Qingyue Zhao¹, Qiaoyu Gu², Zhijun Gao^3,*, Shipian Shao¹, Xinyuan Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1773-1788, 2023, DOI:10.32604/cmes.2023.027500 - 26 June 2023

Abstract Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA) model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series, and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence. Finally, More >

Mingguang Yu^1,2, Xia Zhang^1,2,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1787-1806, 2023, DOI:10.32604/iasc.2023.038798 - 21 June 2023

Abstract As cloud system architectures evolve continuously, the interactions among distributed components in various roles become increasingly complex. This complexity makes it difficult to detect anomalies in cloud systems. The system status can no longer be determined through individual key performance indicators (KPIs) but through joint judgments based on synergistic relationships among distributed components. Furthermore, anomalies in modern cloud systems are usually not sudden crashes but rather gradual, chronic, localized failures or quality degradations in a weakly available state. Therefore, accurately modeling cloud systems and mining the hidden system state is crucial. To address this challenge,… More >

Xu Yang^1,2, Ling Zhuang¹, Yuqiang Sun³, Wenjie Zhang^4,5,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1637-1654, 2023, DOI:10.32604/iasc.2023.039879 - 21 June 2023

Abstract The loss of three-dimensional atmospheric electric field (3DAEF) data has a negative impact on thunderstorm detection. This paper proposes a method for thunderstorm point charge path recovery. Based on the relationship between a point charge and 3DAEF, we derive corresponding localization formulae by establishing a point charge localization model. Generally, point charge movement paths are obtained after fitting time series localization results. However, AEF data losses make it difficult to fit and visualize paths. Therefore, using available AEF data without loss as input, we design a hybrid model combining the convolutional neural network (CNN) and… More >

Xiaoming Fei^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1293-1309, 2023, DOI:10.32604/iasc.2023.038607 - 21 June 2023

Abstract With the acceleration of urbanization in China, the discharge of domestic sewage and industrial wastewater is increasing, and accidents of sewage spilling out and polluting the environment occur from time to time. Problems such as imperfect facilities and backward control methods are common in the urban drainage network systems in China. Efficient drainage not only strengthens infrastructure such as rain and sewage diversion, pollution source monitoring, transportation, drainage and storage but also urgently needs technical means to monitor and optimize production and operation. Aiming at the optimal control of single-stage pumping stations and the coordinated… More >

Mingyong Li, Zheng Jiang^*, Zongwei Zhao, Longfei Ma

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2387-2406, 2023, DOI:10.32604/iasc.2023.037900 - 21 June 2023

Abstract As an essential category of public event management and control, sentiment analysis of online public opinion text plays a vital role in public opinion early warning, network rumor management, and netizens’ personality portraits under massive public opinion data. The traditional sentiment analysis model is not sensitive to the location information of words, it is difficult to solve the problem of polysemy, and the learning representation ability of long and short sentences is very different, which leads to the low accuracy of sentiment classification. This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text… More >

Mohammad S. Al-kahtani¹, Zahid Mehmood^2,3,*, Tariq Sadad⁴, Islam Zada⁵, Gauhar Ali⁶, Mohammed ElAffendi⁶

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2279-2290, 2023, DOI:10.32604/iasc.2023.037673 - 21 June 2023

Abstract Cybersecurity threats are increasing rapidly as hackers use advanced techniques. As a result, cybersecurity has now a significant factor in protecting organizational limits. Intrusion detection systems (IDSs) are used in networks to flag serious issues during network management, including identifying malicious traffic, which is a challenge. It remains an open contest over how to learn features in IDS since current approaches use deep learning methods. Hybrid learning, which combines swarm intelligence and evolution, is gaining attention for further improvement against cyber threats. In this study, we employed a PSO-GA (fusion of particle swarm optimization (PSO) More >

Fulu Wei¹, Xin Li¹, Yongqing Guo^1,*, Zhenyu Wang², Qingyin Li¹, Xueshi Ma³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2001-2018, 2023, DOI:10.32604/iasc.2023.035799 - 21 June 2023

Abstract Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of… More >

Hong Zhang^1,2,*, Gang Yang¹, Hailiang Yu¹, Zan Zheng¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1047-1063, 2023, DOI:10.32604/cmc.2023.039274 - 08 June 2023

Abstract To accurately predict traffic flow on the highways, this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism (CNN-BiLSTM-Attention) traffic flow prediction model based on Kalman-filtered data processing. Firstly, the original fluctuating data is processed by Kalman filtering, which can reduce the instability of short-term traffic flow prediction due to unexpected accidents. Then the local spatial features of the traffic data during different periods are extracted, dimensionality is reduced through a one-dimensional CNN, and the BiLSTM network is used to analyze the time series information. Finally, the Attention Mechanism assigns feature weights and performs… More >

Min Hyung Park¹, Dongyan Nan^2,3, Yerin Kim¹, Jang Hyun Kim^1,2,3,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 121-134, 2023, DOI:10.32604/csse.2023.033247 - 26 May 2023

Abstract After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized More >

Cherifa Nakkach^*, Amira Zrelli, Tahar Ezzedine

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 545-560, 2023, DOI:10.32604/iasc.2023.036385 - 29 April 2023

Abstract Due to the development of diversified and flexible building energy resources, the balancing energy supply and demand especially in smart buildings caused an increasing problem. Energy forecasting is necessary to address building energy issues and comfort challenges that drive urbanization and consequent increases in energy consumption. Recently, their management has a great significance as resources become scarcer and their emissions increase. In this article, we propose an intelligent energy forecasting method based on hybrid deep learning, in which the data collected by the smart home through meters is put into the pre-evaluation step. Next, the More >