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  • Open Access

    ARTICLE

    Selfish Mining and Defending Strategies in the Bitcoin

    Weijian Zhang1,*, Hao Wang2, Hao Hua3, Qirun Wang4

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1861-1875, 2022, DOI:10.32604/iasc.2022.030274

    Abstract As a kind of distributed, decentralized and peer-to-peer transmitted technology, blockchain technology has gradually changed people’s lifestyle. However, blockchain technology also faces many problems including selfish mining attack, which causes serious effects to the development of blockchain technology. Selfish mining is a kind of mining strategy where selfish miners increase their profit by selectively publishing hidden blocks. This paper builds the selfish mining model from the perspective of node state conversion and utilize the function extremum method to figure out the optimal profit of this model. Meanwhile, based on the experimental data of honest mining, the author conducts the simulation… More >

  • Open Access

    ARTICLE

    Blockchain-Enabled Digital Rights Management for Museum-Digital Property Rights

    Liutao Zhao1,2, Jiawan Zhang1,3,*, Hairong Jing4

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1785-1801, 2022, DOI:10.32604/iasc.2022.029693

    Abstract With the rapid development of digitization technology, digital copyright of museum has become more and more valuable. Its collections can be opened to and shared with the people through the Internet. However, centralized authorization, untransparent transaction information and risk of tampering data in traditional digital rights management have a strong impact on system normal operation. In this paper, we proposed a blockchain-based digital rights management scheme (BMDRM) that realizes a distributed digital rights management and authorization system by introducing non-fungible tokens (NFTs) and smart contracts. To ensure the security and efficiency of transactions and authorization, we store all processing data… More >

  • Open Access

    ARTICLE

    Application of CNN and Long Short-Term Memory Network in Water Quality Predicting

    Wenwu Tan1, Jianjun Zhang1,*, Jiang Wu1, Hao Lan1, Xing Liu1, Ke Xiao2, Li Wang2, Haijun Lin1, Guang Sun3, Peng Guo4

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1943-1958, 2022, DOI:10.32604/iasc.2022.029660

    Abstract Water resources are an indispensable precious resource for human survival and development. Water quality prediction plays a vital role in protecting and enhancing water resources. Changes in water quality are influenced by many factors, both long-term and short-term. Therefore, according to water quality changes’ periodic and nonlinear characteristics, this paper considered dissolved oxygen as the research object and constructed a neural network model combining convolutional neural network (CNN) and long short-term memory network (LSTM) to predict dissolved oxygen index in water quality. Firstly, we preprocessed the water quality data set obtained from the water quality monitoring platform. Secondly, we used… More >

  • Open Access

    ARTICLE

    Consensus Mechanism of Blockchain Based on PoR with Data Deduplication

    Wei Zhou1, Hao Wang2, Ghulam Mohiuddin3, Dan Chen4,*, Yongjun Ren1

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1473-1488, 2022, DOI:10.32604/iasc.2022.029657

    Abstract As the basis of cloud computing, distributed storage technology mainly studies how data centers store, organize and manage data. Blockchain has become the most secure solution for cloud storage due to its decentralization and immutability. Consensus mechanism is one of the core technologies of blockchain, which affects the transaction processing capability, security and scalability of blockchain. The current mainstream consensus algorithms such as Proof of Work, Proof of Stake, and Delegated Proof of Stake all have the problem of wasting resources. And with the explosive growth of data, cloud storage nodes store a large amount of redundant data, which inevitably… More >

  • Open Access

    ARTICLE

    Criss-Cross Attention Based Auto Encoder for Video Anomaly Event Detection

    Jiaqi Wang1, Jie Zhang2, Genlin Ji2,*, Bo Sheng3

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1629-1642, 2022, DOI:10.32604/iasc.2022.029535

    Abstract The surveillance applications generate enormous video data and present challenges to video analysis for huge human labor cost. Reconstruction-based convolutional autoencoders have achieved great success in video anomaly detection for their ability of automatically detecting abnormal event. The approaches learn normal patterns only with the normal data in an unsupervised way due to the difficulty of collecting anomaly samples and obtaining anomaly annotations. But convolutional autoencoders have limitations in global feature extraction for the local receptive field of convolutional kernels. What is more, 2-dimensional convolution lacks the capability of capturing temporal information while videos change over time. In this paper,… More >

  • Open Access

    ARTICLE

    SSAG-Net: Syntactic and Semantic Attention-Guided Machine Reading Comprehension

    Chenxi Yu, Xin Li*

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2023-2034, 2022, DOI:10.32604/iasc.2022.029447

    Abstract Machine reading comprehension (MRC) is a task in natural language comprehension. It assesses machine reading comprehension based on text reading and answering questions. Traditional attention methods typically focus on one of syntax or semantics, or integrate syntax and semantics through a manual method, leaving the model unable to fully utilize syntax and semantics for MRC tasks. In order to better understand syntactic and semantic information and improve machine reading comprehension, our study uses syntactic and semantic attention to conduct text modeling for tasks. Based on the BERT model of Transformer encoder, we separate a text into two branches: syntax part… More >

  • Open Access

    ARTICLE

    A New Route Optimization Approach of Fresh Agricultural Logistics Distribution

    Daqing Wu1,2, Jiye Cui1,*, Dan Li3, Romany Fouad Mansour4

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1553-1569, 2022, DOI:10.32604/iasc.2022.028780

    Abstract Under the fierce market competition and the demand of low-carbon economy, the freshness of fresh products directly determines the degree of customer satisfaction. Cold chain logistics companies must pay attention to the freshness and carbon emissions of fresh products to obtain better service development. In the cold chain logistics path optimization problem, considering the cost, product freshness and carbon emission environmental factors at the same time, based on the cost-benefit idea, a comprehensive cold chain vehicle routing problem optimization model is proposed to minimize the unit cost of product freshness and the carbon trading mechanism for calculating the cost of… More >

  • Open Access

    ARTICLE

    Attention Weight is Indispensable in Joint Entity and Relation Extraction

    Jianquan Ouyang1,*, Jing Zhang1, Tianming Liu2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1707-1723, 2022, DOI:10.32604/iasc.2022.028352

    Abstract Joint entity and relation extraction (JERE) is an important foundation for unstructured knowledge extraction in natural language processing (NLP). Thus, designing efficient algorithms for it has become a vital task. Although existing methods can efficiently extract entities and relations, their performance should be improved. In this paper, we propose a novel model called Attention and Span-based Entity and Relation Transformer (ASpERT) for JERE. First, differing from the traditional approach that only considers the last hidden layer as the feature embedding, ASpERT concatenates the attention head information of each layer with the information of the last hidden layer by using an… More >

  • Open Access

    ARTICLE

    Glowworm Optimization with Deep Learning Enabled Cybersecurity in Social Networks

    Ashit Kumar Dutta1,*, Basit Qureshi2, Yasser Albagory3, Majed Alsanea4, Anas Waleed AbulFaraj5, Abdul Rahaman Wahab Sait6

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2097-2110, 2022, DOI:10.32604/iasc.2022.027500

    Abstract Recently, the exponential utilization of Internet has posed several cybersecurity issues in social networks. Particularly, cyberbulling becomes a common threat to users in real time environment. Automated detection and classification of cyberbullying in social networks become an essential task, which can be derived by the use of machine learning (ML) and deep learning (DL) approaches. Since the hyperparameters of the DL model are important for optimal outcomes, appropriate tuning strategy becomes important by the use of metaheuristic optimization algorithms. In this study, an effective glowworm swarm optimization (GSO) with deep neural network (DNN) model named EGSO-DNN is derived for cybersecurity… More >

  • Open Access

    ARTICLE

    Image Steganography Using Deep Neural Networks

    Kavitha Chinniyan*, Thamil Vani Samiyappan, Aishvarya Gopu, Narmatha Ramasamy

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1877-1891, 2022, DOI:10.32604/iasc.2022.027274

    Abstract Steganography is the technique of hiding secret data within ordinary data by modifying pixel values which appear normal to a casual observer. Steganography which is similar to cryptography helps in secret communication. The cryptography method focuses on the authenticity and integrity of the messages by hiding the contents of the messages. Sometimes, it is not only just enough to encrypt the message but also essential to hide the existence of the message itself. As this avoids misuse of data, this kind of encryption is less suspicious and does not catch attention. To achieve this, Stacked Autoencoder model is developed which… More >

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