Home / Journals / CSSE / Vol.48, No.6, 2024
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  • Open AccessOpen Access

    REVIEW

    A Systematic Review of Automated Classification for Simple and Complex Query SQL on NoSQL Database

    Nurhadi, Rabiah Abdul Kadir*, Ely Salwana Mat Surin, Mahidur R. Sarker*
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1405-1435, 2024, DOI:10.32604/csse.2024.051851 - 22 November 2024
    Abstract A data lake (DL), abbreviated as DL, denotes a vast reservoir or repository of data. It accumulates substantial volumes of data and employs advanced analytics to correlate data from diverse origins containing various forms of semi-structured, structured, and unstructured information. These systems use a flat architecture and run different types of data analytics. NoSQL databases are nontabular and store data in a different manner than the relational table. NoSQL databases come in various forms, including key-value pairs, documents, wide columns, and graphs, each based on its data model. They offer simpler scalability and generally outperform… More >

  • Open AccessOpen Access

    REVIEW

    A Systematic Literature Review on Blockchain Consensus Mechanisms’ Security: Applications and Open Challenges

    Muhammad Muntasir Yakubu1,2,*, Mohd Fadzil B Hassan1,3, Kamaluddeen Usman Danyaro1, Aisha Zahid Junejo4, Muhammed Siraj5, Saidu Yahaya1, Shamsuddeen Adamu1, Kamal Abdulsalam6
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1437-1481, 2024, DOI:10.32604/csse.2024.054556 - 22 November 2024
    Abstract This study conducts a systematic literature review (SLR) of blockchain consensus mechanisms, an essential protocols that maintain the integrity, reliability, and decentralization of distributed ledger networks. The aim is to comprehensively investigate prominent mechanisms’ security features and vulnerabilities, emphasizing their security considerations, applications, challenges, and future directions. The existing literature offers valuable insights into various consensus mechanisms’ strengths, limitations, and security vulnerabilities and their real-world applications. However, there remains a gap in synthesizing and analyzing this knowledge systematically. Addressing this gap would facilitate a structured approach to understanding consensus mechanisms’ security and vulnerabilities comprehensively. The… More >

  • Open AccessOpen Access

    REVIEW

    A Survey of Lung Nodules Detection and Classification from CT Scan Images

    Salman Ahmed1, Fazli Subhan2,3, Mazliham Mohd Su’ud3,*, Muhammad Mansoor Alam3,4, Adil Waheed5
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1483-1511, 2024, DOI:10.32604/csse.2024.053997 - 22 November 2024
    Abstract In the contemporary era, the death rate is increasing due to lung cancer. However, technology is continuously enhancing the quality of well-being. To improve the survival rate, radiologists rely on Computed Tomography (CT) scans for early detection and diagnosis of lung nodules. This paper presented a detailed, systematic review of several identification and categorization techniques for lung nodules. The analysis of the report explored the challenges, advancements, and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning (DL) algorithm. The findings also highlighted the usefulness of DL… More >

  • Open AccessOpen Access

    REVIEW

    Software Reliability Prediction Using Ensemble Learning on Selected Features in Imbalanced and Balanced Datasets: A Review

    Suneel Kumar Rath1, Madhusmita Sahu1, Shom Prasad Das2, Junali Jasmine Jena3, Chitralekha Jena4, Baseem Khan5,6,7,*, Ahmed Ali7, Pitshou Bokoro7
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1513-1536, 2024, DOI:10.32604/csse.2024.057067 - 22 November 2024
    Abstract Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class’s data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning… More >

  • Open AccessOpen Access

    ARTICLE

    Performance Analysis of Machine Learning-Based Intrusion Detection with Hybrid Feature Selection

    Mohammad Al-Omari1, Qasem Abu Al-Haija2,*
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1537-1555, 2024, DOI:10.32604/csse.2024.056257 - 22 November 2024
    Abstract More businesses are deploying powerful Intrusion Detection Systems (IDS) to secure their data and physical assets. Improved cyber-attack detection and prevention in these systems requires machine learning (ML) approaches. This paper examines a cyber-attack prediction system combining feature selection (FS) and ML. Our technique’s foundation was based on Correlation Analysis (CA), Mutual Information (MI), and recursive feature reduction with cross-validation. To optimize the IDS performance, the security features must be carefully selected from multiple-dimensional datasets, and our hybrid FS technique must be extended to validate our methodology using the improved UNSW-NB 15 and TON_IoT datasets. More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent PID Control Method for Quadrotor UAV with Serial Humanoid Intelligence

    Linlin Zhang, Lvzhao Bai, Jianshu Liang, Zhiying Qin*, Yuejing Zhao
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1557-1579, 2024, DOI:10.32604/csse.2024.054237 - 22 November 2024
    Abstract Quadrotor unmanned aerial vehicles (UAVs) are widely used in inspection, agriculture, express delivery, and other fields owing to their low cost and high flexibility. However, the current UAV control system has shortcomings such as poor control accuracy and weak anti-interference ability to a certain extent. To address the control problem of a four-rotor UAV, we propose a method to enhance the controller’s accuracy by considering underactuated dynamics, nonlinearities, and external disturbances. A mathematical model is constructed based on the flight principles of the quadrotor UAV. We develop a control algorithm that combines humanoid intelligence with… More >

  • Open AccessOpen Access

    ARTICLE

    Performance-Oriented Layout Synthesis for Quantum Computing

    Chi-Chou Kao1,*, Hung-Yi Lin2
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1581-1594, 2024, DOI:10.32604/csse.2024.055073 - 22 November 2024
    Abstract Layout synthesis in quantum computing is crucial due to the physical constraints of quantum devices where quantum bits (qubits) can only interact effectively with their nearest neighbors. This constraint severely impacts the design and efficiency of quantum algorithms, as arranging qubits optimally can significantly reduce circuit depth and improve computational performance. To tackle the layout synthesis challenge, we propose an algorithm based on integer linear programming (ILP). ILP is well-suited for this problem as it can formulate the optimization objective of minimizing circuit depth while adhering to the nearest neighbor interaction constraint. The algorithm aims… More >

  • Open AccessOpen Access

    ARTICLE

    An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

    Saad M. Darwish1,*, Abdul Rahman M. Sabri2, Dhafar Hamed Abd2, Adel A. Elzoghabi1
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1595-1624, 2024, DOI:10.32604/csse.2024.054615 - 22 November 2024
    Abstract The number of blogs and other forms of opinionated online content has increased dramatically in recent years. Many fields, including academia and national security, place an emphasis on automated political article orientation detection. Political articles (especially in the Arab world) are different from other articles due to their subjectivity, in which the author’s beliefs and political affiliation might have a significant influence on a political article. With categories representing the main political ideologies, this problem may be thought of as a subset of the text categorization (classification). In general, the performance of machine learning models… More >

  • Open AccessOpen Access

    ARTICLE

    Evaluating Public Sentiments during Uttarakhand Flood: An Artificial Intelligence Techniques

    Stephen Afrifa1,2,*, Vijayakumar Varadarajan3,4,5,*, Peter Appiahene2, Tao Zhang1, Richmond Afrifa6
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1625-1639, 2024, DOI:10.32604/csse.2024.055084 - 22 November 2024
    Abstract Users of social networks can readily express their thoughts on websites like Twitter (now X), Facebook, and Instagram. The volume of textual data flowing from users has greatly increased with the advent of social media in comparison to traditional media. For instance, using natural language processing (NLP) methods, social media can be leveraged to obtain crucial information on the present situation during disasters. In this work, tweets on the Uttarakhand flash flood are analyzed using a hybrid NLP model. This investigation employed sentiment analysis (SA) to determine the people’s expressed negative attitudes regarding the disaster. More >

  • Open AccessOpen Access

    ARTICLE

    Software Cost Estimation Using Social Group Optimization

    Sagiraju Srinadhraju*, Samaresh Mishra, Suresh Chandra Satapathy
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1641-1668, 2024, DOI:10.32604/csse.2024.055612 - 22 November 2024
    (This article belongs to the Special Issue: Emerging Artificial Intelligence Techniques for Software Engineering Process Optimization)
    Abstract This paper introduces the integration of the Social Group Optimization (SGO) algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model (COCOMO). COCOMO’s fixed coefficients often limit its adaptability, as they don’t account for variations across organizations. By fine-tuning these parameters with SGO, we aim to improve estimation accuracy. We train and validate our SGO-enhanced model using historical project data, evaluating its performance with metrics like the mean magnitude of relative error (MMRE) and Manhattan distance (MD). Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost More >

  • Open AccessOpen Access

    ARTICLE

    Improving Smart Home Security via MQTT: Maximizing Data Privacy and Device Authentication Using Elliptic Curve Cryptography

    Zainatul Yushaniza Mohamed Yusoff1, Mohamad Khairi Ishak2,*, Lukman A. B. Rahim3, Mohd Shahrimie Mohd Asaari1
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1669-1697, 2024, DOI:10.32604/csse.2024.056741 - 22 November 2024
    Abstract The rapid adoption of Internet of Things (IoT) technologies has introduced significant security challenges across the physical, network, and application layers, particularly with the widespread use of the Message Queue Telemetry Transport (MQTT) protocol, which, while efficient in bandwidth consumption, lacks inherent security features, making it vulnerable to various cyber threats. This research addresses these challenges by presenting a secure, lightweight communication proxy that enhances the scalability and security of MQTT-based Internet of Things (IoT) networks. The proposed solution builds upon the Dang-Scheme, a mutual authentication protocol designed explicitly for resource-constrained environments and enhances it… More >

  • Open AccessOpen Access

    ARTICLE

    A Secure Blockchain-Based Vehicular Collision Avoidance Protocol: Detecting and Preventing Blackhole Attacks

    Mosab Manaseer1, Maram Bani Younes2,*
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1699-1721, 2024, DOI:10.32604/csse.2024.055128 - 22 November 2024
    Abstract This work aims to examine the vulnerabilities and threats in the applications of intelligent transport systems, especially collision avoidance protocols. It focuses on achieving the availability of network communication among traveling vehicles. Finally, it aims to find a secure solution to prevent blackhole attacks on vehicular network communications. The proposed solution relies on authenticating vehicles by joining a blockchain network. This technology provides identification information and receives cryptography keys. Moreover, the ad hoc on-demand distance vector (AODV) protocol is used for route discovery and ensuring reliable node communication. The system activates an adaptive mode for monitoring More >

  • Open AccessOpen Access

    ARTICLE

    SAR-LtYOLOv8: A Lightweight YOLOv8 Model for Small Object Detection in SAR Ship Images

    Conghao Niu1,*, Dezhi Han1, Bing Han2, Zhongdai Wu2
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1723-1748, 2024, DOI:10.32604/csse.2024.056736 - 22 November 2024
    Abstract The high coverage and all-weather capabilities of Synthetic Aperture Radar (SAR) image ship detection make it a widely accepted method for maritime ship positioning and identification. However, SAR ship detection faces challenges such as indistinct ship contours, low resolution, multi-scale features, noise, and complex background interference. This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images, incorporating key structures to enhance performance. The YOLOv8 backbone is replaced by the Slim Backbone (SB), and the Delete Medium-sized Detection Head (DMDH) structure is eliminated to concentrate on shallow features. Dynamically adjusting the… More >

  • Open AccessOpen Access

    ARTICLE

    Machine Learning-Driven Classification for Enhanced Rule Proposal Framework

    B. Gomathi1,*, R. Manimegalai1, Srivatsan Santhanam2, Atreya Biswas3
    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1749-1765, 2024, DOI:10.32604/csse.2024.056659 - 22 November 2024
    Abstract In enterprise operations, maintaining manual rules for enterprise processes can be expensive, time-consuming, and dependent on specialized domain knowledge in that enterprise domain. Recently, rule-generation has been automated in enterprises, particularly through Machine Learning, to streamline routine tasks. Typically, these machine models are black boxes where the reasons for the decisions are not always transparent, and the end users need to verify the model proposals as a part of the user acceptance testing to trust it. In such scenarios, rules excel over Machine Learning models as the end-users can verify the rules and have more… More >

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