Shen Guo^*, Wanxing Sheng, Shuaitao Bai, Jichuan Zhang, Peng Wang

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 799-814, 2023, DOI:10.32604/iasc.2023.038330 - 29 April 2023

Abstract With the full development of disk-resident databases (DRDB) in recent years, it is widely used in business and transactional applications. In long-term use, some problems of disk databases are gradually exposed. For applications with high real-time requirements, the performance of using disk database is not satisfactory. In the context of the booming development of the Internet of things, domestic real-time databases have also gradually developed. Still, most of them only support the storage, processing, and analysis of data values with fewer data types, which can not fully meet the current industrial process control system data… More >

Jianbo Lu^1,2,*, Ruxin Shi², Jin Tong³, Wenqi Cheng⁴, Xiaoya Ma^1,3, Xiaobin Liu²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 525-544, 2023, DOI:10.32604/iasc.2023.038287 - 29 April 2023

Abstract In the deep learning approach for identifying plant diseases, the high complexity of the network model, the large number of parameters, and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources. In this study, a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed. In the proposed model, the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions; the efficient channel attention module is added into… More >

Wenbo Li, Qi Wang^*, Shang Gao

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 921-938, 2023, DOI:10.32604/iasc.2023.038257 - 29 April 2023

Abstract Infrared target detection models are more required than ever before to be deployed on embedded platforms, which requires models with less memory consumption and better real-time performance while considering accuracy. To address the above challenges, we propose a modified You Only Look Once (YOLO) algorithm PF-YOLOv4-Tiny. The algorithm incorporates spatial pyramidal pooling (SPP) and squeeze-and-excitation (SE) visual attention modules to enhance the target localization capability. The PANet-based-feature pyramid networks (P-FPN) are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy. To lighten the network, the standard convolutions other than the backbone More >

S. Deva Kumar, S. Venkatramaphanikumar^*, K. Venkata Krishna Kishore

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1013-1032, 2023, DOI:10.32604/iasc.2023.038165 - 29 April 2023

Abstract Type 2 diabetes patients often suffer from microvascular complications of diabetes. These complications, in turn, often lead to vision impairment. Diabetic Retinopathy (DR) detection in its early stage can rescue people from long-term complications that could lead to permanent blindness. In this study, we propose a complex deep convolutional neural network architecture with an inception module for automated diagnosis of DR. The proposed novel Hybrid Inception U-Net (HIUNET) comprises various inception modules connected in the U-Net fashion using activation maximization and filter map to produce the image mask. First, inception blocks were used to enlarge… More >

Payal Soni, J. Subhashini^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1225-1242, 2023, DOI:10.32604/iasc.2023.038155 - 29 April 2023

Abstract In the current context, a smart grid has replaced the conventional grid through intelligent energy management, integration of renewable energy sources (RES) and two-way communication infrastructures from power generation to distribution. Energy management from the distribution side is a critical problem for balancing load demand. A unique energy management strategy (EMS) is being developed for office building equipment. That includes renewable energy integration, automation, and control based on the Artificial Neural Network (ANN) system using Matlab Simulink. This strategy reduces electric power consumption and balances the load demand of the traditional grid. This strategy is More >

Ebu Yusuf GÜVEN, Zeynep GÜRKAŞ-AYDIN^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 419-437, 2023, DOI:10.32604/iasc.2023.038043 - 29 April 2023

Abstract The Internet of Things (IoT) has aided in the development of new products and services. Due to the heterogeneity of IoT items and networks, traditional techniques cannot identify network risks. Rule-based solutions make it challenging to secure and manage IoT devices and services due to their diversity. While the use of artificial intelligence eliminates the need to define rules, the training and retraining processes require additional processing power. This study proposes a methodology for analyzing constrained devices in IoT environments. We examined the relationship between different sized samples from the Kitsune dataset to simulate the… More >

Kunfu Wang, Yongsheng Hao, Jie Cao^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1033-1052, 2023, DOI:10.32604/iasc.2023.038036 - 29 April 2023

Abstract Meteorological model tasks require considerable meteorological basis data to support their execution. However, if the task and the meteorological datasets are located on different clouds, that enhances the cost, execution time, and energy consumption of execution meteorological tasks. Therefore, the data layout and task scheduling may work together in the meteorological cloud to avoid being in various locations. To the best of our knowledge, this is the first paper that tries to schedule meteorological tasks with the help of the meteorological data set layout. First, we use the FP-Growth-M (frequent-pattern growth for meteorological model datasets) More >

Suhang Li¹, Yunzuo Zhang^1,*, Ruixue Liu², Jiayu Zhang¹, Zhouchen Song¹, Yutai Wang¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1163-1180, 2023, DOI:10.32604/iasc.2023.037902 - 29 April 2023

Abstract The communication system of high-speed trains in railway tunnels needs to be built with leaky cables fixed on the tunnel wall with special fixtures. To ensure safety, checking the regular leaky cable fixture is necessary to eliminate the potential danger. At present, the existing fixture detection algorithms are difficult to take into account detection accuracy and speed at the same time. The faulty fixture is also insufficient and difficult to obtain, seriously affecting the model detection effect. To solve these problems, an innovative detection method is proposed in this paper. Firstly, we presented the Res-Net… More >

Yue Yu¹, Junhua Wu^1,*, Guangshun Li¹, Wangang Wang²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 583-598, 2023, DOI:10.32604/iasc.2023.037875 - 29 April 2023

Abstract As an emerging hot technology, smart grids (SGs) are being employed in many fields, such as smart homes and smart cities. Moreover, the application of artificial intelligence (AI) in SGs has promoted the development of the power industry. However, as users’ demands for electricity increase, traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities. This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation,… More >

Tianshun Xing, Jianjun Chen^*, Taihua Xu, Yan Fan

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 561-581, 2023, DOI:10.32604/iasc.2023.037874 - 29 April 2023

Abstract It is well-known that attribute reduction is a crucial action of rough set. The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations. Normally, the learning performance of attributes in derived reduct is much more crucial. Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct, those measures may have a direct impact on the performance of selected attributes in reduct. However, most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective, which… More >