Gaigai Jin¹, Junsheng Zhou^1,*, Weiguang Qu¹, Yunfei Long², Yanhui Gu¹

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 161-173, 2021, DOI:10.32604/iasc.2021.017440

Abstract Event causality identification is an essential task for information extraction that has attracted growing attention. Early researchers were accustomed to combining the convolutional neural network or recurrent neural network models with external causal knowledge, but these methods ignore the importance of rich semantic representation of the event. The event is more structured, so it has more abundant semantic representation. We argue that the elements of the event, the interaction of the two events, and the context between the two events can enrich the event’s semantic representation and help identify event causality. Therefore, the effective semantic representation of events in event… More >

Jianming Zhao^1,2,3,4, Peng Zeng^1,2,3,4,*, Chunyu Chen^1,2,3,4, Zhiwei Dong⁵, Jongho Han⁶

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 337-350, 2021, DOI:10.32604/iasc.2021.016966

Abstract As networked control systems continue to be widely used in large-scale industrial productions, industrial cyber-attacks have become an inevitable problem that can cause serious damage to critical infrastructures. In practice, industrial intrusion detection has been widely acknowledged to detect abnormal communication behaviors. However, unlike traditional IT systems, networked control systems have their own communication characteristics due to specific industrial communication protocols. Thus, simple cyber-attack modeling is inadequate and impractical for high-efficiency intrusion detection because the characteristics of network control systems are less considered. Based on the status information and transmission connection in industrial communication data payloads, which can properly express… More >

Juan Huang¹, Bifang Zhou¹, Huajun Huang^2,*, Dingwen Qing¹, Neal N. Xiong³

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 303-318, 2021, DOI:10.32604/iasc.2021.016953

Abstract There are various influencing factors in regional FDI (foreign direct investment) and it is difficult to identify the main influencing factors. For this reason, a main factor selection algorithm is proposed in this article for the main factors affecting regional FDI statistics by analyzing the regional economic characteristics and the possible influencing factors in the regional FDI. Then, an example is used to illustrate its effectiveness and its stability. Firstly, the characteristics of regional economy and the regional FDI data are introduced to develop the main factor selection algorithm based on the adaptive Lasso problem for the regional FDI and… More >

Jing Peng^1,2,4, Chaoyang Xia², Yuanwei Xu³, Xiaojie Li², Xi Wu², Xiao Han^1,4, Xinlai Chen⁵, Yucheng Chen³, Zhe Cui^1,4,*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 259-272, 2021, DOI:10.32604/iasc.2021.016749

Abstract Cardiomyopathy is a group of diseases that affect the heart and can cause serious health problems. Segmentation and classification are important for automating the clinical diagnosis and treatment planning for cardiomyopathy. However, this automation is difficult because of the poor quality of cardiac magnetic resonance (CMR) imaging data and varying dimensions caused by movement of the ventricle. To address these problems, a deep multi-task framework based on a convolutional neural network (CNN) is proposed to segment the left ventricle (LV) myocardium and classify cardiopathy simultaneously. The proposed model consists of a longitudinal encoder–decoder structure that obtains high- and low-level features… More >

Adel Hamdan Mohammad^*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 65-80, 2021, DOI:10.32604/iasc.2021.016140

Abstract Intrusion detection is an important topic that aims at protecting computer systems. Besides, feature selection is crucial for increasing the performance of intrusion detection. This paper employs a new hybrid feature selection model for intrusion detection. The implemented model uses Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms in a new manner. In addition, this study introduces two new models called (PSO-GWO-NB) and (PSO-GWO-ANN) for feature selection and intrusion detection. PSO and GWO show emergent results in feature selection for several purposes and applications. This paper uses PSO and GWO to select features for the intrusion detection system.… More >

Mohammad Monirujjaman Khan^1,*, Shamsun Nahar Safa¹, Minhazul Hoque Ashik¹, Mehedi Masud², Mohammed A. AlZain³

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 49-64, 2021, DOI:10.32604/iasc.2021.016077

Abstract Smart wheelchairs play a significant role in supporting disabled people. Individuals with motor function impairments due to some disorders such as strokes or multiple sclerosis face frequent moving difficulties. Hence, they need constant support from an assistant. This paper presents a brain-controlled wheelchair model to assist disabled and paralyzed patients. The wheelchair is controlled by interpreting Electroencephalogram (EEG) signals, also known as brain waves. In the EEG technique, an electrode cap is positioned on the user’s scalp to receive EEG signals, which are detected and transformed by the Arduino microcontroller into motion commands, which drive the wheelchair. The proposed wheelchair… More >

Sobia Wassan¹, Chen Xi^1,*, Nz Jhanjhi², Hassan Raza³

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 187-199, 2021, DOI:10.32604/iasc.2021.015859

Abstract Online banking is an ideal method for conducting financial transactions such as e-commerce, e-banking, and e-payments. The growing popularity of online payment services and payroll systems, however, has opened new pathways for hackers to steal consumers’ information and money, a risk which poses significant danger to the users of e-commerce and e-banking websites. This study uses the selection method of the entire e-commerce and e-banking website dataset (Chi-Squared, Gini index, and main learning algorithm). The results of the analysis suggest the identification and comparison of machine learning and deep learning algorithm performance on binary category labels (legal, fraudulent) between similar… More >

Kye S. Kim¹, Peter M. Kang¹, He Sook N. Kim^2,*

International Journal of Mental Health Promotion, Vol.23, No.3, pp. 303-317, 2021, DOI:10.32604/IJMHP.2021.016470

Abstract The extent of viral spread and strategies in dealing with the COVID-19 pandemic have been different in each country. There are overall increased mental health concerns in many countries but it is unclear what the general public individuals who do not have heighten vulnerability to stressors for existing mental diseases or significant physical illnesses were experiencing during the pandemic. We evaluated the stressors and mental health of general public in South Korea that has a relatively low confirmed cases and deaths. Responses on the on-line survey questions were used to assess the mental and physical symptoms in association with individuals’… More >

Long Liu^1,2, Yanlin Shi^1,*, Xiyan Fei³, Zhenzhen Wang^1,4, Zhi Wang^1,5, Li Li⁶, Lin Ding⁶, Qiaoyuan Yan^7,*

International Journal of Mental Health Promotion, Vol.23, No.3, pp. 319-330, 2021, DOI:10.32604/IJMHP.2021.015699

Abstract Exploring whether medical staff perceive stress on the assigned medical tasks, what are the specific sources of stress, what are the tangible sources of support they expected to be helpful, and individual coping with stress to provide more accurate, personal support for psychological crisis. This study uses a cross-sectional descriptive survey adopting convenience sampling among the medical staff who worked for over seven days in the infected areas of one Grade 2A and three Grade 3A hospitals during the COVID-19 pandemic. The assessment includes attitude when receiving tasks, major stressors, factors relieving stress, and personal management of stress. A total… More >

Soshi Kodama^1,*, Koichi Shido², Nozomu Ikeda³

International Journal of Mental Health Promotion, Vol.23, No.3, pp. 331-352, 2021, DOI:10.32604/IJMHP.2021.016337

Abstract Despite depression being a global mental health disorder, many people with depression do not seek psychiatric help. In particular, it has been reported that only 15.7% of medical students seek treatment. A longer duration of untreated illness (DUI) leads to clinically poor results. To shorten the DUI, the mental health literacy (MHL) with regard to depression needs to be improved, although it is unclear which MHL components will improve the help-seeking process. Additionally, the existing MHL scale for depression is poorly validated for structural validity. Therefore, the purpose of this study was to develop an MHL scale for depression with… More >