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  • Open Access


    A New Hybrid Approach Using GWO and MFO Algorithms to Detect Network Attack

    Hasan Dalmaz*, Erdal Erdal, Halil Murat Ünver

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1277-1314, 2023, DOI:10.32604/cmes.2023.025212

    Abstract This paper addresses the urgent need to detect network security attacks, which have increased significantly in recent years, with high accuracy and avoid the adverse effects of these attacks. The intrusion detection system should respond seamlessly to attack patterns and approaches. The use of metaheuristic algorithms in attack detection can produce near-optimal solutions with low computational costs. To achieve better performance of these algorithms and further improve the results, hybridization of algorithms can be used, which leads to more successful results. Nowadays, many studies are conducted on this topic. In this study, a new hybrid approach using Gray Wolf Optimizer… More >

  • Open Access


    LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

    Sesikala Bapatla1, J. Harikiran2,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 597-617, 2023, DOI:10.32604/csse.2023.034998

    Abstract Diabetes problems can lead to an eye disease called Diabetic Retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated early, DR becomes a significant reason for blindness. To identify the DR and determine the stages, medical tests are very labor-intensive, expensive, and time-consuming. To address the issue, a hybrid deep and machine learning technique-based autonomous diagnostic system is provided in this paper. Our proposal is based on lesion segmentation of the fundus images based on the LuNet network. Then a Refined Attention Pyramid Network (RAPNet) is used for extracting global and local features. To increase… More >

  • Open Access


    A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

    N. Vasudevan*, T. Karthick

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 337-356, 2023, DOI:10.32604/csse.2023.034242

    Abstract Crop protection is a great obstacle to food safety, with crop diseases being one of the most serious issues. Plant diseases diminish the quality of crop yield. To detect disease spots on grape leaves, deep learning technology might be employed. On the other hand, the precision and efficiency of identification remain issues. The quantity of images of ill leaves taken from plants is often uneven. With an uneven collection and few images, spotting disease is hard. The plant leaves dataset needs to be expanded to detect illness accurately. A novel hybrid technique employing segmentation, augmentation, and a capsule neural network… More >

  • Open Access


    Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

    B. Murugeshwari1,*, S. Rajalakshmi1, K. Sudharson2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2293-2307, 2023, DOI:10.32604/csse.2023.029074

    Abstract Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while… More >

  • Open Access


    Non-Negative Minimum Volume Factorization (NMVF) for Hyperspectral Images (HSI) Unmixing: A Hybrid Approach

    Kriti Mahajan1, Urvashi Garg1, Nitin Mittal2, Yunyoung Nam3, Byeong-Gwon Kang4,*, Mohamed Abouhawwash5,6

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3705-3720, 2022, DOI:10.32604/cmc.2022.027936

    Abstract Spectral unmixing is essential for exploitation of remotely sensed data of Hyperspectral Images (HSI). It amounts to the identification of a position of spectral signatures that are pure and therefore called end members and their matching fractional, draft rules abundances for every pixel in HSI. This paper aims to unmix hyperspectral data using the minimal volume method of elementary scrutiny. Moreover, the problem of optimization is solved by the implementation of the sequence of small problems that are constrained quadratically. The hard constraint in the final step for the abundance fraction is then replaced with a loss function of hinge… More >

  • Open Access


    A Hybrid Approach to Neighbour Discovery in Wireless Sensor Networks

    Sagar Mekala1,*, K. Shahu Chatrapati2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 581-593, 2023, DOI:10.32604/iasc.2023.023539

    Abstract In the contemporary era of unprecedented innovations such as Internet of Things (IoT), modern applications cannot be imagined without the presence of Wireless Sensor Network (WSN). Nodes in WSN use neighbour discovery (ND) protocols to have necessary communication among the nodes. Neighbour discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximize percentage of neighbours discovered. The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots. The two methods are found to have certain… More >

  • Open Access


    QL-CBR Hybrid Approach for Adapting Context-Aware Services

    Somia Belaidouni1,2, Moeiz Miraoui3,4,*, Chakib Tadj1

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1085-1098, 2022, DOI:10.32604/csse.2022.024056

    Abstract A context-aware service in a smart environment aims to supply services according to user situational information, which changes dynamically. Most existing context-aware systems provide context-aware services based on supervised algorithms. Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trial-and-error interactions. They also have the ability to build excellent self-adaptive systems. In this study, we aim to incorporate reinforcement algorithms (Q-learning) into a context-aware system to provide relevant services based on a user’s dynamic context. To accelerate the convergence of reinforcement learning (RL) algorithms and provide the correct services in… More >

  • Open Access


    Effectiveness of Bilateral Pulmonary Artery Banding in Patients with Hypoplastic Left Heart Syndrome and Congenital Heart Defects with A Functional Single Ventricle: A Single-Center Retrospective Study

    Vitaliy Suvorov1,*, Vladimir Zaitcev1, Karolina Andrzejczyk2

    Congenital Heart Disease, Vol.17, No.3, pp. 365-374, 2022, DOI:10.32604/chd.2022.019126

    Abstract Background: Bilateral banding of the branches of the pulmonary artery in patients with hypoplastic left heart syndrome (HLHS) and other duct dependent critical neonatal heart malformations can significantly reduce the incidence of severe complications in the postoperative period, especially in severely unstable patients. In our study we compared different surgical techniques of bilateral pulmonary artery banding (PAB) in respect to their success in balancing systemic and pulmonary blood flow. Methods: We included 44 neonates with a HLHS and congenital heart diseases (CHD) with a functional single ventricle underwent a hybrid operation: bilateral PAB and patent ductus arteriosus stenting. The hybrid… More >

  • Open Access


    Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

    Rohit Raja1, Chetan Swarup2, Abhishek Kumar3,*, Kamred Udham Singh4, Teekam Singh5, Dinesh Gupta6, Neeraj Varshney7, Swati Jain8

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2015-2031, 2022, DOI:10.32604/cmc.2022.022904

    Abstract As far as the present state is concerned in detecting the behavioral pattern of humans (subject) using morphological image processing, a considerable portion of the study has been conducted utilizing frontal vision data of human faces. The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach. In this example, hybridization includes an artificial neural network (ANN) with a genetic algorithm (GA). We researched the geometrical properties extracted from side-vision human-face data. An additional study was conducted to determine the ideal number of geometrical characteristics to pick while… More >

  • Open Access


    Hybrid Approach for Taxonomic Classification Based on Deep Learning

    Naglaa. F. Soliman1,*, Samia M. Abd-Alhalem2, Walid El-Shafai2, Salah Eldin S. E. Abdulrahman3, N. Ismaiel3, El-Sayed M. El-Rabaie2, Abeer D. Algarni1, Fatimah Algarni4, Amel A. Alhussan5, Fathi E. Abd El-Samie1,2

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1881-1891, 2022, DOI:10.32604/iasc.2022.017683

    Abstract Recently, deep learning has opened a remarkable research direction in the track of bioinformatics, especially for the applications that need classification and regression. With deep learning techniques, DNA sequences can be classified with high accuracy. Firstly, a DNA sequence should be represented, numerically. After that, DNA features are extracted from the numerical representations based on deep learning techniques to improve the classification process. Recently, several architectures have been developed based on deep learning for DNA sequence classification. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are the default deep learning architectures used for this task. This paper presents a… More >

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