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  • Open Access


    Performance Improvement through Novel Adaptive Node and Container Aware Scheduler with Resource Availability Control in Hadoop YARN

    J. S. Manjaly, T. Subbulakshmi*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3083-3108, 2023, DOI:10.32604/csse.2023.036320

    Abstract The default scheduler of Apache Hadoop demonstrates operational inefficiencies when connecting external sources and processing transformation jobs. This paper has proposed a novel scheduler for enhancement of the performance of the Hadoop Yet Another Resource Negotiator (YARN) scheduler, called the Adaptive Node and Container Aware Scheduler (ANACRAC), that aligns cluster resources to the demands of the applications in the real world. The approach performs to leverage the user-provided configurations as a unique design to apportion nodes, or containers within the nodes, to application thresholds. Additionally, it provides the flexibility to the applications for selecting and… More >

  • Open Access


    Improved Monarchy Butterfly Optimization Algorithm (IMBO): Intrusion Detection Using Mapreduce Framework Based Optimized ANU-Net

    Kunda Suresh Babu, Yamarthi Narasimha Rao*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5887-5909, 2023, DOI:10.32604/cmc.2023.037486

    Abstract The demand for cybersecurity is rising recently due to the rapid improvement of network technologies. As a primary defense mechanism, an intrusion detection system (IDS) was anticipated to adapt and secure computing infrastructures from the constantly evolving, sophisticated threat landscape. Recently, various deep learning methods have been put forth; however, these methods struggle to recognize all forms of assaults, especially infrequent attacks, because of network traffic imbalances and a shortage of aberrant traffic samples for model training. This work introduces deep learning (DL) based Attention based Nested U-Net (ANU-Net) for intrusion detection to address these… More >

  • Open Access


    Enhanced Best Fit Algorithm for Merging Small Files

    Adnan Ali1, Nada Masood Mirza1,2, Mohamad Khairi Ishak1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 913-928, 2023, DOI:10.32604/csse.2023.036400

    Abstract In the Big Data era, numerous sources and environments generate massive amounts of data. This enormous amount of data necessitates specialized advanced tools and procedures that effectively evaluate the information and anticipate decisions for future changes. Hadoop is used to process this kind of data. It is known to handle vast volumes of data more efficiently than tiny amounts, which results in inefficiency in the framework. This study proposes a novel solution to the problem by applying the Enhanced Best Fit Merging algorithm (EBFM) that merges files depending on predefined parameters (type and size). Implementing… More >

  • Open Access


    New Spam Filtering Method with Hadoop Tuning-Based MapReduce Naïve Bayes

    Keungyeup Ji, Youngmi Kwon*

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 201-214, 2023, DOI:10.32604/csse.2023.031270

    Abstract As the importance of email increases, the amount of malicious email is also increasing, so the need for malicious email filtering is growing. Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques, we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering. Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine (SVM), Naïve Bayes, K-Nearest… More >

  • Open Access


    An Imbalanced Dataset and Class Overlapping Classification Model for Big Data

    Mini Prince1,*, P. M. Joe Prathap2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1009-1024, 2023, DOI:10.32604/csse.2023.024277

    Abstract Most modern technologies, such as social media, smart cities, and the internet of things (IoT), rely on big data. When big data is used in the real-world applications, two data challenges such as class overlap and class imbalance arises. When dealing with large datasets, most traditional classifiers are stuck in the local optimum problem. As a result, it’s necessary to look into new methods for dealing with large data collections. Several solutions have been proposed for overcoming this issue. The rapid growth of the available data threatens to limit the usefulness of many traditional methods.… More >

  • Open Access


    Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

    R. Krishnaswamy1, Kamalraj Subramaniam2, V. Nandini3, K. Vijayalakshmi4, Seifedine Kadry5, Yunyoung Nam6,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 391-406, 2023, DOI:10.32604/csse.2023.024901

    Abstract Recently, a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes. At the same time, clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it. Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset. Big data clustering is a trivial process owing to the increasing quantity of data which can be solved… More >

  • Open Access


    Object Detection in Remote Sensing Images Using Picture Fuzzy Clustering and MapReduce

    Tran Manh Tuan*, Tran Thi Ngan, Nguyen Tu Trung

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1241-1253, 2022, DOI:10.32604/csse.2022.024265

    Abstract In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order to perform next steps in image processing. Remote sensing images usually have large size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detect objects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is… More >

  • Open Access


    IoT Based Disease Prediction Using Mapreduce and LSQN3 Techniques

    R. Gopi1,*, S. Veena2, S. Balasubramanian3, D. Ramya4, P. Ilanchezhian5, A. Harshavardhan6, Zatin Gupta7

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1215-1230, 2022, DOI:10.32604/iasc.2022.025792

    Abstract In this modern era, the transformation of conventional objects into smart ones via internet vitality, data management, together with many more are the main aim of the Internet of Things (IoT) centered Big Data (BD) analysis. In the past few years, significant augmentation in the IoT-centered Healthcare (HC) monitoring can be seen. Nevertheless, the merging of health-specific parameters along with IoT-centric Health Monitoring (HM) systems with BD handling ability is turned out to be a complicated research scope. With the aid of Map-Reduce and LSQN3 techniques, this paper proposed IoT devices in Wireless Sensors Networks (WSN)… More >

  • Open Access


    An Enhanced Memetic Algorithm for Feature Selection in Big Data Analytics with MapReduce

    Umanesan Ramakrishnan1,*, Nandhagopal Nachimuthu2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1547-1559, 2022, DOI:10.32604/iasc.2022.017123

    Abstract Recently, various research fields have begun dealing with massive datasets forseveral functions. The main aim of a feature selection (FS) model is to eliminate noise, repetitive, and unnecessary featuresthat reduce the efficiency of classification. In a limited period, traditional FS models cannot manage massive datasets and filterunnecessary features. It has been discovered from the state-of-the-art literature that metaheuristic algorithms perform better compared to other FS wrapper-based techniques. Common techniques such as the Genetic Algorithm (GA) andParticle Swarm Optimization (PSO) algorithm, however, suffer from slow convergence and local optima problems. Even with new generation algorithms such… More >

  • Open Access


    Machine Learning Privacy Aware Anonymization Using MapReduce Based Neural Network

    U. Selvi*, S. Pushpa

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1185-1196, 2022, DOI:10.32604/iasc.2022.020164

    Abstract Due to the recent advancement in technologies, a huge amount of data is generated where individual private information needs to be preserved. A proper Anonymization algorithm with increased Data utility is required to protect individual privacy. However, preserving privacy of individuals whileprocessing huge amount of data is a challenging task, as the data contains certain sensitive information. Moreover, scalability issue in handling a large dataset is found in using existing framework. Many an Anonymization algorithm for Big Data have been developed and under research. We propose a method of applying Machine Learning techniques to protect More >

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