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Zhihan Liu¹, Xiang Li¹, Siqi Liu², Wei Li^1,*, Xiangxu Meng¹, Jing Jia³
Computer Systems Science and Engineering, Vol.47, No.1, pp. 937-954, 2023, DOI:10.32604/csse.2023.039417
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 a lightweight convolutional neural network… More >

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Zhen Zhen¹, Jian Gao^1,2,*
Computer Systems Science and Engineering, Vol.47, No.1, pp. 955-973, 2023, DOI:10.32604/csse.2023.039525
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 Competition show that our proposed… More >

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Jieren Cheng^1,3, Xiulai Li^1,2,3,4,*, Xinbing Xu^2,3, Xiangyan Tang^1,3, Victor S. Sheng⁵
Computer Systems Science and Engineering, Vol.47, No.1, pp. 975-992, 2023, DOI:10.32604/csse.2023.039280
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 of DDoS segmentation, that is,… More >

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Junfeng Sun¹, Chenghai Li¹, Yafei Song^1,*, Peng Ni², Jian Wang¹
Computer Systems Science and Engineering, Vol.47, No.1, pp. 993-1021, 2023, DOI:10.32604/csse.2023.039215
（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, a prediction model of TCAN-BiGRU… More >

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Mengxiao Wang^1,2, Jing Huang^1,2,*
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1023-1042, 2023, DOI:10.32604/csse.2023.039569
（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 and adaptive boosting (AdaBoost) algorithm.… More >

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291

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Aiping Xu^1,2, Xuan Zou³, Chao Wang^2,*
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1043-1059, 2023, DOI:10.32604/csse.2023.039645
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, convert the data set into… More >

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242

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Wen-Tsai Sung¹, Sung-Jung Hsiao^2,*
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1061-1076, 2023, DOI:10.32604/csse.2023.032700
（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 analysis model and information processing… More >

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N. Anand Kumar^1,*, A. Grace Selvarani², P. Vivekanandan³
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1077-1090, 2023, DOI:10.32604/csse.2023.034115
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 been employed to assure a… More >

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321

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Daniyal Amankeldin¹, Lyailya Kurmangaziyeva², Ayman Mailybayeva², Natalya Glazyrina¹, Ainur Zhumadillayeva^1,*, Nurzhamal Karasheva³
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1091-1108, 2023, DOI:10.32604/csse.2023.039503
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 features. The results demonstrated that… More >

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S. Sureshkumar^1,*, 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
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 the purpose of the study… More >

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256

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Yea-Sul Kim¹, Ye-Eun Kim¹, Hwankuk Kim^2,*
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1125-1147, 2023, DOI:10.32604/csse.2023.039550
（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 from generalization errors due to… More >

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327

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Siyu Lu¹, Yueming Ding¹, Zhengtong Yin², Mingzhe Liu^3,*, Xuan Liu⁴, Wenfeng Zheng^1,*, Lirong Yin⁵
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1149-1161, 2023, DOI:10.32604/csse.2023.038598
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 mechanism method. In the case… More >

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249

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Shehab Abdulhabib Alzaeemi¹, Kim Gaik Tay^1,*, 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
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 algorithms, including Genetic Algorithm (GA),… More >

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238

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Muhammad Yasir Khan¹, Muhammad Adnan^1,2, Jawaid Iqbal³, Noor ul Amin¹, Byeong-Hee Roh⁴, Jehad Ali^4,*
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1185-1208, 2023, DOI:10.32604/csse.2023.039352
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 issues, but it still has… More >

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477

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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
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 of deep learning models, this… More >

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238

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Abeer D. Algarni^*
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1233-1247, 2023, DOI:10.32604/csse.2022.026915
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 learning model for sentiment analysis… More >

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210

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Abdelwahed Motwakel^1,*, 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
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 Network (CNN) model and conducted… More >

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247

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Abdelwahed Motwakel^1,*, 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
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 fusion-based predictive tool for energy… More >

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200

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Mohammed Altaf Ahmed¹, T. Satyanarayana Murthy², Fayadh Alenezi³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Yena Kim⁸, Yunyoung Nam^8,*
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1283-1297, 2023, DOI:10.32604/csse.2023.035786
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 where a cluster node nearer… More >

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274

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Hatem A. Alharbi¹, Barzan A. Yosuf², Mohammad Aldossary^3,*, Jaber Almutairi⁴
Computer Systems Science and Engineering, Vol.47, No.1, pp. 1299-1319, 2023, DOI:10.32604/csse.2023.039367
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 efficiently processing and placing IoMT… More >

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