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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 >

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Muhammad Muntasir Yakubu^1,2,*, Mohd Fadzil B Hassan^1,3, 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 >

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137

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Salman Ahmed¹, Fazli Subhan^2,3, Mazliham Mohd Su’ud^3,*, Muhammad Mansoor Alam^3,4, 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 >

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Suneel Kumar Rath¹, Madhusmita Sahu¹, Shom Prasad Das², Junali Jasmine Jena³, Chitralekha Jena⁴, Baseem Khan^5,6,7,*, 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 >

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101

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Mohammad Al-Omari¹, Qasem Abu Al-Haija^2,*
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 >

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227

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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 >

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132

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Chi-Chou Kao^1,*, 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 >

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119

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Saad M. Darwish^1,*, 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 >

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117

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Stephen Afrifa^1,2,*, Vijayakumar Varadarajan^3,4,5,*, 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 >

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119

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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 >

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112

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Zainatul Yushaniza Mohamed Yusoff¹, Mohamad Khairi Ishak^2,*, 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 >

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122

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Mosab Manaseer¹, Maram Bani Younes^2,*
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 >

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307

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107

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Conghao Niu^1,*, 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 >

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109

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B. Gomathi^1,*, 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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