362, 2023, DOI:10.32604/csse.2023.031893

    Abstract Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices… More >

  • Open Access

    ARTICLE

    Early Warning of Commercial Housing Market Based on Bagging-GWO-SVM

    Yonghui Duan1, Keqing Zhao1,*, Yibin Guo2, Xiang Wang2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2207-2222, 2023, DOI:10.32604/csse.2023.032297

    Abstract A number of risks exist in commercial housing, and it is critical for the government, the real estate industry, and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early warning. In this paper, we examine the commodity housing market and construct a risk index for the commodity housing market at three levels: market level, the real estate industry and the national economy. Using the Bootstrap aggregating-grey wolf optimizer-support vector machine (Bagging-GWO-SVM) model after synthesizing the risk index by applying the CRITIC objective More >

  • Open Access

    ARTICLE

    An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

    Walid El-Shafai1,2, Noha A. El-Hag3, Ahmed Sedik4, Ghada Elbanby5, Fathi E. Abd El-Samie1, Naglaa F. Soliman6, Hussah Nasser AlEisa7,*, Mohammed E. Abdel Samea8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2905-2925, 2023, DOI:10.32604/cmc.2023.031936

    Abstract Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. More >

  • Open Access

    ARTICLE

    Cancellable Multi-Biometric Feature Veins Template Generation Based on SHA-3 Hashing

    Salwa M. Serag Eldin1,*, Ahmed Sedik2, Sultan S. Alshamrani3, Ahmed M. Ayoup4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 733-749, 2023, DOI:10.32604/cmc.2023.030789

    Abstract In this paper, a novel cancellable biometrics technique called Multi-Biometric-Feature-Hashing (MBFH) is proposed. The MBFH strategy is utilized to actualize a single direction (non-invertibility) biometric shape. MBFH is a typical model security conspire that is distinguished in the utilization of this protection insurance framework in numerous sorts of biometric feature strategies (retina, palm print, Hand Dorsum, fingerprint). A more robust and accurate multilingual biological structure in expressing human loneliness requires a different format to record clients with inseparable comparisons from individual biographical sources. This may raise worries about their utilization and security when these spread More >

  • Open Access

    ARTICLE

    A Novel Fusion System Based on Iris and Ear Biometrics for E-exams

    S. A. Shaban*, Hosnia M. M. Ahmed, D. L. Elsheweikh

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3295-3315, 2023, DOI:10.32604/iasc.2023.030237

    Abstract With the rapid spread of the coronavirus epidemic all over the world, educational and other institutions are heading towards digitization. In the era of digitization, identifying educational e-platform users using ear and iris based multimodal biometric systems constitutes an urgent and interesting research topic to preserve enterprise security, particularly with wearing a face mask as a precaution against the new coronavirus epidemic. This study proposes a multimodal system based on ear and iris biometrics at the feature fusion level to identify students in electronic examinations (E-exams) during the COVID-19 pandemic. The proposed system comprises four… More >

  • Open Access

    ARTICLE

    Optical Ciphering Scheme for Cancellable Speaker Identification System

    Walid El-Shafai1,2, Marwa A. Elsayed1, Mohsen A. Rashwan3, Moawad I. Dessouky1, Adel S. El-Fishawy1, Naglaa F. Soliman4, Amel A. Alhussan5,*, Fathi E. Abd El-Samie1

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 563-578, 2023, DOI:10.32604/csse.2023.024375

    Abstract Most current security and authentication systems are based on personal biometrics. The security problem is a major issue in the field of biometric systems. This is due to the use in databases of the original biometrics. Then biometrics will forever be lost if these databases are attacked. Protecting privacy is the most important goal of cancelable biometrics. In order to protect privacy, therefore, cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases. One methodology to achieve non-invertibility is the More >

  • Open Access

    ARTICLE

    Face Templates Encryption Technique Based on Random Projection and Deep Learning

    Mayada Tarek1,2,*

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2049-2063, 2023, DOI:10.32604/csse.2023.027139

    Abstract Cancellable biometrics is the solution for the trade-off between two concepts: Biometrics for Security and Security for Biometrics. The cancelable template is stored in the authentication system’s database rather than the original biometric data. In case of the database is compromised, it is easy for the template to be canceled and regenerated from the same biometric data. Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters (cancelable key). Therefore, the cancelable key must be secret to be used in the system authentication process as a second authentication factor in conjunction… More >

  • Open Access

    ARTICLE

    An Optimised Defensive Technique to Recognize Adversarial Iris Images Using Curvelet Transform

    K. Meenakshi1,*, G. Maragatham2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 627-643, 2023, DOI:10.32604/iasc.2023.026961

    Abstract Deep Learning is one of the most popular computer science techniques, with applications in natural language processing, image processing, pattern identification, and various other fields. Despite the success of these deep learning algorithms in multiple scenarios, such as spam detection, malware detection, object detection and tracking, face recognition, and automatic driving, these algorithms and their associated training data are rather vulnerable to numerous security threats. These threats ultimately result in significant performance degradation. Moreover, the supervised based learning models are affected by manipulated data known as adversarial examples, which are images with a particular level… More >

  • Open Access

    ARTICLE

    A Component Selection Framework of Cohesion and Coupling Metrics

    Iyyappan. M1, Arvind Kumar1, Sultan Ahmad2,*, Sudan Jha3, Bader Alouffi4, Abdullah Alharbi5

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 351-365, 2023, DOI:10.32604/csse.2023.025163

    Abstract Component-based software engineering is concerned with the development of software that can satisfy the customer prerequisites through reuse or independent development. Coupling and cohesion measurements are primarily used to analyse the better software design quality, increase the reliability and reduced system software complexity. The complexity measurement of cohesion and coupling component to analyze the relationship between the component module. In this paper, proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository. It measures the interface density modules of coupling and cohesion in a modular software system. This More >

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