Home / Journals / CSSE / Vol.47, No.1, 2023
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  • Open AccessOpen Access

    ARTICLE

    A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19

    Zhihan Liu1, Xiang Li1, Siqi Liu2, Wei Li1,*, Xiangxu Meng1, Jing Jia3
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 937-954, 2023, DOI:10.32604/csse.2023.039417 - 26 May 2023
    Abstract The COVID-19 virus is usually spread by small droplets when talking, coughing and sneezing, so maintaining physical distance between people is necessary to slow the spread of the virus. The World Health Organization (WHO) recommends maintaining a social distance of at least six feet. In this paper, we developed a real-time pedestrian social distance risk alert system for COVID-19, which monitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge, thus avoiding the problem of too close social distance between pedestrians in public places. We design… More >

  • Open AccessOpen Access

    ARTICLE

    CDR2IMG: A Bridge from Text to Image in Telecommunication Fraud Detection

    Zhen Zhen1, Jian Gao1,2,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 955-973, 2023, DOI:10.32604/csse.2023.039525 - 26 May 2023
    Abstract Telecommunication fraud has run rampant recently worldwide. However, previous studies depend highly on expert knowledge-based feature engineering to extract behavior information, which cannot adapt to the fast-changing modes of fraudulent subscribers. Therefore, we propose a new taxonomy that needs no hand-designed features but directly takes raw Call Detail Records (CDR) data as input for the classifier. Concretely, we proposed a fraud detection method using a convolutional neural network (CNN) by taking CDR data as images and applying computer vision techniques like image augmentation. Comprehensive experiments on the real-world dataset from the 2020 Digital Sichuan Innovation… More >

  • Open AccessOpen Access

    ARTICLE

    A Modified PointNet-Based DDoS Attack Classification and Segmentation in Blockchain

    Jieren Cheng1,3, Xiulai Li1,2,3,4,*, Xinbing Xu2,3, Xiangyan Tang1,3, Victor S. Sheng5
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 975-992, 2023, DOI:10.32604/csse.2023.039280 - 26 May 2023
    Abstract With the rapid development of blockchain technology, the number of distributed applications continues to increase, so ensuring the security of the network has become particularly important. However, due to its decentralized, decentralized nature, blockchain networks are vulnerable to distributed denial-of-service (DDoS) attacks, which can lead to service stops, causing serious economic losses and social impacts. The research questions in this paper mainly include two aspects: first, the classification of DDoS, which refers to detecting whether blockchain nodes are suffering DDoS attacks, that is, detecting the data of nodes in parallel; The second is the problem… More >

  • Open AccessOpen Access

    ARTICLE

    Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

    Junfeng Sun1, Chenghai Li1, Yafei Song1,*, Peng Ni2, Jian Wang1
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 993-1021, 2023, DOI:10.32604/csse.2023.039215 - 26 May 2023
    (This article belongs to the Special Issue: Artificial Intelligence for Cyber Security)
    Abstract The accuracy of historical situation values is required for traditional network security situation prediction (NSSP). There are discrepancies in the correlation and weighting of the various network security elements. To solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network is proposed, which is optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO). This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data. Furthermore,… More >

  • Open AccessOpen Access

    ARTICLE

    Detecting Ethereum Ponzi Schemes Through Opcode Context Analysis and Oversampling-Based AdaBoost Algorithm

    Mengxiao Wang1,2, Jing Huang1,2,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1023-1042, 2023, DOI:10.32604/csse.2023.039569 - 26 May 2023
    (This article belongs to the Special Issue: Advances in Mobile Internet Security)
    Abstract Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learning-based methods are believed to be promising for detecting ethereum Ponzi schemes. However, there are still some flaws in current research, e.g., insufficient feature extraction of Ponzi scheme smart contracts, without considering class imbalance. In addition, there is room for improvement in detection precision. Aiming at the above problems, this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis… More >

  • Open AccessOpen Access

    ARTICLE

    Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

    Aiping Xu1,2, Xuan Zou3, Chao Wang2,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1043-1059, 2023, DOI:10.32604/csse.2023.039645 - 26 May 2023
    Abstract To protect the environment, the discharged sewage’s quality must meet the state’s discharge standards. There are many water quality indicators, and the pH (Potential of Hydrogen) value is one of them. The natural water’s pH value is 6.0–8.5. The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard. This paper aims to study the deep learning prediction model of wastewater’s pH. Firstly, the research uses the random forest method to select the data features and then, based on the sliding window,… More >

  • Open AccessOpen Access

    ARTICLE

    Improving the Transmission Efficiency of a WSN with the IACO Algorithm

    Wen-Tsai Sung1, Sung-Jung Hsiao2,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1061-1076, 2023, DOI:10.32604/csse.2023.032700 - 26 May 2023
    (This article belongs to the Special Issue: Information Analytics in Wireless Systems and Internet of Things)
    Abstract The goal of this study is to reduce the energy consumption of the sensing network and enhance the overall life cycle of the network. This study proposes a data fusion algorithm for wireless sensor networks based on improved ant colony optimization (IACO) to reduce the amount of data transmitted by wireless sensor networks (WSN). This study updates pheromones for multiple optimal routes to improve the global optimal route in search function. The algorithm proposed in this study can reduce node energy consumption, improve network load balancing and prolong network life cycle. Through data fusion, regression More >

  • Open AccessOpen Access

    ARTICLE

    An Interoperability Cross-Block Chain Framework for Secure Transactions in IoT

    N. Anand Kumar1,*, A. Grace Selvarani2, P. Vivekanandan3
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1077-1090, 2023, DOI:10.32604/csse.2023.034115 - 26 May 2023
    Abstract The purpose of this research is to deal with effective block chain framework for secure transactions. The rate of effective data transactions and the interoperability of the ledger are the two major obstacles involved in Blockchain and to tackle this issue, Cross-Chain based Transaction (CCT) is introduced. Traditional industries have been restructured by the introduction of Internet of Things (IoT) to become smart industries through the feature of data-driven decision-making. Still, there are a few limitations, like decentralization, security vulnerabilities, poor interoperability, as well as privacy concerns in IoTs. To overcome this limitation, Blockchain has… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Neural Network for Detecting Fake Profiles in Social Networks

    Daniyal Amankeldin1, Lyailya Kurmangaziyeva2, Ayman Mailybayeva2, Natalya Glazyrina1, Ainur Zhumadillayeva1,*, Nurzhamal Karasheva3
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1091-1108, 2023, DOI:10.32604/csse.2023.039503 - 26 May 2023
    Abstract This paper proposes a deep neural network (DNN) approach for detecting fake profiles in social networks. The DNN model is trained on a large dataset of real and fake profiles and is designed to learn complex features and patterns that distinguish between the two types of profiles. In addition, the present research aims to determine the minimum set of profile data required for recognizing fake profiles on Facebook and propose the deep convolutional neural network method for fake accounts detection on social networks, which has been developed using 16 features based on content-based and profile-based… More >

  • Open AccessOpen Access

    ARTICLE

    Adaptive Butterfly Optimization Algorithm (ABOA) Based Feature Selection and Deep Neural Network (DNN) for Detection of Distributed Denial-of-Service (DDoS) Attacks in Cloud

    S. Sureshkumar1,*, G .K. D. Prasanna Venkatesan2, R. Santhosh3
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1109-1123, 2023, DOI:10.32604/csse.2023.036267 - 26 May 2023
    Abstract Cloud computing technology provides flexible, on-demand, and completely controlled computing resources and services are highly desirable. Despite this, with its distributed and dynamic nature and shortcomings in virtualization deployment, the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties. The Intrusion Detection System (IDS) is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources. DDoS attacks are becoming more frequent and powerful, and their attack pathways are continually changing, which requiring the development of new detection methods. Here… More >

  • Open AccessOpen Access

    ARTICLE

    A Model Training Method for DDoS Detection Using CTGAN under 5GC Traffic

    Yea-Sul Kim1, Ye-Eun Kim1, Hwankuk Kim2,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1125-1147, 2023, DOI:10.32604/csse.2023.039550 - 26 May 2023
    (This article belongs to the Special Issue: Advances in Mobile Internet Security)
    Abstract With the commercialization of 5th-generation mobile communications (5G) networks, a large-scale internet of things (IoT) environment is being built. Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service (DDoS) attacks across vast IoT devices. Recently, research on automated intrusion detection using machine learning (ML) for 5G environments has been actively conducted. However, 5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data. If this data is used to train an ML model, it will likely suffer… More >

  • Open AccessOpen Access

    ARTICLE

    Improved Blending Attention Mechanism in Visual Question Answering

    Siyu Lu1, Yueming Ding1, Zhengtong Yin2, Mingzhe Liu3,*, Xuan Liu4, Wenfeng Zheng1,*, Lirong Yin5
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1149-1161, 2023, DOI:10.32604/csse.2023.038598 - 26 May 2023
    Abstract Visual question answering (VQA) has attracted more and more attention in computer vision and natural language processing. Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks. Analysis of all features may cause information redundancy and heavy computational burden. Attention mechanism is a wise way to solve this problem. However, using single attention mechanism may cause incomplete concern of features. This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention More >

  • Open AccessOpen Access

    ARTICLE

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Shehab Abdulhabib Alzaeemi1, Kim Gaik Tay1,*, Audrey Huong1, Saratha Sathasivam2, Majid Khan bin Majahar Ali2
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1163-1184, 2023, DOI:10.32604/csse.2023.038912 - 26 May 2023
    Abstract Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior’s integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the near-optimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN-2SAT). The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different… More >

  • Open AccessOpen Access

    ARTICLE

    A Hierarchal Clustered Based Proactive Caching in NDN-Based Vehicular Network

    Muhammad Yasir Khan1, Muhammad Adnan1,2, Jawaid Iqbal3, Noor ul Amin1, Byeong-Hee Roh4, Jehad Ali4,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1185-1208, 2023, DOI:10.32604/csse.2023.039352 - 26 May 2023
    Abstract An Information-Centric Network (ICN) provides a promising paradigm for the upcoming internet architecture, which will struggle with steady growth in data and changes in access models. Various ICN architectures have been designed, including Named Data Networking (NDN), which is designed around content delivery instead of hosts. As data is the central part of the network. Therefore, NDN was developed to get rid of the dependency on IP addresses and provide content effectively. Mobility is one of the major research dimensions for this upcoming internet architecture. Some research has been carried out to solve the mobility… More >

  • Open AccessOpen Access

    ARTICLE

    Tight Sandstone Image Augmentation for Image Identification Using Deep Learning

    Dongsheng Li, Chunsheng Li*, Kejia Zhang, Tao Liu, Fang Liu, Jingsong Yin, Mingyue Liao
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1209-1231, 2023, DOI:10.32604/csse.2023.034395 - 26 May 2023
    Abstract Intelligent identification of sandstone slice images using deep learning technology is the development trend of mineral identification, and accurate mineral particle segmentation is the most critical step for intelligent identification. A typical identification model requires many training samples to learn as many distinguishable features as possible. However, limited by the difficulty of data acquisition, the high cost of labeling, and privacy protection, this has led to a sparse sample number and cannot meet the training requirements of deep learning image identification models. In order to increase the number of samples and improve the training effect… More >

  • Open AccessOpen Access

    ARTICLE

    Web Intelligence with Enhanced Sunflower Optimization Algorithm for Sentiment Analysis

    Abeer D. Algarni*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1233-1247, 2023, DOI:10.32604/csse.2022.026915 - 26 May 2023
    Abstract Exponential increase in the quantity of user generated content in websites and social networks have resulted in the emergence of web intelligence approaches. Several natural language processing (NLP) tools are commonly used to examine the large quantity of data generated online. Particularly, sentiment analysis (SA) is an effective way of classifying the data into different classes of user opinions or sentiments. The latest advances in machine learning (ML) and deep learning (DL) approaches offer an intelligent way of analyzing sentiments. In this view, this study introduces a web intelligence with enhanced sunflower optimization based deep… More >

  • Open AccessOpen Access

    ARTICLE

    Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis

    Abdelwahed Motwakel1,*, Najm Alotaibi2, Eatedal Alabdulkreem3, Hussain Alshahrani4, Mohamed Ahmed Elfaki4, Mohamed K Nour5, Radwa Marzouk6, Mahmoud Othman7
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1249-1266, 2023, DOI:10.32604/csse.2023.034227 - 26 May 2023
    Abstract Sentiment Analysis (SA) of natural language text is not only a challenging process but also gains significance in various Natural Language Processing (NLP) applications. The SA is utilized in various applications, namely, education, to improve the learning and teaching processes, marketing strategies, customer trend predictions, and the stock market. Various researchers have applied lexicon-related approaches, Machine Learning (ML) techniques and so on to conduct the SA for multiple languages, for instance, English and Chinese. Due to the increased popularity of the Deep Learning models, the current study used diverse configuration settings of the Convolution Neural… More >

  • Open AccessOpen Access

    ARTICLE

    Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

    Abdelwahed Motwakel1,*, Marwa Obayya2, Nadhem Nemri3, Khaled Tarmissi4, Heba Mohsen5, Mohammed Rizwanulla6, Ishfaq Yaseen6, Abu Sarwar Zamani6
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1267-1281, 2023, DOI:10.32604/csse.2023.037735 - 26 May 2023
    Abstract Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal… More >

  • Open AccessOpen Access

    ARTICLE

    Design of Evolutionary Algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks

    Mohammed Altaf Ahmed1, T. Satyanarayana Murthy2, Fayadh Alenezi3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Yena Kim8, Yunyoung Nam8,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1283-1297, 2023, DOI:10.32604/csse.2023.035786 - 26 May 2023
    Abstract Wireless Sensor Networks (WSN) play a vital role in several real-time applications ranging from military to civilian. Despite the benefits of WSN, energy efficiency becomes a major part of the challenging issue in WSN, which necessitate proper load balancing amongst the clusters and serves a wider monitoring region. The clustering technique for WSN has several benefits: lower delay, higher energy efficiency, and collision avoidance. But clustering protocol has several challenges. In a large-scale network, cluster-based protocols mainly adapt multi-hop routing to save energy, leading to hot spot problems. A hot spot problem becomes a problem… More >

  • Open AccessOpen Access

    ARTICLE

    Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

    Hatem A. Alharbi1, Barzan A. Yosuf2, Mohammad Aldossary3,*, Jaber Almutairi4
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1299-1319, 2023, DOI:10.32604/csse.2023.039367 - 26 May 2023
    Abstract In this paper, the Internet of Medical Things (IoMT) is identified as a promising solution, which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service (QoS) in the healthcare sector. However, problems with the present architectural models such as those related to energy consumption, service latency, execution cost, and resource usage, remain a major concern for adopting IoMT applications. To address these problems, this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming (MILP), with the objective of… More >

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