Open Access
ARTICLE
Fatimah Mudhhi Alanazi*, Bothina Abdelmeneem Elsobky, Shaimaa Aly Elmorsy
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.042401
Abstract Proliferation of technology, coupled with networking growth, has catapulted cybersecurity to the forefront of
modern security concerns. In this landscape, the precise detection of cyberattacks and anomalies within networks
is crucial, necessitating the development of efficient intrusion detection systems (IDS). This article introduces a
framework utilizing the fusion of fuzzy sets with support vector machines (SVM), named FSVM. The core strategy
of FSVM lies in calculating the significance of network features to determine their relative importance. Features with
minimal significance are prudently disregarded, a method akin to feature selection. This process not only curtails
the computational burden of the classification… More >
Open Access
ARTICLE
Kai Gao1, Chin-Chen Chang1,*, Chia-Chen Lin2,*
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.038308
Abstract Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,
which allows us to hide sensitive data in image files. In this paper, we propose a novel high-fidelity reversible data
hiding scheme. Based on the advantage of the multipredictor mechanism, we combine two effective prediction
schemes to improve prediction accuracy. In addition, the multihistogram technique is utilized to further improve
the image quality of the stego image. Moreover, a model of the grouped knapsack problem is used to speed up the
search for the suitable embedding bin in each sub-histogram. Experimental results show that the… More >
Open Access
CORRECTION
R. Sangeetha, Usha Devi Gandhi*
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.052487
Abstract This article has no abstract. More >
Open Access
CORRECTION
V. Thanikachalam1,*, M. G. Kavitha2, V. Sivamurugan1
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.052484
Abstract This article has no abstract. More >
Open Access
ARTICLE
Aigerim Baimakhanova1,*, Ainur Zhumadillayeva2, Bigul Mukhametzhanova3, Natalya Glazyrina2, Rozamgul Niyazova2, Nurseit Zhunissov1, Aizhan Sambetbayeva4
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.043273
Abstract As digital technologies have advanced more rapidly, the number of paper documents recently converted into a
digital format has exponentially increased. To respond to the urgent need to categorize the growing number of
digitized documents, the classification of digitized documents in real time has been identified as the primary goal of
our study. A paper classification is the first stage in automating document control and efficient knowledge discovery
with no or little human involvement. Artificial intelligence methods such as Deep Learning are now combined
with segmentation to study and interpret those traits, which were not conceivable ten years ago. Deep… More >
Open Access
CORRECTION
Ahmad Almadhor1, Chitapong Wechtaisong2,*, Usman Tariq3, Natalia Kryvinska4,*, Abdullah Al Hejaili5, Uzma Ghulam Mohammad6, Mohana Alanazi7
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.052412
Abstract This article has no abstract. More >
Open Access
ARTICLE
Xiaonan Li1,2, Qiang Wang1, Conglai Fan2,3, Wei Zhan1, Mingliang Zhang4,*
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.039849
（This article belongs to the Special Issue: Artificial Intelligence for Cyber Security)
Abstract The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software. The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients. Nevertheless, present detection models encounter limitations in their ability to identify malevolent code and its variations, all while encompassing a multitude of parameters. To overcome these obstacles, we introduce a lean model for classifying families of malevolent code, formulated on Ghost-DenseNet-SE. This model integrates the Ghost module, DenseNet, and the squeeze-and-excitation (SE) channel domain attention mechanism. It substitutes the standard convolutional layer in DenseNet… More >
Open Access
ARTICLE
Manal Abdullah Alohali1, Muna Elsadig1, Fahd N. Al-Wesabi2, Mesfer Al Duhayyim3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4
Computer Systems Science and Engineering, DOI:10.32604/csse.2023.037305
Abstract Owing to the rapid increase in the interchange of text information through internet networks, the reliability and security of digital content are becoming a major research problem. Tampering detection, Content authentication, and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies. The authors’ difficulties were tampering detection, authentication, and integrity verification of the digital contents. This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection (ADMDTW-TAD) via the Internet. The DM concept is exploited in the presented ADMDTW-TAD technique to identify the… More >
Open Access
ARTICLE
Chirag Ganguli1, Shishir Kumar Shandilya2, Michal Gregus3, Oleh Basystiuk4,*
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.042607
Abstract Cyber Defense is becoming a major issue for every organization to keep business continuity intact. The presented paper explores the effectiveness of a meta-heuristic optimization algorithm-Artificial Bees Colony Algorithm (ABC) as an Nature Inspired Cyber Security mechanism to achieve adaptive defense. It experiments on the Denial-Of-Service attack scenarios which involves limiting the traffic flow for each node. Businesses today have adapted their service distribution models to include the use of the Internet, allowing them to effectively manage and interact with their customer data. This shift has created an increased reliance on online services to store vast amounts of confidential customer… More >
Open Access
ARTICLE
Yinkong Wei1,2, Mucong Wu1,2,*, Wei Wei3, Paulo R. F. Rocha4, Ziyi Cheng1,2, Weifang Yao5
Computer Systems Science and Engineering, DOI:10.32604/csse.2023.036062
Abstract Ultra-high voltage (UHV) transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment. The ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power grid. Yet, the accurate prediction of the ground total electric field remains a technical challenge. In this work, we collected the total electric field data from the Ningdong-Zhejiang ±800 kV UHVDC transmission project, as of the Ling Shao line, and perform an outlier analysis of the total electric field data. We… More >
Open Access
CORRECTION
Ahmed A. Alsheikhy1, Ahmad S. Azzahrani1, A. Khuzaim Alzahrani2, Tawfeeq Shawly3
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.051630
Abstract This article has no abstract. More >
Open Access
ARTICLE
Leila Safari-Monjeghtapeh1, Mansour Esmaeilpour2,*
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.044892
Abstract Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree “MST”, and meta-heuristic algorithms, including Genetic Algorithm “GA” and Particle Swarm Optimization “PSO”. The Fast Path-based Clustering “FPC” algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and a new hybrid meta-heuristic algorithm… More >
Open Access
REVIEW
Nidhika Chauhan1, Navneet Kaur2, Kamaljit Singh Saini2, Sahil Verma3, Abdulatif Alabdulatif4, Ruba Abu Khurma5,7, Maribel Garcia-Arenas6, Pedro A. Castillo6,*
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.042690
Abstract As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation… More >
Open Access
ARTICLE
Xindi Huang1, Liwei Liang1, Sakirin Tam2, Hao Liang3, Xiong Cai4, Changsong Ding1,5,*
Computer Systems Science and Engineering, DOI:10.32604/csse.2022.029970
Abstract Chinese Medicine (CM) has been widely used as an important avenue for disease prevention and treatment in China especially in the form of CM prescriptions combining sets of herbs to address patients’ symptoms and syndromes. However, the selection and compatibility of herbs are complex and abstract due to intrinsic relationships between herbal properties and their overall functions. Network analysis is applied to demonstrate the complex relationships between individual herbal efficacy and the overall function of CM prescriptions. To illustrate their connections and correlations, prescription function (PF), prescription herb (PH), and herbal efficacy (HE) intra-networks are proposed based on CM theory… More >
Open Access
ARTICLE
Pakorn Santakij1, Samai Srisuay2,*, Pongporn Punpeng1
Computer Systems Science and Engineering, DOI:10.32604/csse.2024.045066
Abstract Social media has revolutionized the dissemination of real-life information, serving as a robust platform for sharing life events. Twitter, characterized by its brevity and continuous flow of posts, has emerged as a crucial source for public health surveillance, offering valuable insights into public reactions during the COVID-19 pandemic. This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets. Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models. An assessment of coherence metrics… More >
Open Access
ARTICLE
Jili Chen1,2, Hailan Wang2, Xiaolan Xie1,2,*
Computer Systems Science and Engineering, DOI:10.32604/csse.2023.037957
Abstract Fuzzy C-Means (FCM) is an effective and widely used clustering algorithm, but there are still some problems. considering the number of clusters must be determined manually, the local optimal solutions is easily influenced by the random selection of initial cluster centers, and the performance of Euclid distance in complex high-dimensional data is poor. To solve the above problems, the improved FCM clustering algorithm based on density Canopy and Manifold learning (DM-FCM) is proposed. First, a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster centers, which improves the self-adaptability… More >
Open Access
ARTICLE
Maha Farouk Sabir1, Mahmoud Ragab2,3,*, Adil O. Khadidos2, Khaled H. Alyoubi1, Alaa O. Khadidos1,4
Computer Systems Science and Engineering, DOI:10.32604/csse.2023.041551
Abstract Big data and information and communication technologies can be important to the effectiveness of smart cities. Based on the maximal attention on smart city sustainability, developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems. Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions. Relating to air pollution occurs a main environmental problem in smart city environments. The effect of the deep learning (DL) approach quickly increased and penetrated almost every domain, comprising air pollution forecast. Therefore, this article develops a new Coot Optimization Algorithm… More >
Open Access
ARTICLE
Abhishek Singhal*, Devendra Kumar Sharma
Computer Systems Science and Engineering, DOI:10.32604/csse.2023.046730
Abstract This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions, employing a deep learning classification algorithm for speech signal analysis. In this study, speech samples are categorized for both training and testing purposes based on their geographical origin. Category 1 comprises speech samples from speakers outside of India, whereas Category 2 comprises live-recorded speech samples from Indian speakers. Testing speech samples are likewise classified into four distinct sets, taking into consideration both geographical origin and the language spoken by the speakers. Significantly, the results indicate a noticeable difference in gender identification accuracy among… More >
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